Time Series Data Mining for Sport Data: a Review

Agarwal, P., Shroff, G., Saikia, S., & Khan, Z. (2015). Efficiency discovering frequent motifs in large-scale sensor data. Proceedings of the second ACM IKDD conference on data sciences, (pp. 98–103).10.1145/2732587.2732601 Search in Google Scholar

Aghabozorgi, S., Shirkhorshidi, A. S., & Wah, T. Y. (2015). Time-series clustering–a decade review. Informion Systems, 53, 16–38.10.1016/j.is.2015.04.007 Search in Google Scholar

Ahmadi, A., Mitchell, E., Richter, C., Destelle, F., Gowing, M., O’Connor, N., & Moran, K. (2014). Toward automatic activity classification and movement assessment during a sports training session. IEE Internet of Things Journal, 2(1), 23–32.10.1109/JIOT.2014.2377238 Search in Google Scholar

Anguera, A., Barreiro, J., Lara, J., & Lizcano, D. (2016). Applying data mining techniques to medical time series: an empirical case study in electroencephalography and stabilometry. Computational and structural biotechnology journal, 14, 185–199.10.1016/j.csbj.2016.05.002488759327293535 Search in Google Scholar

Bagnall, A., Lines, J., Bostom, A., Large, J., & Keogh, E. (2017). The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Mining and Knowledge Discovery, 31(3), 606–660.10.1007/s10618-016-0483-9640467430930678 Search in Google Scholar

Biermann, H., Theiner, J., Bassek, M., Raabe, D., Memmert, D., & Ewerth, R. (2021). A Unified Taxonomy and Multimodal Dataset for Events in Invasion Games. In Proceedings of the 4th International Workshop on Multimedia Content Analysis in Sports.10.1145/3475722.3482792 Search in Google Scholar

Berndt, D., & Clifford, J. (1994). Using dynamic time warping to find patterns in time series. KDD workshop, 10(16), 359–370. Search in Google Scholar

Blank, P., Hoßbach, J., Schuldhaus, D., & Eskofier, B. (2015). Sensor-based stroke detection and stroke type classification in table tennis. Proceedings of the 2015 ACM International Symposium on Wearable Computers, 2018, 93–100.10.1145/2802083.2802087 Search in Google Scholar

Bonidia, R., Rodriges, L., Avila-Santos, A.P., Sanches, D., & Brancher, J. (2018). Computational intelligence in sports: a systematic literature review. Advances Human-Computer Interaction, 2018, 1–13.10.1155/2018/3426178 Search in Google Scholar

Box, G., Jenkins, G., & Reinsel, G. (2016). Time series analysis: forecasting and control. New Jersey: John Wiley & Sons, Inc., Hoboken, fifth ed. Search in Google Scholar

Braei, M., & Wagner, S. (2020). Anomaly detection in univariate time-series: a survey on the state-of-the-art. arXiv preprint arXiv:2004.00433. Search in Google Scholar

Bulling, A., Blanke, U., & Schiele, B. (2014). A tutorial on human activity recognition using body-worn inertial sensors. ACM Computing Surveys (CSUR), 46(3), 1–33.10.1145/2499621 Search in Google Scholar

Chan, K., & Fu, A. (1988). Efficient time series matching by wavelets. In Proceeding of the 15th International Conference on Data Engineering, (Cat. No. 99CB36337), IEEE, (pp. 126–133). Search in Google Scholar

Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: a survey. ACM Computing Surveys, 41(3), 1–58.10.1145/1541880.1541882 Search in Google Scholar

Ding, H., Trajcevski, G., Scheuermann, P., Wang, X., & Keogh, E. (2008). Querying and mining of time series data: experimental comparison of representations and distance measures. PVLDB Endowment, 1(2), 1542–1552.10.14778/1454159.1454226 Search in Google Scholar

Esling, P., & Agon, C. (2012). Time series data mining. ACM Computing Surveys (CSUR), 45(1), 1–34.10.1145/2379776.2379788 Search in Google Scholar

