The Effect of Situational Variables on Teams' Performance in Offensive Sequences Ending in a Shot on Goal. A Case Study

Differences in playing style and technical performance according to the team ranking in the Spanish football LaLiga. A thirteen seasons study

This study aimed to explore the differences in playing style and technical performance according to the ranking level in Spanish football teams. The sample comprised 38 professional teams that competed in LaLiga from 2008/09 to 2020/21, with a total of 4940 matches. The teams were grouped by their final ranking position: Champion (1st); Champions League (2nd-4th); Europa League (5th-6th); middle teams (7th-17th); and relegated teams (18th-20th). Linear mixed models were used to examine the effects of the team ranking on variables related to playing style and technical performance. The F2 statistic was calculated as effect size (ES). Regarding the style of play, the Champion teams initiated offensive sequences from a more advanced field position than the remaining ranking groups with a descending effect as the ranking position decreased (p < 0.001; ES = medium). The sequence duration and passes per sequence showed a decreasing effect across ranking groups (both p < 0.001; ES = small). In contrast, the direct speed showed an increasing effect as the ranking position decreased (p < 0.001; ES = small). A decreasing effect was observed in the number of sequences ending in the final third as the ranking position decreased (p < 0.001; ES = large) while no effect was found for the sequences ending in a shot. There was a reduction effect in ball possession, passing accuracy, through balls and crosses as the teams' ranking decreased (all p < 0.001; ES = small-to-large). In summary, higher-ranked teams had an advanced starting position, longer offensive sequences, slower progression speed, more ball possession, higher passing accuracy, and more crosses and through balls than lower-ranked teams. Football coaches should consider that adopting a playing style focused on regaining the ball possession in advanced field zones and using long passing sequences can be an effective tactical strategy to improve the teams' ranking during the season.

The Influence of Situational Variables on Ball Possession in the South African Premier Soccer League

Although the influence of ball possession in soccer has been well studied in other leagues, such information is sparse concerning the South African Premier Soccer League (PSL). The purpose of this study was to assess the impact of situational variables on ball possession in the PSL. Thirty-two matches played during the 2016–2017 PSL season were analysed using a multiple-camera match analysis system (InStat®). Three situational variables (match outcome, match location, and quality of opposition) and team performance variables (percentage of ball possession, ball possession <5 s, ball possession 5–15 s, ball possession 15–45 s, and ball possession >45 s) were examined. The results showed that losing teams had the highest ball possession (52.35 ± 5.90%) compared to winning (47.65 ± 5.90%) and drawing (50.00 ± 9.98%) teams. Playing away significantly (p < 0.05) decreased ball possession by 5.21% compared to playing at home. Playing against weak opposition was associated with increased ball possession by 4.09%. Conclusively, soccer coaches should be aware of the potential role of situational variables in determining successful team performance in a league season.

Individual ball possession in soccer

This paper describes models for detecting individual and team ball possession in soccer based on position data. The types of ball possession are classified as Individual Ball Possession (IBC), Individual Ball Action (IBA), Individual Ball Control (IBC), Team Ball Possession (TBP), Team Ball Control (TBC) und Team Playmaking (TPM) according to different starting points and endpoints and the type of ball control involved. The machine learning approach used is able to determine how long the ball spends in the sphere of influence of a player based on the distance between the players and the ball together with their direction of motion, speed and the acceleration of the ball. The degree of ball control exhibited during this phase is classified based on the spatio-temporal configuration of the player controlling the ball, the ball itself and opposing players using a Bayesian network. The evaluation and application of this approach uses data from 60 matches in the German Bundesliga season of 2013/14, including 69,667 IBA intervals. The identification rate was F = .88 for IBA and F = .83 for IBP, and the classification rate for IBC was κ = .67. Match analysis showed the following mean values per match: TBP 56:04 ± 5:12 min, TPM 50:01 ± 7:05 min and TBC 17:49 ± 8:13 min. There were 836 ± 424 IBC intervals per match and their number was significantly reduced by -5.1% from the 1st to 2nd half. The analysis of ball possession at the player level indicates shortest accumulated IBC times for the central forwards (0:49 ± 0:43 min) and the longest for goalkeepers (1:38 ± 0:58 min), central defenders (1:38 ± 1:09 min) and central midfielders (1:27 ± 1:08 min). The results could improve performance analysis in soccer, help to detect match events automatically, and allow discernment of higher value tactical structures, which is based on individual ball possession.

Detection and Selection of Behavioral Patterns Using Theme: A Concrete Example in Grassroots Soccer

Observational methodology provides a rigorous yet flexible framework for capturing behaviors over time to allow for the performance of subsequent diachronic analyses of the data captured. Theme is a specialized software program that detects hidden temporal behavioral patterns (T-patterns) within data sets. It is increasingly being used to analyze performance in soccer and other sports. The aim of this study was to show how to select and interpret T-patterns generated by the application of three “quantitative” sort options in Theme and three “qualitative” filters established by the researchers. These will be used to investigate whether 7-a-side (F7) or 8-a-side (F8) soccer is best suited to the learning and skills development needs of 7- and 8-year-old male soccer players. The information contained in the T-patterns generated allowed us to characterize patterns of play in children in this age group. For both formats, we detected technical-tactical behaviors showing that children of this age have difficulty with first-touch actions and controlling the ball after a throw-in. We also found that ball control followed by a pass or a shot at the goal are common in the central corridor of the pitch. Further, depth of play is achieved by ball control, followed by dribbling and a pass or shot. In F8, we saw that depth of play was achieved through ball control, followed by dribbling and passing of one or more opponents leading to a pass or shot. However, in F7, we saw that players succeeded in advancing from their goal area to the rival goal area through a sequence of actions.

Offensive strategies in the European Football Championship 2012

The aim of this study was to identify the independent and interactive effects of possession strategy, pitch location, and game period on the offensive actions performed by the winning teams in the 2012 European Football Championship. The non-clinical magnitude-based inferences method was used to interpret the true effect of the performance indicators on the response variable. The offensive team possessions were grouped into winning (n = 2035) and losing (n = 2071). The winning teams performed offensive processes mainly using the possession play strategy (OR: 0.75, very likely negative effect of the direct play). When the analysis included the pitch location, negative interaction effect was found for the direct play, which ended up in the central path (OR: 0.70, very likely negative effect). On the contrary, the direct play in the second half of the match seemed to produce an effect on the probability of the winning teams performing offensive processes (OR: 1.59, most likely positive effect). The results of multivariate analyses showed that the offensive team possession profiles required a careful investigation because the possession strategy changed under the conjoint effect of pitch location and game period.