Handicapping 2.0

I haven’t posted links to other blogs that much but this post at Equinometry.com from the other side of the Atlantic is a good one. Not necessarily ground breaking stuff but acts as a good reminder. Coming from US not everything is readily applicable but enough is in my opinion to warrant a read.

It briefly covers some new aspects in addition to basic form, speed, pace and class and author promises to followup with Wagering 2.0 article as well.

On related note, there were some good thoughts about data in recent article at Geegeez.co.uk.

Racing Dossier walk through

I have been meaning to write a some kind of description or review of Racing Dossier, the tool that I use as source for all of my data. But author of the tool Michael Wilding has beaten me and made a walk through video of how the tool works.

So, if you are not quite clear what this tool is about or if you have earlier seen the previsous Adobe Air version of the tool and I suggest that you take a look at the new browser based iteration of the tool.

July Smartsigger published

June issue of SmartSigger magazine has been published. If you are a subscriber, check your members area and if you are not, then I would advise you to check it out. There is 30 day trial period which includes access to archive of past issues. This should give you an idea of content available if you were to subscribe.

My article this month is about normalising ratings in order to get a view on how race is shaped for each rating. So answering not only which is better but also by how much it is better.

Raiform – version 2.0

After some unsuccesfull festival betting lets look at some ratings.

I have been in a process of converting my neural network workflow from ai4r to FANN, or Fast Artificial Neural Network and one of the larger tasks was to redo my raiform rating. I originally built that earlier this year and wrote a bit about it in March.

Anyways, here is first glance at the results. Data is from period of 2013 and 2014 and it includes same ratio of different codes as did the training and test datasets. This is now unseen data which was not used for teaching purposes nor for testing. All in all there are some 5000 races and almost 50.000 runs included.

In the chart rating goes up till 500 but theoretical limit is 1000, there were some figures in the 600 range but I dropped from the chart all the occurances where sample size was less than 10, that is the reason why strikerate lines end at different positions for different codes. All in all pretty consistently rising strikerates as rating climbs higher. Highest variance is in Hunter chases but there sample sizes are so small that I wouldn’t worry about it at this time.

This rating is now only thing that I look at when it comes to past few races. It is easier to say that Horse A with a Raiform of 400 is more likely winner than Horse B with rating of 250 rather than Horse A with last three 321 is better than Horse B with 332. I made up those form lines, but I believe point is clear.

Raiform – A new rating

I have been adding new stuff to my bayesian system recently as well as adjusting criterias for ratings.

One new piece of information I have added I call Raiform. I need to call these something in my database and Racealyst AI Form or Raiform for short sounds fancy enough 🙂

Anyway, it is neural network derived rating which combines horses three last finish positions adjusted for number of runners and under the same code as todays run, days since last run and horses age together with information on if race is flat or jumps and if it is handicap race or not. Idea for the rating is from set of ratings Smartsig ran years back called AI form. That rating used also horses sex but for some reason I haven’t deemed that information important enough to include in my database so I have to live without it. I don’t know how people at Smartsig arrived at their ratings but I wanted to do something similar as I felt that just looking at the days since last run or days sincle last good run was not giving out relevant information.

Below is a chart which shows how rating would have performed in 2014. Line is strikerate and bars show return on turnover %. As I expected, ROT is all over the place but strikerate holds a nice upwards trend without plummeting at any point.

Interestingly rating range from 125 to 150 has a return on turnover of over 15% for over 16 thousand selections and strikerate of roughly 11%.

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