Jockey Profiles: Harry Skelton & Sean Bowen

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This is the second article in a series looking at the performance of some of top National Hunt jockeys. Last time, I shared the records of Nico de Boinville and Harry Cobden, which you can read here. In this second piece I will be looking at Harry Skelton and Sean Bowen.

I have analysed NH data for UK racing from 1st Jan 2016 to 31st Oct 2023, and have predominantly used the Geegeez Query Tool for my data collection, but I have also sourced data from the Geegeez Profiler to help with certain sections.

All profits and losses shared have been calculated to Industry SP, but I quote Betfair SP where appropriate; and all tables include A/E indices and, when any data has been pulled from the Geegeez Profiler Tool, I have also shared PRB (Percentage of Rivals Beaten) figures.

Let’s start with Harry Skelton.

Harry Skelton Overall Record

Let me first share Skelton’s overall stats by looking at his performance on all runners during the study period:

 

 

A strike rate of better than one in five is extremely good, but overall losses stand at nearly 15 pence in the £. Having said that Harry's PRB figure is extremely high at 0.62 (higher than both de Boinville and Cobden, who were analysed in the first article). If backing to BSP you would have made a small loss of £67.62 (ROI –1.6%).

Harry Skelton: Record by Year

Yearly stats are the next port of call. Here is a breakdown by both win, and win/placed (Each Way) percentage / Strike Rate (SR%):

 

 

As the graph shows, Skelton has been consistent with six of the eight years seeing win strike rates above 20%, and all years above 19%. There seems to have been a slight dip this year which may or may not be something to keep an eye on.

Harry Skelton: Record by Betting Odds / Price (SP)

A look next at Skelton's results by splitting them into different price bands:

 

 

Patterns are unclear from this market data. Nothing really catches the eye although the 10/1 to 14/1 results are below the average for all jockeys. From a wagering perspective, it looks as though - in general - Skelton rides are slightly overbet.

Harry Skelton: Record by Distance

A look at Harry's record at different distances now. I have grouped them into four distance bands as I did last time, and am comparing the win and each way strike rates:

 

 

A remarkably consistent picture is painted in the chart above with all distance groups showing win strike rates above 20%. The 2m1f-2m2f stats are marginally the strongest for both win and each way. If we look at the PRB figures they all hit 0.60 or above with the 2m1f-2m2f edging it once more, which is highly impressive performance.

 

 

Harry Skelton: Record by Race type

It is time to see if Skelton’s record is better in chases, hurdle races or in bumpers.

 

 

The strike rates for hurdle races and chases is virtually the same, though chases have provided slightly smaller losses. Bumpers (NH Flat races) are poor in comparison with a much lower SR% and hefty losses of 34p in the £. Bumper horses to especially ignore seem to be those priced 8/1 or bigger. Of that cohort, just one win has been achieved from 116 runners.

In non-handicap chases, a tiny profit to SP of £3.56 occurred thanks to 85 winners from 296 rides (SR 28.7%). To BSP these profits stand at £30.82 (ROI +10.4%).

Harry Skelton:  Record by Racecourse

I am now going to look at all courses where Skelton has had at least 80 rides. The courses are listed alphabetically:

 

 

There is quite a mixed bag here with relatively poor strike rates and records at Cheltenham, Chepstow, Haydock, Newbury, and Sandown. These five courses have strike rates ranging from 9.6% to 11%. Compare this with Uttoxeter and Wetherby hitting 33.3% and 33% respectively. The latter two courses have proved profitable to SP, Uttoxeter with stand-out returns of 29 pence for every £1 staked (44p in the £ to BSP). Having said that the most profitable period for the Skelton / Uttoxeter combination occurred between 2016 to 2020 so the cat may be out of the bag now.

Harry Skelton: Record by Trainer

92% of Skelton’s rides are for his brother Dan. The two have combined nearly 4000 times in the past eight years:

 

 

As a result, these are very similar numbers to the jockey's overall set.

Harry Skelton:  Record by Class of Race

In terms of class of race I want to look first at Graded / Listed races:

 

 

Skelton’s record in Grade 1 and 2 events has shown significant betting losses. Indeed, his overall record in these better races is relatively poor. If we now split results by Class of Race, in terms of Class 1 to Class 6, we see the following when comparing win strike rates:

 

 

There appears to be a class bias going on here: specifically, it looks best to avoid Class 1 and 2 events and focus on Class 3 or lower. It should be noted that in Class 3, 4 and 5 events Skelton has made a BSP profit in all three.

Harry Skelton: Record by Run style

Onto one of my favourite areas – run style. Here is a breakdown of Harry Skelton's run style performance in terms of win strike rate across ALL races:

 

 

This breakdown shows a huge front running bias. A strike rate of 36.6% is very impressive. If you had been able to predict pre-race which of his horses would take an early lead you would have secured a small SP profit of £37.01 (ROI +6.4%). Contrast to that the returns on all hold up horses – they would have produced significant losses of £508.62 (ROI -29%).

As one would expect the A/E indices for his Run Style runners correlate with the win rates:

 

 

Any figure above 1.00 suggests value and early leaders / front runners have achieved this edge.

Before winding up the run style section, let me share Skelton's record when riding the favourite:

 

 

More evidence, if it was really needed, of the importance of early positioning in a race.

It is time now to switch to the record of Sean Bowen.

 

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Sean Bowen Overall Record

Bowen’s record across all races is as follows:

 

Despite a strike rate of less than 20%, in terms of returns to SP Bowen has gone close to breaking even. And, to Betfair Starting Price, he has enjoyed a huge overall profit of £1129.71 (ROI +28.6%). However, before we get too excited, there was a single winner that paid over 700/1 on Betfair (was 200/1 Industry SP), so that takes out a significant chunk of the profits. That being said, Bowen has still recorded a BSP profit in six of the eight years.

As with Skelton my next port of call is looking at his yearly figures.

Sean Bowen: Record by Year

Below we see the yearly breakdown by strike rate - both win, and win/placed (Each Way):

 

 

In 2019 there was a bit of a dip, but since then the trend has been upward. The last two seasons have seen the best win strike rates and two of the top three each way ones. 

 

Sean Bowen: Record by Betting Odds / Price (SP)

I would like to look at market factors now and, as before, have split results up by the same Starting Price bands:

 

 

The shorter priced runners (first three rows in the table) have combined to sneak into profit. Despite that 200/1 winner mentioned earlier, horses priced 16/1 or bigger look the group to avoid.  Overall, this is an impressive set of results from a betting perspective, and there does still seem to be some general value in Bowen rides.

Sean Bowen: Record by Distance

A dive next into Bowen’s record at different distances. I am again looking at the win and each way strike rates:

 

 

This is the first jockey across the two articles to date who has achieved his highest win strike rate in the longer races of three miles or more. Let me now look at the Percentage of Rivals Beaten (PRB) splits:

 

There is a slight advantage for the two miles and shorter group with the three miles-plus group edging ‘second’.

Sean Bowen: Record by Race type

Under the microscope next comes Bowen’s record in hurdle races, chases and in bumpers:

 

 

The chase results stand out from all perspectives – strike rate, returns, A/E index. Bowen has turned an SP profit in both handicap and non-handicap chases.

His BSP profits for chases stand at +£304.50 (ROI +18.9%). The BSP figures have not been badly skewed either, and if we concentrate on chase runners that started in the top three in the betting his record reads 242 wins from 872 (SR 27.8%) for a BSP profit of £103.43 (ROI +11.9%). He has also made a profit of £35.49 (ROI +4.1%) to Industry SP.

His NH Flat (bumper) record is modest in comparison. Breaking these bumper results down, horses priced 6/1 or shorter have performed around the norm, but those priced 13/2 or bigger have fared very poorly – just 6 wins from 187 runners (SR 3.2%) for heavy losses of £109.50 (ROI -58.6%).

Sean Bowen: Record by Racecourse

It is course data next for Bowen. Once more 80 runs at a track is the cut off point for the table:

 

 

Bowen has been profitable to follow blindly at six courses, with Taunton showing the biggest returns by far; but as you might have guessed that 200/1 winner mentioned earlier in the piece occurred at the Somerset venue. The stats for Perth are strong and this is mainly because trainer Gordon Elliott has used Bowen regularly at the course in the past two years. They have combined to win 40% of races at the track.

Two courses where Bowen has seemingly struggled a little have been Ludlow and Warwick. Losses have been steep and the PRB figures at both tracks are under 0.50.

Sean Bowen: Record by Trainer

During the period of study, Bowen has 100-plus rides with several trainers, and they are shown in the table below:

 

 

These figures are very solid – you just have to look at the A/E indices which are all 0.90 or higher. To give a comparison, Bowen’s wins to runs record for all other trainers combined stands at 188 wins from 1287 rides (SR 14.6%). That compares to an overall win strike rate from the table above of 19.52%.

The Elliott figures are notably strong. a large factor in which is their potent combination at Perth.

Sean Bowen: Record by Class of Race

A look next at class of race:

 

The best events (Class 1) have been a struggle to this point. Indeed, Bowen has had just two successes from 53 attempts at the very highest level, Grade 1. In contrast, race classes 2 to 4 have provided some good results by all measures.

Sean Bowen: Record by Run Style

Finally, in terms of main sections, let me look at the run style splits in terms of win percentages:

 

 

We can see a familiar pattern here with front runners doing best and hold up horses doing the worst. The A/E indices correlate with the above figures as shown by the following graph:

 

 

Both front runners and prominent racers have A/E indices above 1.00, which is excellent; and both groups secured ‘blind’ SP profits if being able to predict the run style pre-race.

My final graph shows Sean Bowen’s record on favourites by run style group:

 

 

These are very similar to the ones we saw earlier for Skelton: front running favourites perform extremely well, while held up/midfield early favourites performed relatively poorly.

 

Main Takeaways

Here is a table of the main takeaways highlighted in the research above, and which will hopefully help you find some profitable bets going forwards:

 

 

Two for the price of one again this week, and I do hope there are some useful angles, both positive and negative, for you in the above.

- DR

Jockey Profiles: de Boinville and Cobden

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This is the first in a new series of articles looking at the performance of some top National Hunt jockeys. In this initial offering I will be looking at Nico De Boinville and Harry Cobden. Both jockeys have the backing of huge stables with De Boinville riding primarily for Nicky Henderson and Cobden for Paul Nicholls.

I have analysed NH data for UK racing from 1st Jan 2016 to 31st Oct 2023. The main vehicle for my data gathering has been the Geegeez Query Tool, but I have also used the Geegeez Profiler. Hence all profits/losses quoted are to Industry SP, but I will quote Betfair SP where appropriate. All tables include A/E indices, an indicator of sustainable profitability. In addition, when data has been pulled from the Geegeez Profiler Tool, I have also shared the PRB (Percentage of Rivals Beaten) figures.

Let’s start with De Boinville.

Nico De Boinville: Overall Record

Let me first share De Boinville’s overall stats by looking at his performance on every single runner during the period of study:

 

 

This is a very solid set of figures – a win rate of more than one win in five, and an above average A/E index of 0.92 (the figure for all jockeys stands at 0.87). Losses of 11p in the £ to SP are also better than ‘average’ and if backing to BSP you would have made a small blind profit of £142.54 (ROI +5.5%). However, he has had a BSP winner at 130.0 which essentially is the reason for the + figure.

Nico De Boinville: Record by Year

Yearly stats are my next port of call. Here is a breakdown by both win, and win/placed (Each Way) percentage / Strike Rate (SR%):

 

 

As the graph indicates, De Boinville has been consistent in terms of yearly winners / placed efforts. There was a slight dip last year in 2022, but in 2023 he has won nigh on a quarter of all his races.

Nico De Boinville: Record by Betting Odds / Price (SP)

A look at the results by splitting them into different price bands:

 

The Evens and shorter group have performed above the norm, getting close to a break-even situation. These short-priced runners have done especially well in chases hitting a win rate of over 71%. The 7/4 to 5/2 group has edged into profit, so this price range has offered some value. However, I would not be confident that it will continue in subsequent seasons as the slightly inflated results are probably down to statistical variance.

Nico De Boinville: Record by Distance

A look at De Boinville’s record at different distances now. I have grouped them into four distance bands and to begin with I’m comparing win and each way strike rates:

 

 

This graph shows that the shorter the distance the better for De Boinville. If we look at the PRB figures (Percentage of Rivals Beaten) they correlate with the win/EW strike rates:

 

 

The 0.62 figure for the two mile or shorter races group is extremely impressive, as is the 2m1f to 2m2f group; less so the three miles or longer PRB figure of 0.48.

Nico De Boinville: Record by Race code

It is time to see if Nico’s record is better over the bigger obstacles, the smaller obstacles or on the level:

 

 

There are stronger figures across the board in hurdle races – a higher strike rate, better returns, and a higher A/E index. Backing all his mounts over hurdles to Betfair SP would have yielded a profit of £325.45 (ROI +21.1%), with six of the eight years producing a profit. Of course, a few big-priced winners have helped but hurdle races seem to be the races to concentrate on. Handicap hurdle races have produced the bigger profits to BSP but non-handicap hurdle races have also yielded a BSP profit.

Handicap chase results have proved to be poor by comparison. A strike rate of 13.2% has seen SP losses of 33p in the £; the BSP figures are not much better with losses standing at 27p in the £.

Nico De Boinville: Record by Racecourse

Below is a table displaying all courses where De Boinville has had at least 80 rides. The courses are listed alphabetically:

 

 

In general, the course strike rates are over 20% although Aintree and Cheltenham both dip below this mark. This is due to the competitive racing / bigger average field size you get at both tracks, and the A/E figures at those courses are actually top and third in the list. Uttoxeter results also come in at under 20% (15.65%) with a modest A/E index and PRB figure, so this looks a course to be a little wary of.

The Newbury figures are strong and are particularly impressive when focusing just on hurdle races. In these races at the Berkshire track, De Boinville has won 34 of his 116 starts (SR 29.3%) for an SP profit of £57.96 (ROI +50.0%). This profit almost doubles if backing to BSP standing at £106.62 (ROI +91.9%). However, don’t get too excited about the overall profits as a 50/1 winner (BSP 84.9) is almost solely responsible for the bottom line. Having said that, if you look at hurdlers at Newbury priced 2/1 or shorter, the record is very good (and profitable) – 22 wins from 38 (SR 57.9%) for an SP profit of £8.05 (ROI +21.2%); to BSP this edges up to +£10.33 (ROI +27.2%).

 

Nico De Boinville: Record by Trainer

Along with Nicky Henderson, only Ben Pauling has used De Boinville more than 100 times going back to the start of 2016. However, they have not joined forces at all in 2023, and only six times in 2022. Hence, I will simply focus on the combination with Henderson:

 

 

Let me compare these figures with his record with all other trainers combined:

 

 

There is a quite a difference as you can see. The strike rate for Henderson is more than double, and his runners have produced better returns, with both the A/E index and the PRB figures higher. Of course, this was perhaps to be expected as Henderson has a glut of quality horses.

There are a couple of Henderson / De Boinville stats I’d like to share:

  1. Henderson is not a huge fan of horses returning to the track quickly, but the jockey/trainer combo has done well when a horse is returning off a short break of two weeks or less. There have been only 50 qualifiers, but 13 have won (SR 26%) showing a profit to SP of £25.08 (ROI +50.2%).
  2. When De Boinville rides Henderson horses aged 3 or 4 the record reads 80 winners from 258 (SR 31%). Profits have been modest to SP (+£12.66, ROI +4.9%), but to BSP they look healthier at £56.53 (ROI +21.9%).

Nico De Boinville: Record by Class of Race

There are some interesting stats when looking at Graded / Listed races as the graph below of win strike rates show:

 

 

Grade 3 races, which in National Hunt are all handicaps, have provided a strike rate of just 1.1% - this is due to just one winner from 93 attempts. Of these 93 Grade 3 contestants, 39 of them were priced 8/1 or shorter. All 39 were beaten and only nine managed to place. 13 of the 39 were favourites, while 32 were in the top three of the betting.

In races of Class 2 or lower, De Boinville has hit win strike rates above 20% in three separate classes (Class 3, 4 and 5 events). He has only ridden in 41 Class 6 races, winning 6 (SR 14.6%), while in Class 2 events he is 46 from 299 (SR 15.4%).

Nico De Boinville: Record by Run style

Regular readers of my articles will know I am fan of sharing run style data. To begin with here is a breakdown of Nico’s run style performance in terms of win strike rate across ALL races:

 

 

Front runners (led) have edged it over prominent runners in terms of strike rate, both groups have secured a better than one-in-four win rate. If we examine the A/E figures we can see that they correlate with the SR%s as the following chart shows:

 

 

The led A/E index is decent at 1.05 which suggests these runners would have been value investments. Horses that raced mid-division or further back early would have offered punters poor value.

The two sets of run style data clearly show that when De Boinville is riding, a horse racing close to or up with the pace is what, as punters, we are hoping for.

Nico De Boinville: Additional stats

Before moving onto Harry Cobden, here are some extra stats for De Boinville that I feel are worth knowing:

  1. His record in novice events is poor from an ROI perspective. Despite a strike rate around the 25% mark, in novice chases you would have lost over 24p in the £ to SP (19p in the 3 to BSP). In novice hurdle races the figures are similar with 25p in the £ losses to SP, 15p in the £ to BSP.
  2. Horses priced 14/1 or bigger in novice events are 0 from 123.
  3. De Boinville has secured a better strike rate on fillies and mares (22.8%) compared to their male counterparts (21.4%). The female runners would have also produced a blind profit to SP of £27.57 (ROI +5.6%); to BSP this increases to +£128.50 (ROI +26%).
  4. Sticking with fillies and mares, when they have started Evens or shorter, 36 of the 45 have won securing an 80% strike rate. Returns have been positive, too, as one would expect – 31 pence in the £ at SP, 37p using BSP.

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Harry Cobden: Overall Record

Let’s now shift our focus to Harry Cobden and start by looking at his record on all horses in all UK NH races:

 

 

These figures are remarkably similar to those for De Boinville. The strike rates are within 0.21 of a percentage point and the ROIs are both around the -11% mark.

Now it is time to break down these data, firstly by year.

Harry Cobden: Record by Year

Here is a graphical breakdown by both win, and win/placed (Each Way) percentage / Strike Rate (SR%):

 

 

As the graph shows, Cobden has managed a win strike rate of 20% or more in six of the eight years, with the other two years just missing out (18.5% and 19.5%). Likewise, seven of the eight years have seen each way (win & placed) strike rates hitting over 40%. Overall, his figures look very consistent.

Harry Cobden: Record by Betting Odds / Price (SP)

Let us see whether any market / price patterns can be found by breaking down Cobden’s results by Starting Price bands:

 

 

The shorter priced runners (first three rows in the table) have provided similar results to those for De Boinville. Horses priced Evens and shorter have effectively broken even, while the 7/4 to 5/2 have again snuck into profit. As a rule, Cobden’s price stats suggest that horses 5/2 or shorter look the ones to concentrate on.

