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A Monte Carlo simulation for a Basketball game typically involves creating a model that simulates the game's events based on probabilities derived from historical data. The code would involve defining the teams, their strengths (e.g., shooting percentages, points allowed), and simulating multiple games by randomly generating outcomes based on these probabilities. The results are then aggregated to analyze win probabilities, average scores, and other statistics. Here’s a simple pseudocode example:
def simulate_game(team_a, team_b):
score_a = random.poisson(team_a.offense_rating)
score_b = random.poisson(team_b.offense_rating)
return score_a, score_b
num_simulations = 10000
wins_a = 0
wins_b = 0
for _ in range(num_simulations):
score_a, score_b = simulate_game(team_a, team_b)
if score_a > score_b:
wins_a += 1
else:
wins_b += 1
print(f"Team A wins: {wins_a/num_simulations100}%")
print(f"Team B wins: {wins_b/num_simulations100}%")
This code outlines the basic structure for simulating the outcomes of a basketball game between two teams.
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