The aims of this paper are to examine the application of performance indicators in different sports and, using the different structural definitions of games, to make general recommendations about the use and application of these indicators. Formal games are classified into three categories: net and wall games, invasion games, and striking and fielding games. The different types of sports are also sub-categorized by the rules of scoring and ending the respective matches. These classes are analysed further, to enable definition of useful performance indicators and to examine similarities and differences in the analysis of the different categories of game. The indices of performance are sub-categorized into general match indicators, tactical indicators, technical indicators and biomechanical indicators. Different research examples and the accuracy of their presentation are discussed. We conclude that, to enable a full and objective interpretation of the data from the analysis of a performance, comparisons of data are vital. In addition, any analysis of the distribution of actions across the playing surface should also be presented normalized, or non-dimensionalized, to the total distribution of actions across the area. Other normalizations of performance indicators should also be used more widely in conjunction with the accepted forms of data analysis. Finally, we recommend that biomechanists should pay more attention to games to enrich the analysis of performance in these sports.
Positive effects from sports are achieved primarily through physical activity, but secondary effects bring health benefits such as psychosocial and personal development and less alcohol consumption. Negative effects, such as the risk of failure, injuries, eating disorders, and burnout, are also apparent. Because physical activity is increasingly conducted in an organized manner, sport’s role in society has become increasingly important over the years, not only for the individual but also for public health. In this paper, we intend to describe sport’s physiological and psychosocial health benefits, stemming both from physical activity and from sport participation per se. This narrative review summarizes research and presents health-related data from Swedish authorities. It is discussed that our daily lives are becoming less physically active, while organized exercise and training increases. Average energy intake is increasing, creating an energy surplus, and thus, we are seeing an increasing number of people who are overweight, which is a strong contributor to health problems. Physical activity and exercise have significant positive effects in preventing or alleviating mental illness, including depressive symptoms and anxiety- or stress-related disease. In conclusion, sports can be evolving, if personal capacities, social situation, and biological and psychological maturation are taken into account. Evidence suggests a dose–response relationship such that being active, even to a modest level, is superior to being inactive or sedentary. Recommendations for healthy sports are summarized.
Event history data from sports competitions have recently drawn increasing attention in sports analytics to generate data-driven strategies. Such data often exhibit self-excitation in the event occurrence and dependence within event clusters. The conventional event models based on gap times may struggle to capture those features. In particular, while consecutive events may occur within a short timeframe, the self-excitation effect caused by previous events is often transient and continues for a period of uncertain time. This paper introduces an extended Hawkes process model with random self-excitation duration to formulate the dynamics of event occurrence. We present examples of the proposed model and procedures for estimating the associated model parameters. We employ the collection of the corner kicks in the games of the 2019 regular season of the Chinese Super League to motivate and illustrate the modeling and its usefulness. We also design algorithms for simulating the event process under proposed models. The proposed approach can be adapted with little modification in many other research fields such as Criminology and Infectious Disease.
Recent advances in immersive technology have opened new possibilities in sports training, especially for activities requiring precise motor skills, such as tennis. In this paper, we present a virtual reality (VR) tennis training system integrating real-time performance feedback through a wearable sensor device. Ten participants wore the sensor on their dominant hand to capture motion data, including swing speed and swing power, while engaging in a VR tennis environment. Initially, participants performed baseline tests without access to performance metrics. In subsequent tests, participants executed similar routines with their swing data displayed in real-time via a VR overlay. Qualitative and quantitative results indicated that real-time visual feedback led to improved performance behaviors and enhanced situational awareness. Some participants exhibited increased swing consistency and strategic decision-making, though improvements in accuracy varied individually. Additionally, subjective feedback highlighted that the immersive experience, combined with instantaneous performance metrics, enhanced player engagement and motivation. These findings illustrate the effectiveness of VR-based data visualisation in sports training, suggesting broader applicability in performance enhancement.
