Prepping for NH 2025/26, Part 3: Handicap Chases

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Preparing for the Jumps - Part 3, Handicap Chases

In this third article in a series looking forward to the National Hunt season we'll continue last week's chase theme, this time focusing on non-novice handicap chases, writes Dave Renham. That is, any handicap chase without the term ‘novice’ in the title. As before, data has been taken from 1st January 2018 to 31st August 2025 with profits and losses calculated to the Betfair Starting Price (BSP) with a 2% commission applied on any winning bets. Only UK NH races have been researched, so this does not include Irish racing.

We have a good number of qualifying races per year, usually between 850 and 900; in total, then, this equates to nearly 6500 races. I will start as I always do by examining the betting market.

Market factors

I have used Betfair to determine market rank and the table below shows performance by position in the Betfair SP market:

 

 

Favourites performed well and even made a small blind profit. Concentrating on the other market positions, horses ranked fourth in the betting made a solid profit which is a big outlier when comparing to other market positions in the table whose ROI%s are all in the same ballpark. It is interesting that if backing horses fourth in the betting year in year out, a blind profit was achieved in six of the eight years.

Personally, I do not see backing horses fourth in the market as a strategy this coming season because I am assuming it is down to variance. Having said that, I looked back on the previous eight-year subset (2010 to 2017) and horses fourth in the market made a profit then, too. The ROI% was +3% in that time frame. I’ll leave you to decide how to interpret that...

Returning to favourites for a slightly deeper dive, here are the yearly ROI% numbers:

 

 

As we can see, there were flucatuations from year to year ranging from 2018, the year showing the best returns at +7.4%, to the year showing the worst returns (2019) at -12.3%. This is a good example that even annual results for a similar type of horse are rarely uniform. It is something as bettors we really need to appreciate. It’s like the tossing a coin analogy where the first ten tosses see seven heads and three tails, and the second ten see the script flipped somewhat with two heads and eight tails. The previous ten tosses are usually not a statistical or mathematical guide to the next ten. We know over a long period of time that the percentages for the number of heads compared to tails is going to trend towards 50-50, but over shorter periods we will get significant fluctuations.

Moving on, and still with favourites, here is performance by race class.

 

 

As we can see there are far more handicap chases when we get to Class 3 level or lower. The strike rate for Class 1 handicaps was lower than the rest and this was simply down to bigger average field sizes. The lowest class (5) has seen the best results with six of the eight years turning a profit, and the A/E index of 1.07 was extremely solid.

Having looked at the market, it is time to look into other areas. As with the first two articles, I am going to impose a BSP price limit of 20.0 or lower from now on, to avoid any winners at excessively big odds potentially skewing the bottom line. It still includes nearly 40,000 runners so the sample size remains huge.

 

Sex of horse

There will always be more male runners than females but how did their stats stack up against each other? Let me see:

 

 

In terms of performance, both sexes performed in a comparable way across all the key metrics so there was no real edge to either.

 

Age of horse

Moving on to age data now, and I'll begin by sharing the A/E indices across different ages:

 

 

The 4yo stats are based on just 127 runners so, concentrating on the bigger samples of horses aged five and older, there seems nothing much in it between the ages of five and nine, but once we get to handicap chasers aged ten or older we start to see a dip.  Here is a fuller view covering other key metrics:

 

 

When looking at the data as a whole, there seems to be the same age bias as we saw in the graph in play here: lower win rates for horses aged 10 and older, and much poorer ROI percentages too. Even when focusing on horses aged 10+ that started favourite or second favourite, their record was relatively poor in comparison to the other market stats we saw earlier. This cohort of older favs won 374 races from 1671 runners (SR 22.4%) for losses of £160.48 (ROI -9.5%); A/E 0.93. Logic dictates therefore that in general we should concentrate on horses aged five to nine.

 

Country of Breeding

Changing tack to breeding and specifically the country of breeding, below is a breakdown for the four main breeding entities - British, Irish, French and German-breds:

 

 

We have similar strike rates for the three main countries, Britain, Ireland and France. German-bred runners are less common and have performed below the level of the other three.

There are interesting stats for French bred runners when we split their data into different age groups. Combining five- and six-year-old runners together they produced 400 winners from 2056 qualifiers (SR 19.5%) for a small profit of £52.94 (ROI +2.6%); A/E 1.05. Moreover, backing all qualifiers to place on Betfair also nudged into profit to the tune of £36.58.

Compare this to the record of French-breds aged 10 and up. This older cohort produced 168 winners from 1324 runners (SR 12.7%) for a hefty loss of £150.30 (ROI –11.4%), A/E 0.94.

British-bred runners aged eight or younger combined to produce a good set of figures: 832 wins from 4545 runners (SR 18.3%) for a healthy profit to BSP of £420.95 (ROI +9.3%). The A/E index of 1.08 suggests these runners have offered good value. British-bred older runners, though, performed even more poorly than their French counterparts, hitting a strike rate of just 11.6% (229 wins from 1973) for losses of £277.60 (ROI -14.1%); A/E 0.89.

 

Position Last Time Out (LTO)

A quick mention of the most recent piece of form in terms of finishing position LTO. There were no clear patterns that I found but here are three stats that I thought were reasonably interesting.

Firstly, LTO winners performed reasonably well, making only a small loss of 1.5 pence in the £. They won 21.6% of the time, so with a bit of extra digging there may be some value to be found in certain last time winners in handicap chases.

Secondly, horses that fell or were unseated LTO did not perform well. They collectively achieved 201 wins from 1520 (SR 13.2%) for a loss of £114.44 (ROI -7.5%); A/E 0.93.

And thirdly, horses that were pulled up LTO but started favourite next time had a good overall record. Clearly, not a high percentage of horses that were pulled up LTO go off favourite on their next start, but when they did, 71 won from 201 runners (SR 35.3%) for a decent profit of £48.02 (ROI +23.9%); A/E 1.25.

 

Weight

In handicaps of course horses carry weight based on their Official Rating, although it is rare to find anything significant overall as the handicapper does such a good job rating and weighting horses. It can difficult sometimes to decide how best to analyse weight. Weight carried or weight rank are two obvious options, and I have used both many times before.

For these data I have chosen a new idea by comparing different groups in relation to the average weight carried in the race. As Geegeez has recently added the average OR for each race in their racecards it seems a good idea to take this approach. I have split the runners into three groups:

  1. Horses carrying 4lb or more than the race average.
  2. Horses carrying 4lb or less than the race average.
  3. Horses weighted within 4lb of the race average.

Here are the A/E indices of the three groups:

 

 

Horses carrying less weight offered the best value within the timeframe and the ‘4lb or less’ group turned a small profit of £238.04 (ROI +2%). Overall, this group contained 11,106 runners of which 1676 won (SR 15.1%). The other two groups lost 4p in the £ across 14,284 bets and 13,599 bets respectively.

 

Race Class change

A look at change in class next. Here are the splits:

 

 

Horses upped in class did best, albeit by a smallish margin given the number of runners within each group.

 

Trainer Angles: Overall

The final port of call for this piece is to look at some trainer data. Let me start by flagging those trainers with the highest win rates (who saddled at least 150 runners during the time frame). This is a big table containing over 50 trainers. They are ordered alphabetically:

 

 

32 of the 54 trainers made a blind profit which is a higher number than I had expected, while seven had A/E indices of 1.20 or higher – these trainers are Gary Hanmer, Ben Haslam, Matt Sheppard, Henry Daly, Martin Keighley, David Dennis and Richard Hobson. That septet appears to have gone under the radar somewhat. Here are some additional positives for some of those trainers:

  1. Ben Haslam seems to excel with older horses. Those aged 9 or older won 25 times from 107 (SR 23.4%) for a profit of £51.98 (ROI +48.6%); A/E 1.48. There were a few multiple winners but 11 different older horses came home in front so Haslam seems to have good knack of rejuvenating veteran runners.
  2. Henry Daly did especially well in races of three miles or more. In these contests he saddled 31 winners from 154 (SR 20.1%) for a huge £103.14 profit (ROI +67%); A/E 1.33.
  3. Sticking with Daly, his runners won just three times in 47 attempts in Class 1 or 2 events. However, in Class 3 or lower his record was 43 wins from 179 (SR 24%) for a profit of £111.44 (ROI +62.3%).
  4. Martin Keighley has shown excellent consistency when we compare his record across the year. When we split the year into four groups of three months (Jan to Mar, Apr to Jun, Jul to Sept and Oct to Dec), his win strike rates were as follows:

 

 

Not only was Keighley consistent, he also secured returns of at least 14p in the £ in each of the four quarterly groupings.

  1. Richard Hobson produced a profit in seven of the eight years. Hobson’s record with favourites should also be noted – 16 wins from 37 (SR 43.2%) for a profit of £20.09 (ROI +54.3%); A/E 1.48.

 

Trainer Angles: Comparative Data

The final data for these 54 trainers that I would like to share shows their results split into two groups – 2018 to 2021 and 2022 to 2025. I have placed the comparative data in a table covering win strike rate, ROI percentage and A/E indices. Anything highlighted in ‘blue’ is a positive, anything in ‘red’ is a negative. My criteria for each, was as follows:

 

 

By colour coding the table I hope that it helps to compare the data a little more easily. Here is the table:

 

 

Some points I would like to highlight:

Firstly, there is a statistical quirk which, when I saw it, I took a double take. Notice the two strike rates for the different groups of four years for Nick Alexander – they both are 16.77%, but what makes this even more remarkable is that the wins to runs ratio for both timeframes was 26 wins from 155 runners!

Other key points to note:

  1. Messrs. Daly, Haslam, Keighley and Sheppard, four of the seven positive trainers noted earlier, managed to be consistently good across both time frames.
  2. Brian Ellison, Warren Greatrex, Alan King, Ben Pauling, Jeremy Scott and Robert Walford have all shown a significant uptick in performance in the more recent four years.
  3. Nigel Hawke and Tom Lacey have both struggled more recently, well down on their stats for the 2018 to 2021 period.
  4. The Greenall / Guerriero stable, along with the Moore stable have both had decent results in both timeframes. We could say the same about Neil King and Evan Williams.

 

**

 

And that concludes my handicap chase analysis ahead of this jumps season. There's plenty to go at in there and I hope some of the stats will assist you in finding some good value handicap chase winners between now and the spring.

- DR

 

Prepping for NH 2025/26, Part 2: Novice Chases

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Preparing for the jumps – Part 2, Novice Chases

This the second article in a series which is looking forward to the National Hunt season, writes Dave Renham. In the first piece I looked at non-handicap novice hurdle races. In this one, I will focus on novice chasers. I plan to look at both non-handicap and handicap races, starting with the former.

Data has been taken from 1st January 2018 to 31st August 2025 with profits and losses calculated to the Betfair Starting Price (BSP) with a 2% commission applied on any winning bets.

Non-handicap Novice Chases

There has been a serious drop in the number of non-handicap novice chases in recent years so good betting opportunities have proved somewhat limited. However, I feel it is still worthwhile sharing my findings and I would like to begin by looking at market factors for all qualifying races.

Market factors

I have used Betfair in terms of market rank and here is the breakdown:

 

 

Favourites won roughly 50% of all races and backing all of them would have made a small loss equating to just over a penny in the £. Second favourites offered some value and this was especially true when they had been shorter prices. Second favourites with a BSP of 3.0 or lower won 86 races from 194 runners (SR 44.3%) for a profit of £34.98 (ROI +18%). Meanwhile, backing horses positioned 4th or higher in the betting market would have lost a significant amount. That is despite a good looking A/E index.

Having looked at the overall market, it is time to look into other areas. As with the first article, I am going to impose a BSP price limit of 20.0 or lower from now on, to avoid any winners at excessively big odds potentially skewing the bottom line.

 

Sex of horse

Does the sex of the horse make a difference? Let’s see:

 

 

As can be seen, the ratio of male runners to female is around 6:1. In terms of performance, males have won more often but the bottom lines are very similar, as are the A/E indices.

 

Age of horse

Let me move onto the age stats now, beginning with the returns across different ages (the BSP ROI percentages):

 

 

Note that the 4yo stats are based on just 29 runners, so concentrating on the bigger samples of horses aged five and older, we can see that the trend is the younger the horse the better the returns. Once we get to horses aged nine amd up losses become fairly steep. Here are the full stats, including other key metrics:

 

 

It is interesting to note that 5yos have an A/E index below 1. That is surprising based on the strike rate, profit and returns. At the other end of the age spectrum, it does seem that we should be slightly wary of horses aged nine or older.

 

Country of Breeding

I want to look now at breeding and specifically the country of breeding. Here are the splits for the three main countries:

 

 

French breds performed best and showed a healthy profit, while British breds produced the worst returns. French bred runners which won last time out did particularly well, winning once in every three starts (80 wins from 240) for a profit of £71.99 (ROI +30%); A/E (BSP 1.07.

 

Trainers

Finally for the non-handicap novice chases let's look at some trainer data. Below is a table sharing individual trainer data in non-handicap novice chases during the period of study and with the 20.0 BSP price cap. To qualify a trainer must have had at least 50 such runners during the time frame:

 

 

The trainers that stand out are Harry Fry, Alan King and Jamie Snowden with strong metrics across the board. Gordon Elliott’s figures are solid too.

*

Let's now move on to the second part of the article where the focus will be on novice handicap chases. There are around five times the number of these compared to the non-handicap ones, so we have more data as well as more races to potentially attack this winter. I will also be drilling down into some additional areas than I did for the non-handicaps. 

 

Novice Handicap Chases

Market factors

I will start again with market rank. This includes all runners; and, as with the non-handicap novice chases, once we move on from this section I will have imposed the BSP 20.0 price limit:

 

 

Favourites have gone close to breaking even, while horses second and third in the betting market have both lost small (and similar) amounts. Those fifth+ in the betting market have made a profit but essentially this boiled down to three winners at BSP 210, 180 and 324.41. Remove that trio and the cohort would have lost money. Hopefully this helps explain the price cap I will be using once more from now on.

 

Sex of horse

In the non-handicaps discussed earlier there was little in it between male and female runners. Was that the case in novice handicap chases? Let’s compare the strike rates first:

 

 

We see a definite edge to male runners this time. How about the A/E (BSP) indices?

 

 

Again, there is strong edge here for males, so does this correlate when we look at the profit/loss/ROI% figures? Here are the full splits:

 

 

There is a huge discrepancy in terms of the total number of runs for each group, but the male edge is there across the board. For mares, losses of nearly 17p in the £ coupled with a very modest 0.89 A/E index illustrate the struggles they had in such races. I would be wary of backing any female runner in novice handicap chases against the geldings.

 

Age of horse

We saw earlier that in non-handicap novice chases horses aged nine or older produced the poorest returns by some margin. Was that replicated in handicaps? As before I'll start by sharing the returns across different ages (the BSP ROI percentages):

 

 

This time, we see even poorer returns for the aged nine and up group. Losses of more than 18 pence in the £ are negatively noteworthy. Here is the full breakdown for each age group:

 

 

4yo runners were again relatively rare but they had the highest win rate, best ROI% and highest A/E index. Hence, it seems those young'uns require at least a second glance. 6yos had a good record and with over 3500 runners in a very decent sized sample; they made a profit to follow blindly in seven of the last eight years. I cannot give a logical reason why 6yos have done so well, except perhaps that this is the optimal year in their career trajectories from young horses graduating from novice or second season hurdlers.

Going back to the older brigade of runners aged 9yo+, they had the lowest strike rate and the poorest A/E index, coupled with those poor returns noted earlier. I would need a good reason to back an older horse in a novice chase of any description based on these numbers.

 

Country of Breeding

We saw earlier that French bred runners had an edge in non-handicap novice chases. We see a similar pattern in handicap novice chases, too:

 

 

The French breds held sway once more with the best figures across all metrics. It should be noted that German bred runners also performed well from a small sample. There were 79 GER-bred runners of which 18 won (SR 22.8%) for a very healthy profit of £51.59 (ROI +65.3%); A/E (BSP) 1.36. With 14 different winning German bred horses and no horse winning more than twice, nothing can be said to have skewed the data.

Going back to French breds, those who raced in a hurdle race last time saw excellent returns of nearly 25p in the £. Over 700 qualifiers there was a profit to BSP of £175.86 (ROI 24.8%); A/E (BSP) 1.17. Also, younger French bred runners fared extremely well with those aged seven or less producing a strike rate of 20.8% (409 wins from 1968 runners) for a profit of £180.10 (ROI +9.2%). Further, this young cohort was consistent with seven profitable years out of the eight - only 2018 producing a loss, and a small one at that.

 

Position Last Time Out (LTO)

A look now at the most recent piece of form in terms of finishing position LTO.

 

 

These splits are interesting. As we would expect, LTO winners had the best strike rate; and last day runners up achieved a better win rate than horses further back (third or worse) on their most recent start. However, those recent 1-2 finishers incurred losses of over 9p in the £ which is well above the norm for such runners: they're clearly over bet. Also, their A/E indices were lower than we would normally see.

The best value was found with horses that finished sixth or worse LTO and I guess some of these have flown under the radar.

 

Weight Carried

In handicaps horses carry weight based on their Official Rating so I wanted to review this angle. Below are the A/E indices for different weight brackets:

 

 

The higher weighted runners seem to have offered the worst value and when we look at the full splits, we see that this is the case:

 

 

The best value seems to have been with the mid-range weight bracket of 11st to 11st 4lb, followed by the 10st 8lb to 10st 13lb group. Higher weighted runners (11st 5lb or more) were indeed the worst value.

