International Journal of Computer Science in Sport Feed

Deep Learning Sequence Network for Identifying and Analyzing Archery Shooting Patterns

Sun, 03 May 2026 00:00:00 GMT

This study presents a deep learning-based system designed to enhance archery performance by analyzing athletes shooting motions and providing personalized feedback. Video data of four national-level Korean archers were collected between February and May 2024, and 17-joint coordinate data were extracted using pose estimation techniques. The full shooting sequence—from ready position to release—was captured and normalized for consistent analysis. Multiple deep learning sequence models, including RNN, LSTM, GRU, Bi-LSTM, and Bi-GRU, were implemented and evaluated to determine the most effective approach for recognizing distinctive motion patterns of individual archers. The developed system enables objective quantification of motion characteristics, supporting personalized training feedback and performance enhancement. Hyperparameters were optimized using Optuna, and early stopping was applied to prevent overfitting. The system visualized motion consistency and identified joints with high error rates, allowing athletes to recognize and correct deviations in real time. By quantifying individual motion characteristics, the system facilitated the design of personalized training programs, ultimately improving technical performance. This approach offers a novel method for ongoing monitoring and performance evaluation, demonstrating significant potential not only for archery but also for other precision-based sports.

A Framework for Automated Player Identification and Positioning Using Low-Cost Hardware in the Soccer Domain

Thu, 05 Mar 2026 00:00:00 GMT

Soccer has reached a high technical, physical, and tactical level, making data use increasingly common among major clubs. Advances in extracting and processing players’ data have encouraged more teams to explore this information. However, acquiring specialized data remains a challenge due to its complexity and high costs, which limits analysis and research. Publicly available data often include basic information like match results and player lineups, while commercial data, generated manually by individuals watching games, lack consistency. Positional data, crucial for advanced analysis, are typically obtained through expensive wearable GPS devices, limiting access to major soccer clubs. This research aims to propose a low-cost computer vision framework, named Advanced Player Identification and Positioning System, that is useful for extracting player positional data from television broadcast images. This approach enables the creation of advanced datasets, from which essential information for soccer can be extracted, such as tactical formation, for example. The proposed framework was divided into four steps: detection, tracking, identification, and positioning. Experiments were conducted across all stages of the APIPS system using the SoccerNet-GSR dataset, with its manual annotations serving as ground truth. The results indicate that player identification can be improved by the proposed temporal tracking strategy. Furthermore, except for a few outlier cases, the final player positioning error was below 5 meters in 91% of the evaluated instances.

Linear Programming Solutions to the Linear Ordering Problem in Major American Sports

Thu, 05 Mar 2026 00:00:00 GMT

We examine the efficacy of two linear programming models for optimizing the linear ordering problem (LOP) of ranking teams in major American sports, whether measured by head-to-head win counts or by collective victory margins. Using standard solution software, both models are tested over 42 problems involving full seasons from the National Football League, the National Basketball Association, the National Hockey League, and Major League Baseball, as well as major college football, baseball, and men’s basketball. We find that optimal solutions can be achieved rapidly for all four professional sports from either model, regardless of whether victory margins or win counts are used. However, solution speeds were much slower for the collegiate sports, particularly for baseball and men’s basketball where the number of teams were the largest, and especially when win counts were the performance measure. Such problems have a large and sparse pairwise comparison matrix with low values and variation in its non-zero elements, characteristics that have been found to be challenging in the general LOP literature. However, a modified minimum violations model demonstrated dramatic efficiency advantages when solving the collegiate cases, and illustrated that optimal solutions are largely achievable in reasonable time frames even for those sports.

The Anticipated Acceptance of Virtual Reality for Physical Activity with Special Consideration of a Self-Built Low-Cost Setup

Thu, 05 Mar 2026 00:00:00 GMT

The global decline in physical activity (PA) has stimulated interest in technology supported interventions such as virtual reality (VR). While VR-based PA can enhance motivation and enjoyment, its acceptance in the general population remains unclear. This study examined anticipated acceptance of VR for PA, with specific focus on a self-built low-cost setup. A total of 315 participants completed an online Technology Acceptance Model-based survey including Likert-scale and open-ended items. Structural equation modeling assessed predictors of acceptance for (1) commercially available VR and (2) a self-built low-cost setup. Overall acceptance was low, with mean intention-to-use scores below the scale midpoint for commercial VR (2.23 ± 1.18) and the low-cost setup (2.33 ± 1.26), despite moderately positive attitudes toward VR. For commercial VR, perceived ease of use and perceived usefulness significantly predicted acceptance, whereas perceived enjoyment was the strongest predictor for the low-cost setup. External variables such as age, prior use, curiosity, and willingness to pay showed limited or no influence across models. These findings suggest that intuitive usability and enjoyment, rather than technological sophistication or affordability alone, are critical for VR-supported PA acceptance. VR may therefore function best as a complementary PA tool unless future designs further enhance perceived value and engagement.

