Artificial Intelligence Applications in Sports Refereeing: A Systematic Review and Methodological Comparison

Abstract

Artificial Intelligence (AI) technologies are increasingly being used in sports refereeing to support decision-making, object and event tracking, and player- or team-related analysis. This systematic review aimed to examine the application areas of AI technol-ogies in sports refereeing across different sports, identify the analytical methods em-ployed, and compare their methodological performance based on reported accuracy rates. The literature search and study selection process were conducted in accordance with PRISMA guidelines. Scopus, Web of Science, IEEE Xplore, EBSCO, and Google Scholar databases were searched using the string “((Artificial Intelligence) OR (machine learning) OR (deep learning)) AND Sport AND referee”. A total of 404 records were initially identified; after duplicate removal, 374 studies remained. Following title and abstract screening, 45 articles were assessed in full text, and 14 studies met the inclusion criteria. The findings showed that AI applications were predominantly implemented in football. The primary application areas were Referee Decision Support Systems (7 stud-ies), Object and Event Tracking (5 studies), and Player and Team Analysis (2 studies). The most frequently used methods included CNN, LSTM, and YOLO-based deep learning models. Image-processing-based models, particularly YOLO-based approaches, gener-ally demonstrated higher accuracy compared to sensor-based systems. Overall, AI has potential to enhance the accuracy of refereeing decisions and operational efficiency; however, performance limitations, data quality issues, and ethical concerns remain sig-nificant challenges.

Keywords

• Artificial Intelligence
• Deep Learning
• Referee
• Sports

Spor Hakemliğinde Yapay Zekâ Uygulamaları: Sistematik Bir Derleme ve Metodolojik Karşılaştırma

Abstract

Yapay Zekâ (YZ) teknolojileri, karar verme sürecini desteklemek, nesne ve olay takibi yapmak ve oyuncu veya takımlarla ilgili analizler gerçekleştirmek amacıyla spor hakemliğinde giderek daha fazla kullanılmaktadır. Bu sistematik derleme, farklı spor dallarında spor hakemliğinde YZ teknolojilerinin uygulama alanlarını incelemek, kullanılan analitik yöntemleri belirlemek ve bildirilen doğruluk oranlarına dayalı olarak bu yöntemlerin metodolojik performansını karşılaştırmayı amaçlamıştır. Literatür taraması ve çalışma seçimi süreci, PRISMA kılavuzlarına uygun olarak yürütülmüştür. Scopus, Web of Science, IEEE Xplore, EBSCO ve Google Scholar veritabanlarında “((Yapay Zeka) VEYA (makine öğrenimi) VEYA (derin öğrenme)) VE Spor VE hakem” dizesi kullanılarak arama yapılmıştır. Başlangıçta toplam 404 kayıt tespit edildi; yinelenenler çıkarıldıktan sonra 374 çalışma kaldı. Başlık ve özet taramasının ardından 45 makalenin tam metni değerlendirildi ve 14 çalışma dahil edilme kriterlerini karşıladı. Bulgular, yapay zeka uygulamalarının ağırlıklı olarak futbolda uygulandığını gösterdi. Başlıca uygulama alanları Hakem Karar Destek Sistemleri (7 çalışma), Nesne ve Olay Takibi (5 çalışma) ve Oyuncu ve Takım Analizi (2 çalışma) idi. En sık kullanılan yöntemler arasında CNN, LSTM ve YOLO tabanlı derin öğrenme modelleri yer aldı. Görüntü işleme tabanlı modeller, özellikle de YOLO tabanlı yaklaşımlar, sensör tabanlı sistemlere kıyasla genel olarak daha yüksek doğruluk sergiledi. Genel olarak yapay zekâ, hakem kararlarının doğruluğunu ve operasyonel verimliliği artırma potansiyeline sahiptir; ancak performans sınırlılıkları, veri kalitesi sorunları ve etik kaygılar önemli birer zorluk olmaya devam etmektedir.

