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Game plan: what AI can do for football, and what football can do for AI. (English) Zbl 07350699

Summary: The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individual sports, including baseball, basketball, and tennis. More recently, AI techniques have been applied to football, due to a huge increase in data collection by professional teams, increased computational power, and advances in machine learning, with the goal of better addressing new scientific challenges involved in the analysis of both individual players’ and coordinated teams’ behaviors. The research challenges associated with predictive and prescriptive football analytics require new developments and progress at the intersection of statistical learning, game theory, and computer vision. In this paper, we provide an overarching perspective highlighting how the combination of these fields, in particular, forms a unique microcosm for AI research, while offering mutual benefits for professional teams, spectators, and broadcasters in the years to come. We illustrate that this duality makes football analytics a game changer of tremendous value, in terms of not only changing the game of football itself, but also in terms of what this domain can mean for the field of AI. We review the state-of-the-art and exemplify the types of analysis enabled by combining the aforementioned fields, including illustrative examples of counterfactual analysis using predictive models, and the combination of game-theoretic analysis of penalty kicks with statistical learning of player attributes. We conclude by highlighting envisioned downstream impacts, including possibilities for extensions to other sports (real and virtual).

MSC:

68Txx Artificial intelligence
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[1] Abu Farha, Y., Richard, A., & Gall, J. (2018). When will you do what?-Anticipating temporal occurrences of activities. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
[2] Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., & Savarese, S. (2016). Social LSTM: Human trajectory prediction in crowded spaces. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
[3] Alayrac, J.-B., Recasens, A., Schneider, R., Arandjelovi´c, R., Ramapuram, J., De Fauw, J., . . . Zisserman, A. (2020). Self-supervised multimodal versatile networks. InAdvances in Neural Information Processing Systems (NeurIPS).
[4] Albert, J. (2010). Sabermetrics: The past, the present, and the future.Mathematics and 76
[5] Albert, J., Bennett, J., & Mead, C. (2002, 10). Curve ball: Baseball, statistics, and the role of chance in the game.Physics Today,55, 56-57.
[6] Alp G¨uler, R., Neverova, N., & Kokkinos, I.(2018).DensePose: Dense human pose estimation in the wild. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
[7] Anderson, A., Rosen, J., Rust, J., & Wong, K.-P. (2020). Disequilibrium play in tennis. Working paper.
[8] Arandjelovic, R., & Zisserman, A. (2017). Look, listen and learn. InProceedings of the IEEE International Conference on Computer Vision.
[9] Arel, I., Rose, D., & Karnowski, T. (2010, 01). Deep machine learning - a new frontier in artificial intelligence research [research frontier].IEEE Comp. Int. Mag.,5, 13-18.
[10] Azar, O. H., & Bar-Eli, M. (2011). Do soccer players play the mixed-strategy Nash equilibrium?Applied Economics,43(25), 3591-3601.
[11] Barr, G., Holdsworth, C., & Kantor, B. (2008). Evaluating performances at the 2007 cricket world cup.South African Statistical Journal,42(2), 125-142. · Zbl 1397.62618
[12] Bartlett, R. (2006, 12). Artificial intelligence in sports biomechanics: New dawn or false hope?Journal of Sports Science & Medicine,5, 474-479.
[13] Baumer, B., & Zimbalist, A. (2014).The sabermetric revolution: Assessing the growth of analytics in baseball. University of Pennsylvania Press.
[14] Beal, R., Norman, T. J., & Ramchurn, S. D. (2019). Artificial intelligence for team sports: A survey.The Knowledge Engineering Review,34, e28.
[15] Bengio, Y. (2009, January). Learning Deep Architectures for AI.Foundations and TrendsR in Machine Learning,2(1), 1-127. · Zbl 1192.68503
[16] Bhattacharyya, A., Fritz, M., & Schiele, B. (2018). Long-term on-board prediction of people in traffic scenes under uncertainty. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
[17] Bhattacharyya, A., Fritz, M., & Schiele, B. (2019). Bayesian prediction of future street scenes using synthetic likelihoods. InProceedings of the International Conference on Learning Representations.
[18] Bialkowski, A., Lucey, P., Carr, P., Yue, Y., Sridharan, S., & Matthews, I. (2015, 01). Identifying team style in soccer using formations learned from spatiotemporal tracking data.IEEE International Conference on Data Mining Workshops.
[19] Bloembergen, D., Tuyls, K., Hennes, D., & Kaisers, M. (2015). Evolutionary dynamics of multi-agent learning: A survey.J. Artif. Intell. Res.,53, 659-697. · Zbl 1336.68210
[20] Bransen, L., & Van Haaren, J. (2018). Measuring football players’ on-the-ball contributions from passes during games. InInternational Workshop on Machine Learning and Data Mining for Sports Analytics.
