Prediction and evaluation in College Hockey using the Bradley–Terry–Zermelo model
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We describe the application of the Bradley–Terry model to NCAA Divi-sion I Men’s Ice Hockey. A Bayesian construction gives a joint posterior probabilitydistribution for the log-strength parameters, given a set of game results and a choiceof prior distribution. For several suitable choices of prior, it is straightforward to findthe maximum a posteriori point (MAP) and a Hessian matrix, allowing a Gaussianapproximation to be constructed. Posterior predictive probabilities can be esti-mated by 1) setting the log-strengths to their MAP values, 2) using the Gaussianapproximation for analytical or Monte Carlo integration, or 3) applying importancesampling to re-weight the results of a Monte Carlo simulation. We define a methodto evaluate any models which generate predicted probabilities for future outcomes,using the Bayes factor given the actual outcomes, and apply it to NCAA tournamentresults. Finally, we describe an on-line tool which currently estimates probabilitiesof future results using MAP evaluation and describe how it can be refined using theGaussian approximation or importance sampling.
Document typePeer reviewed
Document versionFinal PDF
SourceMathematics for Applications. 2019 vol. 8, č. 2, s. 131-149. ISSN 1805-3629
- 2019/2