Reliability of Teams' Game-Related Statistics in Basketball: Number of Games Required and Minimal Detectable Change

Biomechanical Loads and Their Effects on Player Performance in NCAA D-I Male Basketball Games

Basketball games and training sessions are characterized by quick actions and many scoring attempts, which pose biomechanical loads on the bodies of the players. Inertial Measurement Units (IMUs) capture these biomechanical loads as PlayerLoad and Inertial Movement Analysis (IMA) and teams collect those data to monitor adaptations to training schedules. However, the association of biomechanical loads with game performance is a relatively unexplored area. The aims of the current study were to determine the statistical relations between biomechanical loads in games and training with game performance. Biomechanical training and game load measures and player-level and team-level game stats from one college basketball team of two seasons were included in the dataset. The training loads were obtained on the days before gameday. A three-step analysis pipeline modeled: (i) relations between team-level game stats and the win/loss probabilities of the team, (ii) associations between the player-level training and game loads and their game stats, and (iii) associations between player-level training loads and game loads. The results showed that offensive and defensive game stats increased the odds of winning, but several stats were subject to positional and individual performance variability. Further analyses, therefore, included total points [PTS], two-point field goals, and defensive rebounds (DEF REB) that were less subject to those influences. Increases in game loads were significantly associated with game stats. In addition, training loads significantly affected the game loads in the following game. In particular, increased loads 2 days before the game resulted in increased expected game loads. Those findings suggested that biomechanical loads were good predictors for game performance. Specifically, the game loads were good predictors for game stats, and training loads 2 days before gameday were good predictors for the expected game load. The current analyses accounted for the variation in loads of players and stats that enabled modeling the expected game performance for each individual. Coaches, trainers, and sports scientists can use these findings to further optimize training plans and possibly make in-game decisions for individual player performance.

Implementation of path analysis and piecewise structural equation modelling to improve the interpretation of key performance indicators in team sports: An example in professional rugby union

Analysis of key performance indicators (KPIs) in team sports has frequently involved multiple univariate analyses and modelling of direct associations between each KPI and match outcomes. This study aimed to show a more appropriate framework and modelling process to establish causal plausibility for future confirmatory studies. A cross-sectional design was adopted, using 337 team-match observations of Australian Super Rugby performances. A tentative model was developed in consultation with a domain expert (national analyst) and analysed using piecewise structural equation modelling. Model fit was assessed using Fisher's C and the Akaike Information Criterion (AIC). Hypothesised relationships were modelled using linear mixed effects models and unmodelled pathways were investigated using tests of directed separation. The model was an acceptable fit overall, and adjustments were identified in collaboration with the national head analyst, improving the AIC from 127.15 to 120.77 (Fisher's C = 66.78; p = 0.382). Modelling the hierarchical data structure and developing models that contain more logical hypothesised associations (in consultation with domain experts) is a more useful and important step to analyse and interpret effects of KPIs on team performance. This analysis provides support to the plausibility of the causal structure and generation of new and more precise hypotheses.

Basketball performance is affected by the schedule congestion: NBA back-to-backs under the microscope

Teams experiencing highly competitive densities may be particularly exposed to performance breakdown and injury risk. The aim of this study was to analyse the association between fixture congestion cycles (playing back-to-back games, playing on one day's rest, playing on two day's rest, playing on three or more day's rest) and performance of NBA basketball teams. A total of 82 games from all teams participating in NBA 2016/2017 regular season were considered. Game-related statistics by fixture congestion cycles and game outcome were examined using the Pearson's Chi-Square test, Discriminant Analysis and Binary logistic regression. The results revealed that the likelihood of winning a game increased significantly from playing back-to-back games to having one day rest in between. Shooting efficacy-related statistics presented a considerable discriminatory power of the different fixture congestion cycles. In conclusion, fixture congestion cycles showed a significant impact on the game outcome and team performance. The findings may add value in the re-design of game schedules in the NBA as well as inform coaches to critically manage training load in order to enhance performance and reduce the risk of injury.

Differences in Technical Performance of Players From 'The Big Five' European Football Leagues in the UEFA Champions League

The current study aimed to identify the differences in technical performance between players from clubs of Bundesliga (Germany), La Liga (Spain), Ligue 1 (France), Premier League (England) and Serie A (Italy) when competing in the matches of the UEFA Champions League. Technical performance-related match data of 1,291 players from 1,125 matches (9,799 observations) of the UEFA Champions League (seasons 2009/2010-2017/2018) were collected and analysed. The generalised mixed linear modelling was employed taking the names of the league as the independent variable to predict the count number of 20 technical performance-related match actions and events performed by players belonging to different leagues. The non-clinical magnitude-based inference was used to evaluate the uncertainty in the true effects of the predictor. Results showed that differences in the technical performances between players from La Liga, Premier League and Ligue 1 were all trivial. Bundesliga players made higher numbers of shots than players from La Liga, Premier League and Serie A and achieved more long balls than players from Ligue 1. Serie A players achieved lower numbers of ball touches, passes and lower pass accuracy per match than players from any of the other four leagues. In addition, players from Serie A performed a higher number of long balls per match than Ligue 1 players and lower number of dribbles per match than Premier League players. Non-significant differences in other variables related to passing and organising and all variables related to defending were identified in players between the five leagues. The identified differences in technical performance among leagues could provide a more thorough understanding for practitioners working within the fields of talent identification, player development, player recruitment, coaching and match preparation.