Systematic Analysis of Position-Data-based Key Performance Indicators
References
- Angelini, G., & Angelis, L. de (2019). Efficiency of online football betting markets. International Journal of Forecasting, 35(2), 712-721. https://doi.org/10.1016/j.ijforecast.2018.07.008
- Anzer, G., & Bauer, P. (2021). A goal scoring probability model for shots based on synchronized positional and event data in football (soccer). Frontiers in Sports and Active Living, 53.
- Ben-Naim, E., Vazquez, F., & Redner, S. (2006). Parity and predictability of competitions. Journal of Quantitative Analysis in Sports, 2(4).
- Blomqvist, M., Luhtanen, P., & Laakso, L. (2000). Expert-Novice Differences in Game Performance and Game Understanding of Youth Badminton Players. European Journal of Physical Education, 5(2), 208-219. https://doi.org/10.1080/1740898000050207
- Bradley, P., O’Donoghue, P., Wooster, B., & Tordoff, P. (2007). The reliability of ProZone MatchViewer: a video-based technical performance analysis system. International Journal of Performance Analysis in Sport, 7(3), 117-129. https://doi.org/10.1080/24748668.2007.11868415
- Brito de Souza, D., López-Del Campo, R., Blanco-Pita, H., Resta, R., & Del Coso, J. (2019). An Extensive Comparative Analysis of Successful and Unsuccessful Football Teams in LaLiga. Frontiers in Psychology, 10, 2566. https://doi.org/10.3389/fpsyg.2019.02566
- Brooks, J., Kerr, M., & Guttag, J. (2016). Developing a data-driven player ranking in soccer using predictive model weights. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 49-55).
- Bush, M., Barnes, C., Archer, D. T., Hogg, B., & Bradley, P. S. (2015). Evolution of match performance parameters for various playing positions in the English Premier League. Human movement science, 39, 1-11.
- Castellano, J., Alvarez-Pastor, D., & Bradley, P. S. (2014). Evaluation of research using computerised tracking systems (Amisco and Prozone) to analyse physical performance in elite soccer: A systematic review. Sports Medicine (Auckland, N.Z.), 44(5), 701-712. https://doi.org/10.1007/s40279-014-0144-3.
- Castellano, J., Casamichana, D., & Lago, C. (2012). The Use of Match Statistics that Discriminate Between Successful and Unsuccessful Soccer Teams. Journal of Human Kinetics, 31, 139-147. https://doi.org/10.2478/v10078-012-0015-7
- Chu, W. T., & Situmeang, S. (2017). Badminton video analysis based on spatiotemporal and stroke features. In Proceedings of the 2017 ACM on international conference on multimedia retrieval (pp. 448-451).
- Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155-159. https://doi.org/10.1037/0033-2909.112.L155
- Decroos, T., Bransen, L., Van Haaren, J., & Davis, J. (2019). Actions speak louder than goals: Valuing player actions in soccer. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 1851-1861).
- Drikos, S., & Vagenas, G. (2011). Multivariate assessment of selected performance indicators in relation to the type and result of a typical set in Men’s Elite Volleyball. International Journal of Performance Analysis in Sport, 11(1), 85-95. https://doi.org/10.1080/24748668.2011.11868531
- Dvorak, J., Junge, A., Graf-Baumann, T., & Peterson, L. (2004). Football is the most popular sport worldwide. The American Journal of Sports Medicine, 32(1 Suppl), 3S-4S. https://doi.org/10.1177/0363546503262283
- Evangelos, T., Alexandros, K., & Nikolaos, A. (2005). Analysis of fast breaks in basketball. International Journal of Performance Analysis in Sport, 5(2), 17-22. https://doi.org/10.1080/24748668.2005.11868324
- Fonseca, S., Milho, J., Travassos, B., & Araujo, D. (2012). Spatial dynamics of team sports exposed by Voronoi diagrams. Human Movement Science, 31(6), 1652-1659.
- Forrest, D., Goddard, J., & Simmons, R. (2005). Odds-setters as forecasters: The case of English football. International Journal of Forecasting, 21(3), 551-564. https://doi.org/10.1016/j.ijforecast.2005.03.003
- Goes, F. R., Meerhoff, L. A., Bueno, M. J. O., Rodrigues, D. M., Moura, F. A., Brink, M. S., Elferink-Gemser, M. T., Knobbe, A. J., Cunha, S. A., Torres, R. S., & Lemmink, K. A. P. M [K. A. P. M.] (2021). Unlocking the potential of big data to support tactical performance analysis in professional soccer: A systematic review. European Journal of Sport Science, 21(4), 481-496. https://doi.org/10.1080/17461391.2020.1747552
- Grunz, A., Memmert, D., & Perl, J. (2012). Tactical pattern recognition in soccer games by means of special self-organizing maps. Human Movement Science, 31(2), 334-343. https://doi.org/10.1016Zj.humov.2011.02.008
- Gudmundsson, J., & Horton, M. (2017). Spatio-Temporal Analysis of Team Sports. ACM Computing Surveys, 50(2), 1-34. https://doi.org/10.1145/3054132
- Herold, M., Kempe, M., Bauer, P., & Meyer, T. (2021). Attacking Key Performance Indicators in Soccer: Current Practice and Perceptions from the Elite to Youth Academy Level. Journal of Sports Science & Medicine, 20(1), 158-169. https://doi.org/10.52082/jssm.2021.158
- Hughes, M. D., Cooper, S. M., & Nevill, A. M. (2002). Analysis procedures for nonparametric data in performance analysis of sport. International Journal of Performance Analysis in Sport, 2(1), 6-20.
