Knowledge Management for
UEFA Champions League
By
Harsha Gunnam
Hetal Mehta
Nargis Memon
Manish Wadhwa
Individual Contributions
Introduction
Objectives
Process
Tasks
Initial Research
Basic Findings
Sources
Conclusion
Future Work
Harsha
Hetal
Nargis
Manish
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Literature Review
Data Extraction
Interesting Findings
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Database Creation
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Coefficient Analysis
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Ranking Analysis
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Trend Analysis
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Prediction of Winners
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Testing of Results
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Final Report & Presentation
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Agenda
Introduction
Objectives
Process
Sources
Basic Findings
Interesting Findings
Conclusion
Future Work
Introduction
Objectives
Process
Sources
Basic Findings
Interesting Findings
Conclusion
Future Work
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Introduction
Introduction
Objectives
Process
Sources
Basic Findings
Interesting Findings
Conclusion
Future Work
 UEFA - The governing body of football on the continent of Europe
 Champions League –
 Started in 1992
 Most Prestigious Trophy in the Sport
 Current Champion: AC Milan
 Format –
UEFA Champions League Competition System
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1st qualifying round
24
2nd qualifying round
16+12
3rd qualifying round
18+14
Group stage
16+16
First knock-out round
16
Quarter finals
8
Semi-finals
4
Final
2
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Research Objectives
Objectives
Introduction
Process
Sources
Basic Findings
Interesting Findings
Conclusion
Future Work
Identify the top three leagues for 2008-2009
Identify top 4 clubs for the top 3 leagues
Identify the home and away advantage
Identify the head-to-head probability
Identify possible array of winners for 2008-2009
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Literature Review
Introduction
Objectives
Process
Sources
Basic Findings
Interesting Findings
Conclusion
Future Work
 Papahristodoulou, Christos, "Team Performance in UEFA Champions League
2005-06." Munich Personal RePEc Archive (2006) Unpublished, Paper #138
 Barros, Carlos Pestana, Leach, Stephanie, “Performance evaluation of the
English Premier Football League with data envelopment analysis.” Applied
Economics Vol. 38 No. 12 (2006): 1449-1458
 http://www.betinf.com/champ.htm
 Sports Betting Information
 http://www.betstudy.com/soccer-stats/c/europe/uefa-champions-league/
 Online Betting Guide
 http://en.uclpredictor.uefa.com/
 Online Predictor Game
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Research Design
Objectives
Introduction
Process
Sources
Basic Findings
Interesting Findings
Conclusion
Future Work
Coefficient
Analysis
Head-to-Head
Probability
UEFA Data
Source
Ranking
Analysis
Data
Extraction
Array of
Winners
Top 3
Leagues
MySQL
Excel
Home & Away
Advantage
Top 12 Teams
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Data Sources
Introduction
Objectives
Process
Basic Findings
Sources
Interesting Findings
Conclusion
Future Work
 UEFA Data Source
 http://www.xs4all.nl/~kassiesa/bert/uefa/
 Data collected over 6 seasons
 2002-2008
 Attributes Table Name Team Statistics
Team
Cup
Qualifying Wins
Qualifying Draws
Team Country Team Coefficient Country Coefficient Matches
Team
Team
Country
Year
Country
Team Coefficient
Country Coefficient
Round
Year
Year
Team
Goals
Qualifying Losses
Fields
Number of Wins
Number of Draws
Number of Losses
Bonus
Points
Year
Cardinality
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1185
449
788
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637
8
Basic Findings
Introduction
Objectives
Process
Sources
Basic Findings
Interesting Findings
Conclusion
Future Work
Coefficient Analysis
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Coefficient Analysis
Introduction
Objectives
Process
Sources
Basic Findings
Interesting Findings
Conclusion
Future Work
 Standard UEFA Calculations
 Country Coefficient = Number of Points/Number of Teams
 Calculation Accuracy: 100%
 Team Coefficient = Number of Points + 33% of Country Coefficient
 Calculation Accuracy: 100%
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Basic Findings
Introduction
Objectives
Process
Sources
Basic Findings
Interesting Findings
Conclusion
Future Work
Ranking Analysis
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Predicted Ranking
Introduction
Objectives
Process
Sources
Basic Findings
Interesting Findings
Conclusion
Future Work
 Accuracy: 100%
 Years Considered: 2003-2008
