Hasil untuk "Sports"

M. Messner

Antonio Sinelio Santos Cunha, Vitória Karoline Batista da Silva, Antonio Cícero Frizzi Junior et al.

Este estudo analisou nove fatores associados ao burnout em atletas de futebol Sub-20: idade, índice de massa corporal (IMC), tempo de prática esportiva, frequência semanal de treinos, duração dos treinos, distância da residência familiar, número de visitas à família por ano, moradia com a família e dependência financeira do salário do atleta. A amostra incluiu 251 atletas masculinos de cinco equipes das divisões principais do campeonato. Dados dos participantes foram coletados por ficha estruturada, e o burnout do atleta foi avaliado pelo Questionário de Burnout para Atletas (QBA), que mensura exaustão física e emocional (EFE), reduzido senso de realização esportiva (RSR) e desvalorização esportiva (DES). A idade apresentou associação negativa com a EFE (β = -0,919; p = 0,001), o que indica menores níveis de exaustão em atletas mais velhos. O IMC teve relação positiva com a EFE (β = 1,025; p = 0,023), o que sugere maior exaustão em atletas com maior IMC. Morar com a família foi associado positivamente à DES (β = 1,161; p = 0,005) e ao burnout total (β = 1,068; p = 0,025), o que indica que atletas fora do ambiente familiar tendem a perceber o esporte de forma mais negativa e apresentam maior risco de burnout. Portanto, o suporte familiar e psicológico, aliado ao controle nutricional, é indispensável para o equilíbrio emocional e físico dos atletas, pois previne percepções associadas à síndrome de burnout, fortalece a gestão emocional e contribui para a promoção de um melhor desempenho esportivo e longevidade na carreira.

Yasutaka Shimizu, Atsushi Yamanobe

This study proposes a statistically grounded framework for real-time win probability evaluation and player assessment in score-based team sports, based on minute-by-minute cumulative box-score data. We introduce a continuous dominance indicator (T-score) that maps final scores to real values consistent with win/lose outcomes, and formulate it as a time-evolving stochastic representation (T-process) driven by standardized cumulative statistics. This structure captures temporal game dynamics and enables sequential, analytically tractable updates of in-game win probability. Through this stochastic formulation, competitive advantage is decomposed into interpretable statistical components. Furthermore, we define a latent contribution index, STATS X, which quantifies a player's involvement in favorable dominance intervals identified by the T-process. This allows us to separate a team's baseline strength from game-specific performance fluctuations and provides a coherent, structural evaluation framework for both teams and players. While we do not implement AI methods in this paper, our framework is positioned as a foundational step toward hybrid integration with AI. By providing a structured time-series representation of dominance with an explicit probabilistic interpretation, the framework enables flexible learning mechanisms and incorporation of high-dimensional data, while preserving statistical coherence and interpretability. This work provides a basis for advancing AI-driven sports analytics.

Avery Hinks, Kaitlyn B.E. Jacob, Makenna A. Patterson et al.

Background: Residual force enhancement (rFE), defined as increased isometric force following active lengthening compared to a fixed-end isometric contraction at the same muscle length and level of activation, is present across all scales of muscle. While rFE is always present at the cellular level, often rFE “non-responders” are observed during joint-level voluntary contractions. Methods: We compared rFE between the joint level and single fiber level (vastus lateralis biopsies) in 16 young males. In vivo voluntary knee-extensor rFE was measured by comparing steady-state isometric torque between a stretch-hold (maximal activation at 150°, stretch to 70°, hold) and a fixed-end isometric contraction, with ultrasonographic recording of vastus lateralis fascicle length (FL). Fixed-end contractions were performed at 67.5°, 70.0°, 72.5°, and 75.0°; the joint angle that most closely matched FL of the stretch-hold contraction's isometric steady-state was used to calculate rFE. The starting and ending FLs of the stretch-hold contraction were expressed as % optimal FL, determined via torque-angle relationship. Results: In single fiber experiments, the starting and ending fiber lengths were matched relative to optimal length determined from in vivo testing, yielding an average sarcomere excursion of ∼2.2–3.4 µm. There was a greater magnitude of rFE at the single fiber (∼20%) than joint level (∼5%) (p = 0.004), with “non-responders” only observed at the joint level. Conclusion: By comparing rFE across scales within the same participants, we show the development of the rFE non-responder phenomenon is upstream of rFE's cellular mechanisms, with rFE only lost rather than gained when scaling from single fibers to the joint level.