Harry Cobden: Record by Distance

A dive next into Cobden’s record at different distances. I have grouped them into the same four distance bands I did earlier and am looking at the win and each way strike rates:

 

 

These are a more even set of stats than those for De Boinville, with only a slight drop in longer distance races (3 miles or more). If we look at the PRB figures we get the following splits:

 

 

A much more even grouping for Cobden than we saw for De Boinville. He seems to ride all distances well, especially distances of 2 miles or less.

Harry Cobden: Record by Race code

The next table illustrates Harry’s record by race code.

 

 

Chases and hurdle races offer similar looking stats, certainly as far as strike rate and ROI% is concerned.

The results in bumpers (NH Flat) are poorer when considering the whole-time frame, and losses have been steep at over 27% (27 pence in the £). However, 2022 and 2023 would have seen you break even thanks to a strike rate of just over 20%.

Harry Cobden: Record by Racecourse

I am now going to look at all courses where Cobden has had at least 80 rides. The courses are listed alphabetically:

 

 

Wincanton is the course that initially catches my eye. Not only has he ridden there more than anywhere else, but he has secured the best strike rate of all courses, too. A small profit to SP has also been achieved and the PRB figure of 0.68 is extremely high considering we are talking about over 300 rides. Not only that, but his record there has been very consistent hitting a strike rate of over 25% in each of the eight years. Hurdle races have provided the best results with 61 wins from 171 rides (SR 35.7%) for an SP profit of £40.86 (ROI +23.9%).

Taunton is another track with an excellent PRB of 0.68 – his rides at the course have seen a decent SR% again; this time 28.3%, but no blind profit. Like Wincanton, the hurdle results at the Somerset venue are the best with a 31% strike rate for a break-even scenario.

Other tracks where Cobden has done well include Newbury, Plumpton, and Worcester. Before moving on I will mention his record at Musselburgh. He has only had 24 rides there, but has been successful on 11 of them (SR 45.8%) for a profit of £13.21 (ROI +55%).

Harry Cobden: Record by Trainer

During the period of study, Cobden has had 100 plus rides for two trainers – Paul Nicholls and Colin Tizzard. Cobden had ridden over 600 times for Tizzard when that trainer passed the baton to his son Joe in April 2022. Hence, I will focus on his combination with Paul Nicholls:

 

 

He has secured a strike rate just above one win in four, but losses are slightly bigger than his overall P&L. For comparison purposes, here is his record with all other trainers combined:

 

 

As we can see the strike rate drops markedly to around one win in every six rides, but losses have been smaller. The PRB is lower, while the A/E index remains the same.

Harry Cobden: Record by Class of Race

When sharing Nico De Boinville’s stats earlier, it was shown that his record in Grade 3 contests was extremely poor. We see a similar situation when looking at Cobden’s results as the graph below shows:

 

 

Once again, the results for Grade 3 contests (remember, all of which are handicaps) are quite woeful, especially when we consider his overall record. It should also be noted that 39 of his rides in Grade 3 contests came from horses in the top three in the betting. Of these, just one prevailed.

Harry Cobden: Record by Run Style

Finally, in terms of main sections, let’s look at the run style splits in terms of win percentages:

 

 

This breakdown shows how effective Cobden is when taking the early lead. A strike rate of 35.3% is exceptional. If you had been able to predict pre-race which of his horses went into an early lead you would have secured an SP profit of £126.04 (ROI +16.2%). Conversely, backing all hold up horses would have seen huge losses of £472.71 (ROI -47.8%). As one would expect the A/E indices for his Run Style runners correlate with the win rates:

 

 

The clear takeaway here is that Cobden on a front runner is a potent combination.

Harry Cobden: Additional stats

Before concluding this piece here are some extra stats for Harry Cobden that are worth knowing:

  1. Good to firm ground is relatively rare in NH racing but Cobden has scored 33.6% of the time when racing on this ground. He has won 45 races from 134 rides. A small 4p in the £ profit to SP would have been achieved if backing all such runners blind.
  2. When Cobden rides a horse for a second time in their careers having won on them last time out, he has an excellent strike rate of 28.6%.
  3. He has a modest record in maiden races in terms of returns. Losses of over 28p in the £ would have occurred if backing all qualifiers. If you exclude trainer Paul Nicholls from these figures the losses are even greater at over 40p in the £.

Main Takeaways

Below is a 'cut out and keep' table of the main takeaways from this research.

 

That’s all for this article – two jockeys for the price of one! I hope it has uncovered some angles that may prove useful for readers over the coming months.

- DR

Dave Renham: NH Q&A

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This article is slightly different from my usual pieces in that I am going to address some of the questions you have asked in the comments over the past couple of years, writes Dave Renham.

As the turf flat season has come to an end, I will concentrate on some of the National Hunt questions that have been posed. Any profit and loss figures quoted have been calculated to Betfair Starting Price less 5% commission. Data have been taken from UK NH racing going back to 1st Jan 2017. Here we go...

 

Does the market position of the horse on debut make any difference in their next few runs?

For this answer I am going to combine the stats for all horses having their second, third, fourth and fifth careers starts. Horses that were favourite on their debut have gone on to win just under 20% of the time when grouping together all the results of their next four races. The graph below shows the second to fifth run combined strike rate for different market positions on debut:

 

 

The graph shows that the position a horse appears in the betting market on debut has an influence on their success early in their careers. In terms of returns, favourites on debut have got close to breaking even if you had backed them in all of their next four starts. Compare this to horses that that were 5th or higher in the betting market on debut – they would have combined to lose you over 20 pence in the £.

 

How successful are beaten favourites on their next start?

Horses that were beaten favourites last time out scored in 18.6% of their next starts; but backing all qualifiers would have produced a loss of 6p for every £1 staked. Looking at horses that were beaten favourite two starts back, they have produced a strike rate of 17.3% with slightly higher losses of 7p in the £.

Horses that have been beaten favourites on both of their last two starts have won just under 20% of the time, but losses have been steeper at 9p in the £. These are generally worth swerving!

 

Is wearing blinkers for the first time a positive or a negative?

Trainers turn to headgear in an attempt to make it easier for the horse to focus on what is in front of them rather than being distracted by other horses or the crowd etc in their peripheral vision. Hence, trainers hope that the application of blinkers will help the horse concentrate better and thus perform better. However, the stats point to the fact that horses in first time blinkers are a negative.

First time blinker wearers have won 8.5% of their races (205 wins from 2402 runs) producing losses of £367.95 (ROI -15.3%). One positive stat worth sharing is that if the horse was prominent in the betting (SP of 9/4 or less), this subset of first time wearers made a profit, albeit from a small sample. There were 104 qualifying horses of which 49 won (SR 47.1%) for a profit of £28.75 (ROI +27.6%).

Trainer Gary Moore has a good record with horses wearing blinkers for the first time scoring 7 times from 28 Runners (SR 25%) for a profit of £30.27 (ROI +108.1%). Yes, it is a very small sample but there are two other positives I can share. Firstly, seven other runners were placed, giving Moore a win and placed strike rate of 50%; and secondly, I checked his 2013 to 2016 results also and he won 5 from 18 (SR 27.8%) for a profit of £30.42 (ROI +169.03).

Of course, there are other headgear options and the application of first time cheekpieces has produced much better overall results than first time blinkers. The stats show that 10.7% of these runners won and losses stood at only 1.2% - or just over 1 p in the £ lost for every £1 staked.

 

How does the weight a horse carries in a handicap chase affect the result?

Horses are assigned different handicap marks in handicap races which affects how much weight a horse will carry. Better horses with higher handicap marks (Official Ratings) carry more weight. The idea is that the weight will balance out the differences in ability with the theory that any handicap race will end with all runners finishing at the same time. The handicapper does an excellent job, but higher weighted horses still win more often than lower weighted runners. The graph below shows this:

 

 

Top weights in handicap chases are more than twice as likely to win as horses weighted 7th or lower. However, the market does adjust for this. Top weights, despite their decent win rate, would have lost you around 7 pence in the £ if backing all of them ‘blind’. Horses that were 7th or lower in the weights actually turned a small BSP profit. Of course, a few big-priced winners did skew these results somewhat, but even without those you could legitimately argue that lower weighted horses offer slightly better value in these races.

 

What percentage of races are won by a horse from the top three in the betting?

When combining the results of all National Hunt races, the winner comes from the top three in the betting in 73% of races. However, there is a big variance depending on type of race. Here is a table showing the performance of the top three in the betting in different race types. I have ordered the table row from highest percentage to lowest:

 

 

Handicap races are generally more competitive than non-handicaps which is one of the reasons why the lowest figures are for the handicap groups. Having said that, field size plays a part and handicaps tend to have bigger fields which naturally impacts the percentages. In terms of returns, horses from the top three in the betting have performed best in non-handicap novice chases – these runners would have lost you just 1p for every £10 bet (ROI -0.1%).

 

Have you any breeding stats that will help with my National Hunt betting?

Here are my favourite stats connected with breeding.

  1. When looking at French-bred runners, did you know that female horses have outperformed male horses in terms of strike rate? This is unusual as male horses have a general strike rate edge over female runners in all types of racing be it flat, all weather or National Hunt. French-bred runners that are female have won 13.72% of their races; males have won 13.57%. Females have provided by far the best value of the two also – males would have lost you 9p in the £; females would have won you 14p in the £. I should also say that French-bred females have a strike rate edge in both hurdle races and chases. Male French-bred runners enjoy a small edge in bumpers.
  2. There are a handful of sires whose chase record is considerably better than their hurdles one. Muhtathir is one such sire. His chase SR% stands at 18% while his hurdle one is just 7.9%. In terms of A/E indices, his chase index is 1.04, his hurdle one is just 0.61. Another sire with a similar bias is Schiaparelli. His hurdle SR% has been 9.3% (A/E 0.79); his chase SR% more than double that at 18.9% (A/E 1.02). Fuisse has similar stats to Schiaparelli – he has a 21.4% strike rate in chases (A/E 1.14), in hurdles this drops to 11.9% (A/E 0.81). Both Fuisse and Schiaparelli have made blind profits in chases to BSP.
  3. Sire Trans Island performs much better in hurdle races compared to chases. Strike rates are 13% (hurdle races) versus 8% (chases) with the A/E indices standing at 1.10 and 0.70 respectively. Coastal Path is another sire who seems to have a clear edge when comparing hurdle results to chase ones. In chases he has a 12.1% SR% with his runners showing hefty losses of 44p in the £; in hurdle races the strike rate is 17.7% and his runners have made a blind profit of £322.48 (ROI +107.9%).
  4. Poliglote is a sire that only has a handful of hurdlers these days, but he is the only sire to secure a strike rate of over 20% in these races over the lesser obstacles (44 wins from 215 runners). Returns of 73 pence in the £ is noteworthy and hence any hurdler sired by Poliglote is worth close scrutiny.

 

 

Are there any trainers who do particularly well in bumpers?

Let’s start by looking at the trainers with the highest strike rates in bumpers. In order for them to qualify they must have had at least 75 runners and achieved a 20% win rate or higher:

 

 

It will probably come as no surprise to see Paul Nicholls and Nicky Henderson at the top. However, neither have made an overall profit, losing 4p and 5p in the £ respectively. There is a profitable angle for both Nicholls and Henderson though when we dig a bit deeper into the numbers – and it is the same angle. For both trainers you would have made a profit if ignoring horses on debut and those with just one run under their belt. For Henderson, horses having their second or more career start have won 20 races from 79 (SR 25.3%) for a profit of £23.13 (ROI +29.3%); for Nicholls he has had 21 winners from 69 (SR 30.4%) showing a profit of £18.79 (ROI +27.2%).

There is one more trainer I would like to mention who does not appear on the above graph and that is Hughie Morrison. Morrison is probably better known as a trainer on the level, but he has an excellent record albeit from a smaller sample. He has had 58 bumper runners since 2017 of which 14 have won (SR 24.1%) recording a profit of £24.87 (ROI +42.9%). In fact, 7 of his last 19 runners (SR 36.8%) have won.

Sticking a little bit longer with bumpers, there are two trainers who have done particularly well with bumper debutants. Harry Fry and Anthony Honeyball are the trainers in question. Their figures are impressive:

 

 

Both trainers have been consistent, each making a profit in five of the seven seasons.

 

Which trainers do best with horses that make their career debut in a hurdle race?

Just under 70% of all National Hunt horses have their first ever run in a bumper. However, a good proportion start off their careers by ignoring the bumper route and going straight over hurdles. One trainer excels with these runners, namely Nicky Henderson. His record is outstanding:

 

 

The win strike rate is on another stratosphere with only two other trainers hitting over 20% (20% and 26% for Donald McCain and Paul Nicholls respectively). His biggest priced winner has been at 11/1 (BSP 17) so his results are not skewed in any way. Henderson also seems to target hurdle races at Newbury as 11 of his 18 runners (SR 61.1%) have won there on debut.

There are two stables to avoid, however, when they send an unraced horse over hurdles for their debut. Firstly, Venetia Williams has saddled just three winners from 52 (SR 5.8%) for a loss of £35.65 (ROI -68.6%). In fact, 31 of these runners started at single figure odds so this is not a case of lots of outsiders losing. The other stable to avoid is the Oliver Greenall and Josh Guerriero yard. They have managed just two winners from 64 runners (SR 3.1%) for a hefty loss of £52.38 (ROI -81.8%). In truth, a fair proportion of their runners have been at the higher end of the price spectrum, but regardless of price I think a wide berth is the prudent call.

 

At the Cheltenham Festival Irish trainers seem to excel. What is the record of Irish trainers in all UK National Hunt Races?

Here are the stats for all Irish trained runners in the UK going back to 2017:

 

 

This is an impressive set of figures. Having said that, 80% of the profits occurred at the Cheltenham Festival from around 28% of the total runners.

The Irish seem to target the better meetings and the bigger prizes. There are four courses where they have had at least 200 runners – all four have seen a blind profit as the table below shows:

 

 

It is no surprise that Cheltenham has seen the best returns, but the Perth stats are strong, too. It makes sense to me that when you see an Irish runner declared at any of these four tracks you should look carefully into their chances. If the race is a class 1 or 2 contest, then that has also been a positive.

 

Trainers with sole runners on the day – are any worth following?

The penultimate question for this piece concerns trainers who have had just one runner racing on a particular day. Firstly, let us see the trainers with the best strike rates with these sole runners.

 

 

The usual suspects of Nicholls and Henderson head the list, but it is interesting to see Ann Hamilton in fourth place. Six of the ten earned a blind profit – Henderson, Skelton, Hamilton, Richards, Lacey and Bailey. As far as Henderson is concerned, if you ignored his runners that started favourite, his record is mightily impressive with 53 wins from 249 runners (SR 21.3%) for a profit of £117.99 (ROI +47.4%). This subset of runners would have yielded a profit in six of the seven years.

 

What is the comparison of novice chase debutants with horses having their second chase start in a novice chase?

This is a very recent question which was asked after I wrote a piece on chase debutants. I was asked about trainer improvement between chase debut in a novice chase and the second chase start in a novice chase. Horses that are racing for the first or second time often contest novice chase events so there is a reasonable data set for some trainers.

Firstly, let me compare the overall strike rates in novice chases between horses having their first ever chase run compared with those contesting a novice chase having only their second run over fences:

 

 

As you can see, and might expect, horses improve for the experience. Remarkably, horses having their second chase run have made a blind profit when they raced in a novice chase on this second start. That 18% strike rate has helped turn a 7p in the £ profit.

Now to compare trainer performance. Below is a table displaying individual trainer strike rates on chase debut and 2nd chase run. Once again, these figures apply only when the race in question was a novice chase. To qualify each trainer has had at least 30 runs for each group. I have also created an extra column where I have divided the 2nd run SR% by the debut SR%. To show an improvement this figure needs to be higher than 1.00. I have ordered the table by ‘improvement’:

 

 

Tim Vaughan has shown a remarkable improvement going from 4 wins from 72 on debut to 9 wins from 33 on second start. There is also a significant differential when comparing the Evan Williams stats. There are very few trainers whose chase debutants out-perform their second chase starters but there are a handful (at the bottom of the table). I hope this table will prove useful to assess how likely a horse is to improve from chase debut to second chase run.

As stated, this final question has been geared to novice chase data only. Not all first- and second-time chasers race in a novice chase. Hence you would get bigger data sets if you allowed ‘all chases’ to be analysed for these two groups of runners. Having said that, I have looked briefly at that data, too, and the results for most of the above trainers are similar.

So, there you have it – a selection of answers to the many questions I have been asked about National Hunt racing in recent times. If you have any specific questions, please post them in the comments. It might inspire a new article!

- DR

When Hurdlers Go Chasing

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Some horses are bred to chase, others are not, writes Dave Renham. Some horses are better over hurdles, others are better over the bigger obstacles. In this article I will look at horses making their debut in a chase having switched from hurdling last time out. The data have been taken from UK National Hunt races spanning the seven calendar years from 2016 to 2022. All profits and losses have been calculated to Betfair Starting Price less 5% commission.

All Runners

Firstly let’s review the breakdown for all first-time chasers that qualify having run las time out (LTO) over hurdles:

 

 

These chase debutants have won around one race in every seven and there is not a particularly good bottom line with all runners combining to lose over 10 pence for every £1 staked. In addition, the A/E index is quite modest at 0.88.

Handicap vs Non-handicap

Diving deeper into the stats, we’ll start by splitting the results between handicap and non-handicap races. Here is the win strike rate comparison:

 

 

As can be seen from the graph, debutants that contest a non-handicap have a much higher strike rate, which is to be expected; but, not shown, handicap runners would have lost a little less money overall. A/E indices are similar with 0.89 for non-handicaps, 0.87 for handicaps. It should be noted that most horses making their chase debut do it in a handicap – 72% in fact. With no clear edge to be had let’s move on to market factors.

Betting Market

How good a guide is the betting market is the next question? Here is the breakdown by Industry SP grouping.

 

 

As is shown, very short prices (less than 1/2) have scored enough times to make a profit. Conversely, the very big priced (40/1+) debut chasers have a dreadful record. They have proved extremely unlikely to spring a surprise and losses of nearly 80p in the £ would be painful had you backed them all. Horses priced between 5/2 and 6/1 (combining the price brackets 5/2 to 4/1 and 9/2 to 6/1) edged into profit from a decent sample size. You could argue that, if there is any value, then this is the price bracket which might offer some.

The 25/1 to 33/1 group looks to me to be an anomaly, especially considering the strike rate exceeds the 16/1 to 22/1 group’s strike rate. My guess is that the significant profit seen for the 25/1 to 33/1 bracket is unlikely to be replicated in the years to come. As a writer/researcher it is all well and good quoting profit figures, but if they are unlikely to be sustainable, for whatever reason, it is important to make readers aware of one’s thoughts and the likely bigger picture. Before moving on, I wanted to try and test whether my theory that the recent results for 25/1 to 33/1 runners was likely to be an anomaly. To do that I crossed checked data from 2009 to 2015 and, during that period, 25/1 to 33/1 shots won less than 2% of the time losing 44p in the £.

