Performance Optimization in Football: Advances in Theories and Practices

The aim of this study was to elucidate pattern of attacking actions leading up to goal scoring during the 14 FIFA World Cups from 1966 to 2018. The study analysed 1881 goals scored during a total of 732 matches. We employed observational methodology design. Before goal analysis began, it was developed the observing protocol in which data related to selected variables, by system of notation, was entered after reviewing each individual goal scoring action. The analysis of all video material was carried out independently by four experienced examiners (three of them are Ph.D in sports science and one is Ph.D. candidate in sports science with at least 7 years of coaching and experience as analyst in football). The inter-and intra-observer reliability presented good level of agreement. The kappa values ranged from 0.82 (goal scoring through open play) to 1.00 (action leading up to goal), showing a very high agreement for all performance variables. Interclass correlation was very high (ICC = 0.966, 95% upper and lower confidence intervals were between 0.933 and 1.00). A statistically significant trend (p < 0.05) from 1966 to 2018 was identified towards a higher relative frequency of goals scored from set play and collective actions from open play. The Chi-square did not reveal significant differences in the frequency of goal scoring patterns and goal-scoring zones. The results also revealed that the majority of goals were scored between the 76th and 90th minutes of a match (22.7%), from open play (70.5%), inside the penalty area (54.7%), one touch finishing (62.5%), and collective attacks in open play (55.8%). These findings may provide a possible strategic direction for improving goal-scoring performance in football, as well as practical implementation in World Cup tournament preparation.

Purpose: In football, attacking has seen evolving for decades and attacking pattern detection is an important topic in this sport. The purpose of this study was to identify the general and threatening attacking patterns of different playing styles in world top football matches, which represented the latest evolvement of soccer attacking.

Methods: Attacking sequence data of the top three teams from 21 matches in the 2018 World Cup were collected. The three teams were classified into two playing styles according to a previous study, France was a direct-play team, and Croatia and Belgium were possession-play teams. The football field was divided into 12 zones and Markov transition matrix-based zone models were applied to assess the attacking pattern in the 21 matches. Both descriptive analysis and simulative analysis were conducted using this model.

Results: The results revealed that (1) flanker attacks were frequently taken among all three teams, and possession playing teams (Croatia and Belgium) played more often than direct playing teams (France) in their center of the midfield zone and (2) forward passes across/through zones toward the middle of attacking quarter (A1/4) have a positive impact of creating a chance of a goal.

Conclusion: Using Markov transition matrix, general and threatening attacking patterns were found. The combination of possession play and counterattack was a new trend that emerged in the 2018 World Cup. These findings can help coaches to develop corresponding strategies when facing opponents of different playing styles.

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.