Abstract The purpose of the current investigation was to describe transfers between Icelandic youth sports and to compare drop-out from sport between those doing single sports, those doing multiple sports without transferring, and those transferring between sports. 11,382,013 youth sport invitation records sent to over 40,925 young athletes over a two-year period were analysed. Drop-out and transfers between sports were determined using the first and last attendances of players in different sports. There was net transfer from gymnastics and swimming to other sports, as well as a net transfer from soccer to handball and basketball. Girls had a net transfer from athletics and individual games to team games while boys had a net transfer from team games to athletics and individual games. The percentage of players dropping out of sport was 35.5% for those doing a single sport, 6.5% for players doing multiple sports without transferring, and 18.1% for players doing multiple sports over the two-year period and transferring between sports. These differences between drop-out rates were significant for both girls (p < 0.001) and boys (p < 0.001). Young people should be encouraged to participate in multiple sports to avoid dropping out of sport before they become adults.

Abstract From the observational methodology approach, this study analyses definitive errors or losing blunders, i.e. errors that result in the loss of the game, in elite players at U8 level. An ad hoc observation instrument has been designed as a combination of field format and category systems, based on a thorough theoretical review of the internal logic of chess. The games were compiled in the ChessBase 17 program and analysed using Stockfish 16 NNUE via https://lichess.org/es. The moment in the game when the error occurs is extracted and recorded and coded using Lince software. The reliability of the records from the observation system developed was guaranteed by interobserver agreement, calculated using Cohen’s Kappa coefficient. This paper’s objective is achieved by means of the decision tree analysis technique, obtained using the CHAID procedure, taking the “impact of the error” as the predicted dimension. The results obtained have allowed us to conclude that the errors that lead to the loss of the game for elite U8 players are related to short-term calculation (tactical motifs, undefended pieces or checkmate) as opposed to long-term strategic errors.

Abstract This study evaluates the predictive power of common performance indicators (PIs) in soccer for success- or scoring-related events (SREs) such as shots, corner kicks, and box entries. Using data from 102 Bundesliga matches, we applied five machine learning methods to assess how well 28 widely used PIs (e.g., passes, ball possession time, opponents outplayed) within a past time span (up to 15 minutes) predict an SRE in a future window (up to 15 minutes). We ranked PIs based on the mean Matthews Correlation Coefficient. Results show PI Dangerousity best predicts SRE Goal and SRE ShotTaken , while PI EntriesAttaThird is strongest for SRE Cornerkick , SRE EntryAttaThird , and SRE EntryOppBox . PI Dangerousity and PI SuccPassAttThird consistently rank in the Top 9, highlighting their predictive strength. Combining PI OutplayedOpp and PI TacklingsWon over a five-minute input window improves goal prediction within three minutes, outperforming random guessing by 6%. PIs based on rare events, such as goals and corner kicks, are less effective for SRE prediction, whereas those capturing frequent actions (e.g., final-third possession, Dangerousity, outplayed opponents) perform better. These findings highlight the value of in-game data for short-term event prediction and its potential applications in quantifying match momentum, optimizing live betting odds, and improving performance analysis.

Abstract This paper presents a novel approach to analyzing basketball games. It uses image processing techniques to track player movements, evaluate passes and shots, and visualize game dynamics. The system employs player and ball detection methods, leveraging appearance embedding-based particle filters for robust tracking across consecutive frames. We generate trajectory diagrams that provide insights into team strategies and player performance by applying projective transformation to map coordinates from player feet to the basketball court. Key challenges addressed include improving tracking accuracy under dynamic conditions, minimizing over-detections in pass and shot judgment, and refining ball possession calculations. Experimental results show high tracking accuracy for players, but lower performance in ball tracking and shot detection, particularly in high-speed movements or when objects are occluded. The analysis also revealed that player and team behaviors, such as passing success rates and movement patterns, could be effectively visualized through trajectory diagrams. While the current system provides valuable insights into game strategies, further improvements are needed, particularly in enhancing the reliability of tracking, judgment of passes and shots, and clarity of trajectory in dense sequences of plays.