 

Gender of horse

This is an area I always look at when researching racing data because there are occasions when the sex of the horse makes a real difference in terms of results and returns. It is also a factor that not many punters worry about, so I feel there is a potential edge to be had in certain circumstances. Let’s see whether that is the case here. Firstly, a look at the win strike rates:

 

 

As we can see there is a big variance in terms of win strike rate. Male horses comfortably outperform female runners when making their chase debut. Now it is important to note that male runners do make up 84% of all runners. However, when we look at losses to BSP female runners have actually lost more in absolute terms than males: females produced a loss of £361.51 to £1 level stakes compare with -£344.48 for males.

When we compare the return on investment, there is a chasm between the two groups. Colts and geldings lost just 6% (6p in the £), while fillies and mares lost over 34% (34p in the £). These stats are powerful and can help give is an edge.

Age of horse

A look at what difference the age of the horse makes. Here are the splits:

 

 

4yo chasing debutants are relatively rare but from this modest sample they have performed well. The main takeaway from this table, though, is that horses aged 9 or older are to be avoided. They win far less often than younger runners, and the returns have been dreadful: nigh on 50p in the £. Mares aged 9 or older making the switch from hurdles to chases for the first time have been rare; but of the 71 qualifiers just 2 won!

The 8-year-old group also perform well below the norm and proved very poor value.

Last time out finishing position

The next area to come under the microscope is LTO performance in terms of finishing position. Let us look at the win strike rates first:

 

 

Last time out winners have the highest strike rates followed by LTO runners up, so a better LTO performance seems to be significant from a win probability perspective. It will come as no surprise that horses that were pulled up last time are a cohort to avoid – they have produced a low strike rate at 9% with losses of over 20 pence in the £ from 749 qualifiers.

The anomaly here is the group of horses that fell or unseated LTO. Their strike rate of 16.1% is higher than I had expected. Not only that, but these runners would have secured a profit of £117.10 to £1 level stakes (ROI +99.2%). At just 118 qualifiers, the sample size is quite small, so I think there is a case for remaining sceptical.

Looking at these results in more detail I realised that they were skewed somewhat by three big priced winners. That helps explain the profit figure. I did back check 2009 to 2015 data for LTO fallers/unseated riders to give more context: the strike rate in that period was 16% as well, but in this time frame they made losses more in line with my pre-research expectations of 16p in the £.

Country of Breeding

A quick look at breeding in terms of the origin of the horse. For this I want to compare the record of British-, Irish- and French-bred chase debutants. Here are the strike rates for each:

 

 

There is a big advantage to French-bred chase debutants in terms of their win chance. Remarkably, backing all French-breds blind would have yielded a profit of £70.55 (ROI +6.2%). French-bred chase debutants have shown good consistency, too, having hit a strike rate more than 18% in six of the seven years under review. Four of the seven years turned a blind profit, two years made a loss and one year broke even. Chasing debutants who are French bred demand close scrutiny.

Trainers

The final area for research is usually a popular one, namely trainers. Below is a table of trainers who have had at least 50 runners switching from hurdles to make their chase debut. I have ordered them by strike rate:

 

 

There is a huge difference in strike rate between Henderson at the top (31.82%) and Hawke at the bottom (5.66%). 13 of the 30 trainers have made a BSP profit with 17 in the negative.

It will come as no surprise to see Nicky Henderson and Paul Nicholls occupy the top two spots, but despite excellent strike rates neither have made a profit. This is simply due to the fact punters and bookmakers know these two trainers inside out and finding any value for either is relatively rare regardless of ‘angle’. However, there is one Nicky Henderson positive to share and that is with his odds-on runners. They have won a remarkable 24 times from 30 runners (SR 80%). A profit of £6.19 (ROI +20.6%) would have been achieved if backing all of them.

However, the trainer that catches my eye is Dan Skelton. A strike rate of just over one win in every four and decent profits to boot. Let’s dig deeper into his stats. Firstly, a year-by-year breakdown:

 

 

2016 was the one losing year and the only year where his strike rate dipped below 20%. The 2017 to 2022 results were very consistent, and impressive, showing that his bottom line has not been skewed by a few big priced winners.

Harry Skelton has ridden the vast majority of these runners, and this combination has been responsible for profits of £75.76. Backing this duo would have seen you earn over 44p for every £1 bet during the seven-year period (from 169 qualifiers). Here are three more positive Skelton angles:

  1. He has bucked the female horse trend, scoring with 28% of this cohort (10 wins from 35).
  2. His 5yo runners have done particularly well, winning 15 of their 42 starts (SR 35.7%) for a profit of £38.51 (ROI +93.9%).
  3. Skelton has an outstanding record when his chasing debutants tackle shorter distances. In races of 2m 1f or less he has recorded 25 successes from 67 (SR 37.3%) showing a profit of £46.40 (ROI +70.3%).

Dan Skelton looks a trainer to be on the right side of with chasing debutants.

Summary: bullet points

Before I wind this piece up let me share what I think are the strongest stats both positive and negative from my research on chase debutants making the switch from hurdling.

  1. Horses priced 5/2 to 6/1 (Industry SP) seem to be the range to concentrate on.
  2. Avoid horses priced 40/1 or bigger.
  3. Female horses have a very poor record in terms of both strike rate and returns.
  4. Four-year-olds do well albeit from a modest sample. Avoid chase debut runners aged 9 and up, and it is probably also worth swerving 8yo’s.
  5. Avoid horses that were pulled up LTO.
  6. French-bred horses comfortably outperform British- and Irish-bred runners.
  7. Dan Skelton is a trainer to keep a close eye on as his runners have a very good overall record.

With the National Hunt season clicking into gear now, horses making their chase debuts will be appearing more and more regularly. Hopefully, this article will help to point you in the right direction.

 - DR

Poor Value Favourites in NH Racing

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I wonder, how many readers back favourites from time to time? Is there anyone reading this who only bets on favourites? It certainly would be interesting to know, writes Dave Renham. Maybe you'll share your answer in the comments below this article. I have two reasons for starting with those questions: firstly, I am genuinely interested to know; and, secondly, this article is based around favourites.

I think it is fair to say that favourites have had a bit of a 'bad rap' in the last ten to twenty years, mainly due to punters being bombarded, correctly in the main, with the concept of value. Thirty or forty years ago I am guessing that most punters had less of an understanding of how to assess value prices. Their mindset would probably be more attuned to winners – if they picked enough winners, they would make money. Of course, we know that this is not the case. You can have 50% winners and still lose money; conversely, you can have 15% winners and make a good profit. I still smile to myself when any of my non-racing friends go to a race meeting and ring me up with the question, “Dave, can you pick me some winners please?”

My reply is always the same, “if you want to back winners then back the favourite – this will give you more winners in the long term”. I do clarify that by telling them that is not the right way to bet, but if they want ‘winners’ then my answer gives them the best chance of achieving that on the day in question. And for once a year punters, this is probably the smartest way to bet - trust to luck. But for those of us more regularly engaged with the puzzle, we need to show more discernment.

With the nights drawing in, many of us in the racing world are turning our attention away from the turf flat season and starting to think National Hunt, and the great meetings and races that will take place over the next six months. In this article, then, the focus will be on National Hunt racing and favourites. More specifically, I will be looking to highlight market leaders that the data suggest are showing as offering poor value. Data have been taken from the last six full calendar years of UK NH racing spanning 2017 to 2022, and I will be sharing the results for clear favourites only (excluding races with joint- or co-favourites).

So why look for a race with a poor value favourite? Well, it is not because I am suggesting laying such a horse, although that may be an option in some cases. Rather, it is to do with the fact that if the favourite offers poor value, then there must be value elsewhere in the race on another runner, or runners. Before I start looking for examples of poor value favourites, let me share the breakdown of win strike rate for favourites by year:

 

 

As the chart shows, the strike rates do not fluctuate too much, with a difference of only 2.7% between 2022 (the year with the highest SR%) and 2020 (the year with the lowest).

Here is the overall record of NH clear favourites over these six years:

 

Essentially this is not a bad starting point in terms of returns, with losses of just over 2½ pence in the £. Hence taking National Hunt favourites as a whole, in terms of betting to Betfair SP, they are not bad value. To give some context, the results for backing all fifth favourites would have lost you 7½ pence in the £, sixth favourites 12p in the £. Suddenly 2½p losses look quite good!

So, we have our benchmark figures here for favourites – strike rate of around 37.5%, losses around 2½p in the £, and an A/E index of 0.94. Let's now break this overall group into subsets.

 

Performance in NH Races by Age of favourite

The first area I want to look at is the age of the favourite in question. Below is a bar chart showing the win percentages for different ages of favourites. There is quite a clear pattern as you will see:

 

 

It should be noted that three- and four-year-old favourites make up only 10.6% of all NH favourites and hence I am more interested in the correlation between favourites aged five and older. As the graph indicates, favourites aged five have the best strike rate and, thereafter, that percentage gradually drops for subsequent age groups. Favourites aged ten and older are clearly the worst performing animals in terms of strike rate.

So, have we potentially found a group of favourites that are poor value? We need to see the overall figures for favourites aged ten or older to see if the drop in strike rate effects the bottom line:

 

 

As shown, being of that vintage does affect the bottom line, with losses of over 11 pence in the £. Also, the A/E index has dropped to 0.87 which is a secondary indicator of poorer value.

Interestingly, these older favourites have performed worse in chases than hurdles. Chases make up most of the races with older favourites and the chase stats read 247 wins from 807 runners (SR 30.6%) for losses of £146.28 (ROI -18.3%); A/E index 0.82.

While I was trawling through some of the data, I noticed a horse called Midnight Moss who was a ten-year-old last year (2022). He started favourite in his last three races of 2022 (all chases) with the following results:

 

Clearly punters were happy to keep giving this fella another chance, but in hindsight these races would have given us a good opportunity to look for value elsewhere. For the record, Midnight Moss has run once as an 11yo in 2023... and yes, you’ve guessed it, he started favourite and was beaten into second again.

 

Performance in NH Races of Favourites that were narrowly beaten last time out

The next group of favourites I want to look at is those runners which were just touched off last time out. I'm including horses that were either beaten by a nose, a short head, a head or a neck in their last race. Here are the figures for that subset of runners who started clear favourite next time out:

 

One would expect the strike rate to be decent, and it is, but the returns are very poor for these favourites. Losses of over 15p in the £ is extremely hefty considering the overall stats shared earlier. This looks to me a classic case of horses being overbet, the theory being that they ran so well last time that they have a very good chance of getting their head in front this time. And so, despite a decent enough win rate, their actual starting prices have averaged out to be much shorter than their true odds of winning.

Before moving on, if we focus on handicap races only then the results for these narrowly beaten last time out runners who start favourite get significantly worse. Horses that were beaten a neck or less in a handicap last time out, and who are racing in a handicap again as the clear favourite have produced the following results:

 

Losses of nearly 28p in the £ are quite shocking for this group of NH favourites.

 

Performance in NH Races of Favourites having their second career start

Horses having just their second run are still relative unknown quantities. Plenty of horses run well on their first start but fail to back it up, whereas others run poorly first time out and then improve out of all recognition next time. It makes sense therefore that horses that start favourite on their second career run may not be the best betting proposition. That was my theory at least; below is the evidence, the table showing favourites who have previously run just once:

 

The strike rate here is high, a fair bit above the average for all favourites; but returns are relatively poor – losses approaching 10p in the £. The 0.87 A/E figure is low also. As with horses that were narrowly beaten LTO, this again looks a case of a group of horses being overbet driving the prices below their true odds. It is also worth sharing that horses which won on debut lose a little bit more again when favourite next time (losses of just over 10p in the £).

Performance in NH Races of Heavy ground Favourites

To begin this section, here are the win strike rates for NH favourites in terms of going:

 

 

The figures are relatively even – the best strike rate has occurred when the going has been the firmer side of good. Having said that, this firmer going is relatively rare, making up just 3% of all NH races. In terms of races on heavy ground, clear favourites have also done well, winning slightly above the norm and losing backers just 2p for every £1 staked.

I am guessing most people will be thinking that previous heavy ground winners are a positive if racing again in such conditions especially if starting favourite (me included). However, this has not been the case as the stats show:

 

Losses are close to 10p in the £ for these past heavy ground winners.

On the other hand, clear favourites have fared far better on heavy ground if they are yet to have won a race on this going, as these figures clearly show:

 

Hence, the data for these heavy ground favourites seem clear-cut: be apprehensive of a previous heavy ground winner whereas don’t immediately rule out if not a previous heavy ground winner. What is most interesting, perhaps, is that the win rate of heavy ground win 'virgins' is also higher than those to have won in the deep mud previously.

 

Performance of Favourites in Handicap chases

Maiden Chasers

Finally, in terms of hard data, I want to explore certain favourites in handicap chases. Firstly, let us consider the performance of handicap chase favourites who had never previously won a race over these bigger obstacles. To qualify they must have raced at least once over fences in their career:

 

These losses are 7p in the £ above the average figure for all NH favourites, which means once again there should be value by ignoring the favourite and looking at the other runners in the race.

Let's further consider the subset of these handicap chase favourites who had not only failed to win a chase but in all previous chase runs had not been placed either:

 

Logic suggested to me that these runners might perform relatively poorly as regards favourites and the figures bear that out. Sometimes results turn out like you would expect them to!

*

This article has highlighted several cohorts of potentially poor value NH favourites based on the last six calendar years of UK jump racing. Now, as I always say, articles like this are reporting on the past; there are no guarantees that the figures shared will be replicated in the future. However, most of the sample sizes are decent and the angles are underpinned by credible logic, which gives much more credence to figures. If I was sharing favourite results with only 50 qualifying runners, or where I was unable to explain the results, then it would be right to be sceptical.

Before wrapping up there are a couple of other ideas I have had in regard to finding potentially poor value favourites and both involve using Geegeez tools. The first is looking for NH favourites who display a negative run style. Imagining a two mile handicap chase at Hexham as an example, if we look at the run style figures going back to 2016 (8+ runners) in the Pace Analyser tool, we see the following:

 

 

Clearly this course and distance favours front runners/prominent racers. If the favourite happened to be a habitual hold up horse, then this may be an opportunity where the value lies elsewhere. Using Query Tool, here are the results for this group of favourites, by run style (4 is led, 3 prominent, 2 mid-div, 1 held up). There was just one winner from the twelve clear favourites to race in the latter part of the field in these races, whereas those favourites on the lead or prominent won ten from 22 for a better than 22 point profit (ROI 100%) at SP.

 

 

The second idea involves using the Instant Expert tab. The idea behind this one is to look for favourites that do not have many ‘greens’ within the traffic light ranking system. Green data is positive, and here is a recent example where the traffic light system seemed to highlight a poor value favourite. The race in question was the 3:33 at Wetherby on 18th October 2023. Here is a screenshot of the Instant Expert screen for that contest:

 

The favourite was Deyrann De Carjac, but looking along his row, we can see no greens, three reds and two ambers. Now, in this case the figures are looking at win percentages across each horses’ entire career. Whether this is the optimum setting, I'm not sure. You could look at placed percentages instead, or cut the data to the last two years, race code to chase, select handicaps only etc. However, based on the long-term win percentages for the runners in this race, Deyrann De Carjac looked a poor value favourite. Of the remaining runners Mackenberg priced at 15/2 with four greens was arguably offering some value.

Even looking at handicap chase form in the last two years only, Deyrann De Carjac was unappealing, and Mackenberg well suited to conditions:

 

The result of race was:

 

As we can see, Deyrann De Carjac was last of the five finishers. Also, for eagle-eyed readers you may have noticed that he was a ten-year-old racing in a chase – a poor value favourite stat I shared earlier. Mackenberg didn’t win to give the ‘dream’ result but ran well to finish second beaten a short head.

I appreciate this is just one example and this is a very tricky one for which to collect historical data. However, as I said before, there is plenty of logic to suggest it ought pay off in the long term: these horses are showing themselves to be either unsuited or unproven against today's conditions and they're being sent off favourite. That doesn't appeal to me!

I hope you have found this article interesting and also illuminating. If you have any ideas to test for poor value favourites, please drop them in the comments.

- DR

Steamers and Drifters: Part 3

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I was originally not going to do a ‘part 3’, but in the comments a reader asked about early morning odds and if there was any data available in connection with price movement, writes Dave Renham. As that was data I could access, I thought I would do some digging and share my findings. So here goes...

As in the previous two pieces I am focusing on flat and all weather racing in the UK spanning five years from 2018 to 2022. Bookmaker data is taken from William Hill.

If you missed those articles, you can read the overview one here, and the second part here.

 

Change from Early Odds to Starting Price

To begin with I want to look at early morning odds versus opening show odds. Later on, I will be comparing some of this early odds data with SP. As I alluded to in parts one and two the opening show tends to be around ten minutes before the start of the race. Early morning odds tend to be available around 9am. Indeed, these days most bookmakers price up the night before. Alas, I do not have data for this.

Below is a graphic comparing early morning odds to opening show where I am looking at the percentage of all runners that either shorten in price, stay the same price, or lengthen in price:

 

 

As we can see, nearly 53% of all horses drift / lengthen in price, compared with 36.4% who shorten. Roughly one in nine runners see their price stay the same. These figures follow the pattern of previous research but the differential between drifters and shorteners is much bigger.

These data show us that Early Morning Odds are essentially poor value. If your only option is to bet ‘early’ then I would urge you to use a bookmaker that offers BOG (Best Odds Guaranteed). If you can’t, then I would suggest you do not make the wager at early prices. Regularly ‘taking’ Early Odds will probably lose money for over 95% of punters.

When comparing handicaps with non-handicaps the percentages splits are virtually identical (in handicaps 52.7% of horses drift, in non-handicaps it is 52.4%.) so the market behaves in a very similar way from Early Odds to Opening Show regardless of race type.

 

Effect of Early Odds price movement on Strike Rate and Profitability

Now I want to look at the effect price movement from Early Odds to Opening Show has on strike rate and profit/loss. In terms of profit/loss I am going to calculate returns to Betfair Starting Price. I have split the runners like I did earlier into three groups – horses that shorten in price from ‘early’ to ‘opening’, those that stay the same price, and those that lengthen in price.

 

 

There is the same win percentage pattern here that we saw when looking at Opening Odds versus SP in the previous articles: horses that shorten in price have comfortably the best strike rate. In fact, those that shorten are almost twice as likely to win as those horses that drift. In terms of returns to Betfair Starting Price, horses that shortened in price edged it; but there is less than 1% (1p in the £) between the three groups.

So, we have a very even looking starting point in terms of returns / value. Let's push on...

 

Horses that lengthened in price from Early Odds to Opening Show

I want to look in more detail at horses that either drifted in price from Early Odds to Opening Show Odds or went the other way, i.e. shortened in price. Drifters first. I want to know what percentage of these horses continued to drift in price from Opening Show to SP, having already drifted from the morning price to the first one available on the show ten minutes or so before the 'off'. Here are the splits:

 

 

As we can see, 42.1% of horses that lengthened in price from ‘early’ to ‘opening’ continued to drift out in price. So, there is more chance that the drift will continue compared with the other two scenarios. Roughly a third of these horses shortened, while a quarter remained the same price.

