Leeds Beckett University - City Campus,
Woodhouse Lane,
LS1 3HE
Fully Funded PhD Studentship: Causal modelling of player performance, injury risk, and head accelerations in professional rugby league (RL-InSiGT Project)
The PhD is Sponsored by Catapult Sports, as part of the RL-InSiGT (rugby league integrated study into game and training demands). This fully funded PhD studentship offers a unique opportunity for a mathematically trained researcher to apply modern causal inference and probabilistic modelling to one of the most complex real-world systems in applied data science: professional collision sport.About the Carnegie School of Sport
At Leeds Beckett University, the Carnegie School of Sport is dedicated to making a real difference through world-class research and knowledge exchange. From concussion and player safety to ethics, equality, and talent development, our researchers are tackling the biggest challenges facing sport and society. You’ll be part of a collaborative community where science meets practice, and research delivers tangible impact.
Who we are Looking for:
This studentship is specifically aimed at candidates with strong quantitative training.
Essential:
• 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
• Experience with programming for data analysis (e.g. R, Python, Julia)
• Curiosity about causal reasoning and real-world inference problems
Desirable (but not required):
• Familiarity with causal inference, graphical models, or Bayesian methods
• Experience working with observational or longitudinal data
• Interest in applied problems involving human performance or health
Importantly:
You do not need a background in sport or sports science. Domain knowledge will be supported; methodological strength is the priority.
Start date: 1 June 2026
Duration: 3 years, full-time funded PhD
Primary location: Carnegie School of Sport, Leeds Beckett University (LS6 3QS)
Supervisory team: Professor Ben Jones, Dr Thomas Sawczuk, Dr Cameron Owen, Dr Neil Collins or Professor Mark Gilthorpe (Advisor)
Project: The Rugby League Integrated Study into Game and Training demands; RL-InSiGT project
Causal modelling of player performance, injury risk, and head accelerations in professional rugby league
The project will develop and apply Directed Acyclic Graphs (DAGs) and advanced causal analysis to understand how a) law modifications, b) tactical changes or c) changes in training exposure influence, key metrics measured via Catapult player tracking units, in addition to other outcome measures. These include:
Player physical performance
Players and team match events
Concussion risk
Head acceleration event risk
You will work with large, high-resolution, multimodal datasets, including:
GPS and inertial data from player tracking systems
Match event and contact data
Instrumented mouthguard head-impact data
Longitudinal injury surveillance records
The research is conducted in collaboration with the Rugby Football League, providing access to rare, league-wide datasets and direct pathways to real-world impact.
This PhD is methodologically driven with a focus on the real-world application. While the application domain is men’s rugby league (Super League), the core intellectual challenge lies in causal structure learning, confounding control, missing data, measurement error, and decision-relevant inference in observational settings.
WHY CAUSAL INFERENCE?
Much applied analytics in sport, as in many other fields, focuses on prediction: forecasting injury, performance, or fatigue from historical data. However, prediction alone cannot answer the questions that matter most for real-world decision-making:
What would happen if variables were changed?
Which variables increase concussion risk or performance outcomes, rather than merely correlate with them?
How do interventions trade off performance gains against risks?
These are causal questions, not predictive ones.
This PhD is explicitly built around causal inference as the primary analytical framework. Professional rugby presents a rare and intellectually rich environment for causal analysis: repeated interventions, time-varying confounding, feedback loops, missing data, and noisy measurement systems, all observed longitudinally in a real-world setting.
The project treats causal structure as a first-class object of study, using:
Directed Acyclic Graphs (DAGs) to formalise assumptions, identify confounding, and define valid adjustment sets
Counterfactual reasoning to estimate the effects of hypothetical changes in training, exposure, or match demands
Longitudinal causal models to address cumulative exposure and time-dependent confounding
Transparent and interpretable models designed to support decision-making, rather than black-box prediction
The aim is not simply to model rugby, but to generate robust, decision-relevant causal insights from complex observational data — skills that transfer directly to fields such as health, epidemiology, safety science, and human performance analytics.
RESEARCH AIMS
The PhD will aim to:
Construct and validate causal graphs describing relationships between match events, physical outputs, player and team performance, concussive and head acceleration event outcomes
Apply causal inference techniques (e.g. adjustment sets, mediation analysis, counterfactual estimation) to quantify risk and performance trade-offs
Integrate heterogeneous data sources with differing temporal resolutions and noise characteristics
Develop interpretable, decision-support models for performance optimisation and player welfare
The work will contribute both to applied sport policy and to methodological discussions around causal modelling in complex, real-world systems.
YOUR OPPORTUNITY
As the successful candidate, you will:
Work with large-scale longitudinal datasets rarely available
Develop causal models with direct relevance to critical decision-making, balancing the safety vs. the spectacle of the sport
Collaborate with academic researchers, data scientists, and industry partners
Publish in high-quality peer-reviewed journals at the intersection of statistics, data science, and applied health/performance research
You will be supported to 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 are encouraged to discuss their proposals with a member of the supervisory team: Professor Ben Jones, Dr Thomas Sawczuk, Dr Cameron Owen, Dr Neil Collins or Professor Mark Gilthorpe (Advisor)
Application Reference Number: 2026-June-RFL-Causal/CSS-PHD
Mode of Study: Full-Time (3yrs)
A laptop will be provided
Type of Funding Available: International Fees and Stipend
Stipend Value: £20,780
Stipends are tax-free and paid pro-rata in monthly payments
The successful candidates would ideally have: 2:1 or higher in a relevant undergraduate degree.
For those whose first language is not English you must also have an overall IELTS score of 7.0 with no individual score below 6.5 in order for applicants to obtain a CAS and Visa.
