And now something different

I mostly concentrate on just horse racing but I have been intrigued by baseball betting right from the beginning of my betting career. I think it is combination of statistical emphasis in the game, one of my first system tests and one of the best betting books I have read.

Anyway, new baseball season is looming in the horizon and they are currently wrapping the spring training before commencing season proper and I wanted to have piece of the action. I am not skilled enough in baseball betting to make my own selections but as a result of my Picks Buffet test I have a hypothesis that I wish to test.

I am of opinion that on any given season some teams are easier to predict than others and this information can be used to determine confidence level for a given selection and be used as basis for staking.

What I am going to do is utilize free picks and build a spreadsheet where I track how well teams are predicted. As a source of tips I am going to use free tips released at Zcode System website. Zcode is website that gives out predictions on multiple sports. It is on the expensive side with monthly subscription running at 198$ so you would definately need sizable bankroll if you were to subscribe. I am not a member myself and I haven’t seen what is inside but what can be found from sales page there is quite a lot of content included. For my purposes I am interested in the free picks they publish every day and even though they don’t publicly predict all matches I get 4-6 selections every day which works for me at this point.

I have tracked their selections almost from the beginning of the spring training and before todays games results for 1pt flat stakes show a profit of 8.80 points, strike rate of 58.02% and ROT of 10.87%. So far there are two teams left with 100% success rate, St. Louis Cardinals and San Francisco Giants, several with over 60% rate and some with less than 50% and two with 0% success rate.

When the season begins I am going to use these as confidence factors to decide stakes per match of 1 to 3 points and there is going to be a cutoff point where game is going to be a no bet. Let’s see how this goes.

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%.

New month – New SmartSigger

By lhourahane profile (Flickr) [CC BY 2.0 (http://creativecommons.org/licenses/by/2.0)], via Wikimedia Commons


It is March and Cheltenham is upon us. Coincindentally this months issue of SmartSigger is titled Cheltenham Special  in preparation for the festival. Main point being article about key trends for each race over the four day festival.

My own article for the month is about beaten favourites and if there is any money to be made with that information. Most interesting finding for me was the big difference between top and bottom success rates for trainers when their beaten favourites run again. For full list you need to subscribe the magazine but below I present top and bottom five trainers based on strikerates.

 

 

 

Top 5

TrainerWinnersRunnersSR%P/LROT%VSPVSP%
J Ferguson257035.71%53.6676.66%11.4133.91%
D Lanigan175034.00%45.1090.20%2.8515.25%
A O'Brien5619528.72%-34.12-17.50%-5.20-4.10%
W Mullins8329428.23%21.847.43%-6.43-3.77%
N Henderson5921127.96%16.167.66%3.113.07%

Bottom 5

TrainerWinnersRunnersSR%P/LROT%VSPVSP%
M Dods8829.76%-42.91-52.33%-8.45-44.05%
H Candy5539.43%-36.09-68.10%-3.76-24.06%
R Harris4666.06%-20.45-30.99%-2.71-37.34%
Mrs R Carr3545.56%-37.74-69.88%-6.29-63.85%
Richard Guest51134.42%-72.53-64.18%-7.47-55.23%