Below are the strike rates and returns for this cohort:

 

 

Don’t be fooled by thinking the best value has been with the horses that initially lengthened in price and then stayed the same odds. These figures include BSP winners at prices of 1000.0, 538.81, 403.45 and 358.50. Taking those out the ROI was around -6%.

 

Horses that have shortened in price from Early Odds to Opening Show

A look at the converse group next, those shortening from the morning to opening show, to give us a comparison. Firstly, a look at what happened between Opening Show and SP in terms of percentage splits:

 

 

Horses that shorten from Early Odds to Opening Show are still more likely to subsequently drift than to continue to shorten. However, the percentages for the horses that shortened and those that drifted are the closest they have been in any of the comparisons made, either in this article or the previous two – there is less than a 5% differential between the groups.

Strike rates and returns for these runners are below:

 

 

The strike rates are much higher in this table than the previous one and it seems that horses that shorten initially and then drift on course are slightly better value than the rest. This time the figures for the ‘best’ group are not skewed by huge 300.0 plus BSP winners.

 

Early priced favourites

This is a new departure in terms of what I have looked at previously but I thought it would be interesting to see what happened to horses that were initially favourite on the 'morning line'. I have focused on those horses that were solely at the head of the betting market (no joint or co-favourites) in the morning. Firstly, a look at how all such runners fared:

 

 

The strike rate exceeds 30% which suggests most of the horses remained as favourite. Losses are very modest at just over 2p in the £. If we split these by race type, we get the following:

 

 

Horses that were clear favourite in non-handicaps as the markets opened in the morning have got close to breaking even, which is eye-catching. The difference in strike rate is to be expected, but I had expected that the returns would be very close to the same.

Earlier I suggested that most of the early favourites are likely to have remained favourite at the off (SP) – let's see if that assertion was correct:

 

 

As expected, the figures back up the hypothesis. Nearly two thirds of these runners remained clear favourite at the start of the race and 86.7% of them were either clear favourite, joint favourite or 2nd favourite.

What was even more interesting, though, is what I discovered when I looked at the profit/loss figures in terms of their final market position. Horses that ended up outside the top two in the betting (eg. 3rd fav+ at SP) edged into profit. The strike rate was down as you would expect at 13.1%, (530 wins from 3804 runners, around one winner in eight), but a miniscule profit of £5.41 (ROI +0.1%) was achieved. We have seen in the first two articles that drifters have tended to offer more value – here is another case in point, albeit not a bankable one in isolation.

 

Trainers – Early Odds v SP

To finish off this piece I want to look at some raw trainer data, comparing their respective runners' Early Odds to SP. I have chosen 45 trainers and compared win strike rates and A/E indices for their runners within the three groups: horses that shortened from morning odds to SP, horses that stayed the same price in that time frame, and horses that drifted from morning odds to SP. I have highlighted A/E indices of 0.95 or higher (in green) – these are essentially positive. A/E indices of 0.79 or lower (in red) are essentially negative:

 

 

As one would expect, most trainers have a significant difference in strike rate when comparing drifters to horses that shorten in price. Stuart Williams for example has a three times better strike rate with his horses that shorten in price compared to his drifters (19.01 v 6.28). Indeed, his runners that have shortened in price have made a small profit of around 4p in the £.

Julie Camacho seems to be a trainer to note if her runners shorten from early odds to SP. She has had 451 runners that have contracted in price, of which 86 have won, producing a healthy £99.59 profit to BSP. This equates to impressive returns of 22p in the £.

In terms of Early Odds to SP drifters, Brian Ellison runners that fit that profile look worth avoiding: 44 wins from 838 qualifiers showing hefty losses of £341.35 (ROI -40.7%). Likewise, Ed Dunlop has a similarly poor record with drifters thanks to only 77 wins from 1195 (SR 6.4%) for a loss to BSP of £440.90 (ROI -36.9%).

A few trainers have made profits with their drifters, but many have been helped by the occasional huge priced BSP winner. One trainer who has been less reliant on big-priced winners has been Charles Hills. He has saddled 149 winners from 1240 drifters turning a profit of £277.50 (ROI +22.4%). This record actually improves if you ignore his big-priced runners (BSP 40.0 or higher) – a £433.44 profit returning over 42p in the £. I did check the data for his 2023 drifters, and he has made a decent profit this year so far, too. Very interesting!

 

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So, there we have it. The data collection for these three market movement articles has taken a while, but I hope Geegeez readers are able to take plenty from it. Perhaps the main message is, more horses will drift than shorten be it comparing Early Odds to SP or Opening Show to SP. So, if you can, bet late or bet Betfair SP. Also, when viewing the overall findings there is more value in drifters. This was highlighted especially in the second article where I looked at more significant (i.e. bigger) price movements.

From a trainer perspective, each trainer will have slightly different patterns of price movement, but the trainer tables in articles one and three will assist in that regard.

Good luck.

- DR

Steamers and Drifters: Part 2

/in /by

In this follow-up article I am once again looking at market movement between the bookmaker’s opening show and the returned Starting Price, writes Dave Renham. The data I've used has been taken from 2018 to 2022 for all UK flat and all-weather racing. Bookmaker data is taken from William Hill.

In the first piece, which you can read here, the overall stats showed that a bigger percentage of horses drift (lengthen) in price from their opening price than shorten, when comparing market movement from opening show to SP. The biggest difference can be seen in horses that open 10/1 or bigger; of these over 42% drift in price compared with 29% that shorten.

Up to now in this research, I have not considered how much the price changed. Clearly all price movement is not the same; for example, a horse can drift from 4/1 to 9/2, but another 4/1 shot could drift far more dramatically out to 10/1 as an extreme, but perfectly credible, example. Likewise, horses that shorten in price can vary markedly in terms of how much their price contracts.

At this point I want to discuss what I mean by "significant price changes" as we need to be clear that looking at the difference in two prices does not necessarily tell the whole story. To help explain what I mean, let me give you some context. Let us consider two horses:

Horse A – whose opening price is 40/1 and whose SP is 20/1

Horse B – whose opening price is 6/4 and whose SP is Evens

If we focus solely on the prices, Horse A has shortened more because 40/1 to 20/1 is a 20-point truncation, whereas for Horse B moving from 6/4 to Evens is only a 0.5-point move. However, in terms of the chance of winning, Horse B has actually improved its chances more. In order to appreciate this, we need to understand the percentages behind the odds – in other words the percentage chance of winning according to the odds. The table below illustrates this:

 

 

As the table shows, Horse A has improved its chances of winning by 2.4% (4.8% minus 2.4%). However, Horse B has improved its chance of winning by 10% (50% minus 40%). Knowing and understanding betting odds in terms of the percentage chance of winning is very important. When I wrote my article on trying to create an odds line, the percentage chance for each horse was something I touched upon. Without it, you cannot easily create an accurate odds line.

At the end of this article, I have produced a table with fractional odds, decimal odds and the implied probability (percentage chance of winning) for betting prices. Readers, if required, may use it as a ready reckoner to convert odds into percentages.

It is now time to compare horses that shorten considerably from opening show to SP with those that lengthen/drift considerably. I am going to only consider horses whose prices have changed, in win percentage terms, by 10% or more. Hence the 6/4 to Even money horses mentioned earlier will count, but not the 40/1 into 20/1 ones. (N.B. The word ‘steamer’ is often used about a horse whose price starts to drop dramatically so for the rest of the article I will use ‘steamer’ for horses that shorten in price).

Let's look at strike rates first – for horses whose win chance changes by 10% or more due to their odds move:

 

 

These figures correlate with the findings of the first article and the general data set. Steamers have won 5.5% more often than drifters. However, this does not mean that steamers have produced better returns. In fact, it is quite the contrary as the graph below shows:

 

 

These strong steamers actually lost over 13 pence in the £ betting to Betfair SP; drifters lost just 1p in the £. The full breakdown of the results is as follows:

 

 

A 10%+ change in win percentage between opening odds and SP only occurs roughly once a day on average. Hence, this type of percentage movement can be considered a major price change.

I thought it might be interesting to breakdown the results further by splitting them by the odds at which they opened. Here are the stats for the steamers:

 

 

I have tried to group them so that the number of runs for each group is similar. What seems to be clear is that the worst performing steamers from a profit/returns perspective were those that opened at 11/2 or bigger. The shorter prices of 6/4 or shorter also fared relatively poorly.

Onto drifters now. There are not many big prices here because a 10%-win probability movement is impossible for horses priced 9/1 or bigger. For an extreme example to illustrate this, a horse drifting from 9/1 (10% chance) to 1000/1 (0.1% chance) has a win probability price movement of only 9.9%:

 

 

The results for drifters are more consistent in terms of profits/returns – there is very little difference between the worst performing price band and the best one.

It is clear from this data that if betting at BSP, significant drifters are better value than significant steamers.

 

Trainers

It is not worth looking at individual trainer data for the strength of steamer/drifter (+ / - 10%), as the sample sizes are going to be too small. Therefore, I am going to focus on trainer data for those horses whose prices changed in win probability terms by 5% or more, rather than 10%. This would include, for example, a horse shortening from 8/1 to 5/1, or one drifting from 6/1 to 10/1. I also restricted the runners a little by looking at horses that opened in price between Evens and 50/1. I ignored odds-on runners and those priced 66/1 or bigger for two reasons: firstly, because the majority of punters do not bet in these price brackets and, secondly, the individual trainer sample sizes are extremely small.

Steamers - As noted above I have broken down trainer performance where the win probability implied from opening odds to SP has improved by 5% or more. Trainers with at least 100 qualifying runners are listed. They are ordered by strike rate/win percentage:

 

 

The strike rates are decent as you might expect for steamers, but only a handful of trainers have made a profit. The Johnston and Channon stables have both done well from a profit perspective, as has Mick Appleby. Of course, for steamers, any value is gradually diminishing as the price is contracting.

Ten trainers have produced losses of more than 20 pence in the £, which is quite significant especially considering we are using BSP. The Crisford stable has the worst returns, edging above 30% for their losses.

Of course, if you could predict the steamer before it started shortening in price, and bet before it moves markedly, then the likelihood is that most of the trainers in the list would become profitable. Unfortunately, none of us has a crystal ball, which makes those types of predictions somewhat tricky! What would be interesting to find out is what percentage of horses that initially shorten from opening show, continue to shorten. Likewise, it would be really useful to know what percentage of horses that initially drift from opening show continue to drift. However, I do not have that information and guess the only way to find out would be by doing a live day by day data gathering exercise. That’s for another lifetime!

 

Drifters – Switching to drifters now below is a table of trainer performance where the win probability change from opening odds to SP has decreased by 5% or more. Trainers with at least 100 qualifying runners are once again listed.

 

 

There are six trainers in profit here with Stoute, Haggas and Balding drifters performing particularly well from a returns  and strike rate perspective. Indeed, all trainers in the above table have combined to secure a small profit of £21.52.

The two trainer tables I have shared indicate that at this 5%+ level of steam/drift, bettors might be better off backing drifters rather than steamers. In fact, if we look ALL trainers as a whole for both groups, we get the following overall figures:

 

 

Steamers, as we have come to expect, have the better strike rate, but drifters are offering punters much better value. So, the question is, do you want more chance of winners, or more chance of a profit?

Going back to the stats for Sir Michael Stoute in the second table above, his non-handicap drifters have totally outperformed his handicap drifters as the chart below shows:

 

 

Strike rates are similar but there is a big difference in the profits. Non-handicap drifters of this strength (5%+) would have secured Stoute followers impressive returns of 70 pence in the £ to BSP.

I must admit this has been a very interesting area to research. It is something I have looked at before but not in as much detail. It may be worthwhile comparing early morning odds to SP odds in a future piece, and also at some point I should see if the patterns I have found in these two articles correlate with National Hunt data. Anyway, as ever, I hope you have found the research enlightening, and all comments are appreciated as it helps me with my future work.

- DR

 

p.s. here is the table I mentioned earlier with fractional odds, decimal odds and the implied probability (percentage chance of winning) for betting prices.

Fractional Odds Decimal Odds Implied probability (% chance)
1/100 1.01 99%
1/5 1.2 83.3%
2/9 1.22 81.8%
1/4 1.25 80%
2/7 1.29 77.8%
3/10 1.3 76.9%
1/3 1.33 75%
4/11 1.36 73.3%
2/5 1.4 71.4%
4/9 1.44 69.2%
1/2 1.5 66.7%
8/15 1.53 65.2%
4/7 1.57 63.6%
8/13 1.62 61.9%
4/6 1.67 60%
8/11 1.73 57.9%
4/5 1.8 55.6%
5/6 1.83 54.5%
10/11 1.91 52.4%
Evens 2 50%
21/20 2.05 48.8%
11/10 2.1 47.6%
23/20 2.15 46.5%
6/5 2.2 45.5%
5/4 2.25 44.4%
11/8 2.38 42.1%
7/5 2.4 41.7%
6/4 2.5 40%
8/5 2.6 38.5%
13/8 2.62 38.1%
7/4 2.75 36.4%
9/5 2.8 35.7%
15/8 2.88 34.8%
2/1 3 33.3%
11/5 3.2 31.2%
9/4 3.25 30.8%
12/5 3.4 29.4%
5/2 3.5 28.6%
13/5 3.6 27.8%
11/4 3.75 26.7%
3/1 4 25%
16/5 4.2 23.8%
10/3 4.33 23.1%
7/2 4.5 22.2%
4/1 5 20%
9/2 5.5 18.2%
5/1 6 16.7%
11/2 6.5 15.4%
6/1 7 14.3%
13/2 7.5 13.3%
7/1 8 12.5%
15/2 8.5 11.8%
8/1 9 11.1%
9/1 10 10%
10/1 11 9.1%
11/1 12 8.3%
12/1 13 7.7%
13/1 14 7.1%
14/1 15 6.7%
15/1 16 6.2%
16/1 17 5.9%
18/1 19 5.3%
20/1 21 4.8%
25/1 26 3.8%
33/1 34 2.9%
50/1 51 2%
66/1 67 1.5%
100/1 101 1%
1000/1 1001 0.1%

 

The TRUTH About Steamers and Drifters

/in /by

In this article I am going to look at market movement between the bookmaker’s opening show and the final Starting Price and unearth some truth about steamers and drifters, writes Dave Renham.

In what follows I will be focusing on flat and all-weather racing in the UK spanning five years from 2018 to 2022. Bookmaker data is taken from William Hill.

Typically, the opening show tends to be around ten minutes before the off, and these are the initial prices the bookmakers set. Backing a horse at ‘opening show’ and seeing it shorten in price means you have probably gained a decent edge and potentially some value. Conversely, if you take the opening show price and the horse drifts (lengthens) in price, then you may have lost some value.

However, it is important to note that more horses will lengthen in price than shorten. Here is a graphic to illustrate this by looking at the percentage of all runners that either shorten in price, stay the same, or lengthen in price:

 

 

As can be seen nearly 42% of all horses drift, compared with 34% who shorten. Roughly a quarter of all runners see their price remain steady. It is interesting to note that the percentages are very similar when comparing handicaps with non-handicaps (within 1%), hence the market behaves in a very similar way from opening show until the off regardless of race type.

My starting point for researching this article is very simple – look at opening show versus Starting Price and seeing what effect the differential has on strike rate and profit/loss. In terms of profit/loss I am going to calculate returns to Betfair Starting Price. For the sake of simplicity, I am going to split the runners into three groups:

 

- horses that shorten in price from opening show

- horses that stay the same price as opening show

- horses that lengthen in price from opening show.

 

Using numerical examples:

 

 

Let me start by looking at all races.

 

 

As we can see, horses that shorten in price have comfortably the best strike rate, therefore, unsurprisingly, the market does tend to get it right most of the time. In terms of returns to Betfair Starting Price horses that have remained the same price have proved the best value by a couple of pence in the £. It may be interesting to note that there is little in it between horses which shortened in price compared with those which lengthened.

So, we have a very even looking starting point in terms of returns / value, now it is time to dig deeper.

Horses whose opening show price was 4/1 or shorter

I thought it made sense to look at different price brackets so let’s start with the better fancied runners. It also seemed logical to use the opening show price for this rather than SP as the opening show price is known pre-race. Here are the splits:

 

 

There is the same sliding scale in terms of strike rate, but it is the horses that lengthened / drifted in price that have been better value this time. Horses that shortened in price proved the worst value.

 

Horses whose opening show price was 4/1 or shorter and then lengthened in price

Focusing on this subset of drifters, it is interesting when you compare the results on Grade 1 tracks compared with other tracks. The Grade 1 tracks on the flat are Ascot, Doncaster, Epsom, Goodwood, Newbury, Newmarket, Sandown and York. Firstly, let’s review the win and each-way (win + placed) strike rates:

 

 

There is a difference of 1.6% in the win strike rates; 4.3% in terms of combining win and placed percentages (each way). These differences may look quite modest, but they are significant.

The graph below shows the return on investment (ROI%) to BSP for each group to highlight the significance:

 

 

Now we can see the significance of a 1.6% difference in win strike rates – the returns are over 7p in the £ worse at Grade 1 tracks for these shorter priced runners compared with the non-Grade 1 tracks. Indeed, away from the top tracks we see a situation where one would have virtually broken even backing every single drifter to BSP when it opened at 4/1 or shorter.

Whatever is occurring to create these differences between Grade 1 tracks and non-Grade 1 tracks for drifters, I am not sure. It may be connected with average field size; it may be connected with the quality of racing. It could be a combination of those, or neither.

Sometimes it is not worth speculating, especially as in this case it is nigh on impossible to isolate why. I’m happy on this one that it makes sense to just go with the data.

Before moving on I have checked the 2023 data (up to 27th Sept) and the same pattern for horses that drift/lengthen in price having opened 4/1 or shorter has continued:

 

 

The message according to all the information at my disposal is clear: horses which open at 4/1 or shorter and drift look to be POOR value when racing at Grade 1 tracks; away from these top tracks, such horses seem much better value – taking 2023 into account, going back to 2018 these runners would have lost you just 5p for every £100 bet.

 

Horses whose opening show price was 2/1 or shorter

Going back to data from ALL courses, if we focus on a shorter opening odds criterion of 2/1 or lower, and only look at drifters, we almost get to a break-even scenario. There were 7696 qualifiers of which 2801 won (SR 36.4%). Backing all 7696 runners at £1 stakes to BSP would have lost a meagre £49.13 (ROI -0.6%).

 

Horses whose opening show price was 2/1 or shorter that lengthened in price

If we once again look at the Grade 1 track data compared with other tracks for this subset of drifters, we see the following:

 

 

A similar, if stronger, pattern than with the 4/1 or shorter opening show cohort of drifters. Here, we are looking at a nearly 3% difference in win strike rate which equates to a difference of over 11p in the £ in terms of BSP returns.

