Sport Analytics

A visualisation of functional principal components analysis

Introduction

With the advent of high-fidelity sensors and imaging systems, the applied sports sciences have observed a huge influx of available data originating from different sources, including wearable sensors that are now ubiquitous across many sporting disciplines. These sensors can quantify dynamic human movements over extended periods of time to enable reliable monitoring of athletic performance.

Development of system models without supporting sensor measurements in sporting data is inherently difficult; even the simplest of sporting situations can give rise to non-linear behaviour. For example, the dynamics of a limb, an individual or team members can abruptly change in anticipation of, or response to, a perceived scoring opportunity or an opponent’s action. Consequently, first principle models of most sporting systems are challenging to formalise. However, the confluence of high-fidelity measurements (Big-Data) and the advancement of numerical methods provides the means to account for the non-linearity, high-dimensionality, multiscale nature, and intrinsic uncertainty observed in most sporting systems.

Our specialist area aims to take advantage of Big-Data and applied mathematical and statistical techniques to discover underlying sporting system models. We employ techniques such as regression, machine learning, neural networks and genetic programming to formalise unknown dynamics and provide a means to predict the future state of the sporting system in question. Additionally, we strive to ensure that data-driven models are interpretable and intuitive so the discovered system can be physically understood by practitioners working in applied sporting environments.

Members

Prof. Neil Bezodis

Specialisation: Biomechanics, measurement and sensors

Dr Rowan Brown

Specialisation: Complex dynamical systems, signal processing

Prof. Liam Kilduff

Specialisation: Performance science

Prof. Huw Summers

Specialisation: Dynamical systems, control theory

Dr Mark Waldron

Specialisation: Physiology, human biology, sports medicine

Current Projects

Predicting Team Success

A machine learning and data-driven assessment model for characterising and predicting team success in Championship football

Data Analytics - Rugby

The application of advanced data analytics to quantify load monitoring and performance in professional Rugby Union

Data Analytics - Swimming

The application of advanced data analytics to monitor training load and predict success in swimming

Data Analytics - Olympic Sports

The application of advanced data analytics techniques to pre-existing data to understand performance in Olympic sports

Instrumented-mouthguards in Sports

Optimising data processing methods for the application of instrumented-mouthguards in sport

Pixabay image by Laura Boden Schatz showing women rugby players

Elite Women's Rugby

The physical and fitness characteristics of elite women rugby players and the relationships to match performance

Women's International Rugby Union

The importance of place kicking performance in women’s international Rugby Union

Other Members

Post-Doctoral Research Assistants

Dr Mark White, UK Sports Institute

Postgraduate research students

Georgia Scott, Ospreys Rugby (current)

Lewis Evans, Swansea City AFC (current)

Luke Woodhouse, RFU Women (current)

Sam Jones, UK Sports Institute (current)

Erwan Izri, Sports & Wellbeing Analytics (current)

Mark Bennett (2019) – Investigating match performance in rugby union through the use of isolated and relative performance indicators

Dr Mark White (2021) – Generalisable FPCA-based models for predicting peak power in vertical jumping using accelerometer data

Dr James Wild (2022) – Biomechanics and motor control of early acceleration: enhancing the initial sprint performance of professional rugby union backs

Recent Publications

• Scott, G.A., Edwards, O., Bezodis, N.E., Waldron, M., Roberts, E., Pyne, D.B., Mara, J., Cook, C., Mason, L., Brown, M.R., & Kilduff, L. (2023). Classifying winning performances in international women’s Rugby Union. International Journal of Sports Physiology and Performance, ahead of print. https://doi.org/10.1123/ijspp.2023-0086
• Ullersperger, E., Hills, S., Russell, M., Waldron, M., Shearer, D., Lonergan, B., Farrow, T., Eager, R., & Kilduff, L. (2023). Assessing climatic, travel, and methodological influences on whole-match and worst-case scenario locomotor demands of international men’s rugby sevens match-play. European Journal of Sport Science, 23(7), 1094-1101. https://doi.org/10.1080/17461391.2022.2109065
• Scott, G., Bezodis, N., Waldron, M., Bennett, M., Church, S., Kilduff, L., & Brown, M. R. (2022). Performance indicators associated with match outcome within the United Rugby Championship, Journal of Science and Medicine in Sport, 26(1), 63-68, https://doi.org/10.1016/j.jsams.2022.11.006
• White, M. G. E., Bezodis, N. E., Neville, J. N., Summers, H. D., & Rees, P. (2022). Determining jumping performance from a single body-worn accelerometer using machine learning. PLOS One, 17(2), e0263846. https://doi.org/10.1371/journal.pone.0263846
• White, M. G. E., Neville, J. N., Rees, P., Summers, H. D., & Bezodis, N. E. (2022). The effects of curve registration on linear models of jump performance and classification based on vertical ground reaction forces. Journal of Biomechanics, 140, 111167. https://doi.org/10.1016/j.jbiomech.2022.111167
• Wild, J.J., Bezodis, I.N., North, J.S., & Bezodis, N.E. (2022). Characterising initial sprint acceleration strategies using a whole-body kinematics approach. Journal of Sports Sciences, 40(2), 203-214. https://doi.org/10.1080/02640414.2021.1985759
• Bennett, M., Bezodis, N. E., Shearer, D. A., & Kilduff, L. P. (2021). Predicting performance at the group-phase and knockout-phase of the 2015 Rugby World Cup. European Journal of Sports Science, 21(3), 312-320. https://doi.org/10.1080/17461391.2020.1743764
• Waldron, M., Jones, C., Melotti, L., Brown, R., & Kilduff. L. P. (2021). Collision monitoring in elite male rugby union using a new instrumented mouth-guard. The Journal of Sports & Exercise Sciences, 5(3), 179-187. https://doi.org/10.36905/jses.2021.03.03
• Greybe, D. G., Jones, C. M., Brown, M. R., & Williams, E. M. P. (2020). Comparison of head impact measurements via an instrumented mouthguard and an anthropometric testing device. Sports Engineering, 23, 12. https://doi.org/10.1007/s12283-020-00324-z
• Sheppy, E., Hills, S. P., Russell, M., Chambers, R., Cunningham, D.J., Shearer, D., Heffernan, S., Waldron, M., McNarry, M., & Kilduff, L. P. (2020). Assessing the whole-match and worst-case scenario locomotor demands of international women's rugby union match-play. Journal of Science and Medicine in Sport, 23(6), 609-614. https://doi.org/10.1016/j.jsams.2019.12.0165.
• Bennett, M., Bezodis, N.E., Shearer, D.A., Locke, D., & Kilduff, L.P. (2019). Descriptive conversion of performance indicators in rugby union. Journal of Science and Medicine in Sport, 22(3), 330-334. https://doi.org/10.1016/j.jsams.2018.08.008
• Gray, A. J., Shorter, K., Cummins, C., Murphy, A., & Waldron, M. (2018). Modelling movement energetics using Global Positioning System (GPS) devices in contact team sports: limitations and solutions. Sports Medicine, 48, 1357-1368. https://doi.org/10.1007/s40279-018-0899-z