Faloutsos, C., Ranganthan, M., & Manolopoulos, Y. (1994). Fast subsequence matching in time-series databases. ACM SIGMOD International Conference on Management of Data, 23(2), 419–429.10.1145/191843.191925 Search in Google Scholar

Fu, T.-C. (2011). A review on time series data mining. Engineering Applications of Artificial Intelligence, 24(1), 164–181.10.1016/j.engappai.2010.09.007 Search in Google Scholar

Gao, Y., & Lin, J. (2018). Efficient discovery of variable-length time series motifs with large length range in million scale time series. arXiv preprint arXiv:1802.04883.10.1109/ICDM.2017.8356939 Search in Google Scholar

Gupta, M., Gao, J., Aggarwal, C., & Han, J. (2013). Outlier detection for temporal data: A survey. IEEE Transactions on Knowledge and Data Engineering, 26(9), 250–2267.10.1109/TKDE.2013.184 Search in Google Scholar

Haladjian, J., Schlabbers, D., Taheri, S., Tharr, M., & Bruegge, B. (2020). Sensor-based detection and classification of soccer goalkeeper training exercises. ACM transactions on Internet of things, 1(2), 1–20.10.1145/3372342 Search in Google Scholar

Horvat, T., & Josip, J. (2020). The use of machine learning in sport outcome prediction: A review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(5), e1380.10.1002/widm.1380 Search in Google Scholar

Hossain, H., Khan, M., & Roy, N. (2017). Soccermate: A personal soccer attribute profiler using wearables. In 2017 IEEE International Conference on Parvasive Computing and Communications Workshops (PerCom Workshops), (pp. 164–169).10.1109/PERCOMW.2017.7917551 Search in Google Scholar

Hu, X., Mo, S., & Qu, X. (2020). Basketball activity classification based on upper body kinematics and dynamic time wraping. International journal of sport medicine, 41(4), 255–263.10.1055/a-1065-204431935773 Search in Google Scholar

Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., & Muller, P. (2019). Deep learning for time series classification: a review. Data mining and knowledge discovery, 33(4), 917–963.10.1007/s10618-019-00619-1 Search in Google Scholar

Jamil, M., Phatak, A., Mehta, S., Beato, M., Memmert, D., & Connor, M. (2021). Using multiple machine learning algorithms to classify elite and sub-elite goalkeepers in professional men’s football. Scientific reports, 11(1), 1-7.10.1038/s41598-021-01187-5860902534811371 Search in Google Scholar

Junejo, I., & Al Aghbari, Z. (2012). Using sax representation for human action recognition. Journal of Visual Communication and Image Representation, 23(6), 853–861.10.1016/j.jvcir.2012.05.001 Search in Google Scholar

Kelly, D., Coughlan, G., Green, B., & Caulfield, B. (2012). Automatic detection of collisions in elite level rugby union using a wearable sensing device. Sport Engineering, 15(2), 81–92.10.1007/s12283-012-0088-5 Search in Google Scholar

Keogh, E., & Kasetty, S. (2003). On the need for time series data mining benchmarks: a survey and empirical demonstration. Data Mining and Knowledge Discovery, 7(4), 349–371.10.1023/A:1024988512476 Search in Google Scholar

Keogh, E., Lin, J., Lee, S., & Van Herle, H. (2006). Finding the most unusual time series subsequence: algorithms and applications. Knowledge and Information Systems, 11(1), 1–27.10.1007/s10115-006-0034-6 Search in Google Scholar

Keogh, E., Lin, J., & Truppel, W. (2003). Clustering of time series subsequences is meaningless: Implications for previous and future research. In Proceedings of the third IEEE international conference on data mining, Wahington, DC: IEEE Computer Society, (pp. 115–122). Search in Google Scholar

Keogh, E., & Ratanamahatana, C. (2002). Exact indexing of dynamic time warping. Proceedings of the 26th International Conference on Very Large Data Bases, 7(3), 406–417.10.1016/B978-155860869-6/50043-3 Search in Google Scholar

Li, Y., Wang, L., & Li, F. (2021). A data-driven prediction approach for sports team performance and its application to national basketball association. Omega, 98(102123).10.1016/j.omega.2019.102123 Search in Google Scholar

