Performance Analysis of Basketball Referees by Machine Learning Techniques

Sheng-Wei Wang, Wen-Wei Hsieh

Abstract

Basketball referees are important in a basketball game. In this paper, we analyze the performance of basketball referees in a game from history data and using the machine learning techniques. The data are collected from Taiwan Super Basketball League games. We first observed that the teamwork is a key factor to the performance of referee teams. Furthermore, the degree of teamwork are more important than the personal capabilities. Then, we derived some classifiers by machine learning algorithms to further analyze the data set. Among the three classifiers, a classifier named linear classifier using pocket algorithm, which is able to classify the data points with most correct rate, performs better than the other two classifiers. The classifier also proved the importance of teamwork is much larger than that of personal capability. In the future, the classifier can be used to predict the performance of a referee team in a basketball game.

References

  1. Abu-Mostafa, Y. S., Magdon-Ismail, M., and Lin, H.-T. (2012). Learning from data. AMLBook.
  2. Austin, J. R. (2003). Transactive memory in organizational groups: The effects of content, consensus, specialization and accuracy on group performance. Journal of Applied Psychology, 88(5):866-878.
  3. Balmer, N.J., N., A.M., L., A.M., W., P., W. M., and S.H., F. (2007). Influence of crowd noise on soccer refereeing consistency in soccer. Journal of Sports Behavior, 30:130-145.
  4. Bandura, A. (97). Self-Efficacy: The Exercise of Control. New York: Freeman.
  5. Carron, A. V. (1988). Group dynamics in sport. London: Spodym.
  6. Feinstein, J. (2009). Sometimes an apology is the right call. Sporting News, 233(76).
  7. FIBA (2010a). FIBA Internal Regulations. FIBA.
  8. FIBA (2010b). Referees Manual for Three-Person Officiating. FIBA.
  9. FIBA (2014). Official Basketball Rules 2014. SpainBarcelona.
  10. Gallant, S. (1990). Perceptron-based learning algorithms. Neural Networks, IEEE Transactions on, 1(2):179- 191.
  11. Gladstein, D. L. (1984). Groups in context: A model of task group effectiveness. Administrative Science Quarterly, 29:499-517.
  12. Guillén, F. and Feltz, D., L. (2011). A conceptual model of referee efficacy. Front Psychology, 2(25):1-5.
  13. Hair, J. F. (2006). Multivariate data analysis, volume 6.
  14. Helsen, W. F. and Bultynck, J. (2004). Physical and perceptual-cognitive demands of top-class refereeing in association football. Journal of Sports Sciences, 22:179-189.
  15. Heuze, J. P., Sarrazin, P., M., M., Raimbault, N., and Thomas, J. P. (2006). Relationships of perceived motivational climate to cohesion and collective efficacy in elite female teams. Journal of Applied Sport Psychology, 18:201-218.
  16. Hoseini, S. H., Aslankhani, M. A., Abdoli, B., and Mohammadi (2011). The relationship between the number of crowds with anxiety and the function of the soccer premier leagues referees. Procedia-social and Behavioral Sciences, 30:2374-2378.
  17. Lazarov, V. (2007). Concepts of modern officiating. FIBA Assist Magazine, 24(30-33).
  18. Leicht, A. S. (2008). Physiological demands of basketball refereeing during international competition. Journal of Science and Medicine, 11:357-360.
  19. Magyar, T. M., Feltz, D. L., and Simpson, I, P. (2004). Individual and crew level determinants of collective efficacy in rowing. Journal of Sport & Exercise of Psychology, 26:136-153.
  20. Mirjamali, E., Ramzaninezhad, R., Rahmaninia, F., and Reihani, M. (2013). A study of stress in international and national referees of soccer, volleyball, basketball and handball in iran. World Journal of Sport Sciences, 6(4):347-354.
  21. Nevill, A., Balmer, N., and Williams, A. (2002). The influence of crowd noise and experience upon refereeing decisions in football. Psychology of Sport and Exercise, 3:261-272.
  22. Novikoff, A. B. (1962). On convergence proofs on perceptrons. Symposium on the Mathematical Theory of Automata, 12:615-622.
  23. Orme, J. G. and Combs-Orme, T. (2009). Multiple regression with discrete dependent variables. Oxford Univ. Press, USA.
  24. Rosenblatt, F. (1958). The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review, 65(6):386.
  25. Serkan, H. (2014). Indoor sports incurred by referee mobbing behavior evaluation. Journal of Physical Education and Sport, 14(4):626-631.
  26. Smid, P. (2014). Analysis of teamwork in officiating in basketball. 9th INSHS International Christmas Sport Scientific Conference.
  27. Smith, R. M. and Spinks, W. L. (1995). Discriminant analysis of biomechanical differences between novice, good and elite rowers. Journal of Sports Science, 13:377-385.
  28. Stern, J. (2010). You've kicked the call: Now what? Referee, 35:64-65.
  29. Stewart, M. J. and Ellery, P. (2004). Sources and magnitude of perceived psychological stress in high school officials. Perceptual and Motor Skill, 87:1275-1287.
  30. Tjosvold, D. L. (1988). Cooperative and competitive interdependence: Collaboration between departments to serve customers. Group and Organization Studies, 13(3):274-289.
  31. Wang, J.-L., Wang, Y., and Ma, J.-L. (2013). Training of basketball referees in basketball game based on computer simulation. Journal of Theoretical and Applied Information Technology, 48(2):850-856.
Download


Paper Citation


in Harvard Style

Wang S. and Hsieh W. (2016). Performance Analysis of Basketball Referees by Machine Learning Techniques . In Proceedings of the 4th International Congress on Sport Sciences Research and Technology Support - Volume 1: icSPORTS, ISBN 978-989-758-205-9, pages 165-170. DOI: 10.5220/0006031501650170


in Bibtex Style

@conference{icsports16,
author={Sheng-Wei Wang and Wen-Wei Hsieh},
title={Performance Analysis of Basketball Referees by Machine Learning Techniques},
booktitle={Proceedings of the 4th International Congress on Sport Sciences Research and Technology Support - Volume 1: icSPORTS,},
year={2016},
pages={165-170},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006031501650170},
isbn={978-989-758-205-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Congress on Sport Sciences Research and Technology Support - Volume 1: icSPORTS,
TI - Performance Analysis of Basketball Referees by Machine Learning Techniques
SN - 978-989-758-205-9
AU - Wang S.
AU - Hsieh W.
PY - 2016
SP - 165
EP - 170
DO - 10.5220/0006031501650170