Abstract:
Cricket is primarily played in three formats around the world: test match, one-
day international (ODI), and twenty-twenty (T20). T20 has made a great
revolution in the world of cricket. The Pakistan Cricket Board (PCB) arranges
a tournament named the Pakistan Super League (PSL) every year, which is in
T20 format. PSL is liked and watched by a large number of people, and it has
a greater amount of statistical data. PSL is based on the Draft system for
selecting players for making teams. This draft-based method for selecting
players has different categories, each with its own constraints. 16 player squad
must have five foreign players, and an 18 players squad could have six or five
foreign players. A larger amount of money is used in the draft system. Players’
selection is one of the most important tasks for team formation. PSL team
selection is made by team management, and it is very complex for humans to
analyze all the previous statistics of the players for better selection. It is also
true that human-based systems are not very efficient. It is very important to
analyze players' performances for ease of selection and to make the right
decision for the selection of players for teams by team management, coaches,
and captains. In this thesis, machine learning techniques are used for squad
selection in our model, which is named SFPML (Squad Formation in Pakistan
Super League using Machine Learning). Important features of a batsman and
bowler are used. Our model ranks the batsmen and bowlers based on their
previous performances. If a new player enters the PSL tournament, his
position in the league is determined by finding similarities among PSL
players. Our thesis also attempts to predict the performances of players, such
as how many runs a batsman will score, and how many wickets a bowler will
take.
