Let me be straight with you, I’ve been building a new strategy builder tool which also allows you to do research as well, and it’s because I wanted to play with it that I am writing this article.
However, it’s filled with some very useful information so make sure you keep a copy of it close by.
I’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 it’s not long now until we’re going to be heading into the National Hunt season.
I’ve chosen to look at the following factors:
Days Since Last Won
We’ll start with Beaten Favourite because there is only one option. Either a horse was a beaten favourite or wasn’t.
The first thing we see is that just selecting beaten favourites in these races turns a 4.44% profit, or 0.04p for every £1.00 bet.
I wasn’t expecting that!
For this analysis we’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.
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 we’ll move onto Days Since Last Won…
I’m going to start looking at the IV’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 IV’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 on this because they win 12% more often than the odds suggest.
But it’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 it’s the course winners that should have more weight in your analysis.
Looking at the DSLW we’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, it’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 isn’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.
If you’ve got any questions on this approach then please leave a comment below.