Having a Ball: evaluating scoring streaks and game excitement using in-match trend estimation

TL;DR

This paper introduces a Gaussian process-based model to analyze scoring trends in sports matches, providing new indices to measure game excitement and trend direction, with application to NBA data.

Contribution

It proposes novel probabilistic indices for in-match trend and excitement measurement, enabling detailed analysis of scoring dynamics and game excitement levels.

Findings

Trend indices effectively interpret individual game dynamics.

Excitement index clusters teams by game excitement.

Method applied successfully to NBA season data.

Abstract

Many popular sports involve matches between two teams or players where each team have the possibility of scoring points throughout the match. While the overall match winner and result is interesting, it conveys little information about the underlying scoring trends throughout the match. Modeling approaches that accommodate a finer granularity of the score difference throughout the match is needed to evaluate in-game strategies, discuss scoring streaks, teams strengths, and other aspects of the game. We propose a latent Gaussian process to model the score difference between two teams and introduce the Trend Direction Index as an easily interpretable probabilistic measure of the current trend in the match as well as a measure of post-game trend evaluation. In addition we propose the Excitement Trend Index - the expected number of monotonicity changes in the running score difference - as a measure of overall game excitement. Our proposed methodology is applied to all 1143 matches from the 2019-2020 National Basketball Association (NBA) season. We show how the trends can be interpreted in individual games and how the excitement score can be used to cluster teams according to how exciting they are to watch.