PLOS One Staff

[This corrects the article DOI: 10.1371/journal.pone.0298858.].

Martin Fernando Bruzzese, Gastón César García, Carlos Rodolfo Arcuri et al.

Background: Extensive data exists on external load during training and competition, but a significant gap remains in understanding internal physiological load, particularly in protocols conducted in ecological settings. Given the scarcity of studies on the on-field cardiorespiratory profiles of national-level athletes, especially in Argentine soccer, this study aimed to identify the on-field cardiorespiratory fitness profile of ten highly trained youth field soccer players (13.6 ± 1.3 years old) from both the first league of the Argentine Football Association and members of the national team in their age group category in the current year. Methods: Each athlete performed an on-field cardiorespiratory exercise test (20-m Shuttle Run Test, 20-m SRT) with the COSMED K5 wearable metabolic system (COSMED, Rome, Italy) in dynamic micro-mixing chamber mode. The 20-m Shuttle Run Test involves running back and forth between two lines set 20 m apart, following the pace set by an audio signal. The test starts at a running velocity of 8.5 km·h⁻¹ and increases by 0.5 km·h⁻¹ each min. Results: Mean velocity at maximal oxygen uptake (v<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mover accent="true"><mrow><mi mathvariant="normal">V</mi></mrow><mo mathvariant="normal">˙</mo></mover></mrow></semantics></math></inline-formula>O_2max) was 12.3 ± 0.7 km·h⁻¹. The maximal oxygen uptake (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mover accent="true"><mrow><mi mathvariant="normal">V</mi></mrow><mo mathvariant="normal">˙</mo></mover></mrow></semantics></math></inline-formula>O_2max) on-field was 67.1 ± 5.3 mL·kg⁻¹·min⁻¹. The <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mover accent="true"><mrow><mi mathvariant="normal">V</mi></mrow><mo mathvariant="normal">˙</mo></mover></mrow></semantics></math></inline-formula>O₂ at the first and second ventilatory thresholds (VT1 and VT2) were identified at 67.0 ± 3.0% <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mover accent="true"><mrow><mi mathvariant="normal">V</mi></mrow><mo mathvariant="normal">˙</mo></mover></mrow></semantics></math></inline-formula>O_2max (44.9 ± 3.3 mL·kg⁻¹·min⁻¹) and 84.7 ± 3.7% <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mover accent="true"><mrow><mi mathvariant="normal">V</mi></mrow><mo mathvariant="normal">˙</mo></mover></mrow></semantics></math></inline-formula>O_2max (56.8 ± 3.8 mL·kg⁻¹·min⁻¹), respectively. Conclusions: This is a scarce on-field gas exchange assessment, conducted in an ecological context using a portable analyzer with highly trained national-level youth soccer players from the Argentine youth national team, which underlines their cardiorespiratory fitness, showcases their high-performance potential, offers valuable insights into a selective group of players, and provides a reference for larger-scale research on elite youth soccer and the long-term development of aerobic power and capacity.

Chris Brogly, Saif Rjaibi, Charlotte Liang et al.

