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Improving Player Efficiency Rating in Basketball through Machine Learning

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  • Raghav Seshadri Polygence

DOI:

https://doi.org/10.58445/rars.1000

Keywords:

Lasso Regression, Random Forest Regression, Neural Networks

Abstract

This paper explores the intersection of advanced statistical methodologies and basketball with a focus on improving the Player Efficiency Rating (PER) metric. This research delves into three distinct AI models: Lasso Regression, Random Forest Regression, and Neural Networks. These models, each with unique capabilities, allow for more accurate PER ratings which helps teams and coaches to make informed decisions about player rotations and substitutions.

References

Josh Gonzales, P. (2020, January 21). Problems with per in the NBA. Medium. https://t.ly/bJ7Hn

Vangelis Sarlis, Christos Tjortjis (2020, May 23). Sports analytics - Evaluation of basketball players and Team Performance. https://t.ly/nuSbE

Hollinger, J. (2003). ”Introducing PER.” https://tinyurl.com/bballRefpage

E. Turban, R. Sharda, D. Delen, Decision Support and Business Intelligence Systems, Vol. 9, ninth ed., Pearson, 2011.

A. Senderovich, A. Shleyfman, M. Weidlich, A. Gal, To aggregate or to eliminate? optimal model simplification for improved process performance prediction, Inf. Syst. (2018) 1–16.

B. Gerrard, Moneyball and the role of sports analytics: A decision theoretic perspective, in: North American Society for Sport Management Conference,NASSM 2016, 2016, pp. 2010–2012, no. Nassm

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Posted

2024-03-03

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