As per the table above, drifters at non-Grade 1 tracks opening 2/1 or shorter have edged into profit. For this to happen across such a large sample – over 6500 runners – is interesting and impressive.

 

Horses whose opening show price was between 9/2 and 9/1

Time to look at the data for a bigger odds bracket. Here are the splits for each subset of the cohort whose opening show price was between 9/2 and 9/1:

 

 

It should have been no surprise to see those shortening in price winning more in percentage terms once again. In terms of returns, as with the shorter priced runners, horses that have shortened have been the worst value, albeit by just over 1p in the £.

 

Horses whose opening show price was between 9/2 and 9/1 that lengthened in price

I wanted to continue the comparison between drifters in this price bracket at Grade 1 and non-Grade 1 tracks to see if we get a similar differential as before. I assumed we would, as my expectations were that it would only start to reverse with longer-priced runners:

 

 

As before the non-Grade 1 track data is notably better, both in strike rate terms and BSP returns. However, the gap is starting to narrow, though the difference between the two is still clear. This dynamic has to switch around for bigger-priced runners and we will see whether this is the case shortly.

 

Horses whose opening show price was 10/1 or bigger

Onto double figure priced runners on the opening show now. Here are the results:

 

 

Horses that remained the same price have provided the best returns, while those shortening have marginally out-performed drifters. All groups though show poorer returns than the shorter priced runners we reviewed earlier. Both the shorteners and the drifters offered poor value for the punter.

 

Horses whose opening show price was 10/1 or bigger that lengthened in price

Finally for this section, I wanted to investigate whether this subset of drifters produced converse results whereby horses that raced at Grade 1 tracks performed better than those which did not.

 

 

Here, necessarily, we have the big switch around: horses racing at Grade 1 tracks have the better strike rate for the first time and their returns are much better than their non-Grade 1 track counterparts.

When comparing the results of horses running at a different level of racetrack, splitting up the drifters’ data into opening show price bands has been an eye opener for me. In the future, if my plan is to place my bet late at a Grade 1 track and my horse opens up 9/1 or shorter, I would think twice about backing it if it started to drift. Conversely if I was planning the same at a non-Grade 1 track then I would want to see it drift!

As it stands, the research shared so far has been very general – hence the huge sample sizes. I have not yet considered how much the price has changed, because clearly a horse can drift from 6/1 to 13/2, but another 6/1 shot could drift dramatically out to 10/1 and beyond. In a follow-up article I will be digging deeper into the size of the change in price.

Back to this article and, having looked at the splits in terms of strike rate and returns for different price bands, I thought it would be interesting to go back to where this all started and look at the percentage of all runners that either shorten in price, stay the same or lengthen/drift within each of these bands. Here are my findings:

 

 

This is very enlightening as we can see that the percentage of horses that drifted compared to horses that shortened is similar in the 4/1 or less group, and also in the 9/2 to 9/1 group (green/yellow bars). However, in the 10/1+ group, 42% of all runners drifted, compared to a much lower 28.9% of runners who shortened. These stats are implying that we should delay wagering longer-priced runners by either waiting to the last minute or simply using Betfair SP. For horses that open at prices of 9/1 or shorter, the timing of bet placement seems generally less crucial.

 

Trainers

Changing tack, a quick look at some raw trainer data now. I have chosen 25 high volume flat trainers, and I am simply comparing win strike rates and A/E indices for all of their runners within the three cohorts we’ve used throughout this piece – horses that shortened, horses that stayed the same price and horses that drifted. I have highlighted A/E indices of 0.95 or higher (in green) – these are essentially positive. A/E indices of 0.79 or lower (in red) are essentially negative:

 

 

There are more greens than reds and, as a rule, the strike rates increase as you read across the columns left to right. This is what we would expect based on the overall data presented earlier. However, George Boughey is interesting as his three strike rates sit very close together, between 17.25 and 17.72%. Horses of his that have drifted have proved much better value than those that have shortened.

Clive Cox has a poor record with horses that drift in price as does James Tate, perhaps suggesting these yards know when to bet! Meanwhile, David Menuisier has done extremely well with horses that have shortened in price. They would have provided you with returns of over 17p in the £ to BSP which is impressive: another yard to follow when they’re fancied maybe?

I wanted to delve a little more deeply into trainer statistics and analyse the percentage of runners for individual trainers that either shorten, stay the same price or lengthen.

Below is a list of trainers whose runners drift far more than they. I have ordered them by highest percentage of runners that lengthen in price:

 

 

It was surprising to see George Boughey in the list and even more surprising to see him at the top. I also had not expected to see Sir Mark Prescott or Gary Moore appear either. It may be that these horses are often put in at defensively short prices on the opening show, bookmakers fearful of shrewd trainers/connections landing a gamble. Elsewhere, some less well-known trainers are arguably more predictable entries in the table.

There are not many trainers where this scenario is reversed with the percentage of horses that shorten in price being higher than the percentage that drift. However, five well known handlers have this profile and are shown below:

 

 

There are some big guns in this list. Punters are aware of the skills of these trainers and hence their runners are usually going to be strong in the betting market. It may be that these yards are, generally speaking, less inclined to gamble their horses, though in the case of Aidan O’Brien that’s not typically the case.

*

It is time to wind up this first article into market movement. There are plenty of stats to chew over and hopefully for punters who bet near or around ‘the off’ it has given some useful data to potentially improve your profit/loss bottom line. You can read part two of this article here.

- DR

Introduction to Dobbing: Part 3

/in /by

This is the third in a short series of articles connected with betting on horse races in running, writes Dave Renham. In the first piece, I discussed the idea of DOBBING which essentially means ‘double or bust’. You either double your money or lose your stake. Just to recap, here is a worked example:

Imagine you back a horse at 20.0 for £5; in order to create a potential DOB you try and lay the horse at half the odds for double the stake – so you set a lay at 10.0 for £10. If the horse hits 10.0 or lower in running, your lay bet will be matched and regardless of the result you will win £5 (less commission). If the horse loses but doesn’t hit 10.0 or lower then you lose your £5 stake.

In the second piece I looked at some in-play horse racing data on the flat and this time my attention switches to National Hunt racing. One could argue that National Hunt racing is easier to trade as the races are longer which generally allows the trader more time to make informed decisions. But does the data support that contention?

As before I am going to look at 20 months’ worth of recent UK data which is a very decent sample in terms of size. In fact, I started my research by splitting the data into two and looked at the overall NH DOBBING percentages for each group. One came out at 44.4%, the other at 44.3%. Hence, I feel we can be very confident that this data set will provide us with extremely accurate figures.

Dobbing Percentages by NH Race Type

My first port of call is to split the data into three race tyoe brackets: steeplechases, hurdle races and National Hunt Flat races (bumpers). Here are the findings:

 

 

There is not much difference between the three, but NH Flat races have offered the best chance of DOBBING, followed by chases and finally hurdle races. Most National Hunt Flat races are around two miles so it should be no surprise to see the DOB percentage at 45.9%. If you remember from the previous article the flat results for races of 17f+ saw the DOBBING percentage standing at 45.2%. Hence the NH Flat figure correlates positively with that.

Dobbing Percentages by Handicaps / Non-handicaps

Onto handicaps versus non-handicaps next and, for this data, I am excluding the NH Flat results (which are all non-handicaps) as we have that figure already. In the graph below I have shown the overall handicap versus non-handicaps splits, as well as then splitting this by chase races and hurdle races:

 

 

As can be seen in the chart above, horses are more likely to DOB in handicap races as opposed to non-handicaps. Meanwhile, handicap chases are the most successful DOBBING-wise although handicap hurdlers DOB only 1% below this figure. The lowest figure goes to non-handicap hurdle races where just 41.3% of horses successfully DOB.

 

Dobbing Percentages by Distance

When we looked at the distance splits on the flat in the second part of this series we saw that, as the distance increased, so did the DOBBING percentages. Here now is the National Hunt racing breakdown:

 

 

As we can see, the same pattern occurs here with the longer the race, the better the chance of a horse DOBBING. In the first article I had alluded to the fact that this might be the case. Races of two miles or less give us the lowest overall percentage (41.9%), whereas the longest distances of beyond three miles have seen horses DOB 46.9% of the time. There is a very strong linearity of improved DOB percentage as race distance increases.

It is interesting to note that races beyond three miles have seen a slightly higher percentage of DOBBERS in non-handicaps compared to handicaps, which is a surprise given the handicap/non-handicap stats I shared earlier. However, for the record, non-handicap chases at further than three miles have seen 48% of runners DOB. This is the highest figure based on distance parameters I have found to date.

Dobbing Percentages by Market Odds

Let's now look at the data in terms of Betfair Starting Price (BSP). For the flat data I used market rank rather than price and the flat DOB% stats were quite even although favourites (that were not odds on) had comfortably the highest figure in those findings. I felt it was worth changing it up a bit and using actual market prices, which is arguably a more accurate measure:

 

 

As with the market data from the flat there is no discernible pattern here. It does, however, look best to avoid the essential ‘no hopers’ priced over 500/1. However, it is surprising, to me at least, to see both the 50.01 to 100 price bracket and the 100.01 to 500 price bracket both hitting over 47%. I can’t explain that one, I’m afraid!

Dobbing Percentages by Courses

A look at courses now. Here are the DOB%s for each course, ordered highest to lowest:

 

 

There is quite a difference between the highest figure, at Newbury, 50.6%, and the lowest, Fakenham, 41.2%. What immediately strikes me is the difference in the configuration of these two tracks. Newbury is one of the biggest in terms of circumference being 1m 7f; Fakenham is at the other end of the scale at just one mile all the way round. Cartmel (one mile circumference) and Plumpton (1m 1f circumference) are other tight/sharp tracks that appear down the bottom of the DOBBING percentage list. Meanwhile near the top you have Ascot and Lingfield whose tracks measure 1m5f round, Cheltenham at 1m4f and Donny at 2m. Kelso which is in 4th spot does buck the trend though being just 1m1f in circumference.

Digging a little bit deeper, there could be something in this theory as I decided to find the average circumference of the top ten DOBBING% courses and compare that with the average circumference of the bottom ten. The top ten courses averaged out at 12.6 furlongs, while the bottom ten averaged out at over two furlongs shorter at 10.4 furlongs. Of course, a theory is simply a theory, but the numbers I have uncovered at least seem offer some support.

I need to add a proviso here that these stats come from only 20 months' worth of data. It is still a decent chunk of data for each course but, ideally, I would like four or five years’ worth.

 

National Hunt Horses with good past DOB%s

To finish this piece, I have tried to find a handful of horses that have, in the past, had a high DOB%. My hope is therefore that this will be replicated over the coming season. So here goes – there are nine in total and I have listed them in alphabetical order:

Ahoy Senor – Ahoy Senor is one of the top 3 mile chasers in the country. He has DOBBED in 11 of his 15 races, but with one of those races seeing him start odds on, this improves to 11 out of 14 (78.6%). In those remaining 14 races he has won five of them, but in six of the other nine he has still halved in price or better in running. I am guessing one of the key reasons for this DOBBING success is that he is habitual front runner. 14 of his 15 starts have seen him take the early lead.

Ashtown Lad – Ashtown Lad, trained by Dan Skelton, is a versatile runner who last season switched between hurdling and chasing. He has DOBBED 76.5% of the time (13 races out of 17). He has DOBBED in six of his seven chases, while in hurdle races it stands at seven out of ten. He has shown a mix of running styles with equal DOBBING success.

Before Midnight – Before Midnight is a 10-year-old gelding trained by Sam Thomas. His career DOB% stands at 75% with 18 DOBs from the 24 races when he has priced 2.02 or bigger. The slight concern is that his DOB percentage has been nearer the 50% mark when looking at the last 18 months or so, which is mainly down to a drop in form. However, he could now be well handicapped so it will be interesting to see what the 2023-24 season brings.

Brewinupastorm – Trained by Olly Murphy, Brewinupastorm has achieved 14 DOBs from 25 races, but this becomes 14 from 22 (63.6%) when you ignore his odds on runs (remember, odds on runners cannot dob because they cannot halve in price on Betfair). He has raced mainly over hurdles and his DOBBING percentage in hurdle races stands at a very impressive 75% (12 of 16 qualifying runs). He has raced nine times in the last two NH seasons DOBBING six times (66.7%).

French Dynamite – French Dynamite is an 8yo Irish chaser. He has achieved 13 DOBs from his 20 runs (65%), including ten in his last 12 (83.3%). He races up with the pace (led seven times, raced prominently 11 times, and held up twice) which may be a factor. Six wins helps, but overall, this horse seems to have very solid potential for keeping up a good DOB percentage this season.

Gatsby Grey – Gatsby Grey is a 7yo trained in Ireland by Oliver Kiernan with just 14 NH races under his belt. Of those 14 he has DOBBED in nine (64.3%). It is interesting to note, too, that he has never started shorter than 5.0 BSP. He has three wins to his name and his recent DOBBING record (since Nov 2022) stands at five from his last seven (71.4%).

Guy – Guy is an 8-year-old gelding trained by Nigel Twiston-Davies. He has raced in chases but was switched back to hurdles at the end of the last National Hunt season. He has raced 21 times in his career and has DOBBED an impressive 15 times, equating to 71.4% of races. He has only won twice in these 21 starts, which was what initially caught my eye, as that would account for only two of the 15 DOBs. Digging deeper he has finished second nine times of which he DOBBED on eight of those occasions.

Hatcher – Hatcher is another trained by Dan Skelton. His DOBBING percentage for his career stands at 65%. He has won six races when odds on, so that 65% is based on his other 40 NH runs where he has DOBBED 26 times. Of those 40 he has won eight. What makes his overall record more impressive is that most of his racing has come at around two miles. As we know from the distance data shared earlier, this trip produces the lowest average of all the NH distance DOB%s. However, there is one caveat: as with Before Midnight his more recent DOB% record stands around the 50% mark so this does need to be taken into account.

Le Tueur – Le Tueur is an 8-year-old gelding who has been racing over fences since November 2021. He has DOBBED 14 times in 23 races (60.8%), but again is not a serial winner with just three wins. It is interesting to see that in two of the three races where he was pulled up, he still DOBBED!

So it’s time to wind up this third piece on DOBBING. I am in the process of starting to crunch some run style data for NH races, but it takes a long time - several weeks in fact. Once done, I will report back on that I’m sure in the future.

- DR

Introduction to Dobbing: Part 2

/in /by

This is the second in a short series of articles connected with betting on horse races in running, writes Dave Renham. In the first piece, which you can read here, I discussed the idea of DOBBING, which essentially means ‘double or bust’: you either double your money or lose your stake. Just to recap, here is a worked example:

Imagine you back a horse at 6.0 for £10; in order to create a potential DOB you try and lay the horse at half the odds for double the stake – so you set a lay at 3.0 (half of 6.0) for £20 (double £10). If the horse hits 3.0 or lower in running, your lay bet will be matched and regardless of the result you will win £10 (less commission). If required, there is a little more detail in the first article.

This second article will dig into the numbers in an attempt to see whether we can improve our chances of finding DOBBERS. I will look at 20 months’ worth of recent UK flat data, which equates to over 12,000 races, covering over 100,000 runners. So let’s get cracking!

Dobbing Percentages by Distance

My first port of call is the distance of the race. In terms of dobbing percentage there is a clear pattern when it comes to distance:

 

 

As we can see, the longer the race, the better the chance a horse has of DOBBING. In the first article I had alluded to the fact that this might be the case, and it is always good to see the numbers support the theory. The minimum distance of 5 furlongs gives us the lowest overall percentage (38.8%), whereas the longer distances of 13 to 16 furlongs have seen horses DOB over 44% of the time, while 17f+ races hit 45.2%.

I can think of three logical reasons why there is such a discrepancy when we compare 5f races to races of 17f or more.

Firstly, 5f sprints only take around a minute; races of 17f or more take three or four times as long. These longer races give more time for traders to spot horses that are making eye-catching progress / travelling well.

Secondly, we know that 5f events really disadvantage horses that take up a position near, or at, the rear of the field early. Hence a good proportion of slow starters / held up runners in sprints are not going to get close enough to the action at the front in time. So the chances of these horses DOBBING is relatively low. In contrast, horses that start slowly or are held up in 17f+ races have plenty of time to recover and get into a more competitive position.

And thirdly, keeping on the run style theme, front runners win around 2.5 times as often over 5f as they do over 17f+. It is easy to imagine that, in races where horses that make most or all the running over 5f, very few other runners get in a dangerous enough position to shorten markedly in price and hence DOB.

In fact, having checked that last theory, and although I only looked at 30 5f races where the winner led from start to finish, only 25% of runners dobbed in these races. That’s well below the 38.8% overall percentage for 5f races. I would not expect that 25% DOBBING figure to change too much even if I back-checked 300 races rather than 30. Unfortunately it is not something I can research quickly, therefore the modest sample.

Dobbing Percentages by Market Rank

Moving on to the position the horse holds in the betting market, and as a reminder, odds on runners have been ignored in the figures as they cannot DOB (see first article for full explanation).

 

 

Favourites DOB the most; close to 45% of the time – this is probably because a good proportion of market leaders win and, of those who don’t, most run well. I am slightly surprised to see the other market ranks relatively uniform and not sliding downwards left to right. I thought that would be the case, but there is no clear cut pattern.

Dobbing Percentages by Course

In the first article I mused on whether course configurations can make a difference to DOB percentages. Camera angles are different at certain tracks, for example, and as we know courses in this country vary massively in terms of layout. If we look at Chester’s tight bullring track…

 

 

…we can see the course is roughly a mile in circumference with short straights. Compare this now to York’s expansive gallopers’ paradise:

 

 

The circumference of the track at York is roughly double that of Chester and the finishing straight is nearly five furlongs in length.

Every track in the UK is unique – some are undulating, some have downhill stretches, uphill stretches, long or short straights, sharp bends, etc. Therefore, I would expect to see some variance across the different courses in terms of DOB%. To begin with, let’s look at the DOBBING percentages for each course.

 

 

There is a 7% differential in terms of percentages of runners successfully dobbing between Sandown at the top (46.3%) and Newcastle at the bottom (39.2%). It is interesting to note that three of the four lowest DOBBING courses are all-weather ones (Chelmsford, Wolves and Newcastle). Indeed, the other three all-weather courses also reside in the bottom half of the table. Is this down to the level of competition on the all-weather being slightly below that of the turf? Possibly.

Now we know from earlier findings that the distance of races makes a difference DOBBING wise, so maybe courses that have a lower percentage of sprint races enjoy better DOBBING percentages. Likewise do courses with a higher percentage of sprint races have poorer DOBBING percentages?

To try and test out this theory, I ordered all courses in the previous table from 1 to 37 starting with Newcastle who had the lowest DOB%. Hence I put Newcastle in position 1, Wolves in position 2 and so on up to Sandown in position 37. I wanted to use these ‘positions’ to help make the comparison.

