A Data Science approach analysing the Impact of Injuries on Basketball Player and Team Performance

Quantifying momentum and influencing factors of tennis players using the XGBoost model

Momentum can directly or indirectly affect a tennis player's mentality and the trajectory of the game, thereby changing the outcome of the match. The article provides a clear quantitative description of the concept of momentum in tennis, analyzing the impact of momentum fluctuations on the trajectory of the game and the athlete's scoring; a secondary indicator system is established, and the weights of each indicator are determined through expert analysis method, CRITIC weighting method, and hierarchical analysis method. The final value of momentum is randomly tested with a random walk model. The results show that momentum is not random but influenced by specific factors, indicating that there is a certain correlation between the fluctuation of momentum and the success of the player. Due to the non-normal distribution of momentum, using the XGBoost model and Shap feature importance analysis can determine the significant influence of factors such as the distance run by athletes and the speed at which they hit the tennis ball. Moreover, the model demonstrated excellent performance in five randomly selected matches, with low error indicators and high fitting degrees, and an R2 coefficient of up to 0.9814, showing the model's high precision and strong generalization capability.

Development of sequential winning-percentage prediction model for badminton competitions: applying the expert system sequential probability ratio test

BACKGROUND

This study developed a sequential winning-percentage prediction model for badminton competitions using the expert system sequential probability ratio test (EXSPRT), aiming to calculate the difficulty of each event within a match and establish the initial prior probability.

METHODS

We utilized data from 100 men's singles matches (222 games) held by the Badminton World Federation (BWF) in 2018 to evaluate event difficulty across six models for each determining factor. For setting the initial prior probability calculation method, 30 men's singles matches (74 games) organized by the BWF in 2019 were randomly selected. The odds for these matches were obtained from www.oddsportal.com .

RESULTS

The efficacy of the six models was assessed based on application rates (15%, 20%, 25%, and 30%) of the collected odds, with the initial prior probability reflecting 25% of the odds chosen owing to its superior validity.

CONCLUSIONS

This research yielded six sequential winning percentage prediction models capable of offering real-time predictions during matches in badminton competitions by leveraging EXSPRT. These models enhance spectator engagement and provide foundational data for developing similar prediction models for other sports. Future research should focus on developing a program to identify the most effective model among the six and implement it practically.

The effects of blood flow restriction combined with endurance training on athletes' aerobic capacity, lower limb muscle strength, anaerobic power and sports performance: a meta-analysis

OBJECTIVE

To evaluate the effects of blood flow restriction (BFR) combined with endurance training on aerobic capacity, lower limb muscle strength, anaerobic power, and sports performance to supply effective scientific guidance for training. Two reviewers independently screened the literature, extracted data, and assessed the risk of bias of the included studies. We searched PubMed, Medline, Cochrane, SPORTDiscus and Web of Science databases up to 28 October 2024. Two reviewers independently screened the literature, extracted data, and assessed the risk of bias of the included studies. We calculated the effect size using standardized mean difference values and the random effects model. The results showed a medium effect size on maximal oxygen uptake (V̇O2max), a large effect size on lower limb muscle strength, a small effect size on anaerobic power and sports performance. In conclusion, while BFR training during endurance training had a significant positive effect on lower limb muscle strength and moderate improvement in V̇O2max, its impact on anaerobic power and sports performance was relatively small. These findings suggest that BFR training may be effective for enhancing muscle strength and aerobic capacity, but its benefits on anaerobic power and sport-specific performance may be limited. Therefore, it is important to carefully design BFR training programs to target specific outcomes.

Machine learning models for reinjury risk prediction using cardiopulmonary exercise testing (CPET) data: optimizing athlete recovery

BACKGROUND

Cardiopulmonary Exercise Testing (CPET) provides detailed insights into athletes' cardiovascular and pulmonary function, making it a valuable tool in assessing recovery and injury risks. However, traditional statistical models often fail to leverage the full potential of CPET data in predicting reinjury. Machine learning (ML) algorithms offer promising capabilities in uncovering complex patterns within this data, allowing for more accurate injury risk assessment.

OBJECTIVE

This study aimed to develop machine learning models to predict reinjury risk among elite soccer players using CPET data. Specifically, we sought to identify key physiological and performance variables that correlate with reinjury and to evaluate the performance of various ML algorithms in generating accurate predictions.

METHODS

A dataset of 256 elite soccer players from 16 national and top-tier teams in Iran was analyzed, incorporating physiological variables and categorical data. Several machine learning models, including CatBoost, SVM, Random Forest, and XGBoost, were employed to predict reinjury risk. Model performance was assessed using metrics such as accuracy, precision, recall, F1-score, AUC, and SHAP values to ensure robust evaluation and interpretability.

