A PageRank Model for Player Performance Assessment in Basketball, Soccer and Hockey (1704.00583v1)

Abstract: In the sports of soccer, hockey and basketball the most commonly used statistics for player performance assessment are divided into two categories: offensive statistics and defensive statistics. However, qualitative assessments of playmaking (for example making "smart" passes) are difficult to quantify. It would be advantageous to have available a single statistic that can emphasize the flow of a game, rewarding those players who initiate and contribute to successful plays more. In this paper we will examine a model based on Google's PageRank. Other papers have explored ranking teams, coaches, and captains but here we construct ratings and rankings for individual members on both teams that emphasizes initiating and partaking in successful plays and forcing defensive turnovers. For a soccer/hockey/basketball game, our model assigns a node for each of the n players who play in the game and a "goal node". Arcs between player nodes indicate sport-specific situations (including passes, turnovers, scoring, fouls, out-of-bounds, play-stoppages, turnovers, missed shots, defensive plays etc.), tailored for each sport. As well, some additional arcs are added in to ensure that the associated matrix is primitive and hence there is a unique PageRank vector. The PageRank vector of the associated matrix is used to rank the players of the game. To illustrate the model, data was taken from nine NBA games played between 2014-2016. Many of the top-ranked players (in the model) in a given game had some of the most impressive traditional stat-lines. However, from the model there were surprises where some players who had impressive stat-lines had lower ranks, and others who had less impressive stat-lines had higher ranks. Overall, the model's ranking and ratings reflect more the flow of the game compared to traditional sports statistics.