A Semi-Supervised Machine Learning Approach to Define Pressing Roles in Football

Thu, 25 Dec 2025 00:00:00 GMT

A player’s role for a team can be distinct from their playing position. Positions are generally attributed based on where the players line-up relative to their formation, whereas roles can be defined by frequency of their actions. Hence, the method presented in this research, attributed player roles based on event data. Player role feature selection involved a semi-supervised machine learning approach, that extracted feature importance in the form of Shapley values. These values helped define the KPIs for pressing attacking players. By using the proposed role similarity approach, it is possible for recruitment departments to identify players that occupy similar roles as current players. Furthermore, the evolution of player roles across time can be evaluated, which has applications with performance analysts, as they can interrogate the constituent roles of each player and its influence on overall team performance. Hence, the proposed method can help uncover the optimal KPIs for a given set of roles, while having practitioner applications within elite-level performance analysis and recruitment departments. Future methods should combine physical data sources, such as from tracking data, to enable greater specificity in player role classification.

A Visual Analytics Approach to Basketball Game Understanding Using Image-Based Tracking and Event Detection

Sun, 07 Dec 2025 00:00:00 GMT

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.

Evaluating the Influence of Sensor Configuration and Hyperparameter Optimization on Wearable-Based Knee Moment Estimation During Running

Sun, 14 Sep 2025 00:00:00 GMT

Wearable sensors combined with machine learning (ML) offer a promising approach for estimating joint kinetics in real-world settings, with potential applications in athlete monitoring and injury prevention. However, the variety of sensor configurations in previous studies complicates comparisons and optimal configuration selection. This study compared different wearable sensor configurations, comprising inertial measurement units (IMUs) and pressure insoles (PIs), to determine their influence on the accuracy of ML – based predictions of 3D knee moments during running. Sensor configurations ranged from one to four IMUs, with and without PIs. The dataset consisted of wearable and ground truth knee moment data from 19 recreational runners during treadmill running. Model performance of the convolutional neural networks was evaluated on an independent test set. Hyperparameter optimization (HPO) was applied to refine model architectures and training parameters. Performance gains by PIs and a greater number of IMUs were small but significant. The results after HPO confirmed similar performances between single- and multi-sensor configurations, suggesting only small benefits from additional sensors. Our findings highlight that both sensor configuration and model optimization play critical roles in achieving optimal performance. We provide practical recommendations for sensor selection, balancing accuracy and feasibility, to enable biomechanical assessments in real-world environments.

Which indicators matter? Using performance indicators to predict in-game success-related events in association football

Thu, 31 Jul 2025 00:00:00 GMT

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 PIDangerousity best predicts SREGoal and SREShotTaken, while PIEntriesAttaThird is strongest for SRECornerkick, SREEntryAttaThird, and SREEntryOppBox. PIDangerousity and PISuccPassAttThird consistently rank in the Top 9, highlighting their predictive strength. Combining PIOutplayedOpp and PITacklingsWon 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.

Observational Analysis of Mistakes in Chess Initiation, Using Decision Trees

Mon, 07 Jul 2025 00:00:00 GMT

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.

Transferring between sports: the case of Icelandic youth sport

Tue, 01 Jul 2025 00:00:00 GMT

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.

Advancing Sport Biomechanics with Depth Cameras: Systematic Review of Current Applications and Future Directions

Mon, 30 Jun 2025 00:00:00 GMT

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.

Getting NBA Shots in Context: Analysing Basketball Shots with Graph Embeddings

Wed, 14 May 2025 00:00:00 GMT

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.

Players’ Performance Prediction for Fantasy Premier League, Using Transformer-based Sentiment Analysis on News and Statistical Data

Mon, 05 May 2025 00:00:00 GMT

Fantasy sports have become increasingly popular, with millions of players engaging in strategic team management and competition. In the realm of Fantasy Premier League (FPL), effective player analysis and performance prediction are crucial for success in each game. This paper presents an innovative approach to enhance FPL analysis and performance prediction by integrating news sentiment and players’ injury with statistical data sources. A dataset of weekly news articles was enriched through pretrained transformer-based sentiment analysis toolkit and combined with different boosting and neural network algorithms for prediction tasks. Our findings demonstrate that integrating these features enhances model performance, with the CNN architecture achieving a reduction in MSE from 6.27 to 5.63 outperforming the state of the art model. These results highlight the potential of leveraging diverse data sources for more accurate predictions and informed decision-making in FPL.

