Big data opportunities for the Digitally Transforming Football Industry

1. Big data opportunities for
the Digitally Transforming
Football Industry
Big Data in Sports conference
Francisco Hernández-Marcos
May 25th, 2016
This document has been produced by 11 Goals & Associates. It is not complete unless
supported by the underlying detailed analyses and oral presentation.

2. About me SHAMELESS
SELF-PROMOTION
Education: Universidad Politécnica de Madrid, UNED,
London Business School, University of Chicago – Fundaciò
“laCaixa” & Fundación Rafael del Pino scholarships.
Firms worked for: Abengoa, McKinsey&Co, ABN AMRO,
Real Madrid C.F.
Entrepreneurship: Crisalia
Social Media & Internet consulting: 11goals.com
Lectures & Speaker in 4 continents: The Wall Street Journal, UP Madrid, London
Business School, Cornell, Politecnico Milano, CEIBS (Shanghai), Kungliga Tekniska
högskolan, The Business Factory, Fulbright Spain, ESCP Europe, UIMP, Harvard,
Moscow SU, IE, and several private companies.
Full profile: linkedin.com/in/franciscohm

3. @ Real Madrid
• Former Director of Online Strategy, depending directly from the
Club’s chairman.
• Designed integrated Digital Strategy, both in terms of attracting
traffic and monetising it.
• Designed and implemented Social Media model. Real Madrid
climbed from #3 to #1 team worldwide, in a season where FC
Barcelona won all possible titles, and Real Madrid experienced
the largest decrease of fans of any football team (source: Sport
Markt).
• Most active Facebook page worldwide in any category. A record
that has not been broken by any sports team yet.
• First international team to open presence in Chinese Social
Media.
• Several awards and recognitions, including that of the most
valuable Facebook page in terms of economic value for the club.
• Football and Digital advise to football clubs in 4 countries.

4. 33
Views are our own. All information and
insights contained in this presentation are
either public or common knowledge
Disclaimer

5. Agenda
 Football as a business (summary)
 Digital Transformation in Football (summary)
 Big Data opportunities in the Football industry

6. 55
Competitive dynamics in the Football Industry
• Monopolistic and regulatory power
• Highly corrupt
Leagues/
Federations
• Oligopolistic, based on revenues and ability to attract key players
and coaches
• Most clubs are systematically making loses, only top clubs make
profits, but not extraordinary compared to other “oligopolistic”
industries
• Heavy externalities (e.g. PR)
Clubs
• Extremely competitive environment based on quality of product,
but not price
• Top players having immense bargaining power
Players

7. 66
Football clubs revenue ranking
Source: Deloitte Football Money League (2016); UEFA (2012)
577
561
520
481
474
464
436
420
392
324
281
258
220
199
187
180
169
165
165
161
Real Madrid
FC Barcelona
Manchester United
Paris Saint-Germain
Bayern Munich
Manchester City
Arsenal
Chelsea
Liverpool
Juventus
Borussia Dortmund
Tottenham Hotspur
Schalke 04
AC Milan
Atlético de Madrid
AS Roma
Newcastle United
Everton
Internazionale
West Ham United
Revenue (14/15)
EUR mill.
First
56%
Second
21%
Third
8%
Other
15%
Revenue matters. Securing a significant amount
of recurring revenue is very likely the most
important factor for succeeding in the pitch
Finishing position of highest-
spending club in players wages
(UEFA domestic leagues)

8. 77
Some research shows that league position is strongly
correlated (R2=89%) with wage expenditure
Source: Footballnomics
BACK-UP
…but same research tells us that correlation with transfer spending is low (R2=16%)
Are wealthier clubs profiting
from the non-existence of
superior options for over-
performing players?

9. 88
Revenue breakdown and key drivers
Revenue
Matchday
Broadcast
Commercial
Domestic
International
(Champions League,
Europa League)
Drivers
• Stadium ownership
• Stadium size
• Income per capita
• VIP facilities
• Dynamic pricing (when possible)
Actionable by the club
Drivers
• Lobbying & bargaining to the league
• Team performance
• League salesforce skills
Drivers
• Team performance
• League salesforce skills
Drivers
• Historical Team Performance
• Brand positioning
• Fan base
• Big Ticket contracts bargaining
• Long-tail contracts salesforce
• Loyalty card
• Summer tours
+
+

10. 99
Cost breakdown and key drivers
Expenses
Team wages
Amortizations
G&A
Drivers
• Relative bargaining power with players/agents
‐ Hiring (eg. Revenue-sharing model)
‐ Renewing (e.g. wage steps)
Actionable by the club
Drivers
• Several small factors
Drivers
• Accumulated Net Investment
(more later)
• Legal rate of amortization
+
+