Robot sports, characterized by well-defined objectives, explicit rules, and dynamic interactions, present ideal scenarios for demonstrating embodied intelligence. In this paper, we present VolleyBots, a novel robot sports testbed where multiple drones cooperate and compete in the sport of volleyball under physical dynamics. VolleyBots integrates three features within a unified platform: competitive and cooperative gameplay, turn-based interaction structure, and agile 3D maneuvering. These intertwined features yield a complex problem combining motion control and strategic play, with no available expert demonstrations. We provide a comprehensive suite of tasks ranging from single-drone drills to multi-drone cooperative and competitive tasks, accompanied by baseline evaluations of representative reinforcement learning (RL), multi-agent reinforcement learning (MARL) and game-theoretic algorithms. Simulation results show that on-policy RL methods outperform off-policy methods in single-agent tasks, but both approaches struggle in complex tasks that combine motion control and strategic play. We additionally design a hierarchical policy which achieves 69.5% win rate against the strongest baseline in the 3 vs 3 task, demonstrating its potential for tackling the complex interplay between low-level control and high-level strategy. To highlight VolleyBots' sim-to-real potential, we further demonstrate the zero-shot deployment of a policy trained entirely in simulation on real-world drones.
Traditional approaches -- such as Win Probability Added (WPA)-based ranking or computer vision-driven event detection -- can identify scoring plays but often miss strategic depth, momentum shifts, and storyline progression. Manual curation remains the gold standard but is resource-intensive and not scalable. We introduce DIAMOND, an LLM-driven agent for context-aware baseball highlight summarization that integrates structured sports analytics with natural language reasoning. DIAMOND leverages sabermetric features -- Win Expectancy, WPA, and Leverage Index -- to quantify play importance, while an LLM module enhances selection based on contextual narrative value. This hybrid approach ensures both quantitative rigor and qualitative richness, surpassing the limitations of purely statistical or vision-based systems. Evaluated on five diverse Korean Baseball Organization League games, DIAMOND improves F1-score from 42.9% (WPA-only) to 84.8%, outperforming both commercial and statistical baselines. Though limited in scale, our results highlight the potential of modular, interpretable agent-based frameworks for event-level summarization in sports and beyond.
Ksenia A. Blinova, Irina E. Mishina, Galina E. Ivanova
et al.
INTRODUCTION. The use of antitumor therapy in patients with breast cancer has led not only to an increase in their life expectancy, but also to the need to correct various side effects, including manifestations of cardiotoxicity. Rehabilitation of such patients in Russia is currently lacking.
AIM. To search and analyze the literature on the effectiveness of physical training for the prevention of cardiotoxic complications of antitumor therapy.
MATERIALS AND METHODS. Publications from the PubMed, Scopus, Web of Science, PEDro databases over the past 15 years were collected and analyzed 15 years by keywords in Russian and English: “cardiotoxicity”, “exercise”, “breast cancer”. 126 sources were selected, including systematic reviews and a Cochrane review.
RESULTS AND DISCUSSION. Preclinical studies have shown that physical exercise reduces the accumulation of antitumor drugs in the myocardium and increases the proliferation of cardiomyocyte progenitor cells. Conducting physical training during and after anticancer treatment increases cardiorespiratory endurance and reduces the manifestations of anthracycline cardiotoxicity. This rehabilitation intervention leads to less fatigue, decreased depression, improved physical fitness, cognitive functions, and quality of life. The greatest effectiveness during and after anticancer therapy was shown by aerobic and strength exercises of moderate intensity, performed for 30–40 minutes 3–5 times a week, which provide 150 minutes of physical activity per week. The limitation of the use of physical training in patients is due to the impossibility of predicting the training heart rate by age, as well as the need to take into account concomitant diseases and the patient’s condition.
CONCLUSION. The use of physical training can be used in cancer patients to prevent cardiotoxicity of anticancer therapy. Further research is needed to ensure their successful use in patients with different physical fitness and treatment tolerance.
Abstract Artificial intelligence (AI) models have demonstrated significant success in classifying various types of text. However, the complex nature of these models often complicates the interpretability of their classifications. To address these challenges and to enhance explainability, this study proposes a novel approach to text classification leveraging natural language processing (NLP) techniques and explainable AI (XAI) methods. Text preprocessing steps were essential for improving the quality of text analysis. This was gained by eliminating elements that contribute minimal semantic value. To achieve robust performance and mitigate the risk of overfitting, repeated stratified K-Fold cross-validation was utilized. Furthermore, the synthetic minority oversampling technique (SMOTE) was employed to address dataset imbalance issues. In the classification phase, nine machine learning models and hybrid/multi-model approaches were employed. To validate the explainability of the classifications, the local interpretable model-agnostic explanations (LIME) framework was utilized. The study utilized two datasets containing texts from domains such as sports, medicine, entertainment, politics, technology, and business. Empirical evaluations demonstrated the effectiveness of the proposed approach. The proposed hybrid model achieved exceptional performance across key metrics, including accuracy, precision, recall, and F1-score. The proposed hybrid model achieved results of up to 99% accuracy. This work can be used for various text analysis applications.