Can machine learning distinguish between elite and non-elite rowers?

Thu, 01 May 2025 00:00:00 GMT

A major challenge for sports coaches and analysts is to identify critical elements of athletes’ movement patterns. A potentially relevant tool is machine learning, useful because of its ability to extract patterns from data. In the current study, we employed various deep learning frameworks, including Gated Recurrent Unit networks (GRUs), Convolutional Neural Networks (CNNs), and Multi-Layer Perceptrons (MLPs), to search for differences between elite and non-elite rowers using a rowing ergometer. The MLP model achieved an accuracy of 100% when using all input features, indicating that the problem is suitable as a machine learning task. Our research focused on using a limited amount of the data. Despite using fewer input features, the models managed to classify skill levels with reasonable precision, reaching a best performance of 77% accuracy for the model combining GRU and CNN architectures, 78% for the GRU model, and 94% for the MLP model. From a rowing perspective, the results suggest that movement coordination between upper and lower body limbs, as represented by different feature combinations, is informative in distinguishing between elites and non-elites. The current work suggests that machine learning may supplement human experts in sports coaching, analytics, and talent identification.

NBA Results Forecast: From League Dynamics Analysis to Predictive Model Implementation

Thu, 01 May 2025 00:00:00 GMT

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.

A Deep Learning Approach Based on Interpretable Feature Importance for Predicting Sports Results

Wed, 19 Mar 2025 00:00:00 GMT

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.

Two clusterings to capture basketball players’ shooting tendencies using tracking data: clustering of shooting styles and the shots themselves

Sun, 02 Mar 2025 00:00:00 GMT

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.

Estimating the Relevance of First Offensive Shot Tactics in Table Tennis via Simulation Based on a Finite Markov Chain Model

Sun, 02 Mar 2025 00:00:00 GMT

Finite Markov chain modelling is a commonly used type of stochastic modelling employed in performance analysis of net games. Finite Markov chains are based on a state transition model which can be used to depict the game structure of net games as a succession of states which are defined as equivalence classes for game situations, e.g. service and return. Furthermore, the theory of finite Markov chains allows for the calculation of model variables which are of significant interest not only for validation but also for performance analysis, like wining probabilities or expected rally lengths starting from different states. By simulation, of a more-or-less of tactical behaviors one may study the impact of these tactics on overall success. A novel state transition model for table tennis is introduced in this study as extension of an existing model in the literature containing only the first offensive shot. The new model additionally contains subsequent shots since they may be perceived as being influenced by the first offensive shot. A sample of 105 single matches (49 female, 56 male) at the 2020 Tokyo Olympics was examined. The validation of the Markov property resulted in satisfactory results. The relevance of 26 transitions denoting specific tactical behaviors was obtained using simulation and subsequently compared between sexes. Results provide insights concerning the game structure of table tennis with a particular emphasis on the transition from the initial phase of rallies to the first offensive shot.

Deep Learning with 3D ResNets for Comprehensive Dual-Lane Speed Climbing Video Analysis

Sun, 02 Mar 2025 00:00:00 GMT

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.

Development of Anthro-Fitness Model for Evaluating Firefighter Recruits’ Performance Readiness Using Machine Learning

Wed, 05 Feb 2025 00:00:00 GMT

The role of firefighters has evolved from traditional tasks like rescuing cats from trees and extinguishing house fires to more complex land, sea, and air rescues. The increasing demands for public safety necessitate rigorous training and high fitness levels for firefighters to manage their daily tasks effectively. In this study, final assessments of fitness and anthropometric parameters were gathered from 746 Malaysian firefighter recruits. A k-means clustering algorithm was utilized to group the performance levels of the firefighters whilst a quadratic discriminant analysis model was employed to predict the grouping of firefighters based on these parameters. Feature importance analysis was used to identify the most significant parameters contributing to model performance. Concurrently, the Mann-Whitney test was used to determine the essential anthro-fitness parameters differentiating between the groups of firefighters. The k-means clustering identified two performance groups: excellent and average anthro-fitness readiness (EFR and AFR) groups. The model demonstrated a mean performance accuracy of 91% for training and 87% for independent tests. Feature importance analysis revealed that inclined pull-ups, standing broad jump, shuttle run, 2.4 km run, age, and sit-ups were the most significant parameters. The Mann-Whitney test showed that the EFR group outperformed the AFR group in all anthro-fitness parameters except for height, weight, and age, which showed no significant difference. This study highlights the critical role of specific fitness and anthropometric parameters in distinguishing high-performing firefighters. By identifying the most significant contributors to overall fitness, fire departments can better prepare their personnel to meet the increasing public safety demands. The high accuracy of the predictive model also suggests its potential application in ongoing firefighter assessments and training optimization.