def _pushData(self, prediction):
with DOConnect() as tunnel:
c, conn = connection(tunnel)
try:
c.execute('''delete from modelData.modelResults
where modelYear = %d and modelWeek = %d
and modelPlayerPosition = '%s' ''' %
(self.season, self.week, self.position))
except Exception as e:
print(str(e))
try:
query = '''insert into modelData.modelResults values '''
for i, row in prediction.iterrows():
query += (
"(%d, %d, %d, '%s', '%s', %s, %s, current_timestamp()),"
%
(self.season, self.week, row.playerId, row.Name,
self.position, str(row.prediction), str(row.predVar)))
query = query[:-1]
except Exception as e:
print(str(e))
try:
c.execute(query)
except Exception as e:
print(str(e))
conn.commit()
conn.close()
def buildWeeklyModels(season, week):
with DOConnect() as tunnel:
c, conn = connection(tunnel)
c.execute(
"delete from leagueSims.modelPredictions where modelSeason = %d and predictionWeek = %d"
% (season, week))
conn.commit()
conn.close()
for pos in ['DST', 'K', 'QB', 'RB', 'WR', 'TE']:
print(str(season) + "-" + str(week) + "-" + pos)
c, conn = connection(tunnel)
modelData = meth.pullModelData(season, week, pos, conn)
conn.close()
trainIndex = ((modelData.predictionSeason < season) &
(modelData.predictionWeek == week))
predictIndex = ((modelData.predictionSeason == season) &
(modelData.predictionWeek == week))
treeCount = 200
try:
leagueModel = leaguePredictionTree(modelData[trainIndex],
treeCount)
predResults = leagueModel.returnSimsModels(
modelData[predictIndex])
except:
traceback.print_exc()
c, conn = connection(tunnel)
sqlInsert = "insert into leagueSims.modelPredictions values "
for i, row in predResults.iterrows():
sqlAdd = "(%d, %d, %d, %d, '%s', %f, '%s', %f),"
sqlInsert += sqlAdd % (row['modelSeason'],
row['predictedWeek'],
row['predictionWeek'], row['playerId'],
pos, row['modelPrediction'],
row['predRange'], row['modelPlayProb'])
try:
c.execute(sqlInsert[:-1])
conn.commit()
except Exception as e:
print(str(e))
conn.close()
print('end')
del predResults
del leagueModel
del sqlInsert
del modelData
def _pullData(self, position):
with DOConnect() as tunnel:
c, conn = connection(tunnel)
if position == 'DST':
query = '''select * from modelData.%sData
where playerSeason >= 2014''' % position.lower()
else:
query = '''select * from modelData.%sData
where chartPosition is not null and playerRating is not null and age is not null and experience is not null
and playerSeason >= 2014''' % position.lower()
table = pd.read_sql(query, con=conn)
conn.close()
return table
import sys
sys.path.insert(0, '..')
sys.path.insert(0, '..\..')
from DOConn import connection
from DOsshTunnel import DOConnect
from references import errorSQL
week = 1
year = 2018
with DOConnect() as tunnel:
c, conn = connection(tunnel)
conn.close()
def runSims(season, week, runCount=50):
while True:
print(season, week)
with DOConnect() as tunnel:
c, conn = connection(tunnel)
c.execute('''select standRunCount
from leagueSims.standings
where standYear = %d
and standWeek = %d''' % (season, week))
result = c.fetchone()
if result is None:
None
elif result[0] >= 5000:
conn.close()
break
try:
results = meth.pullResults(season, week, conn)
except Exception as e:
print(str(e))
try:
currentRosters = meth.pullCurrentRosters(season, week, conn)
currentRosters['predictionDistr'] = currentRosters.apply(
lambda x: x['predictionDistr'].split(','), axis=1)
positions = [{
'key': 'QB',
'allow': ['QB']
}, {
'key': 'RB',
'allow': ['RB']
}, {
'key': 'WR',
'allow': ['WR']
}, {
'key': 'TE',
'allow': ['TE']
}, {
'key': 'D/ST',
'allow': ['D/ST']
}, {
'key': 'K',
'allow': ['K']
}, {
'key': 'RBWR',
'allow': ['RB', 'WR']
}, {
'key': 'Flex',
'allow': ['RB', 'WR', 'TE']
}]
rostersDict = {}
for weekNum in currentRosters['predictedWeek'].dropna().unique(
):
for team in currentRosters['playerTeam'].unique():
rostersDict[team + "-" + str(int(weekNum))] = {}
rostersDict[team + "-" + str(
int(weekNum))]['totalRoster'] = currentRosters.loc[
