References
- Aaditya, R., Nicol'as, T. & Marco, C. (2017), On wasserstein two-sample testing and related families of nonparametric tests, Entropy, 19(2), 47. doi: 10.3390/e19020047
- Alagappan, M. (2012), From 5 to 13: Redefining the positions in basketball, in ‘2012 MIT Sloan Sports Analytics Conference.’
- Arjovsky, M., Chintala, S. & Bottou, L. (2017), Wasserstein generative adversarial networks, in D. Precup & Y. W. Teh, eds, Proceedings of the 34th International Conference on Machine Learning, Proceedings of Machine Learning Research, PMLR, 70, 214–223. doi: 10.5555/3305381.3305404
- Barron, B., Sitaraman, S., N. & Arias, A., T. (2024), Analyzing NBA player positions and interactions with density-functional fluctuation theory, in ‘18th annual MIT Sloan Sports Analytics Conference’.
- Bianchi, F., Facchinetti, T. & Zuccolotto, P. (2017), Role revolution: towards a new meaning of positions in basketball, Electronic Journal of Applied Statistical Analysis, 10(3), 712–734.
- Bunker, R., Le Duy, V. N., Tabei, Y., Takeuchi, I. & Fujii, K. (2023), Multi-agent statistical discriminative sub-trajectory mining and an application to nba basketball, arXiv preprint arXiv:2311.16564.
- Chen, R., Zhang, M. & Xu, X. (2023), Modeling the influence of basketball players’ offense roles on team performance, Frontiers in Psychology, 14. doi: 10.3389/fpsyg.2023.1256796
- Fan, Y., Guanyu, H. & Shen, W. (2023), Analysis of professional basketball field goal attempts via a bayesian matrix clustering approach, Journal of Computational and Graphical Statistics, 32(1), 49–60. doi: 10.1080/10618600.2022.2085727
- Fujii, K., Inaba, Y. & Kawahara, Y. (2017), Koopman spectral kernels for comparing complex dynamics: Application to multiagent sport plays, in ‘European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD’17)’, Springer, 127139. doi: 10.1007/978-3-319-71273-4_11
- Fujii, K., Kawasaki, T., Inaba, Y. & Kawahara, Y. (2018), Prediction and classification in equation-free collective motion dynamics, PLoS Computational Biology, 14(11): e1006545.
- Fujii, K., Takeishi, N., Hojo, M., Inaba, Y., and Kawahara, Y. (2020). Physically-interpretable classification of network dynamics for complex collective motions. Scientific Reports, 10(3005).
- Gower, J. C. (1971), A general coefficient of similarity and some of its properties, Biometrics, 27(4), 857–871. doi: 10.2307/2528823
- Hojo, M., Fujii, K., Inaba, Y., Motoyasu, Y. & Kawahara, Y. (2018), Automatically recognizing strategic cooperative behaviors in various situations of a team sport, PLoS One, 13(12): e0209247.
- Hojo, M., Fujii, K. & Kawahara, Y. (2019), Analysis of factors predicting who obtains a ball in basketball rebounding situations, International Journal of Performance Analysis in Sport, 1–14.
- Hu, G., Yang, H.-C. & Xue, Y. (2020), Bayesian group learning for shot selection of professional basketball players, Stat, 10(1), e324, doi: 10.1002/sta4.324.
- Hua, G. & Su, C. (2023), Estimating positional plus-minus in the nba, in ‘17th annual MIT Sloan Sports Analytics Conference.’
- Hvattum, Magnus, L. (2019), A comprehensive review of plus-minus ratings for evaluating individual players in team sports, International Journal of Computer Science in Sport, 18(1), 1–23.
- Ishida, A., Takayanagi, M., Hoshina, I. & Iwayama, K. (2023), Bayesian credible possession based player performance evaluation in basketball, Keisankitoukeigaku [Journal of the Japanese Society of Computational Statistics], 36(2), 99–126.
- Jiao, J., Hu, G. & Yan, J. (2021), A bayesian marked spatial point processes model for basketball shot chart, Journal of Quantitative Analysis in Sports, 17(2), 77–90. doi: 10.1515/jqas-2019-0106
- Kalman, S. & Bosch, J. (2020), NBA lineup analysis on clustered player tendencies: A new approach to the positions of basketball & modeling lineup efficiency of soft lineup aggregates, in ‘14th annual MIT Sloan Sports Analytics Conference’.
