Utilizing Tracking Data to Create Defensive Performance Indicators for Football Players

Suggested by: Philip Schnittkamp, https://www.plaier.com/

Supervisor: Christian Neuwirth 

Short description: Football (or otherwise known as soccer) is one of the most complex sports to analyze with data. The low-scoring nature of the sport, as well as the fact that 22 players are active on a huge pitch at the same time, are just some of the reasons for the huge complexity of modeling team and player strengths. While the analysis of so-called event data has taken huge strides over the last years and is arguably especially helpful when analyzing the individual performance of attacking players, the rise of tracking data allows for much more complex spatio-temporal analysis on both the collective (team) and individual (player) level. Since then, public research has often concentrated on analyzing offensive patterns such as attacking pitch control, passing decision optimization, or off-ball runs to create space. Although there have also been major advances in analyzing defensive patterns like pressing efficacy or defensive compactness, there are still enough areas on the defensive side that remain un- or at least underexplored, especially on the individual player level.

The main idea of this master thesis is to further develop upon existing ideas and models to measure defensive player performance based on tracking data, specifically in terms of optimal defensive positioning to suppress the opposition’s threat. The utilization of several different statistical methods is conceivable to achieve this goal: already existing pitch control frameworks, neural nets, agent-based modelling approaches, etc. While the ultimate goal is to create a defensive performance measure on the individual player level, this is inevitably connected to the team level: Any good individual performance measure should, in the best case, lead to team success in the end to demonstrate its validity.

The following research questions should guide the analysis:

• Which players (and teams) are effective in hindering their opponents from spatio-temporally controlling the most dangerous areas of the pitch?
• What are the optimal defensive positions each player can take on to suppress the opposition’s threat based on their teammates’ and opponents’ positioning at any given time?
• Which players (and teams) are effective in taking on (close to) optimal positions on the defensive side? Which players (and teams) are effective in lowering opponent threat in comparison to expectation based on the opponent’s usual performances?
• Can a team’s defending style influence these results? And does it make sense to quantify this based on different areas of the pitch and/or different phases of defending?
• Are some players better at positioning themselves optimally in different areas of the pitch and/or different phases of defending?
• Are teams that are more effective in terms of defensive positioning better at hindering opponents from scoring goals? Are they more successful overall?

Start/finish: Anytime.

Prerequisites/qualifications: Experience with coding in Python (preferrable) or R is required. Personal interest and at least basic knowledge in football would be highly beneficial.

Suggested reading:

• Spearman, W. (2016, February). Quantifying pitch control. In OptaPro Analytics Forum [Internet]. https://doi.org/10.13140/RG.2.2.22551.93603.
• Fernández, Javier & Bornn, Luke. (2018). Wide Open Spaces: A statistical technique for measuring space creation in professional soccer.
• Higgins, Lewis & Galla, Tobias & Prestidge, Brian & Wyatt, Terry. (2023). Measuring the pitch control of professional football players using spatiotemporal tracking data. Journal of Physics: Complexity. 4. https://doi.org/10.1088/2632-072X/acb67d.
• Spearman, W. (2018, February). Beyond expected goals. In Proceedings of the 12th MIT sloan sports analytics conference (pp. 1-17).
• Peters, Andrew & Parmar, Nimai & Davies, Michael & James, Nic. (2026). Applying an Expected Pass Turnovers model to inform pressing strategies in professional football. International Journal of Performance Analysis in Sport. 1-16. https://doi.org/10.1080/24748668.2026.2671547.
• Forcher, L., Beckmann, T., Wohak, O., Romeike, C., Graf, F., & Altmann, S. (2024). Prediction of defensive success in elite soccer using machine learning - Tactical analysis of defensive play using tracking data and explainable AI. Science and Medicine in Football, 8(4), 317–332. https://doi.org/10.1080/24733938.2023.2239766
• Forcher, L., Forcher, L., Altmann, S., Jekauc, D., & Kempe, M. (2024). Is a compact organization important for defensive success in elite soccer? – Analysis based on player tracking data. International Journal of Sports Science & Coaching, 19(2), 757-768.
• Forcher, L., Altmann, S., Forcher, L., Jekauc, D., & Kempe, M. (2022). The use of player tracking data to analyze defensive play in professional soccer - A scoping review. International Journal of Sports Science & Coaching, 17(6), 1567-1592.