Electrical engineering. Electronics. Nuclear engineering, Information technology
Braillard Olivier, Gaillot Stephane, Mareček Martin
et al.
In the context of experiments aimed at characterizing the thermal exchanges between fluids, the determination of the h thermal exchange coefficient at the wall is requested.
In this objective, the innovative COEFH thermal sensor has been developed and optimized to precisely measure this coefficient. The robustness of its measurement is obtained by performing various tests of the COEFH sensor’s resistance to severe environmental conditions (pressure, temperature, irradiation...). In this case, the tests presented in this paper aim to check the gamma dose tolerance resistance of the sensor (at room temperature, without pressure).
The framework of this action is the European OFFERR program underway over the period [2022-2026] and which aims to carry out R&D actions in the facilities identified in the OFFERR network (https://snetp.eu/offerr/). The present action is called IRRADCOEFH.
This action was carried out in 2024 April-May through a collaboration between the CEA-DES-IRESNE (France) and the CVR located in the Czech Republic.
Tests performed with CVR’s gamma irradiator (*) allowed the electrical characteristics of the COEFH sensor to be continuously tested up to a gamma dose of 50 kGy. These tests were carried out over a period of 3.5 days. The first results obtained show a good behavior of the sensor in the face of gamma irradiation without any observed degradation of its characteristics.
These tests made it possible to validate the use of the sensor under gamma radiative environment.
After a general description of the OFFERR project, the paper describes the irradiation tests performed with the COEFH sensor at the CVR and provides the first results obtained.
Note: the presented results were obtained using the CICRR infrastructure, which is financially supported by the Ministry of Education, Youth and Sports - project LM2023041.
Christoph Rauchegger, Sonja Mei Wang, Pieter Delobelle
The FIFA World Cup in Qatar was discussed extensively in the news and on social media. Due to news reports with allegations of human rights violations, there were calls to boycott it. Wearing a OneLove armband was part of a planned protest activity. Controversy around the armband arose when FIFA threatened to sanction captains who wear it. To understand what topics Twitter users Tweeted about and what the opinion of German Twitter users was towards the OneLove armband, we performed an analysis of German Tweets published during the World Cup using in-context learning with LLMs. We validated the labels on human annotations. We found that Twitter users initially discussed the armband's impact, LGBT rights, and politics; after the ban, the conversation shifted towards politics in sports in general, accompanied by a subtle shift in sentiment towards neutrality. Our evaluation serves as a framework for future research to explore the impact of sports activism and evolving public sentiment. This is especially useful in settings where labeling datasets for specific opinions is unfeasible, such as when events are unfolding.
Keypoint data has received a considerable amount of attention in machine learning for tasks like action detection and recognition. However, human experts in movement such as doctors, physiotherapists, sports scientists and coaches use a notion of joint angles standardised by the International Society of Biomechanics to precisely and efficiently communicate static body poses and movements. In this paper, we introduce the basic biomechanical notions and show how they can be used to convert common keypoint data into joint angles that uniquely describe the given pose and have various desirable mathematical properties, such as independence of both the camera viewpoint and the person performing the action. We experimentally demonstrate that the joint angle representation of keypoint data is suitable for machine learning applications and can in some cases bring an immediate performance gain. The use of joint angles as a human meaningful representation of kinematic data is in particular promising for applications where interpretability and dialog with human experts is important, such as many sports and medical applications. To facilitate further research in this direction, we will release a python package to convert keypoint data into joint angles as outlined in this paper.
Karolina Seweryn, Gabriel Chęć, Szymon Łukasik
et al.
