Instant Replay: Investigating statistical Analysis in Sports

TL;DR

This paper reviews how advanced statistical and AI methods like Markov Chains, Bayesian Inference, and Control Theory can enhance sports analysis by providing objective insights into player and game performance.

Contribution

It introduces the potential of integrating Control Theory and Machine Learning techniques into sports statistical analysis for improved accuracy.

Findings

Markov Chain-based models improve player performance estimation.

Neuro Dynamic Programming shows promise in football analysis.

AI techniques can enhance the objectivity of sports statistics.

Abstract

Technology has had an unquestionable impact on the way people watch sports. Along with this technological evolution has come a higher standard to ensure a good viewing experience for the casual sports fan. It can be argued that the pervasion of statistical analysis in sports serves to satiate the fan's desire for detailed sports statistics. The goal of statistical analysis in sports is a simple one: to eliminate subjective analysis. In this paper, we review previous work that attempts to analyze various aspects in sports by using ideas from Markov Chains, Bayesian Inference and Markov Chain Monte Carlo (MCMC) methods. The unifying goal of these works is to achieve an accurate representation of the player's ability, the sport, or the environmental effects on the player's performance. With the prevalence of cheap computation, it is possible that using techniques in Artificial Intelligence could improve the result of statistical analysis in sport. This is best illustrated when evaluating football using Neuro Dynamic Programming, a Control Theory paradigm heavily based on theory in Stochastic processes. The results from this method suggest that statistical analysis in sports may benefit from using ideas from the area of Control Theory or Machine Learning