 

Trainers

The last port of call for this piece is to review some trainer statistics for novice handicap chases. We have more data to drill into than the ‘nons’ so let me start by sharing trainers who have saddled at least 100 such runners during the time frame. They are ordered alphabetically:

 

 

Almost half of the trainers in the table (15 out of 33) recorded a profit with their runners which is a decent effort. Here are a few individual trainer stats worth sharing:

  1. The Greenall/Guerriero yard have excelled with horses making their second chase start. This cohort won 9 of 24 (SR 37.5%) for a profit of £26.62 (ROI +110.9%); A/E (BSP) 1.96.
  2. Nicky Henderson had only 28 female runners but 9 won (SR 32.1%) for a profit of £21.10 (ROI +75.4%); A/E (BSP) 1.81. He also performed well with horses making their chasing debut thanks to a 25% strike rate (19 wins from 76) for a profit of £20.74 (ROI +27.3%), A/E (BSP) 1.14.
  3. The Hobbs/White yard made an overall loss, but when their runners start favourite we should take note. Their market leaders won 13 from 37 (SR 35.1%) for a profit of £13.04 (ROI +35.2%); A/E (BSP) 1.17.
  4. Paul Nicholls should be noted when saddling the favourite. His record with jollies has been superb. 45 of the 88 won (SR 51.1%) for a healthy profit of £22.83 (ROI +25.9%); A/E (BSP) 1.22.
  5. Evan Williams has bucked the trend when it comes to last time out winners. His record has been excellent with 13 winners from 40 (SR 32.5%) for a profit of £20.31 (ROI +50.8%); A/E (BSP) 1.63.
  6. Venetia Williams has done well with horses aged seven or younger. They produced a profit of £31.11 (ROI +20.6%) thanks to 37 winners from 151 (SR 24.5%); A/E (BSP) 1.09.
  7. Kerry Lee did not make the table due to having saddled just below 100 runners in total, but her favourites did well, winning 12 of 26 starts (SR 46.2%) for a profit of £9.89 (ROI +38%); A/E (BSP) 1.40.

**

That's it for this second part of our 2025/26 NH prep series. There is a banquet of stats and snippets in this piece that hopefully will point us in the right direction when it comes to novice chases this season. Look out for part 3 next week when I'll be lasering in on another cohort of runners performing through the autumn, winter and spring. Until then...

- DR

 

Prepping for NH 2025/26, Part 1: Novice Hurdles

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Preparing for the jumps – Part 1, Novice Hurdles

The days are getting shorter, the nights are getting longer, which means the National Hunt season is beginning to click into gear, writes Dave Renham. October has 51 scheduled meetings in the UK over jumps, more than double the number in September. I will be writing a series of six articles over the coming weeks that share statistics, both positive and negative, that I hope we can use to our advantage this season.

Introduction

In this first piece I am going to concentrate on novice hurdle races, with data taken from 1st January 2018 to 31st August 2025, a period of a little over seven and a half years. Profits and losses have been calculated using Betfair Starting Price (BSP) with a 2% commission applied on any winning bets.

Novice hurdle races can be either handicap or non-handicap contests, and as there are roughly treble the number of non-handicaps, my focus is on this bigger group. Let me start by looking at market factors for all non-handicap novice hurdle races.

Market factors

I will use Betfair's market rank, which may occasionally be slightly different from the industry SP rank - though such differences are unlikely to materially impact any discernible patterns. I will start by sharing the results for the ‘value’ metric, the A/E index. These indices are based on BSP prices and the splits are as follows:

 

 

Third and fourth favourites offered the best value. Is that replicated when studying a wider array of metrics?

 

 

Third favourites performed the best from a profit/loss perspective, while fourth favourites essentially broke even, so the A/E indices were a good guide in terms of value. Meanwhile, favourites won roughly half of all these races but despite that dominance they made a small loss overall. Those sent off fifth market rank or lower on Betfair did extremely poorly, losing over 34p in the £.

Returning to favourites, horses aged five and six provided around 75% of all favourites, and those runners virtually broke even if backing all of them blind – 1293 wins from 2513 (SR 51.5%) for a minimal loss of £5.67 (ROI -0.2%); A/E (BSP) 1.01. There were some positive angles for favourites, but due to the short prices on offer it is difficult to produce significant returns.

Here are a few:

 

 

I will analyse trainers in greater detail below, but it seems that Gordon Elliott should be noted when saddling a favourite in non-handicap novice hurdle races.

Having looked at the market, it is time to move on to other areas. However, I have imposed a BSP price limit of 20.0 or lower from now on, to avoid any winners at excessively big odds potentially skewing the bottom line.

Sex of horse

Is there any difference between the performance of male horses versus female ones? There are three times as many male runners when it comes to non-handicap novice hurdles, so more races are won by males of course. However, was there a difference in the respective win strike rates using the BSP 20.0 or lower limit?

 

 

Males outperformed females by around 2.4% in absolute win percentage terms, which equates to a differential of just over 10%. That also translated to a marginally better bottom line:

 

 

As we can see, male runners broke even during the review period while female runners lost us just over two pence in the £. There was not too much in it overall but, in general terms, male runners were slightly better betting propositions than females over the period of study.

Age

A look at the age of horses racing in non-handicap novice hurdles next. There were only 15 three-year-old qualifiers since 2018 - that age group almost exclusively running in juvenile races - so I have ignored those. Here are the splits for four-year-olds and up:

 

 

It is interesting that the general trend in terms of win strike rate was the older the better, which is unusual in most race types across both flat and NH. Not only that but the best value was also with older horses. Those aged six and up recorded solid overall profits. Sticking with the aged six and older group, if we restrict races to shorter distances (2m 1f or less) their record improved further:

 

 

Returns approached 13p in the £ and this cohort secured a positive return (ROI%) in seven of the eight years as the graph below shows:

 

 

The only losing year has been this current year, 2025, but losses are small and there is still plenty of time to edge back into profit.

Previous hurdle runs

My next port of call looks at the number of hurdles races each runner had previously had. The findings surprised me a little:

 

 

The more experience over hurdles a horse boasted the better the win percentage. This perhaps correlates well with the age stats displayed earlier although it doesn't necessarily follow that an older horse is a more experienced one.

The most successful group in monetary terms were those making their debut over hurdles. These runners secured a profit of over £260 to £1 level stakes, equating to returns in excess of 10p in the £.

Perhaps not surprisingly based on previous findings, hurdle debutants aged six-plus was a strong angle: 116 wins from 560 runners (SR 20.7%) for a profit of £138.24 (ROI +24.7%); A/E (BSP) 1.12. It could be argued that this is back-fitting, but nevertheless the results catch the eye.

Last time out (LTO) Race type

What about LTO Race type? Is there anything to glean from these stats? Let us see:

 

 

The vast majority of runners ran in a hurdle race last time, which is to be expected, but it is the last day NH Flat figures that stand out with a near 9% ROI. If we further restricted those LTO NHF qualifiers to horses that finished in the first five on that prior spin, results improved to 258 wins from 1132 runs (SR 22.8%) for a profit of £156.87 (ROI +13.9%); A/E (BSP) 1.05.

Trainers

Different trainers target different types of races, and of course the quality of horses within their stables differs massively. That has a bearing on the handlers who appear in this section, others performing to better effect further down the line in handicap company.

Below is a table outlining individual trainer data in novice hurdles during the period of study with the 20.0 BSP price cap. To qualify a trainer must have had at least 90 such runners:

 

 

13 of the 32 trainers made a blind profit, with Lucinda Russell’s figures particularly impressive (returns of over 39p in the £). She had an excellent record at Ayr with 11 wins from 34 (SR 32.4%) for a healthy profit of £38.87 (ROI +114.3%). She also made decent profits at Newcastle (6 wins from 16) and Carlisle (7 wins from 19) – each with returns in excess of 100%.

In contrast the O’Neill and Pipe stables have performed relatively poorly, showing significant losses.

Previous hurdle runs by trainer

I would like to expand my trainer research a little by looking at the performance of trainers' runners in relation to the number of previous races they had had over hurdles. Firstly, let me compare win strike rates:

 

 

We saw earlier that, in terms of win percentage, the more exoerience the better over hurdles; but as far as individual trainers are concerned there are not too many that conform to that pattern. Lucinda Russell, for example, saw her strike rate drop as her runners gained more experience over hurdles. Her record with hurdling debutants was excellent – a strike rate of 30.9% thanks to 17 wins from 55 for a profit of £48.56 (ROI +88.3%); A/E (BSP) 1.53.

The stables of Alan King and the Moores also fared particularly well with hurdle debutants although both had fewer qualifiers in the past year or two.

At the other end of the scale was Ben Pauling whose hurdle debutants struggled overall, scoring less than 9% of the time and incurring losses of over 65p in the £. Likewise, Warren Greatrex runners in such races improved steadily from a low (9.68%) novice hurdle debut strike rate.

Trainers to note with horses having their second run over hurdles were Donald McCain and Jamie Snowden. McCain’s 33 wins from 94 (SR 35.1%) notched a profit of £17.93 (ROI +19.1%), A/E (BSP) 1.25; while Snowden’s 16 wins from 39 (SR 41%) was worth £8.27 (ROI +21.1%); A/E (BSP) 1.20.

In terms of the more experienced hurdlers, Kim Bailey's team did well when they'd had at least three previous runs (the 4+ group). His record in that context reads 19 wins from 46 (SR 41.3%) for a healthy profit of £16.73 (ROI +36.4%); A/E (BSP) 1.26. Fergal O’Brien also performed well with these experienced runners, securing 64 wins from 169 (SR 37.9%) and a profit of £31.28 (ROI +18.5%); A/E (BSP) 1.17.

Let me build on that and share a comparison of A/E (BSP) indices. For this table I have highlighted the most positive indices (in blue) and the poorest ones (in red):

 

 

Nigel (now assisted by Willy) Twiston-Davies earns a mention as the only trainer to have managed an A/E index above 1.00 across all four groups. The Hobbs/White yard were close with three 1.00+ figures and a 0.98, as was Kim Bailey whose only figure below 1.00 was 0.99. Not surprisingly I guess, all three of these stables made a blind profit with their novice hurdlers as can be seen in the earlier table.

Onto my final piece of digging, which is...

Trainers and courses

Have any trainers excelled at specific course with their novice hurdlers? These were the strongest combinations ordered by course (20 runs minimum to qualify):

 

 

All 16 combinations proved profitable, with the Hobbs/White performance at Taunton particularly impressive. Long may it continue!

 

**

 

I hope this opening salvo for the National Hunt has highlighted some interesting angles for non-handicap novice hurdles. I'll be back next week with Part 2, looking at the novice chase division.

Until then...

- DR

Handicaps: Today vs Last Run (Part 2)

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Handicap comparisons – last run to current run (Part 2)

This is the second article of two where I am continuing to look at some handicap data focusing on horses in terms of their most recent run compared with their current one, writes Dave Renham. The first article can be viewed here.

Introduction

In the first piece I looked at a variety of criteria including distance, class, weight carried, official rating (OR), average race OR, odds, course and trainer. I looked at each of these individually, but in this second half I plan to combine some of those variables. I also plan to look into trainer-based ‘last run to current run’ stats in greater depth.

I have analysed UK flat handicap races between 2019 and 2024 on both turf and all-weather (AW), with a few caveats. Firstly, horses must have been at least three years old; secondly, they must have had at least six career starts; third, they must have run in a flat handicap last time out; and, finally, their price must have been 12.0 BSP or lower.

Sticking with horses aged three or more seemed logical as far as handicap races go; and with horses needing at least six career starts to qualify, it means that most of them would have found their niche as it were in terms of distance, class, and so on. Using last time out (LTO) flat handicaps meant we could get a proper comparison in terms of changes in distances, official rating, weight etc., while the price limit avoids bottom lines being skewed by the odd huge price making the winner’s enclosure. Profit and loss detail has been calculated to Betfair Starting Price (BSP) less 2% commission on winning bets.

Before sharing my latest findings I want to set the scene, which I hope explains why I started looking at specific combinations of criteria. Hopefully the next paragraph will do this!

Context

The class of race that a horse runs in is restricted somewhat by their OR (also known as their handicap mark), so most horses race at a similar level to the one they raced in last time. Hence, horses tend to run against a similar level of opposition from one race to the next. This may change a little over a period of races as there will be some horses that are improving or running consistently well. For example, horses that win multiple races in a relative short number of races will soon move up class levels and face stronger opposition.

Of course, we can get the opposite with horses struggling and sliding down the handicap, and thus facing easier opposition in subsequent starts. However, as stated earlier, most horse ply their trade in a similar class, and also race over a similar distance, and perhaps there is something to said for horses being familiar with the type of race they are running in.

When back at the trainer’s yard, racehorses tend to be looked after day to day by the same person, kept in the same stable, ridden out by the same jockey: a fairly well defined routine. Horses have familiarity at home all the time, because it provides safety, stability and so on. It makes sense therefore that horses should respond to familiarity on the racecourse.

 

"Doing the same thing"

So the first combinations of criteria I researched were ones that were the same in this race as in the previous run. My initial focus therefore was on familiarity or similarity at the racecourse. I chose six variables - distance, class, weight carried, OR, average race OR, and betting odds - and I combined three of them at a time. This means I looked at 20 different three-way combinations. Here are my findings:

 

 

It was a surprise to find that 17 of the 20 combinations had produced a profit to BSP. Not only that, two of three losing combos came from the two smallest samples (40 and 103 qualifiers respectively). Maybe there is something to be said for familiarity on the racecourse?

Of course, there are numerous other three-way combinations I could try. I could change the parameters so that they all differ, or indeed one or more differ with one or more remaining the same. However, that would take an age to calculate, and we would have perhaps the biggest table of data in the world to try and analyse! Moreover, I could potentially try combining four parameters in any which way, or two... the list is endless.

 

Back in trip with some variables the same

However, before moving on to my trainer analysis I would like to share a few more combinations, because one of the things that came out of the first article was that horses dropping in distance outperformed those that were upped in trip. So it makes sense to look at some combinations with one of the parameters being ‘dropped in distance’. The other two parameters I will keep as the ‘same’. Here are a few of the more profitable combinations in terms of ROI% first:

 

 

All five shown provided solid returns and the full splits with strike rate percentage (SR%) and profit are as follows:

 

 

With so many three-way combinations producing positive results, we could be forgiven for thinking that virtually all of them have been profitable. That is certainly not the case, however, and there were a few that lost a fair few quid. Five such 'dodge or lay' combinations are shown in the table below:

 

 

To summarise, thinking about the original table I shared where three parameters stayed the same and were then combined, it has made me feel that the fewer changes the better for most horses running in handicaps. It makes sense, of course, and from the numbers I have crunched at least it seems this is the case. Perhaps this is largely a case of horses running well and "if it ain't broke, don't fix it".

Trainers

The second part of this article is devoted to a variety of trainer-based stats that came out of my research. For any trainer to qualify in this section, they would have needed to have saddled at least 100 runners within each group.

Change in Course

In the first piece it was noted that horses that were racing at the same course as they did LTO had a slightly better record overall than those that switched courses. I wondered, are there any trainers that show a significant difference in their 'same course' vs 'different course' results? Well, yes, there were six trainers who seemed to strongly fit that profile and the graph below shows a comparison of their win strike rates:

 

 

It is nice to see some trainers that we rarely see in my articles appearing here such as Michael Herrington, Derek Shaw and Adrian Wintle. There were some significant differences in their respective win percentages. However, does that translate to other metrics? The full stats are shown in the table below:

 

 

All six produced far better returns and improved A/E indices with those much better win strike rates. David O’Meara has been profitable with both groups but the ROI% differential is still over 22% or 22p in the £.

One additional stat I would like to share is that horses from these six stables which finished in the first three LTO, when returning to the same track next time combined to produce 149 wins from 599 runners (SR 24.9%) for a profit of £208.33 (ROI +34.8%).

Change in Distance

I next wanted to see if there were any trainers that had significantly different figures in connection with distance change? There were seven trainers where this was the case and, in the table, below I have shared their ROI% and the A/E indices across the three metrics – horses dropping in distance, horses upped in distance and horses racing over the same distance.

Anything highlighted in blue is a strong positive, anything in red is a strong negative and anything black is neutral:

 

 

Again, it is good to see some different names from those who usually appear. As the table shows, Michael Bell, Ruth Carr and the Quinn stable fared particularly well when keeping their runners to the same distance. Brian Ellison, Philip Kirby and Grant Tuer excelled with horses dropping in distance from their last run. Meanwhile Ed Dunlop had a good record with both horses dropping in distance and racing at the same distance. Pretty much all seven, perhaps excepting Messrs. Tuer and Bell, struggled when moving horses up in distance.

Change in Class

A look at change in class of race next and for this section I am going to look at four trainers individually.

 

Ralph Beckett

A look at Beckett’s figures first:

 

 

As we can see, his runners struggled when upped in distance. They had a much lower win rate, lost a significant amount to BSP (unlike the other two which made good profits), and their A/E index was poor at 0.77. For the record, they had a much poorer placed record too. We should be a bit wary of Beckett horses moving up in grade.

 

Julie Camacho

Not a trainer I have studied or shared many stats on in the past, but there are definite differences with Camacho's results for class change:

 

 

Horses racing at the same level as last time performed really well and may be worth looking out for.

 

Scott Dixon

Another trainer rarely discussed in previous articles but Dixon had some very strong stats. Below are the A/E index comparisons first:

 

 

Horses upped in class performed exceptionally well based on these figures. When we analyse a wider set of metrics we can see that this is the case across the board:

 

 

Based on these findings, Scott Dixon’s runners should be noted when upped in class as the data look extremely solid. These runners did slightly better in sprints compared the other distances.

 

R Fell + S Murray

Finally in this section, a look at the Fell/Murray duo. They performed best when keeping runners to the same class. The table below shows the splits:

 

 

A solid performance for the pair when running in the same class, doing particularly well in Class 4 handicaps as well as with their female runners.

 

Change in Odds

Shorter this time than last time

The final trainer area I examined was a change in odds. The sample sizes for trainers with horses which were sent off at the same odds were too small so I compared only lower odds to their last run and higher odds to that previous race. The first table shows trainers who performed far better with runners whose price was shorter (lower) than on their previous run compared with those whose price was higher:

 

 

In general, we would expect much better strike rates for the 'lower' group across all trainers, not just this select band: shorter prices win more often than bigger prices, simple as that. However, these four had much more significant differences between the ‘lower’ and ‘higher’ results than the other trainers I looked at.

Longer this time than last time

Onto the more unusual group of trainers who had more positive results with horses which were bigger prices than they were last time out:

 

 

 

All five had better strike rates for their runners sent off longer than last time, which goes somewhat against the grain. There were decent profits across the board for this cohort and again it was good to see trainers that have rarely appeared in other articles (Declan Carroll, Kevin Frost, Mark Usher and Stuart Williams).