[21] Bransen, L., & Van Haaren, J. (2019). Player chemistry: Striving for a perfectly balanced 77
[22] Bridgeman, L., Volino, M., Guillemaut, J.-Y., & Hilton, A. (2019, June). Multi-person 3D pose estimation and tracking in sports. InIEEE Conference on Computer Vision and Pattern Recognition Workshops.
[23] Brock, A., Donahue, J., & Simonyan, K. (2019). Large scale GAN training for high fidelity natural image synthesis. InProceedings of the International Conference on Learning Representations.
[24] Busoniu, L., Babuska, R., & Schutter, B. D. (2008). A comprehensive survey of multiagent reinforcement learning.IEEE Trans. Syst. Man Cybern. Part C,38(2), 156-172.
[25] Buzzacchi, L., & Pedrini, S. (2014). Does player specialization predict player actions? Evidence from penalty kicks at FIFA World Cup and UEFA Euro Cup.Applied Economics,46(10), 1067-1080.
[26] Camerer, C. (2011).Behavioral game theory: Experiments in strategic interaction. Princeton University Press. · Zbl 1019.91001
[27] Camerer, C., Loewenstein, G., & Prelec, D. (2005). Neuroeconomics: How neuroscience can inform economics.Journal of Economic Literature,43(1), 9-64.
[28] Camerer, C., Loewenstein, G., & Rabin, M. (2011).Advances in behavioral economics. Princeton University Press.
[29] Cao, Z., Simon, T., Wei, S.-E., & Sheikh, Y. (2017). Realtime multi-person 2D pose estimation using part affinity fields. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
[30] Carreira, J., & Zisserman, A. (2017). Quo vadis, action recognition? A new model and the kinetics dataset. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
[31] Chan, C., Ginosar, S., Zhou, T., & Efros, A. A.(2019).Everybody dance now.In Proceedings of the IEEE International Conference on Computer Vision.
[32] Chen, D. L., Kim, J., & Mooney, R. J. (2010). Training a multilingual sportscaster: Using perceptual context to learn language.Journal of Artificial Intelligence Research,37, 397-435. · Zbl 1210.68079
[33] Chen, Y., Tian, Y., & He, M. (2020). Monocular human pose estimation: A survey of deep learning-based methods.Computer Vision and Image Understanding,192, 102897.
[34] Cheng, Y., Yang, B., Wang, B., Yan, W., & Tan, R. T. (2019). Occlusion-aware networks for 3d human pose estimation in video. InProceedings of the IEEE/CVF International Conference on Computer Vision.
[35] Chiappori, P.-A., Levitt, S., & Groseclose, T. (2002). Testing mixed-strategy equilibria when players are heterogeneous: The case of penalty kicks in soccer.American Economic Review,92(4), 1138-1151.
[36] Choi, J., Kwon, J., & Lee, K. M. (2019). Deep meta learning for real-time target-aware visual tracking. InProceedings of the IEEE/CVF International Conference on Com
[37] ChyronHego. (2020).ChyronHego.Retrieved 2020-09-09, fromhttps://chyronhego.com/
[38] Clark, A., Donahue, J., & Simonyan, K. (2019). Adversarial video generation on complex datasets.arXiv, arXiv-1907.
[39] Clarke, A.(2020).Season trends: Quality on rise as midfield takes focus.Retrieved 2020-09-17, fromhttp://www.premierleague.com/news/1745761
[40] Claudino, J., Capanema, D., Souza, T., Serrao, J., Pereira, A., & Nassis, G. (2019, 07). Current approaches to the use of artificial intelligence for injury risk assessment and performance prediction in team sports: A systematic review.Sports Medicine - Open, 5.
[41] Coloma, G. (2012). The penalty-kick game under incomplete information.University of CEMA Economics Serie Documentos de Trabajo(487).
[42] Costa, G., Huber, M., & Saccoman, J. (2009).Practicing sabermetrics: Putting the science of baseball statistics to work. McFarland, Incorporated, Publishers. Retrieved from https://books.google.fr/books?id=Kkf7gowrH UC
[43] CVSports International Workshop on Computer Vision in Sports at CVPR.(2020). Retrieved 2020-09-07, fromhttps://vap.aau.dk/cvsports/
[44] Decroos, T., Bransen, L., Haaren, J. V., & Davis, J. (2019). Actions speak louder than goals: Valuing player actions in soccer. InProceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, KDD 2019, anchorage, ak, usa, august 4-8, 2019(pp. 1851-1861). ACM.