This study explores the potential of super-resolution techniques in enhancing object detection accuracy in football. Given the sport's fast-paced nature and the critical importance of precise object (e.g. ball, player) tracking for both analysis and broadcasting, super-resolution could offer significant improvements. We investigate how advanced image processing through super-resolution impacts the accuracy and reliability of object detection algorithms in processing football match footage. Our methodology involved applying state-of-the-art super-resolution techniques to a diverse set of football match videos from SoccerNet, followed by object detection using Faster R-CNN. The performance of these algorithms, both with and without super-resolution enhancement, was rigorously evaluated in terms of detection accuracy. The results indicate a marked improvement in object detection accuracy when super-resolution preprocessing is applied. The improvement of object detection through the integration of super-resolution techniques yields significant benefits, especially for low-resolution scenarios, with a notable 12\% increase in mean Average Precision (mAP) at an IoU (Intersection over Union) range of 0.50:0.95 for 320x240 size images when increasing the resolution fourfold using RLFN. As the dimensions increase, the magnitude of improvement becomes more subdued; however, a discernible improvement in the quality of detection is consistently evident. Additionally, we discuss the implications of these findings for real-time sports analytics, player tracking, and the overall viewing experience. The study contributes to the growing field of sports technology by demonstrating the practical benefits and limitations of integrating super-resolution techniques in football analytics and broadcasting.
Recent advances in computer vision have made significant progress in tracking and pose estimation of sports players. However, there have been fewer studies on behavior prediction with pose estimation in sports, in particular, the prediction of soccer fouls is challenging because of the smaller image size of each player and of difficulty in the usage of e.g., the ball and pose information. In our research, we introduce an innovative deep learning approach for anticipating soccer fouls. This method integrates video data, bounding box positions, image details, and pose information by curating a novel soccer foul dataset. Our model utilizes a combination of convolutional and recurrent neural networks (CNNs and RNNs) to effectively merge information from these four modalities. The experimental results show that our full model outperformed the ablated models, and all of the RNN modules, bounding box position and image, and estimated pose were useful for the foul prediction. Our findings have important implications for a deeper understanding of foul play in soccer and provide a valuable reference for future research and practice in this area.
Assessment of the performance of a player in any sport is very much needed to determine the ranking of players and make a solid team with the best players. Besides these, fans, journalists, sports persons, and sports councils often analyse the performances of current and retired players to identify the best players of all time. Here, we study the performance of all-time top batters in one-day cricket using physics-based statistical methods. The batters are selected in this study who possess either higher total runs or a high number of centuries. It is found that the total runs increases linearly with the innings number at the later stage of the batter carrier, and the runs rate estimated from the linear regression analysis also increases linearly with the average runs. The probability of non-scoring innings is found to be a negligibly small number (i.e., $\leq 0.1$ ) for each batter. Furthermore, based on innings-wise runs, we have computed the six-dimensional probability distribution vector for each player. Two components of the probability distribution vector vary linearly with average runs. The component representing the probability of scoring runs less than 50 linearly decreases with the average runs. In contrast, the probability of scoring runs greater than or equal to 100 and less than 150 linearly increases with the average runs. We have also estimated the entropy to assess the diversity of a player. Interestingly, the entropy varies linearly with the average runs, giving rise to two clusters corresponding to the old and recent players. Furthermore, the angle between two probability vectors is calculated for each pair of players to measure the similarities among the players. It is found that some of the players are almost identical to each other.
C. Santoyo-Medina, I. Elorriaga Mínguez, I. Galán Cartañá
et al.