RESULTS

CatBoost and SVM exhibited the best performance, with CatBoost achieving the highest accuracy (0.9138) and F1-score (0.9148), and SVM achieving the highest AUC (0.9725). A significant association was found between a history of concussion and reinjury risk (χ² = 13.0360, p = 0.0015), highlighting the importance of neurological recovery in preventing future injuries. Heart rate metrics, particularly HRmax and HR2, were also significantly lower in players who experienced reinjury, indicating reduced cardiovascular capacity in this group.

CONCLUSION

Machine learning models, particularly CatBoost and SVM, provide promising tools for predicting reinjury risk using CPET data. These models offer clinicians more precise, data-driven insights into athlete recovery and risk management. Future research should explore the integration of external factors such as training load and psychological readiness to further refine these predictions and enhance injury prevention protocols.

A Basketball Big Data Platform for Box Score and Play-by-Play Data

This is the second part of a research diptych devoted to improving basketball data management in Spain. The Spanish ACB (Association of Basketball Clubs, acronym in Spanish) is the top European national competition. It attracts most of the best foreign players outside the NBA (National Basketball Association, in North America) and also accelerates the development of Spanish players who ultimately contribute to the success of the Spanish national team. However, this sporting excellence is not reciprocated by an advanced treatment of the data generated by teams and players, the so-called statistics. On the contrary, their use is still very rudimentary. An earlier article published in this journal in 2020 introduced the first open web application for interactive visualization of the box score data from three European competitions, including the ACB. Box score data refer to the data provided once the game is finished. Following the same inspiration, this new research aims to present the work carried out with more advanced data, namely, play-by-play data, which are provided as the game runs. This type of data allow us to gain greater insight into basketball performance, providing information that cannot be revealed with box score data. A new dashboard is developed to analyze play-by-play data from a number of different and novel perspectives. Furthermore, a comprehensive data platform encompassing the visualization of the ACB box score and play-by-play data is presented.

Force plate methodologies applied to injury profiling and rehabilitation in sport: A scoping review protocol

Musculoskeletal injuries are a common health problem among sporting populations. Such injuries come with a high financial burden to the involved organisations and can have a detrimental impact on the career attainment of injured individuals. Force plates are now a common tool available to sport and exercise science and medicine professionals to enable them to profile injury risk predisposition and modulate the rehabilitation process within sporting environments. This is because contemporary force plate technology is portable and affordable and often comes with software that enables the automatic and immediate feedback of test variables to key stakeholders. However, to our knowledge, to date, there has been no comprehensive review of the scientific literature pertaining to clinical applications of force plate technology. Therefore, this article presents a protocol and a methodological framework to perform a scoping review to identify and map the available scientific literature in which force plates have been applied to the injury profiling and rehabilitation of athletes. The specific aims of the scoping review are 1) to identify and describe the force plate tests, methodologies, and metrics used to screen for injury risk and guide the return of injured athletes to full-time training and competition, 2) to identify potential trends and/or differences by participants' age, sex, and/or level of performance in tests, methodologies, and metrics selected, and 3) to identify key gaps in the existing evidence base and new questions that should be addressed in future research. The global aim of the scoping review is to improve practitioner decision-making around force plate test and variable selection when applied to the injury prevention and rehabilitation of sporting populations.

Relationships between contract status and player performance in the Australian Football League

This study analysed the extent to which player performance differs within the Australian Football League (AFL) with respect to the status of a player's contract. AFL Player Ratings (AFLPR) and contract data were obtained during the 2013-2020 AFL seasons for all 827 players listed by an AFL club at the beginning of the 2020 season. A model of "expected performance" was created allowing for an exploration into the differential with actual performance as a function of contract status. Paired t-tests indicated that there was a difference in performance pre- and post-signing their contract for players who signed mid-season (mean change and 95% confidence interval of -1.48 ± 0.93 and -0.49 ± 0.48 AFLPR, at ten match intervals for those in- and out-of-contract at the conclusion of that year's season, respectively). Further differences existed between the groups of players who signed mid-season, as compared to those who signed during the off-season. Correlation analyses indicated that more consistent performers are somewhat less likely to see a reduction in performance post signing as compared to less consistent performers. The applications of these findings have the potential to support organisational decisions relating to the timing and nature of player contracting.