[45] Decroos, T., & Davis, J. (2019). Player vectors: Characterizing soccer players’ playing style from match event streams. InEuropean Conference on Machine Learning and Knowledge Discovery in Databases.
[46] Decroos, T., Van Haaren, J., & Davis, J. (2018). Automatic discovery of tactics in spatiotemporal soccer match data. InProceedings of the international conference on knowledge discovery & data mining.
[47] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009). Imagenet: A largescale hierarchical image database. In2009 IEEE conference on computer vision and pattern recognition(pp. 248-255).
[48] Deo, N., & Trivedi, M. M. (2018). Convolutional social pooling for vehicle trajectory prediction. InProceedings of the IEEE conference on computer vision and pattern recognition workshops(pp. 1468-1476).
[49] Dong, X., & Shen, J. (2018). Triplet loss in siamese network for object tracking. In Proceedings of the european conference on computer vision (ECCV)(pp. 459-474).
[50] Duda, R. O., & Hart, P. E. (1972). Use of the hough transformation to detect lines and curves in pictures.Communications of the ACM,15(1), 11-15. · Zbl 1296.94027
[51] Emara, N., Owens, D. M., Smith, J., & Wilmer, L. (2014). Minimax on the gridiron: Serial correlation and its effects on outcomes in the national football league.Available at
[52] Fang, H.-S., Xie, S., Tai, Y.-W., & Lu, C. (2017). Rmpe: Regional multi-person pose estimation. InProceedings of the IEEE international conference on computer vision (pp. 2334-2343).
[53] Fastovets, M., Guillemaut, J., & Hilton, A. (2013). Athlete pose estimation from monocular tv sports footage. In2013 IEEE conference on computer vision and pattern recognition workshops(p. 1048-1054).
[54] Felsen, P., Lucey, P., & Ganguly, S. (2018). Where will they go? predicting fine-grained adversarial multi-agent motion using conditional variational autoencoders. InComputer vision - ECCV 2018 - 15th european conference, munich, germany, september 8-14, 2018, proceedings, part XI(pp. 761-776).
[55] Fern´andez, J. (2019). Decomposing the immeasurable sport: A deep learning expected possession value framework for soccer. InMit sloan conference.
[56] Fern´andez, J., Bornn, L., & Cervone, D. (2019). Decomposing the immeasurable sport: A deep learning expected possession value framework for soccer. In13 th annual mit sloan sports analytics conference.
[57] 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.
[58] Fernando, T., Denman, S., Sridharan, S., & Fookes, C. (2018). Gd-gan: Generative adversarial networks for trajectory prediction and group detection in crowds. InAsian conference on computer vision(pp. 314-330).
[59] Franks, A., Miller, A., Bornn, L., Goldsberry, K., et al. (2015). Characterizing the spatial structure of defensive skill in professional basketball.The Annals of Applied Statistics, 9(1), 94-121. · Zbl 1454.62538
[60] Gabbett, T. J. (2010). The development and application of an injury prediction model for noncontact, soft-tissue injuries in elite collision sport athletes.The Journal of Strength & Conditioning Research,24(10), 2593-2603.
[61] Gade, R., & Moeslund, T. B. (2018). Constrained multi-target tracking for team sports activities.IPSJ Transactions on Computer Vision and Applications,10(1), 2.
[62] Gauriot, R., Page, L., & Wooders, J. (2016). Nash at wimbledon: evidence from half a million serves.Available at SSRN 2850919.
[63] Giancola, S., Amine, M., Dghaily, T., & Ghanem, B. (2018). Soccernet: A scalable dataset for action spotting in soccer videos. InProceedings of the IEEE conference on computer vision and pattern recognition workshops(pp. 1711-1721).
[64] Godard, C., Mac Aodha, O., & Brostow, G. J. (2017). Unsupervised monocular depth estimation with left-right consistency. InProceedings of the IEEE conference on computer vision and pattern recognition(pp. 270-279).
[65] Goodfellow, I., Bengio, Y., & Courville, A. (2016).Deep learning. MIT Press. (http:// www.deeplearningbook.org) · Zbl 1373.68009
[66] Gupta, A., Johnson, J., Fei-Fei, L., Savarese, S., & Alahi, A. (2018). Social GAN: Socially acceptable trajectories with generative adversarial networks. InProceedings of the IEEE conference on computer vision and pattern recognition (CVPR).
[67] Hanna, J., & Stone, P. (2017, February). Grounded action transformation for robot learning in simulation. InProceedings of the 31st aaai conference on artificial intelligence (AAAI).