Small Language Models (SLMs) have potential to be used for automatically labelling and identifying aspects of text data for medicine/health-related purposes from documents and the web. As their resource requirements are significantly lower than Large Language Models (LLMs), these can be deployed potentially on more types of devices. SLMs often are benchmarked on health/medicine-related tasks, such as MedQA, although performance on these can vary especially depending on the size of the model in terms of number of parameters. Furthermore, these test results may not necessarily reflect real-world performance regarding the automatic labelling or identification of texts in documents and the web. As a result, we compared topic-relatedness scores from Microsofts phi-3-mini-4k-instruct SLM to the topic-relatedness scores from 7 human evaluators on 1144 samples of medical/health-related texts and 1117 samples of sports injury-related texts. These texts were from a larger dataset of about 9 million news headlines, each of which were processed and assigned scores by phi-3-mini-4k-instruct. Our sample was selected (filtered) based on 1 (low filtering) or more (high filtering) Boolean conditions on the phi-3 SLM scores. We found low-moderate significant correlations between the scores from the SLM and human evaluators for sports injury texts with low filtering (\r{ho} = 0.3413, p < 0.001) and medicine/health texts with high filtering (\r{ho} = 0.3854, p < 0.001), and low significant correlation for medicine/health texts with low filtering (\r{ho} = 0.2255, p < 0.001). There was negligible, insignificant correlation for sports injury-related texts with high filtering (\r{ho} = 0.0318, p = 0.4466).

Muhammad Zeeshan Mazhar, Tolga Buz, Yiran Su

Online communities for sports fans have surged in popularity, with Reddit's r/PremierLeague emerging as a focal point for fans of one of the globe's most celebrated sports leagues. This boom has helped the Premier League make significant inroads into the US market, increasing viewership and sparking greater interest in its matches. Despite the league's broad appeal, there's still a notable gap in understanding its online fan community. Therefore, we analyzed a substantial dataset of over 1.1 million comments posted from 2013-2022 on r/PremierLeague. Our study delves into the sentiment, topics, and toxicity of these discussions, tracking trends over time, aiming to map out the conversation landscape. The rapid expansion has brought more diverse discussions, but also a worrying rise in negative sentiment and toxicity. Additionally, the subreddit has become a venue for users to voice frustrations about broader societal issues like racism, the COVID-19 pandemic, and political tensions.

Chang Liu, Chengcheng Ma, XuanQi Zhou

This paper presents a comprehensive framework for time series prediction using a hybrid model that combines ARIMA and LSTM. The model incorporates feature engineering techniques, including embedding and PCA, to transform raw data into a lower-dimensional representation while retaining key information. The embedding technique is used to convert categorical data into continuous vectors, facilitating the capture of complex relationships. PCA is applied to reduce dimensionality and extract principal components, enhancing model performance and computational efficiency. To handle both linear and nonlinear patterns in the data, the ARIMA model captures linear trends, while the LSTM model models complex nonlinear dependencies. The hybrid model is trained on historical data and achieves high accuracy, as demonstrated by low RMSE and MAE scores. Additionally, the paper employs the run test to assess the randomness of sequences, providing insights into the underlying patterns. Ablation studies are conducted to validate the roles of different components in the model, demonstrating the significance of each module. The paper also utilizes the SHAP method to quantify the impact of traditional advantages on the predicted results, offering a detailed understanding of feature importance. The KNN method is used to determine the optimal prediction interval, further enhancing the model's accuracy. The results highlight the effectiveness of combining traditional statistical methods with modern deep learning techniques for robust time series forecasting in Sports.

Eric Ferkel, Theodore Shybut

Yuningsih Erwin, Biyantoro Gigih Adjie, Sugiharto Eko et al.

Optimization of student achievement is carried out with various efforts, including participating in training for preparation for achievement matches. Students of the Faculty of Sports and Health Sciences (FIKK) Surabaya State University (Unesa) who attend training often apply for dispensation not to attend lectures. This has an impact on the quality of learning because students are constrained by place and time so they cannot attend lectures and are only given assignments. So, a system is needed so that student-athletes can take online lectures that are more flexible in time and place. But, FIKK Unesa does not yet have an online lecture system for students. Therefore, this study aims to design and build a Moodle learning management system for student-athletes at FIKK Unesa. The method used in designing and creating the system is waterfall using the Moodle LMS framework. The results showed that the Moodle LMS that had been designed and built could run and function properly.

Yannis P. Pitsiladis, Zongwei Cai

Zexin Chen, Chengxi Li, Xiangyu Xie et al.