**

I think that's plenty for this piece. Both this one and last week's have uncovered some interesting stats and hopefully there will be ways to profit from those in the future.

Until next time...

- DR

Using Official Ratings to Measure Trainers’ Ability

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Using Official Ratings to measure  trainers' ability

Trying to predict whether horses are improving, have reached their ceiling, or are on the downgrade is a part of the racing puzzle that punters have get a handle on, writes Dave Renham. Whether it equates to long term profits is still determined by how much value we are getting, especially on winning selections. If we spot an improver but everyone else does too, it is unlikely to offer value because the price will be driven down by popular opinion. However, if we can spot an improver that most of the crowd do not, then that is a different matter.

Introduction

In this article I will look at fifty trainers with the aim of trying to gauge whether their horses improve, or not, within specific time frames. To do this, I compared their horses in terms of movement in Official Ratings (OR). Specifically, I compared their ORs after three, seven, ten and fifteen career starts.

For each trainer I will first compare horses' OR figure between three and seven starts, then seven and ten starts, and finally ten and fifteen. I have taken data from 2017 to 2024 and, when quoting any profit/loss figures, I have calculated to Betfair Starting Price (BSP) less 2% commission.

It's important to note that only UK and Irish runs are included, so those horses that have raced overseas may have had more than the stated number of runs. Overall, this difference should be negligible but feel free to consider some of the stats approximations and use the intel accordingly.

Three career runs vs. seven career runs

This first group is dominated by younger horses: around 22% of them were aged two and roughly 64% were aged three. I have calculated the percentage of horses within each stable that saw an improved Official Rating between their third and seventh career runs, those with a decreased rating, and those that stayed the same. The trainers are listed in alphabetical order, with any positives and negatives highlighted in the ‘OR Up %’ column. Positive percentages in blue (65% and above); negative percentages in red (40% and below).

 

 

It should come as no surprise to see Charlie Appleby, William Haggas, Sir Mark Prescott, Roger Varian, and the Gosden and Charlton stables with a high percentage of horses who have improved their ORs within this early time frame. Many of the lower scoring trainers are more renowned for handicappers and hence it will be interesting to see whether their figures start to improve as the number of runs increases.

Charlie Appleby’s figures are quite stunning with 88% of his horses increasing their OR figure between career runs three and seven. Digging deeper, backing all Appleby runners that had previously run between three and seven times would have seen the following impressive figures: 245 wins from 956 runners (SR 25.6%) for a profit of £189.87 (ROI +19.9%); A/E 1.08.

Here are some further Appleby stats to keep an eye out for this coming autumn. His horses that had run exactly three times and were racing for the fourth time in either September or October produced 30 wins from 74 runners (SR 40.5%) for a profit of £42.67 (ROI +57.7%); A/E 1.34. Add one run to that - those horses who had four career starts and were having their fifth in September/October - and their record reads 13 wins from 41 (SR 31.7%); A/E 1.26 for a profit of £18.40 (ROI +44.9%); A/E 1.26.

Next, I have calculated the average OR improvement per horse that the ‘better’ trainers achieved within these past runs grouping. [By ‘better’, I mean those with the top ten percentage improvement figures].

 

 

Charlie Appleby again tops the list. On average his horses improved 9.7lbs between those four runs from third to seventh career start, a very decent figure. All trainers in this top ten have performed well above the norm.

 

Seven career runs vs. ten career runs

Onto the second grouping. Logic dictates that the improvement achieved later in horses' careers will be less in OR terms overall than with the previous group of runners. This is indeed the case and hence I have moved the positive percentage figure to 60%+ (in blue); the negative figure remains at 40% or lower:

 

 

Sir Mark Prescott heads the figures here, with 75% of his runners improving their ORs between their seventh and tenth career start. Backing all Sir Mark's runners that had raced between seven and ten times previously across these eight years was worth 100 wins from 388 (SR 25.8%) for a profit of £44.73 (ROI +11.5%).

Next in the list are James Tate (68.6%), James Fanshawe (66.7%), Simon & Ed Crisford (65.4%) and William Haggas (65.0%). Prescott, Tate, Crisford and Haggas all had positive ‘blue’ figures in the first table as well.

At the other end of the scale, there are some surprisingly low improvement percentages for Charlie Johnston (33.3%) and Archie Watson (31.8%).

Let me once again calculate the average OR improvement per horse that the top ten trainers achieved within the 7-10 grouping:

 

 

Sir Mark Prescott is comfortably ahead, averaging an improvement per horse of 5.4lb. For the record, 38 of the 50 trainers managed a positive percentage improvement across their total runner cohorts. The numbers for these top ten are lower overall than we saw for the first grouping, but as horses get more exposed it is harder for them to improve their OR figure.

The horse that showed the biggest improvement was Love So Deep trained by Jane Chapple-Hyam. Her rating went up 27lbs from 74 to 101.

 

Ten career runs vs. fifteen career runs

Finally, a look at this most exposed of the three groupings. This time there are several trainers who have not saddled enough runners to make the figures meaningful. I have included trainers who had at least 30 qualifiers - 20 of the original list missed out on that basis. Here are the splits for the remaining 30 trainers with the same colour coding as for the previous group:

 

 

Charlie Hills is the only trainer to have improved more than 60% of his horses in terms of their OR figure between their tenth and 15th starts. Meanwhile, Messrs. Cox, Dods, Evans and Simcock all ended up below the 40% mark in terms of improvement. These are four trainers that I had expected to have much better figures.

Of the trainers that didn’t make the list due to limited qualifiers, I should mention that the Charlton stable saw improved ORs for 13 of their 15 qualifiers (86.7%). Also, George Boughey saw 20 of his 26 (76.9%) achieve improved ORs.

The final graph shows the average OR improvement per horse that the top ten trainers in this group achieved within the 10-15 career run bracket. Naturally, these are much lower than we have seen previously:

 

 

The horse that improved the most within this whole grouping was Lion Hearted, trained by Mick Appleby. He improved 28lbs from a rating of 58 after 10 starts to a rating of 86 after 15 starts.

*

 

As horses mature there is usually less improvement to be had before they settle at their ability level; however, as we've seen, some trainers find that level quicker than others, and knowing which to follow now and which to be patient with is a big advantage on Joe Punter.

Good luck

- DR

 

When Horses Return to the Same Race

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Horses racing in the same race that they contested last year

Back on January 2nd 2025, Geegeez incorporated an excellent addition to the racecard namely the TRENDS tab, writes Dave Renham.

Geegeez Trends

Before discussing my plan for this article, let;s take a look at the TRENDS tab in a little more detail. Clicking the TRENDS tab will take us to a screen like the one shown below:

 

 

These were the trends for a handicap race at York which occurred fairly recently, on the 25th July 2025. The left-hand half of the screen is focused on standard information, like winning horse, trainer, jockey and going. The middle to right-hand of the screen has further info including the weight the winner carried, the age it was, its SP, official rating, and so on.

Note the red font. Any jockey in red means they are riding in the race again this year. Hence past winning jockeys in this race Oisin Orr and Rowan Scott had rides again in this 2025 renewal. Likewise for trainers, Bailey, Fahey, Nicholls and Ellison had runners this year too. There was one horse highlighted in red namely Dicko The Legend, and this horse was trying to repeat its win in 2024. (As it turned out Dicko The Legend did win the race for the second year running).

When we click on the TRENDS tab for a particular race, if we click on any race date this takes us to the result of that race for the selected/clicked year.

In addition, all of the columns within the TRENDS tab are sortable, making it easy to see if a specific profile is emerging around any of the variables. Hence, some races can offer up some useful past trends that we might be able to use to our advantage. The point of past race trends is not really to pinpoint winners - after all, looking only at win trends is a narrow field of vision for such a thing - but, rather, to highlight potential positives and negatives for horses that you are considering in the round of their overall form profile. Geegeez also has regular articles written by Andy Newton which highlight key trends in certain big races.

As far this article is concerned, I want to examine the performance of horses that ran in the same race the previous year. How often do winners manage to double up? And what about horses that ran in the race last year but did not win? Well, let’s see...

I have looked at UK flat racing (turf & AW) between 1st Jan 2017 and 31st Dec 2024. Any profits and losses have been calculated to Betfair Starting Price (BSP) less 2% commission.

 

All Runners Returning to the Same Race A Year On

The first thing to say is that not all races can have repeat winners or indeed runners from previous years, such as those restricted to horses of a certain specific age group. However, there have been plenty that contested the same race as in the previous year so what do the stats show us?

The table below shows us ALL runners that came back a year later to contest the same race but split into those that won the race last year versus those that did not:

 

 

As we can see the previous year’s winners have won more often, but in terms of returns they would have lost us a bigger percentage of our bankroll. In fact, both ROI figures are disappointing in truth. I had hoped for and expected better.

Perhaps not the start we were hoping for then, but there is an early positive to share when looking at horses that finished runners up the previous year – they have turned a small profit:

 

 

The win rates for last year winners compared to runners up are similar, but have those past winners been overbet driving their average price down? If we compare the average prices for previous year winners compared to previous year runners up, we might get some pointers:

 

 

Both the average decimal odds for the Industry SP and Betfair SP show a lower figure for the horses that won the race the previous year. I tlooks like horses trying to repeat their win a year later have been overbet by the punting fraternity after all. I’ll come back to this later.

 

Horses that Won the race last year

Handicaps vs Non-Handicaps

Despite the overall results for horses that won the same race the previous year not being that positive, let me dig a bit further to see if any positives can be found. Or indeed any strong negatives. I’ll start with non-handicaps versus handicaps:

 

 

As might have been expected there are far more horses trying to repeat a win in the same handicap race than in non-handicap races. There is a higher strike rate in non-handicaps which is also to be expected, but returns have actually been worse.

Race Class

What about splitting the results up by class of race? I have not included Class 7 races as there were only five qualifiers (who all got beaten).

 

 

The strike rate in Class 1 events is decent but this is partly because all races were non-handicaps. If we exclude Class 1 races, there does seem a trend where the lower classes of race have offered last year’s winner less value.

When lumping together the results for horses that tried to repeat their win in Class 4, 5 and 6 races, losses amount to nearly 24 pence in the £, with a poor BSP A/E index of only 0.85. I would be steering clear of these runners unless I had a gilt-edged reason to convince me otherwise.

Class 2 and 3 races have produced the best returns, and an interesting stat is when we look at the results in 3yo+ handicaps across these two classes: winners of the same race the previous year have gone on to repeat their success 53 times from 395 runners (SR 13.4%) for a profit of £89.76 (ROI +22.7%). Also, if we restrict further to the most fancied runners, those priced BSP 4.5 or less, this cohort would have produced 18 winners from just 38 qualifiers (SR 47.4%) for a profit to Betfair SP of £23.22 (ROI +61.1%).

Turf vs All-Weather

I next wanted to compare turf races with all-weather (AW) ones. Would there be similar results? The answer is an emphatic no as the graph below comparing A/E indices shows:

 

 

Horses that won the same race last year have proved far better value in turf races than AW ones as the numbers clearly show. Indeed, the other metrics back these A/E indices up:

 

 

There was a better win strike rate for turf performers and a very clear ‘win’ in terms of their respective returns. OK, turf runners have not proved to be profitable, but they have returned close to 30p more in the £ compared to AW runners.

Sticking with the AW for a minute, the worst time in the calendar for last year’s race to have been run has been during the winter AW season. From November to March the record reads a dismal 37 winners from 337 (SR 11%) for a loss to BSP of £154.55 (ROI -45.9%).

Courses

With turf racing producing the better stats, what about different courses? Has it been easier to repeat wins on certain courses compared to others? One slight issue is limited data for some courses, so let me start by grouping together the results on the ‘best’ tracks - those tracks that are considered Grade 1 tracks. These are Ascot, Doncaster, Epsom, Goodwood, Newbury, Newmarket, Sandown and York:

 

 

These have combined to edge us into blind profit which is pleasing to see. Splitting the results by individual track see the following four hit a profit:

 

 

There is one other course I would like to mention and that is Chester. The figures for horses trying to repeat their win the previous year is decent – 11 wins from 57 (SR 19.3%) for a profit of £23.41 (ROI +41.1%); A/E 1.35.

Trainers

A quick look at trainer performance next. Data again is limited and only a handful of trainers have had more than 30 qualifying runners. They are shown in the table below:

 

 

There are two trainers that stand out, namely Richard Fahey and David O’Meara. Both have strike rates above 20% and have returned good profits with excellent A/E indices. These two trainers are worth noting in the future with the previous year’s winner attempting a repeat.

Overall, it has been hard to find many positives for horses trying to repeat their last year’s win but I have uncovered a small handful.

For the second part of this piece I'll look at runners up from the race the previous year as they gave us a better starting baseline as we saw earlier.

 

Horses that were 2nd in the race last year

Handicaps vs Non-handicaps

I’ll start as before with non-handicap results versus handicaps:

 

 

We see the same type of difference in win percentage but this time non-handicaps have provided far better returns. Having said that, handicappers have still gone close to breaking even.

Race Class

Race class now and it will be interesting to see if the performance in the lower classes of 4 to 6 is poor as it was for the last year’s winners:

 

 

Class 4 and 6 have incurred losses once more although Class 5 results buck the earlier trend. Once again Class 6 results have proved to be the worst from a profit/loss/returns perspective. It is interesting to see the positive performance in Class 1 events with this cohort of runners. Their strike rate is 4.27% below that of the last year winners’ group shared earlier, but they have turned a 22p in the £ loss to a 29p in the £ profit. The reason for this is the same one discussed earlier when looking at each groups’ results as a whole; it comes down to the prices that have been on offer for each group and the averages prices in Class 1 races have been as follows:

 

 

Once again, we see that the average decimal odds for the Industry SP and Betfair SP are showing lower figures for the horses that won the race the previous year. The difference between the average prices for each group is much bigger than we saw for ALL runners. In these better class races it looks like race winners from the prior year are significantly overbet, while the runners up from a year ago were significantly under bet.

Turf vs All-Weather

Turf versus all-weather next. Will we see the same pattern as earlier with turf results outperforming AW ones by some margin?

 

 

Overall, the metrics as a whole are much closer, but the profit/loss and returns still show turf races to be comfortably the better option.

Courses

Onto courses now and a look at the performance of the Grade 1 tracks. They produced solid results earlier, how about now?

 

 

These are even better than we saw for the combined results of last year winners. Doncaster’s figures were strong once again, producing returns in excess of 90p in the £ thanks to a 20.8% strike rate (11 wins from 53).

Trainers

Trainers will be my final port of call, and as before data is limited. Only four trainers had over 30 qualifying runners:

 

 

Four familiar faces from before. Again, O’Meara’s stats are excellent, while Fahey’s are not as good as we saw earlier. Meanwhile, Tim Easterby has a poor record with these runners.

 

Conclusions

I have always been a fan of past race trends over a time frame of 10 or 15 years. This is especially true at the big meetings such as the Cheltenham Festival, Royal Ascot, Glorious Goodwood, etc. I feel better quality races tend to produce stronger profile patterns. We have seen in this piece, although we have only focused on horses that came first or second in a specific race a year prior, that better class races tend to produce the most positive results; likewise the higher profile courses do the same.

Other Key findings

  • Horses looking to repeat a win in the same race the following year are generally over bet, while runners-up from last year’s race are generally under bet.
  • Turf results have been far stronger than AW results for both winners / runners-up from last year’s race.
  • David O’Meara has an excellent record with horses that were first or second last year and returning to run in the same race.
  • Avoid Class 6 races in terms of both last year’s winner / runner-up.

That's all for this week. More racing data crunching next Wednesday. Until then...

- DR

Examining Trainer Consistency

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Gauging Trainer Consistency

I think most of us have favourite trainers or at least ones we prefer, but there is a good proportion of punters who use trainer form, be it long term or recent, as a significant part of their betting selection process, writes Dave Renham.

Introduction

Some people follow trainers at certain courses, others certain jockey/trainer combos, some look for first time runners in handicaps, etc. In this article I am going to try, and please note the word ‘try’, to find a way to determine how consistent an individual trainer has been over the past decade or so.

To do this I have taken data from the last ten full years of flat racing in the UK (turf and AW) and split it into two blocks of five years – 2015 to 2019 and 2020 and 2024. The idea is that I will compare the earlier data set against the more recent one. I have chosen an elite band of trainers to make the research more manageable.

Personally, the more consistent the trainer, the easier it is to assess the chance of any of their runners. And, when I am looking at a potential bet, I prefer the trainer to be consistently good rather than consistently bad!

Methodology

The question I had before I started was, what is the best way to undertake such a comparison of different trainers? What do I use? Win strike rates? Placed strike rates? A/E indices? PRBs? Or a combination of all of those?

The logical starting point for me seemed to be win strike rates. However, I hit a snag immediately. My initial idea felt really logical: compare the win strike rates of different trainers over the two different time frames across different parameters. Then divide the highest winning 5-year percentage by the lowest to give a Comparison Strike Rate (CSR) for each trainer.

I have used this type of CSR method before when comparing win strike rates but that was when I was looking at individual trainers or individual sires and comparing them with their own strike rates across various parameters. That ratio approach generally works well as a metric and it was plan for the second part of the article.

The problem with comparing one trainer’s CSR with other trainers is when the strike rates for each trainer vary significantly. It will probably be easier to give you an example to explain what I mean.

Imagine a 100-race scenario where a trainer had five winners, equating to a 5% win strike rate. Let us then imagine that in the next set of 100 races we saw nine winners (+4 winners). This is a highly plausible scenario, but suddenly the win strike has almost doubled to 9%. This would give us a CSR figure of 1.80. Imagine the same idea with a trainer that hit 25 winners in the first 100 races and then 33 winners in the second 100. Eight more winners is a decent improvement, twice the difference in winners compared with the first trainer, but their CSR figure is much lower at 1.32. To hit a comparable CSR figure of 1.80, 45 winners would have been needed in the second group of 100 races, equating to 20 extra wins.

So, I decided to put the strike rate CSR method on the back burner for the first half of the article, opting instead to use a value metric, A/E index, instead. This seemed a better plan for trainer to trainer comparisons as long as the sample sizes were not too small.

