# Chase Turf Handicaps Analysed

Let me be straight with you, Iβve been building a new strategy builder tool which also allows you to do research as well, and itβs because I wanted to play with it that I am writing this article.

However, itβs filled with some very useful information so make sure you keep a copy of it close by.

Iβve decided to investigate how effective some of the standard factors that can be obtained from any race card are in predicting the result of Chase Turf Handicap races on Soft ground. I want to know if there is anything that can be used from them to predict, or if they are to heavily used by the betting public to make any profits.

Why have I chosen these races?

Because itβs not long now until weβre going to be heading into the National Hunt season.

Iβve chosen to look at the following factors:

Beaten Favourite

Course/Distance Winner

Days Since Last Won

Weβll start with Beaten Favourite because there is only one option. Either a horse was a beaten favourite or wasnβt.

 Runners Winners SR ROI IV 285 55 19.30% 4.44% (1.04) 1.41

The first thing we see is that just selecting beaten favourites in these races turns a 4.44% profit, or 0.04p for every Β£1.00 bet.

I wasnβt expecting that!

For this analysis weβre using 3178 horses in the data sample, of these only 285 were beaten favourites and of those 55 won.

Most importantly the IV (Impact Value) is 1.41 which means these horses win 41% more often than their odds suggest.

I would expect the figures to be lower with a larger sample of data but we can assume that these selections are going to be around break-even overall.

A great start.

Next lets check the Course/Distance winner factor.

 CD Winner Runners Winners SR ROI IV 1 2040 254 12.45% 12.52% (1.13) 0.93 2 296 35 11.82% -5.78% (0.94) 0.88 3 425 44 10.35% -33.02% (0.67) 0.77 4 41 5 12.20% 41.9% (1.42) 0.91

The CD Winner figures mean:

1 = Course winner

2 = Distance winner

3 = Course and distance winner

4 = Course and distance winner in the same race

Horses with a number 4 have a very high ROI, but the sample is very small so this could be an anomaly.

Course and distance winners make a terrible return in these races, most likely because the general betting public put so much weight on this information.

However, the course winner has made a profit but the concern is the IV of 0.93. Although the sample of horses in this analysis has made a profit, they are still winning less than expected. This would indicate that we could expect the profit to reduce over more data.

The IV of 0.93 is the highest however and would indicate that if you were going to use course and distance winner information in your anlaysis,you should apply more weight to course winners than any other sort.

Now weβll move onto Days Since Last Wonβ¦

 DSLW Runners Winners SR ROI IV 1 359 56 15.60% -5.71% (0.94) 1.12 2 312 42 13.46% 0.69% (1.01) 0.96 3 301 35 11.63% -1.84% (0.98) 0.83 4 272 33 12.13% -4.89% (0.95) 0.87 5 197 18 9.14% -3.19% (0.97) 0.65 6 207 21 10.14% -4.71% (0.95) 0.73 7 164 13 7.93% -31.33% (0.69) 0.57 8 142 16 11.27% -23.92% (0.76) 0.81 9 124 18 14.52% -20.02% (0.8) 1.04 10 93 19 20.43% 93.92% (1.94) 1.46 11 75 4 5.33% 132.79% (2.33) 0.38 12 56 3 5.36% -58.27% (0.42) 0.38 13 50 4 8% -13.43% (0.87) 0.57 14 45 5 11.11% -43.64% (0.56) 0.79

Iβm going to start looking at the IVβs in this table as they show something very interesting. Note how horses who won 1 day ago have an IV of 1.12 and the IVβs then steadily decrease until horses who won 9 days ago.

This would indicate that the public think horses coming back after a win one day ago are unlikely to win. However, they put too much weight Β on this because they win 12% more often than the odds suggest.

But itβs the nine to ten day break that seems to be the optimal rest period in these races as they have a combined 1.17 IV value. I would want to see more data here, but this is where I would begin focusing, especially as they make a combined profit.

In summary we have found some very useful information.

We know that Beaten Favourites win far more often than expected and across the sample of data used to write this article have made a good flat bet profit.

Course winners have made a profit in our data sample, but the lower IV is a concern. However, in these race conditions itβs the course winners that should have more weight in your analysis.

Looking at the DSLW weβve discovered that the general public puts far too much weight against a horse who won one day ago as they win 12% more often than expected. However, itβs the nine to ten day timeframe after a winning race that seems to produce the strongest runs.

So what do you do with this knowledge?

Remember that this analysis is just for chase turf handicap races over soft ground. But for these races I would start by marking any horse that meets the above criteria. This isnβt a selection process but rather a strengthening process as it will highlight the runners that statistically win more often than expected in these races.

