Alexandra Martín-Rodríguez, Laura Augusta Gostian-Ropotin, Ana Isabel Beltrán-Velasco
et al.
The symbiotic relationship between sports practice and psychological well-being has, in recent times, surged to the forefront of academic and public attention. The aim of this narrative review is to comprehensively explore the intricate pathways linking physical engagement in sports to its subsequent impacts on mental health and synthesize the multifarious effects of sports on psychological health, offering insights for integrating physical and psychological strategies to enhance well-being. From neurobiological underpinnings to therapeutic applications, this comprehensive manuscript provides an in-depth dive into the multifaceted world of sports and psychology. Highlighting evidence-based interventions, this review aspires to offer actionable insights for practitioners, athletes, and individuals alike, advocating for a holistic approach to mental well-being. This manuscript highlights the profound impact of sports on mental health, emphasizing its role in emotional regulation, resilience, cognitive function, and treating psychological conditions. It details how sports induce neurochemical changes, enhance brain functions like memory and learning, and aid against cognitive decline. This review also notes the benefits of regular exercise in mood improvement, stress management, and social skill enhancement, particularly when combined with mindfulness practices. It underscores the importance of considering cultural and gender perspectives in sports psychology, advocating for an integrated physical–psychological approach to promote overall well-being.
This study examines whether following popular Nigerian sports betting influencers on social media is a financially sound strategy. To avoid the survivorship bias that occurs when influencers only share their winning bets, we tracked 5,467 pre-match betting slips from three prominent tipsters on X (formerly Twitter) and Telegram. We verified the outcomes against official Stake.com records, resulting in a final dataset covering approximately $4.8 million in tracked bets. We analyzed raw performance, assessed risk based on odds sizes, and applied four common staking strategies (Flat, Inverse, Square Root, and Fixed Return) to simulate realistic follower outcomes. The results show a sharp contrast between the wealth these influencers display online and the actual financial results. The influencers themselves collectively lost 25.24% on their promoted bets, while a follower who staked the same amount on every tip would lose 38.27% on their investment. Across all tested strategies, following these influencers consistently led to significant financial losses. These findings raise serious consumer protection concerns in Nigeria's expanding gambling market.
Movement monitoring with wearable technologies is becoming increasingly popular in different fields of application (clinical, sports, entertainment). Particularly, textile-based wearables for movement monitoring are attractive as they follow the body movement, are comfortable to use, and can provide continuous tracking capabilities. Ideally, these wearable devices should be flexible (as opposed to current technologies with rigid electronics on the garments) and transmit data wirelessly to avoid hindering the natural movement with connections. Although fully textile wireless and passive wearable systems — whereby the textile sensing part does not have any rigid components and the data is wirelessly transmitted to an external reader — have been developed, the capability of these technologies is currently limited to a single sensor. In this work, we present a system based on a resonating inductor-capacitor (LC) circuits that can measure multiple sensors to broaden the range of use by tracking more than a single joint. Importantly, the presented system employs multiple capacitive strain sensors but retains the use of a single inductor for data transmission, limiting the complexity of realization and the number of connections. After characterization on the bench for careful design of the circuit components, we demonstrated the capability of the system to be used for human movement monitoring and activity classification by integrating two sensors in sport leggings and performing different static and dynamic activities. The tests with sensorized leggings were performed by a single participant. Among a set of chosen classification algorithms, the best performance (F1-score) was 0.98 for the static activities and 0.96 for dynamic activities. When including three independent sessions (donning and doffing the sensorised leggings) accuracy and F1-score dropped to 0.86 and 0.87 respectively. Overall, the presented system has the potential to be adopted as unobtrusive and comfortable smart clothing for real time movement monitoring.
Abhijeet Vishwasrao, Sai Bharath Chandra Gutha, Andres Cremades
et al.
Rapid urbanization demands accurate and efficient monitoring of turbulent wind patterns to support air quality, climate resilience and infrastructure design. Traditional sparse reconstruction and sensor placement strategies face major accuracy degradations under practical constraints. Here, we introduce Diff-SPORT, a diffusion-based framework for high-fidelity flow reconstruction and optimal sensor placement in urban environments. Diff-SPORT combines a generative diffusion model with a maximum a posteriori (MAP) inference scheme and a Shapley-value attribution framework to propose a scalable and interpretable solution. Compared to traditional numerical methods, Diff-SPORT achieves significant speedups while maintaining both statistical and instantaneous flow fidelity. Our approach offers a modular, zero-shot alternative to retraining-intensive strategies, supporting fast and reliable urban flow monitoring under extreme sparsity. Diff-SPORT paves the way for integrating generative modeling and explainability in sustainable urban intelligence.
Bikash Kumar Badatya, Vipul Baghel, Jyotirmoy Amin
et al.