I then calculated the percentage of sprint races (6f or shorter) held at each course during the same time frame, ordering the courses from 1 to 37, thus:

 

 

Bath top the list with over 50% of all their races being sprints, while Epsom has the lowest figure with just 15.3% of their races being at trips no further than 6f.

Having given the tracks a rank in terms of percentage of races at the course that were sprints, I could compare this with their DOB% rank. For the course DOB% to be strongly affected by race distance then the course ranks for the two variables should be similar.

In some cases they were – Thirsk for example was in position 3 in both: the North Yorkshire track had the third lowest DOB% matching perfectly with the third highest percentage of races that were sprints. In other cases, though, they didn’t match. Wolverhampton, for instance, was in position 2 for DOB%, but position 25 for percentage of sprint races. To try and show the comparison for all the courses I have created line graphs comparing their ranks. I have split it into two so that it fits on the screen:

 

 

For perfect correlation we would need to see the blue and orange lines almost follow the same path. That has not happened here taking all the courses as a whole, so we need to look to see how many courses have their orange and blue dots close to each other. 15 of the 37 courses have their two ranks varying by five or less. Meanwhile, nine of the 37 courses have their two ranks varying by 15 or more.

Thus the jury is still out in terms of saying that the course DOB percentages are affected by distance considerations. My guess is that it is a factor at some courses, but there are other factors also making a difference.

Dobbing Percentages by Run Style

To conclude this second article I want to look at possibly my most favoured area of analysis: run style. It should be noted for the run style research for this piece, I have not been able to use such a big data set, due to the time-consuming nature of this type of research. However, I have been able to analyse 4000 runners looking at how run style impacts the chances of DOBBING.

I mentioned in the first article that horses that lead for the majority of the race, or are leading at the furlong pole while looking like a potential winner, are occasions when the leader’s price is likely to shorten considerably. Obviously, if the price drops enough then the horse will DOB. Hence it would logically follow that front runners should have the highest DOB percentage. This is indeed the case as the chart below clearly illustrates. I have used the run style categories on geegeez.co.uk, and the following stats are pulled from all flat race distances from 5f to 2m 6f:

 

 

Clearly run style is important from a DOBBING perspective. Front runners DOB over 60% of the time across the test sample, and the chart clearly shows the downward trend from front of the field early to back of the field early. 4000 runners across all distances should be a big enough sample for these figures to be accurate. If I was able to look at 100,000 runners, I would be surprised if the percentages for each group changed by more than two or three percentage points. Moreover, I personally researched run style DOBBING percentages back around 2011/2012 and the percentage splits then correlate well with this newer sample.

It should be noted, however, that the distance of the race will cause slight changes to the run style DOB figures. 5-6f races will see the DOB% for ‘Led’ increase slightly to around the 64-65% mark; conversely the DOB% for ‘Held Up’ drops to under the 30% mark. In longer races of 1m4f or more the reverse happens, with the DOB% for ‘Led’ dropping to 55-56% while the ‘Held Up’ DOB% increases to 38-39%. This makes perfect sense as front runners win such a high percentage of sprint races compared to longer races and, as we know, winners will DOB except for that very small proportion that are priced under 2.02.

-----

That’s the end of this review of flat race DOBBING. The Run Style figures should give readers who may be thinking about employing a DOBBING strategy a possible way in. Next time, I’ll perform a similar analysis of National Hunt racing. Until then…

  • DR

An Introduction to ‘Dobbing’

/in /by

DOBBING is a word I came across around ten years ago in connection with in play/in running betting, writes Dave Renham. DOBBING is usually shortened to ‘DOB’ which means ‘double or bust’. Essentially it is an in play trading strategy. If the trade/DOB is successful, we double our original stake, if the trade/DOB is unsuccessful we ‘bust’ or lose our stake.

What is DOBBING?

For people who have not heard of DOBBING before I will give you a worked example which hopefully will help:

Let us imagine you back a horse at 10.0 for £10; in order to create a potential DOB you try and lay the horse at half the odds for double the stake – so you set a lay at 5.0 for £20. If the horse hits 5.0 or lower in running, your lay bet will be matched and regardless of the result you will win £10 (less commission).

Here are the basic mathematics behind the two potential winning outcomes:

- If the horse goes onto win the race, you get £90 returned from the ‘back’ part of the bet while you lose £80 on the ‘lay’ part of the bet. This gives you a £10 profit.

- If the horse does not go onto win but reaches 5.0 or lower in running, then you lose your £10 stake from the ‘back’ bet, but gain £20 from the lay part of wager – again giving a £10 profit.

- If the lay part of the bet is not matched with a horse that does not win the race, you lose your original £10 stake.

 

The table below is another way to look at it, showing the three possible outcomes:

 

For dobbing to be profitable long term, we are probably looking to have a success rate of around 54% or more. This figure has to be a bit higher than a 50.1% baseline as we need to take commission into account.

As Russell Clarke mentioned in the first of his excellent in running articles (which you can read here), only about 20% of all money traded on a horse race occurs in running. Hence, there are far fewer people that trade in running compared with those that don’t. I am sure there are plenty of you out there who have thought about betting in running, but have decided against as it is not for you. There will be some of you who trade and are very successful. Personally, I do dabble in running from time to time, but despite using the market leading trading software, I know I am up against seasoned trading pros. I might be able to produce pre-race plans that are as good as most, but my decision making / speed under pressure is definitely not at the expert level.

An advantage of using this dobbing idea for some punters/traders is that you can place both parts of the bet/trade before the race starts. Therefore there is no need for trading software and you do not have to make quick decisions in running because you have made them already. Hence if you are like me, this is potentially a big plus. However, as the saying goes ‘for every positive there is a negative’. I find I say this phrase regularly in my life away from racing.

I tutor maths and chess online, and my chess students regularly hear this positive/negative quote pertaining to certain moves they make. It is the same here: let us imagine you set your ‘DOB’ pre-race and leave it to run its course. What happens if say you back it at 10.0 and set the lay at 5.0, but by the time the race starts the horse has drifted 15.5? OK the horse might still hit 5.0 in running, but this is going to happen far less often than it would if the starting price was 10.0. Of course the horse could shorten before the off as well, but as a general rule more horses drift than shorten. I know this because I have written about this before, and I have double checked recent data too. As an example, if we look at opening show prices compared to SP in 2023 (UK flat racing), we get the following figures:

 

 

Some pretty strong evidence to back up the general rule I mentioned above.

Of course there are ways round this potential issue by placing your back bet as late as possible; literally as the last horse loads in the stalls. The later you place it, the closer the price will be to its eventual Betfair Starting Price. That will mean however, that you will have to calculate and place the lay part of the bet immediately afterwards, and if you have literally placed your bet at the last second, you will be setting your lay after the race has got underway. Having said that, you should be able to put the lay price and stake in the Betfair machine before the horses have reached the end of the first furlong. This manual approach, though, clearly requires you to be around at the start of every race.

An alternative to avoid either the price fluctuating or needing to be tied to your trading screen at the start of each race, is to use some trading software. It is not too complicated to automate the software to back a horse at Betfair SP and once the Betfair SP is established, a lay at half those odds will be automatically placed. The lay will be calculated immediately the Betfair SP has been established (a few seconds after the off) to create the potential ‘DOB’.

DOB Examples

It is time to look at some races to see what can happen to Betfair prices in running. How many horses tend to DOB in a race, how many do not? Initially let me look at four races run on the same day (August 29th 2023). They are all 10 runner events – I chose those races simply to make the ‘dobbing calculation’ easy to understand.

Race 1 – 2.15 Ripon 1m2f handicap (4yo+)

The result is shown below with the Betfair SPs (BSP) and the lowest price matched in running (IP LOW). The penultimate column (BSP/IPL) is the result of dividing the BSP by the IP LOW. For a successful DOB the BSP needs to have at least halved in price; hence showing a figure of 2 or more. Successful DOBBERS are highlighted in green:

 

 

In this example, despite nine of the ten horses shortening in price, only two (Bollin Margaret and Cedar Rapids) DOBBED. There was one near miss (Tele Red 1.90). If you watch the race back, or even just look at the in running comments, you will probably understand why there were so few DOBBERS. The early leader, King Titan, led for less than two furlongs and hence was never going to shorten in price enough leading for less than a fifth of the race, especially when the lead was a narrow one.

Cedar Rapids took up the running after 2f leading for the next six furlongs and, considering his starting price of 83.06 and that he was still leading 2f from home, it is fairly easy to appreciate why he shortened to 22 and hence DOBBED. Bollin Margaret then took over the lead having just passed the 2f pole and led to the finish. Hence, as a winner at a BSP of 13.36, Bollin Margaret was always going to DOB.

In addition, once Bollin Margaret took over, the nearest challengers never really looked like getting to her. We could have found that out by watching the race replay or by looking at the in running comments. The comments for Bollin Margaret were ‘took keen hold, prominent, switched right over 2f out, ridden to lead over 1f out, kept on well final 110yds, always doing enough’. Hence with none of her closest pursuers really looking like winning this helps explains why they did not DOB.

Obviously each race is different and the number of horses that DOB will not be the same proportion of runners in each race (as we will see).

IMPORTANT NOTE: Before moving onto the second race, it should be noted that race winners do not always DOB, because the BSP has to be at least 2.02 for a horse to halve in decimal price (to 1.01, the lowest value on Betfair). Hence odds on winners cannot DOB.

Race 2 – 4.00 Ripon 6f handicap (3yo+)

Onto a sprint handicap a bit later on the same card:

 

This time, three of the ten runners DOBBED, one more than the first race. In this race three horses led at various points: the winner Twelfth Knight, as well as Abate and Russco. Twelfth Knight and Russco both DOBBED, while Abate was a very near miss with a BSP to IP LOW ratio of 1.98. Horses that lead at some point in the race are usually going to shorten in price, sometimes considerably so. That is the type of pattern I would generally expect to see, and the first two races have conformed to that pattern.

Race 3 – 4.15 Newbury 1m 4f handicap (3yo only)

Over to Newbury now for a handicap over 12 furlongs:

 

This time we see over half the field (six of the ten runners) DOBBING, despite only two horses leading during the race. The high number of DOBBERS is almost certainly due to the fact that the winner Graham, who had been clear 4f out, started to experience that lead steadily eroding. Hence, many in play traders observing the pack close on the leader would have thought / hoped / expected that one or more of those challengers would potentially win. This almost certainly explains why two horses traded so low; Medieval Gold (2nd) traded at 1.5, and Gordon Grey (4th) traded at 2.06.

Race 4 – 6.30 Musselburgh 5f handicap (3yo+)

Here are the facts and figures for this Musselburgh sprint:

 

An even split here with five horses DOBBING and five not. Three different horses led, of which two DOBBED (Sixcor, the winner, and the runner up, The Grey Lass). Two of the other three that DOBBED come as no real surprise if you watch the race back. Beneficiary made good headway mid race and as he was a big price, he would have caught the eye of enough traders to see his price contract sufficiently to DOB. Favourite Aconcagua Mountain travelled strongly and a furlong out looked the most likely winner. He faded in the final furlong but not before trading as low as 1.56.

What these four races tell us is that every race is going to be different from a DOBBING perspective. Just like every race is different if you are simply backing a horse or indeed laying one.

DOB Anomalies

Readers should note, that there are occasions when only the winner DOBS. An example was the two-mile Goodwood Cup this year run on 1st August. In this race, Quickthorn was well clear of the field after four furlongs, and a mile later with just half a mile to go he was still 20 lengths clear. The opposition assumed that Tom Marquand, the jockey of Quickthorn, had gone out too quick but they were sadly mistaken, and no other horse really stood a chance. Knowing how the race panned out explains why it was no surprise that no horse was really that close to halving in price in running.

Here is the result with the accompanying in running data:

 

This scenario of a single DOBBER in a race will occur from time to time especially in very one-sided events. However, it is extremely unusual for every horse in a race to DOB. In previous research from 2018 I had a dim recollection that there was a race at Nottingham where all the runners DOBBED. After doing some digging I found the race in question. It was the 7.25 on 7th August 2018. It was a 10f handicap with six runners. Here is the result:

 

 

Not only did all six DOB, but they all DOBBED fairly comfortably. One reason for this may be that four of the six led the race at some point, while the two who didn’t, were close to the lead making headway at different points.

DOB Success Rates

Going back to the four 10-runner races I shared earlier, of the 40 runners, 16 DOBBED. This equates to 40% of the runners. Previous flat racing DOBBING research I have done, over different time frames, showed the overall DOBBING percentage average out at around this 40% mark. Considering we need a success rate of roughly 14% higher than this, there is clearly a job on to make DOBBING profitable. So, how can we improve upon this base figure of 40%? Here are some thoughts / questions, which I will aim to expand upon in subsequent articles:

  1. How does run style affect DOB success? I have already discussed the fact that horses that take the lead at some point in a race are likely to shorten in price. Horses that lead for the majority of the race, or are leading at the furlong pole while looking like a potential winner, are both occasions when the leader’s price is likely to shorten considerably. Obviously if the price drops enough then the horse will DOB. Front runners, especially in sprints, are horses that are likely to have real potential to DOB; and, of course, the longer they lead the more chance of this happening. Hold up horses are not screaming out DOBBERS, unless they get into a much more forward position at the business end of the race.
  2. You often hear commentators say that a horse is ‘travelling well’. Horses that are ‘on the bridle’ tend to shorten in price as they are not under any pressure, or so it seems. This is a potentially time consuming idea to test, but I want to put it out there.
  3. Does race distance make a difference? Longer races mean greater elapsed time, and logic dictates that there will be more price fluctuation as a result of this. Hence, the chance of DOBBING may increase.
  4. Do courses make a difference? Certain courses, Bath for example, have difficult camera angles in the final couple of furlongs. I remember trying to trade in running in a Bath sprint around ten years ago; never again! It was so difficult to monitor all the horses from a front on angle. Other course considerations I guess that may have a positive bearing on DOBBING percentages, such as those with uphill finishes, or with long home straights.
  5. What effect does the price of a horse have? One sensible argument would be that shorter priced runners may DOB more often than longer prices, simply because the market suggests they will be more competitive: they are more likely to be mounting a serious challenge at some point in the race. An alternative argument would be that horses starting a long odds do not have to go ‘low’ in running to DOB. An 80.0 BSP shot only needs to reach 40.0 to DOB. Whereas a 4.0 (3/1) shot needs to hit 2.0 (even money).

My questions and thoughts don’t stop there, but it is time to wind up this introduction to DOBBING. As you can see we have a fair bit of digging and number crunching to do – or at least I do! And, at this stage I have only really discussed flat racing; I have not even mentioned National Hunt racing as yet...

- DR

An Attempt at Creating 2yo Ratings

/in /by

In recent articles I have looked at a very simple ratings method for all-age handicap races which, on initial testing, seems to have shown more positives than negatives, writes Dave Renham. I hope and expect to write further about these ratings at a later date, but need more time to do some further detailed research. This will take several weeks, probably a couple of months.

In this somewhat related article, I would like to share with you the process I went through when trying to create ratings for two-year-old (2yo) races. My plan was to stick to a similar methodology which in essence was:

  1. a) find what I thought were key factors/variables;
  2. b) use PRB (Percentage of Rivals Beaten) data once more as my metric;
  3. c) combine the PRB figures in the same way as the all-age handicap ratings by simply adding up the relevant scores.

There are a number of different types of 2yo races such as maidens, novice events, Group/Listed races (which are all non-handicaps) and nurseries (handicaps). My idea was to try to rate the maiden and novice races. To me these are quite similar types of race and hence I hoped that one cap could be worn by both. Of course that would not necessarily be the case, but even if the ratings worked well for one of the two then I would have achieved something.

To begin with, let me discuss factors I considered for use. Here was my ‘longlist’:

  1. Trainer record – in 2yo maidens/novices
  2. Sire stats – in 2yo maidens/novices
  3. Debut course
  4. Horse Sex – colt, gelding or filly
  5. Horse purchase price
  6. Most Recent form – Last time out (LTO) finishing position
  7. Recent market data – LTO price
  8. Fitness – days since last race
  9. Draw

The eagle eyed among regular readers will note that the last four factors are ones I used in my original ratings for all-age handicaps.

From this starting point I felt I needed to trim the list down, for two reasons. Firstly, as I mentioned in my very first ratings article, when creating ratings I prefer not to over complicate things. Secondly, some of the above factors would cause some problems for one reason or another.

The draw was the first to be discarded. In all of the articles I have written on the draw in the past, I have mentioned that draw bias works best in handicap races. Hence, although the draw may affect some 2yo races at certain courses, I felt it was not a reliable enough factor to use here. Next to go was purchase price as I had no easy way to source it, or indeed back check it on past results. Further, many horses are home bred and therefore never go through a sales ring. I felt it had importance, which is why it made the longlist, and I wished I had some data I could ‘crunch’ to see how important it actually was, but I felt it was a no go for these ratings.

Fitness using the days since last run metric was the third factor I decided to discard. My main reasoning here is that the advantage of a quick return, that tends to happen in older age handicaps, is not replicated for 2yo runners. I looked briefly at some win and placed stats which were very even across the days ranges, so I felt it was unlikely that the more accurate PRB figures would really give a wide enough spread of figures. I felt it wasn’t worth the hours of data gathering and sorting if the figures were likely to be almost identical across the board. One makes decisions like this all the time when delving into horse racing research. Of course sometimes we make incorrect ones but, with experience, decision making improves.

That left me with six factors/variables so let’s look at each in a little more depth.

1 Trainer record – I am not someone who bets often in 2yo races. Occasionally I will if I spot what I feel is a good betting opportunity. However, my main bets that involve 2yos occur when I play the Tote Placepot. Most meetings have at least one 2yo race in their first six so I have to use some methodology to choose which juvenile runners I am going to put into my ‘pot’. Trainer information is always my first call.

Many trainers do follow a similar path year in, year out; they generally stick to the same training methods, know which races to target, etc. Now it should be noted (albeit it is fairly obvious) that each year trainers have a completely new ‘string’ of 2yos, so variances in overall performance are going to happen from year to year. However, when we think about the bigger stables they tend to keep many of the same owners, and these are likely to be purchasing similar animals to what they have done in the past. Hence, past trainer 2yo data is usually quite a good guide to future performance. The graph below offers a real life illustration through the record of Charlie Appleby in 2yo maidens/novice races over the past four full years:

 

 

These figures are very similar from season to season and, as I am writing this, his current stats for 2023 are in the same ballpark – 31% win strike rate and 55% each way strike rate.

So how best to utilise past 2yo trainer data was my main consideration as there were different stats I could potentially use. One option would be to use PRB figures calculated from all 2yo maiden and novice events for each individual trainer. However, my concern with that was that the number of runs that a 2yo has is usually extremely important. This is a graph I shared in a previous article written in April when examining 2yos on their second starts:

 

 

As we can see there is a significant difference in 2yo performance on debut compared to second starts. Such differences would be replicated when comparing the relevant PRB figures. Not only that, this graph is taking all 2yo runners into account and as you can imagine some specific trainers have even more acute differences. For example, and once again using data from 2017 to 2022, Michael Dods had a 2yo debut win SR% of 5.3%, whereas his second starters won over 16% of the time. William Haggas 2yo debutants scored less than 12% of the time, but on second start won 27% of their races. These are just two examples showing one potential pitfall of using overall 2yo trainer data to produce a trainer rating score.

