Have a personal or library account? Click to login

Success-Score in Professional Soccer – Validation of a Dynamic Key Performance Indicator Combining Space Control and Ball Control within Goalscoring Opportunities

Open Access
|Jan 2023

References

  1. Alves, D. L., Osiecki, R., Palumbo, D. P., Moiano-Junior, J. V. M., Oneda, G., & Cruz, R. (2019). What variables can differentiate winning and losing teams in the group and final stages of the 2018 FIFA World Cup? International Journal of Performance Analysis in Sport, 19(2), 248–257. https://doi.org/10.1080/24748668.2019.159309610.1080/24748668.2019.1593096
  2. 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
  3. Caicedo-Parada, S., Lago-Peñas, C., & Ortega-Toro, E. (2020). Passing Networks and Tactical Action in Football: A Systematic Review. International Journal of Environmental Research and Public Health, 17(18), 6649. https://doi.org/10.3390/ijerph1718664910.3390/ijerph17186649755998632933080
  4. Castellano, J., & Pic, M. (2019). Identification and Preference of Game Styles in LaLiga Associated with Match Outcomes. International Journal of Environmental Research and Public Health, 16(24), 5090. https://doi.org/10.3390/ijerph1624509010.3390/ijerph16245090695029931847147
  5. Collet, C. (2013). The possession game? A comparative analysis of ball retention and team success in European and international football, 2007–2010. Journal of Sports Sciences, 31(2), 123–136. https://doi.org/10.1080/02640414.2012.72745510.1080/02640414.2012.72745523067001
  6. Fujimura, A., & Sugihara, K. (2005). Geometric analysis and quantitative evaluation of sport teamwork. Systems and Computers in Japan, 36(6), 49–58. https://doi.org/10.1002/scj.2025410.1002/scj.20254
  7. Gollan, S., Ferrar, K., & Norton, K. (2018). Characterising game styles in the English Premier League using the “moments of play” framework. International Journal of Performance Analysis in Sport, 18(6), 998–1009. https://doi.org/10.1080/24748668.2018.153938310.1080/24748668.2018.1539383
  8. Hassan, A., Schrapf, N., & Tilp, M. (2017a). The prediction of action positions in team handball by non-linear hybrid neural networks. International Journal of Performance Analysis in Sport, 17(3), 293–302.10.1080/24748668.2017.1336688
  9. Hassan, A., Schrapf, N., Ramadan, W., & Tilp, M. (2017b). Evaluation of tactical training in team handball by means of artificial neural networks. Journal of Sports Sciences, 35(7), 642–647.10.1080/02640414.2016.118380427211106
  10. 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
  11. Jones, P. D., James, N., & Mellalieu, S. D. (2004). Possession as a performance indicator in soccer. International Journal of Performance Analysis in Sport, 4(1), 98–102. https://doi.org/10.1080/24748668.2004.1186829510.1080/24748668.2004.11868295
  12. Kempe, M., Vogelbein, M., Memmert, D., & Nopp, S. (2014). Possession vs. Direct Play: Evaluating Tactical Behavior in Elite Soccer. International Journal of Sports Science, 4(6A), 35–41. http://dx.doi.org/10.5923/s.sports.201401.05
  13. Kirkwood, B. R., Sterne, J. A. C., & Kirkwood, B. R. (2003). Essential medical statistics (2nd ed). Blackwell Science.
  14. Lago-Peñas, C., Lago-Ballesteros, J., & Rey, E. (2011). Differences in performance indicators between winning and losing teams in the UEFA Champions League. Journal of Human Kinetics, 27(2011), 135–146. https://doi.org/10.2478/v10078-011-0011-310.2478/v10078-011-0011-3
  15. Liu, H., Gomez, M.-Á., Lago-Peñas, C., & Sampaio, J. (2015). Match statistics related to winning in the group stage of 2014 Brazil FIFA World Cup. Journal of Sports Sciences, 33(12), 1205–1213. https://doi.org/10.1080/02640414.2015.102257810.1080/02640414.2015.102257825793661
