Sport, Exercise and Health Analytics


To debate, evaluate, recommend and share information on all methods of data analysis that can usefully be applied in sports and exercise science settings, including, but not limited to, traditional biostatistics, Bayesian data analysis, machine learning and causal modelling. The aim being to provide direction, advice and resources for sport and exercise science researchers, reviewers, editors, practitioners, students (undergraduate and postgraduate) as well as those involved in teaching data analysis methods in a sports and exercise science context. 


  • To provide a forum for the debate and evaluation of the different methods of data analysis that can be applied in sport exercise science and related settings
  • To provide guidance on the application of data analysis methods and techniques
  • To recommend and provide resources for sport and exercise science researchers, practitioners, students and those involved in educating undergraduate and postgraduate sport science students
  • To explore ways to better facilitate the open science agenda in sport and exercise science

There has been an
exponential rise in the generation of different types of sport, exercise and
health related data. Data is not only available from traditional data
collection methods but also available from new technology including spatio-temporal
data from Global Positioning System technology (GPS), 3D video movement data,
video performance data, heart rate telemetry and accelerometery data.

While traditional methods can be applied to much of this data, nonetheless, new analysis methods are also being recommended and used. To successfully evaluate the evidence presented, sport and exercise scientists, researchers, editors and reviewers need to have an understanding of the types of analysis used, be able to decide which questions are best answered by which methods, and how to access resources and or expertise in their use and application.

The group will
offer advice on the use of a diverse range of data analysis methods as well
as highlighting useful resources to support their appropriate application in
different sport science contexts. The proposed special interest group would
also be in an excellent position to offer advice for practitioners, and
educators delivering sport science undergraduate and postgraduate programmes,
given what appears to be growing need for a clear pedagogical steer on the
different types of analysis available.
They may also be able to provide BASES Expert Statements on some
aspects of analysis in the future.

Steering Committee

Dr Tony Myers  (Chair)

Professor Alan Nevill 

Professor Mike Duncan 

Professor Kevin Lamb 

Dr Grant Abt 

Dr Greg Roe 

Dr Neil Clarke

Dr Shaun McLaren 

Dr Gavin Sandercock

Dr James Steele

Dr Dan Weaving 

Dr Sean Williams 

Dr Shaun Phillips 

Dr James Dugdale

Dr Paul Swinton 

Dr Jamie Highton 

Dr Chris McLaren-Towlson 

Dr Mark Noon

Dr Ian Lahart

Richard Taylor

Nick Dalton-Barron

Chris Kirk 

Rhys Morris

To contact the Steering Committee, please email Your message will then be sent on to the relevant member of the committee.

How to get involved

You can join our online forum at

BASES members can opt in to communications from this Special Interest Group via their Member Profile (in the Members' Area) to receive updates and information on how to get more involved.

Annual Reports

The overall aims of the Sport, Exercise and Health Analytics Special Interest Group are to provide direction, advice and resources for researchers, reviewers, editors, practitioners, and students as well as those involved in teaching data analysis methods. The first biannual meeting was held on 27 August 2020 via Zoom and was attended by 21 members. Discussion points at the meeting included: i) advice for sport science researchers given the recent calls by the American Statistical Association to ban the use of statistical significance; ii) advice on the use of magnitude-based inference given issues identified with the method; iii) advice for researchers given calls for increased statistical collaboration in sports science, sport and exercise medicine and sports physiotherapy; iv) processes available to check errors in data given some journal article retractions; v) reviewing what is currently taught in undergraduate and postgraduate sport science degree programmes and deciding what should be included; and vi) advice for reviewers when reviewing for articles that include use non-traditional data analysis methods. Actions include putting together a reporting guideline checklist for researchers and reviewers for both traditional and non-traditional analysis with the aim of publishing these in Journal of Sport Sciences and The Sport and Exercise Scientist.

SIG Updates

August 2023

Members of the SIG continue to work towards its overall aims to provide direction, advice and resources for researchers, reviewers, editors, practitioners, students, and those involved in teaching data analysis methods. SIG members have been active in several initiatives:

  • One ongoing project involves SIG members in an academic-industry collaboration. The project is progressing well. The first phase of the project investigated the teaching of research methods and statistics within undergraduate sport and exercise science courses across UK sport science undergraduate programmes has been completed. 94 academics from 60 UK institutions responded to the survey. Findings of this research have been presented at the European College of Sport Science (ECSS) Conference and accepted as a free communication for the BASES 2023 conference. Currently, the second phase of the study is being conducted, featuring focus group interviews to expand upon the survey responses. The aim is to examine the pedagogical approaches adopted and the challenges encountered by those teaching research methods. In addition, SIG members are exploring potential synergies with a parallel project that examines research methods in biology, medicine, and psychology. This project is being conducted by the University of Edinburgh and the Steering Group is looking to see what may be learned from each other’s projects moving forward.
  • Another exciting initiative involves four SIG members — one co-editing and four authoring chapters — producing an open-source online research methods book, specifically tailored for sport and exercise science students. This is planned to be completed in 2024.

To improve the diversity of SIG members, the SIG Chair met with the Chair of the BASES EDI Advisory Group. The two explored potential strategies by which the SIG might achieve increased membership diversity. Looking ahead, plans are in place for a SIG social media account, looking to offer advice on revising the current BASES abstract guidance for quantitative research studies and in line with our continuous efforts to innovate and keep pace with technological advancements in the field, we plan to explore the use of AI in Sport, Exercise and Health data analysis.

There are two other SIGs - The Molecular Exercise Physiology (MEP) SIG, convened by Dr Georgina Stebbings, and Performance Analysis SIG, formerly convened by Donald Barron whose updates were not available at the time of publication.

August 2022

Members of the Sport, Exercise and Health Analytics Special Interest Group actively contributed to the SIG’s overall aims to provide direction, advice and resources for researchers, reviewers, editors, practitioners, students, and those involved in teaching data analysis methods. Members attended the SIG’s latest meeting on 15 July 2022, via Teams to discuss:

  1. Updates on our academic-industry collaboration aimed at improving graduate data analysis skills and industrial performance.
  2. The idea that we should encourage our discipline to report confidence intervals or credible intervals rather than point estimates.
  3. Standardised effects and if the labels such as small, medium, and large are useful or misleading?
  4. How we establish what a meaningful effect looks like in different contexts?

To date, 51 individuals from 35 institutions have taken part in the initial stage of our academic-industry collaboration, with data collection ongoing. There was consensus at the end of our discussion that point estimates (means, beta values, r values etc.) and effect sizes should be reported with confidence or credible intervals and importantly the implications of the full interval — lowest as well as highest values —discussed. Where it makes sense to do so, effect sizes should be translated into something that is tangible for public and practitioners, such as a common language effect size or Number Needed to Treat (NNT). Finally, the group felt that the goals of the analysis — exploring, confirming, predicting etc., — should be reported and justified in relation to the population of interest, which should be clearly defined. If in the study's context, it is possible to define what a meaningful effect is — based on performance, known health outcomes, or concepts such as numbers needed to treat — this should be made explicit, justified, and discussed.

Data analytics SIG article