(currentRosters['playerTeam'] == team) &
(currentRosters['predictedWeek'] == weekNum), [
'playerId', 'playerPosition',
'predictionValue', 'predictionDistr',
'playProb'
]]
rostersDict[team + "-" + str(
int(weekNum))]['totalRoster']['randNum'] = None
except Exception as e:
traceback.print_exc()
try:
replaceValues = meth.pullReplacementNumbers(season, week, conn)
replaceValues['replaceDistr'] = replaceValues.apply(lambda x: [
float(y) if y != '' else 0
for y in x['replaceDistr'].split(',')
],
axis=1)
replaceDict = {}
for replaceWeek in replaceValues['predictedWeek'].unique():
replaceDict[replaceWeek] = replaceValues[
replaceValues['predictedWeek'] ==
replaceWeek].set_index('playerPosition').to_dict(
orient='index')
except Exception as e:
traceback.print_exc()
conn.close()
print('start sims')
start = time.process_time()
sim = leagueSimulation(season, rostersDict, replaceDict, results, week)
status = True
fails = 0
try:
sim.simSeason()
except Exception as e:
fails += 1
traceback.print_exc()
results = sim.leagueResults()
resultsTable = results.copy()
table2 = pd.DataFrame(
data={
'Names': resultsTable.index,
'Points': None,
'Wins': None,
'Losses': None,
'HighPoints': None
})
table2['Points'] = [[round(x, 2)] for x in results['winPoints']]
table2['Wins'] = [[x] for x in results['winWin']]
table2['Losses'] = [[x] for x in results['winLoss']]
table2['HighPoints'] = [[x] for x in results['weeklyHighPoints']]
print(time.process_time() - start)
for i in range(1, runCount):
start = time.process_time()
status = True
try:
sim.simSeason()
status = False
except Exception as e:
fails += 1
with DOConnect() as tunnel:
c, conn = connection(tunnel)
c.execute(
'''insert into leagueSims.simErrors
values (%d, %d, %d, %d, '%s', current_timestamp());'''
% (season, week, fails, i, str(e).replace("'", "\'")))
conn.commit()
conn.close()
results = sim.leagueResults()
resultsTable = resultsTable.add(results, fill_value=0)
table2['Points'] = table2.apply(lambda x: x['Points'] + [
round(
results.loc[results.index == x['Names']].iloc[0][
'winPoints'], 2)
],
axis=1)
table2['Wins'] = table2.apply(
lambda x: x['Wins'] +
[results.loc[results.index == x['Names']].iloc[0]['winWin']],
axis=1)
table2['Losses'] = table2.apply(
lambda x: x['Losses'] +
[results.loc[results.index == x['Names']].iloc[0]['winLoss']],
axis=1)
table2['HighPoints'] = table2.apply(lambda x: x['HighPoints'] + [
results.loc[results.index == x['Names']].iloc[0][
'weeklyHighPoints']
],
axis=1)
print(time.process_time() - start)
sqlStatement = '''insert into leagueSims.standings values %s
on duplicate key update
standRunCount = standRunCount + values(StandRunCount),
standWins = standWins + values(standWins),
standWinsArray = concat(standWinsArray,',',values(standWinsArray)),
standLosses = standLosses + values(standLosses),
standLossesArray = concat(standLossesArray,',',values(standLossesArray)),
standPoints = standPoints + values(standPoints),
standPointsArray = concat(standPointsArray,',',values(standPointsArray)),
standPointsRemain = standPointsRemain + values(standPointsRemain),
standPlayoffs = standPlayoffs + values(standPlayoffs),
standChamp = standChamp + values(standChamp),
standHighPoints = standHighPoints + values(standHighPoints),
standLowPoints = standLowPoints + values(standLowPoints),
standFirstPlace = standFirstPlace + values(standFirstPlace),
standThirdPlace = standThirdPlace + values(standThirdPlace),
standBye = standBye + values(standBye),
standWeeklyHighPoints = standWeeklyHighPoints + values(standWeeklyHighPoints),
standWeeklyHighPointsArray = concat(standWeeklyHighPointsArray,',',values(standWeeklyHighPointsArray)),
updateTime = current_timestamp();
'''
sqlAdd = ''
for i, row in resultsTable.iterrows():
sqlAdd += (
'''(%d,%d,%d,'%s',%d,'%s',%d,'%s',%f,
'%s',%f, %d,%d,%d,%d,%d,%d,%d,%d,%d,'%s',current_timestamp()),'''
% (season, week, runCount, row.name, row['winWin'], ','.join([
str(int(x))
