Estimating Probability Distributions of FIFA-Defined Phases of Play Based on Inter-Analyst Diversity
References
- Alcorn, M. A., & Nguyen, A. (2021). baller2vec: A Multi-Entity Transformer For Multi-Agent Spatiotemporal Modeling.
https://doi.org/10.48550/ARXIV.2102.03291 - Barnerat, T., Crevoisier, J., FIFA, Hoek, F., Redon, P., & Ritschard, M. (n.d.). FIFA Coaching. FIFA. Retrieved December 20, 2025, from
https://www.slideshare.net/slideshow/fifa-coachingmanual/59231173 - Bauer, P. (2022). Automated Detection of Complex Tactical Patterns in Football—Using Machine Learning Techniques to Identify Tactical Behavior [Universitat Tilbingen].
https://doi.org/10.15496/PUBLIKATION-66042 - Bauer, P., & Anzer, G. (2021). Data-driven detection of counterpressing in professional football: A supervised machine learning task based on synchronized positional and event data with expert-based feature extraction. Data Mining and Knowledge Discovery, 35(5), 2009–2049.
https://doi.org/10.1007/s10618-021-00763-7 - Bauer, P., Anzer, G., & Shaw, L. (2023). Putting team formations in association football into context. Journal of Sports Analytics, 9(1), 39–59.
https://doi.org/10.3233/JSA-220620 - BEPRO. (2022, December 2). Cerberus and our Fixed Camera System are FIFA Certified! [Corporate/Technical Blog]. BEPRO. BEPRO.
https://www.bepro11.com/news-updates/cerberus-fifa-certified - BEPRO Dev Team. (2022, April 22). Improving our Football Tracking Data Collection Process [Corporate/Technical Blog]. BEPRO.
https://www.bepro11.com/news-updates/bepro-dev-team-mlops - Bialkowski, A., Lucey, P., Carr, P., Matthews, I., Sridharan, S., & Fookes, C. (2016). Discovering Team Structures in Soccer from Spatiotemporal Data. IEEE Transactions on Knowledge and Data Engineering, 28(10), 2596–2605.
https://doi.org/10.1109/TKDE.2016.2581158 - Chawla, S., Estephan, J., Gudmundsson, J., & Horton, M. (2017). Classification of Passes in Football Matches Using Spatiotemporal Data. ACM Transactions on Spatial Algorithms and Systems, 3(2), 1–30.
https://doi.org/10.1145/3105576 - Decroos, T., Van Haaren, J., & Davis, J. (2018). Automatic Discovery of Tactics in Spatio-Temporal Soccer Match Data. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 223–232.
https://doi.org/10.1145/3219819.3219832 - Dick, U., & Brefeld, U. (2019). Learning to Rate Player Positioning in Soccer. Big Data, 7(1), 71–82.
https://doi.org/10.1089/big.2018.0054 - Duarte, R., Cardinho, H. M., & Folgado, A. (2014). The emergence of team synchronization during the soccer match: Understanding the influence of the level of opposition, game phase and field zone [Master’s thesis, Universidade de Lisboa].
https://www.semanticscholar.org/paper/The-emergence-of-team-synchronization-during-the-of-Duarte-Cardinho/8bf200b1f3d18fad9fc7db89ca804a5bbd5c764b - Fassmeyer, D., Anzer, G., Bauer, P., & Brefeld, U. (2021). Toward Automatically Labeling Situations in Soccer. Frontiers in Sports and Active Living, 3, 725431.
https://doi.org/10.3389/fspor.2021.725431 - Fernandez-Navarro, J., Fradua, L., Zubillaga, A., Ford, P. R., & McRobert, A. P. (2016). Attacking and defensive styles of play in soccer: Analysis of Spanish and English elite teams. Journal of Sports Sciences, 34(24), 2195–2204.
https://doi.org/10.1080/02640414.2016.1169309 - Feuerhake, U. (2016). Recognition of Repetitive Movement Patterns—The Case of Football Analysis. ISPRS International Journal of Geo-Information, 5(11), 208.