Li, Y., & Zhang, Y. (2012). Application of data mining techniques in sports training. In 5th International Conference on BioMedical Engineering and Informatics, (pp. 954–958).10.1109/BMEI.2012.6513050 Search in Google Scholar

Liao, T. (2005). Clustering of time series data—a survey. Pattern Recognition,, 38(11), 1857–1874.10.1016/j.patcog.2005.01.025 Search in Google Scholar

Lin, J., Keogh, E., Lonardi, E., & Patel, S. (2002). Finding motifs in time series. In Proceedings of the Eighth ACM SIGKDD Iternational Conference on Knowledge Discovery and Data Mining 2nd Workshop on Temporal Data Mining, (pp. 53–68). Search in Google Scholar

Lin, J., Keogh, E., Lonardi, W., & Chiu, B. (2003). A symbolic representation of time series, with implications for streaming algorithms. In Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, ACM Press, (pp. 2–11).10.1145/882082.882086 Search in Google Scholar

Lin, J., Keogh, E., Wei, L., & Lonardi, S. (2007). Experiencing sax: a novel symbolic representation of time series. Data Mining and Knowledge Discovery, 15(2).10.1007/s10618-007-0064-z Search in Google Scholar

Lin, J., & Li, Y. (2010). Finding approximate frequent patterns in streaming medical data. In IEEE 23rd International Symposium on Computer-Based Medical Systems (CBMS), IEEE, (pp. 13–18).10.1109/CBMS.2010.6042675 Search in Google Scholar

Linardi, M., Zhu, Y., Palpanas, T., & Keogh, W. (2018). Matrix profile x: Valmod - scalable discovery of variable-length motifs in data series. In Proceedings of the 2018 International COnference on Management Data, (pp. 1053–1066).10.1145/3183713.3183744 Search in Google Scholar

Liu, B., Li, J., Chen, C., Tan, W., Chen, Q., & Zhou, M. (2015). Efficient motif discovery for large-scale time series in healthcare. IEEE Transactions on Industrial Informatics, 11(3), 583–590.10.1109/TII.2015.2411226 Search in Google Scholar

Maeda, T., Fujii, M., Hayashi, I., & Tasaka, T. (2014). Sport skill classification using time series motion picture data. In Industrial Electronics Society, IECON 2014-40th Annual Conference of the IEEE, (pp. 5272–5277).10.1109/IECON.2014.7049304 Search in Google Scholar

Memmert, D., & Raabe, D. (2018). Data Analytics in Football. Positional Data Collection, Modelling and Analysis. Abingdon: Routledge.10.4324/9781351210164 Search in Google Scholar

Memmert, D., Lemmink, K. A. P. M., & Sampaio, J. (2017). Current Approaches to Tactical Performance Analyses in Soccer using Position Data. Sports Medicine, 47(1), 1-10.10.1007/s40279-016-0562-527251334 Search in Google Scholar

Miller, R., Schwarz, H., & Talke, I. (2017). Forecasting sports popularity: application of time series analysis. Academic Journal of Interdisciplinary Studies, 6(2), 75.10.1515/ajis-2017-0009 Search in Google Scholar

Minnen, D., Starner, T., Essa, I., & Isbell, C. (2006). Discovering characteristic actions from on-body sensor data. In Wearable computers, 2006 10th IEEE international symposium on wearable computers. IEEE, (pp. 11–18).10.1109/ISWC.2006.286337 Search in Google Scholar

Mitchell, E., Monaghan, D., & O’Connor, N. (2013). Classification of sporting activities using smartphone. Sensors, 13(4), 5317–5337.10.3390/s130405317367313923604031 Search in Google Scholar

Mitsa, T. (2010). Temporal data mining. Chapman and Hall/CRC.10.1201/9781420089776 Search in Google Scholar

Mueen, A. (2014). Time series motif discovery: dimensions and applications. Wiley Interdiscilinary Reviews: Data Mining and Knowledge Discovery, 4(2), 152–159.10.1002/widm.1119 Search in Google Scholar

