The aim of the study was to evaluate the inter-operator reliability of OPTA Client System which is used to collect live football match statistics by OPTA Sportsdata Company. Two groups of experienced operators were required to analyze a Spanish league match independently. Results showed that team events coded by independent operators reached a very good agreement (kappa values were 0.92 and 0.94) and average difference of event time was 0.06±0.04 s. The reliability of goalkeeper actions was also at high level, kappa values were 0.92 and 0.86. The high intra-class correlation coefficients (ranged from 0.88 to 1.00) and low standardized typical errors (varied from 0.00 to 0.37) of different match actions and indicators of individual outfield players showed a high level of inter-operator reliability as well. These results suggest that the OPTA Client System is reliable to be used to collect live football match statistics by well trained operators.

Identifying match statistics that strongly contribute to winning in football matches is a very important step towards a more predictive and prescriptive performance analysis. The current study aimed to determine relationships between 24 match statistics and the match outcome (win, loss and draw) in all games and close games of the group stage of FIFA World Cup (2014, Brazil) by employing the generalised linear model. The cumulative logistic regression was run in the model taking the value of each match statistic as independent variable to predict the logarithm of the odds of winning. Relationships were assessed as effects of a two-standard-deviation increase in the value of each variable on the change in the probability of a team winning a match. Non-clinical magnitude-based inferences were employed and were evaluated by using the smallest worthwhile change. Results showed that for all the games, nine match statistics had clearly positive effects on the probability of winning (Shot, Shot on Target, Shot from Counter Attack, Shot from Inside Area, Ball Possession, Short Pass, Average Pass Streak, Aerial Advantage and Tackle), four had clearly negative effects (Shot Blocked, Cross, Dribble and Red Card), other 12 statistics had either trivial or unclear effects. While for the close games, the effects of Aerial Advantage and Yellow Card turned to trivial and clearly negative, respectively. Information from the tactical modelling can provide a more thorough and objective match understanding to coaches and performance analysts for evaluating post-match performances and for scouting upcoming oppositions.

Recent research suggests that match-to-match variation adds important information to performance descriptors in team sports, as it helps measure how players fine-tune their tactical behaviours and technical actions to the extreme dynamical environments. The current study aims to identify the differences in technical performance of players from strong and weak teams and to explore match-to-match variation of players’ technical match performance. Performance data of all the 380 matches of season 2012–2013 in the Spanish First Division Professional Football League were analysed. Twenty-one performance-related match actions and events were chosen as variables in the analyses. Players’ technical performance profiles were established by unifying count values of each action or event of each player per match into the same scale. Means of these count values of players from Top3 and Bottom3 teams were compared and plotted into radar charts. Coefficient of variation of each match action or event within a player was calculated to represent his match-to-match variation of technical performance. Differences in the variation of technical performances of players across different match contexts (team and opposition strength, match outcome and match location) were compared. All the comparisons were achieved by the magnitude-based inferences. Results showed that technical performances differed between players of strong and weak teams from different perspectives across different field positions. Furthermore, the variation of the players’ technical performance is affected by the match context, with effects from team and opposition strength greater than effects from match location and match outcome.

Identifying match events that are related to match outcome is an important task in football match analysis. Here we have used generalised mixed linear modelling to determine relationships of 16 football match events and 1 contextual variable (game location: home/away) with the match outcome. Statistics of 320 close matches (goal difference ≤ 2) of season 2012–2013 in the Spanish First Division Professional Football League were analysed. Relationships were evaluated with magnitude‐based inferences and were expressed as extra matches won or lost per 10 close matches for an increase of two within‐team or between‐team standard deviations (SD) of the match event (representing effects of changes in team values from match to match and of differences between average team values, respectively). There was a moderate positive within‐team effect from shots on target (3.4 extra wins per 10 matches; 99% confidence limits ±1.0), and a small positive within‐team effect from total shots (1.7 extra wins; ±1.0). Effects of most other match events were related to ball possession, which had a small negative within‐team effect (1.2 extra losses; ±1.0) but a small positive between‐team effect (1.7 extra wins; ±1.4). Game location showed a small positive within‐team effect (1.9 extra wins; ±0.9). In analyses of nine combinations of team and opposition end‐of‐season rank (classified as high, medium, low), almost all between‐team effects were unclear, while within‐team effects varied depending on the strength of team and opposition. Some of these findings will be useful to coaches and performance analysts when planning training sessions and match tactics.

Performance of football teams varies constantly due to the dynamic nature of this sport, whilst the typical performance and its spread can be represented by profiles combining different performance-related variables based on data from multiple matches. The current study aims to use a profiling technique to evaluate and compare match performance of football teams in the UEFA Champions League incorporating three situational variables (i.e. strength of team and opponent, match outcome and match location). Match statistics of 72 teams, 496 games across four seasons (2008-09 to 2012-13) of this competition were analysed. Sixteen performance-related events were included: shots, shots on target, shots from open play, shots from set piece, shots from counter attack, passes, pass accuracy (%), crosses, through balls, corners, dribbles, possession, aerial success (%), fouls, tackles, and yellow cards. Teams were classified into three levels of strength by a k-cluster analysis. Profiles of overall performance and profiles incorporating three situational variables for teams of all three levels of strength were set up by presenting the mean, standard deviation, median, lower and upper quartiles of the counts of each event to represent their typical performances and spreads. Means were compared by using one-way ANOVA and independent sample t test (for match location, home and away differences), and were plotted into the same radar charts after unifying all the event counts by standardised score. Established profiles can present straightforwardly typical performances of football teams of different levels playing in different situations, which could provide detailed references for coaches and analysts to evaluate performances of upcoming opposition and of their own.

In order to investigate the effect of playing styles on the match performance of football teams, data were analysed on 18 technical performance-related variables and 8 physical performance-related variables from 59 matches in the 2018 FIFA World Cup. A k-means cluster analysis was conducted to classify all match observations into two clusters of tactical approach in order to identify the playing styles of teams (characterised as direct-play, possession-play and mixed-play). Separate Poisson regression models were run in the generalised mixed linear modelling to examine the differences in technical and physical performance between teams classified as using different playing styles when facing different opponents. Results showed that possession-play characterised teams achieved higher values in all the variables related to goal scoring, attacking and passing (ES: 0.32 ~ 1.27) and covered more distance in sprints and high-intensity running (ES: 0.33 ~ 0.47) than direct-play characterised teams. Both possession- and direct-play characterised teams achieved higher values in passing, pass accuracy and delivery into the attacking third playing against direct-play characterised teams than playing against possession-play characterised sides (ES: 0.22 ~ 0.98). These findings may provide insights into the establishment of performance profiles of teams with different tactical styles and the development of specific training drills to optimise playing style.