An Autoencoder Based Approach to Simulate Sports Games

TL;DR

This paper introduces UCLData, a detailed UEFA Champions League dataset, and an autoencoder-based machine learning pipeline to simulate and predict the outcomes of the remaining season games, addressing the impact of COVID-19 disruptions.

Contribution

The paper presents a new dataset and a novel autoencoder-based approach for simulating sports game outcomes and season progression.

Findings

Dataset enables detailed season analysis

Autoencoder model predicts plausible season outcomes

Method offers insights into potential league results

Abstract

Sports data has become widely available in the recent past. With the improvement of machine learning techniques, there have been attempts to use sports data to analyze not only the outcome of individual games but also to improve insights and strategies. The outbreak of COVID-19 has interrupted sports leagues globally, giving rise to increasing questions and speculations about the outcome of this season's leagues. What if the season was not interrupted and concluded normally? Which teams would end up winning trophies? Which players would perform the best? Which team would end their season on a high and which teams would fail to keep up with the pressure? We aim to tackle this problem and develop a solution. In this paper, we proposeUCLData, which is a dataset containing detailed information of UEFA Champions League games played over the past six years. We also propose a novel autoencoder based machine learning pipeline that can come up with a story on how the rest of the season will pan out.