Open AccessThe Dirty League: English Premier League Provides Higher Incentives for Fouling as Compared to other European Soccer Leagues
Author(s) -
Ashwin Phatak,
Robert Rein,
Daniel Memmert
Publication year - 2021
Publication title -
journal of human kinetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.735
H-Index - 37
eISSN - 1899-7562
pISSN - 1640-5544
DOI - 10.2478/hukin-2021-0095
Subject(s) - league , incentive , computer science , statistics , econometrics , psychology , operations research , mathematics , economics , physics , astronomy , microeconomics
Fouling in soccer has been studied from an ethical standpoint as a measure of aggression. However, there is limited research related to fouling for performance. The present study investigated fouling as a factor influencing performance in European soccer leagues. Out of possession fouls (FPGNorm), yellow cards (YCFNorm), and their ratio (YCPFPG) were used as predictors of points (Pts) and goals conceded (GA) at the end of the season using three separate linear regression models. Furthermore, 5-fold cross-validation was used to measure out sample reliability. All the models significantly predicted GA and Pts (p < 0.001). Models predicting GA showed higher reliability than models predicting points. Cross validation (CV) results suggested that FPGNorm and YCPFPG models showed a small standard deviation (SD) in the R 2 results whereas the results from YCFNorm were not reliable to high SD in the 5-fold CV results. In summary, FPGNorm and YCPFPG seem to predict success (low GA and high Pts) across European soccer leagues, with EPL showing the maximum effect. The findings of the current study and the methodology can be applied to an actual game analysis by coaches in multiple invasion sports. Normalizing for out of possession time is a crucial step for the time spent in particular phases of play, which has not been done in previous research while analyzing ‘key performance indices’ (KPIs). Normalization can successfully introduce domain-specific knowledge into predictors, which can be used in complex algorithms improving predictions and investigation of underlying mechanisms.