Using Data and Econometrics to Improve Football Training Sessions and Predict Injuries”

The aim of this project is to combine methods in data science and econometrics to predict relevant outcomes in sports. The project contains two separate, but intrinsically related, lines of research. The first line of the project (henceforth Sub‐project 1) will combine different metrics on individual‐player level data from football (soccer) training sessions to understand what type(s) of training exercises are most effective for players’ short‐term and medium‐term physical conditioning. The second line of the project (henceforth Sub‐project 2) will use a combination of individual‐player data and external factors (i.e.: frequency of matches played, whether team is playing home or away etc.) to predict injuries in professional football players. One of the deliverables of Sub‐project 2 will be an injury forecaster that can be useful to football trainers. I also plan to study how the number of matches and
minutes played affects the probability of muscle injuries in football players. The results of this study will be of interest to tournament organizers worldwide when, for example, putting together football fixtures. The research project will be done in collaboration with Centro Rossi, a diagnostic center in Argentina, and Racing de Montevideo, a football club from Uruguay which has both amateur youth as well as professional players. I plan on disseminating and sharing the results of this project at international conferences, in refereed journal articles, and with relevant institutions in Qatar, such as Aspire Academy and Aspetar.