Oates, T., Boedihardjo, A., Lin, J., Chen, C., Frankenstein, S., & Gandhi, S. (2013). Motif discovery in spatial trajectories using grammar inference. In Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, (pp. 1465–1468).10.1145/2505515.2507820 Search in Google Scholar

Ofoghi, B., Zeleznikow, J., MacMahon, C., & Raab, M. (2013). Data mining in elite sports: a review and a framework. Measurement in Physical Education and Exercise Science, 17(3), 171–186.10.1080/1091367X.2013.805137 Search in Google Scholar

Pimentel, M., Clifton, D., Clifton, L., & Tarassenko, L. (2014). A review of novelty detection. In Signal Processing, 99, 215–249.10.1016/j.sigpro.2013.12.026 Search in Google Scholar

Ratanamahatana, C., Lin, J., Gunopulos, D., Keogh, E., Vlanchos, M., & Das, G. (2010). Mining time series data. Data mining and knowledge discovery handbook, (pp. 1069–1103).10.1007/0-387-25465-X_51 Search in Google Scholar

Raabe, D., Nabben, R., & Memmert, D. (2022). Graph Representations for the Analysis of Multi-Agent Spatiotemporal Sports Data. Applied Intelligence, 1-21.10.1007/s10489-022-03631-z Search in Google Scholar

Rein, R., & Memmert, D. (2016). Big data and tactical analysis in elite soccer: future challenges and opportunities for sports science. SpringerPlus, 5(1), 1–13.10.1186/s40064-016-3108-2499680527610328 Search in Google Scholar

Rein, R., Raabe, D., & Memmert, D. (2017). “Which pass is better?” Novel approaches to assess passing effectiveness in elite soccer. Human Movement Science, 55, 172–181. https://doi.org/10.1016/j.humov.2017.07.01010.1016/j.humov.2017.07.01028837900 Search in Google Scholar

Schmidl, S., Wenig, P., & Papenbrock, T. (2022). Anomaly detection in time series: a comprehensive evaluation. Proceedings of the VLDB Endowment, 15(9), 1779–1797.10.14778/3538598.3538602 Search in Google Scholar

Schmidt, A. (2012). Movement pattern recognition in basketball. Human movement science, 31(2), 360–382.10.1016/j.humov.2011.01.00322402277 Search in Google Scholar

Schumaker, R., Soleiman, O., & Chen, H. (2010). Sports knowledge managemet and data mining. Annual Review of Information Science and Technology, 44(1), 115–157.10.1002/aris.2010.1440440110 Search in Google Scholar

Sempena, S., Maulidevi, N., & Aryan, P. (2011). Human action recognition using dynamic time warping. Proceedings on the 2011 Interantional Conference on Electrical Engineering and Informatics, ICEEI, (pp. 1–5).10.1109/ICEEI.2011.6021605 Search in Google Scholar

Senin, P. (2008). Dynamic time warping algorithm: review. Information and Computer Science Department University of Hawaii at Menoa Honolulu, 855, 1–23. Search in Google Scholar

Seto, S., Zhang, W., & Zhou, Y. (2015). Multivatiate time series classification using dynamic time warping template selection for human activity recognition. IEEE symposium series on computational intelligence, (pp. 1399–1409).10.1109/SSCI.2015.199 Search in Google Scholar

Siirtola, P., Laurinen, P., Haapalainen, E., Roning, J., & Kinnunen, H. (2009). Clustering-based activity classification with a wrist-worn accelerometer using basic features. 2009 IEEE Symposium on Computational Intelligence and Data Mining, (pp. 95–100).10.1109/CIDM.2009.4938635 Search in Google Scholar

Sivaraks, H., & Ratanamahatana, C. (2015). Robust and accurate anomaly detection in ecg artifacts using time series motif discovery. Computational and mathematical methods in medicine, 2015.10.1155/2015/453214432093825688284 Search in Google Scholar

