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Basketball games, real-time adjustment of defense strategy according to the changes on the court can greatly improve the team's chance of success in defense and thus win the game. The development of artificial intelligence technology, especially computer vision, provides a technical basis for real-time tracking of players' positions and body postures. In this paper, a player posture estimation algorithm based on local spatial constraints is proposed based on motion tracking technology and deep learning technology. The algorithm is based on the general human body pose estimation algorithm, which locally constrains the picture based on the athlete detection frame, retains only the content related to the athlete in the picture, and then inputs the processed picture into the general human body pose estimation model for pose detection, and finally maps the detected pose back to the original picture to complete the estimation of the athlete's pose. The model is verified to be able to efficiently complete the real-time defensive strategy optimization task by using NBA game videos for testing. [ABSTRACT FROM AUTHOR]

This study investigated the application of artificial intelligence in real-time prediction of professional basketball games, identifying the variations within performance indicators that are critical in determining the outcomes of the games. Utilizing games data from the NBA seasons 2021 to 2023 as the sample, the study constructed a real-time predictive model for NBA game outcomes, integrating the machine learning XGBoost and SHAP algorithms. The model simulated the prediction of game outcomes at different time of games and effectively quantified the analysis of key factors that influenced game outcomes. The study's results demonstrated that the XGBoost algorithm was highly effective in predicting NBA game outcomes. Key performance indicators such as field goal percentage, defensive rebounds, and turnovers were consistently related to the outcomes at all times during the game. In the first half of the game, assists were a key indicator affecting the outcome of the game. In the second half of the games, offensive rebounds and three-point shooting percentage were key indicators affecting the outcome of the games. The performance of the real-time prediction model for NBA game outcomes, which integrates machine learning XGBoost and SHAP algorithms, is found to be excellent and highly interpretable. By quantifying the factors that determine victory, it is able to provide significant decision support for coaches in arranging tactical strategies on the court. Moreover, the study provides reliable data references for sports bettors, athletes, club managers, and sponsors. [ABSTRACT FROM AUTHOR]

This research investigates the recognition of basketball techniques actions through the implementation of three-dimensional (3D) Convolutional Neural Networks (CNNs), aiming to enhance the accurate and automated identification of various actions in basketball games. Initially, basketball action sequences are extracted from publicly available basketball action datasets, followed by data preprocessing, including image sampling, data augmentation, and label processing. Subsequently, a novel action recognition model is proposed, combining 3D convolutions and Long Short-Term Memory (LSTM) networks to model temporal features and capture the spatiotemporal relationships and temporal information of actions. This facilitates the facilitating automatic learning of the spatiotemporal features associated with basketball actions. The model's performance and robustness are further improved through the adoption of optimization algorithms, such as adaptive learning rate adjustment and regularization. The efficacy of the proposed method is verified through experiments conducted on three publicly available basketball action datasets: NTURGB + D, Basketball-Action-Dataset, and B3D Dataset. The results indicate that this approach achieves outstanding performance in basketball technique action recognition tasks across different datasets compared to two common traditional methods. Specifically, when compared to the frame difference-based method, this model exhibits a significant accuracy improvement of 15.1%. When compared to the optical flow-based method, this model demonstrates a substantial accuracy improvement of 12.4%. Moreover, this method showcases strong robustness, accurately recognizing actions under diverse lighting conditions and scenes, achieving an average accuracy of 93.1%. The research demonstrates that the method reported here effectively captures the spatiotemporal relationships of basketball actions, thereby providing reliable technical assessment tools for basketball coaches and players. [ABSTRACT FROM AUTHOR]

In close basketball games where the final score is within a 5-point difference, end-of-game possessions are very important. However, previous studies have not examined the efficiency of various play types' possessions regarding player positions. Therefore, the study aimed to identify play type actions during end-of-game possessions across player positions (guard, forward, and big) that directly influence the possession's outcome. The analysis evaluated 1009 possessions from 100 EuroLeague games from the 2022-2023 and 2023-2024 regular seasons using Sport Scout observation software (SportScout Group, Thessaloniki, Greece). The variables observed included isolation (ISO), pick and roll ball handler (PnRBH), pick and roll screener (PnRSC), off-ball screens (OBS), transition (TR), cuts (CUT), spot-up shooting (SUP), post-ups (PUP), handoffs (HO), inbound plays (IN), and putbacks (PB). Possession efficiency was evaluated based on points per possession (PPP) and the possession's outcome (positive or negative). Chi-square (χ2) tests compared categorical variables, while the Kruskal-Wallis test assessed PPP differences across player positions. Additionally, the chi-square automatic interaction detector (CHAID) decision tree model was used to classify data and make meaningful predictions for the possession play types. The findings revealed that player positions significantly influenced possession efficiency, with noticeable variations in possession distribution. Decision tree analysis underscored the impact of possession play types on outcomes across player positions. In conclusion, the study highlights the predominant role of guards in end-of-game possessions, relying heavily on isolation plays but achieving higher efficiency with teamwork-oriented strategies. Forwards demonstrate effectiveness in offensive rebounding situations and off-ball movement, while big players exhibit efficiency in proximity to the basket. [ABSTRACT FROM AUTHOR]

Introduction: The confluence of physiological and psychological dynamics is fundamental to athletic performance, particularly in basketball, where physical skill and mental resilience are imperative. While the role of verbal encouragement (VE) as a catalyst for enhancing performance has been explored in various sports disciplines, its specific effects within the basketball have not been adequately examined. Addressing this gap, the current study zeroes in on the influence of coach-delivered VE on the physiological and psychological responses of adolescent basketball players engaged in small-sided games (SSG), providing a focused analysis of how directed encouragement can modulate performance and experience in young athletes. This study aimed to investigate the effects of coach-delivered verbal encouragement on the psychological and physiological responses of adolescent basketball players. Methods: Sixteen male participants (age: 16.93 ± 0.36 years; height: 176.8 ± 0.8 cm; body mass: 73.43 ± 12.57 kg; BMI: 21.70 ± 3.55) were allocated to a Verbal Encouragement Group (VEG, n = 8, mean age: 16.80 ± 0.44) and a Control Group (CG, n = 8, mean age: 17.06 ± 0.26). Each participant engaged in four sessions of small-sided games (SSGs) consisting of four players per side in a 14 x 10 m pitch. Results: The findings revealed significant benefits of coach-delivered verbal encouragement on both the physical and psychophysiological responses of the players (p < 0.05), including increased physical enjoyment, positive mood state, lower heart rate, and higher physical activity intensity level. Discussion: Coaches should incorporate verbal encouragement strategies during SSGs to enhance player performance and optimize both psychological and physiological adaptations. [ABSTRACT FROM AUTHOR]