Methodological trends in machine learning for sport A systematic and quantitative review (2020–2026)
Article Sidebar
Main Article Content
Abstract
Since 2020, the field of machine learning research in sports has expanded. This includes predictive modelling for sports medicine and athlete monitoring using wearables and screening data, sequential modelling for trajectory prediction, and computer vision for broadcast understanding (event spotting, multi-object tracking, pose estimation). Peer-reviewed journal and major conference proceeding evidence published between 2020-01-01 and 2026-02-11 is integrated in this review (search date: 2026-02-11). After outlining repeatable search terms and inclusion criteria, we offer a thematic synthesis arranged by task family (recognition/segmentation, tracking and pose, tactical decision support, biomechanics/engineering prediction, and health/injury risk) and data modality (wearables, clinical/screening data, video, and tracking). Algorithm/sport frequencies throughout the extracted sample are reported in a quantitative synthesis that summarizes representative studies. The findings show that: (i) open benchmarks like SoccerNet-v2 (~300k annotations over 500 broadcast soccer videos) and FineGym (708 hours; 530 fine-grained elements) have fuelled the dominance of deep learning in video and pose pipelines; (ii) tree-based boosting and SVMs continue to be common for structured sports medicine data; and (iii) interpretability tooling (SHAP, permutation importance) is being used more and more to convert models into insights that coaches and clinicians can use. Restrictions include partial PRISMA count reconstruction and limited access to certain paywalled indexing services. We wrap up by discussing unresolved issues with deployment, fairness, domain shift, generalization, and label quality.
Downloads
Article Details
Section
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Each author warrants that his or her submission to the Work is original and that he or she has full power to enter into this agreement. Neither this Work nor a similar work has been published elsewhere in any language nor shall be submitted for publication elsewhere while under consideration by Journal of Human Sport and Exercise (JHSE). Each author also accepts that the JHSE will not be held legally responsible for any claims of compensation.
Authors wishing to include figures or text passages that have already been published elsewhere are required to obtain permission from the copyright holder(s) and to include evidence that such permission has been granted when submitting their papers. Any material received without such evidence will be assumed to originate from the authors.
Please include at the end of the acknowledgements a declaration that the experiments comply with the current laws of the country in which they were performed. The editors reserve the right to reject manuscripts that do not comply with the abovementioned requirements. The author(s) will be held responsible for false statements or failure to fulfill the above-mentioned requirements.
This title is licensed under a Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).
You are free to:
Share — copy and redistribute the material in any medium or format.
Adapt — remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
-
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
-
NonCommercial — You may not use the material for commercial purposes.
-
ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
- You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation.
- No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
How to Cite
References
Bright, J. (2024). Pitchernet: Powering the moneyball evolution in baseball video analytics. IEEE/CVF CVPR. https://doi.org/10.1109/CVPRW63382.2024.00346
Chu, Y. (2022). Machine learning to predict sports-related concussion recovery using clinical data. Annals of Physical and Rehabilitation Medicine, 65(4), 101626. https://doi.org/10.1016/j.rehab.2021.101626
Cioppa, A. (2022). Soccernet-tracking: Multiple objects tracking dataset and benchmark in soccer videos. IEEE/CVF CVPR, (pp. 3491-3502). https://doi.org/10.1109/CVPRW56347.2022.00393
Costa, J. (2021). Framework for intelligent swimming analytics with wearable sensors for stroke classification. Sensors, 21(15), 5162. https://doi.org/10.3390/s21155162
de Beukelaar, T., & Roantree, M. (2025). Estimating oxygen uptake and energy expenditure. PLOS ONE.
Deliège, A. (2021). SoccerNet-v2: A dataset and benchmarks for holistic understanding of broadcast soccer videos. IEEE/CVF CVPR Workshops. https://doi.org/10.1109/CVPRW53098.2021.00508
Gomaz, L. (2021). Individualised ball speed prediction in baseball pitching. Sensors, 21(22), 7442. https://doi.org/10.3390/s21227442
Hauri, S. (2021). Multi-modal trajectory prediction of NBA players. IEEE/CVF WACV. https://doi.org/10.1109/WACV48630.2021.00168
Honda, Y. (2022). Pass receiver prediction in soccer using video and players' trajectories. IEEE/CVF CVPR, (pp. 3503-3512). https://doi.org/10.1109/CVPRW56347.2022.00394
Ingwersen, C. (2023). Sportspose-a dynamic 3d sports pose dataset. IEEE/CVF CVPR, (pp. 5219-5228). https://doi.org/10.1109/CVPRW59228.2023.00550
Liu, P. (2022). MonoTrack: Shuttle trajectory reconstruction from monocular badminton video. IEEE/CVF CVPR, (pp. 3513-3522). https://doi.org/10.1109/CVPRW56347.2022.00395
Luo, Y. (2020). Inverse reinforcement learning for team sports: Valuing actions and players. https://doi.org/10.24963/ijcai.2020/464
Luu, B. (2020). Machine learning outperforms logistic regression analysis to predict next-season NHL player injury: an analysis of 2322 players from 2007 to 2017. Orthopaedic Journal of Sports Medicine, 8(9). https://doi.org/10.1177/2325967120953404
Moore, R. (2025). A context-enhanced deep learning approach to predict baseball pitch location. Sports Engineering, 28, 16. https://doi.org/10.1007/s12283-025-00497-5
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Robles-Palazón, F. (2023). Predicting injury risk using machine learning in male youth soccer players. Chaos, Solitons & Fractals, 167, 113079. https://doi.org/10.1016/j.chaos.2022.113079
Sen, A. (2021). Cricshotclassify: an approach to classifying batting shots from cricket videos using a convolutional neural network and gated recurrent unit. Sensors, 21(8), 2846. https://doi.org/10.3390/s21082846
Shao, D. (2020). FineGym: A hierarchical video dataset for fine-grained action understanding. IEEE/CVF CVPR. https://doi.org/10.1109/CVPR42600.2020.00269
Sheridan, D., Jaspers, A., Cuong, D., Op De Beéck, T., Moyna, N., & de Beukelaar, T. (2025). Estimating oxygen uptake in simulated team sports using machine learning models and wearable sensor data: A pilot study. PLOS ONE, 20(4), e0319760. Récupéré sur https://doi.org/10.1371/journal.pone.0319760
Tsilimigkras, T., Kakkos, I., Matsopoulos, G., & Bogdanis, G. (2024). Enhancing Sports Injury Risk Assessment in Soccer Through Machine Learning and Training Load Analysis. Journal of Sports Science and Medicine, 23, 537-547. https://doi.org/10.52082/jssm.2024.537
Wu, M., Fan, M., Hu, Y., Wang, R., Wang, Y., Li, Y., . . . Xia, G. (2022). A real-time tennis level evaluation and strokes classification system based on the Internet of Things. Internet of Things, 17, 100494. https://doi.org/10.1016/j.iot.2021.100494
Yanai, C. (2022). Q-Ball: Modeling basketball games using deep reinforcement learning. AAAI Conference on Artificial Intelligence, 36, pp. 8806-8813. https://doi.org/10.1609/aaai.v36i8.20861
Yeung, C. (2025). AthletePose3D: A benchmark dataset for 3D human pose estimation and kinematic validation in athletic movements. IEEE/CVF CVPR. https://doi.org/10.1109/CVPRW67362.2025.00592