[68] Hausknecht, M., & Stone, P. (2016, May). Deep reinforcement learning in parameterized action space. InProceedings of the international conference on learning representations (ICLR).
[69] He, K., Gkioxari, G., Doll´ar, P., & Girshick, R. (2017). Mask R-CNN. InProceedings of the IEEE international conference on computer vision(pp. 2961-2969).
[70] He, Y., Yan, R., Fragkiadaki, K., & Yu, S.-I. (2020). Epipolar transformers. InProceedings of the ieee/cvf conference on computer vision and pattern recognition(pp. 7779-7788).
[71] Hernandez-Leal, P., Kartal, B., & Taylor, M. E. (2020). A very condensed survey and critique of multiagent deep reinforcement learning. InProceedings of the 19th international conference on autonomous agents and multiagent systems, AAMAS ’20, auckland, new zealand, may 9-13, 2020(pp. 2146-2148). International Foundation for Autonomous Agents and Multiagent Systems.
[72] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., & Hochreiter, S. (2017). Gans trained by a two time-scale update rule converge to a local nash equilibrium. In Advances in neural information processing systems(pp. 6626-6637).
[73] Huang, S., Gong, M., & Tao, D. (2017). A coarse-fine network for keypoint localization. InProceedings of the IEEE international conference on computer vision(pp. 3028- 3037).
[74] Insafutdinov, E., Pishchulin, L., Andres, B., Andriluka, M., & Schiele, B. (2016). Deepercut: A deeper, stronger, and faster multi-person pose estimation model.InEuropean conference on computer vision(pp. 34-50).
[75] InStat.(2020). Retrieved 2020-09-09, fromhttps://football.instatscout.com/
[76] Iqbal, U., & Gall, J. (2016). Multi-person pose estimation with local joint-to-person associations. InEuropean conference on computer vision(pp. 627-642).
[77] Iskakov, K., Burkov, E., Lempitsky, V., & Malkov, Y. (2019). Learnable triangulation of human pose. InProceedings of the IEEE international conference on computer vision (pp. 7718-7727).
[78] Jin, X., Xiao, H., Shen, X., Yang, J., Lin, Z., Chen, Y., . . . Yan, S. (2017). Predicting scene parsing and motion dynamics in the future. InAdvances in neural information processing systems(pp. 6915-6924).
[79] Kakavas, G., Malliaropoulos, N., Pruna, R., & Maffulli, N. (2019, 08). Artificial intelligence a tool for sports trauma prediction.Injury.
[80] Kalyanakrishnan, S., & Stone, P. (2010). Learning complementary multiagent behaviors: A case study. InRoboCup 2009: Robot soccer world cup XIII(pp. 153-165). Springer
[81] Kampakis, S. (2016).Predictive modelling of football injuries(Unpublished doctoral dissertation). UCL (University College London).
[82] Karras, T., Aila, T., Laine, S., & Lehtinen, J. (2017). Progressive growing of gans for improved quality, stability, and variation.arXiv preprint arXiv:1710.10196.
[83] Kitani, K. M., Ziebart, B. D., Bagnell, J. A., & Hebert, M. (2012). Activity forecasting. In European conference on computer vision(pp. 201-214).
[84] Kocabas, M., Karagoz, S., & Akbas, E. (2018). Multiposenet: Fast multi-person pose estimation using pose residual network. InProceedings of the european conference on computer vision (ECCV)(pp. 417-433).
[85] Kuper, S., & Szymanski, S. (2018).Soccernomics, why england loses, why germany, spain and france win, and why one day, japan, iraq and the united states will become the kings of the world’s most popular sport. HarperCollins Publishers.
[86] Lassner, C., Romero, J., Kiefel, M., Bogo, F., Black, M. J., & Gehler, P. V. (2017). Unite the people: Closing the loop between 3d and 2d human representations. InProceedings of the IEEE conference on computer vision and pattern recognition(pp. 6050-6059).
[87] Le, H., Carr, P., Yue, Y., & Lucey, P. (2017, 03). Data-driven ghosting using deep imitation learning. InMit sloan conference.
[88] Le, H. M., Yue, Y., Carr, P., & Lucey, P. (2017). Coordinated multi-agent imitation learning. InProceedings of the 34th international conference on machine learning, ICML 2017, sydney, nsw, australia, 6-11 august 2017(pp. 1995-2003).
[89] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning.Nature,521(7553), 436-444.
[90] Lennartsson, J., Lidstrom, N., & Lindberg, C. (2015, 05). Game intelligence in team sports. PloS one,10, e0125453.
