Simulating a cricket match scenario provided in the problem statement using weighted random number generation based on probability
Download / clone the repository, setup a virtualenv ( recommended to avoid package dependency conflicts ) and install the requirements using pip
git clone git@github.com:manishbhatias/cricket-match-simulation.git
cd cricket-match-simulation
virtualenv .
pip install -r requirements.txt
You can also use the package manager pip to install cricketmatch_simulator module.
pip install cricketmatch_simulatorThe default parameters provided in the problem statement have already been setup so that we can run it directly in the cloned directory using
python cricketmatch_simulatorYou can also import the module into your own code and setup different match parameters for simulation
from cricketmatch_simulator import CricketMatchSimulator
# Setup match parameters according to problem and call simulate
simulator = CricketMatchSimulator(40, 4, ["Kirat Boli", "N.S Nodhi", "R Rumrah", "Shashi Henra"])
simulator.simulate()
simulator.print_result()4 overs left. 40 runs to win
0.1 Kirat Boli scores 1 run
0.2 N.S Nodhi scores 0 runs
0.3 N.S Nodhi scores 2 runs
0.4 N.S Nodhi scores 1 run
0.5 Kirat Boli scores 6 runs
0.6 Kirat Boli scores 3 runs
3 overs left. 27 runs to win
1.1 Kirat Boli scores 1 run
1.2 N.S Nodhi scores 1 run
1.3 Kirat Boli scores 4 runs
1.4 Kirat Boli scores 3 runs
1.5 N.S Nodhi Out!
1.6 R Rumrah scores 1 run
2 overs left. 17 runs to win
2.1 R Rumrah scores 2 runs
2.2 R Rumrah scores 0 runs
2.3 R Rumrah scores 4 runs
2.4 R Rumrah scores 1 run
2.5 Kirat Boli scores 2 runs
2.6 Kirat Boli Out!
1 overs left. 8 runs to win
3.1 R Rumrah scores 2 runs
3.2 R Rumrah scores 2 runs
3.3 R Rumrah Out!
Bengaluru lost by 4 runs
N.S Nodhi - 4 (5 balls)
Kirat Boli - 20 (8 balls)
Shashi Henra - 0* (0 balls)
R Rumrah - 12 (8 balls)- Refactor player's logic into ts own class
- Allow setting up more match parameters such as team names, player probabliities etc.