Off-ball behavior in association football: A data-driven model to measure changes in individual defensive pressure

References

• Acar, M., Yapicioglu, B., Arikan, N., Yalcin, S., Ates, N., & Ergun, M. (2008). Analysis of goals scored in the 2006 World Cup. Science and football VI. Routledge.
   Google Scholar
• Akenhead, R., Hayes, P. R., Thompson, K. G., & French, D. (2013). Diminutions of acceleration and deceleration output during professional football match play. Journal of Science and Medicine in Sport, 16(6), 556–561. https://doi.org/10.1016/j.jsams.2012.12.005
   PubMed Web of Science ®Google Scholar
• Alves, D. L., Osiecki, R., Palumbo, D. P., Moiano-Junior, J. V., 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, 1–10 doi:10.1080/24748668.2019.1593096.
   Web of Science ®Google Scholar
• Amatria, M., Dios, R. M., Pérez-Turpin, J. A., Gomis-Gomis, M. J., Elvira-Aranda, C., & Suárez-Llorca, C. (2019). Technical-tactical analysis of the players of the left and right wing in elite soccer. Journal of Human Kinetics, 70(1), 233–244. https://doi.org/10.2478/hukin-2019-0045
   PubMed Web of Science ®Google Scholar
• Aminikhanghahi, S., & Cook, D. J. (2017). A survey of methods for time series change point detection. Knowledge and Information Systems, 51(2), 339–367. https://doi.org/10.1007/s10115-016-0987-z
   PubMed Web of Science ®Google Scholar
• Andrienko, G., Andrienko, N., Budziak, G., Dykes, J., Fuchs, G., Von Landesberger, T., & Weber, H. (2017). Visual analysis of pressure in football. Data Mining and Knowledge Discovery, 31, 1793–1839 doi:10.1007/s10618-017-0513-2.
   Web of Science ®Google Scholar
• Barte, J. C., Nieuwenhuys, A., Geurts, S. A., & Kompier, M. A. (2020). Effects of fatigue on interception decisions in soccer. International Journal of Sport and Exercise Psychology, 18(1), 64–75. https://doi.org/10.1080/1612197X.2018.1478869
   Web of Science ®Google Scholar
• Bondia, I. L., González-Rodenas, J., Moreno, F. C., Pérez-Turpin, J. A., & Malavés, R. A. (2017). Creating goal scoring opportunities in elite soccer. Tactical differences between Real Madrid CF and FC Barcelona. RETOS. Nuevas Tendencias en Educación Física, Deporte y Recreación, 233–237.
   Web of Science ®Google Scholar
• Brughelli, M., Cronin, J., Levin, G., & Chaouachi, A. (2008). Understanding change of direction ability in sport. Sports Medicine, 38(12), 1045–1063. https://doi.org/10.2165/00007256-200838120-00007
   PubMed Web of Science ®Google Scholar
• Buchheit, M., & Simpson, B. M. (2017). Player-tracking technology: Half-full or half-empty glass? International Journal of Sports Physiology and Performance, 12(s2), S2-35-S2–41. https://doi.org/10.1123/ijspp.2016-0499
   Web of Science ®Google Scholar
• Carling, C., Reilly, T., & Williams, A. M. (2008). Performance assessment for field sports. Routledge.
   Google Scholar
• Cervone, D., D’amour, A., Bornn, L., & Goldsberry, K. (2014). Pointwise: Predicting points and valuing decisions in real time with NBA optical tracking data. In Proceedings of the 8th MIT Sloan Sports Analytics Conference (pp. 3). Boston, MA.
