With the evenings now sadly drawing in, many punters will soon begin to think about the upcoming National Hunt season, writes Dave Renham. So Matt and I felt it was the right time to revisit pace bias in National Hunt racing. In the past I have written several articles for Geegeez on the topic of pace and for this piece I am going to take an in depth look at non-handicap hurdle races.
I appreciate many of you reading this will have read some or all of my previous articles, but for new readers it is important to explain what pace in a race means and how we measure it. Pace in this context is connected with the running styles of the horses. When I look at pace bias my main focus is the initial pace in a race and the position horses take up early on.
geegeez.co.uk has an excellent pace analyser tool and the stats I am sharing with you in this article are based on that tool’s pace data. The data on Geegeez are split into four styles and accompanying points – Led (4), Prominent (3), Mid Division (2) and Held Up (1). The numbers in brackets are the pace scores assigned to each section.
For this article I have only looked at races with eight or more runners – this avoids falsely run races which often occur when there are small fields.
The first set of data contains the overall pace stats from all 8+ runner National Hunt non-handicaps in the UK from 1/1/09 to 31/7/21:
It is important to keep in mind that the number of runners in each pace group varies: there are far more runners in the prominent and hold up categories as you can see. 'Leaders' is the smallest group as usually you only get one early leader in this type of race, occasionally two when there is a contested early lead. Hence although raw strike rates have relevance, it is more important to look at Impact Values (IV) and the A/E index (Actual winners/Expected winners).
Leaders clearly have an edge as a whole, with prominent racers the next most successful. Therefore, as a general rule of thumb, in non-handicap hurdle races you want to be focusing on those horses that are the most likely to lead early (or at least race prominently and close to the front end).
When we have looked at draw biases on the flat we became aware that such biases can evolve and change over time. In terms of pace bias, though, I have always hoped (or assumed) that they are less likely to change much, if at all, over time. To check this theory out I decided to split the non-handicap data into two and compare 2009 – 2014 with 2015 onwards. The bar chart below compares the A/E values over these time frames:
Excellent correlation across all four pace categories so, because A/E is a measure of market performance, this gives increased confidence that the value in any pace biases is likely to replicated in the foreseeable future. Comparing the strike rates shows a similar level of consistency across the two time periods:
So we have a good starting point from which to start narrowing down the stats into different data sets to establish whether front running bias is stronger or weaker under more specific conditions. As the data seems consistent across the years I will analyse these areas over the whole time period (2009 to July 31st 2021).
Impact of Run Style by Race Distance, Non-Handicap Hurdles
I always feel distance is the best place to start when drilling down into pace data. A look first at the shorter distances.
2 miles 1 furlong or less
These figures are similar to the overall stats for all distances, so let us review by course. The chart below compares A/E values for all courses (min 50 races) – courses with A/E values of 1.00 or bigger are shown:
Bangor On Dee has the highest front-running A/E value at 1.48 and when we break the overall course stats down, we can see other metrics which point to that extremely strong front running bias:
Not only does the front running edge strengthen, it is clear that hold up horses struggle even more than the norm. For the record, if you had been able to predict the front runner(s) in each race at Bangor you would have made an SP profit to tune of 38 pence in the £. If only it was that easy!
The next chart shows the courses with the lowest A/E values for front runners over this trip:
Doncaster racecourse has the poorest figures for front runners and the overall stats for the course are as follows:
I think what this shows is that the course and distance stats are definitely worth drilling down on. The difference between Bangor and Doncaster at this distance range is very significant.
Before moving distances I would like to share some stats around performance of "the favourite" based on their running style:
Again, this shows clearly the importance of pace and running style. It still bemuses me how certain trainers continue to hold up their runners, when surely it is generally worth pushing them up with or close to the pace.
2 miles 2 furlongs to 2 miles 6 furlongs
It is always difficult to group National Hunt distances ‘perfectly’ when analysing large data sets, but for this article I wanted to split the full gamut of race distances into three parts and this seemed like a sensible middle distance grouping.
Here are the pace data for all courses for all non-handicap hurdle races over the 2 mile 2 to 2 mile 6 trip:
The figures are similar to the shorter distances though possibly the front running bias hass very slightly diminished. In terms of courses, amazingly Bangor on Dee is top again from a front running bias perspective – there is unquestionably a marked advantage to those horses that lead early at Bangor.
I thought for this interim distance group I would investigate some run style trainer data. I wanted to see which trainers had been the most successful when sending their runners out into the early lead in non-handicap hurdle races of 2m 2f to 2m 6f.
To that end, below are two graphs – firstly, trainer performance with front runners in terms of win strike rate; and secondly, looking at their respective A/E values.
As you might expect there are a high proportion of trainers that appears in both charts. Nicky Henderson tops both lists but this does not mean he sends a huge proportion of his runners to the front early; it shows, however, that when he does they fare extremely well. For the record here is Henderson's breakdown by running / pace style over this distance block:
His front runners clearly do best in terms of win strike rate, A/E value and IV. It is interesting though that only 11% - one in nine - of Henderson's horses actually take the early lead. But nearly half of them win!
It does make me wonder if trainers are really aware of pace bias... Below is his 'pace pie chart' in terms of percentage of runners that demonstrate a particular pace or running style.
44% of his runners raced off the pace early which is far too large a number in my opinion.
2 miles 7 furlong or more
The third and final grouping are the longer distance non-handicap hurdle races, from just shy of three miles upwards.
There are far fewer longer races as can be seen, but the same pattern emerges. Front runners perform best with prominent runners next best.
Trainers by Run Style (All distances)
I have already touched upon trainers but thought it might be interesting to create some trainer pace figures. To create the trainer pace figures I have simply added up the Geegeez pace points for a particular trainer and divided it by the number of runners. The higher the average the more prominent the trainer tends to race his charges. I have created trainer pace figures which cover all distances in non-handicap hurdles. Here are the trainers with the highest averages:
Rebecca Curtis tops the list and clearly favours positioning her runners nearer the front than the back. Her 'pace pie chart' below demonstrates this even more clearly:
As you can see 25% of Curtis's runners take the early lead, while another nigh on 50% race prominently and close to the pace. Ms Curtis is a trainer who understands the importance of forward run styles. It should come as no surprise therefore that you would have made a profit backing all of her runners ‘blind’ during this time frame. For the record, 53 of Curtis's runners were held up, and only 4 won (SR 7.55%). Compare this to 23% and 21.83% win strike rates for her early leaders and prominent racers.
Let us now review Alan King’s pace pie chart as a comparison to Curtis.
His pace average stands at 1.99 with a measly 2% of his runners sent into an early lead. Overall losses for King have been significant especially with runners that raced mid division or near the back early.
I do believe that pace in a race is something which must be factored in to your betting. Pace biases vary from race type to race type, distance to distance, course to course, etc. However, if you are prepared to do some digging that other punters are not, you will give yourself a significant edge over the crowd.
This article has hopefully offered a good chunk of information to digest, but in reality I have barely scratched the surface. If you really want to profit from run style/pace then the Geegeez tools are there for you to test your own ideas and crunch pace data to your heart’s content.