https://doi.org/10.3390/ijgi5110208 - FIFA. (2022). Enhanced Football Intelligence: Explanation Document. High Performance (TSG) Football Performance Analysis & Insights.
https://www.fifatrainingcentre.com/media/native/world-cup-2022/Enhanced%20Football%20Intelligence%20EN.pdf - Forcher, Leander, Altmann, S., Forcher, Leon, Jekauc, D., & Kempe, M. (2022). The use of player tracking data to analyze defensive play in professional soccer—A scoping review. International Journal of Sports Science & Coaching, 17(6), 1567–1592.
https://doi.org/10.1177/17479541221075734 - Fujii, K. (2025). Machine Learning in Sports: Open Approach for Next Play Analytics. Springer Nature Singapore.
https://doi.org/10.1007/978-981-96-1445-5 - Grunz, A., Memmert, D., & Perl, J. (2012). Tactical pattern recognition in soccer games by means of special self-organizing maps. Human Movement Science, 31(2), 334–343.
https://doi.org/10.1016/j.humov.2011.02.008 - Herold, M., Goes, F., Nopp, S., Bauer, P., Thompson, C., & Meyer, T. (2019). Machine learning in men’s professional football: Current applications and future directions for improving attacking play. International Journal of Sports Science & Coaching, 14(6), 798–817.
https://doi.org/10.1177/1747954119879350 - Hewitt, A., Greenham, G., & Norton, K. (2016). Game style in soccer: What is it and can we quantify it? International Journal of Performance Analysis in Sport, 16(1), 355–372.
https://doi.org/10.1080/24748668.2016.11868892 - Kamiya, K., Nakanishi, W., & Izumi, Y. (2017). Extraction of changes in game situations in soccer matches using tracking data. 65(2), 287–298.
- Kempe, M., Vogelbein, M., Memmert, D., & Nopp, S. (2014). Possession vs. Direct Play: Evaluating Tactical Behavior in Elite Soccer. International Journal of Sports Science, 4, 35–41.
- Kipf, T. N., & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations, 5.
https://doi.org/10.48550/ARXIV.1609.02907 - Kobayashi, Y., Kawamura, H., & Suzuki, K. (2012). Counter attack detection with machine learning from log files of RoboCup simulation. The 6th International Conference on Soft Computing and Intelligent Systems, and The 13th International Symposium on Advanced Intelligence Systems, 1821–1826.
https://doi.org/10.1109/SCIS-ISIS.2012.6505088 - Krippendorff, K. (2011). Computing Krippendorff’s Alpha-Reliability.
https://repository.upenn.edu/handle/20.500.14332/2089 - Kuroda, K., Uchida, I., Fujii, K., & Kameda, Y. (2024). Estimation of Overlapped Tactical Actions from Soccer Match Video: Proceedings of the 12th International Conference on Sport Sciences Research and Technology Support, 257–264.
https://doi.org/10.5220/0013066300003828 - Loshchilov, I., & Hutter, F. (2018). Decoupled Weight Decay Regularization. International Conference on Learning Representations.
https://doi.org/10.48550/ARXIV.1711.05101 - Lucey, P., Bialkowski, A., Carr, P., Foote, E., & Matthews, I. (2021). Characterizing Multi-Agent Team Behavior from Partial Team Tracings: Evidence from the English Premier League. Proceedings of the AAAI Conference on Artificial Intelligence, 26, 1387–1393.
https://doi.org/10.1609/aaai.v26i1.8246 - May, E. (2024, March 7). SkillCorner achieves FIFA Quality Programme certification for Electronic Performance and Tracking Systems I Skillcorner [Corporate News/Press Release]. SkillCorner.
https://skillcorner.com/articles/fifa-epts - Merlin, M., Cunha, S. A., Moura, F. A., Torres, R. D. S., Gonçalves, B., & Sampaio, J. (2020). Exploring the determinants of success in different clusters of ball possession sequences in soccer. Research in Sports Medicine, 28(3), 339–350.
