Hasil untuk "Sports"

J. Hargreaves

S. Cobley, J. Baker, N. Wattie et al.

Herbert P. Susmann, Antonio D'Alessandro

Evaluating sports players based on their performance shares core challenges with evaluating healthcare providers based on patient outcomes. Drawing on recent advances in healthcare provider profiling, we cast sports player evaluation within a rigorous causal inference framework and define a flexible class of causal player evaluation estimands. Using stochastic interventions, we compare player success rates on repeated tasks (such as field goal attempts or plate appearance) to counterfactual success rates had those same attempts been randomly reassigned to players according to prespecified reference distributions. This setup encompasses direct and indirect standardization parameters familiar from healthcare provider profiling, and we additionally propose a "performance above random replacement" estimand designed for interpretability in sports settings. We develop doubly robust estimators for these evaluation metrics based on modern semiparametric statistical methods, with a focus on Targeted Minimum Loss-based Estimation, and incorporate machine learning methods to capture complex relationships driving player performance. We illustrate our framework in detailed case studies of field goal kickers in the National Football League and batters in Major League Baseball, highlighting how different causal estimands yield distinct interpretations and insights about player performance.

Neha Balamurugan, Sarah Wu, Adam Chun et al.

Humans excel at visual social inference, the ability to infer hidden elements of a scene from subtle behavioral cues such as other people's gaze, pose, and orientation. This ability drives everyday social reasoning in humans and is critical for developing more human-like AI agents. We introduce Spot The Ball, a challenging benchmark for evaluating visual social inference in vision-language models (VLMs) using sports as a test domain. The task is to localize a removed sports ball from soccer, basketball, and volleyball images. We present a curated evaluation set with human baselines and a scalable pipeline for generating additional test items. We evaluate four state-of-the-art VLMs (Gemini, GPT, LLaMA, Qwen) using three prompting strategies, finding that humans are consistently two to three times more accurate (20-34%) than models ($\leq$ 17%) across all sports. Our analyses show that models rely on superficial spatial heuristics--such as guessing near the image center or nearby players--while humans leverage social cues like gaze direction and body pose. These findings reveal a persistent human-model gap in visual social reasoning and underscore the need for architectures that explicitly encode structured behavioral cues to achieve robust, human-like inference.

Xusheng He, Wei Liu, Shanshan Ma et al.

Fine-grained analysis of complex and high-speed sports like badminton presents a significant challenge for Multimodal Large Language Models (MLLMs), despite their notable advancements in general video understanding. This difficulty arises primarily from the scarcity of datasets with sufficiently rich and domain-specific annotations. To bridge this gap, we introduce FineBadminton, a novel and large-scale dataset featuring a unique multi-level semantic annotation hierarchy (Foundational Actions, Tactical Semantics, and Decision Evaluation) for comprehensive badminton understanding. The construction of FineBadminton is powered by an innovative annotation pipeline that synergistically combines MLLM-generated proposals with human refinement. We also present FBBench, a challenging benchmark derived from FineBadminton, to rigorously evaluate MLLMs on nuanced spatio-temporal reasoning and tactical comprehension. Together, FineBadminton and FBBench provide a crucial ecosystem to catalyze research in fine-grained video understanding and advance the development of MLLMs in sports intelligence. Furthermore, we propose an optimized baseline approach incorporating Hit-Centric Keyframe Selection to focus on pivotal moments and Coordinate-Guided Condensation to distill salient visual information. The results on FBBench reveal that while current MLLMs still face significant challenges in deep sports video analysis, our proposed strategies nonetheless achieve substantial performance gains. The project homepage is available at https://finebadminton.github.io/FineBadminton/.

Jerrin Bright, Zhibo Wang, Dmytro Klepachevskyi et al.

We present Avatar4D, a real-world transferable pipeline for generating customizable synthetic human motion datasets tailored to domain-specific applications. Unlike prior works, which focus on general, everyday motions and offer limited flexibility, our approach provides fine-grained control over body pose, appearance, camera viewpoint, and environmental context, without requiring any manual annotations. To validate the impact of Avatar4D, we focus on sports, where domain-specific human actions and movement patterns pose unique challenges for motion understanding. In this setting, we introduce Syn2Sport, a large-scale synthetic dataset spanning sports, including baseball and ice hockey. Avatar4D features high-fidelity 4D (3D geometry over time) human motion sequences with varying player appearances rendered in diverse environments. We benchmark several state-of-the-art pose estimation models on Syn2Sport and demonstrate their effectiveness for supervised learning, zero-shot transfer to real-world data, and generalization across sports. Furthermore, we evaluate how closely the generated synthetic data aligns with real-world datasets in feature space. Our results highlight the potential of such systems to generate scalable, controllable, and transferable human datasets for diverse domain-specific tasks without relying on domain-specific real data.