Background: According to the literature, patients with multiple sclerosis (MS) are less active and show higher levels of sedentary behaviour than the general population of the same age range. This study aims to explore the impact of the COVID-19 pandemic on levels of physical activity in these patients. Methods: An online survey was launched between May and June 2021, aimed at patients with MS in Spain, regarding their physical activity habits (performance, intensity, and activities carried out) prior to and during the COVID-19 pandemic. Results: A total of 230 patients responded to the survey, of whom 69.6% were women, 52.6% were between 45 and 64 years old, and 41.3% had moderate disability (Patient-Determined Disease Steps score 3–5). A total of 82.2% of the respondents reported being physically active before the pandemic, decreasing to 75.9% during the pandemic [P=.057 (McNemar test)]. Activities at sports centres decreased and exercise at home, as well as walking, increased. A total of 61.7% reported not using any technology during physical activity practise before the pandemic. For 63.9% of respondents, the preferred format after the pandemic was the mixed format combining in-person and remote physical activity. Conclusions: Physical activity levels decreased during the COVID-19 pandemic among patients with MS. Although SARS-CoV-2 is currently in an endemic phase, this experience should be helpful for the development and implementation of interventions to facilitate physical activity among patients with MS. Resumen: Introducción: Según la literatura, las personas con esclerosis múltiple (PcEM) son menos activas y muestran niveles más altos de sedentarismo que la población de su mismo rango de edad. El objetivo de este estudio es conocer cuál fue el impacto de la pandemia por COVID-19 en el nivel de actividad física (AF) de las PcEM. Métodos: Estudio mediante una encuesta online lanzada entre mayo y junio del 2021, dirigida a PcEM en territorio español, sobre los hábitos de AF (rendimiento, intensidad y actividades realizadas) previos y durante la pandemia por COVID-19. Resultados: Un total de 230 PcEM contestaron la encuesta, de los cuales un 69.6% eran mujeres, un 52,6% tenían entre 45 y 64 años y un 41,3% una discapacidad moderada (PDSS 3–5). Antes de la pandemia, el 82,2% de los encuestados referían ser físicamente activos, reduciéndose a un 75,9% durante la pandemia (McNemar; p = .057). Disminuyeron las actividades en centros deportivos y se incrementaron las desarrolladas en el domicilio, así como caminar como ejercicio. Un 61,7% refería no utilizar tecnología durante la práctica de AF antes de la pandemia. El formato de preferencia tras la pandemia para el 63,9% fue el formato mixto combinando AF presencial y remota. Conclusiones: La AF disminuyó durante la pandemia por COVID-19 entre las PcEM. Aunque actualmente se encuentra en una fase endémica, esta experiencia debería ser un impulso para el desarrollo y aplicación de intervenciones que faciliten su práctica entre las PcEM.
Marília da Silva Alves, Roberto Jerônimo dos Santos Silva, Cleidison Machado Santana
et al.
Apesar dos investimentos realizados ao redor do mundo, seja no âmbito acadêmico, seja na implementação de políticas públicas, os níveis de atividade física não têm aumentado a contento. Assim, o objetivo deste trabalho é identificar quais os fatores que influenciam na participação em Programas Comunitários de Atividade Física na realidade brasileira. Para isso, utilizando a Pesquisa Nacional de Saúde 2019, investigou-se 20.014 sujeitos considerando como desfecho a participação nesses programas, com variáveis independentes divididas em biológicas e sociodemográficas. Para a análise dos dados utilizou-se da regressão logística binária, com p < 0,05, através do software Jamovi® versão 2.3.21. Observou-se que pessoas do gênero feminino (OR = 1,54; IC 95%: 1,40 - 1,69), “pessoas idosas” (OR = 1,10; IC 95%: 1,01 - 1,21) e pessoas “não brancas” (OR = 1,51; IC95%: 1,38 - 1,66) apresentaram chances elevadas de participação nos Programas Comunitários de Atividade Física. Para o segundo bloco, identificou-se que quem apresentou renda acima de cinco salários mínimos tiveram chances reduzidas em 34% (OR = 0,66; IC 95%: 0,57 - 0,76) quando comparados aos que relataram renda de até um salário, e, os que residiam próximo aos locais públicos para lazer apresentaram chances elevadas de participação (OR = 1,71; IC 95%: 1,52 - 1,92). Em suma, aspectos biológicos e sociodemográficos influenciaram na participação em Programas Comunitários de Atividade Física, contudo, a existência de locais públicos de lazer próximos às residências foi o fator de maior impacto evidenciado.
Charina C. Lüder, Tanja Michael, Johanna Lass-Hennemann
et al.
