Spent a few hours courtesy of TotalPerformanceData looking at sectional times for the various courses they cover. I was working on creating average split times for the various split or gate points as they are called for each track by the winners of the races. So in other words what is the average split times for achieved in 2017 for Wolves 7f winners
Final furlong 12.07368421 secs
2f to 1f 11.87368421
3f to 2f 12.33026316
4f to 3f 12.25657895
5f to 4f 11.46710526
6f to 5f 11.83289474
Start to 6f 16.58157895
The beauty of AW racing is that we do not have as much going variation to contend with although this can be eventually factored in with averages for each going type. Class may also have to be factored to get a very fine level of detail on how fast or slow a race was run at different points.
My initial thought was to go down the same line as others and start trying to determine fast/slow run races and then another idea occurred to me. Why not allow Machine learning to take the strain. We are after all interested in which horses to back next time. With a more manual approach we have to determine what type of race a horse has just run in and then figure out which types of performance are best backed next time.
To do this we could feed a Gradient Descent Boosting ML algorithm or some other ML of choice with a file of the following data.
All 7f races at Wolves
Deviation from split 1 average,dev from split 2 average,…….,
dev from final split average,FinPos next time out
It could be possible that with such data machine learning might do a reasonable job of predicting the best split time runs for next time out results. It would not just be a case of best overall time but also the manner in which it was run.
I must confess to not having thought this through fully just yet, it is very much a work/thought in progress and I welcome views.