Abstract Football match result prediction is a challenging task that has been the subject of much research. Traditionally, predictions have been made by team managers, fans, and analysts based on their knowledge and experience. However and recently there has been an increased interest in predicting match outcomes using statistical techniques and machine learning. These algorithms can learn from historical data to identify complex relationships between different variables, and then make predictions about the outcome of future matches. Accordingly, forecasting plays a pivotal role in assisting managers and clubs in making well-informed decisions geared toward securing victories in leagues and tournaments. In this paper, we presented an approach, which is generally applicable in all areas of sports, to forecast football match results based on three stages. The first stage involves identifying and collecting the occurred events during a football match. As a multiclass classification problem with three classes, each match can have three possible outcomes. Then, we applied multiple machine learning algorithms to compare the performance of those different models, and choose the one that performs the best. As a final step, this study goes through the critical aspect of model interpretability. We used the SHapley Additive exPlanations (SHAP) method to decipher the feature importance within our best model, focusing on the factors that influence match predictions. Experiment results indicate that the Multilayer Perceptron (MLP), a neural network algorithm, was effective when compared to various other models and produced competitive results with prior works. The MLP model has achieved 0.8342 for accuracy. The particular significance of this study lies in the use of the SHAP method to explain the predictions made by the MLP model. Specifically, by exploiting its graphical representation to illustrate the influence of each feature within our dataset in predicting the outcome of a football match.

Abstract This study presents a machine learning-based approach to predicting the outcosmes of NBA games, with the aim of enhancing decision-making in sports betting and performance analysis. Using a dataset spanning 20 NBA seasons (2003–2023), we incorporated key features such as team statistics, player performance metrics, and external factors like team fatigue and rankings. The methodology followed the CRISP-DM process, involving data preprocessing, feature selection, and model evaluation. We experimented with multiple classification algorithms, including Logistic Regression, Random Forest, Gradient Boosting, and ensemble methods, to identify the best-performing models. Feature selection techniques such as LASSO and decision tree-based methods were employed to optimize model performance. Our best model, combining team rankings, statistics, and fatigue factors, achieved an accuracy rate of 64.1% and an F1 score of 72.4%, reflecting the complexity of NBA game outcome prediction. The study highlights the importance of key features like team rankings and the challenges posed by the dynamic nature of the NBA. Future research will explore additional qualitative factors, such as emotional states and team dynamics, and employ more advanced machine learning techniques like deep learning to further improve prediction accuracy.

Abstract Evaluating the quality of shots in basketball is crucial and requires considering the context in which they are taken. We introduce a graph neural network to process a graph based on player and ball tracking data to compute expected shot quality. We evaluate this model against other models focusing on calibration. The messages between spatial and temporal features are separated, and an attention mechanism is implemented, making the graph neural network interpretable. We use the GNNExplainer to further show the importance of node features. To demonstrate possible practical applications, we analyse the embeddings of the graph neural network concerning different situations like the mean of all player predictions or similarity between created shots and compare this to existing methods.

Abstract Analyzing dual-lane speed climbing videos provides critical insights into data-driven performance evaluation in sports climbing. This study introduces an enhanced deep learning approach based on 3D ResNets to classify and analyze speed climbing states. Leveraging an annotated dataset of 872 high-resolution videos covering 15 state combinations, the model integrates optimized 3D convolutions and residual connections, achieving significant improvements in classification accuracy and computational efficiency. With a test accuracy of 92.78%, the model significantly outperforms 2D CNNs and C3D models. Additionally, its lightweight architecture and reduced computational complexity equip it with the potential for real-time deployment in controlled environments. While challenges such as data imbalance and limited generalization remain, this research provides a robust technical framework for speed climbing video analysis and lays the groundwork for broader applications in spatiotemporal modeling and intelligent sports analytics.

Abstract Studies to understand the shooting preferences of basketball players relied exclusively on data on shot location, which did not lead to concrete understandings because they contained no information on how they moved to that location. Therefore, this study tried to cluster the players' shooting tendencies using the tracking data of the players' movements during the game. To do this, we first created hand-crafted shot features that included information on the pre-shot movement. Using those features, the dissimilarity of shooting tendencies between players was computed by considering the shot set of each player as a probability distribution and calculating the Wasserstein distance between them. The clustering based on their dissimilarity resulted in more clusters than in previous studies and allowed for specific shooting styles to be defined. Clustering using Gower distance as a dissimilarity measure for shot features, including a categorical feature, extracted clusters of shots that are useful for understanding players' more detailed shooting tendencies. These results prove that it is not only the shot location but also how the player moved before the shot that is important to capture the player's shooting preferences.

Abstract Computer sports methods use computational techniques to analyse and optimise athletic performance. Computer vision (CV) has emerged as a tool that offers objective data on techniques and tactics. Depth camera technology can support markerless kinematic analyses. This systematic review, following the Preferred Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, examined the integration and impact of depth camera technology in sports biomechanics over the past decade. Using databases such as PubMed, Web of Science, and Scopus, we identified and analysed 14 relevant studies. Depth cameras such as Microsoft Kinect and Intel RealSense have been used to analyse performance in various sports by providing biomechanical feedback in real time, improving athlete training, and implementing injury prevention strategies. This review highlights the technology’s cost-effectiveness and accessibility, extending from elite sports to community programs. It suggests further advancements with AI and machine learning to enhance personalised training and integrate virtual and augmented reality, which is promising for the development of sports biomechanics.