It was at this point in retrospect when the alarm bells should have been ringing, about how complicated just creating the trainer part of these ratings would be. However, I thought that using previous runs would almost certainly be the way I would want to go, and trainer stats would make the final ‘cut’. However, before digging any further I wanted to look at the other five factors.

2 Sire Stats – sire stats are often an important part of the 2yo betting picture due to the limited past run data most juveniles have. In some cases, especially early season, all the runners in a 2yo race will be making their debut. Hence we have no past form to go on, so we have to look elsewhere. Sires are the fathers of the respective horses and can have a significant influence on their offspring. When we dig deeper we find that the offspring of a good proportion of sires have clear traits or preferences. These may be going/ground related, distance related, age related, experience related, etc.

Having essentially decided to use previous starts as a key factor in determining the trainer rating PRB score, it would be difficult to do the same for sire stats, as this would potentially overlap somewhat. It is not as bad as using LTO position and Beaten distance LTO as two factors in a system as they are virtually the same metric. However, the improvement from debut to second start for sires would mirror trainer improvement to some extent.

Therefore for sire stats I felt a distance metric made more sense: splitting the 2yo sire PRB data into two, obtaining figures for sprints (5-6f) and for longer 2yo races (7f or more). The majority of 2yo races are contested at a mile or less so this seemed logical to me. To give an example of a sire whose 2yo distance stats differ across these two distance ranges, let me share the non-handicap 2yo win stats for Kingman. In 5-6f races his strike rate has been 12.8%, at 7f or longer this increases to 22.2%.

Interestingly, though, when I calculated PRB figures for Kingman they were closer than I had expected. His progeny’s 2yo PRB for 7f+ was 0.64 compared with 0.60 for 5-6f. This comparison helps to highlight why I believe PRB figures are the most accurate of all the statistical metrics that compare performance. Win stats are a good barometer, but PRB figures are much better because they effective ‘grade’ each run; not just whether the horse won or didn’t, or placed or didn’t.

No Nay Never is another sire whose 2yo offspring show a distance bias. At sprint distances his 2yo non-handicappers score over 19% of the time, at 7f or longer this drops to 13.6%. The PRB figures for No Nay Never this time do underline the strength of this bias as the sprint figure stands at 0.63, while the 7f+ one is much lower at 0.53.

Sire stats using this distance metric looked a good option to use in the ratings.

3 Debut course – this is something I have researched in the past and the track at which a horse makes its debut can be a factor in how it subsequently performs. It particularly affects the second career start as we can see if we compare the PRB figures for second starting 2yo that made their debut at either Ascot, Newmarket, Redcar or Ripon.

 

 

The importance of the debut course becomes less of a factor the more runs a 2yo has, but it still can have a bearing, so I would have to separate out the number of runs since debut in some way or other. Alarm bells were ringing this time as this factor is definitely going to be time consuming from a data gathering aspect, as I would need to collect the LTO course data one at a time and then combine number of last runs to each course. That could mean anything between 100 and 200 separate data ‘dumps’ into excel as well as adding extra columns and data to it. Ouch. However, at this point I was undeterred, as there have been times in the past when I have had to perform an enormous amount of data collection to write an article or series of articles. Also, I felt this factor was really important and would improve the ratings if it was included.

Having slightly buried my head in the sand regarding the enormity of this project, the question I now considered was does factoring in debut course combined with past career runs conflict / overlap with the trainer data idea which was going to use past runs too? I guessed it would to a small extent, but I was open-minded enough not to dismiss using this metric because of that slight concern. Clearly trainers have their preferred starting points for 2yos in terms of races and courses for debut runs, but individual course debut data combines all trainers and hence any significant overlap is extremely unclear. I was fairly confident – hopeful at least! – that the two factors would not conflict enough to make the ratings biased in any way.

Before moving on, I started to think about another problem that I had known would be a real issue in terms of 2yo ratings. What to do if the horse was making its debut? They have no past race data to work with; no debut course stats. What PRB rating could be assigned to those runners? I had several things to ponder, but decided to move onto the next factor as I felt it would at least have fewer issues.

4 Horse Sex – the sex of a horse has relevance and in 2yo races there are essentially three types of runners – colts (entire males), geldings (males who have been gelded) and fillies (females). I did some initial number crunching as this data collection was easy to do and not time consuming. I compared their PRB figures based on about 25,000 2yo runs in maidens and novices. Here are the findings:

 

 

As we can see colts have the best record, followed by fillies and finally geldings. The majority of 2yo runners are colts and fillies (around 87% of all runners combined) leaving geldings that make up a much smaller 13% of the runners.

These stats look promising from a ratings perspective, and I had some data collection completed!

Onto the last two factors now, both of which I used last time.

5 Most Recent form – LTO finishing position is a good barometer of most recent form and it seemed to work well in the handicap ratings. However, I would have same issue with the course debut stats with horses making their debut. What PRB figure would I use?

6 LTO price – LTO price also seemed to work well with the handicap ratings but again the question was what to do about debutants?

*

At this point I was feeling happy that potentially I had six factors to combine to create the ratings. On the flip side, there were a myriad of issues. Perhaps the biggest was the problem of 2yos that were making their debut. These runners would not have PRB figures for three of the six factors (LTO course, LTO position, LTO price). I needed to consider the options.

Option 1 – To use just one of the three LTO factors giving debut runners a standard PRB figure based on all debut run performances.

Option 2 -  Combining the three LTO factors giving debut runners a standard PRB figure based on all debut run performances, and dividing the score by three. This would mean all three factors had some relevance (in essence 1/3 of a rating factor).

Option 3 – Use the ratings only on 2yo races where all the runners had previously run at least once.

Of the three I felt the last option made the most sense as I really wanted to combine all six factors if I could. Based on a look at race data going back to 2019, 33% of 2yo races involved horses that all had run at least once previously. This would still provide around 350 races a year where the ratings could be employed. Added to that I had the facility to pull out all these races.

Having decided that was the preferred way forward, thoughts turned to the enormity of the data collection. As a researcher one is limited by the amount of data one has, or can access. We are also limited to a great extent by our computer skills. If you are able to write and use sophisticated computer programs for example, this gives you a huge advantage over those who cannot. If you have a vast database of results with every single type of variable/factor you can think of you also have a big advantage. Time is such a precious commodity and, without either of the above, my constant issue was the hours required for complicated or detailed research.

My expertise in terms of data number crunching is purely Microsoft Excel-based. I am proficient using Excel and use certain time-saving tricks such as cell formulae, pivot tables, functions like ‘VLOOK up’, and so on. However, I cannot write VBA code for macros, which impinges greatly on what I am able to do in terms of quantity and within certain time frames.

Back to the problem in hand. It was time to look at each factor again and try to work out how much work / hours would be involved with each one.

  1. Trainer record – the advantage I have from a research perspective in terms of trainer data is that when I export thousands of results, the trainer of each horse is part of the data set. Hence as a rule trainer data collection/manipulation is not as time consuming as many others things. On the negative side I would be looking at probably three or four separate data sets which I would need to combine and sort. Once that was done I could create the necessary formulae to calculate individual PRB figures and once those are added for all runners, I could use a pivot table to help calculate each trainer’s individual PRB figure. At least I didn’t have to worry about getting the debut stats; that would save me a little time.

The ideal plan would be to have PRB trainer figures for horses that have raced once, raced twice, and then group those who have raced three or more times together.

That part of the research was not too daunting; definitely doable. It would take several hours probably, but not several days!

  1. Sire stats – when I started thinking about how ‘easy’ it would be pulling and then crunching the sire PRB data for the two distance ranges, I suddenly realised that a trick I often use with sire data collection would not work for PRB figures. I could pull sire data relatively quickly if I was using win strike rates or each way strike rates. BUT not for PRB figures. It suddenly dawned on me that I would have to go one at a time, sire by sire. If that wasn’t bad enough from a time perspective, I also realised that even once I’d done that I’d need to find a way of ‘marrying’ the sire data with the trainer data. That would be even more time consuming and rather fiddly to do.

I thought then, OK I could ditch the sire stats part. I’ll still have five factors to use. The other ratings worked well with five, and even with four when I rated races without the draw factor.

  1. Debut course – back to this potentially tricky factor. I no longer needed to worry about debutants and what figure I would assign to them. However, as I mentioned earlier, I would still need to collect the LTO course data one course at a time combined with the number of career runs the horse had. As with the trainer data collection plan the aim would be to have ‘course on debut’ PRB figures for horses that had raced once, twice, and three or more. Earlier I had reckoned that I would need to collect separate data around 100 to 200 times and marry it together; it was clear that this was going to be within that range, although at the lower end (roughly 110).

-------

It was at this point that, if I had a towel nearby, I would have thrown it in! I had already reached the moment where the data collection and subsequent number crunching was too much to comprehend and hence attempt. It would take several weeks – far too many hours of my time for what I was endeavouring to do. Not only that, I still had three other rating factors where I would need to gather data. That being said, data collection for those three factors would all be far less onerous than the first three. However, it would still be several hours’ worth to add on top.

I was at a crossroads: I needed to decide whether I totally shelved my idea, or adapted it in some way. It has already been established that logically I cannot back test the data over several hundred races as I’d like to, due to the vast amount of time it would take. However, an alternative would be to look to rate races one by one, in real time as it were. Find races for the remainder of the season that qualify and then number crunch each individual race. To be able to do that though, I would still need to have sourced and collated the trainer data from the last few seasons (probably going back to 2015 or thereabouts).

In addition, I would need to source and calculate the PRB figures for LTO position and LTO price. I cannot use the PRB figures I used in the all-age handicap ratings because I used past all-age handicaps to calculate them. To collate the LTO position and LTO price PRB figures for 2yos would not take too long. Again, hours rather than days. On a more positive note the horse sex figures I had already calculated so that rating factor is no problem.

Then, for the sire stats (which I could incorporate doing it this way) and the debut course stats, I would need to check each horse in the race, crunching and then collating the relevant figures. That would take some time, and rating one race would potentially take up to 20 minutes if there was a big field of runners. On the plus side, once I had calculated the individual sire PRB figure that could be added to my 2yo ratings database.

The same would apply for the course on debut/number of career runs PRB figures. Once one was calculated that, too, could be added to the database. After rating, say, 20 to 30 races, the sire PRB stats and the debut course PRB stats would be starting to build up. That would make rating subsequent races far easier as I would start to have some data to hand for some horses that I didn’t need to recalculate.

Hence this is a potential way forward for these ratings should I choose to go that route in future. It will still be a very slow process, and because of that I am undecided in terms of what to do. What is most likely to happen is that I will start to collate some stats over the coming weeks, then try and rate five or six races, and go from there. If the first few races offer some positive signs, it will be easier to plough on and look at more races. If they don’t then it possibly is back to the drawing board.

-----

I hope this article has highlighted the fact that not all horse racing research goes smoothly.

It also shows that, despite all the best intentions, some ideas, no matter how good they may turn out to be, are simply too time-consuming or difficult to research. What has happened to me here is not a one off. In the past I have started researching numerous ideas with the plan of writing about them, only to abort the process at some point. So I’m used to the disappointment!

That was going to be the end of the article, but before checking it through I decided to source and collate the trainer data. As I have now done that I feel it is only fair to share the data with you. If nothing else, you now have some 2yo trainer PRB figures that may prove useful.

Below is a table of 2yo maiden/novice PRB figures for a selection of trainers. I have chosen the 30 trainers who have saddled the most 2yo runners. The figures are grouped as I discussed earlier into horses that have run once previously, horses that have had two career starts and then horses who have run three or more times:

 

 

As you can see most trainers have similar figures in the first two columns, with the third column being the best. My next job will be to source and calculate the PRB trainer figures for horses making their debut. However, that will need to wait for another time.

So I will finish here and ponder what next as far as my attempt to produce ratings for 2yo maiden/novice races is concerned. There will be an update in the future, I promise!

  • DR

My Simple Ratings Method, Part 3

/in /by

This is the third article connected with my attempt at creating simple ratings for certain horse races, writes Dave Renham. The first piece looked at how you could create a simple ratings method; the second tested this simple idea using some historical results. As the results were quite promising, I thought I would extend the number crunching to more past races, and in this third article I will report back my new findings.

To give some context, I was looking for a method for rating handicap races and, as far as weighting was concerned, I decided to give each factor a similar one. In order to do this I used the PRB (Percentage of Rivals Beaten) metric.

The rating method I came up with involved five factors or variables – these were:

  1. Draw – splitting the draw into thirds;
  2. Most Recent form – for this factor I used last time out (LTO) finishing position;
  3. Recent Market data – LTO price was used for this one – so the Industry Starting Price the horse was returned in its most recent race;
  4. Long term form – for long term form I used career placed percentages in handicap races.
  5. Fitness –I used days since last run to create the PRB figures for this final variable.

For more ‘meat on the bones’, the first article explains in depth what the individual PRB values were within each of the above categories. Here's the link.

Essentially each horse ends up with five PRB values and therefore when rating a race, we simply add up the five PRB figures for each horse giving them a final total or rating score. The horse with the highest total becomes top rated, the second highest total becomes second rated and so on.

In the second article I did some back testing on the ratings, although for this stat-busting exercise I decided to ignore the draw factor by focusing on longer distance races. ‘Time’ was the main reason for ignoring the draw – it was something that was going to take far too long to collate the necessary information. Hence my ratings were ‘trimmed’ – now they would be created by using the four other PRB figures produced from LTO position, LTO price, career handicap placed percentages and days since last run.

I back tested 324 races with the following rules:

  1. Year - 2018 (UK racing)
  2. Age group - 3yo+ / 4yo+ handicaps
  3. Distance - 1m1f or longer
  4. Runners – 8 or 9

The results showed promise, and you can look at the in depth findings here.

For this next batch of testing, I kept the first three rules mentioned above, but changed the runners rule to 10 to 12 runner races. Before sharing the results of this second phase of testing, let me quickly share my thoughts on what I perceive to be the most important finding. For any ratings to have the ‘potential’ to be useful, they need to show strong similarities with the actual betting market. The first phase of testing did see this happening. For example, the top rated runner started favourite in over 41% of the races rated. Just 3.7% of top rated runners started 7th, 8th or 9th in the betting. Ultimately if your ratings do not mirror the betting market that well, then the chances are they are going to be dud.

Of course, as punters, we are looking for value, and the hope is also that the ratings throw up value selections. The first set of results shared in the second article offered some promise in that regard.

OK, it is time to look in detail at the 10 to 12 runner 2018 results. For the record this comprised of 362 races in total.

Firstly, let's review how the top rated horses matched up against the actual betting market.

 

 

This graph perfectly shows the type of sliding scale one wishes to see. It is similar to the one we saw when analysing the 8 to 9 runner race data.

Over 35% of the top rated runners started as the favourite, while more than 72% of the top rated runners started in the top three in the betting. Compare this to 8th or bigger in the betting which accounted for just 5.1% of all top rated runners.

Let's now look at the second top rated runners next in the same way:

 

 

Again, this graph gives the type of results that suggest the ratings are fairly accurate in terms of assessing potential chance of winning. We would expect the highest bars in the graph to be on the left hand side once more with a sliding scale going from left to right. 57.2% of the 2nd rated runners ended up in the top three of the betting compared with 2.3% ending up in the bottom three of the betting (10th or worse).

So once again the top section of the ratings are looking good. Time to take a look at the lowest rated to see how they fit against the market. Firstly let us look at the market rank percentages for the lowest rated runners. To begin with let's review the 12th rated runner in 12 runner handicap races:

 

 

This graph is effectively a mirror image of the first two we saw, which is exactly what I would hope to see. Over 66% of the bottom rated runners ended up 10th or lower in the betting.

Now a look at the 11 runner races (bottom rated):

 

 

An even better set of figures here in terms of correlation. This is probably due to the fact that the 11 runner sample size was around 33% bigger than for 12 runner races.

I don’t see the need to show the whole graph for the 10 runner races as well, but the results were similar once more. The bottom rated runner appeared 8th, 9th or 10th in the betting market over 63% of time, while only 12% of them ended up in the top three market positions.

It is very pleasing to see that the results we got for 8 to 9 runner races are being replicated here. Essentially these simple PRB based ratings are looking like providing a relatively sound framework in terms of forming our own market – if nothing else. I discussed some ideas about how to form a betting tissue/market in this article which preceded this series. These ratings could be used in conjunction with that – or even be used in a stand-alone manner.

Time to see how the ratings fared in terms of winning – their win strike rate. In the 8 to 9 runner results, the higher rated runners comfortably out-performed the lower rated ones. Obviously, I'm hoping for the same scenario here:

 

 

These results are reasonably positive – the top rated runner has done extremely well and we do get the type of sliding scale one would hope for. In truth, the 8 to 9 runner data looked stronger, but when you analyse win and placed data, the picture looks more clear-cut:

 

 

This graph gives excellent correlation with higher rated runners hitting higher win and placed strike rates; lower rated runners doing the reverse.

It is time now to look at the performance of the individual rating positions in terms of profit/loss to BSP. At this point it should be noted that in the whole data set for this article, there have been some unusually big-priced winners. The ten highest BSP prices of winners during this study were:

 

 

Within the type of sample size used for this piece, huge priced winners are a common problem when trying to use BSP as a value metric. That is why in other Geegeez articles, where appropriate, I have quoted BSP on shorter priced runners only. Unfortunately using this shorter priced idea will not work effectively on the rating positions data due to very small sample sizes (for the lower rated positions in particular). Therefore, in this case, the BSP profit/loss figures for individual ratings positions shown below may confuse matters for some readers, but hopefully you'll still get the gist at least:

 

 

We do have to take the profit/loss figures here with a pinch of salt however; especially the lower rated ones. Ordinarily strike rates of 4.1% and 5.2% are not going to produce stunning returns of 78.6 pence in the £ or 57.2 pence in the £.

Thus, instead of dwelling on these skewed figures, it makes more sense to dig deeper into the top-rated runner results as these prices as a whole are much shorter. In fact 88% of all top rated runners were priced 12.0 BSP shorter.

I want to look at two main areas when it comes to top-rated runners. Firstly I want to delve into profit / returns, so here are the profit/loss stats for top-rated runners in terms of their market position.

 

 

It is interesting to see that the top-rated runner has made a decent looking profit when actually starting as the favourite. From a ratings value perspective though, I would have liked to have seen slightly better figures from the poorer market positions. Having said that, 362 races is too small a sample to see a potential pattern emerge such as that, especially when just 63 top rated runners started 5th or higher in the actual betting market. It is heartening to know that top rated runners that were 5th or bigger in the betting did make a profit of £11.15 from these 63 runners, but we need much more data. Not just on top rated runners but on other rating positions too.

