Using AI and Statistical Techniques to Correct Play-by-play Substitution Errors
Play-by-play is an important data source for analysis, particularly for basketball leagues that cannot afford the infrastructure for collecting video tracking data; it enables advanced metrics like adjusted plus-minus and lineup analysis like With Or Without You (WOWY). However, this analysis is not possible unless all substitutions are recorded and are correct. In this paper we use six seasons of play-by-play from the Canadian university league to derive a framework for automated cleaning of play-by-play that is littered with substitution logging errors. These errors include missing substitutions, unequal number of players subbing in and out, substitution patterns of a player not alternating between in/out, and more. We define features to build a prediction model for identifying correct/incorrect recorded substitutions and outline a simple heuristic for player activity to use for inferring the players who were not accounted for in the substitutions. We define two performance measures for objectively quantifying the effectiveness of this framework. The play-by-play which results from the algorithm opens up a set of statistics that were not obtainable for the Canadian university league which improves their analytics capabilities; coaches can improve strategy leading to a more competitive product, and media can introduce modern statistics in their coverage to increase engagement from fans.