Using Market Rank to Assess Trainer Performance

When it comes to horse race betting, the role of the trainer is of pivotal importance to a great many punters, writes Dave Renham.

That may simply be the trainer themselves, with no filters applied: just as some punters have favourite jockeys, many have favourite trainers and, equally, other trainers they tend to ignore. Trainer form at the course, trainer form over the past 14 days, trainer records with 2yos, the trainer / jockey combo are examples of slightly more refined potential ‘trainer weapons’ in a punter’s armoury.

Personally, I feel trainers and trainer stats have their place but for me they are far from the ‘be all and end all’. Having said that, I believe that digging a little deeper into trainer performance can be a useful exercise. Looking for an edge that most punters would be unaware of is always worth investigating!

A Different Approach to Assessing Trainer Form

In this article, then, I am attempting to evaluate trainer performance in a different way from the ‘norm’. The basic idea is to compare the odds rank of each trainer’s runners with their finishing positions. This is intended to offer a much broader perspective of trainer performance, rather than simply focusing on winners, strike rate and/or returns. I am hoping that we may find a few lesser known trainers whose horses tend to outrun their odds – an ‘over performance’ if you like.

The data I have collated covers three full seasons of UK flat racing (2018 to 2020) and I have focused solely on handicaps. I am using handicaps because they are generally a more consistent data set to use where runners have a theoretically more equal chance in the round.

As with any method there are potential flaws or issues that need to be discussed. Principally, the comparison of finishing position with odds position is going to hinder horses that start favourite as they will be unable to ‘over perform’. The best they can do is match their odds position by winning the race. Hence trainers who have had a good number of favourites will be at a disadvantage using this approach. Having said that, there are ways to try and balance the data as I will attempt to demonstrate later.

There are three possible outcomes in terms of position in the market compared to finishing position:

1. Expected result – eg a horse 3^rd in the betting rank finishes 3^rd, a horse 7^th in the betting rank finishes 7^th etc;
2. Positive result – eg a horse 5^th in the betting rank finishes 2^nd, a horse 9^th in the betting rank finishes 5^th etc;
3. Negative result - eg a horse 4^th in the betting rank finishes 6^th, a horse 2^nd in the betting rank finishes 4^th etc.

Trainer Performance

So let us look at trainer performance using these parameters. Firstly, here are the top 25 trainers in terms of positive results using this approach. The table shows the breakdown of terms of total runs, number of positive results, number of negative results and number of expected results. It also breaks these down into percentages – positive percentages, expected and positive percentages combined, and negative percentages:

All trainers have a ‘positive and expected combined’ percentage of at least 60%. Lisa Williamson tops the list with 61.12% of her runners outperforming their position in the odds market (82.54% positive / expected combined). However, her overall win strike rate is around 3% so although her runners tend to run above expectations she is not a trainer that we can easily exploit. Indeed, most of the trainers in this list have relatively modest overall win strike rates, but I would say if you fancy one of their runners, you can at least expect it to run well and more likely than not to run better than its price suggests.

In order to try and find a group of trainers that we may be able to profit from, it makes sense to look at a more focused type of runner nearer the head of the market. To that end, I narrowed the search to horses that were not favourite but were priced from 4/1 to 12/1. The theory is that these runners will go closer to winning if they outperform their odds position. It also eliminates favourites who ultimately can only match their market rank, not exceed it:

Brett Johnson, who is second in the list, has made an SP profit within this price bracket of 18p in the £. His runners have hit the frame an impressive 42% of the time. Indeed, several of the trainers in this table made a profit to SP and they are shown in a bar chart below. It shows their win strike rate% in blue and their percentage profit in orange. They are ordered with the most profitable starting from the left:

For the record, four other trainers would have made a profit betting to Betfair SP – they were Christine Dunnett, Antony Brittain, Grace Harris and Linda Perratt.

Trainer Performance: Comparison Values

Another way to compare finishing position with market position is to calculate what I will call a ‘Comparison’ Value. In order to explain clearly what I mean, let me show you how to calculate this figure by using a simple example.

We start by looking at the difference between the market position and the finishing position. Let us imagine trainer ‘A’ has had 10 runners with the following results:

So ten races and we then add up the difference column. This gives us a total of 12. To get our ‘Comparison’ Value we then divide this total difference by the total number of races. So in this example we have 12 divided by 10. This gives us a ‘Comparison Value’ of 1.20. In other words, on average the runners from this hypothetical trainer have finished 1.2 places higher than their odds rank suggested they should.

Clearly trainers can achieve positive or negative Comparison Values depending on their overall performance. I have calculated these figures for all trainers over this three year period – again I have ignored any favourites for the same reason as discussed earlier in the article.

Below are the trainers who have achieved a figure of 1.00 or greater.

The figures are slightly skewed due to the fact that most of these trainers primarily run less fancied horses. It therefore makes it easier for them to outperform their odds rank over time. Thus it again makes sense to use this ‘comparison’ method closer to the head of the market, by deploying our 4/1 to 12/1 price bracket (and again excluding favourites). This creates a more level playing field.

At this point the figures for all trainers drop markedly and only three have managed a positive Comparison Value – Michael Attwater, Derek Shaw and Mike Smith (R Michael Smith). However, here are the top 30 trainers within this price bracket in terms of highest Comparison Values:

I do feel these trainers are ones to keep on the right side of with runners that are priced around the 4/1 to 12/1 mark in handicaps. Not only would I look to exploit them for occasional straight win bets, I would look closely at each way options (doubles and trebles) as well as placepot options. For any spread bettors out there, an unconventional way of evaluating trainers and their likely performance has definite potential. Maybe one of the ideas mentioned here could provide the genesis of that sought after edge.

Summary

No method, idea, or rating is fool proof. Ideas I have discussed in this article certainly come into that category. However, in order to try and stay ahead of the game, it is worth our while thinking ‘outside the box’. Going against the crowd often pays dividends as you are more likely to obtain value for your selection, if the masses aren’t backing it too.

There are countless ways to analyse data: I’m not saying what I have done here is perfect, but it is a different slant and was interesting and enlightening from my personal perspective. I hope you’ve taken something from it as well.

• DR

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• Handicap Winners and Official Rating Change: A Study
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• Is Recent Trainer Form Important?
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