Small sample sizes can make A/E indices look far better or worse than they are in reality. That is the same for most metrics, of course, and is one of the perils of working with racing data. However, for decent sample sizes, A/E indices tend to be a good metric when it comes to comparing different trainers (and horses and jockeys and sires and courses, and so on).

For this article I will be using a minimum of 30 runs within each area to qualify and, as I mentioned earlier, will be using A/E indices to make comparisons for this first half of the piece. The indices are based on Betfair Starting Prices.

Trainer Consistency: 2yo runners

Let me look at some two-year-old (2yo) data first, starting with the individual trainer A/E indices for horses making their debuts. I will divide the bigger A/E index by the smaller one to create a comparison A/E figure using a similar idea to the one mentioned earlier with the Comparison Strike Rate (CSR). I will call it the CAE figure:

 

 

The closer the CAE figure is to 1.00, the more consistent the trainer has been in relation to comparing their A/E indices over the two-time frames. Based on this method, as far as 2yo debutants go, the trainers that have shown the most consistency are Ralph Beckett (1.05), J & T Gosden (1.07), David Simcock (1.09), Hugo Palmer (1.10), Simon & Ed Crisford (1.12) and David O’Meara (1.13).

Owen Burrows has shown a real uptick in performance from 2015 to 2019 compared with 2020 and 2024. His CAE figure of 1.90 underscores this. In fact, when we drill into his performance with 2yos on debut we see that in the past two full years (2023 and 2024) these runners won nine races from just 30 starts (SR 30%) for a BSP profit of £32.79 (ROI +109.3%).

Moving onto 2yos on their second career start, here is a graphical comparison of the trainers’ A/E indices across the two-time frames. I have split the trainers into two groups in order to fit in each graph:

 

 

The closer the orange and blue dots are to each other, the more consistent the trainer’s A/E indices have been across the two periods.

Converting these into CAE figures we see the most consistent trainers from this group with second time starters aged two have been Ralph Beckett (1.01), Charlie Appleby (1.02), Simon & Ed Crisford (1.03), Andrew Balding (1.10), Michael Dods (1.10), Richard Fahey (1.12) and Michael Bell (1.13). Interestingly, when we look at the two win strike rates for these seven trainers, their strike rates have been very similar, which adds further confidence in the findings.

Onto the second batch of trainers now:

 

 

In this group the trainers with the closest CAE figures to 1.00 are Roger Varian (1.01), David O’Meara (1.01), Charles Hills (1.07), David Simcock (1.08), Sir Mark Prescott (1.08) and Archie Watson (1.10). These trainers have produced some consistent performances across the board with their 2yo second starters.

Trainer Consistency: 3yo runners

I want to move on to three-year-old (3yo) races next and am going to look at a much bigger data set, namely all 3yo non-handicaps. In theory, we should see the CAE figures much closer to 1.00 than before due to the sample size.

 

 

With 18 of the 25 trainers having a CAE figure of less than 1.10, this is an indication that most of these top trainers do perform to a similar level year in year out with specific horses in specific races – in this case 3yos in 3yo non-handicaps. Larger samples of data are less affected by those occasional unusual results which can impact on smaller data sets.

However, it should be noted that Richard Fahey and Sir Mark Prescott have both seen a dip in performance in 3yo non-handicaps over the past five years. Fahey’s record across both time frames has been particularly contrasting as the table below shows:

 

 

The strike rate has almost halved, and the returns have gone from a strong positive figure to a poor negative one. Conversely, James Fanshawe has seen an uptick in performance over the past five years, turning an 8% loss at BSP from 2015 to 2019 into a 22% profit from 2020 to 2024.

It’s now time to switch methods for the second half of the article where I aim to examine some trainer course data.

Trainer Consistency: Racecourse Angles

For the trainer course data, I plan to look at a selection of individual trainers comparing their course records and so, as I stated earlier, I will revert to the CSR (comparison strike rate) concept. Again, to help make comparisons easier when I divide the strike rates, I will divide the bigger by the smaller to give figures of 1.00 or higher.

Charlie Appleby

A look at the Godolphin trainer Charlie Appleby first. Here are the courses where he has had at least 30 runners in both timeframes:

 

 

I think this table shows why as punters need to be a little careful when it comes to some trainer course stats. Yes, certain trainers do target certain courses, and some are able to consistently repeat successes year on year. However, even for someone like Appleby, who has a yard chock full of top-quality horses, not all courses have delivered similar strike rates in the two five-year batches. At Sandown his win record has been excellent in the past five years but was relatively modest in the earlier five, giving a CSR figure of 2.01. The same applies for Haydock and the splits for Appleby at the Warrington track are as follows:

 

 

In terms of returns, we can see that Appleby’s figures have improved by around 40p in the £, although despite this he did not manage to get into overall profit.

Looking at which courses it might be worthwhile considering backing his runners in the future, I would say the following: Doncaster, Lingfield, Newbury and the Rowley course at Newmarket. My thinking is that these five have not only seen consistent performances (CSR figures all between 1.00 and 1.16) but have produced blind profits to BSP in both of the two five-year time frames. Ascot also falls into that category but his figures there are skewed by a BSP winner priced 36.0 in 2017 and a BSP 75.0 winner in 2022.

Before moving on, Appleby’s record at the Newmarket Rowley course is worth sharing in more detail; from 2015 to 2019 he had 54 winners from 200 (SR 27%) for a profit of £58.62 (ROI +29.3%). From 2020 to 2024 his record read 104 winners from 353 runners (SR 29.5%) for a profit of £105.76 (ROI +30%). Eight of the ten years saw the Godolphin trainer produce a blind profit on all his runners.

Andrew Balding

There are three courses where Balding has turned a profit in both five-year time frames and hit a low, i.e. consistent, CSR figure. These are Chester, Doncaster and Newbury. Of the three, Chester has the most consistent feel to the stats. He has a good record there with shorter priced runners (BSP 10.0 or lower) hitting a strike rate of 26.2% (71 wins from 271) for a profit to BSP of £60.34 (ROI +22.3%).

With bigger priced runners (above 10.0) at the track, he has made a profit of £73.52 (ROI +61.3%) thanks to 10 winners from 120. Overall, taking all prices into account, he has made a blind profit there in seven of the ten years.

Ralph Beckett

For Beckett I have produced a table of his CSR figures for different courses and these are shown below:

 

 

Doncaster, Wolverhampton and York have seen consistent CSR figures of 1.03, 1.13 and 1.01 respectively, with all three of them proving profitable across both time periods.

Chelmsford has a slightly higher CSR at 1.29 but this is a fourth course I would look out for Beckett runners as these splits are decent:

 

 

In contrast, his record at Lingfield (turf and AW courses combined) has been all over the place. The 2.30 CSR screams this and, if we look at the yearly win strike rates, coupled with the win & placed (EW) ones, we see the following:

 

 

We can see the huge discrepancies comparing 2016 and 2024, where the win rates were over 30%, with 2019 and 2021, where the win rates were 6.3% and 8.8% respectively. These results are based on fairly decent yearly sample sizes, too, with eight of the ten years having 30+ runners at the course.

I have said it many times before in articles that some stats can be misleading, and the more digging we can do behind the numbers the better.

Other Profitable Trainer Consistency Angles

Time precludes further trawling of the full list of trainers in such detail but I will share the remaining positive trainer/course stats, based on the combination of low CSR figures combined and two profitable five-year time frames. Trainers not shown failed to complete that double qualification for any course:

 

 

Outro

As I stated at the outset, this was a piece of research where I wanted to try to establish when trainers show consistency within certain parameters. Hopefully all the hours of research combined with my approach has at least offered some tasty food for thought. I am sure the ideas are not foolproof, but I believe they have merit and utility.

Comments are always welcome and if there are any tweaks to the methods that you’d like to discuss, please let me know in the space below.

  • DR

Does Gelding Improve Racehorse Performance?

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Most male horses begin their life on the flat as an entire, writes Dave Renham. At some point sooner or later - usually sooner - most horses disappoint their connections with racecourse performance and thus, in search of improvement, are gelded.

Introduction

The term "gelded" refers to the process of castrating a male horse, and this procedure is common within the horse racing industry especially with horses that race on the flat. The main reason for gelding a horse are to improve temperament and focus which it is hoped will improve performance on the track. Further, gelding a horse can improve a horse's compatibility with other horses, reducing tensions and possible distractions during races and on the gallops.

First Time Gelding

Overall

In this article I will primarily focus on data concerned with a first run after being gelded. The data has been taken from six full years of UK flat racing (2019 to 2024) and any profit / losses have been calculated to Betfair Starting Price (BSP) less 2% commission. The A/E indices have also been calculated to BSP.

Let me start by looking at the results for ALL horses that were having their first run since being gelded. The stats read as follows:

 

 

As we can see these horses win close to one time in every eight starts, and a small profit would have been made from backing them all. Of course, the chances are that we have had a few big prices which have skewed the returns a little. Below then are the results by different BSP price bands:

 

 

The best value has been with those horses priced BSP 7.01 to 19.00. In terms of the huge prices, there were five horses that won at three figure odds (BSP 100.0 +) with the biggest winning price being a whopping BSP 612.25.

It makes sense for the remainder of the article to have a BSP price ceiling in place to avoid skewing.

For the rest of the article therefore I will impose a BSP price limit of 19.00. This still gives us over 4000 horses to examine.

 

Age of horse

My first port of call with this price limitation is to look at different ages of horses to see if that had made any difference. The graph below looks at the BSP returns (ROI%) across different age groups:

 

 

Three-year-olds have by far the worst record (they also have the lowest win strike rate of all the age groups). The Classic generation have struggled particularly in Class 6 events. Their overall strike rate stands at 17% but in Class 6 races this drops to 13.8% as a result of just 70 winners from 506 runners. Backing all qualifiers would have seen significant losses of £117.03 (ROI -23.1%).

Juveniles (two-year-olds) having their first run after being gelded have done particularly well when contesting Novice races. Of the 123 runners, 33 have won (SR 26.8%) for a healthy BSP profit of £53.84 (ROI +43.8%).

Four-year-olds on their first run after the op have performed best in handicaps with 133 winners from 692 runners (SR 19.2%) for a decent profit of £178.61 (ROI +25.8%).

 

Career starts

One interesting finding relates to career starts. Horses that have raced three times in their careers previously and then were gelded have a good record. This is especially true when they race in handicap races as virtually all of them were making their handicap debut having qualified to run in handicaps for the first time. This cohort of runners have run 1044 times with 191 winning (SR 18.3%). Backing these runners blind would have produced a healthy profit of £188.03 (ROI +18%). This is especially impressive considering the BSP price limit that I imposed earlier.

 

Position LTO

Onto last time out performance now, specifically position last time out. Here are the splits:

 

 

It is interesting to note that 335 horses that won last time out were still subsequently gelded; that is a bigger number than I had expected. Having said that, they performed well on their next starts, making a solid profit – returns are just over 9p in the £. When we look at the strike rates across different finishing positions, there is not the drop off I had expected. Horses that finished sixth or worse have still won over 15% of the time which is a decent effort.

 

Days since last run

Horses that are gelded are rarely rushed back to the racecourse for obvious reasons. Only 3.5% of all horses that have been gelded return to the track within four weeks. The rest tend to be given much longer to recover. Indeed, 55% of all gelded horses are given at least five months until they are asked to race again, the surgery often coinciding with a horse's scheduled winter break.

In reality, the longer the break the better in terms of offering punters value, as the graph below displaying the A/E indices for different ‘days off track’ groupings shows:

 

 

Once we get to 85 days plus (more than 12 weeks), we can see an improvement in the A/E indices. Those off the track for five months or more (151+ days) provided good value. This cohort of runners had an excellent A/E index of 1.09 (as seen above), and their overall figures read an impressive 431 wins from 2294 runners (SR 18.8%) for a level stakes profit of £258.69 (ROI +11.3%).

 

Sires

I thought it would be interesting to see if any sires showed a pattern in terms of when their offspring were gelded. It is generally agreed that sires can influence their progeny from the perspective of distance requirements, ideal ground conditions, etc. I wondered if that also applied to behaviour, attitude, temperament etc? Below is a list of sires who have had at least 50 of their offspring running for the first time after being gelded coupled with the 19.0 BSP or lower price restriction:

 

 

Eight sires have turned a profit but two stand out, namely Iffraaj and Invincible Spirit. Both have seen strike rates of more than 30% (with Invincible Spirit nigh on 40%), and both have made excellent profits. Progeny of New Approach, however, have really struggled on their first run after being gelded.

I did take a quick look at sire performance when having their second, third or fourth runs after being gelded. Iffraaj and Invincible Spirit’s strike rates both dipped back to around 20%, but both still turned a profit. New Approach on the other hand saw a huge improvement in strike rate (22%), although backing all qualifiers would still have made a small loss.

 

Trainers

1st run after gelding

All trainers will have a slightly different approach to what happens after one of their horses has been gelded from the viewpoint of methods of recovery and rehabilitation. They will also plan out horses' return to the track slightly differently from each other. Below is a list of trainers who have saddled at least 50 qualifiers (1st run after being gelded / BSP 19.00 or less). The list is order alphabetically:

 

 

The first thing that stands out to me is the difference in the figures for the Godolphin trainers Charlie Appleby and Saeed bin Suroor. Appleby’s figures, by his high standards, are poor, while bin Suroor’s are excellent.

There do seem a be a handful of trainers to avoid; namely David Simcock (whose record is dreadful), Karl Burke, Richard Fahey, Charlie Johnston and David O’Meara.

1st vs 2nd run after gelding

Before winding this piece up, I would like to compare win strike rates for individual trainers comparing the first run after being gelded with the second run. I have split the data up into three graphs – the first one contains the eight trainers with the highest strike rates from the first run data shown earlier. The second graph shows the next eight trainers, and the final graph shows the nine that had the lowest strike rates. Presenting in this way makes each graph comparison easier to see:

 

 

As we can see William Haggas, Ralph Beckett and the Gosden yard have very similar strike rates. However, there is a marked difference with some of the others. Most have seen a significant drop, such as Marco Botti 22% down to 10.29%, Saeed bin Suroor down from 29.41% to 20.83%, and Archie Watson down from 27.66% to just 15.6%.

Roger Varian is the only trainer to really buck the trend with a big jump in the opposite direction from 21.74% to 33.09%. I will share the full figures of all the trainers including profit/losses/returns after the third graph.

 

Onto the second group of trainers now:

 

 

In this group we have five trainers with very similar strike rates, while Richard Hughes and Charlie Appleby have seen significant improvements in terms of win success. Michael Bell’s figures look more akin to the first group of trainers with a drop from just under 21% to 13.7%.

And finally to the third group:

 

 

This third group were the trainers with the lowest strike rates with horses having their first run after being gelded. As we can see, every single trainer improved their strike rate when the horses were having their second run. Charlie Johnston (9.84% to 23.21%), David Simcock (3.77% to 18.99%) and Clive Cox (15.38% to 26.92%) all showing significant improvements.

As promised, here are the full facts and figures for these trainers with their runners that are having their second run after being gelded:

 

 

There is a stark difference between some individual trainer performances when comparing first run versus second run.

To make it easier to digest, in the table below I have listed those trainers that have either a positive record or a negative one across both first and second runs after being gelded:

 

 

This article has thrown up a fair few positive angles – more than I had expected.

I hope it has been an interesting read, and it is time for me to start thinking about my next piece. Until then...

- DR

A Preliminary Look at Race Class in Flat Handicaps

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Dipping my toe into Race Class in flat handicaps

Introduction

I would like to start this piece with a question, writes Dave Renham. “When analysing a handicap race, how many of us look at the class of the race and other class factors in detail?”

Clearly, I cannot speak for everyone, just for myself, but in terms of the pecking order of race factors I’ll look at, class considerations are at the lower end of my list of priorities. I would always look at run style/pace, the draw, Peter May’s ratings, last time out (LTO) factors (position, odds, course) first. Once I move on to class factors though, there are five main things I will look at:

  1. which class of race each horse raced in last time and over its last few starts
  2. which horses have won at this level before, or indeed at a higher class level
  3. recent handicap marks or Official Ratings (ORs) over the past few races noting how well the horses ran on each occasion
  4. highest winning handicap marks, as long as they are relatively recent (within 18 months or so)
  5. past placed form and the relative handicap mark at the time

I must admit there are times when, especially if I am looking at a big field handicap, I’ll only do this for the main contenders, or at least the horses I believe are the main contenders! Also, in terms class factors, I know from past research that it is quite difficult to find an edge when taking on the Official handicapper, as the handicapping system in this country is extremely accurate. However, I still want to review what I think I know and compare it against recent evidence.

Data has been taken from UK racing, turf and AW, handicaps spanning the four full years from 2021 to 2024.

Before sharing my research let me discuss the different classes of handicaps. In 2021 and 2022 the European Free Handicap was the only remaining Class 1 handicap, but that race was discontinued thereafter, so the highest class of handicap is now Class 2 - and the lowest level is Class 6.

Within each class there are races open to slightly different ability levels as regards their Official Ratings. Every classification has at least two different rating bands within it, and there may be some variants of these which I've not listed, for example 56-72:

 

Class 2 includes Heritage Handicaps. The rating bands for this class are 86-100, 91-105 and 96-110.

Class 3 The ratings band for this class are 76-90 and 81-95

Class 4 For horses rated 66-80 and 71-85

Class 5 For horses rated 56-70 and 61-75

Class 6 For horses rated 46-60 and 51-65

 

Class 2 Races Overview

Therefore, within each classification there are races where the quality or class level of the runners is slightly different.

Let me give two race examples of Class 2 handicaps run during the period of study. The first race, run on October 16th 2021 at Ascot, was the Class 2 Balmoral Handicap, a 20-runner race where all horses were rated between 101 and 110. The average OR for the race was 105. The second race, run on October 17th 2024 at Southwell, was the Class 2 Download the At the Races App Handicap, a 12-runner race where 11 of the 12 runners were rated 94 or less with the highest rated horse having an OR of 98. The average OR rating for the race was just 91, a difference therefore between the two race OR averages of 14 pounds.

Hence, we need to be aware that the level of competition within each class band can differ, and sometimes quite markedly. Of course, the handicapper, as I have already stated, does an excellent job when rating horses, so in theory the rating adjustments made should ensure races are equally as hard to win regardless of the average OR. Having said that, higher rated runners in Class 2 handicaps do win slightly more often than lower rated ones.