ABSTRACTBackground: Refugees with exposure to multiple traumatic events are at high risk for developing posttraumatic stress disorder (PTSD) and depression. Narrative exposure therapy (NET) is an effective treatment for the core symptoms of PTSD, but it does not reliably reduce depressive symptoms. Endurance exercise on the other hand was consistently found to be effective in treating depression making it a promising adjunct to NET. Up to date, no studies exist investigating the combination of NET and endurance exercise in a sample of refugees with PTSD and comorbid depression.Objectives: In the proposed randomized controlled trial, we aim to investigate whether a combination of NET and moderate-intensity aerobic exercise training (MAET) enhances treatment outcome for refugees with PTSD and comorbid depressive symptoms. We expect a greater improvement in psychopathology in participants who receive the combined treatment.Methods and analysis: 68 refugees and asylum seekers with PTSD and clinically relevant depressive symptoms will be recruited in the proposed study. Participants will be randomly assigned to receive either NET only (NET-group) or NET plus MAET (NET+-group). All participants will receive 10 NET sessions. Participants in the NET+-group will additionally take part in MAET. Primary (PTSD, depression) and secondary (general mental distress, agoraphobia and somatoform complaints, sleep quality) outcome measures will be assessed before treatment, after treatment, and at six-month follow-up. The hypotheses will be tested with multiple 2 × 3 mixed ANOVA's.Trial registration: German Clinical Trials Register identifier: DRKS00022145.
Ashish Singh, Antonio Bevilacqua, Thach Le Nguyen
et al.
Technological advancements have spurred the usage of machine learning based applications in sports science. Physiotherapists, sports coaches and athletes actively look to incorporate the latest technologies in order to further improve performance and avoid injuries. While wearable sensors are very popular, their use is hindered by constraints on battery power and sensor calibration, especially for use cases which require multiple sensors to be placed on the body. Hence, there is renewed interest in video-based data capture and analysis for sports science. In this paper, we present the application of classifying S\&C exercises using video. We focus on the popular Military Press exercise, where the execution is captured with a video-camera using a mobile device, such as a mobile phone, and the goal is to classify the execution into different types. Since video recordings need a lot of storage and computation, this use case requires data reduction, while preserving the classification accuracy and enabling fast prediction. To this end, we propose an approach named BodyMTS to turn video into time series by employing body pose tracking, followed by training and prediction using multivariate time series classifiers. We analyze the accuracy and robustness of BodyMTS and show that it is robust to different types of noise caused by either video quality or pose estimation factors. We compare BodyMTS to state-of-the-art deep learning methods which classify human activity directly from videos and show that BodyMTS achieves similar accuracy, but with reduced running time and model engineering effort. Finally, we discuss some of the practical aspects of employing BodyMTS in this application in terms of accuracy and robustness under reduced data quality and size. We show that BodyMTS achieves an average accuracy of 87\%, which is significantly higher than the accuracy of human domain experts.
In combat sports, athletes are divided into categories based on gender and body mass. Athletes attempt to compete against a lighter opponent by losing body mass prior to being weighed (i.e., ‘weight-cutting’). The purpose of this narrative review was to explore the current body of literature on weight-cutting and outline gaps for further research. Methods of weight-loss include energy intake restriction, total body fluid reduction and pseudo extreme/abusive medical practice (e.g., diuretics). The influence of weight-cutting on performance is unclear, with studies suggesting a negative or no effect. However, larger weight-cuts (~5% of body mass in <24 h) do impair repeat-effort performance. It is unclear if the benefit from competing against a smaller opponent outweighs the observed reduction in physical capacity. Many mechanisms have been proposed for the observed reductions in performance, ranging from reduced glycogen availability to increased perceptions of fatigue. Athletes undertaking weight-cutting may be able to utilise strategies around glycogen, total body water and electrolyte replenishment to prepare for competition. Despite substantial discussion on managing weight-cutting in combat sports, no clear solution has been offered. Given the prevalence of weight-cutting, it is important to develop a deeper understanding of such practices so appropriate advice can be given.
Based on various existing wireless fingerprint location algorithms in intelligent sports venues, a high-precision and fast indoor location algorithm improved weighted k-nearest neighbor (I-WKNN) is proposed. In order to meet the complex environment of sports venues and the demand of high-speed sampling, this paper proposes an AP selection algorithm for offline and online stages. Based on the characteristics of the signal intensity distribution in intelligent venues, an asymmetric Gaussian filter algorithm is proposed. This paper introduces the application of the positioning algorithm in the intelligent stadium system, and completes the data acquisition and real-time positioning of the stadium. Compared with traditional WKNN and KNN algorithms, the I-WKNN algorithm has advantages in fingerprint positioning database processing, environmental noise adaptability, real-time positioning accuracy and positioning speed, etc. The experimental results show that the I-WKNN algorithm has obvious advantages in positioning accuracy and positioning time in a complex noise environment and has obvious application potential in a smart stadium.