https://doi.org/10.1080/15438627.2020.1716228 - Mgaya, G. B., Liu, H., & Zhang, B. (2021). A Survey on Applications of Modern Deep Learning Techniques in Team Sports Analytics. In A. Abraham, Y. Ohsawa, N. Gandhi, M. A. Jabbar, A. Haqiq, S. McLoone, & B. Issac (Eds.), Proceedings of the 12th International Conference on Soft Computing and Pattern Recognition (Vol. 1383, pp. 434–443). Springer International Publishing.
https://doi.org/10.1007/978-3-030-73689-742 - Michael, O., Obst, O., Schmidsberger, F., & Stolzenburg, F. (2018). Analysing Soccer Games with Clustering and Conceptors. RoboCup 2017: Robot World Cup XXI, lecture Notes in Computer Science, 11175, 120–131.
https://doi.org/10.1007/978-3-030-00308-1_10 - Niu, Z., Gao, X., & Tian, Q. (2012). Tactic analysis based on real-world ball trajectory in soccer video. Pattern Recognition, 45(5), 1937–1947.
https://doi.org/10.1016/j.patcog.2011.10.023 - Oliveira, J. (2004). Conhecimento especifico em futebol: Contributos para a defini9ao de uma matriz dindmica do processo ensino aprendizagem-treino do jogo [Universidade do Porto; Application/pdf].
https://doi.org/10.34626/FTBD-GE25 - Scott, A., Uchida, I., Kuroda, K., Kim, Y., & Fujii, K. (2025). SoccerTrack v2: A Full-Pitch Multi-View Soccer Dataset for Game State Reconstruction.
https://doi.org/10.48550/ARXIV.2508.01802 - Sigari, M.-H., Soltanian-Zadeh, H., Kiani, V., & Pourreza, A.-R. (2015). Counterattack detection in broadcast soccer videos using camera motion estimation. The International Symposium on Artificial Intelligence and Signal Processing, 101–106.
https://doi.org/10.1109/AISP.2015.7123487 - Suzuki, G., Takahashi, S., Ogawa, T., & Haseyama, M. (2019). Team Tactics Estimation in Soccer Videos Based on a Deep Extreme Learning Machine and Characteristics of the Tactics. IEEE Access, 7, 153238–153248.
https://doi.org/10.1109/ACCESS.2019.2946378 - Tenga, A., Kanstad, D., Ronglan, L. T., & Bahr, R. (2009). Developing a New Method for Team Match Performance Analysis in Professional Soccer and Testing its Reliability. International Journal of Performance Analysis in Sport, 9(1), 8–25.
https://doi.org/10.1080/24748668.2009.11868461 - Torres-Ronda, L., Beanland, E., Whitehead, S., Sweeting, A., & Clubb, J. (2022). Tracking Systems in Team Sports: A Narrative Review of Applications of the Data and Sport Specific Analysis. Sports Medicine - Open, 8(1), 15.
https://doi.org/10.1186/s40798-022-00408-z - Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention Is All You Need.
https://doi.org/10.48550/ARXIV.1706.03762 - Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., & Bengio, Y. (2018). Graph Attention Networks. International Conference on learning Representations. International Conference on Learning Representations.
https://doi.org/10.48550/ARXIV.1710.10903 - Wade, A. (with Internet Archive). (1996). Principles of team play. Spring City, PA : Reedswain, Inc.
http://archive.org/details/principlesofteam0000wade - Wang, Q., Zhu, H., Hu, W., Shen, Z., & Yao, Y. (2015). Discerning Tactical Patterns for Professional Soccer Teams: An Enhanced Topic Model with Applications. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2197–2206.
https://doi.org/10.1145/2783258.2788577
DOI: https://doi.org/10.2478/ijcss-2026-0007 | Journal eISSN: 1684-4769
Language: English
Page range: 111 - 135
Published on: Sep 9, 2026
Published by: International Association of Computer Science in Sport
In partnership with: Paradigm Publishing Services
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© 2026 K. Kuroda, K. Fujii, Y. Kameda, published by International Association of Computer Science in Sport
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