This can be seen when we look at the splits for horses rated 101 to 110 compared with those rated 90 and lower when racing in Class 2 handicaps. Let me look first at qualifiers with a BSP winning price set at a maximum of 20.0. This is to avoid the results potentially being skewed by a huge priced winner or two:

 

 

Horses with a higher rating have outperformed those with a lower one as mentioned above, albeit the difference in absolute win rate is only 1.7%. In relative terms, it is nearly 14% greater. The hugher rated runners recorded a small profit with a difference of just under 7p in the £ when comparing the two rating groups. The A/E index was also higher for the 101 to 110 group.

Just for the record here are the figures for qualifiers priced over 20.0 BSP for each rating band:

 

 

Hence, in this cohort of genuine outsiders, the higher rated Class 2 runners have comfortably outperformed the lower rated runners across strike rate, returns and A/E indices. It should be noted that the 101 to 110 group did not have any 100/1+ winners that would have completely skewed their profit figure. Indeed, it was the 90 or below group which had the biggest priced winner and comfortably so at BSP 146.4.

Class 3-6 Races Overview

We see a similar pattern in different class groupings when comparing the highest rated runners with the lowest, including a price cap of BSP 20.0. Across all class groups the higher rated runners won more often. Differences in win strike rates vary from 3% or higher to 4.5% or higher depending on the class level. In terms of A/E indices the higher rated runners have proved better value in Class 3, 4 and 5 contests. At Class 6 level the lower rated runners have had the edge value wise.

 

Comparing Average Race OR with LTO Average Race OR

Having given us some background, for the remainder of this article, I am going to dig a bit deeper into the whole question of class. I will set an odds range of BSP 20.0 or shorter once more to avoid very big prices skewing the findings. In addition, the horse data I am checking requires the horse to have run in a flat handicap (turf or AW) last time out as well.

I have discussed already that within each race class we can get a significant differential in terms of average race ORs of the runners. For the main body of this article, I want to examine the performance of horses racing in a handicap when comparing the average OR of their current race with the average OR of their last race. I have decided to split the OR race average differences up thus:

 

 

Just to clarify, a horse that raced in a handicap that had an average OR of 76 last time out and races in a handicap with an average OR of 82 this time would go in the '+6 to 9' group as the difference is six higher. A horse racing in a handicap where the average was the same would go into the '+1 to –1' group, and so on.

Let's examine and compare the win strike rates of each group first. I wasn’t sure what I would find but these results come from huge sample sizes so we can be very confident in the numbers:

 

 

As we can see the strike rates are very similar which again perhaps highlights how accurately calculated handicap ratings in this country are overall. The two highest percentages lie at the right of the graph with the '-6 to -9' group and the '-10 or lower' group. If we now compare the A/E indices (to BSP) we again see a very level playing field:

 

 

The figures vary by just 0.03 from the lowest, 0.99, to the highest, 1.02. For the second graph running the '-6 to -9' group and the '-10 or lower' group are marginally better performing than the rest. Both these groups also made a small profit to BSP with their cohort of runners that were BSP 20.0 or less.

 

There are so many different routes I could take at this point, but I elected to fully focus on the '-10 or lower' group for the remainder of this article, as based on these initially findings this could be the group where we find an edge.

 

Handicap Races where Average OR dropped by 10 pounds or more from previous race (also a handicap)

Race Class Change

The first area which I want to look at for this cohort is ‘Race Class Change’ in terms of official classifications (Class 2, 3, 4 etc). Clearly, with a drop in race average OR of 10 or more from LTO, there were always going to be very few qualifiers in terms of being upped in class next time – just 18 horses went up in class to be precise (for the record this small sample made an ROI% loss of 14.4%). Here are the splits for qualifiers racing in the same class or dropped in class:

 

 

According to these figures, the bigger the drop in race class the better. There is positive correlation across strike rates, returns and A/E indices.

What is really interesting is when we look at the race class last time out for any qualifying class dropper. For example, if a horse raced in Class 2 LTO, then raced in Class 3 or lower next time, their record when the average race OR drops by 10 or more is quite remarkable:

 

 

These runners have shown a BSP profit in each of the four years showing excellent consistency with A/E values for each year ranging from 1.08 to 1.21. I, for one, will keep a close eye on these runners in the future.

The Betting Market

Moving onto market factors, and horses that start in the top three of the betting, after seeing the race average OR drop by 10 or more produced a blind profit:

 

 

Returns equated to a smidge under 3p in the £ and, if we stuck to favourites only, returns were marginally better at 3.3p in the £.

When we look at BSP prices, those qualifiers priced between 15.0 and the price cap of 20.0 won just 5.2% of the time (48 wins from 932) for a hefty loss of £133.20 (ROI –14.3%).

Based on this evidence, it seems to make sense to be looking for horses nearer the top end of the betting market more often than not.

Position LTO

How about last time out finishing position? The past stats have favoured horses that finished either second or third last time out. These runners have combined to produce the following figures:

 

 

We see a strike rate close just better one win in every five, and positive returns of over 5p in the £. The A/E index of 1.04 is also very solid. Logic dictated to me that there would not be too many LTO winners that qualified considering the average race class OR drop, but actually there were 204 such winners of which 43 won (SR 21.8%). However, they are very well found in the market and have proved quite costly to follow, losing £28.56 (ROI –14.0%).

Trainers

Trainer data is always something punters are drawn to and for this area of research I think the findings may be more illuminating than usual. One skill that all trainers need is an ability to find the right race for their horses. However, in handicaps there will be times when this may not the case and a trainer will be quite happy to see a moderate or even poor run in the hope that the horse's handicap mark drops. But how many trainers have been adept at finding a much weaker race in an attempt to exploit it? Let’s see.

The table below shows those trainers who had at least 50 runners with a drop in race average OR of 10 or more compared with the average rating of the race they contested last time out. The table is ordered alphabetically:

 

 

We have quite a mixed bag here, but there is a good proportion of trainers who stand out positively including George Boughey, John Butler, David and Mick Easterby, Brian Ellison, David Evans, Richard Fahey, Paul Midgley, Gary and Josh Moore, Nigel Tinkler and Archie Watson.

What may be even more enlightening is to compare the record of the trainers in the above table against their overall record with all other runners under all other circumstances, (e.g. runners whose race average OR changed by -9 or more). Below are the trainers with the biggest differentials between the two in terms of win strike rate:

 

 

We could argue that these nine trainers have been very adept at placing horses in much weaker races compared with the handicap LTO over the past four years. Not only do the strike rates suggest that, the A/E indices for each group show that too:

 

 

 

There are some excellent A/E indices there in the blue columns. Overall, of the 33 trainers in the original table 24 had better records when their charges raced in handicaps with an average race OR of 10 or less compared to their most recent start.

 

Summary

As with a lot of areas I research, I have literally scratched the surface in terms of race class. However, it has been an interesting journey with several positives noted on the way. The second half of the article focused on just one specific group of runners - those who were contested a handicap where the average official rating was at least 10lb lower than their previous race - so there is scope to look in detail at other groupings in the future. For those who want more class articles (no pun intended) please suggest any ideas in the comments.

- DR

The Vagaries of Early Speed: Soft Leads and Pace Collapses

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The Vagaries of Early Speed: Soft Leads and Pace Collapses

In recent years the question of pace in a race has become much more on punter’s radars, writes Dave Renham. When we watch flat racing on the television, pundits or commentators will almost always include a discussion about where the pace in the race is, and who is likely to lead.

Introduction

The pace in a race is linked to run style, and I have written numerous articles analysing the run style of horses in both flat and National Hunt racing on this site. These articles, which can be found here, have often highlighted the edge front runners enjoy in races over certain distances at certain tracks. In this article I want to try as best I can to compare races where front runners get an uncontested lead, as opposed to races where there is strong competition for the early lead. I have decided to focus on handicaps of 1 mile or less in UK flat racing. I have looked at around 3000 races which is a very decent sized dataset.

I had two objectives. Firstly, to look at races where the leader got a ‘soft’ or ‘uncontested’ lead. In other words, a race where the front runner was not pestered in front and could potentially dictate the race. In an ideal world that would see the leader setting slow fractions thus saving more energy for later in the race, meaning there was more chance of the horse making all of the running to win. And secondly, I wanted to look at the opposite type of race, where there was strong competition for the lead with multiple horses up with or very close to the pace. In this type of scenario, the expectation is that those near the front will typically go off too hard, using too much energy, which makes it easier for a more conservatively ridden horse to come late and win from off the pace.

The problem with researching these ideas, is how to accurately determine which races fall into which category. It is not easy to do. If I had sectional timing for all courses and all races, with all the times for all horses at all call points in a database or spreadsheet, that would be a tool that could do the job. However, sadly I do not have access to such data. If I had the time to watch the replays of all 3000 or so races in this sample, then that potentially could help in analysing my ideas (although it would be subjective). That though is not a sensible use of my time, even if I had all the time in the world! Hence, I needed to come up with a different plan, one that could hopefully do the job...

Methodology

So I came up with a plan which, while I appreciate is not foolproof, I think it gives us a good overview if nothing else - a flavour, if you will - of how pace affects these types of races.

Firstly, I decided to find the average pace rating for every single race in the sample. To do this I used the same numerical values found on Geegeez where led (early leader) gets 4 points, prominent 3, mid division 2 and held up 1. Hence to get the average pace rating for each race I added up the pace scores of all the runners and divided it by the total number of runners in the race. Let me give a worked example to help explain it more clearly.

Below is a result from Chelmsford over 6f from July 2023:

 

 

As we can see there were 10 runners, one early leader (L), two prominent racers (P), four horses that raced mid-division (M), and three that were held up (H). Hence the calculation is as follows:

 

 

Thus, if we add up the total points column, we get 21 and then we divide it by 10 runners to give us the average pace rating for that race:

 

 

I did this calculation for each race using some Excel wizardry and the averages ranged between a lowest pace average of 1.96 and the highest standing at 3.50. The vast majority of race averages landed between 2.10 and 2.90.

The reason for creating these race pace averages was to help determine how much pace there was in each race. The lower the average, the more horses would have been held up or raced in mid pack. Hence, there was likely to be far less pressure on the early leader. Higher race pace averages would see more horses contesting for the early lead and/or being on the heels of the leader(s).

Although I could not be 100% certain for every race, logic dictated - and my working assumption was - that lower pace race averages would conform to races where the early leader gets a soft or uncontested lead, while the higher averages would have a greater chance of producing races where the early pace might collapse. I decided for any run style or pace comparison from now on I was going to lump together the results for mid-division horses with hold up horses. This makes sense to me as mid-division horses still have plenty of ground to make up in order to win. From now on this joint group will be referred to as simply 'the hold-up group' – making it less of a mouthful than to keep calling it the hold-up/mid-division  group.

Having calculated the race pace averages I needed to decide on the best way was to interpret the results. The problem we always have with any pace/run style data is how to calculate win rates etc. It is not as simple as what proportion of races are won by early leaders compared to hold up horses. This is because, on average, there are far more horses that are held up compared with horses that lead early. In my example Chelmsford race, there was one front runner, four midfield and three held up horses.

Normally, I simply divide the number of winners to runners within each run style group but when I did this looking across different pace race averages, I realised we could not compare strike rates in that way. I won’t go into the mathematical reasoning as it is quite complicated to understand - and even more difficult to explain!

I decided the only way to have a fair comparison was to create a type of impact value where I divided the win percentage of leaders within their leaders’ group by the win percentage of hold up runners within their hold up group. I will call it the Comparison Strike Rate, or CSR for short.

This way I could get a straight comparison, with the idea being that the CSR figure would help to show any significant differences in performance (according to the relative strike rates). Hence a CSR of 2 would suggest that a lone front runner has double the chance of winning as compared to one of the hold up horses. [I have used this CSR idea before in two 2yo articles back in 2019.]

 

All Distances (5f-1m)

I split the race pace averages up into four groups where there was a similar number of races within each group. Here are the CSR splits:

 

 

Essentially, the 2.39 or less group are the races where it is more likely that a front runner got an uncontested lead; and we can see that front runners have been far more successful within their run style group than hold up runners have within theirs, winning 2.44 times as often.

Fast forward to the races with the highest race pace averages (the 2.68+ group) and we can see the CSR drops to 1.34. It is in these races, where there is the most pace in the race, that we would expect an overly fast early tempo - and the numbers back this up. Front runners do still have an edge, but it is vastly diminished. Therefore, it does seem that what pundits and punters have believed for years is true: front runners that get an uncontested lead win far more often than front runners that have horses pressing them for the lead or snapping at their heels.

What is also pleasing when looking at the graph is that not only do both ends of the spectrum match the expectation, but there is excellent correlation as the race pace averages increase (from left to right on the graph). This gives me more confidence in the findings and my initial theory.

Having found the types of patterns that I was hoping for I felt I wanted to dig deeper and split the data further by looking at different distances, starting with the minimum distance.

For the rest of this article though I am just going to compare the two ‘extremes’ – the races with the lowest race pace average band (2.39 or less) with races with the highest (2.68 or more).

5f handicaps

Anyone who has read my run style/pace articles over the years will know that 5f races give front runners the strongest edge when taking all courses into account. Hence, I would expect the CSR figures for both groups to be higher than the average figures for all 5f-1m races we saw in the first graph. I would also hope that we still see a significant differential between the two CSR figures.

Here is what I found:

 

 

As the graph shows, front runners had a CSR figure of 3.68 in races where the early leader got an easy time of it. This is well above the 5f-1m average figure of 2.44. In 5f handicaps when there was plenty of competition up or near the front early, front runners were still 2.27 times more likely to win than one of the hold up horses.

Ultimately, punters should not be put off backing a potential front runner in a 5f handicap even if there is likely to be a lot of pace on. They still have a decent edge and offer value. If the shape of the pace suggests an easy lead, then we can be even more confident of a good run from the horse that takes the early lead.

 

6f handicaps

Front runners maintain a healthy edge generally over 6f, but it is nowhere near as potent as it is over 5f. Hence, we should expect a drop in both CSR figures. Here are my findings:

 

 

In six-furlong races where there is less competition for the lead, we see a strong CSR figure of 2.76. Front runners continue to enjoy a huge edge over hold up horses. However, when we get to races over 6f where there is plenty of early pace and possible battles up front, we see the figure drop markedly to just 1.27. Front runners still offer better value than hold ups under such conditions, but the edge is small. 

 

7f-1m handicaps

I am lumping together 7f and 1m handicaps simply because once we hit 7f, although front runners still hold sway, the advantage is much diminished when compared to the stats at 6f and especially 5f contests. There are a handful of courses where over 7f front runners enjoy a very strong edge; Beverley, Chelmsford, Epsom, Leicester and Musselburgh immediately spring to mind. However, overall, it is a more even playing field. Let’s see what the CSR figures show this time:

 

 

Races where the pace average was 2.39 or less still favoured front runners more than those where the pace average was 2.68 or higher. However, both figures have dropped further from those we saw over 6f. This is as we would expect but it always good to have it confirmed in black and white, or in this case red.

I would like to share one final graph which combines the preceding trio of charts so we can perhaps more easily compare the CSR figures across the different distances / race pace averages:

 

 

Conclusions

So, what has this research told us? Well, assuming that my theory is correct in terms of races with a pace average of 2.39 or lower being races where the early leader has an easier time of it, and that those 2.68 or higher are when there is more competition for the lead, then theories about easy leads and pace collapses are almost certainly true. At least the numbers shared suggest this is the case in handicaps of 1m or less. Moreover, the shorter the distance the easier it seems for front runners with uncontested leads to win.

Before I finish, what I have done for my own peace of mind is to watch back some replays of races where the pace race averages were 2.39 or less, and virtually all races saw the early leader lacking strong competition for its lead. Likewise, I did the same for some races averaging 2.68 or more and a high percentage of those saw some very strong competition for the lead early. This adds some qualitative confidence that my race pace averages do the job I was hoping for.

I appreciate that, until the race has been run, we are unable to calculate the race pace averages. However, the Geegeez racecards give us past running styles for each horse to a maximum of their most recent four runs so this is a tool we can use to try and work up the likely pace make-up for any race in question.

There is plenty more scope for me to keep researching races with different pace make-ups. I do have data for non-handicaps up to 1 mile, as well as data at longer distances. I also could try to look at other angles within the 5f-1m handicap bracket. Ideas include do front runners with uncontested leads win more often in lower class races? That has some logic behind the theory so I may test that. I may also analyse different courses in more detail, and I might expand to try and find any positives or negatives regarding prominent racers across different race pace averages.

So many ideas, why are days only 24 hours in length?!

- DR

An Analysis of Place Betting on Betfair

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An Analysis of Place Betting on Betfair

One of the comments about an article I wrote a couple of months ago asked whether I could write an article on the subject of place betting on Betfair, writes Dave Renham. I have written around a thousand horse racing articles in my life and, consequently, have covered a fair few different topics; but I have never delved into the subject of place betting. So, thank you to Keith for requesting a piece on this subject.

This article covers ten full years of flat racing in the UK (2015 to 2024) across both turf and AW racing. All profits/losses are calculated to Betfair Place SP with 2% commission taken out. I have excluded races of 4 or fewer runners as only winners count under those circumstances.

Betfair Place SP: All Races

Overall, if we had bet every runner in every race in this ten-year time span to Betfair Place SP, we would have lost around 2.5 pence in the £. This is actually a smidge less than we would have lost if backing all horses to win. Let me split the BSP Place data up by individual year to see how it has panned out:

 

 

The worst yearly return was a loss of 4% in 2020, the best was a loss of 0.6% in 2022. For the record, the current figure for 2025, as of early July, is showing a loss of 2.2%. Looking at the graph, it could be argued that since 2021 betting on the Place market has offered punters better value than previously. Whether that has truly been the case, or the results are just down to variance, it is impossible to say.