The PHD Studentship will be awarded to the strongest applications assessed on the applicant’s academic excellence, the strength of the research proposal and how the proposal fits with the research project.
To apply, please go to the application portal which can be found through the 'Apply Now' button.
Please make sure that you complete the application process in full and also provide the following additional information:
1. RESEARCH PROPOSAL (include title and project reference)
Your research proposal must outline the topic of your proposed research, the questions it will address and some indication of how you will conduct your research. It is an integral part of the application process. It should be no more than 2000 words in length (not including references) and must include the research project title and reference.
The criteria listed below will be used in both selecting those applicants who will be called for interview and those who will be successful in securing a PGR award, and these should help you form your research proposal.
a) Context and significance of your research
Please outline the significance and originality of your proposed research, indicating: aims, relationship to previous research in the field, research question(s) you are seeking to answer.
b) Research design and methods
Please outline the design of your proposed research, indicating: methodology and methods, a timetable for completion of the PGR award, ethical considerations that your research may raise.
c) Dissemination and impact
Please identify: possible opportunities to disseminate your research to academic audiences during your PGR programme, the ways in which your research might be relevant outside academia.
You are strongly advised to discuss your proposal with a named Supervisor before making your application.
How applications will be assessed Your application will be considered by a Carnegie School of Sport selection panel. The panel will evaluate applications based on the quality of the proposal, preparedness of the applicant and feasibility of the research project.
2. STATEMENT OF PURPOSE
This should be a maximum of 1000 words outlining: What knowledge, skills, and training would you bring to the proposed research? This may include relevant academic study, relevant experience as a professional or practitioner, and any specific training in research skills/methods. Why do you want to undertake this research in the Carnegie School of Sport at Leeds Beckett University. How does the proposed research relate to your career goals.
3. CV
A current CV, including your employment history or other professional experience, including internships.
- Please state clearly that you are applying for a Carnegie School of Sport studentship and include the reference - 2026-May-RFL-Causal/CSS-PHD
- The closing date for applications is midnight on 30 March 2026.
- Shortlisted candidates will be invited for interview.
- We aim to hold in person interviews for shortlisted applicants during the week commencing 11th May 2026.
- For queries about applying please contact Research Admissions
- We regret that we are not able to respond to all applications. Applicants who have not received a response within four weeks of the closing date should consider their application has been unsuccessful on this occasion.
Application Deadline: 30 March 2026
Hopkinson M, Hendricks S, Jones B, Nicholson G, Patricios JS, Dane K, Gardner AJ, Howell DR, Owen C, Quarrie KL, Tierney G, Till K, Wilson F, Johnston RD. Impacting the rugby tackle: risk factors and mechanisms for concussion and musculoskeletal tackle-related injury - a systematic review and Delphi consensus to inform intervention strategies for risk reduction. Br J Sports Med. 2025 Oct 10;59(20):1397-1409. doi: 10.1136/bjsports-2024-108992. PMID: 40830034.
Rennie G, Hart B, Dalton-Barron N, Weaving D, Williams S, Jones B. Longitudinal changes in Super League match locomotor and event characteristics: A league-wide investigation over three seasons in rugby league. PLoS One. 2021 Dec 2;16(12):e0260711. doi: 10.1371/journal.pone.0260711. PMID: 34855846; PMCID: PMC8638883.
Tooby J, Owen C, Sawczuk T, Roe G, Till K, Phillips G, Vishnubala D, White R, Rowson S, Tucker R, Tierney G, Jones B. Instrumented Mouthguards in Men's Rugby League: Quantifying the Incidence and Probability of Head Acceleration Events at a Group and Individual Level. Sports Med. 2025 Nov;55(11):2879-2890. doi: 10.1007/s40279-025-02253-y. Epub 2025 Jun 6. PMID: 40478418; PMCID: PMC12559052.
Scantlebury S, Jones B, Owen C, Brown J, Collins N, Fairbank L, Till K, Phillips G, Stokes K, Whitehead S. Time to level the playing field between men and women - Given similar injury incidence: A two-season analysis of match injuries in elite men and women's (Super League) rugby league. J Sci Med Sport. 2024 Nov;27(11):765-771. doi: 10.1016/j.jsams.2024.07.001. Epub 2024 Jul 9. PMID: 39043494.
Kalkhoven, J.T., 2024. Athletic injury research: frameworks, models and the need for causal knowledge. Sports Medicine, 54(5), pp.1121-1137.
Kalkhoven, J.T., Watsford, M.L., Coutts, A.J., Edwards, W.B. and Impellizzeri, F.M., 2021. Training load and injury: causal pathways and future directions. Sports Medicine, 51(6), pp.1137-1150.
Tennant, P.W., Murray, E.J., Arnold, K.F., Berrie, L., Fox, M.P., Gadd, S.C., Harrison, W.J., Keeble, C., Ranker, L.R., Textor, J. and Tomova, G.D., 2021. Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations. International journal of epidemiology, 50(2), pp.620-632.
Greenland, S., 1990. Randomization, statistics, and causal inference. Epidemiology, pp.421-429.
Contact us
To discuss your application and research proposal please contact a member of the supervisory team Professor Ben Jones, Dr Thomas Sawczuk, Dr Cameron Owen, Dr Neil Collins or Professor Mark Gilthorpe (Advisor)
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Professor Ben Jones
Professor / Carnegie School of Sport -
Neil Collins
Post Doctoral Research Fellow / Carnegie School of Sport -
Dr Thomas Sawczuk
Research Fellow / Carnegie School of Sport -
Dr Cameron Owen
Senior Research Fellow / Carnegie School of Sport -
Professor Mark Gilthorpe
Professor / Carnegie School of Sport