Bayesian Inference 2018 Ice Hockey World Cup outcomes

I’ve tuned my Bayesian model a bit. Previously, it used the cumulative sum of historical results point spread as its data input, now it uses each individual game spread. Perhaps an example can make this clearer:

Consider two teams, A and B, who have met 5 times in the past, with following results:


The above series gives a cumulative spread of +2, which normalized by number of games becomes 2/5 = 0.4 for team A, indicating that team A is more likely to win. However, that result is highly influence by the 10-0 result of the first game, while not putting enough weight on the fact that the four last games team A actually lost.

To cope with that, I changed the model, instead of considering the cumulative spread in its calculations, to consider each historical outcome individually, which in the above example would result in team B being regarded as the winner.

Below the new predicted outcomes and corresponding odds.





About swdevperestroika

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

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