Where to go: Computational and visual what-if analyses in soccer

References

• Abade, E. A., Gonçalves, B. V., Leite, N. M., & Sampaio, J. E. (2014). Time–motion and physiological profile of football training sessions performed by under-15, under-17, and under-19 elite portuguese players. International Journal of Sports Physiology and Performance, 9(3), 463–470.
   PubMed Web of Science ®Google Scholar
• Alexander, J. P., Spencer, B., Mara, J. K., & Robertson, S. (2019). Collective team behaviour of australian rules football during phases of match play. Journal of Sports Sciences, 37(3), 237–243.
   PubMed Web of Science ®Google Scholar
• BBC. (2018, June 27). Fifa’s hi-tech world cup ‘performance tracker’. Retrieved from https://www.bbc.com/news/av/technology-44546889/fifa-s-hi-tech-world-cup-performance-tracker
   Google Scholar
• Benito Santos, A., Theron, R., Losada, A., Sampaio, J. E., & Lago-Peñas, C. (2018). Data-driven visual performance analysis in soccer: An exploratory prototype. Frontiers in Psychology, 9, 2416. Retrieved from https://www.frontiersin.org/article/10.3389/fpsyg.2018.02416
   PubMed Web of Science ®Google Scholar
• Boren, T., & Ramey, J. (2000). Thinking aloud: Reconciling theory and practice. IEEE Transactions on Professional Communication, 43(3), 261–278.
   Web of Science ®Google Scholar
• Castellano, J., Puente, A., Echeazarra, I., Usabiaga, O., & Casamichana, D. (2016, January). Number of players and relative pitch area per player: Comparing their influence on heart rate and physical demands in under-12 and under-13 football players. PloS One, 11(1), 1–13.
   Web of Science ®Google Scholar
• Clemente, M. F., Couceiro, S. M., Martins, F. M., Mendes, R., & Figueiredo, A. J. (2013). Measuring collective behaviour in football teams: Inspecting the impact of each half of the match on ball possession. International Journal of Performance Analysis in Sport, 13(3), 678–689.
   Web of Science ®Google Scholar
• Dobson, S., & Goddard, J. A. (2001). The economics of football. Cambridge: Cambridge University Press.
   Google Scholar
• Ericsson, K. A., & Simon, H. A. (1984). Protocol analysis. Cambridge, MA: MIT Press.
   Google Scholar
• FIFA. (2018, June 27). Facts and figures: Impact and legacy of 2018 fifa world cup russia. Retrieved from https://resources.fifa.com/mm/document/tournament/competition/02/86/77/63/Impactandlegacyof2018FIFAWorldCupENneutral.pdf
   Google Scholar
• Folgado, H., Bravo, J., Pereira, P., & Sampaio, J. (2019). Towards the use of multidimensional performance indicators in football small-sided games: the effects of pitch orientation. Journal of Sports Sciences, 37(9), 1064–1071. (PMID: 30426856).
   PubMed Web of Science ®Google Scholar
• Frencken, W. G., Lemmink, K. A., & Delleman, N. J. (2010). Soccer-specific accuracy and validity of the local position measurement (lpm) system. Journal of Science and Medicine in Sport, 13(6), 641–645.
   PubMed Web of Science ®Google Scholar
• Gonçalves, B., Marcelino, R., Torres-Ronda, L., Torrents, C., & Sampaio, J. (2016). Effects of emphasising opposition and cooperation on collective movement behaviour during football small-sided games. Journal of Sports Sciences, 34(14), 1346–1354 (PMID: 26928336).
   PubMed Web of Science ®Google Scholar
• Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2019). A survey of methods for explaining black box models. ACM Computing Surveys, 51(5), 93:1–93: 42.
   Web of Science ®Google Scholar
• Johnston, R. J., Watsford, M. L., Kelly, S. J., Pine, M. J., & Spurrs, R. W. (2014). Validity and interunit reliability of 10 hz and 15 hz gps units for assessing athlete movement demands. The Journal of Strength & Conditioning Research, 28(6), 1649–1655.