Arnau Barrera Roy, Albert Clapés Sintes

Computer vision and video understanding have transformed sports analytics by enabling large-scale, automated analysis of game dynamics from broadcast footage. Despite significant advances in player and ball tracking, pose estimation, action localization, and automatic foul recognition, anticipating actions before they occur in sports videos has received comparatively little attention. This work introduces the task of action anticipation in basketball broadcast videos, focusing on predicting which team will gain possession of the ball following a shot attempt. To benchmark this task, a new self-curated dataset comprising 100,000 basketball video clips, over 300 hours of footage, and more than 2,000 manually annotated rebound events is presented. Comprehensive baseline results are reported using state-of-the-art action anticipation methods, representing the first application of deep learning techniques to basketball rebound prediction. Additionally, two complementary tasks, rebound classification and rebound spotting, are explored, demonstrating that this dataset supports a wide range of video understanding applications in basketball, for which no comparable datasets currently exist. Experimental results highlight both the feasibility and inherent challenges of anticipating rebounds, providing valuable insights into predictive modeling for dynamic multi-agent sports scenarios. By forecasting team possession before rebounds occur, this work enables applications in real-time automated broadcasting and post-game analysis tools to support decision-making.

Kazutaka Fukushima, Kazuyuki Kamahara, Anna Tomori et al.

Objectives To describe the characteristics of COVID-19 among elite Japanese athletes and their return-to-play (RTP) time.Methods We retrospectively reviewed clinical records at the Japan Institute of Sports Sciences between June 2022 and May 2023. Elite athletes who underwent periodic health examinations were examined by a physician to confirm COVID-19 history, symptoms and the RTP time.Results Of 994 athletes, 456 had a COVID-19 history (mean±SD, 23.3±4.6 years; 56% male). Most infections occurred during the sixth wave (Omicron variant), followed by the seventh and eighth waves, with 88% recorded after the fifth wave. Indoor athletes were more frequently affected than outdoor athletes (306 vs 150, p<0.05). Badminton athletes were the most commonly affected athletes (16%), followed by volleyball (10%) and handball (7%). Among those with a history of COVID-19, 89% reported symptoms, while 11% were asymptomatic. Fever was the most common symptom (80%), followed by sore throat (58%) and cough (44%). The median (IQR) RTP time was 10 (7–14) days. Overall, 472 athletes resumed play within 28 days, while 20 returned after 28 days. RTP delays were more frequent before Omicron (9/59 athletes) than after (11/433 athletes, p<0.05).Conclusion COVID-19 was more common among indoor sports athletes, primarily during the Omicron wave, with most cases being symptomatic but resolving quickly. These findings likely reflect factors such as close-contact training, immune changes during intensive training and international travel and may help characterise COVID-19 outcomes in elite athletes.

Julianna Podolec, Silvia Ciraolo, Joanna Wojda et al.

Purpose of Research: The research aims to provide an in-depth understanding of CLOVES syndrome, detailing its clinical features, epidemiology, etiology, and diagnostic criteria. It focuses on the role of PIK3CA gene mutations, challenges in diagnosis, and treatment options, including PI3K/AKT/mTOR pathway inhibitors. Research Materials and Methods: This article is based on a review of the current literature and clinical reports from various sources. The methodology includes a collection and synthesis of clinical data, imaging findings, and genetic analyses from published case studies and medical literature. The primary materials used in the research include: clinical case reports and studies, imaging studies, genetic analysis, therapeutic interventions, epidemiological data, literature review and data analysis. Basic Results: CLOVES syndrome affects fewer than 200 individuals worldwide, with symptoms appearing at birth or early childhood. It can lead to serious complications such as nerve compression, deep vein thrombosis, and pulmonary embolism. Diagnosis involves genetic testing and imaging, and sirolimus shows potential in managing symptoms. Conclusions: CLOVES syndrome is a rare, non-hereditary overgrowth disorder caused by a PIK3CA gene mutation. Early diagnosis and a multidisciplinary approach are vital for managing this complex condition and improving patient outcomes.

Wojciech Urban, Kornelia Karamus, Rafał Wojciech Rejmak et al.

