The aim of this study was to define and categorise different styles of play in elite soccer and associated performance indicators by using factor analysis. Furthermore, the observed teams were categorised using all factor scores. Data were collected from 97 matches from the Spanish La Liga and the English Premier League from the seasons 2006–2007 and 2010–2011 using the Amisco® system. A total of 19 performance indicators, 14 describing aspects of attacking play and five describing aspects of defensive play, were included in the factor analysis. Six factors, representing 12 different styles of play (eight attacking and four defensive), had eigenvalues greater than 1 and explained 87.54% of the total variance. Direct and possession styles of play, defined by factor 1, were the most apparent styles. Factor analysis used the performance indicators to cluster each team’s style of play. Findings showed that a team’s style of play was defined by specific performance indicators and, consequently, teams can be classified to create a playing style profile. For practical implications, playing styles profiling can be used to compare different teams and prepare for opponents in competition. Moreover, teams could use specific training drills directed to improve their styles of play.

The aim of the present study was to evaluate the effect of match status, venue, and quality of opposition on the styles of play in soccer. Data were collected from 380 games of the English Premier League from the 2015–2016 season. Linear mixed models were applied to evaluate the influence of these contextual variables on membership scores for Direct Play, Counterattack, Maintenance, Build Up, Sustained Threat, Fast Tempo, Crossing, and High Pressure. The results showed that match status had a significant effect on the eight styles of play (all P < 0.001), venue had a significant effect on all styles of play (P < 0.01) except Counterattack and Maintenance, and quality of opposition had a significant effect on all styles of play (P < 0.05) except Counterattack. Moreover, the interaction between match status and quality of opposition, and venue and quality of opposition showed significant effects on some styles of play. The results of this study imply that contextual variables influence the use of styles of play in soccer match play. Consequently, this provides meaningful recommendations for practitioners in soccer.

The aim of this study was to evaluate the effectiveness of styles of play in soccer and the influence of contextual variables (i.e. match status, venue and quality of opposition). Team possessions (n = 68,766) from the 380 matches of the 2015–2016 English Premier League season were collected for this study. The Possession Effectiveness Index, based on Expected Goals and Ball Movement Points metrics, was used to measure the effectiveness of team possessions. Linear mixed models were applied to analyse the influence of contextual variables on the effectiveness score for each style. Results showed that the effectiveness of Direct Play, Counterattack, Maintenance and Crossing significantly increased when teams were winning by two or more goals. Counterattack increased its effectiveness when teams were winning by one goal and reduced its effectiveness when losing by one goal. The effectiveness of Direct Play increased when losing by two goals or more. Playing away negatively affected the effectiveness of Direct Play, Maintenance and High Pressure. In addition, playing against a stronger opposition reduced the effectiveness of all styles of play. The results suggest that the effectiveness of styles of play changes under specific circumstances and that not all contextual variables affect them in the same way.

Attacking tactical variables have been commonly studied in soccer to analyze teams’ performance. However, few studies investigated defensive tactical variables during match-play and the influence of contextual variables on them. The aims of the present study were (1) to examine the defensive behaviors of soccer teams when gaining the ball in advanced zones of the pitch and (2) to evaluate the effect of contextual variables on these defensive behaviors. A sample of 1,095 defensive pieces of play initiated in the opposing half of the pitch obtained from 10 matches of the season 2010/11 of La Liga and involving 13 teams was collected using the semiautomated tracking system Amisco Pro. Five defensive tactical variables, the outcome of defensive pieces of play, and contextual variables (i.e., match status, venue, quality of opposition, and match period) were recorded for every defensive piece initiated in the opposing half of the pitch. Results showed that there were significant differences among teams in the outcome of defensive pieces of play originating from the opposing half (χ² = 111.87, p < 0.01, φ_c = 0.22), and in the outcome of offensive pieces of play following ball gains (χ² = 49.92, p < 0.001, φ_c = 0.22). Cluster analysis revealed four groups describing different defensive behaviors from high-pressure to a defense close to their own goal. Match status (χ² = 25.87, p < 0.05, φ_c = 0.11) and quality of opposition (χ² = 21.19, p < 0.05, φ_c = 0.10) were the contextual variables that showed a significant effect on defensive pieces of play initiated in the opposite half of the pitch. Teams winning gained more balls in the zone close to their own goal, and losing teams gained more balls in advanced zones of the pitch. Moreover, the greater the quality of the opponent the lesser the chance of gaining the ball in advanced zones of the pitch. Neither venue or match period influenced the defensive pieces of play analyzed. Soccer teams could employ a similar analysis to improve their performance and prepare for opposition teams in competition.

The present study aimed to analyse the playing surface area, dispersion and distance covered of professional football teams comparing the pre-lockdown and post-lockdown periods. Positional and match physical demands data were collected from all matches played in the First Spanish Division (n = 760) during season 2019/2020. Pre-lockdown (1^st–27^th matchday) and post-lockdown periods (28^th–38^th matchday) were compared. Variables related to team-level spatial (i.e., convex hull, team width and team length) and physical performance (i.e., total distance and high-speed running distance) were analysed using an optical tracking system (i.e., ChyronHego). In addition, these variables were concerned with respect to the match location contextual variable. Linear mixed models were used to examine the difference between the pre-lockdown and post-lockdown periods following a hierarchical structure considering players, matches and teams. The results revealed that the pandemic lockdown affected the teams’ performance when comparing the periods before and after lockdown, showing a general decrease in the average values of the spatial and physical variables measured. The current data could assist practitioners in making informed decisions in order to design and improve training plans for similar situations in which teams return to competition after an unusual period with no training.

(1) Background: This study analyzed the differences in match movement profiles according to opponent quality (i.e., match balance) in the professional Spanish soccer leagues over four consecutive seasons (from 2015/2016 to 2018/2019); (2) Methods: The ChyronHego^® system was used to record competition movement data from all matches played in the First (Liga Santander; n = 1520) and Second Spanish Division (Liga Smartbank; n = 1848). The total distance (TD) and high-intensity running distance (TD > 21 km∙h⁻¹) covered with and without ball possession (TDWP and TDWOP, respectively) were analyzed using a Linear Mixed Model, considering the opponent quality contextual variable; (3) Results: Results showed that teams covered a significantly greater TD when played against the lowest quality teams in L1 (p < 0.05), while in L2 teams covered a significantly greater TD when played against the highest quality teams (p < 0.05). Teams covered a significantly greater TDWP and TDWP > 21 km∙h⁻¹ when playing against the highest quality teams in both L1 and L2 (p < 0.05). On the contrary, playing against the lowest quality teams in L1 versus the highest quality teams in L2 implied more TDWOP and TDWOP > 21 km⋅h⁻¹ (p < 0.05); (4) Conclusions: The present study indicates that match movement profiles depend on contextually related variables.