Winning ages in jumps

I was inspired to do this post after reading a thread at Raceadvisor forums about ages of winners in races over 3 miles. I thought that this might be of interest so decided to calculate strikerates by age and race distance. Leftmost column is age and distances on top row. Data for these statistics is from all races ran in UK and Ireland in 2012 and 2013.

First, chase in races between 16 and 24 furlongs.


161718192021222324
30811000000
4262191525191000
5161416131116141115
6171414141513121715
713141491413131113
813101313101191112
991110610101098
1091211886867
1188196487711
121113071082138
132000077000
14000000000
150000000020
16000000000

Chases over 25 to 36 furlongs.


2526272829303132333436
300000000000
400000000000
51414000000000
6121819201790001000
715181313461431400
811111087101412400
99111588141490140
1099118126013000
1185123157132510015
1255680000000
131000025000000
14017000000000
1500000000000
1600000000000

And finally Hurdles ran from 16 to 28 furlongs.


16171819202122232425262728
311129100033000000
4910998998761100
5991191011101091312320
6911710910119101311833
789910898118911110
889888878710797
97775777810614130
1052645675568413
11671334856630725
12300601354242500
1300001400000000
140000000000000
150000000000000
160000000000000

 

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.

SmartSigger – Making Your Betting Pay

SmartSigger_logoI have been writing for SmartSigger-magazine since last spring and I have to say that I have really liked it. Writing about something forces one to give more thought into subject at hand and I feel that my best articles are still to come.

SmartSigger is spiritual successor to two previous betting magazines and / or online communities. First Smartsig and after that Smartersig. Almost complete Smartsig archives can be accessed from here and I think they are a great resource, naturally game has changed over the years but I think there are ideas and thoughts that can be utilized even today.

One of the recurring features in Smartsig was KISS, or Keep It Simple Stupid -systems. Collection of simple approaches to betting. So far I have encountered at least few that I would like to check with recent data and see if they still work and if not, if they could be made to work.

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Exacta system test

A while back I was looking through results of my bayesian forecasting system and noticed that my first and second rated selections finish either first or second pretty consistently. About 30% of a time and pretty much regardless of race type or number of runners (unless really high number of runners).

Naturally betting exactas came into mind and I wanted to test if there was any profit to be found from these. Unfortunately I don’t have tote results in handy database so I had to approach testing this manually. For this reason I restricted this to only UK races in classes 2-5 and number of runners in race to 4-12. This way I got roughly four suitable races per day to check results from. For each race I paper played two bets, 1-2 and 2-1 so two points worth of bets per race.

I now have data for the past two months and over 186 races I have had 55 winning exactas for a strike rate of almost 30%. Unfortunately profits weren’t there, loss of 27.4 points. I am not willing to give up yet as class 5 seems to be making profits as well as shorter distances, meaning turf and all weather flat races.

I am going to continue papertrading atleast over the next month to see if there is anything worth backing here.

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