- Hughes, M., & Franks, I. M. (2004). Notational analysis of sport: Systems for better coaching and performance in sport. Routledge.
- Hughes, M., Caudrelier, T., James, N., Donnelly, I., Kirkbride, A., & Duschesne, C. (2012). Moneyball and soccer - an analysis of the key performance indicators of elite male soccer players by position. Journal of Human Sport and Exercise, 7(2), 402-412. https://doi.org/10.4100/jhse.2012.72.06
- Hughes, M. D., & Bartlett, R. M. (2002). The use of performance indicators in performance analysis. Journal of Sports Sciences, 20(10), 739-754. https://doi.org/10.1080/026404102320675602
- Hvattum, L. M., & Arntzen, H. (2010). Using ELO ratings for match result prediction in association football. International Journal of Forecasting, 26(3), 460-470. https://doi.org/10.1016/j.ijforecast.2009.10.002
- 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), 22703. https://doi.org/10.1038/s41598-021-01187-5
- Kim, S. (2004). Voronoi Analysis of a Soccer Game. Nonlinear Analysis: Modelling and Control, 9(3), 233-240. https://doi.org/10.15388/NA.2004.9.3.15154
- Lago, C. (2009). The influence of match location, quality of opposition, and match status on possession strategies in professional association football. Journal of Sports Sciences, 27(13), 1463-1469. https://doi.org/10.1080/02640410903131681
- Lago-Peñas, C., & Gomez-Lopez, M. (2014). How important is it to score a goal? The influence of the scoreline on match performance in elite soccer. Perceptual and motor skills, 119(3), 774-784.
- Lago-Peñas, C., & Dellal, A. (2010). Ball possession strategies in elite soccer according to the evolution of the match-score: the influence of situational variables. Journal of Human Kinetics, 25, 93-100.
- 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/v10078-011-0011-3
- Lames, M., & McGarry, T. (2007). On the search for reliable performance indicators in game sports. International Journal of Performance Analysis in Sport, 7(1), 62-79. https://doi.org/10.1080/24748668.2007.11868388
- Lepschy, H., Wäsche, H., & Woll, A. (2020). Success factors in football: an analysis of the German Bundesliga. International Journal of Performance Analysis in Sport, 20(2), 150-164. https://doi.org/10.1080/24748668.2020.1726157
- Leite, W., & Barreira, D. (2014). Are the teams sports soccer, futsal and beach soccer similar. International Journal of Sports Science, 4(6A), 75-84.
- Liu, H., Gómez, 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, 22(12), 1205-1213. https://doi.org/10.1080/02640414.2015.1022578
- Liu, H., Gómez, M.-A., Gonçalves, B., & Sampaio, J. (2016). Technical performance and match-to-match variation in elite football teams. Journal of Sports Sciences, 34(6), 509-518. https://doi.org/10.1080/02640414.2015.1117121
- Low, B., Coutinho, D., Gonçalves, B., Rein, R., Memmert, D., & Sampaio, J. (2019). A systematic review of collective tactical behaviours in football using positional data. Sports Medicine,50, 343-385.
- Low, B., Rein, R., Raabe, D., Schwab, S., & Memmert, D. (2021a). The porous high-press? An experimental approach investigating tactical behaviours from two pressing strategies in football. Journal of Sports Sciences, 39(19), 2199-2210.
- Low, B., Schwab, S., Rein, R., & Memmert, D. (2021b). Defending in 4-4-2 or 5-3-2 formation? Small differences in footballers’ collective tactical behaviours. Journal of Sports Sciences, 40(3), 351-363.
- Lüdin, D., Donath, L., Cobley, S., & Romann, M. (2021). Effect of bio-banding on physiological and technical-tactical key performance indicators in youth elite soccer. European Journal of Sport Science, 1-9. https://doi.org/10.1080/17461391.2021.1974100
- Lutz, J., Memmert, D., Raabe, D., Dornberger, R., & Donath, L. (2019). Wearables for Integrative Performance and Tactic Analyses: Opportunities, Challenges, and Future Directions. International Journal of Environmental Research and Public Health, 17(1). https://doi.org/10.3390/ijerph17010059
- Mackenzie, R., & Cushion, C. (2013). Performance analysis in football: A critical review and implications for future research. Journal of Sports Sciences, 31(6), 63W676. https://doi.org/10.1080/02640414.2012.746720
- Memmert, D. (Ed.) (2021). Match Analysis. Abingdon: Routledge.
