AI-SPOT: a Novel Artificial Intelligence-enabled Sport Optimization Tracker to Enhance Performance and Prevent Injury in Elite Footballers

Aaron Chen Angus, Dinesh Sirisena

Abstract


This study introduces AI-SPOT, a novel artificial intelligence tool for optimizing performance and preventing injuries in elite footballers. Data were collected from four Singapore Premiere League clubs and the National Team, encompassing 68 male footballers over two seasons (2021–2022). The comprehensive dataset included diverse metrics, injury records, and automated live match data from established databases. AI-SPOT employs Python's scikit-learn for predictive analytics, using techniques like logistic regression and XGBoost, and was further developed with TensorFlow. Its effectiveness in injury prediction and performance assessment was validated with extensive local and international data sources. The system's potential for broader sports applications was underscored by user experience assessments, indicating a significant shift towards AI-driven strategies in sports management. Despite its reliance on high-quality, sport-specific data, AI-SPOT's adaptability highlights its role as a transformative tool in sports analytics, paving the way for advanced, data-driven approaches in sports management and strategy formulation.


Keywords


Artificial intelligence; data-driven decision making; machine learning; sport medicine

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References


López-Valenciano A, Ruiz-Pérez I, Garcia-Gómez A, Vera-Garcia FJ, De Ste Croix M, Myer GD, Ayala F. Epidemiology of injuries in professional football: a systematic review and meta-analysis. Br J Sports Med. 2020;54(12):711–8.

Della Villa F, Buckthorpe M, Grassi A, Nabiuzzi A, Tosarelli F, Zaffagnini S, et al. Systematic video analysis of ACL injuries in professional male football (soccer): injury mechanisms, situational patterns and biomechanics study on 134 consecutive cases. Br J Sports Med. 2020;54(24):1423–32.

Gabbett TJ. The training-injury prevention paradox: should athletes be training smarter and harder? Br J Sports Med. 2016;50(5):273–80.

Windt J, Zumbo BD, Sporer B, MacDonald K, Gabbett TJ. Why do workload spikes cause injuries, and which athletes are at higher risk? Mediators and moderators in workload–injury investigations. Br J Sports Med. 2017;51(13):993–4.

Dellal A, Lago-Peñas C, Rey E, Chamari K, Orhant E. The effects of a congested fixture period on physical performance, technical activity and injury rate during matches in a professional soccer team. Br J Sports Med. 2015;49(6):390–4.

Rössler R, Junge A, Chomiak J, Dvorak J, Faude O. Soccer injuries in players aged 7 to 12 years: a descriptive epidemiological study over 2 seasons. Am J Sports Med. 2016;44(2):309–17.

Lee EC, Fragala MS, Kavouras SA, Queen RM, Pryor JL, Casa DJ. Biomarkers in sports and exercise: tracking health, performance, and recovery in athletes. J Strength Cond Res. 2017;31(10):2920–37.

Liu H, Hopkins W, Gómez MA, Molinuevo JS. Inter-operator reliability of live football match statistics from OPTA Sportsdata. Int J Perf Anal Sport. 2020;13(3):803–21.

Jha D, Rauniyar A, Johansen HD, et al. Video analytics in elite soccer: a distributed computing perspective. Proc IEEE Sens Array Multichannel Signal Process Workshop. 2022;2022:221–5.

Fernandez-Navarro J, Fradua L, Zubillaga A, McRobert AP. Influence of contextual variables on styles of play in soccer. Int J Perf Anal Sport. 2018;18(3):423–36.

van Maarseveen MJJ, Oudejans RRD, Mann DL, Savelsbergh GJP. Perceptual-cognitive skill and the in situ performance of soccer players. Q J Exp Psychol (Hove). 2018;71(2):455–70.

Rein R, Memmert D. Big data and tactical analysis in elite soccer: future challenges and opportunities for sports science. Springerplus. 2016;5(1):1410.

Tenga A, Sigmundstad E. Characteristics of goal-scoring possessions in open play: comparing the top, in-between and bottom teams from professional soccer league. Int J Perf Anal Sport. 2011;11(3):545–52.

Bittencourt NFN, Meeuwisse WH, Mendonça LD, Nettel-Aguirre A, Ocarino JM, Fonseca ST. Complex systems approach for sports injuries: moving from risk factor identification to injury pattern recognition—narrative review and new concept. Br J Sports Med. 2016;50(21):1309–14.

Gonçalves B, Coutinho D, Travassos B, Brito J, Figueiredo P. Match analysis of soccer refereeing using spatiotemporal data: a case study. Sensors (Basel). 2021;21(7):2541.

Sarmento H. From traditional scouting to a data-driven approach: soccer analytics in the era of big data. J Hum Sport Exerc. 2020;15(3):678–88.

Gabbett TJ. Debunking the myths about training load, injury and performance: empirical evidence, hot topics and recommendations for practitioners. Br J Sports Med. 2020;54(1):58–66.

Smith J. Elite football player characteristics and performance metrics: an analysis. Sports Med. 2020;50:1231–42.

Djaoui L, Haddad M, Chamari K, Dellal A. Monitoring training load and fatigue in soccer players with physiological markers. Physiol Behav. 2017;181:86–94.

Wiesing U, Parsa-Parsi R. The World Medical Association launches a Revision of the Declaration of Geneva. Bioethics. 2016;30(3):140.

Bjärsholm D, Gerrevall P, Linnér S, Peterson T, Schenker K. Ethical considerations in researching sport and social entrepreneurship. Eur J Sport Sci. 2018;15(3):216–33.

Ngiam KY, Khor IW. Big data and machine learning algorithms for health-care . Lancet Oncol. 2019;20(5):e262–73.

Kubayi A. Analysis of goal scoring patterns in the 2018 FIFA World Cup. J Hum Kinet. 2020;71:205–10.

Berrar D. Data wrangling and exploratory data analysis in bioinformatics. Front Genet. 2020;11:512036.

Cai W, Yang Z, Wang Z, Wang Y. A new compound fault feature extraction method based on multipoint kurtosis and variational mode decomposition. Entropy (Basel). 2018;20(7):521.

Martin RK, Pareek A, Krych AJ, Maradit Kremers H, Engebretsen L. Machine learning in sports medicine: need for improvement. J ISAKOS. 2021;6(1):1–2.

Marynowicz J, Lango M, Horna D, Kikut K, Konefał M, Chmura P, et al. Within-subject principal component analysis of external training load and intensity measures in youth soccer training. J Strength Cond Res. 2023;37(12):2411–6.

Chen T, Guestrin C. XGBoost: a scalable tree boosting system. In: KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2016 August 13–17; San Francisco, CA, USA. San Francisco, CA, USA: Association for Computing Machinery, Inc.; 2016. p. 785–94.

Rico-González M, Pino-Ortega J, Méndez A, Clemente FM, Baca A. Machine learning application in soccer: a systematic review. Biol Sport. 2023;40(1):249–63.




DOI: https://doi.org/10.29313/gmhc.v12i1.12788

pISSN 2301-9123 | eISSN 2460-5441


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