[91] Levitt, S., Chiappori, P., & Groseclose, T. (2002, 02). Testing mixed-strategy equilibria when players are heterogeneous: The case of penalty kicks in soccer.American Economic Review,92, 1138-1151.
[92] Li, M. G., Jiang, B., Zhu, H., Che, Z., & Liu, Y. (2020). Generative attention networks for multi-agent behavioral modeling. InAaai(pp. 7195-7202).
[93] Lin, K. (2014). Applying game theory to volleyball strategy.International Journal of Performance Analysis in Sport,14(3), 761-774.
[94] Lin, T., Yang, Y., Beyer, J., & Pfister, H. (2020). Sportsxr - immersive analytics in sports. ArXiv,abs/2004.08010.
[95] Liu, G., Luo, Y., Schulte, O., & Kharrat, T. (2020, 07). Deep soccer analytics: learning an action-value function for evaluating soccer players.Data Mining and Knowledge Discovery. doi: 10.1007/s10618-020-00705-9
[96] Liu, G., & Schulte, O. (2018). Deep reinforcement learning in ice hockey for context-aware player evaluation. In (p. 3442-3448). AAAI Press.
[97] Liu, J., Carr, P., Collins, R. T., & Liu, Y. (2013). Tracking sports players with contextconditioned motion models. In2013 IEEE conference on computer vision and pattern recognition(p. 1830-1837).
[98] Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. InProceedings of the IEEE conference on computer vision and pattern recognition(pp. 3431-3440).
[99] Lowe, Z.(2013, March).Lights, Cameras, Revolution.Retrieved 2020-09-07, fromhttps://grantland.com/features/the-toronto-raptors-sportvu-cameras -nba-analytical-revolution/
[100] Lu, W., Ting, J., Little, J. J., & Murphy, K. P. (2013). Learning to track and identify players from broadcast sports videos.IEEE Transactions on Pattern Analysis and Machine Intelligence,35(7), 1704-1716.
[101] Luc, P., Clark, A., Dieleman, S., Casas, D. d. L., Doron, Y., Cassirer, A., & Simonyan, K. (2020). Transformation-based adversarial video prediction on large-scale data.arXiv preprint arXiv:2003.04035.
[102] Luc, P., Couprie, C., Lecun, Y., & Verbeek, J. (2018). Predicting future instance segmentation by forecasting convolutional features. InProceedings of the european conference on computer vision (ECCV)(pp. 584-599).
[103] Luc, P., Neverova, N., Couprie, C., Verbeek, J., & LeCun, Y. (2017). Predicting deeper into the future of semantic segmentation. InProceedings of the IEEE international conference on computer vision(pp. 648-657).
[104] Machine Learning and Data Mining for Sports Analytics.(2020). Retrieved 2020-09-08, fromhttps://dtai.cs.kuleuven.be/events/MLSA20/
[105] Madry, A., Makelov, A., Schmidt, L., Tsipras, D., & Vladu, A. (2017). Towards deep learning models resistant to adversarial attacks.arXiv preprint arXiv:1706.06083.
[106] Mahasseni, B., Lam, M., & Todorovic, S. (2017, July). Unsupervised video summarization with adversarial lstm networks. InProceedings of the IEEE conference on computer vision and pattern recognition (CVPR).
[107] Maksai, A., Wang, X., & Fua, P. (2016). What players do with the ball: A physically constrained interaction modeling. InProceedings of the IEEE conference on computer vision and pattern recognition(pp. 972-981).
[108] Meerhoff, L. A., Goes, F. R., Knobbe, A., et al. (2019). Exploring successful team tactics in soccer tracking data. InJoint european conference on machine learning and knowledge discovery in databases(pp. 235-246).
[109] Mehrasa, N., Zhong, Y., Tung, F., Bornn, L., & Mori, G. (2018). Deep learning of player trajectory representations for team activity analysis. In11th mit sloan sports analytics conference(Vol. 2, p. 3).
[110] Merler, M., Joshi, D., Mac, K.-N. C., Nguyen, Q.-B., Hammer, S., Kent, J., . . . Feris, R. S. (2018). The excitement of sports: Automatic highlights using audio/visual cues. In CVPR workshops(pp. 2520-2523).
[111] Michael, L. (2004).Moneyball : the art of winning an unfair game. New York: W. W. Norton.
[112] Miech, A., Alayrac, J.-B., Smaira, L., Laptev, I., Sivic, J., & Zisserman, A. (2020, June). End-to-end learning of visual representations from uncurated instructional videos. InProceedings of the ieee/cvf conference on computer vision and pattern recognition (CVPR).
[113] Minsky, M. (1961). Steps toward artificial intelligence.Proceedings of the IRE,49(1), 8-30.