Keywords

• Yapay Zeka
• Derin Öğrenme
• Hakem
• Spor

References

1. AbuKhait, J., Alaqtash, M., Aljaafreh, A., & Othman, W. (2023). Automated detection and classification of soccer field objects using YOLOv7 and computer vision techniques. International Journal of Advanced Computer Science and Applications, 14(11). https://doi.org/10.14569/IJACSA.2023.0141191
2. Aleza, M. E., & Vetrithangam, D. (2023). Use of artificial intelligence to avoid errors in referring a football match. In 2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1) (pp. 1–6). IEEE. https://doi.org/10.1109/ICAIA57370.2023.10169463
3. Babaee Khobdeh, S., Yamaghani, M. R., & Khodaparast Sareshkeh, S. (2024). Basketball action recognition based on the combination of YOLO and a deep fuzzy LSTM network. The Journal of Supercomputing, 80(3), 3528–3553. https://doi.org/10.1007/s11227-023-05611-7
4. Barra, S., Carta, S. M., Giuliani, A., Pisu, A., Podda, A. S., & Riboni, D. (2023). FootApp: An AI-powered system for football match annotation. Multimedia Tools and Applications, 82(4), 5547–5567. https://doi.org/10.1007/s11042-022-13359-0
5. Blockeel, H., Devos, L., Frénay, B., Nanfack, G., & Nijssen, S. (2023). Decision trees: From efficient prediction to responsible AI. Frontiers in Artificial Intelligence, 6, Article 1124553. https://doi.org/10.3389/frai.2023.1124553
6. Bonaccorso, G. (2018). Machine learning algorithms. Packt Publishing.
7. Chen, X. (2021). Research on the application of artificial intelligence technology in the field of sports refereeing. Journal of Physics: Conference Series, 1952(4), Article 042048. https://doi.org/10.1088/1742-6596/1952/4/042048
8. Cheng, Y. H., Nguyen, D. M., & Kuo, C. N. (2023). An innovative table tennis scoring system using deep residual network. Engineering Letters, 31(1), 315–319.

