How smart is Football Data Analytics today?

Dr. Stefan Kühn data2day - Karlsruhe29.09.2015

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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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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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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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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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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Ignorance-

Part 1

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Corners

Claim: Long corners are overrated, short corners are better, see e.g. Barca.

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Long corners versus Short corners

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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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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Corners

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Ignorance-

Part 2

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Marginal Goals

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Claim: Some goals count more than others, one should rate players according to this.

Marginal goals

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Why they should have bought Darren Bent

What do you think?

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.

Ignorance-

Part 3

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Substitutions and Scoring

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Substitutions and Scoring

ClaimSubs 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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Substitutions and Scoring

Only forwardsControlled for time on the field

• Claim: Fatigue is the cause of this effect!

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Substitutions and Scoring

A closer lookEstimates 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?

Substitutions and Scoring

Detected ReasonFatigue, subs are fitter

• What do you think, when looking at this graph?

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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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Alternatives

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What keeps Football Data Analytics from being smart?

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Requirements

+ Scientific Method!

Reality

Tools Data

Money

???

+ Severe Time Constraint + Results must impress

What keeps Data Analytics from being smart?

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Requirements

+ Scientific Method!

Reality

Tools Data

Money

???

+ Severe Time Constraint + Results must impress

Alternatives

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Thanks a lot!

And enjoy the game :-)

www.codecentric.deblog.codecentric.de

stefan.kuehn@codecentric.de