1. MIS 480/580 Final Project Presentation Knowledge Management in Cricket – A Research Project By: Luis Barreda Deepika Nim Jagadish Ramamurthy James Sanford

2. 2/12/2016 2 MIS 580 - Knowledge Management RolesDeepikaJagadishLuisJames Project Management Literature Review Data Source Identification Data Extraction Defining Research Objectives Analysis Methods and Tools identification Analysis Documentation

3. Research Objectives Data Sources and Data Collection Methods Analytical Methods Interesting Findings KM in Cricket 2/12/2016 3 MIS 580 - Knowledge Management

4. 2/12/2016 4 MIS 580 - Knowledge Management In general… Sports statistics are perfect for knowledge generation Lots of data being generated constantly Data is widely available, but underused What we tried… To analyze this data and find patterns To understand the causes behind our observations To predict outcomes and create knowledge

5. 2/12/2016 MIS 580 - Knowledge Management 5 CRICKET TEST CRICKET 5-day timed match One team bowls, one team bats, for 2 innings each ONE DAY INTERNATIONALS One day, 2 Innings

6. 2/12/2016 MIS 580 - Knowledge Management 6 Baseball: pitcher vs. batter Cricket: bowler vs. batsmen Offense scores runs by hitting and running Defense gets batsmen out by bowling and fielding Basic positions are the same More players to get out Runs are also easier to score Runs per inning are much higher in cricket In cricket innings last much longer

7. 2/12/2016 MIS 580 - Knowledge Management 7 De Silva, Basil M. & Swartz, Tim B. Winning the coin toss and the home team advantage in one-day international cricket matches A statistical analysis of 427 one day international cricket matches played during the1990’s. Brooks, Robert & Bussiere, Luc F. Sinister strategies succeed at the cricket world cup Analysis of batting records from the 2003 Cricket World Cup that showed that left-handed batsmen were more successful than right- handers, and that the most successful teams had close to 50% left- handed batsmen Allsopp, Paul (2005), Measuring team performance and modeling the home advantage effect in cricket Statistical analysis thesis paper that establishes that in all forms of cricket, that a team’s scoring potential and its capacity to win are both significantly amplified when it plays at its home ground

8. 2/12/2016 MIS 580 - Knowledge Management 8 Analysis of the successful strategy of the Australian Cricket Team during the 2000s Patterns in wins and losses of Australia Effect of key players and external factors like match venues

9. 2/12/2016 9 MIS 580 - Knowledge Management

10. 2/12/2016 10 MIS 580 - Knowledge Management

11. 2/12/2016 MIS 580 - Knowledge Management 11 Patterns in Individual Player performance Batsmen Vs. Bowlers Significance of Partnerships between Left-Handed and Right-Handed Batsmen Age of the Players Effect of Match Venues Australia’s performance at Home and Away venues Run Rate Correlation between Run Rate and Match Win/Loss Ratio Early Declarations Analysis of the outcomes of early declarations of all teams

12. 2/12/2016 MIS 580 - Knowledge Management 12 Finalization of Research Objectives Identification of Data Sources Data Extraction Selection of Analysis Methods Data AnalysisConclusions

13. 2/12/2016 MIS 580 - Knowledge Management 13 www.cricinfo.com

14. 2/12/2016 14 MIS 580 - Knowledge Management

15. 2/12/2016 MIS 580 - Knowledge Management 15 Team - A group of players on the same side in a game Win/Loss – Ration of total matches won to total matches lost Players – Individuals in a team Runs Scored - Basic unit of scoring Overs – An over has 6 plays Runs/Over – Ratio of total runs scored to total overs in a game Venue - Place where a game is held Age – Age of a player

16. 2/12/2016 MIS 580 - Knowledge Management 16 Individual value plot Bar chartLine plotScatter plot Correlation and Regression MinitabMS Excel

17. 2/12/2016 MIS 580 - Knowledge Management 17 To analyze new Australian Strategy: Declare Early Elect to end innings while batting Gives Australia a chance to win rather than draw Objective Statistics for all teams, limited to declared matches Only early declarations : 350 or fewer runs Query Looked at wins, losses, draws and total matches played Wins over non-wins Represents success over failure Analysis

