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Monte Carlo for calculating the st.dev of an all skill NHL league
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import csv, random, math | |
# STuff do once | |
schedule = [] | |
n = 10000 | |
avgsd = 0.0 | |
pskill = 24 #percent skill | |
# store the schedule | |
with open('schedule.csv', 'rU') as csvfile: | |
spamreader = csv.reader(csvfile, delimiter=',', quotechar='|') | |
for row in spamreader: | |
schedule.append(row) | |
strengths = range(1,31) | |
for i in range(n): | |
# stuff do on each loop | |
wins = [0]*30 | |
startScore = 0 # strength, and score | |
teams = {'PITTSBURGH' : startScore, 'PHILADELPHIA' : startScore, 'OTTAWA' : startScore, 'WINNIPEG' : startScore, 'CHICAGO' : startScore, | |
'LOS ANGELES' : startScore, 'BOSTON' : startScore, 'NY RANGERS' : startScore, 'TORONTO' : startScore, 'MONTREAL' : startScore, | |
'NEW JERSEY' : startScore, 'NY ISLANDERS' : startScore, 'TAMPA BAY' : startScore, 'WASHINGTON' : startScore, 'FLORIDA' : startScore, | |
'CAROLINA' : startScore, 'ST. LOUIS' : startScore, 'DETROIT' : startScore, 'COLUMBUS' : startScore, 'NASHVILLE' : startScore, | |
'DALLAS' : startScore, 'PHOENIX' : startScore, 'MINNESOTA' : startScore, 'COLORADO' : startScore, 'ANAHEIM' : startScore, | |
'VANCOUVER' : startScore, 'BUFFALO' : startScore, 'SAN JOSE' : startScore, 'CALGARY' : startScore, 'EDMONTON' : startScore} | |
# assign strengths randomly | |
random.shuffle(strengths) | |
sIndex = 0 | |
for t in teams: | |
teams[t] = strengths[sIndex] | |
sIndex += 1 | |
# "compete" the teams | |
for game in schedule: | |
if random.randint(1,100) <= (100-pskill): | |
if random.randint(1,2) == 1: | |
wins[teams[game[0]]-1] += 1 | |
else: | |
wins[teams[game[1]]-1] += 1 | |
else: | |
if teams[game[0]] < teams[game[1]]: | |
wins[teams[game[0]]-1] += 1 | |
else: | |
wins[teams[game[1]]-1] += 1 | |
wins = [x/82.0 for x in wins] | |
mean = sum(wins, 0.0) / len(wins) | |
d = [ (i - mean) ** 2 for i in wins] | |
std_dev = math.sqrt(sum(d) / len(d)) | |
avgsd += std_dev | |
print std_dev | |
print "Average St.Dev: %s" % (avgsd/n) | |
print "Mean: %s" % (mean) |
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