Precise analysis of athletic motion is central to sports analytics, particularly in disciplines where nuanced biomechanical phases directly impact performance outcomes. Traditional analytics techniques rely on manual annotation or laboratory-based instrumentation, which are time-consuming, costly, and lack scalability. Automatic extraction of relevant kinetic variables requires a robust and contextually appropriate temporal segmentation. Considering the specific case of elite javelin-throw, we present a novel unsupervised framework for such a contextually aware segmentation, which applies the structured optimal transport (SOT) concept to augment the well-known Attention-based Spatio-Temporal Graph Convolutional Network (ASTGCN). This enables the identification of motion phase transitions without requiring expensive manual labeling. Extensive experiments demonstrate that our approach outperforms state-of-the-art unsupervised methods, achieving 71.02% mean average precision (mAP) and 74.61% F1-score on test data, substantially higher than competing baselines. We also release a new dataset of 211 manually annotated professional javelin-throw videos with frame-level annotations, covering key biomechanical phases: approach steps, drive, throw, and recovery.
Ultimate is a sport where points are scored by passing a disc and catching it in the opposing team's end zone. In Ultimate, the player holding the disc cannot move, making field dynamics primarily driven by other players' movements. However, current literature in team sports has ignored quantitative evaluations of when players initiate such unlabeled movements in game situations. In this paper, we propose a quantitative evaluation method for movement initiation timing in Ultimate Frisbee. First, game footage was recorded using a drone camera, and players' positional data was obtained, which will be published as UltimateTrack dataset. Next, players' movement initiations were detected, and temporal counterfactual scenarios were generated by shifting the timing of movements using rule-based approaches. These scenarios were analyzed using a space evaluation metric based on soccer's pitch control reflecting the unique rules of Ultimate. By comparing the spatial evaluation values across scenarios, the difference between actual play and the most favorable counterfactual scenario was used to quantitatively assess the impact of movement timing. We validated our method and show that sequences in which the disc was actually thrown to the receiver received higher evaluation scores than the sequences without a throw. In practical verifications, the higher-skill group displays a broader distribution of time offsets from the model's optimal initiation point. These findings demonstrate that the proposed metric provides an objective means of assessing movement initiation timing, which has been difficult to quantify in unlabeled team sport plays.
Shomoita Alam, Erica E. M. Moodie, Lucas Y. Wu
et al.
Causal inference has become an accepted analytic framework in settings where experimentation is impossible, which is frequently the case in sports analytics, particularly for studying in-game tactics. However, subtle differences in implementation can lead to important differences in interpretation. In this work, we provide a case study to demonstrate the utility and the nuance of these approaches. Motivated by a case study of crossing in soccer, two causal questions are considered: the overall impact of crossing on shot creation (Average Treatment Effect, ATE) and its impact in plays where crossing was actually attempted (Average Treatment Effect on the Treated, ATT). Using data from Shandong Taishan Luneng Football Club's 2017 season, we demonstrate how distinct matching strategies are used for different estimation targets - the ATE and ATT - though both aim to eliminate any spurious relationship between crossing and shot creation. Results suggest crossing yields a 1.6% additive increase in shot probability overall compared to not crossing (ATE), whereas the ATT is 5.0%. We discuss what insights can be gained from each estimand, and provide examples where one may be preferred over the alternative. Understanding and clearly framing analytics questions through a causal lens ensure rigorous analyses of complex questions.
Accurate, real-time collision detection is essential for ensuring player safety and effective refereeing in high-contact sports such as rugby, particularly given the severe risks associated with traumatic brain injuries (TBI). Traditional collision-monitoring methods employing fixed cameras or wearable sensors face limitations in visibility, coverage, and responsiveness. Previously, we introduced a framework using unmanned aerial vehicles (UAVs) for monitoring and real time kinematics extraction from videos of collision events. In this paper, we show that the strategies operating on the objective of ensuring at least one UAV captures every incident on the pitch have an emergent property of fulfilling a stronger key condition for successful kinematics extraction. Namely, they ensure that almost all collisions are captured by multiple drones, establishing multi-view fidelity and redundancy, while not requiring any drone-to-drone communication.
We present a method for 3D ball trajectory estimation from a 2D tracking sequence. To overcome the ambiguity in 3D from 2D estimation, we design an LSTM-based pipeline that utilizes a novel canonical 3D representation that is independent of the camera's location to handle arbitrary views and a series of intermediate representations that encourage crucial invariance and reprojection consistency. We evaluated our method on four synthetic and three real datasets and conducted extensive ablation studies on our design choices. Despite training solely on simulated data, our method achieves state-of-the-art performance and can generalize to real-world scenarios with multiple trajectories, opening up a range of applications in sport analysis and virtual replay. Please visit our page: https://where-is-the-ball.github.io.
Sanchayan Santra, Vishal Chudasama, Pankaj Wasnik
et al.