Secondly, I wanted a breakdown of how far clear of the 2nd rated the top rated was. This is something that I omitted to think about when penning the last article. Hence for this next table I have combined the relevant stats from both articles to include all 8 to 12 runner handicap results. This gives us a bigger data set for analysing the gap between the top two rated runners. Here are the findings:

 

 

Before commenting on this, it must be stressed that despite expanding the sample size, it is still a relatively modest one. However, one could not have dreamed of a much better set of results (well, I suppose I could have, but you have to have some sense of realism!) The bigger the gap, the better the results – both from a strike rate and returns perspective.

My next port of call was looking at ratings position versus betting market position. I wanted to compare the performance of horses that are rated better than their odds position, compared to those who are not. Here are the results:

 

 

As with the 8 to 9 runner data, horses ranked better than their price ranking have done best from a profit/loss perspective. There is a big differential here, but as I have already stated, the BSP data for all these races is not too reliable, and hence I would not read too much into this.

Before finishing, I have one more set of figures I want to share. As I did in the previous piece, I want to look at the actual rating scores and group the lower rated runners as a whole, and compare them with the higher rated runners. Tthe lowest possible rating using my PRB scores is 1.64; the highest possible is 2.39. The groupings I have used are horses that were rated 1.64 to 1.84, and horses that were rated 2.18 to 2.39. These are exactly the same groupings I used in the 8 to 9 runner article. Here is the comparison of wins, runs and strike rate for our two groups in 10 to 12 runner handicaps:

 

 

These stats are what one would have expected based on all the previous data shared in this article. However, it is always nice to have expectation validated in black and white.

I have not added the BSP profit figures as the 1.64 to 1.84 group had two of those huge priced winners I alluded to before (353.78 and 137.3). Such winners totally skew the profit/loss column making a comparison a mockery (as we have seen twice before in this piece, with the profit/loss figures for individual ratings positions, and with the rating rank v market rank BSP comparison). For the record, the 2.18 to 2.39 group, which did not have big priced winners skewing the results, lost a modest 20 points to BSP equating to an ROI of -4.7%.

So that’s currently where I’m at. There has definitely been further promise in this latest piece of research. I will decide where I go next with this over the next few days and any new ratings research will be written up and shared with you in the very near future.

- DR

My Simple Ratings Method Revisited

/in /by

In the last piece I wrote on Geegeez I attempted to demonstrate to readers how you could go about creating a rating method in order to help you to analyse a race, writes Dave Renham. This article continues on from that simple ratings method, as I have decided to dig into the past and do some testing using historical results.

To recap, I was looking for a method for rating handicap races and, as far as weighting was concerned, I decided to give each factor a similar emphasis. In order to do this I used the PRB (Percentage of Rivals Beaten) metric.

The rating method I came up with involved five factors, as follows:

  1. Draw – Using the Draw Analyser tool rather the draw tab in the racecard in order to define a more precise date range, I set what I felt were relevant parameters. These parameters had to ideally a) match the race in question; and b) give me a big enough sample size.

My ideal date range is a recent one such as 2016 to 2023, for handicaps only and, in terms of runners, covering a spread of plus or minus two runners compared with the field size of the race I was rating. So, for example, if it was a 10-runner race, I would set 8 to 12 runners on the Draw Analyser. The Draw Analyser gives PRB figures for individual stalls as well as grouping them into thirds. I used the thirds method for the ratings, grouping low draws together, middle draws together and high draws together.

  1. Most Recent form – for this factor I used last time out (LTO) finishing position. For the relevant PRB figures I looked at two full years of handicap race data (2021 and 2022) to give what should be incredibly accurate readings. This amounted to several thousand races. The PRB figures had a range from 0.60 for winners last time out to 0.41 for horses that finished 9th or worse LTO.
  2. Recent Market data – for this factor I used LTO price – so the Industry Starting Price the horse returned in its most recent race. Once again I used 2021 and 2022 handicap races to create these PRB figures. The PRB figures had a range from 0.60 for horses priced 6/4 or shorter LTO to 0.36 for horses that priced 40/1 or bigger LTO.
  3. Long term form – for long term form I used career placed percentages in handicap races. Again the data for the PRB figures was taken from the two years of 2021-22 handicap results. The PRB had a range from 0.58 for career placed percentages of 51% or more, down to 0.44 for those who hit 20% or less.
  4. Fitness – for this final factor I used days since last run to create the PRB figures. To give the most accurate scores I used the same data set (’21-’22 handicap races) as I had done for the previous factors. The PRB figures ranged from 0.61 for horses that returned to the track within three days to 0.43 for horses off the track for 71 or more days.

So, essentially when rating each race, I took the relevant five PRB figures for each horse and added them up. The horse with the highest total became top rated, the second highest total became second rated and so on.

After writing the original article my plan was to rate a few races and see how the figures worked out. This is something I am still in the process of doing and will feed back my findings in a future article.

[I initially had no intention of back testing results because I thought it would take far too long. However, using a bit of excel, a fair amount of copying / pasting, and a few shortcuts I thought of as I was going along, I managed to get a year’s worth of ratings data in a few hours. The only ‘problem’ is that to do this I had to ignore the draw factor. The main reason for this was that it would take me far too long to gather the draw data (probably several months). But there were other reasons as well, one being that a good proportion of course and distances do not have a significant draw bias so trawling through masses of these types of race would not really improve the ratings or make them more accurate.]

So my ratings would be created using the four other PRB figures based on LTO position, LTO price, career handicap placed percentages and days since last run.

When you create ratings or systems and then back test them on past results, it is important to ensure that you use a different data set. This is a common mistake people make – one I made the first time I tried to create systems back in the early 90s. Hence, having used a data set of 2021 to 2022 to create all the PRB figures, I needed to choose a different year for the testing phase. I chose 2018.

A year of handicaps gives me plenty of data to work with. I did however want to narrow that down by looking only at 3yo+ and 4yo+ handicaps, as this would avoid handicaps with younger, less exposed runners. My assumption is that these ratings will work far better in races that involve older horses. I also chose to try and eliminate any draw factors by choosing handicaps races of 1 mile 1 furlong or more. Without the draw in the ratings, it made no sense to test shorter distance handicaps where draw bias can be extremely relevant and potential skew some ratings results (without the draw PRB being considered). Finally, I looked at 8- or 9-runner 3yo+/4yo+ handicap races for the basis of this article.

Just to reiterate I am back testing my ratings on:

  1. Year - 2018 (UK racing)
  2. Age group - 3yo+ / 4yo+ handicaps
  3. Distance - 1m1f or longer
  4. Runners – 8 or 9

Before sharing the results of my testing, let me discuss briefly what I am hoping to find. For the ratings to have the potential to be useful/effective, more often than not, they need to show strong correlation with the actual betting market. If your top two rated horses are consistently near the head of the actual betting market this is a far more positive sign than if they are consistently near the foot of the betting market. Of course in terms of making a profit from your ratings, you are looking for them to be more accurate than the actual betting market and throw up value selections. Not easy!

OK, let’s dig into my findings:

Firstly let's see how the top rated horses matched up against the actual betting market.

 

 

This graph is extremely positive with over 41% of the top rated runners starting as the favourite. Indeed 79% of the top rated runners started in the top three in the betting. There is a definite sliding scale, too, showing the type of correlation you would be hoping for. Let me look at the second top rated runners next in the same way:

 

 

Again, this graph gives positive results. You would expect the higher bars in the graph to be on the left hand side once more, and they are. Just over 72% of the 2nd rated runners ended up in the top four of the betting.

So the top section of the ratings are looking good. How about the lowest rated? Firstly let us look at the market rank percentages for the lowest rated runners. To begin with let me look at the 8th rated runner in 8-runner handicap races:

 

 

The graph is reversed compared with the first two we saw, which is exactly what we are looking for. 44% of the lowest rated runners were at the bottom of the betting market in 8th place. Less than 17% of them ended up in the top four of the betting. Further positive news as far as the ratings are concerned.

Now a look at the 9-runner races (bottom rated):

 

 

A similar lay out to the 8-runner races with nearly 72% of 9th rated runners ending up 7th, 8th or 9th in the actual betting market.

I have to say that I am extremely pleased with the correlation to date between my ratings and the betting market. For something that is relatively simple (just four parameters), it is mirroring the betting market well.

So these ratings, on the evidence we have so far (based on 324 races), definitely show some potential. Time to see how the ratings fared in terms of winning – their win strike rate. Clearly I was hoping that the top rated runners would comfortably out-perform lower rated ones. Here are the findings:

 

 

More positive news with the top two rated runners both securing strike rates in excess of 20%. Also a clear break between the top four rated and those rated fifth to ninth. The 7th rated is very slightly out of kilter, but this can happen – the important fact is the trend from top rated to bottom rated is downwards.

What I now want to look at is how the ratings would have performed if betting on them. I am assuming that we are backing at £1 level stakes to Betfair Starting Price (BSP). Here are the findings:

 

 

The results for the top rated runner are a little disappointing, losses of around 17p in the £. Horses ranked 7th have made surprisingly high profits, but most of the big priced BSP winners happened to pop up in this specific ranking position. I doubt these figures would be replicated again – this is just the type of outlier you can get when analysing BSP profit/loss.

When taking the top four rated as a whole, they have outperformed horses rated fifth to ninth as the table below shows:

 

 

Considering how big priced runners on Betfair can skew the figures, these grouped results are very heartening.

I must admit I am pleasantly surprised with these initial findings. I am intrigued to see how the ratings work with shorter distance races where I can include the fifth parameter – draw bias.

My next port of call was looking at ratings position versus betting market position. I wanted to compare the performance of horses that are rated better than their odds position, compared to those who are not.

Just to clarify, some examples of horses that are rated better than their odds position would be as follows (I appreciate for many I am just stating the obvious, but just in case there is any confusion in my English/grammar):

 

 

And here are examples where they are not (these include identical positions in the rank of the ratings compared to the actual market rank):

 

 

My hope is that I see better returns for horses that are rated better than their odds position, compared to those who are not. This would suggest that the ratings can potentially pinpoint some value selections.

Here are the returns for each:

 

These figures suggest the rankings are doing a pretty good job – it seems there has been more value when the rating rank has been better than the market position.

Before winding this piece up, I have one more set of data to share with you. I am looking at the actual rating scores and grouping the lower rated runners as a whole, and comparing them with the higher rated runners. Now the lowest possible rating using my PRB scores is 1.64; the highest possible is 2.39. The groupings I have used are horses that were rated 1.64 to 1.84, and horses that were rated 2.18 to 2.39. These groupings from 2018 3yo+/4yo+ handicaps would have produced the following results:

 

 

The strike rates should come as no surprise based on the evidence of the ‘Ratings Win SR%’ graph shown earlier, but the differences in returns are even wider than I had expected. It is another indication that these simple ratings have some real potential.

I'm to park things here for now and start further number crunching for the follow up article. The data set of 324 races is a decent one, but before making too many bold claims, I think we need to look to how these ratings fare in other races. Research wise, I plan to analyse the 2018 data from 10- to 12-runner races next. Once that’s done, I will write it up and share my findings.

Stay tuned!

- DR

How To Create Simple Horse Racing Ratings: Example

/in /by

In my previous article I went through some basic ideas in terms of trying to create your own odds line or betting tissue, writes Dave Renham. In this piece I am going to show you how to go about trying to create a rating method in order to help you when analysing a race. There is no perfect way to rate a race; there are no perfect ratings, so this idea / method I am sharing is just one of thousands of potential ways to rate a horse race.

Two problems in the past I have found with rating a race have been firstly which factors to use, and secondly what ‘weighting’ or importance do I give to each one. Let’s look at factors first:

Factors / variables to use – to start with, one important thing to be aware of is to make sure the factors you ‘rate’ do not overlap in any way. A simple example of this would be using ‘last time out (LTO) finishing position’ but using ‘LTO beaten distance’ as well. These two factors are very similar as they are both measuring last time out performance and they should not be used in combination in terms of rating races; rather, choose one or the other.

For me I do not want to over complicate things so I would be looking for a handful of factors/variables to use in my ratings. Here are the factors I tend to concentrate on when trying to develop a rating system, and what ‘measure’ I would use:

  1. Most Recent form – either LTO finishing position or LTO distance beaten
  2. Recent market data – LTO price or prices from last 2-3 runs
  3. Long term form – some stat connected with the horses’ overall career
  4. Fitness – days since their last race
  5. Draw – past C&D draw stats split into thirds

Weighting of factors/variables – this is tricky in my opinion, and I have no magic bullet to share with you I’m afraid. What I have struggled with in the past is which stats to use for each factor – win strike rate, placed strike rate, A/E indices, etc. Not only that, but how on earth do you ‘weight’, for example, LTO position versus days since last run? How do the individual LTO finishing positions compare with a grouping of days since the horse ran last? What grouping for days since last run do I use? I cannot use individual days, so do I group it in weeks, blocks of 10 days, etc?

There are lots of questions, but no clear cut answers. Suffice to say, you just have to go with your gut instinct in terms of weighting factors. Once you have rated a few races, you will get a feel for what you may have to adjust to improve them.

For this article I will be using the five variables mentioned above in an attempt to create simple ratings for horse races. As far as weighting is concerned, I am basically going to weight each factor in a similar way. In order to do this, my stat of choice is going to be the PRB stat (Percentage of Rivals Beaten).

Percentage of rivals beaten (PRB) – Before becoming a member of the Geegeez family I had not really delved into this metric much. However, now, I think it is arguably the most important racing stat I consider. For more information on PRB (and the other metrics used on geegeez.co.uk) check out this article.

On Geegeez you can find the PRB stat in a variety of areas which can be accessed from the racecard – individual horse records such as:

 

On the Profiler tab:

 

In the pace / run style tab:

 

And in the draw tab:

Hence, we can find the draw PRB stats needed for my simple rating method on Geegeez. For the remaining stats we need to make use of several hours of number crunching I did prior to writing this article.

How is this simple rating system going to work?

Essentially, I am going to use the five factors mentioned earlier and find the relevant PRB figures for each horse within each factor. Then I am going to add up the five PRB scores to give me their final scores or rating. I said it was simple! I would suggest trying this idea in handicap races; I would use a different idea for say 2yo races or 3yo maiden races.

Right, let’s go through each factor one by one:

  1. Draw – I would like to start with one of the tabs you can use on Geegeez. Let us imagine we have a 1m handicap at Pontefract with 9 runners. I would actually go to the Draw Analyser tool rather the draw tab in the racecard in order to use a more precise date range. Hence this is what I would enter in terms of parameters:

 

 

As you can see, I have chosen a recent date range (2016 to 2023); handicaps only due to it being a handicap, 7 to 11 runners (+ or -2 from 9 runners), full ‘going’ range from hard to heavy, and ‘Actual’ rather than ‘Card’ as this takes non-runners into account.

In this imaginary 9-runner handicap example, any horse drawn 1 to 3 would get a ‘0.60’ PRB ‘rating’ figure, those drawn 4 to 6 would get ‘0.48’ and draws 7 to 9 the figure would be ‘0.42’.

  1. Most Recent form – for this factor I am going to use LTO finishing position. For the PRB figures I have looked at thousands of handicap races to give the most accurate readings. Here are the PRB figures for LTO finishing position:

 

 

As you can see LTO winners have a PRB figure of 0.60 when running in their next race; compare this with horses that finished 9th or worse LTO whose figure is much lower, not surprisingly, at just 0.41.

Therefore, when rating each horse you simply need to look for their LTO finishing position and assign the relevant figure from this graph.

  1. Recent Market data – for this factor I am going to use LTO price – so the price the horse was in its most recent race. Here are the PRB figures, again taken from thousands of races:

 

 

Another sliding scale here as you would expect with shorter priced runners LTO producing higher PRB figures. Hence a horse that was priced 11/2 LTO would be assigned a rating figure of 0.55, if they were priced 25/1 LTO their figure would be 0.44, etc.

  1. Long term form – for long term form I am going to use career placed percentages in handicap races. The data shared again is taken from thousands of races to give us the most accurate figures possible. I have split the percentages into four groups as the graph below shows:

 

 

The beauty of this stat from a Geegeez perspective is that you can find these percentages by using the ‘Instant Expert’ tab from the racecard. All you need to do is to adjust the distance tab (top left of screenshot) to include all races (I use the full range from 5f to 4m4f), click the handicap tab (top right of screenshot), and for ALL flat races click the ‘Flat & AW’.

 

 

In the above example, the horse at the top has a career placed percentage of 43% and as this lies between the ’36 to 50%’ grouping, this horse would be worth a PRB figure of 0.54.

  1. Fitness – for this metric I am using days since last run (DSLR) with once more thousands of races analysed to create accurate PRB figures. Here are the splits:

 

 

As you can see this metric is going to be quite even for most horses, as the vast majority of runners will have been off the track for between 8 and 70 days. The 8 to 14, 15 to 28, 29 to 42 and 43 to 70 groupings are very close together in terms of PRB figures.

And that’s it for configuring my factors. Hopefully it has been fairly self-explanatory.

However, let me give you a fictitious example which hopefully will help. I will stick to the 9-runner mile handicap race at Pontefract that I used for the draw data earlier.

Firstly here are our imaginary horses and their relevant stats:

 

 

From here we can convert these into the relevant PRB figures for each individual stat:

 

 

I have totalled up the five PRB figures for each horse to give them a final total (furthest column on the right). These totals are their final rating figures and I have ordered our imaginary horses highest to lowest below:

 

 

From these ratings, you would hope there is good chance that the winner will come from one of the top three (Fireball, Frazzle and Dobbin); likewise you would hope the bottom three rated (Monty, The Closer and The Gooner) are unlikely to produce the winner.

How you deploy your ratings is going to be different for each individual. You may want to use them as a basis for an odds line – in a 9-runner race, each horse theoretically has an 8/1 chance of winning so you could initially price up the middle rated horse at 8/1. This horse is Plodder – from here you could assign the rest of the prices using Plodder as your starting point, and then adjust them to get a book percentage of close to 100%. Once done you could compare them to the actual prices on offer to see if there are any horses that potentially offer you value.

An alternative approach is to simply compare the actual market position with your rating positions. Let’s say for argument’s sake Dobbin, your third rated horse, is priced up at 10/1 and is 6th in the betting, you might perceive this to offer value. Likewise if the top rated horse Fireball is third in the betting priced 5/1, again you might perceive this horse to offer you some value.

Essentially how you interpret the ratings is going to be personal to you – again there is no right or wrong way. What ultimately matters is how predictive your ratings are. I have not back tested this rating idea as yet, but it is on my ‘to do list’, as I have enjoyed researching and writing this piece. What is likely with a first ratings attempt is that I will need to make some adjustments – for this idea it may be that I am underrating one particular factor and overrating another. If that proves to be the case, I could apply some multipliers to the relevant PRB figures to help with that adjustment. For example, I may need to multiply the underrated PRB figures by 1.10 and the overrated ones by 0.90 to give more accurate overall ratings. However, that is for another time.

Until next time, I’d encourage you to experiment for yourself and if you find anything interesting, feel free to share in the comments below.

  • DR
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