11. 1010
Cost drivers
Source: own analysis based on Annual Statements (2013/14); UEFA (2012)
ILLUSTRATIVE
EXAMPLES
Ratios to revenue
44% 48%
74% 69% 69% 69% 61%
51%
65%
17% 12%
Real
Madrid
FC
Barcelona
Average
Turkey
Average
Italy
Average
England
Average
Russia
Average
Spain
Average
Germany
Average
UEFA
Other
Amortization
Wages
•Team wages account for the most part of an average club costs
•There are significant differences in cost management among clubs (e.g. wage steps)
•57% of UEFA member clubs are loss-making
•Cost is the main driver of a Club’s profitability. Most Clubs are loss-making because
they are not enough diligent on the cost base
•UEFA is concern about these issues and is implementing “Financial Fair Game” policies
0%-8%-11% 2% -8%
Net profit to revenue ratio
EBITDA:
164 M.€
EBITDA:
134 M.€
-9%-22%

12. 1111Source: SportYou
EUR mill. - Estimation
Net (after tax) player’s wages (2015/16)
17
11
10
8
6
6
5
4,5
3,8
3
2,8
2,5
2,4
2,4
2
2
2
1,2
1,2
1,2
1,2
1
Cristiano
Bale
Ramos
Benzema
James
Kroos
Marcelo
Modric
Pepe
Casemiro
Arbeloa
Danilo
Kovacic
Varane
Carvajal
Isco
Keylor
Jesé
Nacho
K. Casilla
Chersyshey
L. Vazquez
21,2
10
10
7,5
6,5
6
6
5,8
5,5
4
4
3,5
3,5
3
3
2,5
2,5
2
2
1,5
1,5
1
1
1
Messi
Neymar
Suárez
Iniesta
Rakitic
Busquets
Alves
Piqué
Mascherano
Jordi Alba
Arda Turan
Claudio Bravo
Vermaelen
Ter Stegen
Mathieu
Adriano
Aleix Vidal
Rafinha
Barta
Sergi Roberto
Douglas
Masip
Munir
Sandro
Total: EUR 96,2 mill
Average: EUR 4,4 mill
Wage to Turnover ratio: 41%
Net profit: EUR 42 mill.
Real Madrid CF FC Barcelona
Total: EUR 114,5 mill
Average: EUR 4,8 mill
Wage to Turnover ratio: 47%
(73% with amortizations)
Net profit: EUR 15 mill.

13. 1212
Source:
Revenue-sharing model in the football industry
Revenue-
sharing model
A case of
sustainable
advantage based
on asymmetric
information?

14. 1313
Net Investment breakdown and key drivers
Net Investment
Arrivals
expenditure
Departures
income
Actionable by the club
-
Drivers
• Relative bargaining power with players/agents
• Revenue Sharing Model
• Flexible , performance driven, terms
Drivers
• Relative bargaining power with players/agents
• Alternative departure models (lease, free, etc)

15. 1414
Source: Transfer Markt; own analysis
Note: Some transfers data are estimations
Player transfers of selected clubs
-200
-100
0
100
200
300
Ronaldo;
Kaká;
Alonso;
Benzema
Departures income (GBP mill.)
Arrivals expenditure (GBP mill.)
Net income (GBP mill.)
Real Madrid CF
05/06 06/07 07/08 08/09 09/10 10/11 11/12 12/13 13/14Season
473
998
-525
Total 05/06 to 13/14
Ramos;
Robinho
Diarra;
Gago
Robben;
Pepe;
Sneijder
Huntelar
DiMaría;
Özil;
Khedira
Coentrão
Modric
Bale;
Isco James;
Kroos
14/15
-20
-10
0
10
20
30
40
Athletic Bilbao
84
49
35
Del Horno
Aduritz
Martínez Herrera

16. 1515
Football: The rules of the Business
1) Football has social value, and business value too
2) The better players the team has (measured by market wage), the better the team does on the
pitch
3) Revenue drives long-term team’s performance
4) Commercial the most important source of revenue: larger and growing faster
5) Matchday revenue drivers: Stadium ownership, VIP
6) Broadcast revenue drivers: domestic league value and team distribution, and success on
international tournaments
7) Commercial: Big-ticket contracts (dependant on TV audience) are key. Stadium naming rights will
bring significant growth soon
8) Main cost driver is team wages. It determines (un)profitability
More info: Football: 10 rules of the Business
FURTHER
READING

