Rory Campbell, Head of Technical Analysis at West Ham United. CEO of C&N Sporting Risk talks about data and Analytics have been slow to penetrate football the way it has other sports. However, the high variance of the game and the lack of effective data strategies being utilized means there is a huge competitive advantage to be gained by using analytics properly in different sectors throughout the sport. Today, with the World Cup in full swing, Rory discusses how the role of analytics in football has changed, why more football clubs are being run like data-driven buesinesses, and how a new generation is enabling the next level of competitive gains to be made on and off the pitch.

1. FOOTBALL’S COMPETITIVE
ADVANTAGE: FROM TALENT, TO
TECHNOLOGY & ANALYTICS
HOW IS THE USE OF ANALYTICS AND TECHNOLOGY PROVIDING PEOPLE
WITH A COMPETITIVE ADVANTAGE IN FOOTBALL?

2. DATA IN FOOTBALL – WHERE ARE WE?
• Moneyball
• What did it do to data analysis in sport?
• What did Moneyball actually mean?
• Beating the betting markets and the successful use of data in football.
• To Describe -> To Analyse -> To Predict -> To Implement.
• Use and process across sectors in football and beyond.

3. DATA IN FOOTBALL – WHAT DO WE HAVE?
• Basic understanding of what we have – appearances, goals, assists, saves etc
• Physical data vs technical data
• Most significant development has been the ability to apply x,y coordinates to technical data to provide more
context to the event
• Events: Shots, Tackles, Passes, Interceptions…….
• Types of events: Through ball or Cross, Foot or Head…….
• Contextualised for every impacting factor – location, strength of opposition, strength of own team, game
state…….

4. DOES ANALYTICS HAVE VALUE IN FOOTBALL?
• Football is a low scoring game and therefore there is huge variance in results and performance.
• Where does analytics have more value?
1. Golf
2. Basketball
3. Football
• The harder the challenge of properly turning data into analytics the bigger the competitive gain of getting it right.
• Uncertainty and variance are good things.

5. UNDERSTANDING STATISTICS AND ANALYTICS
• The key premise behind all data analysis
• What are statistics?
• What is analytics?
• Importance of generating predictive utility
• Analytics is a process of properly understanding the information that we have about the past and turning that
into valuable insight that we can use to better understand and predict the future.

6. PREDICTIVE UTILITY – IMPORTANCE AND CHALLENGE
• Does a piece of information help me better predict expected performance and outcome in the future?
• This question has to be at the heart of every statistical and analytical process.
• Sample size – do we have a big enough sample of data?
• Like all industries now the wealth of information presents opportunities and challenges.
• Distinguishing between what is important and the noise…….

7. STATISTICS VS ANALYTICS – 2017/2018 PL SEASON
Player Key Passes/90 Added Creating Goal Value/90
Kevin De Bruyne 2.9 0.97
Sadio Mane 1.7 0.88
Raheem Sterling 1.7 0.72
Pascal Gross 2.2 0.71
Mezut Ozil 3.2 0.59
Christian Eriksen 2.6 0.55
David Silva 2.1 0.42
Cesc Fabregas 2.8 0.41
Dele Alli 1.8 0.39

8. GENERATING PREDICTIVE UTILITY
• Every event on the pitch has an impact on the chances of scoring or conceding a goal and therefore the
chances of winning or losing.
• To provide an accurate value to every action.
• To invest in understanding that value.
• To invest in creating and developing that value.

9. EXAMPLES OF SPECIFIC PROCESSES
• What are basic expected goal models?
• Logistic regression or random forest.
• Moving beyond expected goals – The Burnley and Atletico Madrid factor.
• Player and Team Suitability using Machine Learning.
• Modelling all events in a time series.
• Game flow models.
• The peak of modelling football – does it exist?

10. STRATEGIC
IMPLEMENTATION

11. INEFFICIENT BUT COMMON USE OF ‘ANALYTICS’
• ‘We know this data thing might be important so we should collect it, buy it, do something with it,
probably employ some smart people to do something with it, we will look at it and find it interesting
and maybe use it every now and again.’
• Data and analytics become a bolt on tool to the decision making process in an inefficient way.
• This is so common……… smart people, smart ideas, smart work but is it integrated into objective,
strategy, decision making and process in the right way?
• What is the challenge of big data in implementation?

12. ANALYTICS DEFINING OBJECTIVES AND DRIVING
DECISION MAKING
• What can numbers do?
• Numbers enable you to define things properly.
• What was the score?
• How much did we make?
• How much did we win/lose by?
• All of these common questions are thrown around defining success and they are answered by numbers.
• Numbers allow us to define success or failure very clearly.
• Analytics allow you to clearly define objectives and in a lot of sectors objectives are becoming a lot
more difficult to define.

13. WHAT IS THE OBJECTIVE?
• Most objectives are grand, bold and broad statements and that is good but what do they actually
mean?
• A proper analytical process allows us to define and understand the objective.
• What is the objective?
• What does that objective look like?
• Where are we now and what does that look like?
• What are the gaps to the objective?

14. OBJECTIVE: QUALIFY FOR THE CHAMPIONS LEAGUE
QUALIFY FOR CHAMPIONS LEAGUE TOP 4 IN PREMIER LEAGUE
How many points? ?
How many goals? ?
How many goals conceded? ?
How many shots and from where? ?
How many shots conceded and from where? ?
How many entries into areas on the pitch and how? ?
How to prevent entries into areas on the pitch? ?
Most valuable actions in volume and quality? ?
Most valuable actions in volume and quality to prevent? ?

15. PROCESS
• Can we bridge the gap?
• How do we bridge the gap?
• When are we going to bridge the gap?
• In what stages are we going to bridge the gap?
• Examples from other sports

16. COMMUNICATION OF ANALYTICS IN CHANGING
ORGANISATIONAL STRUCTURES
• Analytics presents challenges for decision making in traditional structures.
• Analytics in a business model vs traditional top down decision making.
• Decision makers fed with information and processes they may be less comfortable with.
• Challenges this presents the analytics world:
1. Ego/Insecurity
2. Channels of communciation
3. Ensuring understanding and proper implementation
• Often the challenge is the following: Turning complex information into presentable information that better influences:
1. Communicate the logic and sense behind the analysis and the process
2. Make results understandable and logical
3. Understand the audience and decision makers

17. WHEN IS ANALYTICS MOST VALUABLE
When analytics is used at its most efficient it is at the forefront of defining
objective and strategy and therefore enables the process and decision making to
be driven by a clear process of value and implementation with qualitative
analysis and instinct working alongside that in a more structured process.