Then do some form reading to determine whether or not the runner looks strong enough in the race it is in to contend.

### 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.

1. Carl Abbott says:

Are you mixing Impact Value up with Actual/Expected A/E ratio? Impact Value doesn’t relate to the odds. So, for example, if there were an equal number of male and female horses running and males won 55% of races then males would have an IV of 55/50 = 1.1. However if this aspect was overvalued by the public such that the betting market strongly favoured male horses it would be possible to have a A/E ratio of less than 1 for males horses and a value of above 1 for female horses.

1. Hi Carl, thank you for the comment. I actually call A/E ratio, PIV (Pool Impact Value) which is it’s term in the US where I first started using it. Named that way because it came from the impact value calculations. But you’re right I should have been writing PIV instead of IV.

2. Josephine says:

Exciting stuff as ever. Roll on NH season! However after many years’ hard labour with suchlike stats (ever so many possibilities aren’t there?) I am reluctantly reaching the conclusion that once you have identified the eligible contender(s) in such races, with rare exceptions they will come out as being 1 to 4 in the betting forecast order because 1. Bookies, tipsters and some of Joe Public have already factored at least some of the data into their assessments some intuitively, others with “the knowledge” 2. Lots of these “finds” will be running in small fields anyway because of the conditions…..So what do I do these day now I am long in the tooth &… as they say..”trying not to worry about old age because it doesn’t last that long.”? I glance down the RP Newspaper Challenge Naps of the Day Table & working from the top of the list select the named beast with a forecast SP above Evens in a field of 8 runners or fewer – Flat season – 9 runners or fewer NH. in the top half of the list. There are days without bets but you also pick up other useful vibes from the exercise. e.g. tipsters for courses etc.so you can use this list in all sorts of ways . It’s all for free & these guys are employed to get winners…so they do know their stuff….

1. Thank you for the comment and info Josephine. As you say, a lot of the time the contenders are in the top four, but often there is a disparity in the odds between them which is where we can make your profits. An interesting approach you are using there, has it yielded good long-term results for you?

1. Josephine says:

Good enough for me to have filed every day’s selections for the last 12 years – (minus shortish holiday periods spent overseas) and can be made safer by a Tote place bet on the selection additional to the win with a bookie as early as you can get on. This gives some protection from gambles and dark horses, & is cream on top when the Tote can return nicely on fancied horses in races where there are 7 runners or less. If I had the time I could work out the strike rate of course – prob 15/20% – better than Santander’s finest hey? The raw data being provided by guys on the top of their game – & as you know I am a reluctant fan of tipsters as long as i dont have to pay them – makes it pretty reliable. Why haven’t I made money? 1. A very busy lifestyle so I cant bet consistently even though minimal work is involved. 2. Too impoverished and timid to put on substantial money or
to reinvest what i do win. 3. Too indisciplined to avoid wagers on horses I get attached to
or to avoid betting on every race on the card when I get the opportunity to actually go
racing – not very often fortunately. I love it….Just yer average Jo(sephine) Punter/Mug.

3. stan jones says:

can we narrow the winners of 9-10 days by the place the fin in last race just and idea ?

1. You could do this Stan, but if a horse won it’s last race and hasn’t run for 90 days then the form won’t be as relevant.

4. Pete says:

Great stuff Michael – but how do you get the combined IV of 1.17 from 1.04 and 1.46.
Av = 1.25 Product =1.52

1. Well spotted Pete. I worked out the IV again for both groups rather than merging them.

5. I have been keeping statistics on favourites for nine years and I now have a method of selecting winners after many years of profitless methods. From the statistics you can gleam hundreds of facts but not a method of selecting a winner with certainty.

However, the purpose of this e-mail is to mention a system I devised and programmed using data created and maintained by Adrian Massey. As Michael is always trying to get the message across – monitor your results.

I monitored the results from my system and found lots of little pearls of knowledge. For each horse in the race I calculated 95 factors from its past performance (Adrian detailed every run from its first). These 95 factors were scored and and the horses were listed in order of score. The highest rated being first.

Again, Michaels preaching of “monitor the results” came into its own. I found that the second rated horse won more than the first rated.

The system was successful at finding winners and even came up with 100/1 winners. Regrettably Adrian stopped producing the data and my system had to end.

There is a saying in racing circles that a horse dropping in grade from its last race is a certain winner. These statistics obtained from Adrian’s data disprove that.

Horses dropping 4 grades win 7.5%

Horses climbing 1 grade win 7.45%