Decision Support System for School Cricket in Sri Lanka (CricDSS) Hansinie M. Jayathilake Dushyanthi U. Vidanagama Faculty of Computing General Sir John Kotelawala Defence University

Presentation Outline Objectives Drawbacks of current process High level System Architecture Technologies Used Available Team Selection Methods Genetic Algorithms (GA) for Team Selection Evaluation and Suggestions Limitations and Recommendations Conclusion and Future Work

Drawbacks of Current Process Tournament scheduling problems Match score entering card updating problems Communicating with SLC after a match Invisibility of player performance time to time Selecting the right team for the tournament

Objectives Upgrade the Sri Lankan school cricket with the blend of information technology Introducing a web based system (CricDSS) to solve the issues regarding school cricket match management and analysis. Introducing Genetic Algorithm based advanced team selection model in line with the web solution to enhance the decision making.

High level System Architecture

Technologies Used JavaEE Netbeans IDE Glassfish web server MySQL Database server Hibernate ORM Java RMI/Ajax JGAP Framework for GA implementation

Available Team Selection Methods Selection of multi-functional teams based on quality function deployment (QFD) and analytic hierarchy process (AHP) Heuristic algorithms Axiomatic design principles - selected based on the required skills and preparing a skill development procedure Fuzzy set theory - selection of project teams, audit teams and worker teams Fuzzy Inference System (FIS)- Player selection and team formation Neural Networks - Applied to forecast the cricketer’s near term performance

Genetic Algorithms for Team Selection Cricket team selection is an optimization problem which looks for the best combination of players Evolutionary algorithms such as GA can be used for optimization of real world problems. Less complex, more straightforward and flexible Chromosome approach Used to handle multiple solution search spaces Easier to be transferred and applied in different platforms Integer variables are easier to accommodate in GA than continuous variables GA is less complex and more straightforward compared to other algorithms. In addition, Genetic algorithms are easier to be transferred and applied in different platforms, thereby increasing its flexibility [21]. Also unlike other solution methods, integer variables are easier to accommodate in GA than continuous variables [22].

How GA Works? Current players’ data Initial selection of chromosomes Fitness function Selection Crossover Mutation Evaluate new chromosomes Update Termination Condition (Fitness Value) Output How GA Works? Y N

Fitness Function Implementation One chromosome = team with 15 players Let Overall Average Performance (Pi) of i th player for the opponent team denoted as: (Pi) = i th player (Batting average + Bowling average + Fielding average) Fitness of the ith player: F(Pi) = (Pi) Fitness of a k th chromosome: F (Ck) = ∑ F (Pi) 15 i =1

Crossover P1 = n P2 = n +2 C1 C2 P1 1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 P14 P15 P1 = n P1 1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 P14 P15 P2 = n +2 P1 1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 P14 P15 C1 P1 1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 P14 P15 C2

Mutation Before After P1 1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 P14 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 P14 P15 Before P1 1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 P14 P15 After

Evaluation and Suggestions All critical features of the system were tested with Coachers and School Cricket Officers Test execution procedure was started by distinguishing the CricDSS components and features that need to be tested It was identified that the administrator dashboard was horizontal resulting user confusion. Also they suggested the match scheduling function needed to be more visible.

Limitations and Recommendations Less accessibility to Internet by the schools in rural areas of Sri Lanka. Only statistical data of the players are used in model creation Usage of Glassfish server to host the application. Also the score values of players in the database have to regularly update in order to give the correct result from the genetic algorithm. It is highly recommended to host or deploy this application in Glassfish server since it is the most advanced server which can support for all the features and technologies in CricDSS.

Conclusion and Future Work Use of open source technologies results in making CricDSS an affordable product for Sri Lankan schools. More work on training people in remote areas where computer literacy is low. Adaptation to mobile devices Explore more technologies to build GA implementation in mobile devices.

Thank You