Before moving on, I would like to point readers in the direction of an excellent and informative article written by Russell Clarke on here back in 2020. It explores each way betting with traditional bookmakers and, amongst many useful pointers, it highlights under what circumstances it is better value to bet each way. The link is here www.geegeez.co.uk/money-without-work-5-bookmaker-concessions-each-way-betting/

 

Betfair Place SP: Number of Runners

Having read that piece I wondered if there was a number of runners ‘edge’ we could find on the Betfair Place markets. Here is the breakdown of all races backing all runners across different field sizes:

 

 

Note that the fourth column, Place%, is different from traditional bookmaker place percent, because when there are non-runners that affect the number of placed positions, on Betfair this figure is not adjusted. Therefore, in a race with eight runners declared, if there was a non-runner, people betting with the bookmakers will see the number of places drop from three to two. On Betfair if an 8-runner race becomes a 7-runner one, they still pay three places. We could get 9-runner races having two non-runners and the same thing would happen. This is almost certainly why races with seven runners have almost broken even if betting all runners blind on the Betfair Place Market. Overall, 28.6% of runners in 7-runner contests would have paid two places with bookmakers, this figure increases to 35% on Betfair. And this is totally due to 7-runner races with non-runners paying out still on the extra place on Betfair. Now, of course, prices on the Betfair Place Market will contract a little under such circumstances but not enough it seems to give punters more chance of parity.

We see a similar thing occurring with 15-runner races. Losses are 1.12% for all 15-runner races, and more specifically for handicaps this drops further to 0.84%. Here we are seeing a similar dynamic where 16-runner handicaps that have one non-runner still pay four places on Betfair. Again, this scenario will occur in races with 17 runners declared and two non-runners, 18 declared and three non-runners, and so on. We can also see that 16-runner races have edged into profit. In terms of handicaps, it is not surprising perhaps to note, that races with 15 to 17 runners have offered the best value on the Betfair Place Market (losses of only 0.6% across all runners).

At this point it is worth sharing that non-handicaps with 12 or more runners have offered place betting Betfair players the worst value over the past ten years as losses move up from an overall average loss of 2.5% to a punishing 5.5%.

Betfair Place SP: By Course

I want to look at individual courses next to see if any patterns can be found. Firstly, let me look at the courses where placed runners did best in terms of returns:

 

 

I had wondered before starting this ‘course digging’ whether the bigger named tracks would produce better results. However, this is not the case with only Ascot and Newbury of the Grade 1 courses in the table. I’m not really seeing a pattern as yet so let me look at the worst performing courses from a Betfair place perspective:

 

 

No Grade 1 courses here, but a good mix of different calibre tracks with again no clear pattern. Sometimes we check ideas, and nothing can be found or indeed explained. That seems to be the case here.

 

Betfair Place SP: Actuall Odds

Moving swiftly on, one key area worth looking at is actual Betfair Place odds. I would hope this would give us some useful information. The graph below shows the Befair Place Returns to different BSP Place Odds Groupings:

 

 

Ahh! This is an extremely useful set of figures. The shorter BSP Place prices on the left of the graph have produced far better returns than the bigger priced groupings on the right. It seems that BSP Place prices of 5.0 or shorter gives punters the best chance of being profitable. Combining these runners at 5.0 or shorter would have seen us lose less than 1p in the £ for every £1 staked. Also, remember this is still sharing the data across all runners so we are talking about backing horses blind with no other considerations than price. We should be able to improve upon this with a bit of extra work.

Clearly, as with any BSP data be it win or place, we do not know the exact BSP price until the race has started, but as I have stated before betting seconds before the off will get us very similar results.

Betfair Place SP: Early Morning Odds to Opening Show Odds

I now would like to examine price movement based on William Hill prices in terms of Early Morning Odds to Opening Show Odds. The Early Morning Odds I've used are those available around 9am, while the Opening Show Odds occur roughly 10 minutes before race time. Does price movement between these time frames make any difference to the returns for placed runners? Let’s see:

 

 

Based on these figures, horses that shorten in price between Early Morning Odds and Opening Show Odds have provided the better returns. Still an overall loss as we would expect but only equating to just over one penny in the pound.

Digging a bit further with those horses that shortened in price from Early to Opening, if we restrict qualifiers to those that continued to shorten from Opening Odds to SP that would have produced a Betfair Place Profit of £213.09 from 68,778 qualifiers. Not a huge profit based on the number of qualifiers but a profit, nonetheless.

Betfair Place SP: Trainers

A look at trainers now, focusing first on the trainers with the highest placed percentages. Below are the top 20 in terms of place rank.

 

 

Nine of the 20 trainers have made profits in the Betfair Place Market. The Crisford stable have the best returns (an impressive 6.65% ROI), and they have had seven winning years out of ten. It should also be noted that their 3yo runners have returned over 10p in the £ if betting to BSP Place. Charlie Appleby’s figures are very solid when we drill down to the jockey data. There have been seven jockeys that have ridden 50 or more times for the stable and six have turned a place profit.

The Charlton stable on the other hand has very poor place returns. Betting their runners to place each year would have seen ten losing years out of ten. The Haggas stable has also struggled through this lens.

Other trainers to have made significant positive place returns over the past decade include Jane Chapple-Hyam (17% ROI), Grant Tuer (11% ROI), Bryan Smart (8% ROI), William Muir / Chris Grassick (7% ROI), and Paul and Oliver Cole (7% ROI).

Betfair Place SP: Trainers by Odds Band

I am now going to look at 75 trainers and their ROI Place Percentages across different Befair Place odds bands. The biggest place price in the table is 5.00, and I have split the prices into seven groupings. Figures in black show a positive return within that group, those in red show a negative return. The minimum number in each price band is 100 runners; if there a trainer saddled less than 100 runners in a price band the box is empty in the table:

 

 

You can pick out the detail of interest for yourself in this table, both positive and negative. But I will share a few personal observations to get you started:

C Appleby – we saw in the first table that he had shown a Betfair profit on his placed runners. In this table we perhaps see why, with five of the seven price bands turning a profit. His runners have offered the best value between place odds of 3.01 and 5.00.

R Beckett – all price bands have shown a negative ROI%. The 3.51 to 5.00 price band have shown the biggest losses.

Jane Chapple-Hyam – it was noted earlier that Jane Chapple-Hyam had secured a decent profit across all her runners on the place market. She had enough runners to qualify on six of the seven price bands and four showed a profit. For the record she showed good returns on bigger priced runners too.

H+R Charlton – seven reds out of seven. We saw earlier how poor the yard's place returns were overall.  This helps to explain why.

B Ellison – he has secured a positive ROI% in six of the seven price bands. Yard seems to offer solid value in the place markets.

J Goldie – some good stats especially with the shorter prices on the place market. Generally strong looking figures when horses are priced 3.50 or lower.

B Millman – five of the six bands to have enough runners have secured a profit for Millman. It should be noted however that his performance is poor once we get to prices of 6.5 or bigger.

G Tuer – as with Millman, Tuer has secured five profitable returns out of six. Tuer has also made significant returns with horses priced 9.00 or bigger on the place market.

I hope readers will get an opportunity to analyse the table in their own time and find further useful angles for other trainers.

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This has been my first foray into Betfair place betting, and I have been pleasantly surprised in terms of the overall stats. It's an area that certainly will demand more of my attention in future. Do please continue suggest any ideas for future place (or indeed other) articles in the comments. I can't guarantee to write to your specific request but, as this piece shows, if I feel there are sufficient data and angles then it'll get some focus.

- DR

Newmarket July Meeting: A Ten Year View

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Newmarket July Meeting – a look at the past 10 years

Later this week racing fans will enjoy the three-day Newmarket July Meeting, writes Dave Renham. Expect top quality action headlined by two Group 1s, the July Cup and the Falmouth Stakes, as well as four Group 2s. There will also be several decent handicaps to get stuck into. In this article I am going to look at stats for the most recent ten meetings going back to 2015. Profit / loss has been calculated to £1 level stakes using Betfair Starting Price (BSP), with 2% commission taken out of any winning selections.

Newmarket July Meeting: Market Rank (Exchange)

I would like to start by examining the betting market, more specifically the market rank of Betfair. Here are the splits:

 

 

It seems the sweet spot over the past decade was horses ranked third to fifth in betting preference. Favourites by contrast suffered fairly significant losses equating to more than 17 pence in the £. Splitting the favourite data into handicaps versus non-handicaps, it was handicaps that offered better results, albeit losses were still over 11p in the £. Losses in non-handicaps were over 23p in the £ for the jollies. Eye-watering stuff!

If we look at the Betfair A/E indices for Market Rank, we can see excellent correlation with the earlier table:

 

 

Horses ranked third to fifth in the betting have offered excellent value over the past ten years. It will be interesting to see if this pattern continues this year.

 

Newmarket July Meeting: Price movement

Now I want to look at price movement data comparing Early Morning Odds to final Starting Prices. I am going to share the win strike rates for horses that lengthened in price over the day, stayed the same price, and shortened in price.

 

 

Horses that shortened in price were the most successful, as is the norm, but the differential between ‘shorteners’ and those that drifted in price is huge. Horses that were backed over the day won more than 2.5 times more often than those that drifted. If we now look at the profit / loss for the three groups, based on their final BSP we see the following:

 

 

It seems that following the money would have been a good strategy over the past ten meetings. Despite the prices shortening, 'line trackers' still would have seen a 10p in the £ return to BSP on horses that were shorter prices at the off than they were in the morning.

There are two trainers in particular that were worth noting when the money was down: Andrew Balding and Ralph Beckett. Balding was 7 from 31 (SR 22.6%) with shorteners, just 2 from 28 (SR 7.1%) with drifters. Beckett meanwhile was 7 from 29 (SR 24.1%) with shorteners and just 1 from 21 (SR 4.8%) with drifters.

 

Newmarket July Meeting: Age in 3yo+ & 4yo+ races

Let me next look at the performance of different aged runners in races open to 3yos and over / 4yos and over. It should be noted that the vast majority (87% of them) were 3yo+ races. Here are the age splits:

 

 

This is illuminating. Three- and four-year olds have clearly outperformed their elders both from a win rate perspective as well as a profit / loss one. It looks like we can pretty much write off any horse aged seven or older based on these figures.

The contrast between younger and older has been strongest in non-handicaps where horses aged five or older have won just three times from 97 attempts (SR 3.1%) for losses of over 80 pence in the £. In handicaps three-year-olds fared extremely well as the graph for win strike rate below shows:

 

 

I would think the starting point for any all-age handicap would be the 3yos based on this data. It should be noted, too, that 3yos that shortened in price from Early Odds to SP in these races won over 24% for a healthy 18% ROI%.

Newmarket July Meeting : Recent form

Moving on to ‘last time out’ factors, let's start with recent form and the position the horse finished on their most recent start. Here is the breakdown:

 

 

 

It is unusual to see similar win rates for horses finishing first, second, third or fourth last time out (LTO). The LTO winners’ group made a BSP profit, but those figures are skewed somewhat by three decent priced winners – BSP 66.34, 50.0 and 40.0. All in all, I would not be paying too much heed to LTO performance as in the past, for this meeting at least, it has not been much of a guide.

Newmarket July Meeting: Course LTO

Another LTO factor is the course at which the horse ran last time. Below are the LTO courses that have provided at least 50 of the Newmarket July meeting runners:

 

 

A good chunk of the runners raced at Ascot last time, many of which were racing at the Royal meeting a few weeks previously. There were also a significant number of horses that raced at Newmarket LTO and these had the best record in terms of strike rate as well as delivering a fair profit. Sandown and York were the other two LTO courses to turn a profit here, and York’s figures look particularly solid. LTO York runners that started in the top three in the betting at the Newmarket July festival secured a 31% strike rate and returns of over 100p in the £. In contrast, the Newcastle and Windsor figures have been poor, and both have commensurately disappointing place percentages, too.

Newmarket July Meeting: Course form

Onto course form now and looking at the performance of past course winners compared to those that had not won at the track. To make this a fair comparison I will compare only horses that had already run at the track: clearly horses that were yet to run there could not have previously won! I am going to compare the win strike rates and the win & placed (Each Way) strike rates first:

 

 

It seems therefore that a previous course win is preferable both from a win and a place perspective. This is also reflected in the profit/loss and return columns in terms of win bets:

 

 

In terms of betting on the Place market on Betfair, there has been a big discrepancy there as well. Previous course winners produced a £38.41 profit to £1 level stakes; non-course winners lost £52.24.

With regards horses that raced at the course previously and had been placed before (includes winners of course), they too have a strong edge as these stats show:

 

 

Taking all these stats into account a previous win or placed effort at the track has been a definite positive at this meeting.

 

Newmarket July Meeting: Trainers

The final area I want to look at is trainers: which handlers have excelled at this meeting and which ones have found it a struggle? Only trainers with 30 runners or more are shown. As far as Charlie Johnston is concerned, I have combined his record with his father’s, Mark. The table is ordered by return on investment at Betfair Starting Price (BSP ROI).

 

 

Several trainers were in profit through the decade although Ralph Beckett, Michael Bell, Karl Burke and the Johnston stable's performance figures were boosted by a few bigger priced winners dropping in. Charlie Appleby, Andrew Balding, Richard Hannon, Aidan O’Brien and Saeed bin Suroor have more solid looking overall profiles and all five showed good profits with horses in the short to mid-price range.

There are a couple of extra stats worthy of note. Firstly, when William Buick rides for Appleby the strike rate has been a smidge under 30% for a 49p in the £ return. Secondly, Aidan O’Brien should be noted when any of his runners pivot from Royal Ascot. This cohort won 27% of the time for a 63p in the £ return.

The records of William Haggas, Charlie Hills, Hugo Palmer and Kevin Ryan have been poor, although Haggas has had plenty of near misses. Palmer and Hills have a poor record in terms of placed runners as well, and they may be two trainers worth avoiding at the fixture.

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Newmarket July Meeting: Key Positives

The key positives are as follows:

  1. Horses third to fifth in the betting market
  2. Horses that shorten in price from Early Morning Odds to SP
  3. 3yos in 3yo+ handicaps
  4. Ran at Newmarket or York LTO
  5. Previous course form (both win and placed)
  6. Trainers - Appleby, Balding, Hannon, O’Brien and bin Suroor

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I hope this piece has provided a few pointers that will prove useful over the three days of Newmarket's always excellent July meeting. Good luck to those punting, and don’t forget to use the Tix software if playing the Jackpot, Placepot, Scoop 6 or Quadpot.

- DR

 

How to Use Tix for Multi-Race (Placepot) Bets

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Using Tix for Jackpots, Placepots, Quadpots & the Scoop 6, primarily focusing on Placepots

Geegeez readers should by now be aware of the online software called Tix, which Matt built in conjunction with the developer who built much of the coding for the original geegeez.co.uk racecards and form tools, writes Dave Renham. The Tix software is designed to be used for tote multi-race pool bets such as the Jackpot, Placepot, Quadpot and Scoop 6. It enables punters to produce more sophisticated and strategic permutations than the bog-standard perm approaches most punters use.

Tote Bets: A Quick Intro

Before discussing the software, it should be noted that the Tote take a percentage out of any final pool, the amount depending on the bet. Below is a table showing the percentage take-outs for the main pool bets:

 

Pool bet Percentage taken out
Jackpot 29%
Placepot 27%
Quadpot 26%
Scoop 6 30%

As we can see they are all in the same ballpark. If we consider the Placepot, therefore, if 27% is taken out that leaves 73% of the original pool being shared between winners.

To help understand the maths, here is an example. For a final total of £100,000 bet into a particular Placepot pool where there was £200 remaining at the end of the six races, the dividend would be worked out thus:

£100,000 x 73% = £73,000

£73,000 divided by 200 = £365

£365 is the dividend is to a £1 stake.

The lure of Placepots and Jackpots is the chance of a big payout for relatively small outlays. Personally, I have never regularly played the Jackpot but play plenty of Placepots. I’ve been fortunate enough to have enjoyed some reasonable wins, and one very big win, but of course there have been many occasions when I have lost all of my stake. As far as this article is concerned, I am going to focus on using Tix for Placepots, because it is the most commonly played of the tote multi-race bets.

Playing Placepots the Traditional Way

Let's first look at how we could play these pool bets without the aid of Tix.

One line 'Hail Mary'

The first method is to simply pick one horse in each race. In a Placepot, there are six legs and so that would be six horses. In order to win a share of the Placepot all six must either win or place. This would be the case even if we pick six favourites. For those wanting to put the favourite in as the only selection in each of the six races, this is possible because there is a Placepot option to back the unnamed favourite.

Tthere are plenty of races where the market is quite tight at the top and we would be guessing which horse is sent off favourite, so for ‘favourite’ fans this is a useful option. However, the chance of all six favourites winning or placing is surprisingly rare. Indeed, looking at the 177 flat race meetings held in the UK in April and May of this year only 13 times did six favourites win or place in each of the first six races on the card.

However, that did not mean there would have been 13 theoretical winning Placepots for favourite only backers. This is because three of these 13 did not count due to a situation where a joint favourite won or placed, but the other joint favourite did not. When this happens there can only be one horse deemed to be favourite so the horse with the lowest racecard number becomes the favourite for pool bet purposes. Hence, if we had gone down the unnamed favourite Placepot route in April and May we would have had 177 Placepots bets of which 10 won.

The problem with all favourites placing is that the dividend tends to be very low when this happens, and that was the case with all ten dividends as the table below shows:

 

Date Course Dividend to £1 stake
7th April 2025 Kempton £6.20
12th April 2025 Brighton £12.40
12th April 2025 Thirsk £8.00
1st May 2025 Redcar £11.50
3rd May 2025 Goodwood £9.90
5th May 2025 Windsor £10.50
9th May 2025 Nottingham £7.30
21st May 2025 Chepstow £13.20
23rd May 2025 Goodwood £5.90
26th May 2025 Windsor £8.00

 

If we had placed let’s say a £2 bet on each of the 177 Placepots our outlay would have been £354. Our returns would have been £185.80 showing a LOSS of £168.20. Ouch!

Favourites obviously command the most amount of money wagered in Placepots which is why, when all six win or place, the dividends are so low. Interestingly, there were two meetings in April and May where no favourites placed in any of the six races – the dividends for these meetings were somewhat different.

 

Date Course Dividend to £1 stake
19th April 2025 Musselburgh £1954.50
31st May 2025 Lingfield £4022

 

The '2x2'

For seasoned Placepot players selecting a single horse in each race is not a credible strategy. In the period discussed we have seen that putting the favourite as the only selection in each race secured a winning Placepot less than 6% of the time, and delivered significant losses.

