Predicting the NHL playoffs with PageRank

Swanson Nathan (), Koban Donald () and Brundage Patrick ()
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Swanson Nathan: US Military Academy, Department of Mathematical Sciences, West Point, NY, USA
Koban Donald: US Military Academy, Department of Mathematical Sciences, West Point, NY, USA
Brundage Patrick: US Military Academy, Department of Mathematical Sciences, West Point, NY, USA

Journal of Quantitative Analysis in Sports, 2017, vol. 13, issue 4, 131-139

Abstract: Applying Google’s PageRank model to sports is a popular concept in contemporary sports ranking. However, there is limited evidence that rankings generated with PageRank models do well at predicting the winners of playoffs series. In this paper, we use a PageRank model to predict the outcomes of the 2008–2016 NHL playoffs. Unlike previous studies that use a uniform personalization vector, we incorporate Corsi statistics into a personalization vector, use a nine-fold cross validation to identify tuning parameters, and evaluate the prediction accuracy of the tuned model. We found our ratings had a 70% accuracy for predicting the outcome of playoff series, outperforming the Colley, Massey, Bradley-Terry, Maher, and Generalized Markov models by 5%. The implication of our results is that fitting parameter values and adding a personalization vector can lead to improved performance when using PageRank models.

Keywords: Corsi statistics; Markov chains; National Hockey League (NHL) (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1515/jqas-2017-0005

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