The aim of this study was two folds: (i) to explain the relationships between game-related statistics and game outcome based on team quality and (ii) to explore the effects of opposition interaction on the players’ technical and physical performances according to playing position. Game-related statistics of all the 692 balanced games of regular season 2016–2017 in the National Basketball Association were analysed. All the analyses were conducted by decision-tree algorithm (exhaustive CHAID) and magnitude-based inferences. Our study indicated that defensive rebounds, blocked shots, and assists determined between winning and losing games for stronger teams while defensive rebounds and turnovers were the key performance indicators for weaker teams. Subsequently, in stronger vs stronger games, players from winning teams in home games ran slower than their peers from losing teams, whereas an opposite trend was found in away games. In stronger vs weaker games, players from winning teams in home games covered more distance and ran faster than their peers from losing teams. In weaker vs weaker games, effective defence played an important role in winning the games. The findings provide references for coaches and performance analysts to design training plans accounting for quality of teams and oppositions.

The aim of the present study was to identify the game-related statistics which discriminate between winning, drawing and losing teams in Chinese Soccer Association Super League. The sample included 1056 balance games from the 2012–2017 Chinese Soccer Association Super League. Physical and technical game-related statistics were gathered. A one-way analysis of variance and discriminant analysis of data was done. The results showed that winning teams were significantly higher for the following game statistics: shots, shots on target, 50–50 challenge won, offsides, sprinting distance, sprinting effort, sprinting distance in ball possession and high-speed-running distance in ball possession. Losing teams had significantly higher averages in the variable crosses, passes, forward passes, sprinting distance out of ball possession and high-speed-running distance out of ball possession. Discriminant analysis concluded the following: the variables that discriminate between winning, drawing and losing teams were the shots on target, sprinting distance in ball possession, quality of opposition, passes and forward passes. Coaches and players should be aware of these different profiles in order to design and evaluate practices and competitions for their teams.

Performance analysis in soccer has attained greater importance for coaching staff in order to gather and manage useful information (i.e., physical, technical, and tactical) of their teams during consecutive seasons. Accordingly, we examined the evolution of physical and technical performance...

To investigate the effects of situational factors (match location, strength of team and opponent) and environmental factors (relative air humidity, temperature and air quality index) on the technical and physical match performance of Chinese Soccer Super League teams (CSL). The generalized mixed modelling was employed to determine the effects by using the data of all 240 matches in the season 2015 collected by Amisco Pro^®. Increase in the rank difference would increase the number of goal-scoring related, passing and organizing related actions to a small-to-moderate extent (Effect size [ES]: 0.37–0.99). Match location had small positive effects on goal-scoring related, passing and organizing related variables (ES: 0.27–0.51), while a small negative effect on yellow card (ES = −0.35). Increment in relative air humidity and air quality index would only bring trivial or small effects on all the technical performance (ES: −0.06–0.23). Increase in humidity would decrease the physical performance at a small magnitude (ES: −0.55–−0.38). Teams achieved the highest number in the physical performance-related parameters at the temperature between 11.6 and 15.1 °C. In the CSL, situational variables had major effects on the technical performance but trivial effects on the physical performance, on the contrary, environmental factors affected mainly the physical performance but had only trivial or small impact on the technical performance.

This study aimed to determine whether the role of technical, physical performance indicators and situational variables in determining match outcome has varied from a long-term analysis (seasons 2012 to 2017) of the Chinese Soccer Super League (CSL). The sample included 1,429 matches where 17 technical performance-related indicators, 11 physical performance-related indicators and two situational variables (match location and quality of opposition) were analysed. Three binary logistic regression models (inclusion of different variables) were used to measure the level of association between factors and match outcome over the six seasons studied. Results of models 1 and 2 revealed that shots on target, possession, total distance in ball possession, total distance out of ball possession, and match location exerted a decreased influence on winning the matches from 2012 to 2014 seasons. However, these indicators play a more important role in winning matches from 2014 to 2017 seasons. Additionally, the quality of opposition has a continuously increased negative effect on the match outcome. In model 3, more variables, such as high-speed distance, high-speed out of ball possession, had a meaningful influence on winning the match. These results provide valuable information about performance indicators and situational variables on winning the matches from a long-term approach.

The aim of this study was to identify playing styles adopted by teams in the Chinese Soccer Super League (CSL) and to investigate their evolution across a 6-season period. Data were collected from 1,429 CSL matches from 2012 to 2017 seasons using the Amisco system. Seventeen technical performance-related indicators and eleven physical performance-related indicators were included in the factor analysis (PCA: principal components analysis) in order to group them into performance factors (styles of play). Seven factors were obtained (eigenvalues greater than 1) and explained 74.44% of the total variance. Multivariate analyses of covariance (MANCOVA) were used to check differences among seven styles of play (team’s ranking was used as a covariate) during the six seasons under study. The main findings showed that Factor 1 (high intensity play) and factor 3 (offensive actions) of CSL soccer increased substantially along the seasons. Coaches and sports scientists should take into consideration these performance trends when preparing training and controlling for matches