{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T07:39:01Z","timestamp":1771659541489,"version":"3.50.1"},"reference-count":37,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T00:00:00Z","timestamp":1646092800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Classifying multi-agent cooperative behavior is a fundamental problem in various scientific and engineering domains. In team sports, many cooperative plays can be manually labelled by experts. However, it requires high labour costs and a large amount of unlabelled data is not utilised. This paper examines semi-supervised learning methods for the classification of strategic cooperative plays (called screen plays) in basketball using a smaller labelled dataset and a larger unlabelled dataset. We compared the classification performance of two basic semi-supervised learning methods: self-training and label-propagation. Results show that the classification performance of the semi-supervised learning approaches improved upon the conventional supervised approach (SVM: support vector machine) for minor types of screen-plays (flare, pin, back, cross, and hand-off screen). For the feature importance, we found that self-training obtained similar or higher Sharpley values than SVM. Our approach has the potential to reduce manual labelling costs for detecting various cooperative behaviors.<\/jats:p>","DOI":"10.2478\/ijcss-2022-0006","type":"journal-article","created":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T10:58:24Z","timestamp":1668682704000},"page":"111-121","source":"Crossref","is-referenced-by-count":8,"title":["Cooperative play classification in team sports via semi-supervised learning"],"prefix":"10.2478","volume":"21","author":[{"given":"Zhang","family":"Ziyi","sequence":"first","affiliation":[{"name":"Graduate School of Informatics , Nagoya University , Nagoya, Aichi , Japan"}]},{"given":"Kazuya","family":"Takeda","sequence":"additional","affiliation":[{"name":"Graduate School of Informatics , Nagoya University , Nagoya, Aichi , Japan"}]},{"given":"Keisuke","family":"Fujii","sequence":"additional","affiliation":[{"name":"Graduate School of Informatics , Nagoya University , Nagoya, Aichi , Japan"},{"name":"RIKEN Center for Advanced Intelligence Project, Fukuoka , Fukuoka , Japan"},{"name":"PRESTO, Japan Science and Technology Agency, Kawaguchi , Saitama , Japan ."}]}],"member":"374","published-online":{"date-parts":[[2022,11,17]]},"reference":[{"key":"2024042807225899934_j_ijcss-2022-0006_ref_001","doi-asserted-by":"crossref","unstructured":"Ai, S., Na, J., De Silva, V., and Caine, M. (2021). A novel methodology for automating spatiotemporal data classification in basketball using active learning. In 2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML), pages 39\u201345. IEEE.10.1109\/PRML52754.2021.9520715","DOI":"10.1109\/PRML52754.2021.9520715"},{"key":"2024042807225899934_j_ijcss-2022-0006_ref_002","doi-asserted-by":"crossref","unstructured":"Capraro, V. (2013). A model of human cooperation in social dilemmas. PloS one, 8(8):e72427.","DOI":"10.1371\/journal.pone.0072427"},{"key":"2024042807225899934_j_ijcss-2022-0006_ref_003","doi-asserted-by":"crossref","unstructured":"Capraro, V. and Rand, D. G. (2018). Do the right thing: Experimental evidence that preferences for moral behavior, rather than equity or efficiency per se, drive human prosociality. Forthcoming in Judgment and Decision Making.10.1017\/S1930297500008858","DOI":"10.2139\/ssrn.2965067"},{"key":"2024042807225899934_j_ijcss-2022-0006_ref_004","unstructured":"Cervone, D., D\u2019Amour, A., Bornn, L., and Goldsberry, K. (2014). Pointwise: Predicting points and valuing decisions in real time with nba optical tracking data. In Proceedings of the MIT Sloan Sports Analytics Conference."},{"key":"2024042807225899934_j_ijcss-2022-0006_ref_005","doi-asserted-by":"crossref","unstructured":"Cervone, D., D\u2019Amour, A., Bornn, L., and Goldsberry, K. (2016). A multiresolution stochastic process model for predicting basketball possession outcomes. Journal of the American Statistical Association, 111(514):585\u2013599.28110.1080\/01621459.2016.1141685","DOI":"10.1080\/01621459.2016.1141685"},{"key":"2024042807225899934_j_ijcss-2022-0006_ref_006","unstructured":"Dickinson, T. L. and McIntyre, R. M. (1997). A conceptual framework for teamwork measurement. 