Lineup Regularized Adjusted Plus-Minus (L-RAPM): Basketball Lineup Ratings with Informed Priors

TL;DR

This paper introduces L-RAPM, a regression model that improves basketball lineup ratings by accounting for opposition and player information, especially effective with sparse data.

Contribution

The paper presents a novel lineup rating method that incorporates informed priors and controls for opposition, addressing data sparsity in sports analytics.

Findings

L-RAPM outperforms baseline models in predictive accuracy.

Improvement is more significant with smaller sample sizes.

Method effectively handles sparse lineup data.

Abstract

Identifying combinations of players (that is, lineups) in basketball - and other sports - that perform well when they play together is one of the most important tasks in sports analytics. One of the main challenges associated with this task is the frequent substitutions that occur during a game, which results in highly sparse data. In particular, a National Basketball Association (NBA) team will use more than 600 lineups during a season, which translates to an average lineup having seen the court in approximately 25-30 possessions. Inevitably, any statistics that one collects for these lineups are going to be noisy, with low predictive value. Yet, there is no existing work (in the public at least) that addresses this problem. In this work, we propose a regression-based approach that controls for the opposition faced by each lineup, while it also utilizes information about the players making up the lineups. Our experiments show that L-RAPM provides improved predictive power than the currently used baseline, and this improvement increases as the sample size for the lineups gets smaller.