[114] MIT Sloan Sports Analytics Conference.(2020). Retrieved 2020-09-07, fromhttp://www .sloansportsconference.com/
[115] Miyato, T., Kataoka, T., Koyama, M., & Yoshida, Y. (2018). Spectral normalization for generative adversarial networks.arXiv preprint arXiv:1802.05957.
[116] Mora, S. V., & Knottenbelt, W. J. (2017). Deep learning for domain-specific action recognition in tennis. In2017 IEEE conference on computer vision and pattern recognition workshops (CVPRW)(p. 170-178).
[117] Moschini, G. (2004). Nash equilibrium in strictly competitive games: live play in soccer. Economics Letters,85(3), 365 - 371. · Zbl 1254.91075
[118] Murphy, K. P. (2013).Machine learning: a probabilistic perspective. MIT press. · Zbl 1295.68003
[119] Nax, H. (2015).Behavioral game theory(Unpublished doctoral dissertation). ETH Zurich, Zurich.
[120] Newell, A., Huang, Z., & Deng, J. (2017). Associative embedding: End-to-end learning for joint detection and grouping. InAdvances in neural information processing systems (pp. 2277-2287).
[121] Ng, A. Y., & Russell, S. J. (2000). Algorithms for inverse reinforcement learning. In Proceedings of the seventeenth international conference on machine learning (ICML 2000), stanford university, stanford, ca, usa, june 29 - july 2, 2000(pp. 663-670). Morgan Kaufmann.
[122] Omidshafiei, S., Papadimitriou, C., Piliouras, G., Tuyls, K., Rowland, M., Lespiau, J.-B., . . . Munos, R. (2019).α-rank: Multi-agent evaluation by evolution.Scientific reports, 9(1), 1-29.
[123] Op De Be´eck, T., Meert, W., Sch¨utte, K., Vanwanseele, B., & Davis, J. (2018). Fatigue prediction in outdoor runners via machine learning and sensor fusion. InProceedings of the 24th acm sigkdd international conference on knowledge discovery & data mining (p. 606-615). Association for Computing Machinery.
[124] Opta.(2020). Retrieved 2020-09-09, fromhttps://www.optasports.com
[125] Palacios-Huerta, I. (2003, 04). Professionals Play Minimax.The Review of Economic Studies,70(2), 395-415. · Zbl 1106.91301
[126] Palacios-Huerta, I. (2016).Beautiful game theory: How soccer can help economics. Princeton University Press. · Zbl 1291.00002
[127] Panait, L., & Luke, S. (2005). Cooperative multi-agent learning: The state of the art. Auton. Agents Multi Agent Syst.,11(3), 387-434.
[128] Papandreou, G., Zhu, T., Chen, L.-C., Gidaris, S., Tompson, J., & Murphy, K. (2018). Personlab: Person pose estimation and instance segmentation with a bottom-up, partbased, geometric embedding model. InProceedings of the european conference on computer vision (ECCV)(pp. 269-286).
[129] Papandreou, G., Zhu, T., Kanazawa, N., Toshev, A., Tompson, J., Bregler, C., & Murphy, K. (2017). Towards accurate multi-person pose estimation in the wild. InProceedings of the IEEE conference on computer vision and pattern recognition(pp. 4903-4911).
[130] Pavlakos, G., Choutas, V., Ghorbani, N., Bolkart, T., Osman, A. A., Tzionas, D., & Black, M. J. (2019). Expressive body capture: 3d hands, face, and body from a single image. InProceedings of the IEEE conference on computer vision and pattern recognition (pp. 10975-10985).
[131] Pavlakos, G., Zhou, X., Derpanis, K. G., & Daniilidis, K. (2017). Coarse-to-fine volumetric prediction for single-image 3d human pose. InProceedings of the IEEE conference on computer vision and pattern recognition(pp. 7025-7034).
[132] Pavllo, D., Feichtenhofer, C., Grangier, D., & Auli, M. (2019). 3d human pose estimation in video with temporal convolutions and semi-supervised training. InProceedings of the IEEE conference on computer vision and pattern recognition(pp. 7753-7762).
[133] Pishchulin, L., Insafutdinov, E., Tang, S., Andres, B., Andriluka, M., Gehler, P. V., & Schiele, B. (2016). Deepcut: Joint subset partition and labeling for multi person pose estimation. InProceedings of the IEEE conference on computer vision and pattern recognition(pp. 4929-4937).
[134] Puerzer, R. (2002, 01). From scientific baseball to sabermetrics: Professional baseball as a reflection of engineering and management in society.Nine: A Journal of Baseball History and Culture,11, 34-48.