   Google Scholar
• Chawla, S., Estephan, J., Gudmundsson, J., & Horton, M. (2017). Classification of passes in football matches using spatiotemporal data. ACM Transactions on Spatial Algorithms and Systems (TSAS), 3(2), 1–30. https://doi.org/10.1145/3105576
   Web of Science ®Google Scholar
• Clemente, F. M., Couceiro, M. S., Martins, F. M. L., Dias, G., & Mendes, R. (2013). Interpersonal dynamics: 1v1 sub-phase at sub-18 football players. Journal of Human Kinetics, 36(1), 179. https://doi.org/10.2478/hukin-2013-0018
   PubMed Web of Science ®Google Scholar
• Di Salvo, V., Gregson, W., Atkinson, G., Tordoff, P., & Drust, B. (2009). Analysis of high intensity activity in premier league soccer. International Journal of Sports Medicine, 30(3), 205–212. https://doi.org/10.1055/s-0028-1105950
   PubMed Web of Science ®Google Scholar
• Dos’santos, T., Thomas, C., Comfort, P., & Jones, P. A. (2018). The effect of angle and velocity on change of direction biomechanics: An angle-velocity trade-off. Sports Medicine, 48(10), 2235–2253. https://doi.org/10.1007/s40279-018-0968-3
   PubMed Web of Science ®Google Scholar
• Duarte R, Araújo D, Correia V and Davids K. (2012). Sports Teams as Superorganisms. Sports Medicine, 1 10.2165/11632450-000000000-00000
   PubMed Web of Science ®Google Scholar
• Duarte, R., Araújo, D., Correia, V., & Davids, K. (2012a). Sports teams as superorganisms. Sports Medicine, 42(8), 633–642. https://doi.org/10.1007/BF03262285
   PubMed Web of Science ®Google Scholar
• Duarte, R., Araújo, D., Davids, K., Travassos, B., Gazimba, V., & Sampaio, J. (2012b). Interpersonal coordination tendencies shape 1-vs-1 sub-phase performance outcomes in youth soccer. Journal of Sports Sciences, 30(9), 871–877. https://doi.org/10.1080/02640414.2012.675081
   PubMed Web of Science ®Google Scholar
• Faude, O., Koch, T., & Meyer, T. (2012). Straight sprinting is the most frequent action in goal situations in professional football. Journal of Sports Sciences, 30(7), 625–631. https://doi.org/10.1080/02640414.2012.665940
   PubMed Web of Science ®Google Scholar
• Fernandez, J., & Bornn, L. (2018). Wide open spaces: A statistical technique for measuring space creation in professional soccer. In Proceedings of the Sloan Sports Analytics Conference.
   Google Scholar
• Fernandez-Navarro, J., Fradua, L., Zubillaga, A., Ford, P. R., & Mcrobert, A. P. (2016). Attacking and defensive styles of play in soccer: Analysis of Spanish and English elite teams. Journal of Sports Sciences, 34(24), 2195–2204. https://doi.org/10.1080/02640414.2016.1169309
   PubMed Web of Science ®Google Scholar
• Fernandez-Navarro, J., Fradua, L., Zubillaga, A., & Mcrobert, A. P. (2018). Influence of contextual variables on styles of play in soccer. International Journal of Performance Analysis in Sport, 18(3), 423–436. https://doi.org/10.1080/24748668.2018.1479925
   Web of Science ®Google Scholar
• Fleiss, J. L. (1971). Measuring nominal scale agreement among many raters. Psychological Bulletin, 76(5), 378. https://doi.org/10.1037/h0031619
   Google Scholar
• Frencken W, Lemmink K, Delleman N and Visscher C. (2011). Oscillations of centroid position and surface area of soccer teams in small-sided games. European Journal of Sport Science, 11(4), 215–223. 10.1080/17461391.2010.499967
   Web of Science ®Google Scholar
• FRIAS, T., & DUARTE, R. (2014). Man-to-man or zone defense? Measuring team dispersion behaviors in small-sided soccer games. Trends in Sport Sciences, 21, 135.
   Google Scholar
• Goes, F. R., Kempe, M., Meerhoff, L. A., & Lemmink, K. (2018). Not every pass can be an assist: A data-driven model to measure pass effectiveness in professional soccer matches. Big Data.
   Google Scholar
• Goes, F., Meerhoff, L., Bueno, M., Rodrigues, D., Moura, F., Brink, M., Elferink-Gemser, M., Knobbe, A., Cunha, S., & Torres, R. (2020). Unlocking the potential of big data to support tactical performance analysis in professional soccer: A systematic review. European Journal of Sport Science, 1–16.
   Web of Science ®Google Scholar
• Grehaigne, J.-F., Bouthier, D., & David, B. (1997). Dynamic-system analysis of opponent relationships in collective actions in soccer. Journal of Sports Science, 15, 137–149. https://doi.org/10.1080/026404197367416
   PubMed Web of Science ®Google Scholar
• Gudmundsson, J., & Horton, M. (2017). Spatio-temporal analysis of team sports. ACM Computing Surveys (CSUR), 50, 22.
   Web of Science ®Google Scholar
• Gyarmati, L., Kwak, H., & Rodriguez, P. (2014). Searching for a unique style in soccer. arXiv preprint arXiv:1409.0308.
