Predicting a T20 Cricket Match Result While The Match is in Progress By Fahad Munir 11201014 Md. Kamrul Hasan 11201032 Sakib Ahmed 11201009 Sultan Md. Quraish 11201017 A thesis submitted to the Department of Computer Science and Engineering in partial fulfillment of the requirements for the degree of B.Sc. in Computer Science Department of Computer Science and Engineering Brac University August 2015 © 2015. Brac University All rights reserved. Declaration It is hereby declared that 1. The thesis submitted is my/our own original work while completing degree at Brac University. 2. The thesis does not contain material previously published or written by a third party, except where this is appropriately cited through full and accurate referencing. 3. The thesis does not contain material which has been accepted, or submitted, for any other degree or diploma at a university or other institution. 4. We have acknowledged all main sources of help. Student’s Full Name & Signature: Fahad Munir Md. Kamrul Hasan 11201014 11201032 Sakib Ahmed Sultan Md. Quraish 11201009 11201017 ii Approval The thesis/project titled “Predicting a T20 Cricket Match Result While The Match is in Progress” submitted by 1. Fahad Munir (11201014) 2. Md. Kamrul Hasan (11201032) 3. Sakib Ahmed (11201009) 4. Sultan Md. Quraish (11201017) Of Summer, 2015 has been accepted as satisfactory in partial fulfillment of the requirement for the degree of B.Sc. in Computer Science on August 23, 2015. Examining Committee: _______________________________ Rubel Biswas Senior Lecturer, Department of Computer Science and Engineering Brac University _______________________________ Professor Md. Haider Ali, PhD Chairperson, Department of Computer Science and Engineering Brac University iii Ethics Statement iv Abstract Data Mining and Machine learning in Sports Analytics, is a brand new research field in Computer Science with a lot of challenge. In this research the goal is to design a result prediction system for a T20 cricket match while the match is in progress. Different machine learning and statistical approach were taken to find out the best possible outcome. A very popular data mining algorithm, decision tree were used in this research along with Multiple Linear Regression in order to make a comparison of the results found. These two model are very much popular in predictive modeling. Forecasting a T20 cricket match is a challenge as the momentum of the game can change drastically at any moment. As no such work has done regarding this for-mat of cricket, we have decided to take the challenge as T20 cricket matches are very much popular now a days. We are using decision tree algorithm to design our forecasting system by depending on the previous data of matches played between the teams. This system will help the teams to take major decision when the match is in progress such as when to send which batsman or which bowler to bowl in the middle overs. It significantly expands the exposure of research in sports analytics as it was previously bound between some other selected sports. Keywords: Data Mining; Machine Learning; Cricket; T20; Prediction; Decision tree; Linear Regression Analysis v Dedication (Optional) A dedication is the expression of friendly connection or thanks by the author towards another person. It can occupy one or multiple lines depending on its importance. You can remove this page if you want. vi Acknowledgement Firstly, all praise to the Great Allah for whom our thesis have been completed without any major interruption. Secondly, to our co-advisor Mr. Moin Mostakim sir for his kind support and advice in our work. He helped us whenever we needed help. Thirdly, Jon Van Haaren and the whole judging panel of Machine Learning in Sports Analytics Conference 2015. Though our paper not accepted there, all the reviews they gave helped us a lot in our later works. And finally to our parents without their throughout sup-port it may not be possible. With their kind support and prayer we are now on the verge of our graduation. vii Table of Contents Declaration................................................................................................................................ ii Approval ................................................................................................................................. iii Ethics Statement...................................................................................................................... iv Abstract ..................................................................................................................................... v Dedication (Optional) ............................................................................................................. vi Acknowledgement .................................................................................................................. vii Table of Contents ................................................................................................................. viii List of Tables ............................................................................................................................ x List of Figures .......................................................................................................................... xi List of Acronyms .................................................................................................................... xii Chapter 1 Introduction............................................................................................................ 1 1.1 Thoughts behind the Prediction Model .................................................................... 1 1.2 Aims and Objectives ................................................................................................ 2 1.3 History of T20 Cricket ............................................................................................. 2 1.3 Game Method........................................................................................................... 3 Chapter 2 Related Work ......................................................................................................... 5 Chapter 3 Weka ....................................................................................................................... 7 Chapter 4 Data Description .................................................................................................... 8 Chapter 5 Prediction Modeling using Decision Tree .......................................................... 12 5.1 Entropy Calculation ............................................................................................... 13 viii 5.2 Information Gain .................................................................................................... 13 5.3 Data Training ......................................................................................................... 14 Chapter 6 Analysis and Result.............................................................................................. 14 6.1 Win Predicting ....................................................................................................... 15 Chapter 7 Prediction Modeling using Multiple Linear Regression .................................. 18 7.1 Multiple Regression Analysis Components ........................................................... 20 7.2 Problem Formulation ............................................................................................. 22 Chapter 8 Bat-First Prediction ............................................................................................. 22 8.1 First Segment ......................................................................................................... 22 8.2 Second Segment ..................................................................................................... 23 8.3 Third Segment ........................................................................................................ 25 Chapter 9 Bat Second Run Prediction Model ..................................................................... 27 9.1 First Segment ......................................................................................................... 28 9.2 Second Segment ..................................................................................................... 28 9.3 Third Segment ........................................................................................................ 31 Chapter 10 First and Second ................................................................................................ 33 10.1 Result Prediction based on First and Second Segment (Bat First) ...................... 33 10.2 Result Prediction based on First and Second Segment (Bat Second) .................. 35 Chapter 11 Future Work....................................................................................................... 37 Chapter 12 Conclusion .......................................................................................................... 37 References ............................................................................................................................... 38 ix List of Tables Table 1 Training set Batting First ............................................................................................ 15 Table 2 Test set Batting First ................................................................................................... 16 Table 3 Training set Batting Second .......................................................................................