Predicting the Scoring Time in Hockey

This paper proposes a Bayesian predictive density estimator of time to goal in a hockey game, using ancillary information such as performance in the past, points, and specialists’ opinions about teams. To be more specific, we model time to r-th goal as a gamma distribution. The proposed Bayesian predictive density estimator using the ancillary information belongs to an interesting new version of a weighted beta prime distribution and it outperforms the other estimators in the literature such as the one that does not incorporate this information as well as the plug-in estimator. The efficiency of our estimator is evaluated using frequentist risk along with measuring the prediction error from the old dataset, 2016–2017, to the season 2018–2019 of the National Hockey League.