def generate_score(self): total_score = 0 overs = 50 # assume 50 overs for over in range(overs): for ball in range(6): runs_scored = self.ball_by_ball_score_generator(total_score, overs - over) total_score += runs_scored return total_score
print(f"Mean of generated scores: {mean_generated}") print(f"Standard Deviation of generated scores: {std_dev_generated}")
# Verify the score generator score_generator = CricketScoreGenerator() generated_scores = [score_generator.generate_score() for _ in range(1000)] random cricket score generator verified
def innings_score_generator(self): return np.random.normal(self.mean, self.std_dev)
# Calculate mean and standard deviation of generated scores mean_generated = np.mean(generated_scores) std_dev_generated = np.std(generated_scores) def generate_score(self): total_score = 0 overs = 50
# Plot a histogram of generated scores import matplotlib.pyplot as plt
import numpy as np import pandas as pd
To verify the random cricket score generator, we compared the generated scores with historical cricket data. We collected data on international cricket matches from 2010 to 2020 and calculated the mean and standard deviation of the scores.