Sports Data Analytics: A Case Study of off-Field Behavior of Players

  • Malini PatilEmail author
  • Neha Sharma
  • B. R. Dinakar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1016)


The field of sports science is highly emerging and has made the headlines in the research and development activities, with new challenges and trends in the recent past. Sports analytics is the analysis of historical big data mainly available in the form of statistics to provide a proper insight into the entire team, an individual, or even the coach. It also facilitates decision-making for both on-field and off-field performances of all the concerned. The term “sports analytics” was popularized in 2011. It has also created a platform under the confluence of many disciplines such as data mining, machine learning, big data analytics, artificial intelligence, and predictive analytics. The paper aims at analyzing the off-field behavior of players using a statistical approach. The focus of the study is to comprehend the nature of data set, explore the relation between the attributes of the data set, and create a model to understand how the data relates to the underlying population using a real world data set. It comprises motion sensor data of 19 activities among both male and female categories. The sports data set is referred from the UCI repository. It consists of motor sensor data collected for both male and female players. The data set also clearly displays the three dimensions of big data namely, volume, variety, and veracity.


Sports data analytics Predictive analytics Big data Players behavior Recurrent neural network LSTM 



The authors wish to acknowledge the UCI Machine Learning repository, Centre for Machine Learning, and Intelligent Systems for providing the data set.


  1. 1.
    ParmezanBonidia, R., DuilioBrancher, J., & Marques Busto, R. (2018). Data mining in sports: A systematic review. IEEE Latin America Transactions, 16(1), 232–239.CrossRefGoogle Scholar
  2. 2.
    Shih, H.-C. (2017). A survey on content-aware video analysis for sports. IEEE Transactions on Circuits and Systems for Video Technology, 28(5), 1212–1231.CrossRefGoogle Scholar
  3. 3.
    Takahashi, M., Ikeya, K., Kano, M., Ookubo, H., & Mishina, T. (2016). Robust volleyball tracking system using multi-view cameras. In 2016 23rd International Conference on Pattern Recognition Pattern Recognition (ICPR) (pp. 2740–2745).Google Scholar
  4. 4.
    Knobbe, A., Orie, J., Hofman, N., et al. (2017). Sports analytics for professional speed skating. Data Mining and Knowledge Discovery, 31, 1872. Scholar
  5. 5.
    Cheng, X., Ikoma, N., Honda, M., & Ikenaga, T. (2017). Ball state based parallel ball tracking and event detection for volleyball game analysis. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 100(11), 2285–2294.CrossRefGoogle Scholar
  6. 6.
    Baumer, B., Jensen, S., & Matthews, G. (2015). openWAR: An open source system for evaluating overall player performance in major league baseball. Journal of Quantitative Analysis in Sports, 11(2), 69–84. Retrieved October 31, 2018 from
  7. 7.
    Mulholland, J., & Jensen, S. T. (2014). Predicting the draft and career success of tight ends in the national football league. Journal of Quantitative Analysis in Sports, 10(4), 381–396. Scholar
  8. 8.
    Lopez, M. J., & Matthews, G. (2015). Building an NCAA men’s basketball predictive model and quantifying its success. Journal of Quantitative Analysis in Sports, 11(1), 5–12.CrossRefGoogle Scholar
  9. 9.
    Becker, A., & Sun, X. A. (2016). An analytical approach for fantasy football draft and lineup management. Journal of Quantitative Analysis in Sports, 12(1), 17–30.CrossRefGoogle Scholar
  10. 10.
    Kolbush, J., & Sokol, J. H. (2017). A logistic regression/Markov chain model for American college football. International Journal of Computer Science in Sport, 16(3), 185–196.CrossRefGoogle Scholar
  11. 11.
    Bartolucci, F., & Murphy, T. B. (2015). A finite mixture latent trajectory model for modeling ultrarunners’ behavior in a 24-hour race. Journal of Quantitative Analysis in Sports, 11(4), 193–203.CrossRefGoogle Scholar
  12. 12.
  13. 13.
    Altun, K., Barshan, B., & Tunçel, O. (2010). Comparative study on classifying human activities with miniature inertial and magnetic sensors. Pattern Recognition, 43(10), 3605–3620.CrossRefGoogle Scholar
  14. 14.
    Barshan, B., & Yüksek, M. C. (2014). Recognizing daily and sports activities in two open source machine learning environments using body-worn sensor units. The Computer Journal, 57(11), 1649–1667.CrossRefGoogle Scholar
  15. 15.
    Altun, K., & Barshan, B. (2010). Human activity recognition using inertial/magnetic sensor units. In Proceedings First International Workshop on Human Behavior Understanding (in conjunction with the 20th International Conference on Pattern Recognition), August 22, 2010. Istanbul, Turkey,Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.J.S.S. Academy of Technical EducationBengaluruIndia
  2. 2.Society for Data SciencePuneIndia
  3. 3.Dayanand Sagar Academy of Technology and ManagementBengaluruIndia

Personalised recommendations