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.

Roster-Based Optimisation for Limited Overs Cricket

<p>The objective of this research was to develop a roster-based optimisation system for limited overs cricket by deriving a meaningful, overall team rating using a combination of individual ratings from a playing eleven. The research hypothesis was that an adaptive rating system accounting for individual player abilities, outperforms systems that only consider macro variables such as home advantage, opposition strength and past team performances. The assessment of performance is observed through the prediction accuracy of future match outcomes. The expectation is that in elite sport, better teams are expected to win more often. To test the hypothesis, an adaptive rating system was developed. This framework was a combination of an optimisation system and an individual rating system. The adaptive rating system was selected due to its ability to update player and team ratings based on past performances. A Binary Integer Programming model was the optimisation method of choice, while a modified product weighted measure (PWM) with an embedded exponentially weighted moving average (EWMA) functionality was the adopted individual rating system. The weights for this system were created using a combination of a Random Forest and Analytical Hierarchical Process. The model constraints were objectively obtained by identifying the player’s role and performance outcomes a limited over cricket team must obtain in order to increase their chances of winning. Utilising a random forest technique, it was found that players with strong scoring consistency, scoring efficiency, runs restricting abilities and wicket-taking efficiency are preferred for limited over cricket due to the positive impact those performance metrics have on a team’s chance of winning. To define pertinent individual player ratings, performance metrics that significantly affect match outcomes were identified. Random Forests proved to be an effective means of optimal variable selection. The important performance metrics were derived in terms of contribution to winning, and were input into the modified PWM and EWMA method to generate a player rating. The underlying framework of this system was validated by demonstrating an increase in the accuracy of predicted match outcomes compared to other established rating methods for cricket teams. Applying the Bradley-Terry method to the team ratings, generated through the adaptive system, we calculated the probability of teami beating teamj. The adaptive rating system was applied to the Caribbean Premier League 2015 and the Cricket World Cup 2015, and the systems predictive accuracy was benchmarked against the New Zealand T.A.B (Totalisator Agency Board) and the CricHQ algorithm. The results revealed that the developed rating system outperformed the T.A.B by 9% and the commercial algorithm by 6% for the Cricket World Cup (2015), respectively, and outperformed the T.A.B and CricHQ algorithm by 25% and 12%, for the Caribbean Premier League (2015), respectively. These results demonstrate that cricket team ratings based on the aggregation of individual player ratings are superior to ratings based on summaries of team performances and match outcomes; validating the research hypothesis. The insights derived from this research also inform interested parties of the key attributes to win limited over cricket matches and can be used for team selection.</p>

Roster-Based Optimisation for Limited Overs Cricket

<p>The objective of this research was to develop a roster-based optimisation system for limited overs cricket by deriving a meaningful, overall team rating using a combination of individual ratings from a playing eleven. The research hypothesis was that an adaptive rating system accounting for individual player abilities, outperforms systems that only consider macro variables such as home advantage, opposition strength and past team performances. The assessment of performance is observed through the prediction accuracy of future match outcomes. The expectation is that in elite sport, better teams are expected to win more often. To test the hypothesis, an adaptive rating system was developed. This framework was a combination of an optimisation system and an individual rating system. The adaptive rating system was selected due to its ability to update player and team ratings based on past performances. A Binary Integer Programming model was the optimisation method of choice, while a modified product weighted measure (PWM) with an embedded exponentially weighted moving average (EWMA) functionality was the adopted individual rating system. The weights for this system were created using a combination of a Random Forest and Analytical Hierarchical Process. The model constraints were objectively obtained by identifying the player’s role and performance outcomes a limited over cricket team must obtain in order to increase their chances of winning. Utilising a random forest technique, it was found that players with strong scoring consistency, scoring efficiency, runs restricting abilities and wicket-taking efficiency are preferred for limited over cricket due to the positive impact those performance metrics have on a team’s chance of winning. To define pertinent individual player ratings, performance metrics that significantly affect match outcomes were identified. Random Forests proved to be an effective means of optimal variable selection. The important performance metrics were derived in terms of contribution to winning, and were input into the modified PWM and EWMA method to generate a player rating. The underlying framework of this system was validated by demonstrating an increase in the accuracy of predicted match outcomes compared to other established rating methods for cricket teams. Applying the Bradley-Terry method to the team ratings, generated through the adaptive system, we calculated the probability of teami beating teamj. The adaptive rating system was applied to the Caribbean Premier League 2015 and the Cricket World Cup 2015, and the systems predictive accuracy was benchmarked against the New Zealand T.A.B (Totalisator Agency Board) and the CricHQ algorithm. The results revealed that the developed rating system outperformed the T.A.B by 9% and the commercial algorithm by 6% for the Cricket World Cup (2015), respectively, and outperformed the T.A.B and CricHQ algorithm by 25% and 12%, for the Caribbean Premier League (2015), respectively. These results demonstrate that cricket team ratings based on the aggregation of individual player ratings are superior to ratings based on summaries of team performances and match outcomes; validating the research hypothesis. The insights derived from this research also inform interested parties of the key attributes to win limited over cricket matches and can be used for team selection.</p>

Opening up the court: analyzing player performance across tennis Grand Slams

In tennis, the Australian Open, French Open, Wimbledon, and US Open are the four most prestigious events (Grand Slams). These four Grand Slams differ in the composition of the court surfaces, when they are played in the year, and which city hosts the players. Individual Grand Slams come with different expectations, and it is often thought that some players achieve better results at some Grand Slams than others. It is also thought that differences in results may be attributed, at least partially, to surface type of the courts. For example, Rafael Nadal, Roger Federer, and Serena Williams have achieved their best results on clay, grass, and hard courts, respectively. This paper explores differences among Grand Slams, while adjusting for confounders such as tour, competitor strength, and player attributes. More specifically, we examine the effect of the Grand Slam on player performance for matches from 2013 to 2019. We take two approaches to modeling these data: (1) a mixed-effects model accounting for both player and tournament features and (2) models that emphasize individual performance. We identify differences across the Grand Slams at both the tournament and individual player level.

