This document discusses the role of data analytics in football. It provides details about the author's education and qualifications in sports performance analysis and football analytics. It then discusses some of the main applications of data analytics in football, including performance analysis, opposition analysis, and recruitment analysis. For each application, it provides examples of the types of data, visualizations, and reports that can be generated. It also includes some case studies on leveraging data for defending set pieces and analyzing an individual player's performance in different positions. The document notes some limitations of relying only on data but emphasizes that data analysis can effectively inform and complement video analysis when used as part of an integrated process.

1. Analytics in
Football:
Changing the
Perspective

2. Education:
• BSc Computer Science – UCY
• MSc Sports Performance Analysis – LBU
• Professional Diploma in Football Tactical Analysis –
Barca Innovation Hub
• Mathematical Modelling of Football – UU
Qualifications:
• UEFA C Coaching License
• PFSA Level 1 courses
• The FA courses
• Statsbomb football analytics course
• Telestration software certificates
Experience:
• York City FC
• Chesterfield FC
• Pafos FC
• Dinamo Tbilisi FC
About Me

3. Data Analytics in Football
• Performance Analysis
• Opposition Analysis
• Recruitment Analysis
Data Providers
Extraction/Manipulation
Visualisations

4. Recruitment Analysis
• Data analysis provides a way of “filtering” players
and reducing scouts’ biases.
• In its depth, it can predict players’ performance
levels (dangerous!).
• Having benchmarks and minimum standards can
provide a clear image of a player.
• Numbers never lie, wrong interpretation does.
• A way of providing an objective opinion for a
player.
• Custom metrics.
Data
Analysis
Traditional
Scouting
Safer
Predictions
Data Report Scout Report
Dossier (Data
+ Scout
Reports)
Deeper
Analysis
Created a scouting platform: FBRefScout

5. Recruitment Analysis
1. Percentile ranking bar chart & target values.

6. Recruitment Analysis
2. Pizza plots for specific position metrics.

7. Recruitment Analysis
3. Plot events according to the player’s
position/team’s importance.

8. Recruitment Analysis
4. Projecting performance levels to our league.

9. Recruitment Analysis
5. Physical Data

10. Opposition Analysis
• Both video and data analysis are important.
• Data analysis can reduce the waste of time.
• Efficiency.
• Focus on what you need to know.
• Easy to review and recreate analysis.
• Things that the eye might not see.
Ways to look at teams with data:
• Direct
• Compared to league
• Over time
•Data Analyst
creates data
report
•Data report is
given to
performance
analyst
Data
Report
•Performance
analyst creates
pre-match
report
•Pre-match
report is given
to assistant
coach.
Pre-
match
report
Team
preparation
based on pre-
match report
Coaches'
strategies

11. Opposition Analysis - Examples

12. Opposition Analysis - Examples

13. Opposition Analysis - Examples

14. Opposition Analysis - Examples
@DanielKatona17
@BeGriffis

15. Opposition Analysis - Examples
Case Study: Leveraging Tableau and Data to Break Down Defending Set Pieces in Football: A Look into the Cypriot 1st Division
Platform: Set-Pieces Analytics Platform
Outcome: Creates a much faster and more objective analysis
cycle for the club (Data & Performance Analysis)

16. Performance Analysis
General information about the league.
General team assessment.
Individual player’s assessment.
Comparisons.

17. Performance Analysis
General information about the league
@yureedelahi

18. Performance Analysis
General team assessment

19. Performance Analysis
Individual player’s assessments
@Dynamo Tbilisi – Fajrin Buyang Daffa

20. Performance Analysis
Comparisons
Comparisons

21. Case Study
• Conversation with friends arguing whether Willy
Semedo is more dangerous in a wider position, or
in an inside forward position in the pitch.
• I insisted that Semedo was much more dangerous
in a wider position on the pitch.
• How can I prove that to my friends, but more
importantly to the coaching staff?
@Soccermatics

22. Other Uses
@markrstats
@BtoBAnalytics

23. Other Uses
@EredivisiePlots
@BeGriffis

24. Goals
1
Develop self-
sustaining
automated
dashboards
and reports to
minimize
maintenance.
2
Reduced
maintenance
leads to free
time, allowing
for continuous
learning,
research, and
further
creation.
3
Design
processes and
methodologies
in collaboration
with the
coaching staff.
4
While data
offers objective
insights, it's
crucial to
interpret and
visualize them
accurately to
uphold their
integrity.
5
Launch
initiatives to
design custom
metrics and
visualizations to
enhance
performance.
6
Deliver an
unbiased
perspective on
performance
analysis.
7
Video analysis
remains a
crucial tool,
and its
effectiveness is
enhanced when
integrated with
data.
8
Maintain open
and consistent
communication
, ensuring
alignment with
the coaching
staff and the
game model.

25. Limitations
Concluding from data alone
can be dangerous, especially
when making broad
assumptions.
It’s crucial to view data
analysis as a tool to inform
and complement video
analysis, guiding potential
implications and
recommendations.
Approached in this manner,
the process becomes more
reliable, effective, and
efficient.
Countries with limited
resources, such as Cyprus,
may face challenges in
partnering with external data
providers.
While manual event logging is
possible, it demands
significant time and effort.

26. THANK YOU!
“It’s a testament to the fact that passion, innovation, and determination can
bridge the gap that financial constraints might pose.”