La integración de los datos en el fútbol de élite. Un nuevo paradigma de investigación

  1. Felipe, José Luis 1
  2. Alonso-Callejo, Antonio 2
  1. 1 Universidad de Castilla-La Mancha y Unión Deportiva Las Palmas
  2. 2 Universidad de Castilla-La Mancha
    info

    Universidad de Castilla-La Mancha

    Ciudad Real, España

    ROR https://ror.org/05r78ng12

Revista:
Cuadernos económicos de ICE

ISSN: 0210-2633

Any de publicació: 2023

Número: 106

Pàgines: 55-66

Tipus: Article

DOI: 10.32796/CICE.2023.106.7695 DIALNET GOOGLE SCHOLAR lock_openAccés obert editor

Altres publicacions en: Cuadernos económicos de ICE

Resum

Technology has become established in elite sports in recent years and is used routinely,especially in elite football. It should be noted that, while the processes underlyingtactics in elite football have improved over the years, scientific approaches have notevolved at the same pace. The solution to this problem is the integration of new technologiesand big data into the day-to-day operations of elite football coaching staffs.In this way, the world of football must learn to record, store, analyze, and apply thewide variety and volume of data available for the sake of improving the game and the spectacle. Thus, the three major challenges that loom in the world of football in thecoming years are: injury prevention, orientation of training tasks and technical-tacticaldevelopment. The new big data systems and techniques applied to the world of footballallow for the implementation of the PDCA (Plan, Do, Check, and Act) cycle, considereda reliable and valid tool for implementing a problem-solving model in the context ofelite football.