[135] Quiroga, J., Carrillo, H., Maldonado, E., Ruiz, J., & Zapata, L. M. (2020, June). As seen on tv: Automatic basketball video production using gaussian-based actionness and game states recognition. InProceedings of the ieee/cvf conference on computer vision and pattern recognition (CVPR) workshops.
[136] Ramos, G., Vaz, J., Mendonca, G., Pezarat-Correia, P., Rodrigues, J., Alfaras, M., & Gamboa, H. (2020, 01). Fatigue evaluation through machine learning and a global fatigue descriptor.Journal of Healthcare Engineering,2020, 1-18.
[137] Ren, S., He, K., Girshick, R., & Sun, J.(2015).Faster R-CNN: Towards realtime object detection with region proposal networks.InAdvances in neural information processing systems 28(pp. 91-99).Curran Associates, Inc.Retrieved fromhttp://papers.nips.cc/paper/5638-faster-r-cnn-towards-real -time-object-detection-with-region-proposal-networks.pdf
[138] RoboCup project.(2020). Retrieved 2020-09-09, fromhttps://www.robocup.org 85
[139] Rossi, A., Pappalardo, L., Cintia, P., Iaia, F., Fern´andez, J., & Medina, D. (2018, 06). Machine learning approach to injury prediction.
[140] Schmidhuber, J. (2015). Deep learning in neural networks: An overview.Neural Networks, 61, 85-117.(Published online 2014; based on TR arXiv:1404.7828 [cs.NE])doi: 10.1016/j.neunet.2014.09.003
[141] Shih, H.-C. (2017). A survey of content-aware video analysis for sports.IEEE Transactions on Circuits and Systems for Video Technology,28(5), 1212-1231.
[142] Shoham, Y., Powers, R., & Grenager, T. (2007). If multi-agent learning is the answer, what is the question?Artif. Intell.,171(7), 365-377. · Zbl 1168.68493
[143] Sindik, J., & Vidak, N. (2008, 06). Application of game theory in describing efficacy of decision making in sportsman’s tactical performance in team sports.Interdisciplinary Description of Complex Systems - scientific journal,6, 53-66.
[144] Skinner, B. (2010). The price of anarchy in basketball.Journal of Quantitative Analysis in Sports,6(1).
[145] Song, A., Severini, T., & Allada, R. (2017). How jet lag impairs major league baseball performance.Proceedings of the National Academy of Sciences,114(6), 1407-1412.
[146] Spearman, W. (2016, 02).Quantifying pitch control.doi: 10.13140/RG.2.2.22551.93603
[147] Spearman, W. (2018). Beyond expected goals. InProceedings of the 12th mit sloan sports analytics conference(pp. 1-17).
[148] Statsbomb.(2020). Retrieved 2020-09-09, fromhttps://statsbomb.com/
[149] Stats perform.(2020).Retrieved 2020-09-17, fromhttps://www.statsperform.com/ resource/stats-playing-styles-introduction/
[150] Stone, P., Sutton, R. S., & Kuhlmann, G. (2005). Reinforcement learning for RoboCupsoccer keepaway.Adaptive Behavior,13(3), 165-188.
[151] StriVR Immersive Sports Analytics.(2020).Retrieved 2020-09-09, fromhttps://www .strivr.com/use-cases/sports/
[152] Su, S.-Y., Hajimirsadeghi, H., & Mori, G.(2019).Graph generation with variational recurrent neural network.arXiv preprint arXiv:1910.01743.
[153] Sun, C., Karlsson, P., Wu, J., Tenenbaum, J. B., & Murphy, K.(2019).Stochastic prediction of multi-agent interactions from partial observations.arXiv preprint arXiv:1902.09641.
[154] Sun, J., Xie, J., Hu, J.-F., Lin, Z., Lai, J., Zeng, W., & Zheng, W.-s. (2019). Predicting future instance segmentation with contextual pyramid convlstms. InProceedings of the 27th acm international conference on multimedia(pp. 2043-2051).
[155] Sun, K., Xiao, B., Liu, D., & Wang, J. (2019). Deep high-resolution representation learning for human pose estimation. InProceedings of the IEEE conference on computer vision and pattern recognition(pp. 5693-5703).
[156] Sun, X., Davis, J., Schulte, O., & Liu, G. (2020). Cracking the black box: Distilling deep sports analytics. InProceedings of the 26th acm sigkdd international confer
[157] Sypetkowski, M., Kurzejamski, G., & Sarwas, G. (2019). Football players pose estimation. InImage processing and communications challenges 10(pp. 63-70). Cham: Springer International Publishing.