  16. Liu, H., Hopkins, W. G., & Gómez, M.-A. (2016). Modelling relationships between match events and match outcome in elite football. European Journal of Sport Science, 16(5), 516–525. https://doi.org/10.1080/17461391.2015.104252710.1080/17461391.2015.104252726190577
  17. Liu, H., Yi, Q., Giménez, J.-V., Gómez, M.-A., & Lago-Peñas, C. (2015). Performance profiles of football teams in the UEFA Champions League considering situational efficiency. International Journal of Performance Analysis in Sport, 15(1), 371–390. https://doi.org/10.1080/24748668.2015.1186879910.1080/24748668.2015.11868799
  18. Liu, T., Yang, L., Chen, H., & García-de-Alcaraz, A. (2021). Impact of Possession and Player Position on Physical and Technical-Tactical Performance Indicators in the Chinese Football Super League. Frontiers in Psychology, 12, 722200. https://doi.org/10.3389/fpsyg.2021.72220010.3389/fpsyg.2021.722200851140134659035
  19. Lord, F., Pyne, D. B., Welvaert, M., & Mara, J. K. (2020). Methods of performance analysis in team invasion sports: A systematic review. Journal of Sports Sciences, 38(20), 2338–2349. https://doi.org/10.1080/02640414.2020.178518510.1080/02640414.2020.178518532583724
  20. Mackenzie, R., & Cushion, C. (2013). Performance analysis in football: A critical review and implications for future research. Journal of Sports Sciences, 31(6), 639–676. https://doi.org/10.1080/02640414.2012.74672010.1080/02640414.2012.74672023249092
  21. Mao, L., Peng, Z., Liu, H., & Gómez, M.-A. (2016). Identifying keys to win in the Chinese professional soccer league. International Journal of Performance Analysis in Sport, 16(3), 935–947. https://doi.org/10.1080/24748668.2016.1186894010.1080/24748668.2016.11868940
  22. Memmert, D. (Ed.) (2021). Match Analysis. Abingdon: Routledge. Link10.4324/9781003160953
  23. Memmert, D., & Raabe, D. (2018). Data Analytics in Football. Positional Data Collection, Modelling and Analysis. Abingdon: Routledge.10.4324/9781351210164
  24. 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
  25. Memmert, D., & Rein, R. (2018). Match analysis, Big Data and tactics: Current trends in elite soccer. Deutsche Zeitschrift Für Sportmedizin, 2018(03), 65–72. https://doi.org/10.5960/dzsm.2018.32210.5960/dzsm.2018.322
  26. Perl, J., & Memmert, D. (2011). Net-Based Game Analysis by Means of the Software Tool SOCCER. International Journal of Computer Science in Sport, 10(2), 77–84.
  27. Perl, J., & Memmert, D. (2017). A Pilot Study on Offensive Success in Soccer Based on Space and Ball Control – Key Performance Indicators and Key to Understand Game Dynamics. International Journal of Computer Science in Sport, 16(1), 65–75. https://doi.org/10.1515/ijcss-2017-000510.1515/ijcss-2017-0005
  28. Perl, J., & Memmert, D. (2018). Soccer: Process and interaction. In A. Baca & J. Perl, Modelling and Simulation in Sport and Exercise (S. 73–94). Routledge.10.4324/9781315163291-4
  29. Perl, J., Grunz, A., & Memmert, D. (2013). Tactics Analysis in Soccer – An Advanced Approach. International Journal of Computer Science in Sport, 12(1), 33–44.
  30. 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
  31. Rein, R., & Memmert, D. (2016). Big data and tactical analysis in elite soccer: Future challenges and opportunities for sports science. SpringerPlus, 5(1), 1410. https://doi.org/10.1186/s40064-016-3108-210.1186/s40064-016-3108-2499680527610328
  32. 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
