PIRI

Injury Analytics in Sport: Beyond the Black Box

Recent advances in techniques to explain machine learning models are opening up models that were previously considered black box models. This, in conjunction with the injection of sports domain knowledge and club context through robust data explorations, feature engineering and feature selection, combine to allow practitioners to see the reasoning behind the model outputs and enable them to incorporate into their decision-making.

Player Profiling and Injury Risk Research

NEW RESEARCH INITIATIVE: Impact of advanced player profiling on injury risk assessment and injury prevention You’re Invited: Join our innovative experiment to examine the impact of improved screening methods on injury risk assessment and interventions on injury prevention.  We’ve been leading the way in injury risk analytics R&D, and are excited to create an all …

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Planning Survey

Programming Practices and Loading Patterns in Team Sports

The Performance Intelligence Research Initiative  (PIRI) is excited to embark on new research for planning and programming! In sports, planning and programming for physical training alongside technical and tactical sequences is supposed to be an easy practice—especially when you have been doing this for a long time. If you’re working with Olympic sports and preparing …

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Narrowing the Gap from Research to Results

The Kitman Labs Performance Intelligence Research Initiative has identified the most urgent needs confronting elite sports practitioners – as they define them. Our just-published practitioner survey surfaced the most pressing needs across sports and roles, pointing a path for future research to sharpen its focus on what matters most for on-field results. Why did we launch …

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