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.

Football: Discovering elapsing-time bias in the science of success / Galli, L.; Galvan, G.; Levato, T.; Liti, C.; Piccialli, V.; Sciandrone, M.. - In: CHAOS, SOLITONS AND FRACTALS. - ISSN 0960-0779. - 152:(2021). [10.1016/j.chaos.2021.111370]

Football: Discovering elapsing-time bias in the science of success

Galvan G.
;
Piccialli V.
;
Sciandrone M.
2021

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11573/1623000
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