Scientific Gambling on Ice Hockey Worlds – identifying potentially exploitable games

One of the most difficult aspects of dealing with lots of data, is to present the information obtained from various computations in a clear and meaningful way. For instance, in order to identify games where there is a potentially exploitable gambling advantage between the odds given by Unibet, and the probabilities obtained from my Bayesian engine, I must compute all predited game outcomes (WIN/DRAW/LOSS) for both Unibet and my system, and then identify potential exploitable differences. The graph below is a new attempt to consolidate all this information into a single graph.
Each game has potentially 4 bars. The 3 leftmost are predictions from my system, where the third is the average prediction of the two statistical models I use. The rightmost bar (where it exists) is Unibet’s odds converted to probabilities (taking into account the markup that all betting shops place on their odds).
So, with this graph, the basic process to identify potentially exploitable games is to compare each of the colored sub-bars from my program, with the corresponding Unibet bar. Those bets where any of my sub-bars are taller than Unibet’s, are the high risk/high reward games that might be exploitable.
[there are two reasons not all games have all four bars: if there’s no previous historical games between the two teams, like for FIN-KOR, or if Unibet have not yet publicized their odds, as for FRA-BLR and DEN-USA]

About swdevperestroika

High tech industry veteran, avid hacker reluctantly transformed to mgmt consultant.
This entry was posted in Bayes, Data Analytics, Data Driven Management, Gambling, HOCKEY-2018, Math, Numpy, Probability, PYMC, Python, Statistics and tagged , , , , , , , , , , , , . Bookmark the permalink.

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