An alternative and more popular approach is to choose two horses in each race giving players more coverage. We call this a permutation, or perm. If we choose two horses per race rather than one, the number of bets or lines goes up drastically from one to 64 because we multiply the number of selections per race to get the total number of selections.

1 x 1 x 1 x 1 x 1 x 1 = 1 while 2 x 2 x 2 x 2 x 2 x 2 = 64

Take three horses per race and we are looking at 729 bets or lines.

Obviously, the chances of winning part of the pot increase but the more bets/lines we have the more we are staking, which will have an impact on any final returns.

Variable perms

To try and reduce the number of perms, some Placepot players vary the number of horses chosen for each race. Hence, they may have a couple of races where they choose just one horse – a so called ‘banker’; perhaps they have three horses in two of the other races, and five in each of the final two races.

In this scenario the number of bets or lines would be calculated 1 x 1 x 3 x 3 x 5 x 5 which equals 225 bets. This idea covers 18 horses in total (the same as the three horses in every race perm) but cuts the number of bets/lines down considerably.

Thus, varying the number of horses chosen per race is the most sensible method discussed to date; but it is time to talk Tix and a more sophisticated approach to adjusting the Placepot perms.

Introducing Tix

The Tix software allows us to use what is known as the ‘ABCX’ approach. This approach essentially allows players to group horses by order of confidence / perceived chance. In terms of a Placepot the thinking would be along these sorts of lines (or at least this is the way I think!) -

 

A Horses – horses that I believe are genuine contenders to win or place; or horses that I perceive to be overpriced within the mid-range of prices such as a 10/1 shot that I think ought to be 5/1, or a 12/1 shot that is 6/1 on my reading of the race.

B Horses – the next best options that we can make a case for especially if one or more of the A contenders underperform.

C Horses – horses that are unlikely to win but have some chance of placing. An example may be a horse overpriced at 33/1 we perceive should half that price at least. Or a less fancied horse well drawn over a course and distance that has a strong bias.

X Horses – horses that are excluded from calculations as their win or place chance seems extremely unlikely or I feel they are significantly over-factored in the market.

 

For Placepots my preferred approach is to have more A’s than B’s and maybe one or two C’s. However, for bigger meetings such as Royal Ascot, I tend to load up on A’s and have more C’s than B’s. I am sure others will have alternative approaches that may well be better than mine. Hopefully the more I use the software the more I can finesse my methods.

In terms of the Tix software the A horses will occur in more bets/lines than the B’s that in turn occur in more bets/lines than the C’s. The table below shows all the possible combinations or perms for each individual Placepot ticket – I have colour coded them to help make it clearer. A rated selections are in red, B are in black and C are in green.

 

 

This way of combining the horses is far more efficient and a lot cheaper! The way Tix is designed is that we can have a maximum of 28 individual tickets and this only occurs if we pick at least one horse in each of A, B and C positions in every race - as per the image above.

Tix Selection Flexibility

Keeping to the ‘three horses in a race scenario’, here are total number of bets/lines based on the Tix options, assuming we keep to the same combination for all six races. It includes the two I have already shared:

 

Combos All 3 on A 2 on A, 1 on B 2 on A, 1 on C 1A, 1B, 1C 1 on A,  2 on B 1 on A, 2 on C
Total Bets 729 496 256 28 73 13

 

The table shows the flexibility of the Tix software in terms of being able to offer various ‘number of bet’ scenarios, and remember, these example numbers are based on choosing the same A, B and/or C combination for all six races. Assuming we wanted to put three horses into each race we of course could choose a different three-way combination for each race such as:

 

Race no. Column A (no. of selections) Column B (no. of selections) Column C (no. of selections) Total no. of horses in race
1 2 1 0 3
2 1 1 1 3
3 1 2 0 3
4 3 0 0 3
5 1 0 2 3
6 2 0 1 3

 

This particular Tix construction of three horses per race would equate to 138 lines. It would take several pages to list all possible Tix bet constructions of three horses in each of the six races, so I’ll spare readers that! On the Tix site, our ticket with this type of perm/construction would potentially look something like this:

 

 

To be clear, the green column is for A picks, the yellow is for B picks, and C picks are in the right hand sandy coloured column. And these numbers in the specific columns would give us the following ten tickets:

 

 

As we can see, for this example there are ten specific groupings (tickets), and we would need at least one of those of ten groupings to have a win or placed horse in each of the six races to get a return. Of course, we may achieve a return that is less than our original stake, so six ‘win or placers’ on one of the tickets does not guarantee a profit.

If all eighteen horses manage to place then we probably would be dreaming but in that unlikely scenario these ten specific groupings/tickets would combine to have all 138 bets/lines as winning ones.

Tix Staking Flexibility

So, one of the brilliant parts about using Tix is this selection flexibility. A further feature in terms of flexibility is that we can adjust our stakes in terms of the four main groupings. This is the default position with the same stakes on each:

 

 

However, anyone who has seen Matt post his Tix selections on the site (like he did brilliantly at Royal Ascot 2025, I might say) will know he has a favoured strategy thus:

 

- All A's: 4x unit stake

- Five A's with one B pick: 3x unit stake

- Four A's with two B picks: 2x unit stake

- Five A's with one C pick: 1x unit stake

 

Using the ‘Matt Method’ we would simply tick the relevant boxes thus:

 

 

Using the example of my ten tickets shared above, this means ticket 1 (all A's) has a 4x amplification, tickets 2 to 4 (any 5 A's with 1 B) are 3x unit stake, tickets 5 to 7 (any 4 A's with 2 B's) are 2x normal stakes, and tickets 8 to 10 (any 5 A's with 1 C) are 1x stakes.

Of course, this stake amplification on certain tickets will increase the overall outlay but we're pressing up our strongest opinions whilst mixing in some 'big dividend' prospects.

In this specific example based on an original 1p per bet/line, and having no increase in stakes (so betting all lines with the same stake of 1p), it would cost £1.38.

Using the 4-3-2-1 Matt method would increase stakes to £3.00. The reasoning behind Matt’s staking plan is logical. The A horses are more likely to win or place than the Bs, who in turn should outperform the Cs. Hence the all-A column should have the highest stake, the 5A 1B column should come next and so on.

This staking method is one option, possibly the best one; obviously there are plenty of others that could be used. Also, at this point, it should be noted there is another way to adjust our stakes. We can adjust individual tickets by clicking on the ‘stake’ box at the bottom of each ticket and changing the default stake.

 

 

For those readers who have yet to use Tix, how to use the software is specific to each individual. Some I’m sure will not adjust stakes, some will. Some will load up with A’s, some may spread their horses more evenly. However, it is important to appreciate that each race meeting is different, and we are likely to play a Placepot at Carlisle with very few runners on the card differently to one at Royal Ascot where field sizes are much bigger and very competitive.

Wider Coverage

Thinking of the bigger meetings like Royal Ascot with their huge and competitive fields, it is likely that there will be an increase in the number of horses that will be used in our placepots. Earlier I looked at an imaginary three horses per race scenario sharing how placing them in different columns affected the total number of lines. Now let's look at the same idea using four horses per race (24 horses in total). Again, I have assumed that we have split the horses into the same columns for each race. Obviously placing four horses in the exact same columns for each of the six races is something that in practice we would almost definitely not do, but my reasoning is two-fold. Firstly, it is easy for me to calculate and share the total number of bets for each grouping. And secondly it gives us a decent understanding of the ‘number of total bets’ differences we can get using this flexible software:

 

Combos All 4 on A 3 on A, 1 on B 3 on A, 1 on C 2 on A, 2 on B 2 on A,  2 on C
Total Bets 4096 3402 2187 1408 448
Combos 2 on A, 1 on B, 1 on C 1 on A,  3 on B 1 on A, 2 on B, 1 on C 1 on A, 1 on B, 2 on C 1 on A, 3 on C
Total Bets 688 154 79 34 19

 

We can see that if selecting all 24 horses in the A column (four in each race) the number of bets/lines is a massive 4096. However, when we spread them more evenly but keep mostly A’s, such as a 2A, 1B and 1C scenario for each race, this cuts the bets/lines down to 688.

As I mentioned earlier for ease of calculations, I have assumed that each race has the same A, B, C combo or grouping. But, of course, Tix players will play each race according to its make-up. Considerations will be affected by the number of runners, the individual strengths of the runners, the relative prices of those runners, etc. For example, a three-runner race with a 1/12 favourite could see us choose that favourite on A as a stand-alone banker. A three-runner race where all three horses are priced between 13/8 and 2/1 may mean we choose all three in the A column. Only one of them will count in a final Placepot dividend while the other two will be losers and all lines involving those two will ‘die’.

Example Tix Play: Royal Ascot

I now want to share my Tuesday Placepot at Royal Ascot this year and how I played it using Tix. In terms of staking, I didn’t use Matt’s 4-3-2-1 method, I simply kept to the same 1p stakes per ticket.

Leg 1 - Queen Anne Stakes:

This was the race I previewed for Geegeez on the Tuesday and happily my two selections came first and second. The winner, Docklands, returned 14/1 (backed in from 25/1) so that was a good start to the week on an individual punting front. The runner up Rosallion was favourite and pre-race I was tempted to leave him as the stand-alone ‘A’ selection in my Placepot; but the race did have a very competitive look about it. So I played safe taking five selections across two columns. I also split Rosallion and Docklands up putting Docklands on C – silly me as that turned out.

Leg 1 selections

A – numbers 4 and 10

C  - numbers 3, 5 and 6

Horses that won/placed: one A, and one C

 

Leg 2 - Coventry Stakes:

These 2yo races with loads of runners and little form are the ones I fear most in Placepots with only three places available (and so it proved here). I went big trying to cover as many bases as possible with four A’s and four C’s:

A – numbers 1, 2, 13 and 20

C  - numbers 8, 9, 11 and 17

Horses that won/placed: one C

This was frustrating from the point of view that two of my A selections finished fourth and fifth. On the flip side, I was still in the pot with one of my C’s placing, and two of the placers were 66/1 and 80/1 meaning very few tickets had those runners on them.

Having played just A’s and C’s I was now needing at least one A horse to win or place in the final four races.

 

Leg 3 - King Charles III Stakes:

This was another horrible race with 23 runners and only three places up for grabs. My only strong opinion on the race was that American Affair was overpriced and I was happy for that to be one of my A’s. I went four A’s and two C’s. American Affair won.

A – numbers 1, 7, 14 and 16

C  - numbers 3 and 12

Horses that won/placed: two A’s

 

Leg 4 - St James's Palace Stakes:

Although there were only two places available in this seven-runner race, there were four rags and an odds-on fav in Field Of Gold. I had him and Henri Matisse as my A’s. No need for any ‘C’ cover.

A – numbers 1, 3

Horses that won/placed: two A’s

 

Leg 5 - Ascot Stakes:

There were two at a price I liked here in Nurburgring and Ascending. I decided to split them with Nurburgring on A and Ascending on C. I put one of the well fancied Mullins pair on A and what I hoped was another live outsider on C.

A – numbers 13 and 20

C  - numbers 3 and 9

Horses that won/placed: one A, and one C

Ascending beat Nurburgring for a £665 exacta (and no I didn’t have it!). At least I had one A selection that counted so was still in the Placepot game with one to play.

 

Leg 6 - Wolferton Stakes:

With no eventual non-runners this 16-runner Listed race had only three horses to count in the Placepot. Before the race I was very keen on Sons And Lovers thinking this must finish in the frame. I decided two have two A’s and one C.

A – numbers 9 and 14

C  - number 15

Horses that won/placed: one A

Sons And Lovers faded into fifth annoyingly, but fortunately my other A got the job done.

Here's how these selections would have looked in the Tix columns.

 

Leg Column A Column B Column C
1 4, 10 3, 5, 6
2 1, 2, 13, 20 8, 9, 11, 17
3 1, 7, 14, 16 3, 12
4 1, 3
5 13, 20 3, 9
6 9, 14 15

 

 

The numbers in bold are the horses that won or placed, but two of them ended up being redundant (number 5 in leg 1 and number 9 in leg 5). The rest, in red, counted on one of the '5 on A, 1 on C' lines and, because I had two win/placed horses in two of the races, I ended up with four winning lines (1 x 1 x 2 x 2 x 1 x 1).

The Placepot to a £1 stake paid £2767.40 meaning each of my four 1p lines netted £27.67, so the overall return on that winning ticket was £110.68 (£27.67 x 4 winning lines). Taking my stake into account and the 5% bonus the Tote pays on winning Tix tickets (yet another reason for using Tix!), I ended up with a profit on the bet of just over £102.

What if?

One two-word phrase we are all too familiar with is ‘what if?’ - so, just for fun, I am going to play that game now. What if I had put six of my original selections in different columns? More specifically, what if my three ‘placers’ on C had been put on A instead; and three of my ‘losers’ from A had been put on C instead?

To achieve this scenario, I could have swapped horses 4 and 5 over in race one, horses 1 and 9 in race two, and horses 20 and 3 in race five. If I had instead done that, I would have had two places in legs 1, 3, 4 and 5, and one place each in legs 2 and 6. That would have given me 16 winning lines quadrupling the return to over £400. Considering all my selections were in A and C this scenario could have happened. Likewise, if a few of my winning A’s ended up as C's I would have won diddly!

Sticking with the ‘what if?’ line, what if my original ticket had been staked differently using Matt’s 4,3,2,1 method? Well, due to only having one successful 5A 1C combo the same payout of £110.68 would have occurred on that ticket (same 1p stake), but the cost of the overall bet would have increased by £7.68 meaning my overall profit would be slightly down at just over £94. (I appreciate that an extra £7.68 stake would have impacted the real-life pot, but it is such a small amount if I had played the bet this way instead my profit would have been virtually the same, give or take a penny or two).

I also looked at what would have happened if I had put all my C selections as B’s instead, sticking to my original 1p per line staking. This would have added an extra £20 or so to the overall stake but I would have had 12 winning lines so my return would have been around the £300 mark (allowing again for any marginal change in the actual Placepot payout due to the extra £20 of staked funds).

Summary

In this article I feel I have only scratched the surface when it comes to the potential and scope of the Tix software. In the first half of the article, I gave a general overview of how Tix works coupled with the flexibility it has in terms of limiting/varying the number of lines using certain configurations. In the second half I have delved into one of my recent Placepot plays looking at what happened, and what could have happened if I had made some slight alterations via Tix to the make-up of my Placepot.

Before writing this, I was a regular user of Tix. Having spent time researching and writing about it, my appreciation and confidence in Tix has improved even more. I am expecting Tix to help me profit further when tackling Placepots in the future. I might even be tempted into a few Jackpots too...

- DR

Evaluating Jockeys by Percentage of Rivals Beaten, Part 2

/in , /by

This is the second half of an analysis of jockey performance using the Percentage of Rivals Beaten metric, following this one I wrote back at the beginning of June, writes Dave Renham. This time, I will put the same 35 jockeys – those that have had the greatest number of rides on average per year over the past four years - under the microscope.

Introduction

The data has been taken from UK flat racing (turf + AW) from 2021 to 2024. I have also limited findings to horses the jockeys rode sent off at an Industry Starting Price of 20/1 or less in order to try and eliminate most of the horses that had little or no chance. Also, very big priced winners skew the data.

In the first article I primarily examined the data by using and comparing numbers based on Percentage of Rivals Beaten (PRB). PRB is a calculation based on a horse's finishing position in relation to field size. It makes key distinctions between a horse finishing, say, fourth in seven-horse race (PRB 50%, three rivals beaten, beaten by three rivals) and finishing fourth in a sixteen-horse race (PRB 80%, twelve rivals beaten, beaten by three rivals). We express the PRB as a number between 0 and 1. So, in the examples above, 50% is 0.5 and 80% is 0.8.

For this piece I will be primarily using PRB once more, and I will also be looking at strike rates, profit/loss, returns and A/E indices where appropriate. Using other metrics in conjunction with the PRBs should help to give us a clearer overall picture.

Before I start, I noted last time that certain jockey to jockey comparisons were difficult to evaluate from a PRB perspective due to what I will call ‘jockey price bias’. Essentially, some jockeys have more shorter priced rides than others, and thus conversely, other jockeys have more bigger priced rides. This could potentially skew the PRB, so it is something I am aware of and will address in what follows.

My starting point today is going to field size.

Number of race runners

It should be noted that in races of 2 to 6 runners the average PRB figure for all jockeys riding horses priced 20/1 or shorter stands at 0.55; for 7 to 9 runners, it is 0.57; for 10 to 12 it is 0.59 and for 13+ runner races it is 0.60. Knowing these figures is important to help evaluate each jockeys’ performance within each 'number of runners' grouping. However, based on my findings last time connected with jockey price bias, I also need to consider the average PRB for each jockey to provide better context. Using these two factors in tandem I have used a mathematical formula to establish what are positive PRBs and which are negative for each individual. As in the first article, positives will be highlighted in green, negatives in red:

 

 

Let’s look at the very small field size of 2 to 6 runners first. The PRB positives highlight Ghiani, Havlin, Loughnane, Stott and Watson. Let’s see if that translates into an overall profit:

 

 

Four of the five made a blind profit with only Rob Havlin in the red. When digging deeper into Jason Watson’s figures it is impressive to note that he made a profit in three of the four years, and his losing year was only 5p in the £. Also, most of Watson's winners were at the shorter priced end of market; if we restrict to runners priced 8/1 or less his record actually improves further to 63 wins from 233 (SR 27.04%) for a profit of £ 50.78 (ROI +21.79).

Meanwhile, Kevin Stott managed four profitable years in a row which is even more eye-catching. Finally for this group of jockeys, geegeez-sponsored rider Marco Ghiani proved himself to be an exceptional judge of pace in these smaller field contests winning 19 of 46 (SR 41.3%) on horses that took the lead early.

Time to look at the jockeys that had a negative PRB. Here are their figures:

 

 

All six made a loss although Oisin Murphy and Sean Levey's deficits were modest. Murphy and Danny Muscutt struggled when the runners were bigger prices: restricting to horses priced 10/1 to 20/1 saw Murphy win just once from 40 for losses of 68p in the £, and Muscutt was beaten on all 40 horses he rode in that price range.

