What is a good run?

Earlier I wrote about my plan to add a new kind of rating called a Suitability Score. And for that I needed to know if run horse had was a good one even if it didn’t finish first.

Initially I used a concept from Peter Mays book, but it was obviusly only for flat races as the system broke down when distances went further and  this lead to chases and hurdles having a lot more good runs compared to flat races.

After staring at lengths lost for flat and jumps for a while I came to conclusion that one-size-fits all solution wont work here and I also started thinking about going, it has to have effect on lengths a horse lost by, or atleast that is the hypothesis at the moment.

I was surprised that there seems to be greater variance between race distances in flat races compared to jump races, as can be seen from two charts below which describe average lengths lost per BSP for different distances in furlongs.

As I have recently dabbled with Artificial Neural Networks I decided to see if they could be of help in determining a good run. Plan is to train an ANN to give out expected lengths lost based on distance, going and bsp. I have now done first version of this with All Weather in mind and just by putting in criteria that run where actual lengths lost was less than half of expected distance behind winner was a good run and run where actual was more than 1.5 times the expected would be considered a poor run I were able to divide runs to roughly 25% good runs, 50% of ok runs and 25% of poor runs.

Next up is to check if this classification has any bearing on how horse performs next time out.

Average lengths lost per BSP range – Jumps

Yesterday I posted average lengths lost in different odds ranges for flat races and now it is time to post the same table for jumps. Should not come as huge surprise that when race distances are longer then so are the losing lengths as well.

>=<Avg lengths lostAvg BSPLengths / BSP
123.451.662.08
236.982.542.75
349.983.532.83
4511.744.482.62
5613.815.472.52
6715.136.462.34
7815.917.462.13
89178.462.01
91017.329.451.83
101219.0910.91.75
121419.6112.871.52
141620.7214.891.39
161821.9516.891.3
182022.7218.911.2
202224.3220.841.17
222424.8222.811.09
242625.6224.821.03
262825.6126.830.95
283025.9228.790.9
303527.0132.250.84
354028.5237.320.76
404529.8342.240.71
455029.5947.340.63
505531.5451.770.61
556031.5956.620.56

Average lengths lost per BSP range – Flat

While researching the criteria for a good race for my suitability score I did some comparison on what are the average lengths lost in specific odds range. Below is a table where this shows. First two columns tell the odd range and the other columns should be self explanatory.

This table is for flat races, I am going to post same data for jumps in a few.

>=<Avg lengths lostAvg BSPLengths / BSP
121.411.670.85
232.932.551.15
343.973.531.12
454.714.491.05
565.455.490.99
675.976.460.92
786.527.450.88
896.758.450.8
9107.289.460.77
10127.5310.890.69
12148.412.880.65
14168.814.890.59
16189.3316.90.55
18209.7118.910.51
202210.2520.840.49
222410.4522.810.46
242610.7824.820.43
262811.1726.840.42
283011.4328.810.4
303511.8232.230.37
354012.7637.330.34
404512.9342.190.31
455013.8747.320.29
505513.9351.860.27
556013.556.650.24

 

Suitability Score

For a while now I have been planning to add a new kind of rating to my bayesian system. Something that I have now named suitability score.

Previously I have calculated strike rate for class, course and distance. Similarly to what is presented in the Instant Expert portion of the race cards at Geegeez. But I have had a feeling that it is a bit harsh and haven’t added  it as one of the factors calculated into my odds.

After reading Peter Mays forecasting book I realized that I wanted to create something to measure if run was successfull regardless if the horse won or not. From May’s book I ripped the concept of distance beaten in pounds (and conversion from lengths lost to pounds).

I looked through the races ran in 2013 and decided on a bit different parameters on what to consider a good run and what constitutes a bad run. I ended up with values where roughly 40% of runs are deemed good, 50% ok and 10% bad.

While figuring that out I was also bouncing out ideas on how to use these as a factor in my system. First and easiest would be to just calculate strikerates but I wasn’t happy with that when I effectively had three possbile outcomes for a race, good, ok and bad. In the end as a first version I decided steal a concept of Net Promoter Score, or NPS.

I will calculate suitability score as follows:

((Good runs / All runs) – (Bad runs / All runs)) * 100

This gives me a score between -100 and 100.

Once I have calculated bunch of these and done some analysis I will post a followup.

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