This fully funded PhD studentship at Leeds Beckett University’s Carnegie School of Sport offers a unique opportunity to advance causal modelling in professional rugby league. Sponsored by Catapult Sports and part of the RL-InSiGT (Rugby League Integrated Study into Game and Training Demands) project, the position focuses on causal inference, probabilistic modelling, and applied data science in collision sports. The research aims to understand player performance, injury risk, and head accelerations using advanced statistical and computational methods.

As a PhD student, you will develop and apply Directed Acyclic Graphs (DAGs) and causal analysis to explore how law modifications, tactical changes, and training exposures influence key metrics measured via Catapult player tracking units. Outcome measures include player physical performance, match events, concussion risk, and head acceleration event risk. You will work with large, high-resolution, multimodal datasets such as GPS and inertial data, match event and contact data, instrumented mouthguard head-impact data, and longitudinal injury surveillance records. The project is conducted in collaboration with the Rugby Football League, providing access to rare league-wide datasets and direct pathways to real-world impact.

The intellectual challenge centers on causal structure learning, confounding control, missing data, measurement error, and decision-relevant inference in observational settings. The project treats causal structure as a first-class object of study, employing DAGs to formalize assumptions, identify confounding, and define valid adjustment sets. Counterfactual reasoning and longitudinal causal models will be used to estimate effects of hypothetical changes and address cumulative exposure and time-dependent confounding. Transparent and interpretable models are designed to support decision-making, not just prediction.

Research aims include constructing and validating causal graphs describing relationships between match events, physical outputs, player and team performance, and concussive/head acceleration outcomes. You will apply causal inference techniques such as adjustment sets, mediation analysis, and counterfactual estimation to quantify risk and performance trade-offs. The integration of heterogeneous data sources with differing temporal resolutions and noise characteristics is a key methodological focus. The work will contribute to applied sport policy and methodological discussions around causal modelling in complex, real-world systems.

As the successful candidate, you will collaborate with academic researchers, data scientists, and industry partners, and publish in high-quality peer-reviewed journals at the intersection of statistics, data science, and applied health/performance research. You will build strong transferable skills in causal analysis, statistical computing, and applied machine learning, with clear relevance beyond sport (e.g., health, epidemiology, human performance, and safety analytics). In addition to the PhD, you will have the opportunity to provide real-world data insight back to the sport.

Applicants must have a first-class or upper second-class degree (or Master’s) in Mathematics, Statistics, Data Science, Physics, Computer Science, Engineering, or a closely related discipline. Strong foundations in probability, statistics, and modelling are essential, along with experience in programming for data analysis (R, Python, Julia). Domain knowledge in sport is not required; methodological strength is the priority. The position is full-time for three years, starting 1 June 2026. Funding includes international fees, a tax-free stipend of £20,780 per year, paid monthly, and a laptop. Interviews will take place from 11 May to 14 May 2026. The application deadline is 30 March 2026.

For further information and application instructions, visit the project link and contact a member of the supervisory team. Reference number: 2026-June-RFL-Causal/CSS-PHD.