EFFECTS OF HIGH-INTENSITY TRAINING ON BASKETBALL PLAYERS

ABSTRACT Introduction: Basketball represents much more than a high-intensity exercise. Like most ball games, it is a continuous movement system. Objective: Study the effect of high-intensity interval training (HIT) on the aerobic metabolism of young basketball players. Methods: The author randomly divided male basketball players into an upper limb HIT group, lower limb HIT group, and control group by experimental method and statistical analysis, the control group received routine training, and aerobic exercise capacity was measured by increasing load test before and after the experiment. Results: During the lower extremity experiment, the mean power (MP) and peak power (PP) of the 4th full-force pedal stroke in the lower extremity HIT group increased (P<0.05), and the T/C ratio of the lower extremity HIT group was also implemented (P<0.05). There was no significant change in the indices of the control group (P>0.05). Conclusion: Upper extremity HIT in young male basketball players improved only upper extremity aerobic exercise capacity. In contrast, lower-extremity HIT improved both upper-extremity aerobic exercise capacity and lower-extremity anaerobic exercise capacity. Level of evidence II; Therapeutic studies - investigating treatment outcomes.

STRENGTHENING THE ABDOMINAL CORE ON BALANCE IN BASKETBALL PLAYERS

ABSTRACT Introduction: Basketball has the characteristics of physical struggle. This demands from its training and teaching that the participants and coaches work on the qualities of strength, speed, and tactics of the sport. Objective: Study the impacts of abdominal core strengthening on balance and agility in basketball players. Methods: After a literature survey, we used basketball players as experimental volunteers, divided into experimental and control groups, to validate an experimental protocol to strengthen the abdominal core added to routine training. T-run tests and Z-run tests were also performed before and after training. The method of mathematical statistics was used to compare the results in the research-relevant data Results: After training, the two sensitivity test results in the young basketball players were significantly higher than the control group, with significant differences (P<0.05), there was no significant difference in the two sensitive quality indices of the control group athletes before and after training (P>0.05). Conclusion: It was evidenced that abdominal core stability training plays a positive role in improving the balance and agility of young basketball players. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.

Study on Strength and Quality Training of Youth Basketball Players

In order to scientifically explore the effective path of strength quality training of basketball players and improve the effect of strength quality training of basketball players, this paper takes young basketball players as the research object and comprehensively observes the changes and improvement of strength quality by building a strength training monitoring system for basketball players. On this basis, it is proposed to integrate blood flow restriction and basketball players' special strength training. Through the comparison with the traditional resistance strength training method, it is found that after 8 weeks of experimental comparison, the athletes' strength quality test indicators show that the average 3RM of the experimental group 1 bench press is 65.2 kg, the experimental group 2 is 65.7 kg, and the experimental group 3 is 72.2 kg. The average performance of the traditional control group was 55.4 kg. Compared with the traditional group, the average performance of the three experimental groups in bench press was significantly improved, which also verified the feasibility of this method in strength quality training.

The Attack-Block-Court Defense Algorithm: A New Volleyball Index Supported by Data Science

Spiker–blocker encounters are a key moment for determining the result of a volleyball rally. The characterization of such a moment using physical–statistical parameters allows us to reproduce the possible ball’s trajectory and then make calculations to set up the defense in an optimal way. In this work, we present a computational algorithm that shows the possible worst scenarios of ball trajectories for a volleyball defense, in terms of the covered area by the block and the impact time of the backcourt defense to contact the ball before it reaches the floor. The algorithm is based on the kinematic equations of motion, trigonometry, and statistical parameters of the players. We have called it the Attack-Block-Court Defense algorithm (the ABCD algorithm), since it only requires the 3D-coordinates of the attacker and the blocker, and a discretized court in a number of cells. With those data, the algorithm calculates the percentage of the covered area by the blocker and the time at which the ball impacts the court (impact time). More specifically, the structure of the algorithm consists of setting up the spiker’s and blocker’s locations at the time the spiker hits the ball, and then applying the kinematic equations to calculate the worst scenario for the team in defense. The case of a middle-hitter attack with a single block over the net is simulated, and an analysis of the space of input variables for such a case is performed. We found a strong dependence on the average impact time and the covered area on both the attack–block height’s ratio and the attack height. The standard deviation of the impact time was the variable that showed more asymmetry, respecting the input variables. An asymmetric case considering more variables with a wing spiker and three blockers is also shown, in order to illustrate the potential of the model in a more complex scenario. The results have potential applications, as a supporting tool for coaches in the design of customized defense or attack systems, in the positioning of players according to the prior knowledge of the opponent team, and in the development of replay and video-game technologies in multimedia systems.