- Memmert, Lemmink, K. A. P. M, & Sampaio, J. (2017). Current Approaches to Tactical Performance Analyses in Soccer Using Position Data. Sports Medicine (Auckland, N.Z.), 47(1), 1-10. https://doi.org/10.1007/s40279-016-0562-5
- Memmert, D., Raabe, D., Schwab, S., & Rein, R. (2019). A tactical comparison of the 4-2-3-1 and 3-5-2 formation in soccer: A theory-oriented, experimental approach based on positional data in an 11 vs. 11 game set-up. PloS One, 14(1), e0210191. https://doi.org/10.1371/journal.pone.0210191
- Memmert, D., & Raabe, D. (2018). Data analytics in football: Positional data collection, modelling and analysis. Routledge.
- Memmert, D., Klemp, M., Schwab, S. & Low, B. (2023). Individual attention capacity enhances in-field group performances in soccer. International Journal of Sport and Exercise Psychology, 1-18.
- Nakagawa, S., & Cuthill, I. C. (2007). Effect size, confidence interval, and statistical significance: A practical guide for biologists. Biological Reviews, 82(4), 591-605. doi: 10.1111/j .1469-185X.2007.00027.x
- Nevill, A., Atkinson, G., & Hughes, M. (2008). Twenty-five years of sport performance research in the Journal of Sports Sciences. Journal of Sports Sciences, 26(4), 413–426. https://doi.org/10.1080/02640410701714589
- 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.
- Pettersen, S. A., Johansen, H. D., Baptista, I. A. M., Halvorsen, P., & Johansen, D. (2018). Quantified Soccer Using Positional Data: A Case Study. Frontiers in Physiology, 9, 866. https://doi.org/10.3389/fphys.2018.00866
- Phatak, A. A., Rein, R., & Memmert, D. (2021). The dirty league: English premier league provides higher incentives for fouling as compared to other European soccer leagues. Journal of Human Kinetics, 80(1), 263-276.
- Phatak, A. A., Mehta, S., Wieland, F. G., Jamil, M., Connor, M., Bassek, M., & Memmert, D. (2022). Context is key: normalization as a novel approach to sport specific preprocessing of KPI’s for match analysis in soccer. Scientific Reports, 12(1), 1117.
- 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.. DOI 10.1186/s40064-016-3108-2
- 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.2017.07.010
- Sarlis, V., & Tjortjis, C. (2020). Sports analytics — Evaluation of basketball players and team performance. Information Systems, 93, 101562. https://doi.org/10.1016/j.is.2020.101562
- 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/10.1080/02640414.2014.898852
- Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological science, 22(11), 1359-1366.
- Strumbelj, E., & Sikonja, M. R. (2010). Online bookmakers’ odds as forecasts: The ease of European soccer leagues. International Journal of Forecasting, 26(3), 482-488. https://doi.org/10.1016/j.ij forecast.2009.10.005
- Taki, T., & Hasegawa, J. (2000). Visualization of dominant region in team games and its application to teamwork analysis. In Proceedings Computer Graphics International 2000 (pp. 227-235). IEEE Comput. Soc. https://doi.org/10.1109/CGI.2000.852338
- Tavares, F., & Gomes, N. (2003). The offensive process in basketball - a study in high performance junior teams. International Journal of Performance Analysis in Sport, 3(1), 34-39. https://doi.org/10.1080/24748668.2003.11868272
- Tenga, A., Holme, I., Ronglan, L. T., & Bahr, R. (2010a). Effect of playing tactics on achieving score-box possessions in a random series of team possessions from Norwegian professional soccer matches. Journal of Sports Sciences, 28(3), 245-255. https://doi.org/10.1080/02640410903502766
- Tenga, A., Holme, I., Ronglan, L. T., & Bahr, R. (2010b). Effect of playing tactics on goal scoring in Norwegian professional soccer. Journal of Sports Sciences, 28(3), 237-244. https://doi.org/10.1080/02640410903502774
- Weimar, D., & Wicker, P. (2017). Moneyball Revisited. Journal of Sports Economics, 18(2), 140-161. https://doi.org/10.1177/1527002514561789
- Wunderlich, F., Berge, F., Memmert, D., & Rein, R. (2020). Almost a lottery: the influence of team strength on success in penalty shootouts. International Journal of Performance Analysis in Sport, 20(5), 857-869. https://doi.org/10.1080/24748668.2020.1799171
- Wunderlich, F., & Memmert, D. (2018). The Betting Odds Rating System: Using soccer forecasts to forecast soccer. PloS One, 13(6), e0198668. https://doi.org/10.1371/journal.pone.0198668
- 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, 1-16. https://doi.org/10.1080/02640414.2021.1930685
- Yiannakos, A., & Armatas, V. (2006). Evaluation of the goal scoring patterns in European Championship in Portugal 2004. International Journal of Performance Analysis in Sport, 6(1), 178-188. https://doi.org/10.1080/24748668.2006.11868366
DOI: https://doi.org/10.2478/ijcss-2023-0006 | Journal eISSN: 1684-4769
Language: English
Page range: 80 - 101
Published on: Jun 16, 2023
Published by: International Association of Computer Science in Sport
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© 2023 Justus Schlenger, Fabian Wunderlich, Dominik Raabe, Daniel Memmert, published by International Association of Computer Science in Sport
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