17. Agenda
 Football as a business (summary)
 Digital Transformation in Football (summary)
 Big Data opportunities in the Football industry

18. 1717
Football industry is heavily intermediated, specially when
trying to reach a global audience
Source: Xxxxx
•TV channels
•Other media
•Retailers
•Small-deal
agents
•Games
•Other

19. 1818
Strategic shift in the football industry: Digital as enabler
of a global, fan-centric organization
Content and
Brand provider
DT
Leading
relationship
with fans
(customers)
E.g.: Nespresso, Apple, Ferrari USA, Tesla Motors, Zara
Customer-centric
organizations always
create value

20. 1919
Customer Value Vs Brand value
Source: Harvard Business Review; Markables

21. 2020
Threat of media content piracy: Use Social Tech when it is
strategic for you to be closer to your end-customers
Low
•The sports industry is about to be seriously threatened by Internet piracy.
•Getting closer to the end-user would help clubs to gain insights and knowledge of the
customer, and to react and change value propositions to fight piracy.
•Also social technologies can create value-added, harder to be copied, services to bundle
with the base product.
Live Sport
Events
HighRequired Broadband
Non-Live
Live
Nature of
content
News
MusicBooks
Movies,
TV Series

22. 2121
Why Digital Transformation in Football?
Global revenue
opportunities
Threat
of piracy
Digital
Transformation
Strategic
problem

23. 2222
Strategic Digital Transformation in Soccer
More info: Strategic Digital Transformation in Soccer
FURTHER
READING

24. Agenda
 Football as a business (summary)
 Digital Transformation in Football (summary)
 Big Data opportunities in the Football industry

25. 2424
Big Data opportunities for the Revenue stream
Valuation of
contracts
Revenue
Matchday
Broadcast
Commercial
Domestic
International
(Champions League,
Europa League)
Drivers
• Stadium ownership
• Stadium size
• Income per capita
• VIP facilities
• Dynamic pricing (when possible)
Drivers
• Lobbying & bargaining to the league
• Team performance
• League salesforce skills
Drivers
• Team performance
• League salesforce skills
Drivers
• Historical Team Performance
• Brand positioning
• Fan base
• Big Ticket contracts bargaining
• Long-tail contracts salesforce
• Loyalty card
• Summer tours
+
+
Dynamic
pricing
Brand Management
(own&sponsors)
Where &
when to Tour
Team
Performance
1-to-1
communications
TV Freemium
model

26. 2525
Big Data opportunities for the Expenses stream
Expenses
Team wages
Amortizations
G&A
Drivers
• Relative bargaining power with players/agents
‐ Hiring (eg. Revenue-sharing model)
‐ Renewing (e.g. wage steps)
Drivers
• Several small factors
Drivers
• Accumulated Net Investment
• Legal rate of amortization
+
+
Player valuation
(sport+commercial)

27. 2626
Big Data opportunities for the Net Investment
stream
Net Investment
Arrivals
expenditure
Departures
income
-
Drivers
• Relative bargaining power with players/agents
• Revenue Sharing Model
• Flexible , performance driven, terms
Drivers
• Relative bargaining power with players/agents
• Alternative departure models (lease, free, etc)
Player valuation
(sport+commercial)

28. Agenda
 Football as a business (summary)
 Digital Transformation in Football (summary)
 Big Data opportunities in the Football industry

29. •Strategic consulting services in technology and digital marketing for top executives
•We advise companies on digital transformation
Francisco Hernández
•MBA London Business School.
•IEP University of Chicago.
•11 years of digital experience.
•Ex Director Online Strategy Real
Madrid C.F.
•Other companies: ABN Amro,
Abengoa, McKinsey&Company.
•Professor at ESCP Europe.
•Lecturer in Europe, Latam and
Asia
•PWC: 10 e-Business talents in
Spain.
Sonia Fernández
•MBA Stanford.
•15 years of digital experience.
•Ex CEO Vindico Europe.
•Ex CEO Match.com Spain.
•Ex CEO MercadoLibre Spain.
•Other companies: Fon, Grupo
Prisa, 3i, Lehman Brothers.
•Professor at OBS-UB, EOI and MIB
•Lecturer at universities and in-
company training
•Author of two books on
networking and social networks
published in 2004 and 2001 franciscohm
francisco.hernandez@11goals.com | (+34) 605 58 66 55
soniafernandez
sonia.fernandez@11goals.com | (+34) 619 721 781

30. Thanks very much for your
attention and interaction
Francisco Hernández
francisco_hernandez@11goals.com
(+34) 605 58 66 55