Goal or Miss? A Bernoulli Distribution for In-Game Outcome Prediction in Soccer

Due to a colossal soccer market, soccer analysis has attracted considerable attention from industry and academia. In-game outcome prediction has great potential in various applications such as game broadcasting, tactical decision making, and betting. In some sports, the method of directly predicting in-game outcomes based on the ongoing game state is already being used as a statistical tool. However, soccer is a sport with low-scoring games and frequent draws, which makes in-game prediction challenging. Most existing studies focus on pre-game prediction instead. This paper, however, proposes a two-stage method for soccer in-game outcome prediction, namely in-game outcome prediction (IGSOP). When the full length of a soccer game is divided into sufficiently small time frames, the goal scored by each team in each time frame can be modeled as a random variable following the Bernoulli distribution. In the first stage, IGSOP adopts state-based machine learning to predict the probability of a scoring goal in each future time frame. In the second stage, IGSOP simulates the remainder of the game to estimate the outcome of a game. This two-stage approach effectively captures the dynamic situation after a goal and the uncertainty in the late phase of a game. Chinese Super League data have been used for algorithm training and evaluation, and the results demonstrate that IGSOP outperforms existing methods, especially in predicting draws and prediction during final moments of games. IGSOP provides a novel perspective to solve the problem of in-game outcome prediction in soccer, which has a potential ripple effect on related research.

Kinect and Few-Shot Technology-Based Simulation of Physical Fitness and Health Training Model for Basketball Players in Plateau Area

Players in modern basketball have a lot of physical contact, a lot of bumps, and a lot of physical struggles. The competition for the ball, whether in the air or on the ground, is fierce, putting higher demands on the players' physical abilities. Coaches frequently use plateau physical training, which is very effective in developing athletes' cardiopulmonary function, among many other training methods. The proportional length and active area of arms are obtained using the skin color model of the human body, the angle and posture information of each joint is extracted from dynamics, and the 3D posture of arms and dynamic arms is trained and recognized in this paper, which is based on Kinect. The findings revealed that mild hypoxia in the plateau significantly lowered basketball players' performance and that basketball players' maximum heart rate and 1-minute heart rate recovery in high-intensity exercise were lower than those in flat area training.

Optimization of a Sports Activity Development Model Using Artificial Intelligence under New Curriculum Reform

The recent curriculum reform in China puts forward higher requirements for the development of physical education. In order to further improve students' physical quality and motor skills, the traditional model was improved to address the lack of accuracy in motion recognition and detection of physical condition so as to assist teachers to improve students' physical quality. First, the physical education teaching activities required by the new curriculum reform were studied with regard to the actual needs of China's current social, political, and economic development; next, the application of artificial intelligence technology to physical education teaching activities was proposed; and finally, deep learning technology was studied and a human movement recognition model based on a long short-term memory (LSTM) neural network was established to identify the movement state of students in physical education teaching activities. The designed model includes three components: data acquisition, data calculation, and data visualization. The functions of each layer were introduced; then, the intelligent wearable system was adopted to detect the status of students and a feedback system was established to assist teaching; and finally, the dataset was constructed to train and test the designed model. The experimental results demonstrate that the recognition accuracy and loss value of the training model meet the practical requirements; in the algorithm test, the motion recognition accuracy of the designed model for different subjects was greater than 97.5%. Compared with the traditional human motion recognition algorithm, the designed model had a better recognition effect. Hence, the designed model can meet the actual needs of physical education. This exploration provides a new perspective for promoting the intelligent development of physical education.

Hybrid Basketball Game Outcome Prediction Model by Integrating Data Mining Methods for the National Basketball Association

The sports market has grown rapidly over the last several decades. Sports outcomes prediction is an attractive sports analytic challenge as it provides useful information for operations in the sports market. In this study, a hybrid basketball game outcomes prediction scheme is developed for predicting the final score of the National Basketball Association (NBA) games by integrating five data mining techniques, including extreme learning machine, multivariate adaptive regression splines, k-nearest neighbors, eXtreme gradient boosting (XGBoost), and stochastic gradient boosting. Designed features are generated by merging different game-lags information from fundamental basketball statistics and used in the proposed scheme. This study collected data from all the games of the NBA 2018-2019 seasons. There are 30 teams in the NBA and each team play 82 games per season. A total of 2460 NBA game data points were collected. Empirical results illustrated that the proposed hybrid basketball game prediction scheme achieves high prediction performance and identifies suitable game-lag information and relevant game features (statistics). Our findings suggested that a two-stage XGBoost model using four pieces of game-lags information achieves the best prediction performance among all competing models. The six designed features, including averaged defensive rebounds, averaged two-point field goal percentage, averaged free throw percentage, averaged offensive rebounds, averaged assists, and averaged three-point field goal attempts, from four game-lags have a greater effect on the prediction of final scores of NBA games than other game-lags. The findings of this study provide relevant insights and guidance for other team or individual sports outcomes prediction research.