Introduction   Irritable bowel syndrome (IBS) is a prevalent condition involving the gut–brain interaction where individuals commonly experience recurring abdominal pain, changes in bowel movements, and frequently bloating.   Aim The aim of this study was to review literature studies on IBS and non-pharmacological methods for treating this condition.    Method Data for the article were retrieved by using Pub Med setting the time descriptor to 2019-2024.   Conclusions Treating irritable bowel syndrome (IBS) continues to be challenging, as each patient needs a tailored approach. Once the correct IBS subtype is diagnosed, treatment should target the primary symptoms, such as bloating or diarrhea. Non-pharmacological treatment plays a initial, crucial and  rapidly developing role in IBS therapy.

Kai Biedermann, Gian-Andri Baumann, Christina M. Spengler et al.

Introduction Advanced Footwear Technology (AFT) enhances running economy, which is partly attributed to midsole foam properties such as high resilience (the ability of a material to absorb and recover energy under elastic deformation) and high compliance (the tolerance of a material to deformation). While compliant midsoles are known to improve running economy over non-compliant ones, the impact of further increasing compliance in already compliant midsoles remains unclear. Also unclear is the effect of increased compliance on the perception of effort and comfort during running, as this might transform a feeling of cushioning into one of instability. This study aimed to address these issues by comparing three current AFT models with similar resilience but varying compliance: Nike AlphaFly 2 (NAF), Nike VaporFly 3 (NVF), and On Cloudboom Echo 3 (CBE), with the NAF being the most compliant shoe, and thus the one with the highest level of energy return (11.1J) compared with both the NVF (6.5 J) CBE (6.0 J). Methods Sixteen well-trained runners (age 31 ± 5 years, height 178 ± 9 cm, body mass 66 ± 10 kg, body fat 14 ± 4%, V̇O2peak 59 ± 4 ml･kg-1･min-1) performed, on different days, sub-maximal running for 6 min at 16 km·h-1 (80 ± 7% V̇O2peak) on a treadmill and a 400-m track. Treadmill tests included two runs each in NAF and NVF, while track tests included three runs each in NAF, NVF and CBE, with shoe order varied systematically. Gas exchange was continuously monitored while perceived exertion and comfort were rated post-run using a 100mm visual analogue scale. Spatiotemporal data, including impact loading, ground contact time, and cadence, were assessed using accelerometry. Results The NVF improved running economy compared with the NAF (-0.8 ± 0.3 ml･kg-1･min-1, P < 0.05) and CBE (-0.7 ± 0.2 ml･kg-1･min-1, P < 0.05). These findings were corroborated by lower heart rate and ventilation with NVF, present during both treadmill and overground running. However, there was no correlation between the shoe differences seen on both surfaces. No significant differences were found between the shoes concerning perceived effort and comfort. Participants experienced lower impact magnitudes in the NVF (5.4 ± 1.5 g) compared with the NAF (5.6 ± 1.5 g, P < 0.05) and CBE (5.5 ± 1.5 g, P < 0.05). No changes in spatiotemporal data were associated with the differences in running economy between the shoes. Discussion/Conclusion These findings indicate that improvements in running economy with AFT are not a matter of endlessly pursuing increased compliance and energy return. Furthermore, perception seems to be unaffected by higher midsole compliance when different shoe models are tested, suggesting that many other factors are at play. As perception of exertion did not differ between shoes despite noticeable differences in physiological variables, it remains to be seen whether such minute differences are relevant for performance, or whether perhaps longer trials are needed to detect differences in exertion.

Onkar Sadekar, Sandeep Chowdhary, M. S. Santhanam et al.

Advancements in technology have recently allowed us to collect and analyse large-scale fine-grained data about human performance, drastically changing the way we approach sports. Here, we provide the first comprehensive analysis of individual and team performance in One-Day International cricket, one of the most popular sports in the world. We investigate temporal patterns of individual success by quantifying the location of the best performance of a player and find that they can happen at any time in their career, surrounded by a burst of comparable top performances. Our analysis shows that long-term performance can be predicted from early observations and that temporary exclusions of players from teams are often due to declining performances but are also associated with strong comebacks. By computing the duration of streaks of winning performances compared to random expectations, we demonstrate that teams win and lose matches consecutively. We define the contributions of specialists such as openers, all-rounders and wicket-keepers and show that a balanced performance from multiple individuals is required to ensure team success. Finally, we measure how transitioning to captaincy in the team improves the performance of batsmen, but not that of bowlers. Our work emphasizes how individual endeavours and team dynamics interconnect and influence collective outcomes in sports.