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In European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD\u201917), pages 127\u2013139. Springer.10.1007\/978-3-319-71273-4_11","DOI":"10.1007\/978-3-319-71273-4_11"},{"key":"2024042807225899934_j_ijcss-2022-0006_ref_010","doi-asserted-by":"crossref","unstructured":"Fujii, K., Isaka, T., Kouzaki, M., and Yamamoto, Y. (2015). Mutual and asynchronous anticipation and action in sports as globally competitive and locally coordinative dynamics. Scientific Reports, 5.10.1038\/srep16140463360426538452","DOI":"10.1038\/srep16140"},{"key":"2024042807225899934_j_ijcss-2022-0006_ref_011","doi-asserted-by":"crossref","unstructured":"Fujii, K., Kawasaki, T., Inaba, Y., and Kawahara, Y. (2018). Prediction and classification in equation-free collective motion dynamics. 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In International Conference on Machine Learning, pages 235\u2013243."},{"key":"2024042807225899934_j_ijcss-2022-0006_ref_023","unstructured":"Miller, A. C. and Bornn, L. (2017). Possession sketches: Mapping NBA strategies. In Proceedings of the MIT Sloan Sports Analytics Conference."},{"key":"2024042807225899934_j_ijcss-2022-0006_ref_024","unstructured":"Nistala, A. (2018). Using deep learning to understand patterns of player movement in basketball. PhD thesis, Massachusetts Institute of Technology."},{"key":"2024042807225899934_j_ijcss-2022-0006_ref_025","doi-asserted-by":"crossref","unstructured":"Papalexakis, E. and Pelechrinis, K. (2018). thoops: A multi-aspect analytical framework for spatio-temporal basketball data. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pages 2223\u20132232.10.1145\/3269206.3272002","DOI":"10.1145\/3269206.3272002"},{"key":"2024042807225899934_j_ijcss-2022-0006_ref_026","doi-asserted-by":"crossref","unstructured":"Tian, C., De Silva, V., Caine, M., and Swanson, S. (2019). Use of machine learning to automate the identification of basketball strategies using whole team player tracking data. Applied Sciences, 10(1):24.","DOI":"10.3390\/app10010024"},{"key":"2024042807225899934_j_ijcss-2022-0006_ref_027","doi-asserted-by":"crossref","unstructured":"Van Engelen, J. E. and Hoos, H. H. (2020). A survey on semi-supervised learning. Machine Learning, 109(2):373\u2013440.","DOI":"10.1007\/s10994-019-05855-6"},{"key":"2024042807225899934_j_ijcss-2022-0006_ref_028","unstructured":"Vapnik, V. (2013). The nature of statistical learning theory. Springer science & business media."},{"key":"2024042807225899934_j_ijcss-2022-0006_ref_029","unstructured":"Wang, K.-C. and Zemel, R. (2016). Classifying nba offensive plays using neural networks. In Proceedings of the MIT Sloan Sports Analytics Conference."},{"key":"2024042807225899934_j_ijcss-2022-0006_ref_030","doi-asserted-by":"crossref","unstructured":"Wang, Q., Zhu, H., Hu, W., Shen, Z., and Yao, Y. (2015). Discerning tactical patterns for professional soccer teams: an enhanced topic model with applications. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 2197\u20132206.10.1145\/2783258.2788577","DOI":"10.1145\/2783258.2788577"},{"key":"2024042807225899934_j_ijcss-2022-0006_ref_031","doi-asserted-by":"crossref","unstructured":"Werfel, J., Petersen, K., and Nagpal, R. (2014). Designing collective behavior in a termiteinspired robot construction team. Science, 343(6172):754\u2013758.","DOI":"10.1126\/science.1245842"},{"key":"2024042807225899934_j_ijcss-2022-0006_ref_032","doi-asserted-by":"crossref","unstructured":"Woolley, A. W., Chabris, C. F., Pentland, A., Hashmi, N., and Malone, T. W. (2010). Evidence for a collective intelligence factor in the performance of human groups. science, 330(6004):686\u2013688.","DOI":"10.1126\/science.1193147"},{"key":"2024042807225899934_j_ijcss-2022-0006_ref_033","doi-asserted-by":"crossref","unstructured":"Yarowsky, D. (1995). Unsupervised word sense disambiguation rivaling supervised methods.In Proceedings of the 33rd annual meeting on Association for Computational Linguistics, pages 189\u201396, Cambridge, Massachusetts. Association for Computational Linguistics.10.3115\/981658.981684","DOI":"10.3115\/981658.981684"},{"key":"2024042807225899934_j_ijcss-2022-0006_ref_034","doi-asserted-by":"crossref","unstructured":"Yokoyama, K., Shima, H., Fujii, K., Tabuchi, N., and Yamamoto, Y. (2018). Social forces for team coordination in ball possession game. Physical Review E, 97(2):022410.","DOI":"10.1103\/PhysRevE.97.022410"},{"key":"2024042807225899934_j_ijcss-2022-0006_ref_035","unstructured":"Zhou, D., Bousquet, O., Lal, T., Weston, J., and Sch\u00f6lkopf, B. (2003). Learning with localand global consistency. Advances in Neural Information Processing Systems, 16."},{"key":"2024042807225899934_j_ijcss-2022-0006_ref_036","unstructured":"Zhu, X. and Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, CMU CALD."},{"key":"2024042807225899934_j_ijcss-2022-0006_ref_037","unstructured":"Zhu, X. J. (2005). Semi-supervised learning literature survey. 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