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.

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

@ 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.

3 3

Views are our own. All information and insights contained in this presentation are

either public or common knowledge

Disclaimer

Agenda

Football as a business (summary)

Digital Transformation in Football (summary)

Big Data opportunities in the Football industry

5 5

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

6 6

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)

7 7

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?

8 8

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

+

+

9 9

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

+

+

10 10

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%

11 11 Source: 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.

12 12

Source:

Revenue-sharing model in the football industry

Revenue-sharing model

A case of sustainable

advantage based on asymmetric information?

13 13

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)

14 14

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 13/14 Season

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

15 15

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

Agenda

Football as a business (summary)

Digital Transformation in Football (summary)

Big Data opportunities in the Football industry

17 17

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

18 18

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

19 19

Customer Value Vs Brand value

Source: Harvard Business Review; Markables

20 20

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

High Required Broadband

Non-Live

Live

Nature of content

News

Music Books Movies,

TV Series

21 21

Why Digital Transformation in Football?

Global revenue opportunities

Threat of piracy

Digital Transformation

Strategic problem

22 22

Strategic Digital Transformation in Soccer

More info: Strategic Digital Transformation in Soccer

FURTHER READING

Agenda

Football as a business (summary)

Digital Transformation in Football (summary)

Big Data opportunities in the Football industry

24 24

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

25 25

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)

26 26

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)

Agenda

Football as a business (summary)

Digital Transformation in Football (summary)

Big Data opportunities in the Football industry

•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

Thanks very much for your attention and interaction

Francisco Hernández francisco_hernandez@11goals.com

(+34) 605 58 66 55