Quantifying and Comparing the Match Demands of U18, U23, and 1ST Team English Professional Soccer Players

The aim of this study was to quantify and compare the match load demands of U18, U23, and 1ST team players during the official season. A total of 65 matches and 495 (U18 = 146, U23 = 146, and 1ST team = 203) individual player game observations were included in this analysis. A 10-Hz global navigation satellite systems (GNSS) and 100-Hz triaxial accelerometer (STATSports, Apex, Northern Ireland) were used to monitor the following metrics during official matches: total distance, high-speed running distance (HSR), sprint distance, high metabolic distance, explosive distance, high-intensity bursts distance, speed intensity, and dynamic stress load (DSL) were analyzed. A multivariate analysis of variance test reported significant (p &lt; 0.001) differences among the groups. HSR during matches was lower (d = small) for U18 players than the U23 and 1ST team players. Sprint distance and high-intensity bursts distance were lower (small) in U18 compared with the U23 and 1ST team. DSL was greater in 1ST compared with U18 (small) and U23 (small). This study reported that the differences between groups were greater for HSR, sprint distance, high-intensity bursts distance, and DSL, while total distance, high metabolic load distance, explosive distance, and speed intensity did not differ between the groups. These findings could be used to design training programs in the academy players (i.e., U18) to achieve the required long-term physical adaptations that are needed to progress into the U23 and 1ST teams.

Self-Learning Mechanism for Mobile Game Adjustment towards a Player

Mobile app markets have faced huge expansion during the last decade. Among different apps, games represent a large portion with a wide range of game categories having consumers in all age groups. To make a mobile game suitable for different age categories, it is necessary to adjust difficulty levels in such a way to keep the game challenging for different players with different playing skills. The mobile app puzzle game Wonderful Animals has been developed consisting of puzzles, find pairs and find differences game (available on the Google Play Store). The game testing was conducted on a group of 40 players by recording game level completion time and conducting a survey of their subjective evaluation of completed level difficulty. The study aimed to find a mechanism to adjust game level difficulty to the individual player taking into account the player’s achievements on previously played games. A pseudo-algorithm for self-learning mechanism is presented, enabling level difficulty adaptation to the player. Furthermore, player classification into three classes using neural networks is suggested in order to offer a user-specific playing environment. The experimental results show that the average recognition rate of the player class was 96.1%.

Position specific peak impact and running demands of professional rugby union players during game play

This study quantifies the maximum number of impacts and peak running demands during 1– to 10– minute rolling window periods in elite rugby union matches using a multi-team dataset (n = 2232 player-games). Maximum values for impacts (impacts·min−1) and running (m·min−1) were calculated for 161 athletes from four teams across the 2018 and 2019 Super Rugby seasons. The effect of window duration and playing position on peak impact and running demands were estimated using linear mixed effect models and prediction intervals. The peak impact and running demands for a 1-min period were 4.5 – 5.5 impacts·min−1 and 150 – 180 m·min−1, depending on playing position. While small variations in mean impact and running movements could be observed by position, the large prediction interval and individual player variation meant that there was no practically meaningful difference by position. As such, when prescribing training drills to replicate the peak demands in rugby union, impact and running movements of players can be similar, regardless of position. Using a prediction interval allows us to identify the range where the demands in a future game may fall, and are beneficial to use when also trying to prepare players for the demands of rugby union.

Injury rates decreased in men’s professional football: an 18-year prospective cohort study of almost 12 000 injuries sustained during 1.8 million hours of play

BackgroundThe UEFA Elite Club Injury Study is the largest and longest running injury surveillance programme in football.ObjectiveTo analyse the 18-season time trends in injury rates among male professional football players.Methods3302 players comprising 49 teams (19 countries) were followed from 2000–2001 through 2018–2019. Team medical staff recorded individual player exposure and time-loss injuries.ResultsA total of 11 820 time-loss injuries were recorded during 1 784 281 hours of exposure. Injury incidence fell gradually during the 18-year study period, 3% per season for both training injuries (95% CI 1% to 4% decrease, p=0.002) and match injuries (95% CI 2% to 3% decrease, p<0.001). Ligament injury incidence decreased 5% per season during training (95% CI 3% to 7% decrease, p<0.001) and 4% per season during match play (95% CI 3% to 6% decrease, p<0.001), while the rate of muscle injuries remained constant. The incidence of reinjuries decreased by 5% per season during both training (95% CI 2% to 8% decrease, p=0.001) and matches (95% CI 3% to 7% decrease, p<0.001). Squad availability increased by 0.7% per season for training sessions (95% CI 0.5% to 0.8% increase, p<0.001) and 0.2% per season for matches (95% CI 0.1% to 0.3% increase, p=0.001).ConclusionsOver 18 years: (1) injury incidence decreased in training and matches, (2) reinjury rates decreased, and (3) player availability for training and match play increased.