   PubMed Web of Science ®Google Scholar
• Krustrup, P., Mohr, M., Ellingsgaard, H., & Bangsbo, J. (2005). Physical demands during an elite female soccer game: importance of training status. Medicine and Science in Sports and Exercise, 37(7), 1242–1248.
   PubMed Web of Science ®Google Scholar
• Le, H. M., Carr, P., Yue, Y., & Lucey, P. (2017). Data-driven ghosting using deep imitation learning. Proceeding of the 11th MIT Sloan Sports Analytics Conference, Boston, MA. Boston: MIT.
   Google Scholar
• Link, D. (2018). Data analytics in professional soccer - performance analysis based on spatiotemporal tracking data. Berlin: Springer Vieweg. doi:10.1007/978-3-658-21177-6
   Google Scholar
• Redwood-Brown, A., Cranton, W., & Sunderland, C. (2012). Validation of a real-time video analysis system for soccer. International Journal of Sports Medicine, 33(08), 635–640.
   PubMed Web of Science ®Google Scholar
• Rein, R., & Memmert, D. (2016, August 24). Big data and tactical analysis in elite soccer: future challenges and opportunities for sports science. SpringerPlus, 5(1), 1410.
   PubMedGoogle Scholar
• 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. Retrieved from http://www.sciencedirect.com/science/article/pii/S0167945716302676
   PubMed Web of Science ®Google Scholar
• Sathyan, T., Shuttleworth, R., Hedley, M., & Davids, K. (2012). Validity and reliability of a radio positioning system for tracking athletes in indoor and outdoor team sports. Behavior Research Methods, 44(4), 1108–1114.
   PubMed Web of Science ®Google Scholar
• Seidl, T., Cherukumudi, A., Hartnett, A., Carr, P., & Lucey, P. (2018). Bhostgusters: Realtime interactive play sketching with synthesized nba defenses. Proceeding of the 12th MIT Sloan Sports Analytics Conference, Boston, MA. Boston: MIT.
   Google Scholar
• Sha, L., Lucey, P., Yue, Y., Wei, X., Hobbs, J., Rohlf, C., & Sridharan, S. (2018). Interactive sports analytics: An intelligent interface for utilizing trajectories for interactive sports play retrieval and analytics. ACM Transactions on Computer-human Interaction : A Publication of the Association for Computing Machinery, 25(2), 13:1–13: 32.
   Web of Science ®Google Scholar
• Stein, M., Janetzko, H., Breitkreutz, T., Seebacher, D., Schreck, T., Grossniklaus, M., … Keim, D. A. (2016). Director’s cut: Analysis and annotation of soccer matches. IEEE Computer Graphics and Applications, 36(5), 50–60.
   PubMed Web of Science ®Google Scholar
• Stein, M., Janetzko, H., Lamprecht, A., Breitkreutz, T., Zimmermann, P., Goldlücke, B., … Keim, D. A. (2018). Bring it to the pitch: Combining video and movement data to enhance team sport analysis. IEEE Transactions on Visualization and Computer Graphics, 24(1), 13–22.
   PubMed Web of Science ®Google Scholar
• Stein, M., Janetzko, H., Seebacher, D., Jäger, A., Nagel, M., Hölsch, J., … Grossniklaus, M. (2017). How to make sense of team sport data: From acquisition to data modeling and research aspects. Data, 2(1), 2.
   Web of Science ®Google Scholar
• Valter, D. S., Adam, C., Barry, M., & Marco, C. (2006). Validation of prozone®: A new video-based performance analysis system. International Journal of Performance Analysis in Sport, 6(1), 108–119.
   Google Scholar
• Varley, M. C., Fairweather, I. H., & Aughey, R. J. (2012). Validity and reliability of gps for measuring instantaneous velocity during acceleration, deceleration, and constant motion. Journal of Sports Sciences, 30(2), 121–127.
   PubMed Web of Science ®Google Scholar
• Waldron, M., Worsfold, P., Twist, C., & Lamb, K. (2011). Concurrent validity and test–retest reliability of a global positioning system (GPS) and timing gates to assess sprint performance variables. Journal of Sports Sciences, 29(15), 1613–1619.
   PubMed Web of Science ®Google Scholar