Master's thesis/research internship in sports data analytics at University of Caen, Normandie

[Albrecht Zimmermann]

**Towards predicting optimal team lineups**
Master's thesis/Research internship, M2, 6 months, 2023

*Laboratory/company*
Laboratory: GREYC CNRS UMR 6072
Team CODAG – Contraintes, Ontologies, Data mining, Annotations, Graphes
Université de Caen Normandie
14000 Caen, France
Company: Skriners
38 rue de Metz
92000 Nanterre

*Remuneration*
Standard gratification for a research internship according to French legislation: ~570 euros/month

*Context*
Using computational methods to analyze sports data gives practitioners (coaches, agents, athletes themselves) powerful tools to make more objective decisions when it comes to a variety of questions that arise in professional sports.
The company Skriners already offers a tool for supporting decision makers for player acquisition or replacement, based on sophisticated statistics derived from video recordings of matches. Skriners is a SaaS software for sports professionals to compare, recommend and manage players based on statistical criteria. Thanks to its comprehensive database, Skriners can also help find promising talent. The software also offers a workforce management feature. This decision support is limited to individual players, so far, not taking teammates or eventual information about opponents into account.
In the long term, the tool is to be enriched to automatically suggest team lineups, based on available players, intended match strategy, information about the opposing team etc. This will require taking synergies between players into account, as well as the performance of particular players in given defensive or offensive systems.
The work to be performed in this internship will lay the groundwork for this future research, by exploring whether and how existing work on team chemistry [1], the context of players’ performance [2], and the automatic identification of tactical formations [3] can be applied to the data currently available to Skriners. Based on this evaluation, the intern will either start implementing and applying those techniques to the data to derive additional statistics, or identify in which way data and/or methods need to be adapted.

*Objectives*
- Evaluate the applicability of existing methods to the data available to Skriners
- Evaluate the needs for and possible sources for additional data

*Activities*
- Familiarize oneself with the data at Skriners’ disposal
- Familiarize oneself with existing work in the literature
- Identify whether there are data that would be needed but are currently missing
- Implement and apply existing methods to the data, generating additional statistics
- Identify additional data sources

*Profil*
Student in computer science or sports/movement science. Programming, as well as machine learning/data mining or statistics knowledge necessary. Candidates are encouraged to apply as soon as possible.

*To apply*
Send the following documents (as .pdf) to the contact addresses listed below:
- Motivation letter
- Résumé
- Grades for M2 (as far as available) and M1
- If possible, contact data for one or more persons (teachers, prior internship supervisors) who can be contacted for references

*Contact*
albrecht....@unicaen.fr
gill....@skriners.fr

*References*
[1] Bransen, Lotte, and Jan Van Haaren. "Player chemistry: Striving for a perfectly balanced soccer team." arXiv preprint arXiv:2003.01712 (2020).
[2] Bransen, Lotte, Pieter Robberechts, Jesse Davis, Tom Decroos, Jan Van Haaren, Angel Ric, Sam Robertson, and David Sumpter. "How does context affect player performance in football?." (2020).
[3] Bialkowski, A., Lucey, P., Carr, P., Yue, Y., Sridharan, S. and Matthews, I., 2014, December. Large-scale analysis of soccer matches using spatiotemporal tracking data. In 2014 IEEE international conference on data mining (pp. 725-730). IEEE.