There are four other jockeys I would like to highlight when racing in field sizes of 2 to 6 runners and they are Joe Fanning, Paul Mulrennan, Saffie Osborne and David Probert. The ‘graph’ below shows they all had very similar win strike rates (red numbers) and made decent returns (BSP ROI%, black numbers) too:

 

 

It should be noted that both Saffie Osborne and Paul Mulrennan were very good from the front in small fields. Both won 35%  of the time in such small field races when taking the early lead. It seems likely, then, that both are good judges of pace.

I am not going to go into any depth regarding the 7 to 9 and 10 to 12 runner stats. However, I will take a quick look at the bigger field contests of 13 or more runners in terms of the jockeys who achieved a positive PRB. In bigger fields, being poorly positioned and/or avoiding trouble in running becomes far more relevant. I am guessing that some jockeys are simply better than others at avoiding trouble in running or being poorly positioned.

In terms of those who had positive PRBs the table below shows the splits:

 

 

Despite the positive PRBs the results of Callum Shepherd, and particularly Kieran O’Neill, were not good for punters. We cannot really expect all eight to have returned a profit, but the losses for those two were steeper than I expected. On the flip side four made a blind profit, of which three (Jason Hart, Billy Loughnane and Rossa Ryan) produced a very significant profit. The other two made a small loss.

These figures do highlight that PRBs on their own, for this type of research at least, can be flawed. This is the same for any metric – for example a high strike rate does not guarantee profits, a good return on investment can be skewed due to a single big priced winner, and so on. That's why reviewing different metrics where possible is the ideal.

Going back to the table, Rossa Ryan’s figures are outstanding across the board. His returns (ROI%) to BSP by year are shown below:

 

 

I believe that one of the reasons for his success in bigger fields has been his ability to win on hold up horses. The win percentage for all jockeys combined in 13+ runner events when they held their mounts up stood at just 7.5% in the study period; Ryan’s was nearly double that on 14.2%. Not surprisingly, his strike rate on hold ups was the highest of all the jockeys. There is definitely something in this because Jason Hart, who also made significant profits in 13+ runner events recorded a strike of 13.6% on hold ups which is the second best of all the jockeys. Being able to manoeuvre your mount successfully through traffic in bigger fields will naturally lead to more wins overall.

Race Class

I want to look at class of race next with the starting focus on the better quality Class 1 and 2 races. I have split their results by price in order to help eliminate any bias. Combining the data for both classes, as some jockeys have limited Class 1 data within certain price bands, helps to get more meaningful datasets. I have chosen four ISP price bands – 7/2 or shorter, 4/1 to 7/1, 15/2 to 10/1, and 11/1 or bigger.

The average figures for all jockeys in the list are shown in blue at the bottom of each column and, because we are dealing with price bands, we have a more level playing field to compare one jockey’s PRB with another. Therefore, I have highlighted any PRB that is at least 3% above the average or at least 3% below the average. The 3% ‘above group’ (positive) are highlighted in green, the 3% ‘below group’ (negative) in red. Any PRB with an asterisk (*) means the dataset was limited so we should probably ignore that number. Here are the findings:

 

 

Connor Beasley and Danny Tudhope stand out with three greens and no reds. Those two jockeys seem to have excelled in the better class races, at least according to the PRBs. If we look at their profit and loss figures, we see that Beasley was +£90.43 (ROI +34.8%); while Tudhope was +£60.11 (ROI +9.6%).  At the other end of the scale David Allen and Sam James have three reds and no greens. Overall, they lost 17p and 12p in the £ respectively.

What this PRB research is telling us is that some jockeys are almost certainly better than their win rates suggest, they just don’t ride enough horses with good chances. Take Saffie Osborne as an example in Class 1 and 2 races when riding bigger priced runners. Her PRB for the 11/1 to 20/1 price band was an excellent 0.52 qualifying for a ‘green’. However, when you look at her actual overall record with these runners she has won just once in 75 attempts for huge losses if you were backing them all to win. However, if you had backed her horses to place on Betfair she would have made a profit! Her rides within this subset have been outperforming their odds more often than not.

Let's now look  at the lower end of the class scale, namely Class 5 and 6 races. I'm using the same price splits and the same colour coding:

 

 

There are fewer reds and greens here in total compared to the higher grade of race. James Doyle, David Egan and Rob Havlin have hit two greens, while Joe Fanning's performance looks more modest with three reds.

Courses by Jockey

Finally, in this piece, although there are still plenty of stats to share, I am looking at a selection of the 35 jockeys and comparing their PRB figures at different courses. This should be a very effective use of PRB data as a comparison tool because the comparison is with the individual jockey themselves. I am only using courses where a jockey had enough rides to be meaningful. I am not sharing the course data of all 35 jockeys due to space, but more importantly due to personal time constraints!

David Allan

A look at Allan’s PRBs – the graph below shows the results:

 

 

The PRBs range from 0.53 at Haydock and Musselburgh to 0.63 at Pontefract and Southwell. Indeed, Southwell is the course where Allen fared best in terms of profitability having had 25 winners from 97 rides (SR 25.8%) for a BSP profit of £98.13 (ROI +101.16). He did not made a blind profit at Pontefract, but this is probably more down to luck as he had numerous seconds (22 second places compared with 12 wins from 104 rides). Some of those seconds were at fair prices such as BSP 10.63, 11.83, 18.5, 21 and 30. Going back to Haydock and Musselburgh where he had his lowest PRBs, both showed significant losses of 52p and 25p in the £ respectively.

Connor Beasley

A look at Connor Beasley now:

 

 

The one course well below the rest in terms of PRBs, Carlisle, has been a poor hunting ground for winners for Beasley, too. He had just had five winners from 68 (SR 7.4%) for losses of £37.33 (ROI -54.9%). The highest PRB came from Southwell, but Beasley made a loss there; although he made an 18% profit if backing place only to Betfair Place SP. Beasley hit a PRB of 0.61 at Beverley, Doncaster and Thirsk, the first two named both producing a blind profit. His record at Doncaster was the best with 16 wins from 82 (SR 19.5%) for a profit of £38.29 (ROI +46.7%).

William Buick

William Buick is the next jockey to share – for his figures I have put them in a table. The five highest have been highlighted in green:

 

 

The five greens (Haydock 0.66, Kempton 0.68, Leicester 0.66, Windsor 0.66, Yarmouth 0.69) have produced the following:

 

 

Four of the five saw a blind profit with only Kempton failing. However, in the better races (Class 1 and 2) at the Sunbury track he made a small profit thanks to 10 wins from 28 (SR 35.7%) for a profit of £4.65 (ROI +16.6%). There are five more positive course angles for Buick to share which I will share in the following table:

 

 

Hollie Doyle

Hollie’s PRBs were perhaps a tad disappointing as I’m a big advocate of hers and of women’s sport generally. Having said that, her overall record speaks for itself and her win percentages across different price bands match up well with other leading jockeys. For her course PRB data I am sharing all the qualifying courses combined with other key metrics. The courses are ordered by PRB highest to lowest:

 

 

What this table illustrates, other than Doyle’s individual course PRBs, is that four of the five courses with the highest PRBs produced a blind profit – Bath, Chepstow, Brighton and Kempton. Not only that, the further you go down the list the redder the BSP PL and ROI columns become. The correlation between PRBs and profit/loss and return on investment is more positive than negative.

There are a few extras to share as far as Hollie is concerned - at Bath her record in handicaps has been excellent with 14 wins from 61 (SR 23%) for a profit of £21.58 (ROI +35.4%). Sticking with Bath her 15 winners came from 14 different trainers. Not only that, but her boss Archie Watson is not one of them! At Chepstow she is 8 from 23 for Watson producing a return of 91p in the £, and on favourites at the same track she won 13 of 25 (SR 52%) for a profit of £12.50 (ROI +36.7%).

Joe Fanning

Joe Fanning is still going strong at 54 years old. The PRBs for the courses he rides the most are shown below:

 

 

Let’s look at more metrics at the four courses with PRB figures of over 0.60:

 

 

As we can see there is excellent correlation between the PRB figures and profit/return columns with all four in profit. Three of the four had very high A/E indices which is equally positive.

In terms of negatives the two courses with the lowest PRBs Hamilton (0.49) and Wolves (0.50) produced the following overall results:

 

 

There have been significant losses at both courses, with Hamilton’s win rate particularly poor also.

Oisin Murphy

Oisin Murphy has been Champion jockey four times in the last six years, and he is undoubtably one of the top riders around. There are 18 courses where he has had enough rides / rivals and the PRBs are as follows (courses with a PRB figure of 0.67 or higher are highlighted in green):

 

 

The 0.71 figure for Newcastle stands out and his overall record there is impressive as can be seen in the table below. The table shows the results for the six courses with his highest PRBs:

 

 

Four of the six secured a profit to BSP, with two (Kempton and Yarmouth) showing very small losses. It is interesting that four of the six courses were all-weather tracks. It is also worth noting that when riding for Andrew Balding at Newcastle Murphy had a 50% strike rate (9 wins from 18) for a profit of £30.50 (ROI +169.5%). For Hugo Palmer he rode six times at Salisbury, winning five, while at Wolves the pair were five from 10. Admittedly these are small samples but eye-catching, nonetheless.

Saffie Osborne

Saffie Osborne has had some solid looking PRBs across both articles and it will be interesting to drill down into her course PRBs. The graph below shows the different course figures:

 

 

The four courses with the highest PRBs were Southwell (0.66), Newmarket (0.65), Brighton (0.64) and Newcastle (0.62). The table shows the results for these four courses:

 

 

Osborne recorded excellent profits at all four, with very high A/E indices showing positive correlation with the PRBs. She has ridden those four courses very well in the past four years.

The three courses with the lowest PRBs - Doncaster (0.50), Bath (0.51) and Chelmsford (0.52) - saw returns correlate positively too as each showed significant losses. Losses stood at 30p, 49p and 26p in the £ respectively.

Rossa Ryan

Ryan is the last jockey I am looking at here and his course PRB figures are shown in the table below:

 

 

The four highest PRBs are highlighted in green, Chelmsford (0.65), Newbury (0.64), Wolverhampton (0.64) and Lingfield (0.63). Here were his overall results at these four tracks.

 

 

All four again were in profit suggesting positive correlation once more between the PRBs and other key metrics. For the record most of his rides at Lingfield came on the all-weather (AW) and his AW ROI% at the track stood at 11%.

The lowest PRB occurred when racing at Sandown (0.51) – his overall record there has been relatively poor, as one might suspect. He had 11 winners from 89 (SR 12.4%) for a loss of £30.71 (ROI 34.5%).

-

Phew! That was a lot of research and a lot of stats.

Ultimately, as punters we 'live or die' by our final profit/loss figure. Having a high PRB does not necessarily equal profit, but this article has shown that higher PRBs tend to outperform lower ones on the ledger front. As I have always said, the more metrics we can use the better. PRBs are definitely a metric we should use where possible in combination with others: they add a lot more depth, especially in smaller sample sizes.

- DR

Royal Ascot 2025: Analysing The Group 1 Races

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There are three festivals a year I get really excited about, writes Dave Renham. The Cheltenham Festival and Glorious Goodwood are two; and the third, which is soon to be upon us, is of course Royal Ascot. Around this time last year I looked in detail at the big 1-mile handicaps at the meeting - you can catch up with that piece here. This year I am concentrating on the Group 1 races.

Introduction

There are eight Group 1s run at the Royal meeting and they are as follows:

 

 

As would be expected, there is a good mix of different race distances and conditions although there is only one Group 1 race at a distance beyond 1m 2f, the Gold Cup.

In this article I am looking back on the last ten years of these Group 1 contests, trying to find any snippets that may help us when tackling the races this year. Profit and losses have been calculated to Betfair Starting Price (BSP) less 2% commission.

Royal Ascot Group 1s by Market Rank

Let me start by examining the market. I have ranked the runners based on BSP, which is the most accurate way of doing it. Further, it eliminates almost all of the ‘joint’ market positions.

 

 

These races have definitely been market-friendly with the top three betting positions each producing a blind profit. Those fourth in the Betfair betting lists have performed poorly but due to the modest sample we can perhaps assume this is an anomaly. Regardless, it seems that the top three in the betting are the ones to concentrate on.

Group 1 Favourites at Royal Ascot

Narrowing in on favourites, below are the Percentage of Rivals Beaten (PRB) figures for each individual race to help give a better overview of favourite performance in specific races. For the record the average PRB figures for favourites across the eight races combined stands at 0.76.

 

 

There has been quite a variance with Gold Cup favourites performing best, and by some margin. Their actual performance in the Gold Cup has been as follows:

 

 

During the ten year study period the Gold Cup favourite secured five wins, two seconds, a third and two fourths, so no BSP jolly has completely bombed out.

Royal Ascot Group 1s: Top 3 Market Ranks

If we undertake the same type of PRB analysis across the top three in the betting, the graph generally becomes more even:

 

 

The St James’s Palace has the highest figure now with the Gold Cup a close second. Nine of the ten winners of the St James’s Palace came from the top three in the betting (four favourites, three second favs, two third favs).

Conversely, of all the races the Commonwealth Cup has seen fancied runners struggle the most. Favourites have won three of the last ten Commonwealth Cup renewals, but there were no wins for second favourites (two wins for third favs). Quite a few horses that were in the top three of the betting have bombed out with 10 of the 30 failing to finish in the top ten, three of them being favourites.

It should be noted that the four winning Commonwealth Cup favourites in the past decade more than paid for the other six losing jollies, returning a profit of 2.55 units at BSP.

Royal Ascot Group 1s by Last Time Out (LTO) Position

The second area I want to look at is recent performance and specifically LTO finishing position. Here is a breakdown of performance by last day finishing position (I have grouped all horses together that finished fifth or worse LTO):

 

 

The BSP profit for those that finished third LTO has been totalled skewed by the 140.0 BSP winner Khaadem. LTO winners do look the group to concentrate on with over half of the 80 winners having also won on their most recent start. If we combine LTO winners with a position in the top three in the betting, then we see some excellent results: 36 winners from 115 runners (SR 31.13%) for a profit of £30.50 (ROI +26.5%); A/E 1.14.

It is also worth keeping an eye out for LTO winners that won by at least a length in the race prior to Ascot. These runners have scored 18.9% of the time (30 wins from 159) for a profit of £29.13 (ROI +18.3%).

 

Royal Ascot Group 1s by Last Time Out (LTO) Race Class

Onto looking at the class of race LTO. Here are the splits:

 

 

As we would expect horses that ran in Group 1 company last time have won most often. Those that raced in Group 3 or Listed Class LTO have been profitable, but both have been skewed by very big priced winners going in. Still, Royal Ascot is a meeting where horses fairly consistently win at massive odds.

LTO winners that contested a Group 1 race have actually offered poor value despite a strike rate of close to 30%. The 51 qualifiers lost over 28p in the £ if backing them to repeat the Group 1 win at Royal Ascot.

Royal Ascot Group 1s by Days Since Last Run (DSLR)

It is time to see whether the timing of the last run before Royal Ascot makes a difference. It should be noted this data does not include French, American or Australian runners as I do not have facility to check those. However, it still applies to over 90% of Ascot runners. Here are my findings:

 

 

As the table shows, I have included 50 to 240 days as a single group simply because there are very few runners within that grouping, and their performance has been poor. I wanted to help highlight the difference between that group and the group absent 241+ days (or eight months-plus).

The biggest cohort had a run between 22 and 35 days prior to Ascot and their results have been positive given the overall context. To give a broader overview let me share the PRB figures for each ‘days off track’ grouping:

 

 

These figures correlate with the win strike rates. The figures for 22 to 35 days and 241 days+ are clearly best. Finally in this section, below is the ten-year performance in Royal Ascot Group 1s of horses from the top three in the betting by days since last run:

 

 

Again, this points to the same two groups (22 to 35 days; 241+ days) as the areas in which to focus from a positive perspective. They would have offered punters very good value over the past ten years.

Royal Ascot Group 1 Trainers

The final area I will consider is that of trainers although it should be noted that data is limited. There are a handful of trainers who have saddled at least 20 runners in Royal Ascot Group 1s in the last ten years, and they are shown in the following table:

 

 

It is important not to take these figures (especially big profit lines) too literally due to the sample sizes. It is probably more prudent to look at the PRB values to give a better general indication of how each trainer's horses have run:

 

 

William Haggas, despite having just one winner from 24, has an excellent PRB figure so it looks like he has been somewhat unlucky in recent years. He has endured five second places,  as well as four thirds and five fourths. Haggas looks a trainer that may offer some placepot/ each way value at the very least next week.

By contrast, Roger Varian’s runners have really struggled although a good proportion of his charges have been bigger prices. Indeed, Charyn, in last year's Queen Anne Stakes is Varian's sole Royal Ascot G1 winner to date. There are sure to be more in future but his seem a little over-bet.

Other trainer titbits to share include the fact that Aidan O’Brien's 13 Group 1 winners in the past decade have all been ridden by Ryan Moore (from 61 rides). All other jockeys riding for O'Brien are a combined 0 from 43 since 2015, although again most of these runners were outsiders. Sticking with O’Brien it seems best to concentrate on those starting favourite or second favourite. They have combined to produce 12 of his 13 winners (from 34 qualifiers) returning a small 2p in the £. Finally, albeit from a very small sample, the Gosden stable has had four winners and four placed runners from just 13 runners aged four.

Summary

The Group 1 races at Royal Ascot are the races that owners, trainers and jockeys covet the most, although any win at the Royal meeting is huge.

In terms of the Group 1s, the most fancied runners - those in the top three in the betting - have fared much the best. Don’t be put off by horses having their first run in more than eight months (241+ days) and we might also consider a break of 22 to 35 days (three to five weeks) as more of a positive than a negative.

A last day win is preferable to other finishing positions and a last time out win coupled with a top three position in the betting market has been a very strong positive. From the training ranks, William Haggas appears to have been quite unlucky in the past decade and certainly I’ll be popping a few of his runners in my placepots at the very least. Aidan O’ Brien runners are worth noting if starting in the top two of the betting and particularly when ridden by Ryan Moore.

Wishing you the best of luck with your Royal Ascot Group 1 wagers.

- DR

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