  33. Rice, M. E., & Harris, G. T. (2005). Comparing Effect Sizes in Follow-Up Studies: ROC Area, Cohen’s d, and r. Law and Human Behavior, 29(5), 615–620. https://doi.org/10.1007/s10979-005-6832-710.1007/s10979-005-6832-716254746
  34. Ruan, L., Ge, H., Gómez, M.-Á., Shen, Y., Gong, B., & Cui, Y. (2022). Analysis of defensive playing styles in the professional Chinese Football Super League. Science and Medicine in Football, 1–9. https://doi.org/10.1080/24733938.2022.209996410.1080/24733938.2022.209996435796256
  35. Ruiz-Ruiz, C., Fradua, L., Fernández-GarcÍa, Á., & Zubillaga, A. (2013). Analysis of entries into the penalty area as a performance indicator in soccer. European Journal of Sport Science, 13(3), 241–248. https://doi.org/10.1080/17461391.2011.60683410.1080/17461391.2011.60683423679140
  36. Sarmento, H., Marcelino, R., Anguera, M. T., CampaniÇo, J., Matos, N., & LeitÃo, J. C. (2014). Match analysis in football: A systematic review. Journal of Sports Sciences, 32(20), 1831–1843. https://doi.org/10.1080/02640414.2014.89885210.1080/02640414.2014.89885224787442
  37. Schrapf, N., Alsaied, S., & Tilp, M. (2017). Tactical interaction of offensive and defensive teams in team handball analysed by artificial neural networks. Mathematical and Computer Modelling of Dynamical Systems, 23(4), 363–371.10.1080/13873954.2017.1336733
  38. Schulze, E., Julian, R., & Meyer, T. (2022). Exploring Factors Related to Goal Scoring Opportunities in Professional Football. Science and Medicine in Football, 6(2), 181–188. https://doi.org/10.1080/24733938.2021.193142110.1080/24733938.2021.193142135475738
  39. Taki, T., & Hasegawa, J. (2000). Visualization of dominant region in team games and its application to teamwork analysis. Proceedings Computer Graphics International 2000, 227–235. https://doi.org/10.1109/CGI.2000.85233810.1109/CGI.2000.852338
  40. Tenga, A., Ronglan, L. T., & Bahr, R. (2010). Measuring the effectiveness of offensive match-play in professional soccer. European Journal of Sport Science, 10(4), 269–277. https://doi.org/10.1080/1746139090351517010.1080/17461390903515170
  41. Vogelbein, M., Nopp, S., & Hökelmann, A. (2014). Defensive transition in soccer – are prompt possession regains a measure of success? A quantitative analysis of German Fußball-Bundesliga 2010/2011. Journal of Sports Sciences, 32(11), 1076–1083. https://doi.org/10.1080/02640414.2013.87967110.1080/02640414.2013.87967124506111
  42. Winter, C., & Pfeiffer, M. (2016). Tactical metrics that discriminate winning, drawing and losing teams in UEFA Euro 2012®. Journal of Sports Sciences, 34(6), 486–492. https://doi.org/10.1080/02640414.2015.109971410.1080/02640414.2015.109971426508419
  43. Wunderlich, F., Seck, A., & Memmert, D. (2021). The influence of randomness on goals in football decreases over time. An empirical analysis of randomness involved in goal scoring in the English Premier League. Journal of Sports Sciences, 39(20), 2322–2337. https://doi.org/10.1080/02640414.2021.193068510.1080/02640414.2021.193068534024249
  44. Zhou, C., Lago-Peñas, C., Lorenzo, A., & Gómez, M.-Á. (2021). Long-Term Trend Analysis of Playing Styles in the Chinese Soccer Super League. Journal of Human Kinetics, 79(1), 237–247. https://doi.org/10.2478/hukin-2021-007710.2478/hukin-2021-0077833654434401003
Language: English
Page range: 32 - 42
Published on: Jan 17, 2023
Published by: International Association of Computer Science in Sport
In partnership with: Paradigm Publishing Services
Publication frequency: 2 times per year

© 2023 David Brinkjans, Daniel Memmert, Jonas Imkamp, Jürgen Perl, published by International Association of Computer Science in Sport
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.