Soto-Valero, C., González-Castellanos, M., & Pérez-Morales, I. (2017). A predictive model for analysing the starting pitchers’ performance using time series classification methods. International Journal of Performance Analysis in Sport, 17(4), 492–509.10.1080/24748668.2017.1354544 Search in Google Scholar

Srivastava, R., Patwari, A., Kumar, S., Mishra, G., Kaligounder, L., & Sinha, P. (2015). Efficient characterization of tennis shots and game analysis using wearable sensor data. 2015 IEEE sensors, (pp. 1–4).10.1109/ICSENS.2015.7370311 Search in Google Scholar

Stein, M., Jenezko, D., H.and Seebacher, Jäger, A., Negel, J., M.and Hölsch, Kosub, S., Schreck, T., Kleim, D., & Grossniklaus, M. (2017). How to make sense of team sport data: from acquisition to data modeling and research aspects. Data, 2(1).10.3390/data2010002 Search in Google Scholar

Tanaka, Y., Iwamoto, K., & Uehara, K. (2005). Discovery of time series motif from multidimensional data based on mdl principle. Machine Learning, 58(2), 269–300.10.1007/s10994-005-5829-2 Search in Google Scholar

Torkamani, S., & Lohweg, V. (2017). Survey on time series motif discovery. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 7(2).10.1002/widm.1199 Search in Google Scholar

Wang, J., Wang, Z., Gao, F., Zhao, H., Qui, S., & Li, J. (2020). Swimming stroke phase segmentation based on wearable motion capture technique. IEEE Transactions on Instrumentation and Measurement, 69(10), 8526–8538.10.1109/TIM.2020.2992183 Search in Google Scholar

Wang, X., Mueen, A., Ding, H., Trajcevski, G., Scheuermann, P., & Keogh, E. (2012). Experimental comparison of representation methods and distance measures for time series data. Data Mining and Knowledge Discovery, 26(2), 275–309.10.1007/s10618-012-0250-5 Search in Google Scholar

Wang, Z., Li, J., Wang, J., Zhao, H., Qiu, S., Yang, N., & Shi, X. (2018). Inertial sensor-based analysis of equestrian sports between beginner and professional riders under different horse gaits. IEEE Transactions on Instrumentation and Measurement, 67(11), 2692–2704.10.1109/TIM.2018.2826198 Search in Google Scholar

Worsey, M., Jones, B., Cervantes, A., Chauvet, S., Thiel, D., & Espinosa, H. (2020). Assessment of head impacts and muscle activity in soccer using a t3 inertial sensor and a porable electromyography (emg) system: A preliminary study. Electronics, 9(5), 834.10.3390/electronics9050834 Search in Google Scholar

Wu, H., & Keogh, E. (2021). Current time series anomaly detection benchmarks are flawd and are creating the illusion of progress. IEEE Transaction on Knowledge and Data Engineering.10.1109/TKDE.2021.3112126 Search in Google Scholar

Xi, X., Keogh, E., Shelton, C., Wei, L., & Ratanamahatana, C. (2006). Fast time series classification using numerosity reduction. In Proceedings of the 23rd international conference on Machine learning, (pp. 1033–1040).10.1145/1143844.1143974 Search in Google Scholar

Xing, Z., Pei, J., & Keogh, E. (2010). A brief survey on sequence classification. ACM SIGKDD Explorations Newsletter, 12(1), 40–48.10.1145/1882471.1882478 Search in Google Scholar

Yeh, C., Kavantzas, N., & Keogh, E. (2017). Matrix profile vi: meaningful multidimensional motif discover. In IEEE international conference on data mining (ICDM). IEEE, (pp. 565–574).10.1109/ICDM.2017.66 Search in Google Scholar

Yong, W., Lingyun, P., & Jia, W. (2020). Statistical analysis and arma modeling for the big data of marathon score. Science & Sports, 35(6), 375–385.10.1016/j.scispo.2020.01.009 Search in Google Scholar

Zolhavarieh, S., Aghabozorgi, S., & Teh, Y. (2014). A review of subsequence time series clustering. The Scientific World Journal,, 2014.10.1155/2014/312521413031725140332 Search in Google Scholar