 Country Ranking = Summation of 5
years of Country Coefficients
 Team Ranking = Summation of 5
years of Team Coefficients
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Ranking Analysis - Leagues
Objectives
Introduction
Process
Sources
Basic Findings
Interesting Findings
Conclusion
Future Work
Country Ranking
2003
2004
2005
2006
2007
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
2008
England
France
Germany
Italy
Netherlands
Portugal
Romania
Russia
Spain
Turkey
 Observations:
 Spain, England and Italy: Top three leagues for the past six seasons
 Romania: Rapid Improvement
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Top Leagues & Teams
Introduction
Objectives
Process
Sources
Basic Findings
Interesting Findings
Conclusion
Future Work
Spain
• FC Barcelona
• Real Madrid
• Sevilla
• Valencia
England
• Arsenal
• Chelsea
• Liverpool
• Manchester United
Italy
• AC Milan
• AS Roma
• Internazionale
• Juventus
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Ranking Analysis - Teams
Objectives
Introduction
Process
Sources
Basic Findings
Interesting Findings
Conclusion
Future Work
Team Ranking
2003
2004
2005
2006
2007
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
2008
AC Milan
Arsenal
AS Roma
Chelsea
FC Barcelona
Internazionale
Juventus
Liverpool
Manchester United
Real Madrid
Sevilla
Valencia
 Observations:
 Consistent Team: FC Barcelona
 Rapid Improvement: Chelsea
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Interesting Findings
Introduction
Objectives
Process
Sources
Basic Findings
Interesting Findings
Conclusion
Future Work
Probability Analysis
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Head-to-Head Probability Analysis
Objectives
Introduction
Process
Sources
Basic Findings
Interesting Findings
Conclusion
Future Work
Arsenal Vs Others
1.2
1
0.8
0.6
0.4
Win Probability
Lose Probability
0.2
0
 Technique:
 Naive Bayes
 Observations:
 Strength: 6 Teams
 Weakness: FC Barcelona
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Head-to-Head Probability Testing
Introduction
Objectives
Process
Sources
Basic Findings
Interesting Findings
Team
Opponent
2003-07
AC Milan
Celtic
0.5
0
OK
AC Milan
Shakhtar Donetsk
1
1
OK
Arsenal
Sparta Praha
1
1
OK
AS Roma
Dinamo Kiev
-1
1
NOT OK
AS Roma
Manchester United
0
-0.75
OK
AS Roma
Real Madrid
-0.5
1
NOT OK
Chelsea
Valencia
0.5
1
OK
FC Barcelona
Celtic
0.5
1
OK
Manchester United
Olympique Lyon
0
0.5
OK
Real Madrid
Olympiakos Piraeus
0.5
0.5
OK
Conclusion
Future Work
2008 Prediction
 Technique:
 Comparison of the signs of the difference between the win probability and
the lose probability
 Matched signs – Correct Prediction
 Different signs – Incorrect Prediction
 Assumption
 Difference of zero (win-loss) favors both ways
 Accuracy: 80%
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Interesting Findings
Introduction
Objectives
Process
Sources
Basic Findings
Interesting Findings
Conclusion
Future Work
Home-Away Analysis
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Home – Away Analysis
Introduction
Objectives
Process
Sources
Basic Findings
Interesting Findings
Conclusion
Future Work
Home Advantage Analysis
14
12
10
8
Strong
6
Very Strong
4
Weak
2
Very Weak
0
 Technique:
 Mapped advantage on the basis of strength.
 Strength level decided by the difference in goals scored
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Very Strong – Win with a difference of 2 or more goals
Strong – Win with a difference of 1 goal
Weak – Draw or lose with a difference of 1 goal
Very Weak – Lose with a difference of 2 or more goals
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Home – Away Analysis
Introduction
Objectives
Process
Sources
Basic Findings
Interesting Findings
Conclusion
Future Work
Away Advantage Analysis
18
16
14
12
10
8
Strong
6
Very Strong
4
Weak
2
Very Weak
0
 Observations:
 Strongest Home Team: AC Milan
 Weakest Home Team: Real Madrid
 Strongest Visiting Team: Liverpool
 Weakest Visiting Team: Chelsea
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Interesting Findings
Introduction
Objectives
Process
Sources
Basic Findings
Interesting Findings
Conclusion
Future Work
Winners Analysis
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Prediction of Winners
Objectives
Introduction
Process
Home Win Probability
Team
Probability
FC Barcelona
0.7115625
Manchester United
0.61601563
Liverpool
0.60256579
Real Madrid
0.58089286
Valencia
0.578125
Internazionale
0.56819444
Arsenal
0.52309783
AC Milan
0.50989318
Chelsea
0.5002425
Juventus
0.45089286
Sevilla
0.38125
AS Roma
0.29888889
Sources
Basic Findings
Array of Winners
FC Barcelona
Manchester United
Liverpool
Real Madrid
Interesting Findings
Conclusion
Future Work
Away Win Probability
Team
Probability
FC Barcelona
0.62822917
Liverpool
0.52776316
Manchester United
0.50664063
Real Madrid