Precise Event Spotting (PES) aims to identify events and their class from long, untrimmed videos, particularly in sports. The main objective of PES is to detect the event at the exact moment it occurs. Existing methods mainly rely on features from a large pre-trained network, which may not be ideal for the task. Furthermore, these methods overlook the issue of imbalanced event class distribution present in the data, negatively impacting performance in challenging scenarios. This paper demonstrates that an appropriately designed network, trained end-to-end, can outperform state-of-the-art (SOTA) methods. Particularly, we propose a network with a convolutional spatial-temporal feature extractor enhanced with our proposed Adaptive Spatio-Temporal Refinement Module (ASTRM) and a long-range temporal module. The ASTRM enhances the features with spatio-temporal information. Meanwhile, the long-range temporal module helps extract global context from the data by modeling long-range dependencies. To address the class imbalance issue, we introduce the Soft Instance Contrastive (SoftIC) loss that promotes feature compactness and class separation. Extensive experiments show that the proposed method is efficient and outperforms the SOTA methods, specifically in more challenging settings.
Clara Martinez-Perez, Henrique Nascimento, Ana Roque
et al.
Background: Sports vision training improves perceptual–motor skills crucial for performance and injury prevention. Despite proven benefits, little is known about its perception and use among coaches in Portugal. Methods: A cross-sectional online survey was completed by active coaches from various sports, gathering sociodemographic data, awareness of visual training, perceived importance of ten visual skills, and implementation in training plans. Statistical analyses included descriptive tests to summarize sample characteristics, t-tests and two-way ANOVA to compare perceived importance of visual skills across sex and sport modalities, Spearman correlations to assess associations with age, and Firth-corrected logistic regression to identify predictors of incorporating visual training into practice plans. Results: Among 155 participants (88.5% men; mean age 36.9 ± 11.8 years), 73.2% reported incorporating visual training, with no association with self-reported knowledge (p = 0.413). Regarding perceived importance, reaction time was rated highest (1.20 ± 0.44), followed by hand–eye/body coordination (1.61 ± 0.71) and anticipation (1.34 ± 0.55). Age negatively correlated with importance given to visual memory, peripheral vision, concentration, depth perception, coordination, and moving-object recognition (p < 0.05). Multivariable analysis showed age (OR = 1.05; p = 0.0206) and volleyball (OR = 2.45; p = 0.031) positively associated with implementation, while higher perceived importance for visual concentration was negatively associated (OR = 0.54; p = 0.0176). Conclusions: Visual training implementation is high but not always linked to formal knowledge. Adoption is influenced by sport and demographics, and the counterintuitive role of visual concentration underscores the need for tailored educational programs to enhance performance and reduce injury risk.
Understanding human actions from videos is essential in many domains, including sports. In figure skating, technical judgments are performed by watching skaters' 3D movements, and its part of the judging procedure can be regarded as a Temporal Action Segmentation (TAS) task. TAS tasks in figure skating that automatically assign temporal semantics to video are actively researched. However, there is a lack of datasets and effective methods for TAS tasks requiring 3D pose data. In this study, we first created the FS-Jump3D dataset of complex and dynamic figure skating jumps using optical markerless motion capture. We also propose a new fine-grained figure skating jump TAS dataset annotation method with which TAS models can learn jump procedures. In the experimental results, we validated the usefulness of 3D pose features as input and the fine-grained dataset for the TAS model in figure skating. FS-Jump3D Dataset is available at https://github.com/ryota-skating/FS-Jump3D.
The analysis of high-intensity runs (or sprints) in soccer has long been a topic of interest for sports science researchers and practitioners. In particular, recent studies suggested contextualizing sprints based on their tactical purposes to better understand the physical-tactical requirements of modern match-play. However, they have a limitation in scalability, as human experts have to manually classify hundreds of sprints for every match. To address this challenge, this paper proposes a deep learning framework for automatically classifying sprints in soccer into contextual categories. The proposed model covers the permutation-invariant and sequential nature of multi-agent trajectories in soccer by deploying Set Transformers and a bidirectional GRU. We train the model with category labels made through the collaboration of human annotators and a rule-based classifier. Experimental results show that our model classifies sprints in the test dataset into 15 categories with the accuracy of 77.65%, implying the potential of the proposed framework for facilitating the integrated analysis of soccer sprints at scale.
Problem definition: Professional sports leagues may be suspended due to various reasons such as the recent COVID-19 pandemic. A critical question the league must address when re-opening is how to appropriately select a subset of the remaining games to conclude the season in a shortened time frame. Academic/practical relevance: Despite the rich literature on scheduling an entire season starting from a blank slate, concluding an existing season is quite different. Our approach attempts to achieve team rankings similar to that which would have resulted had the season been played out in full. Methodology: We propose a data-driven model which exploits predictive and prescriptive analytics to produce a schedule for the remainder of the season comprised of a subset of originally-scheduled games. Our model introduces novel rankings-based objectives within a stochastic optimization model, whose parameters are first estimated using a predictive model. We introduce a deterministic equivalent reformulation along with a tailored Frank-Wolfe algorithm to efficiently solve our problem, as well as a robust counterpart based on min-max regret. Results: We present simulation-based numerical experiments from previous National Basketball Association (NBA) seasons 2004--2019, and show that our models are computationally efficient, outperform a greedy benchmark that approximates a non-rankings-based scheduling policy, and produce interpretable results. Managerial implications: Our data-driven decision-making framework may be used to produce a shortened season with 25-50\% fewer games while still producing an end-of-season ranking similar to that of the full season, had it been played.