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Sports Analytics.

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Presentation on theme: "Sports Analytics."— Presentation transcript:

1 Sports Analytics

2 LottoNL-Jumbo Speed Skating Team
Skaters: Sven Kramer, Wouter Olde Heuvel, Kjeld Nuis, … Some 16 Olympic medals + numerous championships

3 Block Periodization Models
Issurin

4 Taper/Peak Superposition of residual training effects - timing
competition Meso-blocks most complex phase Accumulation Transmutation Realisation residuals 12-30 days 12-25 days 8-14 days Issurin

5 Historical Training Data
15 years of data collected Some 40 athletes, currently nine: seven men, two women Daily training details Morning and afternoon training Six days per week Training type, intensity (subjective), duration, load Roughly bi-weekly physical test Aerobic Anaerobic Competition data Corrected for track-differences

6 Speed Skating Data duration intensity Kjeld Nuis 1000 m

7 Research Aims Service LottoNL-Jumbo with insights
provide dashboard What factors in the training routines affect performance? load, periodization, sickness, atmospheric conditions detailed optimization, tweaking Personalised training advice Male vs. female Discipline-specific Athlete-specific pre-season tests predictiveness?

8 The Effect of Training test moment tapering windows

9 Aspects of Periodization
Within each window: count How many exercises? sum (duration, load) How many minutes, …? max (duration, intensity, load) Did you recently …? stddev (duration, intensity, load) How varying was …? Determiners of specific activities just in the morning/afternoon certain intensity ranges (zones) Sum of duration over 14-day period Max of intensity over 2-day period Sum of duration over 21-day period, morning sessions Sum of duration over 21-day period, intensities 6, …, 9 Maximum of load over 7-day period, cycling

10 Modelling Training/Response
Each variable will have a U-shape Neither too little, nor too much In theory non-linear, in practice only a sample linear model threshold model relative time optimum training load available data

11 Kjeld Nuis 178 races On average 2.89% above track record
Specialises on 1000 m (2.1%) Dutch champion 1000 m, 1500 m WC Distances: bronze 1000 m, silver 1500 m WC Sprint: ‘silver’ ISU World Cup: gold 1000 m, silver 1500 m

12 Total sum of load over last 5 days, morning sessions
undesired result due to over-training advised upper limit

13 Maximum intensity 8 or higher in the two days prior to the race
advised lower limit

14 Football, PSV Cooperation with Luc van Agt & Ruud van Elk
Tactical data derived from video, proprietary event data from Ortec Sports 52 matches

15 Tactical Data x/y coordinates of all players
Apply geometric algorithms to extract features Statistical analysis of extracted features

16 Visualisation defenders direction of play

17 Visualisation midfielders

18 Visualisation attackers

19 Visualisation loss of ball

20 Visualisation gain of ball

21 Snowboarding

22 lift lunch bending

23 Activity Recognition sitting lying standing lift skiing ski tow

24 Olympic Committee NOC*NSF: Funnels
Talent tracking and development Large database of sports results visualisation

25 Funnel 100m sprint Usain Bolt Churandy Martina

26 Head to head sports: Elo ratings

27 Funnel tennis (Elo) Roger Federer David Farrer


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