0.48178571
Chelsea
0.4796875
AC Milan
0.47955909
Valencia
0.4609375
Juventus
0.45089286
Internazionale
0.445
Arsenal
0.42663043
Sevilla
0.4125
AS Roma
0.295511111
 Technique:
 Assigned values to strength levels
 Aggregated the values of the strength levels
 Team Win Probability = (Aggregated Strength Value * Probability) /
Number of Matches
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Testing of Final Prediction
Objectives
Introduction
Process
Home Win Probability
Team
Probability
FC Barcelona
0.64766447
Manchester United
0.58220109
Valencia
0.57211538
Liverpool
0.52321023
Real Madrid
0.5140625
Juventus
0.51171875
AC Milan
0.503115
Internazionale
0.4919325
Arsenal
0.4880475
Chelsea
0.4759375
AS Roma
0.21875
Basic Findings
Sources
Winners for 2008
Chelsea
Manchester United
FC Barcelona
Liverpool
Interesting Findings
Conclusion
Future Work
Away Win Probability
Team
Probability
FC Barcelona
0.58799342
Liverpool
0.48579545
Chelsea
0.48263889
Manchester United
0.46535326
AC Milan
0.45249
Valencia
0.44711538
Real Madrid
0.44375
Juventus
0.4390625
Internazionale
0.43638205
Arsenal
0.43074604
AS Roma
0.27083333
 Dataset considered – 2003-2007
 Accuracy:
 Home Win: 90%
 Away Win: 100%
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Conclusion
Introduction
Objectives
Process
Sources
Basic Findings
Interesting Findings
Conclusion
Future Work
Good visiting teams have a better chance at the trophy
Sports knowledge can be transferred from tacit to explicit
Field with a wide scope for research
Good career choice – Sports Consultant
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Applications & Future Work
Introduction
Objectives
Process
Sources
Basic Findings
Interesting Findings
Conclusion
Future Work
Applications
Sports Betting
Sports Consulting
Future Work
Extended data set
Round level analysis
Player level analysis
Team strategy analysis
Consideration of UEFA Cup
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References
Introduction
Objectives
Process
Sources
Basic Findings
Interesting Findings
Future Work
Conclusion
Papahristodoulou, Christos, "Team Performance in UEFA Champions League 200506." Munich Personal RePEc Archive (2006) Unpublished, Paper #138
Barros, Carlos Pestana, Leach, Stephanie, “Performance evaluation of the English
Premier Football League with data envelopment analysis.” Applied Economics Vol.
38 No. 12 (2006): 1449-1458
 Websites:
 http://www.betinf.com/champ.htm
 http://www.betstudy.com/soccer-stats/c/europe/uefa-champions-league/
 http://en.uclpredictor.uefa.com/
 http://www.uefa.com/competitions/ucl/index.html
 http://en.wikipedia.org/wiki/Uefa_Champions_League
 http://en.wikipedia.org/wiki/Bayes_theorem
 http://www.xs4all.nl/~kassiesa/bert/uefa/
 http://www.soccerbase.com/
 http://europeancups.altervista.org/
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Thank You
Introduction
Objectives
Process
Sources
Basic Findings
Interesting Findings
Future Work
Conclusion
Thank You
Dr. Hsinchun Chen
Yulei Zhang (Gavin)
Yan Dang (Mandy)
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Questions
Introduction
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Objectives
Process
Sources
Basic Findings
MIS 580: Knowledge Management
Interesting Findings
Future Work
Conclusion
29
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MIS 580: Knowledge Management
Sparta Praha
Slovan Liberec
Shakhtar Donetsk
Schalke 04
Red Star Belgrade
Basic Findings
Real Madrid
RC Lens
PSV Eindhoven
Olympique Lyon
Manchester United
Lokomotiv Moscow
Liverpool
Sources
Lille OSC
Internazionale
Fenerbahçe
FC Barcelona
Process
Deportivo La Coruña
Club Brugge
Celtic
Celta de Vigo
Objectives
Borussia Dortmund
Bayern München
Anderlecht
Introduction
Ajax
AEK Athens
Head-to-Head Probability Analysis
Interesting Findings
Conclusion
0.400
0.300
Future Work
AC Milan Vs Others
1.000
0.900
0.800
0.700
0.600
0.500
Win Probability
Lose Probability
0.200
0.100
0.000
30
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Anderlecht
AS Monaco
Bayer Leverkusen
Benfica
CSKA Sofia
Deportivo La Coruña
FBK Kaunas
FC Basel
Galatasaray
Girondins Bordeaux
Grazer AK
Llansantffraid
Maccabi Haifa
Olympiakos Piraeus
PSV Eindhoven
Real Betis
Spartak Moscow
Valencia
Process
Werder Bremen
Udinese
Shakhtar Donetsk
Panathinaikos
Newcastle United
Objectives
Lokomotiv Moscow
Liverpool
Levski Sofia
Legia Warsaw
Juventus
Introduction
Internazionale
Galatasaray
Club Brugge
Celtic
Benfica
Bayer Leverkusen
Head-to-Head Probability Analysis
Sources
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Basic Findings
MIS 580: Knowledge Management
Interesting Findings
Conclusion
Liverpool Vs Others
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Future Work
FC Barcelona Vs Others
Win Probability
Lose Probability
Win Probability
Lose Probability
31