Black cat in a Ballpark

Random enough headline? I picked up a book called “How to Find a Black Cat in a Coal Cellar” after encouraging review by James Butler of Betfair Pro Trader. And the review didn’t lie, it is a good book and I can wholeheartedly recommend it. And as mentioned by James in his review, while it is written from a viewpoint of analysing tipping services nothing prevents one from using the same ideas when analysing homegrown systems and strategies. I plan to use these tests for my own systems later on but as a practice run I will begin with analysing a betting record I have from last year.

At the beginning of my betting life I ran a tipster proofing blog in Finnish. One of the tests and the longest running one was for a service called Sports Picks Buffet. Service collected tips from several sources and then chose best bets based on ratio between opinions of different tipsters. Service alternated between seasons with MLB and NBA and during my test all tips were for baseball. Buffet was ran under a pseudonym and fake Phd credentials which person behind the system came clean about a short while after my test concluded. Currently the service is run under a presumably correct name and shortened name of The Picks Buffet.

First. lets look at basic info about the betting record. There were total of 336 bets over roughly a three month period and all were baseball moneyline bets. During the test period meagre profit of 6.38 points or return on turnover of 1.90% was achieved. Strikerate during the test was 60.12% and average odds were 1.71. In the chart below you can see how bets unfolded.

As you can see, there was an healthy upwards trend in the results, which was followed by a decline and a new climb. Profits never rose to any extreme heights.

How about statistical significance? Testing this was biggest takeaway from the book I mentioned at the beginning of the post. Anyway, for these bets we can calculate a p value of 34.04% meaning that there is over one third of a chance that results above are purely due to chance alone. Black cat book recommends a conservative limit of 0.01% and when value should be below of that for results to be considered statistically significant I think it is safe to say that these results are not.

If we were to draw a normal distribution with average results above we would get a figure as below. But ofcourse there were many bets above and below of 1.71 that was the average. Betting with odds of 1.71 and fair odds of 1.66 there would still be a 26% chance of negative return.


(Sport) Picks Buffet is priced at pretty expensive 125$ per month and I don’t think these results quite justify that kind of a pricetag. Especially when there is service providing basically a same kind of service for cheaper. I haven’t personally tried this, but based on info on their site it could be used in similar way. So to me Scamdicappers seems to offers the same thing, only for multiple US sports and for 50$ per month.

 

First view on Suitability Score results

Finally managed to do some analysis on effect of Suitability Score to strikerate. Below is chart where data from 1st Nov 2013 to 31st October 2014 is charted base on different Suitability scores. Scores mobe throughout the range og -100 to 100.

Different scores are:

  • Weight: +/- 5% from the current days weight carried
  • Class: Same purse class (my own class division based on purse of winner)
  • Going: On same going
  • Distance: Rounded to full furlongs
  • Type: Same type of race, Flat, AW, etc.
  • CGDT: Same (Purse)Class, Going, Distance and Type as today

Progress in my hunt for a good run

Now that I finally found  a way to calculate if a race was good, bad or merely OK. I took all races run between 1.10.2012 – 30.9.2014 and looked at last run each runner had had and determined if it was good bad or ok.

Now I was able to calculate if my method was up to anything, reasoning is that if last run was a good one then that should improve the odds of winning for that horse. And it actully did, from the table below you can see how performance in previous race affected strike rate.

LTOSR%ROI%
Poor6.33%-13.90%
Ok9.62%-5.44%
Good15.34%-5.69%

None of the the above made any profits but at this point I am more interested in ways that let me forecast the winner and I am happy with anything that has a better strikerate than choosing randomly. And in races in question betting randomly one would have achieved strikerate of approximately 10%. My good runs last time out perform better than that and as importantly, races deemed bad perform worse.

I didn’t expect to find profits by using only one indicator so I was surprised that Hunter Chases and NH Flats combined made almost 600 points of profit over two years using just this one indicator. And that is over almost thousand races with roughly 450 winners. Not too shabby I’d say.

New Smartsigger and Artificial Neural Networks

Latest issue of Smartsigger was released a few days ago. Among other great articles there is mine where I document my first forays into Artificial Neural Networks. As I am relative beginner and wrote that article from that perspective I am constantly learning new things. One outcome of this continuous learning is that after writing that article I have already changed my toolset. Instead of Fann I am now using AI4R which I actually find to suit my workflow a lot better.

Speaking of ANN’s, I am almost done with my second version of network which determines if run a horse was a good, ok or in worst case a poor one. As I am writing this I teaching it flat races and once that is done I am able to calculate results for each last time out for runs ran in 2013 and have a first look at what effect (if any) this has on strike rate and profits. So one step closer to Suitability Score.