[158] Sypetkowski, M., Sarwas, G., & Trzcinski, T. (2019). Synthetic image translation for football players pose estimation.J. UCS,25(6), 683-700.
[159] Szymanski, S. (2020). Sport analytics: Science or alchemy?Kinesiology Review,9(1), 57-63.
[160] Tuyls, K., P´erolat, J., Lanctot, M., Hughes, E., Everett, R., Leibo, J. Z., . . . Graepel, T. (2020). Bounds and dynamics for empirical game theoretic analysis.Auton. Agents Multi Agent Syst.,34(1), 7.
[161] Tuyls, K., & Weiss, G. (2012). Multiagent learning: Basics, challenges, and prospects.AI Mag.,33(3), 41-52.
[162] Unterthiner, T., van Steenkiste, S., Kurach, K., Marinier, R., Michalski, M., & Gelly, S. (2018). Towards accurate generative models of video: A new metric & challenges. arXiv preprint arXiv:1812.01717.
[163] Urieli, D., MacAlpine, P., Kalyanakrishnan, S., Bentor, Y., & Stone, P. (2011, May). On optimizing interdependent skills: A case study in simulated 3d humanoid robot soccer. InProc. of 10th int. conf. on autonomous agents and multiagent systems (AAMAS) (Vol. 2, pp. 769-776). IFAAMAS.
[164] Villegas, R., Yang, J., Zou, Y., Sohn, S., Lin, X., & Lee, H. (2017). Learning to generate long-term future via hierarchical prediction.arXiv preprint arXiv:1704.05831.
[165] Visser, U., & Burkhard, H.-D. (2007, 06). Robocup: 10 years of achievements and future challenges.AI Magazine,28, 115-132.
[166] Vondrick, C., Pirsiavash, H., & Torralba, A. (2016). Anticipating visual representations from unlabeled video. InProceedings of the IEEE conference on computer vision and pattern recognition(pp. 98-106).
[167] Vu, T.-H., Choi, W., Schulter, S., & Chandraker, M. (2018). Memory warps for learning long-term online video representations.arXiv preprint arXiv:1803.10861.
[168] Wang, J. M., Fleet, D. J., & Hertzmann, A. (2007). Gaussian process dynamical models for human motion.IEEE transactions on pattern analysis and machine intelligence, 30(2), 283-298.
[169] Weissenborn, D., T¨ackstr¨om, O., & Uszkoreit, J. (2019). Scaling autoregressive video models.arXiv preprint arXiv:1906.02634.
[170] Wellman, M. P. (2006). Methods for empirical game-theoretic analysis. InAaai(pp. 1552-1556).
[171] Wyscout.(2020). Retrieved 2020-09-09, fromhttps://wyscout.com/ 87
[172] Xiong, B., Kalantidis, Y., Ghadiyaram, D., & Grauman, K. (2019, June). Less is more: Learning highlight detection from video duration. InProceedings of the ieee/cvf conference on computer vision and pattern recognition (CVPR).
[173] Xu, J., Ni, B., Li, Z., Cheng, S., & Yang, X. (2018). Structure preserving video prediction. InProceedings of the IEEE conference on computer vision and pattern recognition (pp. 1460-1469).
[174] Yang, H., Wang, B., Lin, S., Wipf, D., Guo, M., & Guo, B. (2015). Unsupervised extraction of video highlights via robust recurrent auto-encoders. InProceedings of the IEEE international conference on computer vision(pp. 4633-4641).
[175] Yee, T., Lis‘y, V., & Bowling, M. H. (2016). Monte carlo tree search in continuous action spaces with execution uncertainty. InIjcai(pp. 690-697).
[176] Yeh, R. A., Schwing, A. G., Huang, J., & Murphy, K. (2019). Diverse generation for multiagent sports games. InProceedings of the IEEE conference on computer vision and pattern recognition(pp. 4610-4619).
[177] Zhang, F., Zhu, X., & Ye, M. (2019, June). Fast human pose estimation. InProceedings of the ieee/cvf conference on computer vision and pattern recognition (CVPR).
[178] Zhang, H., Goodfellow, I., Metaxas, D., & Odena, A. (2019). Self-attention generative adversarial networks. InInternational conference on machine learning(pp. 7354- 7363).
[179] Zhang, H., Sciutto, C., Agrawala, M., & Fatahalian, K. (2020). Vid2player: Controllable video sprites that behave and appear like professional tennis players.arXiv preprint arXiv:2008.04524.
[180] Zhang, K., Chao, W.-L., Sha, F., & Grauman, K. (2016). Video summarization with long short-term memory. InECCV (7)
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