   Google Scholar
• Gyarmati, L., & Anguera, X. (2015). Automatic extraction of the passing strategies of soccer teams. arXiv preprint arXiv:1508.02171.
   Google Scholar
• Haugen, T. A., Tønnessen, E., Hisdal, J., & Seiler, S. (2014). The role and development of sprinting speed in soccer. International Journal of Sports Physiology and Performance, 9(3), 432–441. https://doi.org/10.1123/ijspp.2013-0121
   PubMed Web of Science ®Google Scholar
• Herold, M., Goes, F., Nopp, S., Bauer, P., Thompson, C., & Meyer, T. (2019). Machine learning in men’s professional football: Current applications and future directions for improving attacking play. International Journal of Sports Science & Coaching, 1747954119879350.
   Google Scholar
• James, N. (2006). Notational analysis in soccer: Past, present and future. International Journal of Performance Analysis in Sport, 6(2), 67–81. https://doi.org/10.1080/24748668.2006.11868373
   Google Scholar
• 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, 35–41.
   Google Scholar
• Lago-Peñas, C., Lago-Ballesteros, J., Dellal, A., & Gómez, M. (2010). Game-related statistics that discriminated winning, drawing and losing teams from the Spanish soccer league. Journal of Sports Science & Medicine, 9, 288.
   PubMed Web of Science ®Google Scholar
• Link, D., Lang, S., & Seidenschwarz, P. (2016). Real time quantification of dangerousity in football using spatiotemporal tracking data. PloS one, 11(12), e0168768. https://doi.org/10.1371/journal.pone.0168768
   PubMed Web of Science ®Google Scholar
• Liu, H., Hopkins, W., Gómez, A. M., & Molinuevo, S. J. (2013). Inter-operator reliability of live football match statistics from OPTA sportsdata. International Journal of Performance Analysis in Sport, 13(3), 803–821. https://doi.org/10.1080/24748668.2013.11868690
   Web of Science ®Google Scholar
• 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
   PubMed Web of Science ®Google Scholar
• Memmert, D., Lemmink, K. A., & Sampaio, J. (2017). Current approaches to tactical performance analyses in soccer using position data. Sports Medice, 47(1), 1–10. https://doi.org/10.1007/s40279-016-0562-5
   PubMed Web of Science ®Google Scholar
• Moura, F. A., Santana, J. E., Vieira, N. A., Santiago, P. R. P., & Cunha, S. A. (2015). Analysis of soccer players’ positional variability during the 2012 UEFA European championship: A case study. Journal of Human Kinetics, 47(1), 225–236. https://doi.org/10.1515/hukin-2015-0078
   PubMed Web of Science ®Google Scholar
• O’donoghue, P. (2004). Sources of variability in time-motion data; measurement error and within player variability in work-rate. International Journal of Performance Analysis in Sport, 4(2), 42–49. https://doi.org/10.1080/24748668.2004.11868303
   Google Scholar
• Orth, D., Davids, K., Araújo, D., Renshaw, I., & Passos, P. (2014). Effects of a defender on run-up velocity and ball speed when crossing a football. European Journal of Sport Science, 14(sup1), S316–S323. https://doi.org/10.1080/17461391.2012.696712
   PubMed Web of Science ®Google Scholar
• Passos, P., Amaro E Silva, R., Gomez-Jordana, L., & Davids, K. (2020). Developing a two-dimensional landscape model of opportunities for penetrative passing in association football–stage I. Journal of Sports Sciences, 38(21), 2407–2414. https://doi.org/10.1080/02640414.2020.1786991
   PubMed Web of Science ®Google Scholar
• Power, P., Ruiz, H., Wei, X., & Lucey, P. (2017). Not all passes are created equal: Objectively measuring the risk and reward of passes in soccer from tracking data. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining ACM, 1605-1613.
   Google Scholar
• Priyadarshana, W., & Sofronov, G. (2014). Multiple break-points detection in array CGH data via the cross-entropy method. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 12(2), 487–498. https://doi.org/10.1109/TCBB.2014.2361639
   Web of Science ®Google Scholar
• Rampinini, E., Impellizzeri, F. M., Castagna, C., Coutts, A. J., & Wisløff, U. (2009). Technical performance during soccer matches of the Italian Serie A league: Effect of fatigue and competitive level. Journal of Science and Medicine in Sport, 12(1), 227–233. https://doi.org/10.1016/j.jsams.2007.10.002
   PubMed Web of Science ®Google Scholar
• 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/s40064-016-3108-2
   PubMedGoogle Scholar
• Rice-Coates, CA. (2017). Thomas Müller: The first and possibly last Raumdeuter.