Referències bibliogràfiques

  • Ayala, F., López-Valenciano, A., Martín, J. A. G., Croix, M. D. S., Vera-Garcia, F. J., del Pilar Garcia-Vaquero, M., Ruiz-Pérez, I., & Myer, G. D. (2019). A preventive model for hamstring injuries in professional soccer: Learning algorithms. International journal of sports medicine, 40(05), 344-353.
  • Baca, A., Dabnichki, P., Heller, M., & Kornfeind, P. (2009). Ubiquitous computing in sports: A review and analysis. Journal of Sports Sciences, 27(12), 1335-1346.
  • Bahr, R. (2016). Why screening tests to predict injury do not work—and probably never will…: a critical review. British journal of sports medicine, 50(13), 776-780.
  • Balagué, N., & Torrents, C. (2005). Thinking before computing: Changing approaches in sports performance. International Journal of Computer Science in Sport, 4(1), 5-13.
  • Buchheit, M., Gray, A., & Morin, J. B. (2015). Assessing stride variables and vertical stiffness with GPS-embedded accelerometers: preliminary insights for the monitoring of neuromuscular fatigue on the field. Journal of Sports Science & Medicine, 14(4), 698.
  • Claudino, J. G., Capanema, D. D. O., de Souza, T. V., Serrão, J. C., Machado Pereira, A. C., & Nassis, G. P. (2019). Current approaches to the use of artificial intelligence for injury risk assessment and performance prediction in team sports: a systematic review. Sports medicine-open, 5, 1-12.
  • 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, (2), 123-136.
  • Cunniffe, B., Proctor, W., Baker, J. S., & Davies, B. (2009). An evaluation of the physiological demands of elite rugby union using global positioning system tracking software. The Journal of Strength & Conditioning Research, 23(4), 1195-1203.
  • Deo, R. C. (2015). Machine learning in medicine. Circulation, 132(20), 1920-1930.
  • Di Salvo, V., Baron, R., Tschan, H., Montero, F. C., Bachl, N., & Pigozzi, F. (2007). Performance characteristics according to playing position in elite soccer. International journal of sports medicine, 28(03), 222-227.
  • Edouard, P., Verhagen, E., & Navarro, L. (2022). Machine learning analyses can be of interest to estimate the risk of injury in sports injury and rehabilitation. Annals of physical and rehabilitation medicine, 65(4), 101431.
  • Ekstrand, J., Bengtsson, H., Waldén, M., Davison, M., Khan, K. M., & Hägglund, M. (2022). Hamstring injury rates have increased during recent seasons and now constitute 24% of all injuries in men’s professional football: the UEFA Elite Club Injury Study from 2001/02 to 2021/22. British Journal of Sports Medicine, 57(5), -298.
  • Felipe, J. L., Garcia-Unanue, J., Viejo-Romero, D., Navandar, A., & Sánchez-Sánchez, J. (2019). Validation of a video-based performance analysis system (Mediacoach®) to analyze the physical demands during matches in LaLiga. Sensors, 19(19), 4113.
  • Felipe, J. L., Garcia-Unanue, J., Gallardo, L., & Sanchez-Sanchez, J. (2021). Tracking Systems Used to Monitor the Performance and Activity Profile in Elite Team Sports. Sensors, (24), 8251.
  • Folgado, H., Lemmink, K. A., Frencken, W., & Sampaio, J. (2014). Length, width and centroid distance as measures of teams tactical performance in youth football. European Journal of Sport Science, 14(sup1), S487-S492.
  • Glazier, P. S. (2017). Towards a grand unified theory of sports performance. Human movement science, 56, 139-156.
  • Goecks, J., Nekrutenko, A., Taylor, J., & Galaxy Team team@ galaxyproject. org. (2010). Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome biology, , 1-13.
  • Gonçalves, B. V., Figueira, B. E., Maçãs, V., & Sampaio, J. (2014). Effect of player position on movement behaviour, physical and physiological performances during an 11-a-side football game. Journal of sports sciences, 32(2), 191-199.
  • Gréhaigne, J. F., & Godbout, P. (1995). Tactical knowledge in team sports from a constructivist and cognitivist perspective. Quest, 47(4), 490-505.
  • Gréhaigne, J. F., & Godbout, P. (2014). Dynamic systems theory and team sport coaching. Quest, 66(1), 96-116.
  • Higgins, T., Naughton, G. A., & Burgess, D. (2009). Effects of wearing compression garments on physiological and performance measures in a simulated game-specific circuit for netball. Journal of Science and Medicine in Sport, 12(1), 223-226.
  • Hughes, M. D., & Bartlett, R. M. (2002). The use of performance indicators in performance analysis. Journal of sports sciences, 20(10), 739-754.
  • Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260.
  • Kempe, M., Vogelbein, M., Memmert, D., & Nopp, S. (2014). Possession vs. direct play: evaluating tactical behavior in elite soccer. International Journal of Sports Science, (6A), 35-41.
  • Kononenko, I. (2001). Machine learning for medical diagnosis: history, state of the art and perspective. Artificial Intelligence in medicine, 23(1), 89-109.
  • 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, (13), 1463-1469.
  • 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, (12), 1205-1213.
  • López-Valenciano, A., Ayala, F., Puerta, J. M., Croix, M. D. S., Vera-García, F., Hernández- Sánchez, S., ... & Myer, G. (2018). A preventive model for muscle injuries: a novel approach based on learning algorithms. Medicine and science in sports and exercise, (5), 915.
  • Low, B., Coutinho, D., Gonçalves, B., Rein, R., Memmert, D., & Sampaio, J. (2020). A systematic review of collective tactical behaviours in football using positional data. Sports Medicine, 50, 343-385.
  • 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.
  • Memmert, D., & Rein, R. (2018). Match analysis, big data and tactics: current trends in elite soccer. German Journal of Sports Medicine, 69(3), 65-72.
  • Memmert, D., Lemmink, K. A., & Sampaio, J. (2017). Current approaches to tactical performance analyses in soccer using position data. Sports Medicine, 47(1), 1-10.
  • Moura, F. A., Martins, L. E. B., Anido, R. D. O., De Barros, R. M. L., & Cunha, S. A. (2012). Quantitative analysis of Brazilian football players' organisation on the pitch. Sports biomechanics, 11(1), 85-96.
  • Moura, F. A., Martins, L. E. B., Anido, R. O., Ruffino, P. R. C., Barros, R. M., & Cunha, S. A. (2013). A spectral analysis of team dynamics and tactics in Brazilian football. Journal of sports sciences, 31(14), 1568-1577.
  • Nakanishi, R., Murakami, K., & Naruse, T. (2008). Dynamic positioning method based on dominant region diagram to realize successful cooperative play. In RoboCup 2007: Robot Soccer World Cup XI 11 (pp. 488-495). Springer Berlin Heidelberg.
  • Noor, A. M., Holmberg, L., Gillett, C., & Grigoriadis, A. (2015). Big Data: the challenge for small research groups in the era of cancer genomics. British Journal of Cancer, (10), 1405-1412.
  • 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.
  • Rommers, N., Rössler, R., Verhagen, E., Vandecasteele, F., Verstockt, S., Vaeyens, R., Lenoir, M., D’Hondt, E., & Witvrouw, E. (2020). A machine learning approach to assess injury risk in elite youth football players. Medicine and science in sports and exercise, (8), 1745-1751.
  • Sampaio, J., & Maçãs, V. (2012). Measuring tactical behaviour in football. International journal of sports medicine, 33(05), 395-401.
  • 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, (20), 1831-1843.
  • 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.
  • 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.
Fuente de los datos: Dialnet