In this talk we analyse several typical mistakes in use cases from the field of football data analytics.

1. How smart is
Football Data
Analytics today?
Dr. Stefan Kühn
data2day - Karlsruhe
29.09.2015

2. Topic
Why Football Data Analytics?
• It’s about Football
• There is a lot of data out there
• There is a lot of ignorance out there
• Three examples
• Corners
• Marginal goals
• Substitutions
• Alternatives
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3. Infos
Why Football is an interesting Use Case
• 209 FIFA federations - worldwide
• Most popular sport - 3.3-3.5 billion fans
• Monetary facts - revenue (Deloitte Money League)
• Real Madrid 2013/4: 549.5 Million € (Position 1)
• Bayern Munich 2013/4: 487.5 Million € (Position 3)
• Everton 2013/4: 144.1 Million € (Position 20)
• Social Media facts (Deloitte Money League)
• Facebook: FC Barcelona - 81.4 Million Likes
• Twitter: Real Madrid - 14.4 Million Followers
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4. Some Stats
Why Football is a Data Use Case
• 306 Bundesliga matches per season
• 2000+ recorded events per match
• 512 Bundesliga players
• Live Statistics (Opta, Prozone etc.):
• Shots, Passes, Assists
• Tacklings, Blocks, intercepted Passes
• Saves and other actions of Goalkeepers
• Fouls and Foul types
• Position Data including time stamps
• 1.8 Million Amateur matches (Deutschland)
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5. Some Remarks
Is there anything left to do?
• Big companies like SAP are involved
• Players are tracked in training and matches (and
sometimes at home as well)
• Physiological data, nutrition data, training plans
★ BUT:
Big data is not about the data.
(Gary King, Harvard University, 2013)
It’s about Analytics.
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6. Some Remarks
Where is the ignorance?
• „The Number’s Game - Why Everything You
Know About Football Is Wrong“
• Book by Chris Anderson (former Cornell University
Prof) and David Sally (Economics and Behavioral
Game Theory)
• „Is it easier to score as a sub“?
• Blogpost by Dan Altman, founder of North Yard
Analytics
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7. Ignorance
-
Part 1
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8. Corners
Claim: Long corners are overrated, short
corners are better, see e.g. Barca.
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Long corners versus Short corners

9. Corners
Some useful stats
• Average number of goals per team per match: 1.3
• Average number of corners per team per match: 5
• Long corners account for ~8.5% of all goals
• Silly question: The average team scores once
every ten games from a penalty, shall they give
up on penalties as well?
• Lack of relevant context
• How efficient are the alternatives?
• How efficient is the average possession?
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10. Corners
Average Possession
• Average number of possessions per team per match: 200
• Average number of goals per team per match: 1.3
• Expectation value per possession: 0.0065
• Normalized per match (200 possessions):
• All possessions are corners: 4.4 goals
• Half of the possessions are corner: 2.85 goals
• 10% of the possessions are corners: 1.46 goals
• The efficiency of long corners is more than three times
as high as the efficiency of the average possession.
• Still unknown:
• How efficient are the alternatives?
• Are there any negative counter effects?
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11. Corners
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12. Ignorance
-
Part 2
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13. Marginal Goals
13
Claim:
Some goals count
more than others,
one should rate
players according
to this.

14. Marginal goals
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Why they should have bought Darren Bent
What do you think?

15. Marginal goals
Why they should have bought a book on hypothesis testing
• How many second goals could have been scored without the first goal?
• Do the samples for matches with one (own) goal, two goals etc. differ,
and if yes (it’s a definite yes, selection bias): how?
• Is it more likely to score more against weaker teams and less against
stronger teams?
• And of course: The events considered here are not statistically
independent.
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What they should have done
• Compute marginal goals per sample group (e.g. fixed number of own goals).
Here, the first goal cannot have less marginal points than the second goal etc.
which is the only reasonable result.
• Do not compare apples and pies. (In some sense Simpson’s paradox)
• Or: Hire the best striker for first goals and the best striker for second goals.

16. Ignorance
-
Part 3
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17. Substitutions and Scoring
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18. Substitutions and Scoring
Claim
Subs score more
than expected
• This is the first
correct claim!
• But still weak
effect, unknown
reason(s)
• Do opponents
score more as
well?
• Corrections needed
• 36% of subs are
forwards
• Individual Orders
• Tactical changes
• Lots of other things
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19. Substitutions and Scoring
Only
forwards
Controlled
for time on
the field
• Claim:
Fatigue is
the cause
of this
effect!
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20. Substitutions and Scoring
A closer look
Estimates for
the mean for
first and
second half
• Analysis:
No control for
fatigue
possible, only
control for
time spent on
the field.
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From minute 60
on the share of
subs starts to
rise. Effect on
number of goals?

21. Substitutions and Scoring
Detected
Reason
Fatigue,
subs are
fitter
• What do
you think,
when
looking at
this graph?
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22. Summary
What are the commonalities in all cases?
• „New“ spectacular insights
• Preconceptions
• Confirmation Bias
• Lack of reflection
• Challenging own results?
• Alternative explanations?
• Do not mix up a variable and your interpretation
of this variable (fatigue vs. time on field)
• BUT: Data and Tools have been good!
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23. Alternatives
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24. What keeps Football Data Analytics from being smart?
24
Requirements
+ Scientific Method!
Reality
Tools Data
Money
???
+ Severe Time Constraint
+ Results must impress

25. What keeps Data Analytics from being smart?
25
Requirements
+ Scientific Method!
Reality
Tools Data
Money
???
+ Severe Time Constraint
+ Results must impress

26. Alternatives
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27. 27
Thanks a lot!
And enjoy the game :-)
www.codecentric.de
blog.codecentric.de
stefan.kuehn@codecentric.de