Data Warehouse for Professional Soccer Organization

1. Team 007- Monica Deborah | Hiren Shah | Vikal Gupta | Saket Chandra Singh
Data Warehouse for Utah Soccer
League
IS 6480: Data Warehousing

2. Soccer Analysis: Outline
Outline
• Introduction
• Fun Facts
• Objective
• Lifecycle
• Planning
• Requirements
• Logical Design
• Physical Design
• Data Integration
• Deployment
• Analysis

3. Soccer Analysis: Introduction
Team Introduction
Monica Deborah Vijaykumar
MSIS-David Eccles School of Business
Vikal Gupta
MSIS-David Eccles School of Business
Saket Singh
MSIS-David Eccles School of Business
Hiren Shah
MSIS-David Eccles School of Business

4. Soccer Analysis: Introduction
Project Introduction
• Started in Early 1800s.
• Involves 11 players on each side. 1 goal keeper, defenders,
midfielders and forwards.
• The game is played on a rectangular field with goal at each end.
The objective of the game is to score into the opposing goal
without using arms or hands.
• The international governing body of soccer is FIFA.

5. Soccer Analysis: Fun Facts
Fun Facts
• Soccer also known as football is the most popular sport in the
world. It is being played by over 250 million people every year
in more than 200 countries.
• On average, soccer players run as far as 9.5 miles in a single
match.
• FIFA has more member countries than the U.N.
• A refereeing decision in a soccer match between Argentina and
Peru in 1964 led to a riot in which 300 fans were killed.

6. Soccer Analysis: Mission
MISSION OF UTAH SOCCER LEAGUE
• Organizing inspiring tournaments

7. Soccer Analysis: Objective
Objective
• Offensive Production
• Fan Satisfaction
• Significance of location of an event for Offensive Production

8. Soccer Analysis: Lifecycle
Data Warehousing Life Cycle
Planning
Requirements
Logical
Design
Physical
Design
Data
integration
Deployment
Maintenance
Analysis

9. Soccer Analysis: Planning
Planning

10. Soccer Analysis: Planning
ASANA DASHBOARD

11. Soccer Analysis: Requirements
Requirements
• To create a dimensional model to
measure the offensive production
based on location and events
• To perform analysis based on the
model created

12. Soccer Analysis: Brain Storming
Regions

13. Soccer Analysis: Brain Storming
Region Wise Offensive Level

14. Soccer Analysis: Brain Storming
Defining offensive production
Region Location
Location
Weight
1
(Left Side)
(-60,70) to (0,56) 1
2
(Left Side)
(0,56) to (60,70) 1
3
(Penalty Area)
(-60,56) to (-42,14) 4
4
(Penalty Area)
(42,14) to (60,56) 4
5
(Right Side)
(-60,0) to (0,14) 1
6
(Right Side)
(0,14) to (60,0) 1
7
(Middle Area)
(-42,14) to (0,56) 2
8
(Middle Area)
(0,56) to (42,14) 2
Location
Weight
Offensive Level
1 Low Offensive
2 Medium Offensive
4 High Offensive
Event
Weight
Offensive Level Events
0 Not Offensive End of half, throw in
1 Low Offensive Pass, tackle
2 Medium Offensive Offside, Corner Cross
3 High Offensive Crossbar, Post
4 Very High Offensive Goal, Penalty Shot

15. Soccer Analysis: Logical Design
Logical Design

16. Soccer Analysis: Physical Design
Physical Design

17. Soccer Analysis: DI
Data Integration (Dimensions)

18. Soccer Analysis: DI
Data Integration (Location Transformation)

19. Soccer Analysis: DI
Data Integration (Event Transformation)

20. Soccer Analysis: DI
Data Integration (ETL for Fact Input)

21. Soccer Analysis: DI
Data Integration (Event Fact Transformation)

22. Soccer Analysis: DI
Data Integration (Location Fact Transformation)

23. Soccer Analysis: DI
Data Integration

24. Soccer Analysis: Deployment
Deployment (Environment Setup)
• Improved the AWS EC2 instance type from t2.medium to t2.2xLarge
• Allocated high process memory to spoon.bat Data Integration
java process
• Added indexing on attributes used to join tables in MySQL
• Time out and performance tuning for MySQL server

25. Soccer Analysis: Analysis
OLAP Analysis

26. Soccer Analysis: Analysis
Analysis
2641.75
2458.25
2159.00
2101.40 2079.33
1923.75 1873.50
1803.50 1773.13 1752.00
1691.67
1607.67 1588.60 1571.33
1457.33
0
1
2
3
4
5
6
7
8
9
0.00
500.00
1000.00
1500.00
2000.00
2500.00
3000.00
team15 team8 team19 team4 team17 team6 team20 team10 team16 team13 team7 team9 team2 team18 team22
OffensiveProduction
Average Offensive Production per Match (Team)
Average Offensive Production Matches

27. Soccer Analysis: Analysis
Analysis
367.33 362.25
334.80
318.75
304.25
293.50 291.33 291.00 283.50 277.00 274.00 274.00 266.33 266.25
0
1
2
3
4
5
6
7
0.00
50.00
100.00
150.00
200.00
250.00
300.00
350.00
400.00
OffensiveProduction
Average Offensive Production per Match (Player)
Average Offensive Production Games

28. Soccer Analysis: Analysis
OLAP Analysis

29. Soccer Analysis: Analysis
OLAP Analysis

30. Soccer Analysis: Analysis
Conclusion
• We calculate offensive production with 2 parameters, that are location weight and event weight.
Based on our analysis, we came to conclusion that team 15 produces the highest average offens
ive production as compared to other teams on ground.
• And also the top 2 players who produces the most offensive weight are player no- 154 and 195,
who belongs to the same team 15.
• Hence we can conclude that team 15 is more towards fan satisfaction.

31. It’s a Goal!
THANK YOU