A method to identify defensive assignments in team-based invasion sports using spatiotemporal trajectories

References

• Adhikari, R. and Agrawal, R.K., 2013. An Introductory Study on Time Series Modeling and Forecasting. CoRR, abs/1302.6613. Saarbrücken, Germany: LAP LAMBERT Academic Publishing, 68.
   Google Scholar
• Bak, P., et al., 2012. Scalable Detection of Spatiotemporal Encounters in Historical Movement Data. Computer Graphics Forum, 31 (3pt1), 915–924. doi:10.1111/j.1467-8659.2012.03084.x
   Google Scholar
• Bialkowski, A., et al., 2014. Large-Scale Analysis of Soccer Matches Using Spatiotemporal Tracking Data. In: 2014 IEEE International Conference on Data Mining, December, Shenzhen, China, 725–730. doi:10.1109/ICDM.2014.133
   Google Scholar
• Chin, S.L., et al.. 2005. An Application Based on Spatial-Relationship to Basketball Defensive Strategies. In: T. Enokido, et al., eds.. Embedded and Ubiquitous Computing – EUC 2005 Workshops. Berlin, Heidelberg: Springer Berlin Heidelberg, 180–188.
   Google Scholar
• de Lucca Siqueira, F. and Bogorny, V., 2011. Discovering chasing behavior in moving object trajectories. Transactions in GIS, 15 (5), 667–688. doi:10.1111/j.1467-9671.2011.01285.x
   Web of Science ®Google Scholar
• Dodge, S., Weibel, R., and Lautenschütz, A.K., 2008. Towards a Taxonomy of Movement Patterns. Information visualization, 7 (3), 240–252. doi:10.1057/PALGRAVE.IVS.9500182
   Web of Science ®Google Scholar
• Dos Santos, A.A., et al., 2015. Inferring Relationships from Trajectory Data. In: Proceedings XVI GEOINFO, November 29th to December 2nd, 2015, Campos do Jordão, Brazil, 68–79.
   Google Scholar
• Franks, A., et al., 2015. Characterizing the spatial structure of defensive skill in professional basketball. The annals of applied statistics, 9 (1), 94–121. doi:10.1214/14-AOAS799
   Web of Science ®Google Scholar
• Goldsberry, K. and Weiss, E., 2013. The Dwight effect: a new ensemble of interior defense analytics for the NBA. In: Proc. 7th Annual MIT Sloan Sports Analytics Conference, February Boston, MA, 1–11.
   Google Scholar
• Grosfeld-Nir, A., Ronen, B., and Kozlovsky, N., 2007. The Pareto managerial principle: when does it apply? International Journal of Production Research, 45 (10), 2317–2325. doi:10.1080/00207540600818203
   Web of Science ®Google Scholar
• Gudmundsson, J. and Horton, M., 2017. Spatio-Temporal Analysis of Team Sports. ACM Computing Surveys, 50 (2), 22:1–22:34. doi:10.1145/3054132
   Web of Science ®Google Scholar
• Gudmundsson, J. and van Kreveld, M., 2006. Computing Longest Duration Flocks in Trajectory Data. In: Proceedings of the 14th Annual ACM International Symposium on Advances in Geographic Information Systems, Arlington, Virginia, USA New York, NY, USA: ACM, 35–42.
   Google Scholar
• Gudmundsson, J., van Kreveld, M., and Speckmann, B., 2004. Efficient Detection of Motion Patterns in Spatio-temporal Data Sets. In: Proceedings of the 12th Annual ACM International Workshop on Geographic Information Systems, GIS ’04, Washington DC, USA New York, NY, USA: ACM, 250–257.
   Google Scholar
• Huang, W., et al., 2015. Predicting human mobility with activity changes. International Journal of Geographical Information Science, 29 (9), 1569–1587. doi:10.1080/13658816.2015.1033421
   Web of Science ®Google Scholar
• Hyndman, R.J. and Athanasopoulos, G., 2018. Forecasting: principles and practice. 2nd ed. Australia: OTexts.
   Google Scholar
• Jeung, H., Shen, H.T., and Zhou, X., 2008. Convoy queries in spatio-temporal databases. In: Proceedings of the 2008 IEEE 24th International Conference on Data Engineering, Washington, DC, USA: IEEE Computer Society, 1457–1459.
   Google Scholar
• Krzanowski, W.J. and Hand, D.J., 2009. ROC curves for continuous data. Boca Raton, FL: Crc Press.
   Google Scholar
• Laube, P. and Imfeld, S., 2002. Analyzing Relative Motion within Groups of Trackable Moving Point Objects. In: M.J. Egenhofer and D.M. Mark, eds.. Geographic Information Science. Berlin, Heidelberg: Springer Berlin Heidelberg, 132–144.
   Google Scholar
• Laube, P., van Kreveld, M., and Imfeld, S., 2005. Finding REMO — Detecting Relative Motion Patterns in Geospatial Lifelines. In: Peter F. Fisher, ed. Developments in Spatial Data Handling. Berlin, Heidelberg: Springer Berlin Heidelberg, 201–215.
