montecarlo.py

from scipy.stats import ks_2samp

# Run a montecarlo simulation over 1,000 turns to get distribution of KS scores for each
def KS_MonteCarlo(euro_league, n_teams, n_seasons):
    simulation = np.arange(1,1001,1)
    ks_run =[]
    for run in simulation:
        flat20 = perfect_competition(n_teams, n_seasons).T.to_numpy().flatten()
        ks_run.append(ks_2samp(euro_league.Pts, flat20).statistic)
    
    return pd.DataFrame(ks_run)