Sport, Exercise and Health Analytics


Mission

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. 

Objectives

  • 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 officemanager@bases.org.uk. 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 https://sport-exercise-and-health-analytics-sig.tribe.so

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.