Assessing Offensive/Defensive Strategies in a Football Match Using DEA

Villa, Gabriel; Lozano, Sebastián

doi:10.32731/ijsf/143.082019.01

Cited by 5 publications

(1 citation statement)

References 21 publications

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“…In light of such phenomenon, quantifying the effectiveness of more styles of play has also been analyzed to explain a broader concept of tactical behaviors, to which these tactical variables and performance indicators contribute. Recent studies proposed a theoretical framework for measuring styles of play ( Fernandez-Navarro et al, 2018 ), quantify the use of attacking and defensive strategies in football matches ( Villa and Lozano, 2019 ), and predict a pattern of tactical–strategic behaviors with major probabilities of success in transitions ( Casal et al, 2016 ). Behavior indicators ( Kempe et al, 2014 ), multivariate statistical methods ( Moura et al, 2014 ), and spatio-temporal analysis ( Memmert et al, 2016 ) have also been used for potential tactical evaluation.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the Effectiveness of Defensive Playing Styles in the Chinese Football Super League

Ruan

Ge²,

Shen³

et al. 2022

Front. Psychol.

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Establishing and illustrating a predictive and prescriptive model of playing styles that football teams adopt during matches is a key step toward describing and measuring the effectiveness of styles of play. The current study aimed to identify and measure the effectiveness of different defensive playing styles for professional football teams considering the opponent’s expected goal. Event data of all 1,120 matches played in the Chinese Football Super League (CSL) from the 2016 to 2020 seasons were collected, with fifteen defense-related performance variables being extracted. The PCA model (KMO = 0.76) output eight factors that represented 7 different styles of play (factor 6 and 8 represent one style of play) and explained 85.17% of the total variance. An expected goal (xG) model was built using data related to 27,852 shots. Finally, the xG of the opponent was calculated in the multivariate regression model, outputting five factors that (p < 0.05) explained 41.6% of the total variance in the xG of the opponent and receiving a dangerous situation (factor 7) was the most apparent style (31.3%). Finally, the predicted model with defensive styles correlated with actual xG of the opponent at r = 0.62 using the 2020 season as testing data which showed that the predicted xG was correlated moderately with the actual. The result indicated that if the team strengthened the defense closed to the own goal, high intensity confrontation, and defense of goalkeeper, meanwhile making less errors and receiving less dangerous situations, the xG of the opponent would be greatly reduced.

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