The Integration of Data in Elite Football. A New Paradigm of Research

José Luis Felipe
Antonio Alonso-Callejo

##plugins.themes.bootstrap3.article.main##

Published: Dec 29, 2023
Abstract

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 underlying
tactics in elite football have improved over the years, scientific approaches have not
evolved at the same pace. The solution to this problem is the integration of new technologies
and 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 the
wide 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 the
coming years are: injury prevention, orientation of training tasks and technical-tactical
development. The new big data systems and techniques applied to the world of football
allow for the implementation of the PDCA (Plan, Do, Check, and Act) cycle, considered
a reliable and valid tool for implementing a problem-solving model in the context of
elite football.

##plugins.themes.bootstrap3.article.details##

Keywords:
Big Data, Elite Football, Injury Prevention, Training Tasks, Technical-Tactical Development
References

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.