- Kanda, S., Takeuchi, K., Fujii, K. & Tabei, Y. (2020), Succinct trit-array trie for scalable trajectory similarity search, Proceedings of the 28th International Conference on Advances in Geographic Information Systems, 518–529.
- Kempe, M., Grunz, A. & Daniel, M. (2014), Detecting tactical patterns in basketball: Comparison of merge self-organising maps and dynamic controlled neural networks, European Journal of Sport Science, 15(4), 249–255.
- Lutz, D. (2012), A cluster analysis of nba players, in ‘2012 MIT Sloan Sports Analytics Conference.’
- McInnes, L., Healy, J., Saul, N. & Großberger, L. (2018), Umap: Uniform manifold approximation and projection, Journal of Open Source Software, 3(29), 861. doi: 10.21105/joss.00861
- McIntyre, A., Brooks, J., Guttag, J. & Wiens, J. (2016), Recognizing and analyzing ball screen defense in the NBA, Proceedings of the MIT Sloan Sports Analytics Conference, 11–12.
- McQueen, A., Wiens, J. & Guttag, J. (2014), Automatically recognizing on-ball screens, in Proceedings of the MIT Sloan Sports Analytics Conference.
- Miller, A., Bornn, L., Adams, R. & Goldsberry, K. (2014), Factorized point process intensities: A spatial analysis of professional basketball, in E. P. Xing & T. Jebara, eds, Proceedings of the 31st International Conference on Machine Learning, Proceedings of Machine Learning Research, PMLR, 32, 235–243. doi: 10.5555/3044805.3044833
- Miller, A. C. & Bornn, L. (2017), Possession sketches: Mapping NBA strategies, in Proceedings of the MIT Sloan Sports Analytics Conference.
- Muniz, M. & Flamand, T. (2022), A weighted network clustering approach in the nba, Journal of Sports Analytics, 8(4), 1–25.
- Murtagh, F. & Legendre, P. (2011), Wards hierarchical clustering method: Clustering criterion and agglomerative algorithm.
- Nistala, A. (2018), Using deep learning to understand patterns of player movement in basketball, PhD thesis, Massachusetts Institute of Technology.
- Papalexakis, E. & Pelechrinis, K. (2018), thoops: A multi-aspect analytical framework for spatiotemporal basketball data, Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 2223–2232.
- Peruše, M., Kristan, M., Kovačič, S., Vučkovič, G. & PeGs, J. (2009), A trajectory-based analysis of coordinated team activity in a basketball game, Computer Vision and Image Understanding, 113(5), 612–621.
- Sha, L., Lucey, P., Yue, Y., Carr, P., Rohlf, C. & Matthews, I. (2016), Chalkboarding: A new spatiotemporal query paradigm for sports play retrieval, in ‘International Conference on Intelligent User Interfaces’, 336–347.
- van der Maaten, L. & Hinton, G. (2008), Visualizing data using t-sne, Journal of Machine Learning Research, 9(86), 2579–2605.
- Wang, K.-C. & Zemel, R. (2016), Classifying nba offensive plays using neural networks, Proceedings of the MIT Sloan Sports Analytics Conference.
- Wang, X., Han, B., Zhang, S., Zhang, L., Lorenzo Calvo, A. & Gomez, M.- (2022), The differences in the performance profiles between native and foreign players in the chinese basketball association, Frontiers in Psychology, 12. doi: 10.3389/fpsyg.2021.788498
- Whitehead, T. (2018), Nylon calculus: Defining 23 offensive roles using the NBAs play-type data. Retrieved November 22, 2024, from
https://fansided.com/2018/10/15/nylon-calculus-defining-23-offensive-roles-nba-play-type-data - Yanai, C., Solomon, A., Katz, G., Shapira, B. & Rokach, L. (2022), Q-ball: Modeling basketball games using deep reinforcement learning, Proceedings of the AAAI Conference on Artificial Intelligence, 36, 8806–8813.
- Yeung, C., Ide, K. & Fujii, K. (2024), Autosoccerpose: Automated 3d posture analysis of soccer shot movements, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 3214–3224.
- Zhang, L., Lu, F., Liu, A., Guo, P. & Liu, C. (2016), Application of k-means clustering algorithm for classification of nba guards, International Journal of Science and Engineering Applications, 5(1), 1–6.
- Zhang, Z., Takeda, K. & Fujii, K. (2022), Cooperative play classification in team sports via semisupervised learning, International Journal of Computer Science in Sport, 21(1), 111–121.