# Calculate mean and standard deviation of generated scores mean_generated = np.mean(generated_scores) std_dev_generated = np.std(generated_scores)
plt.hist(generated_scores, bins=20) plt.xlabel("Score") plt.ylabel("Frequency") plt.title("Histogram of Generated Scores") plt.show() random cricket score generator verified
# Plot a histogram of generated scores import matplotlib.pyplot as plt # Calculate mean and standard deviation of generated
def ball_by_ball_score_generator(self, current_score, overs_remaining): # probability distribution for runs scored on each ball probabilities = [0.4, 0.3, 0.15, 0.05, 0.05, 0.05] runs_scored = np.random.choice([0, 1, 2, 3, 4, 6], p=probabilities) return runs_scored 0.05] runs_scored = np.random.choice([0
import numpy as np import pandas as pd
class CricketScoreGenerator: def __init__(self): self.mean = 245.12 self.std_dev = 75.23
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