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Predicting future elite athletes based on scouting data

Type of thesis: Masterarbeit / location: Leipzig / Status of thesis: Open theses

In the German sports performance system, the goal is to conduct the best possible talent identification and development. German sports federations invest significant efforts at regional and national levels to support suitable young athletes across various sports. One of the methods employed is the regular organization of talent scouting events in individual and team sports. These talent scouting events typically occur at least once a year in respective sports and involve tests measuring various attributes (e.g., strength, endurance, anthropometry, and sport-specific features) of young athletes.

Statistical models to predict the future success of young athletes can significantly improve the talent identification and development process.
Using advanced methods from the statistical and machine learning area, the task is to analyze scouting data to understand how young athletes develop over time and to create accurate prediction models to support decisions about further funding of young athletes.
One challenge is figuring out which factors indicate success and how to include each player’s unique development path in the prediction models.

Hence, we are in search of a passionate student in Computer science or comparable study programs who is interested in sports analytics. The topic is advertised as a Master’s thesis.

Kontakt: Dr. Christian Saal Sportwissenschaftliche Fakultät (https://www.uni-leipzig.de/personenprofil/mitarbeiter/dr-christian-saal)