def getTeamTimeDist(id, homeOrAway):
team = teams.getTeamById(id) if type(id) == int else teams.getTeamByName(id)
teamPos= possessions[(possessions['off'] == team['id'])]
if homeOrAway is not None:
validGames = [int(game[1]['gameId']) for game in games.iterrows() if game[1][homeOrAway] == team['id']]
teamPos = pd.DataFrame([p[1] for p in teamPos.iterrows() if p[1]['gameId'] in validGames])
tm = teamPos['dur'].values
mean = sum(tm)/len(tm)
textstr = "mean="+str(round(mean, 2))
return s.weibull_min.fit(tm, floc=0)
def getTeamTrans(id, offenseOrDefense, homeOrAway):
#get transitional matrix for a team, filter by offensive or defensive possessions
global ends
team = teams.getTeamById(id) if type(id) == int else teams.getTeamByName(id)
thisTeam = possessions[possessions[offenseOrDefense] == team['id']]
if homeOrAway is not None:
validGames = [int(game[1]['gameId']) for game in games.iterrows() if game[1][homeOrAway] == team['id']]
thisTeam = pd.DataFrame([p[1] for p in thisTeam.iterrows() if p[1]['gameId'] in validGames])
ends = np.zeros(shape=(10,10))
thisTeam.apply(getTransMatrix, axis=1)
ends = (ends/ends.sum())*100
return ends
def getTeamTimeDist(id, homeOrAway):
team = teams.getTeamById(id) if type(id) == int else teams.getTeamByName(
id)
teamPos = possessions[(possessions['off'] == team['id'])]
if homeOrAway is not None:
validGames = [
int(game[1]['gameId']) for game in games.iterrows()
if game[1][homeOrAway] == team['id']
]
teamPos = pd.DataFrame(
[p[1] for p in teamPos.iterrows() if p[1]['gameId'] in validGames])
tm = teamPos['dur'].values
mean = sum(tm) / len(tm)
textstr = "mean=" + str(round(mean, 2))
return s.weibull_min.fit(tm, floc=0)
def plotTeamTimeDist(id):
team = teams.getTeamById(id)
teamPos= possessions[(possessions['off'] == id) & (possessions['dur'] >= 0)]
tm = teamPos['dur'].values
if len(tm) == 0:
print(id, len(tm))
raise ValueError("wha")
mean = sum(tm)/len(tm)
textstr = "mean="+str(round(mean, 2))
p0, p1, p2=s.weibull_min.fit(tm, floc=0)
ydata=s.weibull_min.pdf(np.linspace(0, 50, 100), p0, p1, p2)
plt.plot(np.linspace(0, 50, 100),ydata, '-')
plt.title(team['name'] +" Weibull")
plt.hist(tm,24, normed=1)
plt.text(35, .04, textstr)
#plt.show()
plt.savefig("vis/"+team['name']+"Weibull.png")
plt.clf()
def getTeamTrans(id, offenseOrDefense, homeOrAway):
#get transitional matrix for a team, filter by offensive or defensive possessions
global ends
team = teams.getTeamById(id) if type(id) == int else teams.getTeamByName(
id)
thisTeam = possessions[possessions[offenseOrDefense] == team['id']]
if homeOrAway is not None:
validGames = [
int(game[1]['gameId']) for game in games.iterrows()
if game[1][homeOrAway] == team['id']
]
thisTeam = pd.DataFrame([
p[1] for p in thisTeam.iterrows() if p[1]['gameId'] in validGames
])
ends = np.zeros(shape=(10, 10))
thisTeam.apply(getTransMatrix, axis=1)
ends = (ends / ends.sum()) * 100
return ends
def plotTeamTimeDist(id):
team = teams.getTeamById(id)
teamPos = possessions[(possessions['off'] == id)
& (possessions['dur'] >= 0)]
tm = teamPos['dur'].values
if len(tm) == 0:
print(id, len(tm))
raise ValueError("wha")
mean = sum(tm) / len(tm)
textstr = "mean=" + str(round(mean, 2))
p0, p1, p2 = s.weibull_min.fit(tm, floc=0)
ydata = s.weibull_min.pdf(np.linspace(0, 50, 100), p0, p1, p2)
plt.plot(np.linspace(0, 50, 100), ydata, '-')
plt.title(team['name'] + " Weibull")
plt.hist(tm, 24, normed=1)
plt.text(35, .04, textstr)
#plt.show()
plt.savefig("vis/" + team['name'] + "Weibull.png")
plt.clf()