9. Cui, C. (2018). Player detection based on support vector machine in football videos. International Journal of Performability Engineering, 14(2), 309–319.
10. Dai, X., & Li, S. (2021). Application analysis of wearable technology and equipment based on artificial intelligence in volleyball. Mathematical Problems in Engineering, 2021, Article 5572389. https://doi.org/10.1155/2021/5572389
11. Das, S., Tariq, A., Santos, T., Kantareddy, S. S., & Banerjee, I. (2023). Recurrent neural networks (RNNs): Architectures, training tricks, and introduction to influential research. In O. Colliot (Ed.), Machine learning for brain disorders (pp. 117–138). Springer. https://doi.org/10.1007/978-1-0716-3195-9_4
12. Date, C. J. (2004). An introduction to database systems (8th ed.). Pearson/Addison Wesley.
13. FIFA. (2023, October 12). Semi-automated offside technology. FIFA official page
14. Han, S.-H., Kim, K. W., Kim, S., & Youn, Y. C. (2018). Artificial neural network: Understanding the basic concepts without mathematics. Dementia and Neurocognitive Disorders, 17(3), 83–89. https://doi.org/10.12779/dnd.2018.17.3.83
15. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
16. Ikotun, A. M., Ezugwu, A. E., Abualigah, L., Abuhaija, B., & Heming, J. (2023). K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data. Information Sciences, 622, 178–210. https://doi.org/10.1016/j.ins.2022.11.139
17. Jin, G. (2022). Player target tracking and detection in football game video using edge computing and deep learning. The Journal of Supercomputing, 78(7), 9475–9491.
18. Khurana, D., Koli, A., Khatter, K., & Singh, S. (2023). Natural language processing: State of the art, current trends and challenges. Multimedia Tools and Applications, 82(3), 3713–3744. https://doi.org/10.1007/s11042-022-13428-4
19. Li, G., Huang, J., & Guo, Q. (2022). Multi-category gesture recognition modeling based on sEMG and IMU signals. Sensors, 22(15), 5855.
20. Lopes, J. M., Mota, L. P., Mota, S. M., Torres, J. M., Moreira, R. S., Soares, C., Pereira, I., Gouveia, F. R., & Sobral, P. (2024). Object and event detection pipeline for rink hockey games. Future Internet, 16(6), Article 179. https://doi.org/10.3390/fi16060179
21. Ma, E., & Kabala, Z. J. (2024). Refereeing the sport of squash with a machine learning system. Machine Learning and Knowledge Extraction, 6(1), 506–553. https://doi.org/10.3390/make6010025
22. Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. Annals of Internal Medicine, 151(4), 264–269. https://doi.org/10.7326/0003-4819-151-4-200908180-00135
23. Munoz-Macho, A. A., Domínguez-Morales, M. J., & Sevillano-Ramos, J. L. (2024). Performance and healthcare analysis in elite sports teams using artificial intelligence: A scoping review. Frontiers in Sports and Active Living, 6, Article 1383723. https://doi.org/10.3389/fspor.2024.1383723
24. Naik, B. T., Hashmi, M. F., Geem, Z. W., & Bokde, N. D. (2022). DeepPlayer-Track: Player and referee tracking with jersey color recognition in soccer. IEEE Access, 10, 32494–32509. https://doi.org/10.1109/ACCESS.2022.3161441
25. Pan, T.-Y., Tsai, W.-L., Chang, C.-Y., Yeh, C.-W., & Hu, M.-C. (2022). A hierarchical hand gesture recognition framework for sports referee training-based EMG and accelerometer sensors. IEEE Transactions on Cybernetics, 52(5), 3172–3183. https://doi.org/10.1109/TCYB.2020.3007173
26. Pan, W., Zhou, M., Wang, J., Su, J., & Stefanidis, A. (2024). Unsupervised clustering in football analysis: A color-segmentation and lighting adaptation approach. IEEE Access, 12, 178127–178141. https://doi.org/10.1109/ACCESS.2024.3506827
27. Passfield, L., & Hopker, J. G. (2017). A mine of information: Can sports analytics provide wisdom from your data? International Journal of Sports Physiology and Performance, 12(7), 851–855. https://doi.org/10.1123/ijspp.2016-0644
28. Powers, D. M. W. (2011). Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation. Journal of Machine Learning Technologies, 2(1), 37–63. https://doi.org/10.9735/2229-3981.2.1
29. Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 779–788). IEEE. https://doi.org/10.1109/CVPR.2016.91
30. Rodríguez-Pérez, R., & Bajorath, J. (2022). Evolution of support vector machine and regression modeling in chemoinformatics and drug discovery. Journal of Computer-Aided Molecular Design, 36(5), 355–362. https://doi.org/10.1007/s10822-022-00442-9
31. Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.
32. Samuel, R. D., Galily, Y., Filho, E., & Tenenbaum, G. (2020). Implementation of the Video Assistant Referee (VAR) as a career change-event: The Israeli Premier League case study. Frontiers in Psychology, 11, Article 564855. https://doi.org/10.3389/fpsyg.2020.564855
33. Spitz, J., Wagemans, J., Memmert, D., Williams, A. M., & Helsen, W. F. (2021). Video assistant referees (VAR): The impact of technology on decision making in association football referees. Journal of Sports Sciences, 39(2), 147–153. https://doi.org/10.1080/02640414.2020.1809163
34. Taborri, J., Palermo, E., & Rossi, S. (2023). WARNING: A wearable inertial-based sensor integrated with a support vector machine algorithm for the identification of faults during race walking. Sensors, 23(11), Article 5245. https://doi.org/10.3390/s23115245
35. Wang, T., Geng, J., Wang, J., & Yan, X. (2024). Video refereeing model of soccer match based on fuzzy clustering and cuckoo optimization algorithm. IEEE Access, 12, 58495–58504. https://doi.org/10.1109/ACCESS.2024.3401705
36. Yamashita, R., Nishio, M., Do, R. K. G., & Togashi, K. (2018). Convolutional neural networks: An overview and application in radiology. Insights into Imaging, 9(4), 611–629. https://doi.org/10.1007/s13244-018-0639-9
37. Yang, Z., Shen, Y., & Shen, Y. (2024). Football referee gesture recognition algorithm based on YOLOv8s. Frontiers in Computational Neuroscience, 18, Article 1341234. https://doi.org/10.3389/fncom.2024.1341234
38. Zanganeh, A., Jampour, M., & Layeghi, K. (2022). IAUFD: A 100k images dataset for automatic football image/video analysis. IET Image Processing, 16(9), 2408–2420. https://doi.org/10.1049/ipr2.12543
39. Zhang, K., & Aslan, A. B. (2021). AI technologies for education: Recent research and future directions. Computers and Education: Artificial Intelligence, 2, Article 100025. https://doi.org/10.1016/j.caeai.2021.100025
40. Zhang, T., & Fu, C. (2022). Application of improved VMD-LSTM model in sports artificial intelligence. Computational Intelligence and Neuroscience, 2022, Article 3410153. https://doi.org/10.1155/2022/3410153
41. Zhekambayeva, M., Yerekesheva, M., Ramashov, N., Seidakhmetov, Y., & Kulambayev, B. (2024). Designing an artificial intelligence-powered video assistant referee system for team sports using computer vision. Retos, 61, 1162–1170.