Advice

Market Bias in 5f Handicaps

(Last Updated On: July 20, 2010)

Guest post by David Renham from Racing Trends

In this article I am looking to see whether market forces are the same at different course and distances. The focus is 5f turf handicaps (excluding 2yo nurseries) and I have concentrated on races with 7 or more runners. In order to investigate this area as thoroughly as I can, I decided to use two different approaches. Method 1 was the simplest – splitting the betting market into thirds – as I do when I analyse draw bias. Method 2 was to use the number of runners in comparison to the prices on offer – I will explain this method in detail later. I was hoping that the two methods would produce very similar results – let us see.

The data has been taken from a recent 7 year period and let us first look at the “thirds” method (method 1).

Method 1 – “spitting market positions into thirds” – the idea here is fairly straight-forward. Horses with the shortest prices will be classified in the top third of the market, more mid range prices will be classified in the middle third of the market, while bigger prices/outsiders will be classified in the bottom third of the market. One slight downfall of this method is that there is not always an even split, but it should balance out as the table below shows:

Number of runners Top 1/3 of market Middle 1/3 of market Bottom 1/3 of market
10 3 4 3
11 4 3 4
12 4 4 4

Hence, with 10 runners, the middle ‘third’ gets the extra runner, while with 11 runners the top and bottom ‘thirds’ get the extra runner. This idea continues for all other groups of three (eg 13, 14 and 15 runners). Hopefully therefore, we will get a fairly accurate reflection of market bias overall.

For all 5f courses the handicap market bias stats for winning horses are as follows:

Top third of market (%) Middle third of market (%) Bottom third of market (%)
58.8 28.4 12.8

No surprises that the top end of the market have produced the majority of winners – essentially the top ‘third’ of the market have produced the winner 4.6 times more often than the bottom ‘third’ of the market.

Let us now break down these stats by course. There is quite a variance between courses – I have ordered them initially alphabetically:

Number of runners Top third of market (%) Middle third of market (%) Bottom third of market (%)
Ascot 53.8 38.5 7.7
Ayr 51.6 37.5 10.9
Bath 73.5 22.4 4.1
Beverley 76.1 16.3 7.6
Brighton 55.6 30.9 13.6
Carlisle 60.0 25.0 15.0
Catterick 69.9 19.2 11.0
Chepstow 65.0 25.0 10.0
Chester 53.6 37.5 8.9
Doncaster 55.3 26.3 18.4
Epsom 47.8 34.8 17.4
Folkestone 63.3 20.0 16.7
Goodwood 52.8 26.4 20.8
Hamilton 64.3 21.4 14.3
Haydock 53.1 35.9 10.9
Leicester 53.8 38.5 7.7
Lingfield 75.0 18.8 6.3
Musselburgh 66.7 20.7 12.6
Newbury 64.3 32.1 3.6
Newcastle 50.0 34.0 16.0
Newmarket 45.3 37.7 17.0
Nottingham 47.3 34.5 18.2
Pontefract 66.0 27.7 6.4
Redcar 55.0 32.5 12.5
Ripon 51.7 24.1 24.1
Salisbury 31.8 45.5 22.7
Sandown 54.1 35.3 10.6
Thirsk 70.3 14.9 14.9
Warwick 42.9 50.0 7.1
Windsor 66.7 22.8 10.5
Yarmouth 50.0 33.3 16.7
York 34.9 41.9 23.3

Beverley tops the list for the top ‘third’ of the market with 76.1%; Salisbury has the lowest on 31.8% – a huge difference between them. A question that needs to be addressed at this juncture is how valid are these course figures?

In many cases my hypothesis is that they are fairly accurate. The figures for each course cover a fair number of races – Musselburgh for example has had 111 races, Beverley 92. Hence, in most cases we are dealing with decent sample sizes.

Also, as a punter who has tried to specialize in sprint handicaps, many of the courses with low or lowish top ‘third’ percentages are courses I have really struggled at – York, Salisbury and Newmarket are three such examples. The percentages for such courses indicate that results have not been as market biased as one would expect, so in other words these races have been far more open contests – my losses at these courses can vouch for that!!

Method 2 – “price to runner ratio” – this second method uses the number of runners compared with the specific prices of the horses. It is best explained by an example:

Assume we have a 16 runner race; divide the number of runners in half to give us 8. We now use the half runners figure of 8, and the total number of runners figure of 16 to split our runners into our three market groups. Horses priced 8/1 or shorter would then go in the top third of the market, horses priced between 17/2 (first price above 8/1) and 16/1 would go in the middle third of the market, horses priced above 16/1 would go in the bottom third of the market.