This paper explores the potential of a small, domain-specific language model trained exclusively on sports-related data. We investigate whether extensive training data with specially designed small model structures can overcome model size constraints. The study introduces the OnlySports collection, comprising OnlySportsLM, OnlySports Dataset, and OnlySports Benchmark. Our approach involves: 1) creating a massive 600 billion tokens OnlySports Dataset from FineWeb, 2) optimizing the RWKV architecture for sports-related tasks, resulting in a 196M parameters model with 20-layer, 640-dimension structure, 3) training the OnlySportsLM on part of OnlySports Dataset, and 4) testing the resultant model on OnlySports Benchmark. OnlySportsLM achieves a 37.62%/34.08% accuracy improvement over previous 135M/360M state-of-the-art models and matches the performance of larger models such as SomlLM 1.7B and Qwen 1.5B in the sports domain. Additionally, the OnlySports collection presents a comprehensive workflow for building high-quality, domain-specific language models, providing a replicable blueprint for efficient AI development across various specialized fields.

Fodil Fadli, Yassine Himeur, Mariam Elnour et al.

Anomaly detection in sport facilities has gained significant attention due to its potential to promote energy saving and optimizing operational efficiency. In this research article, we investigate the role of machine learning, particularly deep learning, in anomaly detection for sport facilities. We explore the challenges and perspectives of utilizing deep learning methods for this task, aiming to address the drawbacks and limitations of conventional approaches. Our proposed approach involves feature extraction from the data collected in sport facilities. We present a problem formulation using Deep Feedforward Neural Networks (DFNN) and introduce threshold estimation techniques to identify anomalies effectively. Furthermore, we propose methods to reduce false alarms, ensuring the reliability and accuracy of anomaly detection. To evaluate the effectiveness of our approach, we conduct experiments on aquatic center dataset at Qatar University. The results demonstrate the superiority of our deep learning-based method over conventional techniques, highlighting its potential in real-world applications. Typically, 94.33% accuracy and 92.92% F1-score have been achieved using the proposed scheme.

G. Atkinson, T. Reilly

W. Helsen, J. Starkes, N. Hodges

Violeta-Elena MOIS, Iulia HĂRĂNGUŞ

Bobâlna Hill is the highest part of the Cluj and Dej Hills, an integrated part of the Someșan Plateau, with an altitude of 693 m. From a geomorphological point of view, it falls within the erosive-structural level formed on the horizon of the Dej tuff, of Lower Pannonian age. At the foot of the hill, a series of subsequent valleys developed, such as Olpret Valley to the north, northeast and east, Măr Valley and Lujerdiu Valley to the south, and Luna Valley to the west and southwest respectively. From a geological point of view, Bobâlna Hill is composed by the Dacitic tuff of Dej, interspersed with marls, clays, sandstones with coals and marly shales. Due to its altitude and the rocks it is made of, Bobâlna Hill determined the meaning of the evolution of the other natural components of the environment, flora, fauna, soils, as well as the hydrography. The development of human communities in the adjacent communes was deeply influenced by the presence of Bobâlna Hill. The hill is located about 30 km west of Dej City. Its area of polarization is more visible on the territories of Bobâlna, Aluniș, Cornești, Recea-Cristur and Panticeu communes. Bobâlna Hill has been the source of building materials for houses and other buildings for a long time. There are still buildings made of “Băbdiu stone” today. In addition to resistance over time, tuff has a beautiful appearance and was accessible to people. In June 1437, an army of peasants gathered on Bobâlna Hill, armed with pitchforks and scythes, ready to fight to regain their rights and freedom. The uprising in Bobâlna took place as a result of burdensome feudal obligations towards the state, the feudal lord and the Catholic church, but also because of numerous abuses. On June 8, 1937, the leaders of the villages in the Olpret area formed an initiative committee to build a monument in honour of the peasants who revolted in 1437, on Bobâlna Hill, at an altitude of 693 meters. The initiative was successful and a limestone monument was built, unveiled on December 21, 1957. Starting from 1968 and up to 1989, popular celebrations were organized, most of them just above, on the Bobâlna plateau. The heroes of the 1437 uprising were commemorated and it was an occasion of joy, reunion and party for the inhabitants of the area. Between the years 1989-1999, the celebrations stopped, but they were resumed in 1999, 2001 and 2002. The last celebration was in 2015, in the school yard in Bobâlna commune, but it had a smaller scale. In the years 2021 and 2022, the first two editions of the Revolution Race event took place, on Bobâlna Hill. Revolution Race is a sporting event that highlights nature, history and rural traditions. The “Înflorești” Sports Club Association participated to the organization of the two events in partnership with the Go4Fun Association and the administration of the municipalities of Bobâlna and Cornești. Over 300 runners from Romania and abroad participated in each edition. Starting from the advantages offered by the natural setting, the presence of Bobâlna Hill, and the significance of the historical event of 1437, Bobâlna municipality is currently designing a long-term development strategy based on ecological rural tourism.

