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.

Trainer angle from the archives

I was looking for something else from my archives when I laid my eyes on some freebie system called The Marathon Chase System. As we are heading to jump season proper I decided to take a look inside as I didn’t recall how this particular one worked.

Well, inside was pretty simple system with a name of a trainer and criteria for distance the race is to be ran over as well as odds range for when to take the bet. System was available from MakeYourBettingPay.co.uk, but I was not able to locate it from there anymore but to be on the safe side I am not going to give out the details of the system.

But I did check on the results in 2014 so far. Meagre 6 point profit over 36 bets so far and strike rate of 17%. Both 2012 and 2013 were profitable for this trainer and I had a look if there were other trainers who had been successfull with same criteria for races and there were. I was able to narrow it down to three trainers who had had sufficient amount of runners since 2012, wins were not only from super high SP’s and had consistent strikerate.

These three trainers have made a profit of over 150 points in last couple of years and up 20 points this year already. Which means that while, this is my first look at creating a trainer based system myself, I am going to elevate this to tracking and see how these perform for me.

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

 

Which is more important, strikerate or ROI?

I was looking through selections calculated by my bayesian system and came up with two approaches to a set of selections. Other is high strikerate low ROI and other is other way around. Naturally in perfect world one would take both, but what would you choose if you had to choose either or from below?

System A

  • 867 selections
  • 246 winners
  • Profit of 53.25 points
  • Strikerate of 28.37%
  • ROI of 6.14%

System B

  • 289 selections
  • 8 winners
  • Profit of 225.83 points
  • Strikerate of 2.77%
  • ROI of 78.14%

What would you choose?

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