Intractable Likelihood

Main Organisers: Gareth Roberts, David Firth, Chris Holmes, Iain Murray, Alex Beskos, Yee Whye Teh, John Aston

Sports and fitness have developed into activities that are rich in diverse and complex data types produced in high frequency and volume (e.g. a combination of data from multiple sensors, motion trackers, questionnaires, images, and videos), and in associated research questions on topics like Sports Health, Outcomes, Performance, Measurement and Planning. The challenges involved in identifying, capturing, combining and extracting topical knowledge from such data movitate new methodological developments in Data Science, and provide a broad scope for applied and methodological research in scientific fields such as Statistical Science, Machine Learning, Computer Science, Engineering, Economics and Cognitive Neuroscience.

 

This workshop is an academia-led response to those challenges, aiming to identify promising Data Scientific approaches with potential to innovate on Sports applications and beyond. During the workshop:

  • Current scientific developments at the intersection of Data Science and Sport will be presented, and new applications and associated methodological challenges will be identified and discussed.
  • Subject-matter knowledge will be exchanged between academic and non-academic experts across the multiple scientific disciplines contributing to Data Science and Sports Science.
  • Researchers from the cross-disciplinary scientific core of ATI and beyond will engage with researchers and key professionals from leading UK sports organizations and from the sports industry.

 

The key aims of this workshop are:

  • Scope the Data Science methodological framework and define the corresponding application areas that can identify, capture, combine and analyze relevant data sources towards producing insights and knowledge on aspects of Sport, and on the impact of Sport on Society.
  • Boost the engagement of academic researchers on topics at the intersection of Data Science and Sport, and maximize interaction with leading UK sports organizations and the sports industry.
  • Kick-start the formation of a framework within ATI for developing and maintaining cross-institutional, cross-disciplinary academic and industrial research collaborations, on both applications and methodology, at the intersection of Data Science and Sport, both nationally and internationally.

 

Website: https://atidatasciencesports.wordpress.com