Xiaotong Liu, Binglu Wang, Zhijun Li

Outdoor sports pose a challenge for people with impaired vision. The demand for higher-speed mobility inspired us to develop a vision-based wearable steering assistance. To ensure broad applicability, we focused on a representative sports environment, the athletics track. Our efforts centered on improving the speed and accuracy of perception, enhancing planning adaptability for the real world, and providing swift and safe assistance for people with impaired vision. In perception, we engineered a lightweight multitask network capable of simultaneously detecting track lines and obstacles. Additionally, due to the limitations of existing datasets for supporting multi-task detection in athletics tracks, we diligently collected and annotated a new dataset (MAT) containing 1000 images. In planning, we integrated the methods of sampling and spline curves, addressing the planning challenges of curves. Meanwhile, we utilized the positions of the track lines and obstacles as constraints to guide people with impaired vision safely along the current track. Our system is deployed on an embedded device, Jetson Orin NX. Through outdoor experiments, it demonstrated adaptability in different sports scenarios, assisting users in achieving free movement of 400-meter at an average speed of 1.34 m/s, meeting the level of normal people in jogging. Our MAT dataset is publicly available from https://github.com/snoopy-l/MAT

Sushant Gautam, Mehdi Houshmand Sarkhoosh, Jan Held et al.

The application of Automatic Speech Recognition (ASR) technology in soccer offers numerous opportunities for sports analytics. Specifically, extracting audio commentaries with ASR provides valuable insights into the events of the game, and opens the door to several downstream applications such as automatic highlight generation. This paper presents SoccerNet-Echoes, an augmentation of the SoccerNet dataset with automatically generated transcriptions of audio commentaries from soccer game broadcasts, enhancing video content with rich layers of textual information derived from the game audio using ASR. These textual commentaries, generated using the Whisper model and translated with Google Translate, extend the usefulness of the SoccerNet dataset in diverse applications such as enhanced action spotting, automatic caption generation, and game summarization. By incorporating textual data alongside visual and auditory content, SoccerNet-Echoes aims to serve as a comprehensive resource for the development of algorithms specialized in capturing the dynamics of soccer games. We detail the methods involved in the curation of this dataset and the integration of ASR. We also highlight the implications of a multimodal approach in sports analytics, and how the enriched dataset can support diverse applications, thus broadening the scope of research and development in the field of sports analytics.

Moch. Irfan Rifa’i, Wasis Himawanto, Ruruh Andayani Bekti et al.

This research aims to see how interested students are in taking part in extracurricular swimming activities. The method used by researchers is a survey method using a questionnaire. By using the survey method, data can be obtained which will be analyzed using descriptive and percentage statistical techniques. Based on the results of the research and discussion, the following conclusions can be drawn from this research: Students' interest in participating in extracurricular swimming activities in elementary schools from the overall data tends to be in the High category. Students' interest in participating in extracurricular swimming activities in elementary schools from the overall data tends to be in the High category. In this category, student interest was very high, accounting for 12 students (26.67%), in the high category, accounting for 13 students (28.89%), in the sufficient category, accounting for 9 students (20%), and in the low category. a total of 11 students (24.44%). The results of the implications used in this research provide input for schools to create better extracurricular swimming programs.

Kavya Anand, Pramit Saha

Fitts' law has been widely employed as a research method for analyzing tasks within the domain of Human-Computer Interaction (HCI). However, its application to non-computer tasks has remained limited. This study aims to extend the application of Fitts' law to the realm of sports, specifically focusing on squash. Squash is a high-intensity sport that requires quick movements and precise shots. Our research investigates the effectiveness of utilizing Fitts' law to evaluate the task difficulty and effort level associated with executing and responding to various squash shots. By understanding the effort/information rate required for each shot, we can determine which shots are more effective in making the opponent work harder. Additionally, this knowledge can be valuable for coaches in designing training programs. However, since Fitts' law was primarily developed for human-computer interaction, we adapted it to fit the squash scenario. This paper provides an overview of Fitts' law and its relevance to sports, elucidates the motivation driving this investigation, outlines the methodology employed to explore this novel avenue, and presents the obtained results, concluding with key insights. We conducted experiments with different shots and players, collecting data on shot speed, player movement time, and distance traveled. Using this data, we formulated a modified version of Fitts' law specifically for squash. The results provide insights into the difficulty and effectiveness of various shots, offering valuable information for both players and coaches in the sport of squash.

Ryan S. Brill, Ronald Yurko, Abraham J. Wyner

The standard mathematical approach to fourth-down decision making in American football is to make the decision that maximizes estimated win probability. Win probability estimates arise from machine learning models fit from historical data. These models attempt to capture a nuanced relationship between a noisy binary outcome variable and game-state variables replete with interactions and non-linearities from a finite dataset of just a few thousand games. Thus, it is imperative to knit uncertainty quantification into the fourth-down decision procedure; we do so using bootstrapping. We find that uncertainty in the estimated optimal fourth-down decision is far greater than that currently expressed by sports analysts in popular sports media.

