def ProcessScoresTeamwise(pairs):
"""Average number of goals for each team.
pairs: map from (team1, team2) to (score1, score2)
"""
# map from team to list of goals scored
goals_scored = {}
for key, entries in pairs.iteritems():
t1, t2 = key
for entry in entries:
g1, g2 = entry
goals_scored.setdefault(t1, []).append(g1)
goals_scored.setdefault(t2, []).append(g2)
# make a list of average goals scored
lams = []
for key, goals in goals_scored.iteritems():
lam = thinkbayes2.Mean(goals)
lams.append(lam)
# make the distribution of average goals scored
cdf = thinkbayes2.MakeCdfFromList(lams)
thinkplot.Cdf(cdf)
thinkplot.Show()
mu, var = thinkbayes2.MeanVar(lams)
print('mu, sig', mu, math.sqrt(var))
def PlotCdfs(d, labels):
"""Plot CDFs for each sequence in a dictionary.
Jitters the data and subtracts away the mean.
d: map from key to sequence of values
labels: map from key to string label
"""
thinkplot.Clf()
for key, xs in d.items():
mu = thinkbayes2.Mean(xs)
xs = thinkbayes2.Jitter(xs, 1.3)
xs = [x - mu for x in xs]
cdf = thinkbayes2.MakeCdfFromList(xs)
thinkplot.Cdf(cdf, label=labels[key])
thinkplot.Show()