   Google Scholar
• Li, R. and Chellappa, R., 2010. Group motion segmentation using a Spatio-Temporal Driving Force Model. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June. San Francisco, CA, 2038–2045. doi:10.1109/CVPR.2010.5539880
   Google Scholar
• Li, Z., et al., 2010. Swarm: mining relaxed temporal Moving Object Clusters. Proceedings of the VLDB Endowment, 3 (1–2), 723–734. doi:10.14778/1920841.1920934
   Google Scholar
• Metulini, R., 2017. Filtering Procedures for Sensor Data in Basketball. Statistica & Applicationi, 15 (2), 133–150.
   Google Scholar
• Metulini, R., Manisera, M., and Zuccolotto, P., 2018. Modelling the dynamic pattern of surface area in basketball and its effects on team performance. Journal of Quantitative Analysis in Sports, 14 (3), 117–130. doi:10.1515/jqas-2018-0041
   Web of Science ®Google Scholar
• Mikołajec, K., Maszczyk, A., and Zajac, T., 2013. Game Indicators Determining Sports Performance in the NBA. Journal of human kinetics, 37, 145–151. doi:10.2478/hukin-2013-0035
   PubMed Web of Science ®Google Scholar
• Miller, A.C. and Bornn, L., 2017. Possession sketches: Mapping nba strategies. In: MIT Sloan Sports Analytics Conference, March 3–4 at the Hynes Convention Center in Boston, the Sloan Sports Analytics Conference (SSAC).
   Google Scholar
• Moore, A.B. and Rodda, J., 2015. Adaptive Relative Motion Representation of Space- Time Trajectories. The Cartographic journal, 52 (2), 204–209. doi:10.1080/00087041.2015.1119463
   Web of Science ®Google Scholar
• Mu, E. and Pereyra-Rojas, M., 2016. Practical decision making: an introduction to the Analytic Hierarchy Process (AHP) using super decisions. Vol. 2. Switzerland: Springer International Publishing.
   Google Scholar
• Parent, C., et al., 2013. Semantic Trajectories Modeling and Analysis. ACM Computing Surveys, 45 (4), 42:1–42:32. doi:10.1145/2501654.2501656
   Web of Science ®Google Scholar
• Pelekis, N. and Theodoridis, Y., 2014. Mobility data management and exploration. New York, NY: Springer Publishing Company, Incorporated.
   Google Scholar
• Saaty, T.L., 1990. How to make a decision: the analytic hierarchy process. European journal of operational research, 48 (1), 9–26. doi:10.1016/0377-2217(90)90057-I
   Web of Science ®Google Scholar
• Scholz, R.W. and Lu, Y., 2014. Detection of dynamic activity patterns at a collective level from large-volume trajectory data. International Journal of Geographical Information Science, 28 (5), 946–963. doi:10.1080/13658816.2013.869819
   Web of Science ®Google Scholar
• Shen, J. and Cheng, T., 2016. A framework for identifying activity groups from individual space-time profiles. International Journal of Geographical Information Science, 30 (9), 1785–1805. doi:10.1080/13658816.2016.1139119
   Web of Science ®Google Scholar
• Spaccapietra, S., et al., 2008. A conceptual view on trajectories. Data Knowledge Engineering, 65 (1), 126–146. doi:10.1016/j.datak.2007.10.008
   Web of Science ®Google Scholar
• Tang, L.A., et al., 2014. A Framework of Traveling Companion Discovery on Trajectory Data Streams. ACM Transactions on Intelligent Systems and Technology, 5 (1), 3:1–3:34.
   Web of Science ®Google Scholar
• Van Haaren, J., et al.. 2015. Automatically Discovering Offensive Patterns in Soccer Match Data. In: E. Fromont, T. De Bie, and M. van Leeuwen, eds.. Advances in Intelligent Data Analysis XIV. Cham: Springer International Publishing, 286–297.
   Google Scholar
• Vilar, L., et al., 2012. The Role of Ecological Dynamics in Analysing Performance in Team Sports. Sports Medicine, 42 (1), 1–10. doi:10.2165/11596520-000000000-00000
   PubMed Web of Science ®Google Scholar
• Wu, S. and Bornn, L., 2018. Modeling Offensive Player Movement in Professional Basketball. The American statistician, 72 (1), 72–79. doi:10.1080/00031305.2017.1395365
   Web of Science ®Google Scholar
• Yuan, G., et al., 2017. Multi-granularity periodic activity discovery for moving objects. International Journal of Geographical Information Science, 31 (3), 435–462. doi:10.1080/13658816.2016.1205194
   Web of Science ®Google Scholar
• Zheng, Y., 2015. Trajectory data mining: an Overview. ACM Transactions on Intelligent Systems and Technology, 6 (3), 29:1–29:41. doi:10.1145/2743025
   PubMed Web of Science ®Google Scholar
• Zheng, Y. and Zhou, X., 2011. Computing with spatial trajectories. Vol. 1. New York, NY: Springer Publishing Company, Incorporated.
   Google Scholar