18. 2/12/2016 MIS 580 - Knowledge Management 18

19. 2/12/2016 MIS 580 - Knowledge Management 19 Are players responsible for Australia’s success? If an individual plays more, does Australia win more? Looking at top batters and bowlers should tell us Objective Australian match outcomes by player Select best 6 bowlers and batsmen Query Looked at wins and losses Win/loss ratio is the measure of success Australia’s ratio should increase when impact players play Analysis

20. 2/12/2016 MIS 580 - Knowledge Management 20

21. 2/12/2016 MIS 580 - Knowledge Management 21

22. To check with hard data if the fact the Australia has an equal number left and right hand batsmen been a factor in their success Objective Retrieve runs scored by “same handed” batting partners and compare it with those of “different handed” batting partners Query Categorize the partnerships into five categories. See which kind of batting partners have contributed significantly to the wins of Australia Analysis 2/12/2016 22 MIS 580 - Knowledge Management

23. 2/12/2016 23 MIS 580 - Knowledge Management Individual Value Plot Displays all data values – so more informative Grouping the data values in different categories to make comparisons Drawing conclusions based on the density of data values in each categories Data sets considered X – Axis: Categories of runs scored in partnerships Insignificant – 0-50 runs Good Enough – 50 -75 runs Very Good – 75 – 100 runs Significant – 100 – 200 runs Match Winner – 200 runs and above Y – Axis: Runs scored in Partnerships

24. 2/12/2016 24 MIS 580 - Knowledge Management

25. To see if the “home” factor favored Australia Objective Win/Loss Ratio for all teams at home and away Query Analyze and see if the ‘home’ factor favored them more than any other team Analyze and see if they played most of their matches at home during the 2000s Analysis 2/12/2016 25 MIS 580 - Knowledge Management

26. 2/12/2016 26 MIS 580 - Knowledge Management

27. 2/12/2016 27 MIS 580 - Knowledge Management

28. 2/12/2016 28 MIS 580 - Knowledge Management

29. To check if data proves the myth about batsmen And to analyze if that was a factor in Australia’s success Objective Run/Match for all 5 regular batsmen of Australia from their debut year Match the year of play with their age Query Analyze the trends in the runs/match of the batmen and check when they all peaked Analysis 2/12/2016 29 MIS 580 - Knowledge Management

30. 2/12/2016 30 MIS 580 - Knowledge Management

31. 2/12/2016 MIS 580 - Knowledge Management 31 To analyze the runs per over of all teams and their corresponding wins and see if there is any correlation Objective Statistics for all teams, Runs per over and wins Query Draw a regression scatter plot between runs per over and wins, and see the r- squared value and draw conclusions Analysis

32. 2/12/2016 32 MIS 580 - Knowledge Management

33. 2/12/2016 MIS 580 - Knowledge Management 33 Key Players peaking at the right time Diversity of batsmenHigh Run Rate Use of positive strategy – always aiming to win

34. 2/12/2016 34 MIS 580 - Knowledge Management Simple Analysis Methods Clear Objectives Rock Solid Data Source Knowledge Creation Knowledge Creation

35. 2/12/2016 35 MIS 580 - Knowledge Management Comparison of the West Indies of the 80s to Australia of the 2000s Comparison of the top for four teams in the world with Australia Predictions of the outcome of a test match series

36. 2/12/2016 MIS 580 - Knowledge Management 36  CricInfo.com  EspnStar.com  CricketAustralia  De Silva, Basil M. & Swartz, Tim B. Winning the coin toss and the home team advantage in one-day international cricket matches  Brooks, Robert & Bussiere, Luc F. Sinister strategies succeed at the cricket world cup  Allsopp, Paul (2005), Measuring team performance and modeling the home advantage effect in cricket

37. 2/12/2016 MIS 580 - Knowledge Management 37 Title SlideData Sets/ Attributes ObligationsAnalysis Methods and Tools AgendaEarly Declarations IntroductionBatsmen Vs. Bowlers CricketPartnerships Similarities to BaseballMatch Venue Literature ReviewBatsmen Age Research ObjectivesRuns Per Over (Run Rate) Research DesignConclusions Data SourceLessons Learned Sample DataReferences