I started thinking about how to measure if trainer is in form or not. As I already determine if a run was a good or not, why not use this for all of trainers horses and not only per individual horse.
So, I calculated two different measures in similar way as suitability score. One with score since the beginning of my dataset (2012) called Tform and one for 20 previous starts of trainers horses, Tform20. Idea was that if Tform20 was higher than all time Tform then trainer would be considered to be in form and if it was lower trainer would be considered to be out of form. Tform rating has range from -100 to +100.
Below is a chart showing strike rate based on Tfrom and for different rating ranges. Included is line for all, only those considered in form and those considered to be out of form.
Interestingly there is pretty steep drop at top end of the scale while for most of the time strike rate increases rather linearilly as Tform rating increases. And as planned, in form trainers have slightly higher strike rate almost throughout the range.
Let’s see if situation changes when we use Tform20 as the base rating.
Interesting. In form trainers don’t differ that much from average strike rate for given rating range and out of form trainers perform really erratically.
One reason could be that I have only two years worth of data here, 2013 and 2014. I need to see how this develops.
In last chart let’s look at how many runs there were in each rating range for both of the ratings.
For one system test I am running I have been collecting early Betfair prices the night before for all of the selections before making the final decision. And I started wondering that how accurate they are. I only have data for little over 800 selections and they are part of currently profitable set of selections so that might color the results but still, I am surprised at the accuracy of the early odds. Chart over different odds ranges is shown below.
Actual number winners over the data is 179 and expected number based on early odds is 183 while expected number of winners based on BSP is only 162. Well, that shouldn’t surprise as system wouldn’t be making profit against BSP unless it was beating the accuracy of BSP.
I need to see if I can get hold of more and more complete set of data that wouldn’t be coloured by my selection process to be able to do more throughout analysis.
This subject actually touches on very first article I wrote for SmartSigger about accuracy of Racing Post forecast and if it would be possible to make profits when betting against the public.
Finally managed to do some analysis on effect of Suitability Score to strikerate. Below is chart where data from 1st Nov 2013 to 31st October 2014 is charted base on different Suitability scores. Scores mobe throughout the range og -100 to 100.
Different scores are:
- Weight: +/- 5% from the current days weight carried
- Class: Same purse class (my own class division based on purse of winner)
- Going: On same going
- Distance: Rounded to full furlongs
- Type: Same type of race, Flat, AW, etc.
- CGDT: Same (Purse)Class, Going, Distance and Type as today
Now that I finally found a way to calculate if a race was good, bad or merely OK. I took all races run between 1.10.2012 – 30.9.2014 and looked at last run each runner had had and determined if it was good bad or ok.
Now I was able to calculate if my method was up to anything, reasoning is that if last run was a good one then that should improve the odds of winning for that horse. And it actully did, from the table below you can see how performance in previous race affected strike rate.
None of the the above made any profits but at this point I am more interested in ways that let me forecast the winner and I am happy with anything that has a better strikerate than choosing randomly. And in races in question betting randomly one would have achieved strikerate of approximately 10%. My good runs last time out perform better than that and as importantly, races deemed bad perform worse.
I didn’t expect to find profits by using only one indicator so I was surprised that Hunter Chases and NH Flats combined made almost 600 points of profit over two years using just this one indicator. And that is over almost thousand races with roughly 450 winners. Not too shabby I’d say.
While this is not about horse racing it would be cool to see something similar from different horse race courses. Something like this could possibly be used for grouping similar tracks together.