Timilehin B. Aderinola, Hananeh Younesian, Cathy Goulding et al.

$\textbf{Goal:}$ This study investigates the feasibility of monocular 2D markerless motion capture (MMC) using a single smartphone to measure jump height, velocity, flight time, contact time, and range of motion (ROM) during motor tasks. $\textbf{Methods:}$ Sixteen healthy adults performed three repetitions of selected tests while their body movements were recorded using force plates, optical motion capture (OMC), and a smartphone camera. MMC was then performed on the smartphone videos using OpenPose v1.7.0. $\textbf{Results:}$ MMC demonstrated excellent agreement with ground truth for jump height and velocity measurements. However, MMC's performance varied from poor to moderate for flight time, contact time, ROM, and angular velocity measurements. $\textbf{Conclusions:}$ These findings suggest that monocular 2D MMC may be a viable alternative to OMC or force plates for assessing sports performance during jumps and velocity-based tests. Additionally, MMC could provide valuable visual feedback for flight time, contact time, ROM, and angular velocity measurements.

Ewerton Borges deSouza Lima, Gabriel Paris deGodoy, Guilherme Ladeira Osés et al.

Abstract Purpose To evaluate epidemiological data of upper limb injuries in professional athletes who participated in two major Brazilian soccer championships between 2016 and 2019. Methods A prospective cohort study was conducted to evaluate the teams of two divisions over four seasons within the Brazilian Soccer Championship and the Paulista Soccer Championship. Clubs and their doctors were contacted to participate in the study and guided on the correct way to enter data via online platforms: Transfermarkt (Transfermarkt GmbH & Co. KG) and Survey Monkey (Momentive.AI). Demographic data, injury characteristics, and FIFA Incidence Formula were analyzed. Results Overall, the study analyzed 3,828 matches and 126,357 hours of play. Upper limb injuries were registered 169 times, representing 6.8% of total injuries, with a FIFA incidence of 1.34. Most lesions occurred in forward players (21.3%), the shoulder exhibited the highest number of injuries (63.3%). The player’s position was related to the location on the field where the injury occurred (p < 0.001); however, there was no relationship between the type of injury and the location on the body (p > 0.001). The average time to return to play was 19.1 days (range 0–200 days) and it was longer for goalkeepers. The necessity of surgical treatment was statistically associated with additional time to return to play (p < 0.001). Conclusions Shoulder injuries were the most frequent upper limb injury sustained during the two major Brazilian soccer championships. Forward players suffered the most upper limb injuries and goalkeepers experienced the longest time to return to play.

Atefe Eftekhari, Jalal Dehghanizade

The aim of this study was to investigate the effect of self-movement on distance perception. The method of the present study is quasi-experimental. The statistical population of the present study was the students of Urmia University who were randomly selected as a sample of 60 students who were eligible to participate in the study and were randomly divided into three groups of 20 people including static group, dynamic group with treadmill speed 4 Km/h and the dynamic group were divided at a speed of 8 km/h. The test of walking blindfolded in a straight line to a previously observed target was used to measure distance perception. The results of analysis of variance showed that there was no significant difference in the perception of estimating the distance between groups, while there was a significant difference in the perception of distance with closed eyes between groups and also in perception scores. There is no significant difference in distance with closed eyes between the movement group with treadmill speeds 4 and 8, but the difference between these two groups with the group without movement is significant and Finally, the results showed that although self-motion affects the perception of distance with the eyes closed, but the speed of movement does not affect this perception. Therefore, movement can affect the perception of the surrounding environment.