Weiqi Wu, Chengyue Jiang, Yong Jiang et al.

Ontological knowledge, which comprises classes and properties and their relationships, is integral to world knowledge. It is significant to explore whether Pretrained Language Models (PLMs) know and understand such knowledge. However, existing PLM-probing studies focus mainly on factual knowledge, lacking a systematic probing of ontological knowledge. In this paper, we focus on probing whether PLMs store ontological knowledge and have a semantic understanding of the knowledge rather than rote memorization of the surface form. To probe whether PLMs know ontological knowledge, we investigate how well PLMs memorize: (1) types of entities; (2) hierarchical relationships among classes and properties, e.g., Person is a subclass of Animal and Member of Sports Team is a subproperty of Member of ; (3) domain and range constraints of properties, e.g., the subject of Member of Sports Team should be a Person and the object should be a Sports Team. To further probe whether PLMs truly understand ontological knowledge beyond memorization, we comprehensively study whether they can reliably perform logical reasoning with given knowledge according to ontological entailment rules. Our probing results show that PLMs can memorize certain ontological knowledge and utilize implicit knowledge in reasoning. However, both the memorizing and reasoning performances are less than perfect, indicating incomplete knowledge and understanding.

Jiaben Chen, Huaizu Jiang

Human-centric video frame interpolation has great potential for improving people's entertainment experiences and finding commercial applications in the sports analysis industry, e.g., synthesizing slow-motion videos. Although there are multiple benchmark datasets available in the community, none of them is dedicated for human-centric scenarios. To bridge this gap, we introduce SportsSloMo, a benchmark consisting of more than 130K video clips and 1M video frames of high-resolution ($\geq$720p) slow-motion sports videos crawled from YouTube. We re-train several state-of-the-art methods on our benchmark, and the results show a decrease in their accuracy compared to other datasets. It highlights the difficulty of our benchmark and suggests that it poses significant challenges even for the best-performing methods, as human bodies are highly deformable and occlusions are frequent in sports videos. To improve the accuracy, we introduce two loss terms considering the human-aware priors, where we add auxiliary supervision to panoptic segmentation and human keypoints detection, respectively. The loss terms are model agnostic and can be easily plugged into any video frame interpolation approaches. Experimental results validate the effectiveness of our proposed loss terms, leading to consistent performance improvement over 5 existing models, which establish strong baseline models on our benchmark. The dataset and code can be found at: https://neu-vi.github.io/SportsSlomo/.

Kostiantyn Kolomiiets

Актуальність ролі робочих програм спортивно-оздоровчих занять в організації діяльності клубу з єдиноборств визначається сучасними тенденціями в спорті та фізичній активності, а також потребами суспільства в збереженні й покращанні фізичного та психологічного здоров’я людей. Зростаючий інтерес до фітнесу, спорту й здорового способу життя спонукає клуби з єдиноборств до пошуку нових підходів до організації тренувань та занять. Робочі програми спортивно-оздоровчих занять у цьому контексті стають необхідним інструментом для забезпечення ефективного тренування, адаптованого до потреб різних груп населення. Мета дослідження – визначити роль робочих програм спортивно-оздоровчих занять в організації діяльності клубу з єдиноборств. Методи дослідження. У дослідженні застосовано метод аналізу, синтезу, індукції, системного аналізу та ін. Результати. Визначено роль робочих програм спортивно-оздоровчих занять в організації діяльності клубу з єдиноборств. Сучасна спортивна діяльність у галузі єдиноборств неможлива без упровадження та реалізації науково обґрунтованих методів тренувань. Для забезпечення високої ефективності й досягнення найкращих результатів потрібний систематичний і цілеспрямований підхід до процесів тренування. Один із провідних інструментів цього підходу – розробка робочих програм для спортивно-оздоровчих занять. Ці програми становлять вагомий директивний документ для тренерського колективу та спортсменів, визначаючи структуру, завдання, цілі й послідовність тренувального процесу. Висновки. Дослідження дало змогу визначити роль робочих програм спортивно-оздоровчих занять в організації діяльності клубу з єдиноборств. Проведений дослідницький аналіз етапів формування робочих програм спортивно-оздоровчих занять і їх ролі в організації діяльності клубу з єдиноборств розкриває важливість функціонування спортивної підготовки в цьому контексті.