   Google Scholar
• Santos, P., Lago-Peñas, C., & García-García, O. (2017). The influence of situational variables on defensive positioning in professional soccer. International Journal of Performance Analysis in Sport, 17(3), 212–219. https://doi.org/10.1080/24748668.2017.1331571
   Web of Science ®Google Scholar
• Schreurs, M. J., Benjaminse, A., & Lemmink, K. A. (2017). Sharper angle, higher risk? The effect of cutting angle on knee mechanics in invasion sport athletes. Journal of Biomechanics, 63, 144–150. https://doi.org/10.1016/j.jbiomech.2017.08.019
   PubMed Web of Science ®Google Scholar
• Schulze E, Julian R and Meyer T. (2022). Exploring Factors Related to Goal Scoring Opportunities in Professional Football. Science and Medicine in Football, 6(2), 181–188. 10.1080/24733938.2021.1931421
   PubMedGoogle Scholar
• Spearman, W. (2018). Beyond expected goals. In MIT Sloan Sports Analytics Conference Boston.
   Google Scholar
• Steiner, S. (2018). Passing decisions in football: Introducing an empirical approach to estimating the effects of perceptual information and associative knowledge. Frontiers in Psychology, 9, 361. https://doi.org/10.3389/fpsyg.2018.00361
   PubMed Web of Science ®Google Scholar
• Steiner S, Rauh S, Rumo M, Sonderegger K and Seiler R. (2019). Outplaying opponents—a differential perspective on passes using position dataGegner aus dem Spiel nehmen – eine differenzielle Betrachtung von Pässen mittels Positionsdaten. Ger J Exerc Sport Res, 49(2), 140–149. 10.1007/s12662-019-00579-0
   Web of Science ®Google Scholar
• Szczepański, Ł., & Mchale, I. (2016). Beyond completion rate: Evaluating the passing ability of footballers. Journal of the Royal Statistical Society, 178(2), 513–533. https://doi.org/10.1111/rssa.12115
   Google Scholar
• Taylor, J. B., Wright, A. A., Dischiavi, S. L., Townsend, M. A., & Marmon, A. R. (2017). Activity demands during multi-directional team sports: A systematic review. Sports Medicine, 47(12), 2533–2551. https://doi.org/10.1007/s40279-017-0772-5
   PubMed Web of Science ®Google Scholar
• Tenga, A., Mortensholm, A., & O’donoghue, P. (2017). Opposition interaction in creating penetration during match play in elite soccer: Evidence from UEFA champions league matches. International Journal of Performance Analysis in Sport, 17(5), 802–812. https://doi.org/10.1080/24748668.2017.1399326
   Web of Science ®Google Scholar
• Topál, D., Matyasovszkyt, I., Kern, Z., & Hatvani, I. G. (2016). Detecting breakpoints in artificially modified-and real-life time series using three state-of-the-art methods. Open Geosciences, 8(1), 78–98. https://doi.org/10.1515/geo-2016-0009
   Web of Science ®Google Scholar
• Vilar, L., Araújo, D., Davids, K., & Button, C. (2012). The role of ecological dynamics in analysing performance in team sports. Sports Medice, 42(1), 1–10. https://doi.org/10.2165/11596520-000000000-00000
   PubMed Web of Science ®Google Scholar
• Vilar, L., Araújo, D., Davids, K., & Bar-Yam, Y. (2013). Science of winning soccer: Emergent pattern-forming dynamics in association football. Journal of Systems Science and Complexity, 26(1), 73–84. https://doi.org/10.1007/s11424-013-2286-z
   Web of Science ®Google Scholar
• (2013). World Medical Association Declaration of Helsinki. JAMA, 310(20), 2191 10.1001/jama.2013.281053
   PubMed Web of Science ®Google Scholar
• Young, W. B., & Murray, M. P. (2017). Reliability of a field test of defending and attacking agility in Australian football and relationships to reactive strength. The Journal of Strength & Conditioning Research, 31(2), 509–516. https://doi.org/10.1519/JSC.0000000000001498
   PubMed Web of Science ®Google Scholar