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DATA VIEWSJOOST N. KOK
ELECTRICAL ENGINEERING, MATHEMATICS AND COMPUTER SCIENCE
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Correlations follow directly from the data
▪ Availability of large quantities of data
▪ Large progress in methods
▪ Excellent computational infrastructure
Reason about the correlations to find the causal structures
DATA SCIENCE & AI
THE COMPUTATIONAL TURN
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New England Journal of Medicine (Messerli, 2012)
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publications.tno.nl/publication/34616450/tHet1v/vlasblom-2014-intelligente.pdf
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Main elements
1. Camera installation
• 14 camera’s with pitch view
• Recording, encoding and storing video
• Connected and powered over ethernet
2. Network installation
• 4 edge switches that merge 1 to 6 video data streams
• 1 core server that connects internal network to external network and cloud
3. BallJames platform
• Processing cloud with GPU’s and CPU’s to detect and track players and ball
• Training cloud to incrementally retrain our deep-learning models
• Operations cloud where internal administration, scheduling and maintenance is run
4. End user front-end
• API for direct access to Live Stream data
• Portal for after-match downloads, insights and much more
11
1
2
3
4
Core switch
video
datavideo
Cloud
• Processing• Training• Operations
1
2
data events
End users
4
3
Central infra
Stad
ium
Clo
ud
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▪ Cooperation between KNVB, Sportinnovator & Sport Data Center.
▪ Use WEURO to collect data and add value to the data
▪ Knowledge institutions & Companies
▪ Data available in the Sport Data Valley
WOMEN’S EURO 2017
SHOWCASE FOR SPORT DATA VALLEY
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PERFORMANCE ANALYSIS DASHBOARD
HTTPS://FFBPAD.APPSPOT.COM/ HTTPS://TINYURL.COM/SOCCERLAB
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14
SPRINTS
ATTACKING: DISTANCE > 24 KM/H
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EVENT PREDICTION
IN-GAME, NEAR REAL-TIME
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Arno Knobbe
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advised upper
limit
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Rating
Tournament
Strength of Tournament
Home Advantage
Team
Prediction
PlayerRating
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1.1. El o Rat ing Fomul a 8
Figure 1.3: The expected score E i j
Note that equat ion (1.4) is a base-ten version of a logist ic funct ion of the di↵erence
in rat ings of two players; however, in Sect ion 1.4.1 I will int roduce that home-field
advantagecan also be implemented into theexpected score funct ion, stated as follows:
Eh =1
1 + 10− (h+ r h − r a ) / 400, (1.5)
where h is the amount of home advantage boost. The constants 10 and 400 in the
formula (1.4) come from the empirical distribut ion of the chess world. Together, they
simply imply a change in the scale of the x-axis in Figure 1.2.
In Figure 1.3, it is easy to see that the expected score model (see (1.4)) is a logist ic
model in terms of the rat ing di↵erences r i − r j , and that the constants 400 and 10 in
(1.4) only lead to a rescaling of the x-axis.
1.1. El o Rat ing Fomul a 8
Figure 1.3: The expected score E i j
Note that equat ion (1.4) is a base-ten version of a logist ic funct ion of the di↵erence
in rat ings of two players; however, in Sect ion 1.4.1 I will int roduce that home-field
advantagecan also be implemented into theexpected score funct ion, stated as follows:
Eh =1
1 + 10− (h+ r h − r a )/ 400, (1.5)
where h is the amount of home advantage boost. The constants 10 and 400 in the
formula (1.4) come from the empirical distribut ion of the chess world. Together, they
simply imply a change in the scale of the x-axis in Figure 1.2.
In Figure 1.3, it is easy to see that the expected score model (see (1.4)) is a logist ic
model in terms of the rat ing di↵erences r i − r j , and that the constants 400 and 10 in
(1.4) only lead to a rescaling of the x-axis.
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