The idea here was to take into account that each market is different. Some markets are stronger than others; some races have a couple of short priced horses and several outsiders, while others are more competitive with several runners vying for favouritism at bigger prices. This method has been used before by Eric Bowers when analyzing market biases and anyone who has read his articles will know that he has an excellent racing brain – hence the method should be a good one!

The only problem I could see is that the split of runners in each third was not as even as it was for the simple “thirds” approach. Using the “thirds” approach the splits for each third had been virtually perfect at 33.3%, 33.4% and 33.3%. This price to runner ratio method saw the splits at 23.8% (top), 35.4% (middle) and 41.4% (bottom).

This means that to compare the overall win percentages for each group would not give us comparable figures to our results for the “thirds” method. I then decided to use a formula to take these variances into account. I won’t bore you with the mechanics of it, but essentially it meant that we had an equal split once again in terms of the total runners in each group – ideal to compare the results for both methods.

The overall handicap market bias stats for winning horses at all courses for method two are as follows:

Top third of market (%) Middle third of market (%) Bottom third of market (%)
59.6 28.8 11.6

To recap the method 1 results saw a split of 58.8, 28.4 and 12.8. So this is pleasing to see the figures for both methods virtually mirroring each other. Next stop was to look at the individual course stats to see how well they matched up:

Number of runners Top third of market (%) Middle third of market (%) Bottom third of market (%)
Ascot 61.3 27.5 11.2
Ayr 54.9 29.9 15.3
Bath 64.9 27.7 7.4
Beverley 75.2 18.4 6.4
Brighton 58.6 28.6 12.7
Carlisle 56.6 32.8 10.6
Catterick 69.6 23.4 7.1
Chepstow 70.3 23.2 6.5
Chester 59.5 28.3 12.1
Doncaster 47.1 32.2 20.7
Epsom 53.9 32.6 13.5
Folkestone 68.3 18.8 12.9
Goodwood 52.4 31.0 16.7
Hamilton 56.5 34.1 9.4
Haydock 45.0 43.8 11.2
Leicester 48.8 40.4 10.8
Lingfield 77.3 14.0 8.7
Musselburgh 64.1 26.2 9.7
Newbury 64.8 32.5 2.6
Newcastle 53.8 31.8 14.4
Newmarket 51.2 35.1 13.7
Nottingham 47.7 32.8 19.5
Pontefract 63.9 29.9 6.1
Redcar 59.0 27.3 13.8
Ripon 58.8 21.7 19.5
Salisbury 29.0 46.9 24.1
Sandown 59.7 30.7 9.6
Thirsk 67.6 21.1 11.3
Warwick 44.7 39.6 15.7
Windsor 70.1 22.8 7.2
Yarmouth 55.1 24.4 20.5
York 38.6 43.9 17.5

The figures for some courses had bigger variances than expected (eg. Ascot and Hamilton), but generally the figures matched very well. For example, ordering by percentages for the top third of the market, 8 courses appeared in both top 10’s.

For me, the question now is ‘Do I use these figures in the future when analyzing 5f sprint handicaps?’ The answer is a simple “yes”. I think the information that has been collated is going to prove useful.

Both methods have produced very similar results and I will now think twice about backing an outsider at certain courses such as Beverley, Bath, Lingfield, Newbury and Pontefract. Conversely at the courses such as York, Salisbury and Warwick, I will think twice about backing a horse at the favoured end of the betting.

The beauty of this type of research is that it can be extended to all race types, all distances, etc, etc. My next port of call will be 6f handicaps to see if the course stats for 6f correlate with those for 5. If they do not, then maybe I will have to go back to the drawing board.

Dave Renham is a leading uk horse racing researcher. He has worked in the past for the RacingPost and the Racing and Football Outlook newspaper. His own website at www.racingtrends.co.uk is a great spot for finding out about profitable angles for your UK horse racing betting.

Michael Wilding

Michael started the Race Advisor in 2009 to help bettors become long-term profitable. After writing hundreds of articles I started to build software that contained my personal ratings. The Race Advisor has more factors for UK horse racing than any other site, and we pride ourselves on creating tools and strategies that are unique, and allow you to make a long-term profit without the need for tipsters. You can also check out my personal blog or my personal Instagram account.

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