Football: Discovering elapsing-time bias in the science of success. (November 2021)

• Record Type: Journal Article
• Authors: Galli, L.; Galvan, G.; Levato, T.; Liti, C.; Piccialli, V.; Sciandrone, M.
• Is Part Of: Chaos, solitons and fractals. Volume 152(2021)
• Publication Date: 2021-11
• Subjects: Football -- Science of success -- Match analysis -- Machine learning -- Sports analytics; Chaotic behavior in systems -- Periodicals; Solitons -- Periodicals; Fractals -- Periodicals; Chaotic behavior in systems; Fractals; Solitons; Periodicals; 003.7
• DOI: 10.1016/j.chaos.2021.111370
• Languages: English
• ISSNs: 0960-0779

Abstract

Highlights: We conjecture that players' behavior is more and more correlated with the match outcome as the 90 minutes elapse. We demonstrate the effect of this elapsing-time bias by applying a host of machine learning techniques on a large corpus of finely detailed football matches of European leagues. We show that we can predict the output of a match with high confidence simply by looking at the last 15 minutes of the game. We design a new task and we show that is not affected by elapsing-time bias . Abstract: One of the fundamental topics in sports analytics is the science of success, i.e., the study of the correlation between players' performances and their success. This is a very challenging task especially in the case of team sports, among which football is a prominent example. This paper is concerned with uncovering a dangerous bias that is present in most of the approaches proposed in the literature that apply statistical techniques or machine learning models to study the correlation between team performances and match outcome. In particular, we find out that players' behavior on a time interval is more and more correlated with the match outcome as the 90 minutes elapse. As an extreme example, we show that we can predict the output of a match with high confidence simply by looking at the last 15 minutes of the game. We call this effect elapsing-time bias . We conduct a quantitative analysis that proves the existence of this phenomenon and shows its consequences. We then propose a novel way to address the problem. Namely, we design a new machine learning task that is not affected by elapsing-time bias . All the experiments are conducted on a large corpus of finely annotated football matches of European leagues.