Football Pass Prediction Using Player Locations

  • Philippe Fournier-VigerEmail author
  • Tianbiao Liu
  • Jerry Chun-Wei Lin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11330)


In many sports, predicting the passing behavior of players is desirable at it provides insights that can help to understand and improve player performance. In this paper, we describe a novel model for football pass prediction, developed to participate in the Prediction Challenge of the 5th Workshop on Machine Learning and Data Mining for Sports Analytics, collocated with ECML PAKDD 2018. The model called Football Pass Predictor (FPP) considers various aspects to generate predictions such as the distance between players, the proximity of players from the opposite team, and the direction of each pass. Experimental results shows that the model can achieve a prediction accuracy of 33.8%, and more than 50% if two guesses are allowed. This is considerably more than the random predictor, which obtains 8.3%.


Football Pass prediction Prediction challenge 



This work is partially financed by the Youth 1000 talent funding of Philippe Fournier-Viger.


  1. 1.
  2. 2.
    Garratt, K., Murphy, A., Bower, R.: Passing and goal scoring characteristics in Australian A-League football. Int. J. Perform. Anal. Sport 17(1–2), 77–85 (2017)CrossRefGoogle Scholar
  3. 3.
    Plummer, B.T.: Analysis of attacking possessions leading to a goal attempt, and goal scoring patterns within men’s elite soccer. J. Sports Sci. Med. 1(1), 1–38 (2013)Google Scholar
  4. 4.
    Liu, T.: Systematische Spielbeobachtung im internationalen Leistungsfußball. Ph.D. dissertation. University of Bayreuth (2014)Google Scholar
  5. 5.
    Liu, T., Hohmann, A.: Apriori-based diagnostical analysis of passings in the football game. In: Proceedings of IEEE 2016 International Conference on Big Data Analysis, pp. 1–4. IEEE (2016)Google Scholar
  6. 6.
    Liu, T., Fournier-Viger, P., Hohmann, A.: Using diagnostic analysis to discover offensive patterns in a football game. In: Tavana, M., Patnaik, S. (eds.) Recent Developments in Data Science and Business Analytics. SPBE, pp. 381–386. Springer, Cham (2018). Scholar
  7. 7.
    Fournier-Viger, P., Nkambou, R., Tseng, S.M.: RuleGrowth: mining sequential rules common to several sequences by pattern-growth. In: Proceedings of 26th Symposium on Applied Computing, pp. 954–959. ACM Press (2011)Google Scholar
  8. 8.
    Fournier-Viger, P., Lin, J.C.-W., Vo, B., Chi, T.T., Zhang, J., Le, H.B.: A survey of itemset mining. WIREs Data Min. Knowl. Discov. e1207 (2017). Scholar
  9. 9.
    Fournier-Viger, P., Lin, J.C.-W., Kiran, R.U., Koh, Y.S., Thomas, R.: A survey of sequential pattern mining. Data Sci. Pattern Recogn. (DSPR) 1(1), 54–77 (2017)Google Scholar
  10. 10.
    Stöckl, M., Cruz, D., Duarte, R.: Modelling the tactical difficulty of passes in soccer. In: Chung, P., Soltoggio, A., Dawson, C.W., Meng, Q., Pain, M. (eds.) Proceedings of the 10th International Symposium on Computer Science in Sports (ISCSS). AISC, vol. 392, pp. 139–143. Springer, Cham (2016). Scholar
  11. 11.
    Rein, R., Raabe, D., Memmert, D.: “Which pass is better?” Novel approaches to assess passing effectiveness in elite soccer. Hum. Mov. Sci. 55, 172–181 (2017)CrossRefGoogle Scholar
  12. 12.
    Gyarmati, L., Stanojevic, R.: QPass: a merit-based evaluation of soccer passes. Preprint on arXiv:1608.03532 (2016)
  13. 13.
    McHale, I.G., Relton, S.D.: Identifying key players in soccer teams using network analysis and pass difficulty. Eur. J. Oper. Res. 268(1), 339–347 (2018)CrossRefGoogle Scholar
  14. 14.
    Cakmak, A., Uzun, A., Delibas, E.: Computational modeling of pass effectiveness in soccer. J. Adv. Complex Syst. (2018, in press)Google Scholar
  15. 15.
    Lida, R., Mase, K.: Ball passing course creating behavior in soccer game detection from player trajectory. IEICE Technical report, vol. 113, no. 432, pp. 171–176 (2014)Google Scholar
  16. 16.
    Dhar, J., Singh, A.: Game analysis and prediction of ball positions in a football match from video footages. In: Proceedings of International Conference on Recent Advances and Innovations in Engineering, pp. 1–6. IEEE (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Philippe Fournier-Viger
    • 1
    Email author
  • Tianbiao Liu
    • 2
  • Jerry Chun-Wei Lin
    • 3
  1. 1.School of Natural Sciences and HumanitiesHarbin Institute of Technology (Shenzhen)ShenzhenChina
  2. 2.College of Sports and Physical EducationBeijing Normal UniversityBeijingChina
  3. 3.Department of Computing, Mathematics and PhysicsWestern Norway University of Applied Sciences (HVL)BergenNorway

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