Data Science & Sports An overview Delft, April 7 th , 2016 - PowerPoint PPT Presentation

Data Science & Sports An overview Delft, April 7 th , 2016 Kamiel Maase, Netherlands Olympic Committee

Sport Science & Innovation Program MAIN GOAL: performance enhancement (medals!)

Support athletes, coaches, and their staff. By: 1. Distributing/ implementing knowledge Factsheets, protocols  Seminars  Sport science information center and helpdesk (Topsport Topics)  2. Hiring experts Sport dietitians  transfer of expertise, dietary logs, anthropometry  Embedded scientists  daily support, measurements  3. Infrastructure Field labs, climate room  testing, training  Embedded scientists platform & network  4. Research and innovation Research Program Sport  Sportinnovator Program, own projects  often with companies  Eat2Move (research and innovation on nutrition) 

Picture: LinkedIn.com But, there is more DATA IN SPORTS, A POSSIBLE CLASSIFICATION

Types of data, a possible classification Recreational sports (‘organized’) : monitors : participation data, • club members, KISS (kengetallen sportparticipatie) ; Recreational sports (‘not organized’) : e.g. GPS data, social • data and city characteristics  quantified self, large datasets ; Elite sports 1 : sports intelligence (competition results, • performance progression, performance outlook (‘funnels’), benchmarking  tool for investment decisions; Elite sports 2 : deliberate measurements • Athlete measurements (physical, mental…)  External/environmental measurements  Competition analysis (technical, tactical)  Involves technology like sensors, video …  Zoom in …

Examples (elite sports 2) Regular measurements Heart rate, speed, contact times, position, power, personal logs; • Technical and tactical parameters (“tagging/ scouting”); • Anthropometry • Reasons to measure and record Steering of training • Direct feedback, learning  • Match/race preparation and Match/race analysis prior to next round  Protests 

The era of (big) data science OPPORTUNITIES & CHALLENGES

Opportunities Larger data sets (elite sports: pooling of data, mass sports: Q-self) (Potentially) better analyses • Danger: haystack • Combine data From one-dimensional to multi-dimensional (multi-disciplinary) • Expected and unexpected correlations – data science • Computing power Power of the Crowd • Research programs Get the max of of your data: •  Better performance  Vital society  Understanding the value of sports Picture: LinkedIn.com

Challenges Quantity and quality of data Most sports data sets are not ‘big’ • However: mixed nature (from research projects to regular • measurements) FAIR data: findable, accessible, interoperable, reusable •  data formats , filing , and analysis Rules and regulations Privacy issues • Competitive edge (data of elite athletes) • Sports Data Valley •  Learn from existing initiatives! This is just the beginning…