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.

The aim of the present study was to identify the game‐related statistics that discriminate between season‐long successful and unsuccessful basketball teams participating in the Spanish Basketball League (LEB1). The sample included all 145 average records per season from the 870 games played between the 2000–2001 and the 2005–2006 regular seasons. The following game‐related statistics were gathered from the official box scores of the Spanish Basketball Federation: 2‐ and 3‐point field‐goal attempts (both successful and unsuccessful), free‐throws (both successful and unsuccessful), defensive and offensive rebounds, assists, steals, turnovers, blocks (both made and received), and fouls (both committed and received). To control for season variability, all results were normalized to minutes played each season and then converted to z‐scores. The results allowed discrimination between best and worst teams’ performances through the following game‐related statistics: assists (SC=0.47), steals (SC=0.34), and blocks (SC=0.30). The function obtained correctly classified 82.4% of the cases. In conclusion, season‐long performance may be supported by players’ and teams’ passing skills and defensive preparation.

Comparison was made of home advantage in 157 national domestic soccer leagues throughout the world for six seasons between 2006 and 2012, a total of 169,752 games. Quantified as the number of competition points won by the home team that was expressed as a percentage of all points gained in the league, the advantage was present in all continents, but showed considerable variation between countries. It was by far the highest in Nigeria (86.82%), followed by Bosnia-Herzegovina, Guatemala, Indonesia, Algeria, Bolivia and Ghana, all between 70% and 80%. Regionally, there were pockets of high home advantage in the Andes, the Balkans, West Africa and Central America, while in the Baltic republics and in most of the Arabian Peninsula it was low. A multivariate model was developed by using proxy variables for the main explanations of home advantage and its worldwide variation. After controlling for the competitive balance in each league, significant predictors of home advantage were: Fédération Internationale de Football Association ranking (a proxy for crowd support), the maximum geographical distance between teams, the majority of teams coming from a single city, at least two teams playing at a high altitude, the recent occurrence of a civil war and the Corruption Perception Index. The model accounted for 43% of the variability in home advantage, the rest of which needs to be investigated after developing more precise measures for territoriality, the ethnic and cultural factors involved and the potential for referee bias.

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.

The aim of this study was to identify the key physical and technical performance variables related to team quality in the Chinese Super League (CSL). Teams’ performance variables were collected from 240 matches and analysed via analysis of variance between end-of-season-ranked groups and multinomial logistic regression. Significant physical performance differences between groups were identified for sprinting (top-ranked group vs. upper-middle-ranked group) and total distance covered without possession (upper and upper-middle-ranked groups and lower-ranked group). For technical performance, teams in the top-ranked group exhibited a significantly greater amount of possession in opponent’s half, number of entry passes in the final 1/3 of the field and the Penalty Area, and 50–50 challenges than lower-ranked teams. Finally, time of possession increased the probability of a win compared with a draw. The current study identified key performance indicators that differentiated end-season team quality within the CSL.