for x in table2.loc[table2['Names'] == i].iloc[0]['Wins']
]), row['winLoss'], ','.join([
str(int(x))
for x in table2.loc[table2['Names'] == i].iloc[0]['Losses']
]), round(row['winPoints'], 2), ','.join([
str(round(x, 1))
for x in table2.loc[table2['Names'] == i].iloc[0]['Points']
]), round(row['winPointsRemain'], 2), row['playoffs'],
row['champ'], row['runnerup'], row['thirdPlace'],
row['highPoints'], row['lowPoints'], row['firstPlace'],
row['bye'], row['weeklyHighPoints'], ','.join([
str(int(x)) for x in table2.loc[table2['Names'] ==
i].iloc[0]['HighPoints']
])))
with DOConnect() as tunnel:
c, conn = connection(tunnel)
try:
c.execute(sqlStatement % sqlAdd[:-1])
except:
traceback.print_exc()
conn.commit()
conn.close()
def runWeeklyData(weekVariable):
with DOConnect() as tunnel:
c, conn = connection(tunnel)
weekRun = weekVariable
data = pd.read_sql("""select avg(a.winPoints)
as donePoints,
avg(weightPoints) as weightPoints,
avg(ifnull(preDraftCapital,(select avg(preDraftCapital) from analysis.preDraftCapital))) as preDraftCapital,
avg(b.winPoints) as predictPoints, lcase(ifnull(a.winTeam,preDraftTeam)) as winTeam,
ifnull(a.winSeason,preDraftYear) as winSeason,
max(a.winWeek) as maxWeek
from analysis.preDraftCapital
left join (select winSeason, winTeam, avg(winPoints) as winPoints, max(winWeek) as winWeek,
sum(winPoints*(1-.05*((select max(winWeek) from la_liga_data.wins where winSeason = 2018 and winWeek <= replaceVar and winWeek <= 13)-winWeek)))/
sum((1-.05*((select max(winWeek) from la_liga_data.wins where winSeason = 2018 and winWeek <= replaceVar and winWeek <= 13)-winWeek))) as weightPoints
from la_liga_data.wins
where winWeek <= (select max(winWeek) from la_liga_data.wins where winSeason = 2018 and winWeek <= replaceVar and winWeek <= 13)
group by 1,2) a on a.winSeason = preDraftYear and a.winTeam = preDraftTeam
left join la_liga_data.wins b on ifnull(a.winSeason,preDraftYear) = b.winSeason and b.winWeek > ifnull(a.winWeek,0) and ifnull(a.winTeam,preDraftTeam) = b.winTeam
and b.winWeek <= 13
group by winTeam, winSeason""".replace('replaceVar', str(weekRun)),
con=conn)
matchups = pd.read_sql(
"""select matchYear, lcase(matchTeam) as matchTeam,
group_concat(lcase(matchOpp) order by matchWeek asc) as matchOpp from la_liga_data.matchups
group by 1,2;""",
con=conn)
standings = pd.read_sql(
"""select lcase(winTeam) as winTeam, ifnull(count(distinct(winWeek)),0) as weekNumber,
sum(winWin) as win,
sum(winLoss) as loss,
sum(winTie) as ties,
sum(winPoints) as points,
count(
case when winPoints =
(select max(winPoints) from la_liga_data.wins a
where a.winSeason = b.winSeason and a.winWeek = b.winWeek)
then 1 end) as highPoints
from la_liga_data.wins b
where winSeason = 2018 and winWeek <= %d
group by 1""" % weekRun,
con=conn)
pointTotals = pd.read_sql(
"""select winPoints from la_liga_data.wins where winWeek <= 13""",
con=conn)
pointAverages = pd.read_sql(
"""select winSeason, winTeam, avg(winPoints) as avgPoints
from la_liga_data.wins where winWeek <= 13 group by 1,2""",
con=conn)
data2 = data.loc[data['winSeason'] <= 2017]
pointsMean = np.mean(pointTotals['winPoints'])
pointsSd = np.std(pointTotals['winPoints'])
randseasonMean = np.mean(pointAverages['avgPoints'])
randseasonSd = np.std(pointAverages['avgPoints'])
if weekRun == 0:
weekStart = weekRun
else:
weekStart = standings.iloc[0]['weekNumber'].item()
models = ['recentPoints', 'coin', 'draft', 'points', 'all']
def randModel(preDraftCap, pointsAvg, weightPoints):
result = (np.random.normal(randseasonMean, randseasonSd, 1))
return result
def draftModel(preDraftCap, pointsAvg, weightPoints):
result = (
np.random.normal(coefs['const'], se['const'], 1) +
(np.random.normal(coefs['preDraftCapital'],
se['preDraftCapital'], 1)) * preDraftCap)
return result
def pointsModel(preDraftCap, pointsAvg, weightPoints):
result = (
np.random.normal(coefs['const'], se['const'], 1) +
(np.random.normal(coefs['donePoints'], se['donePoints'], 1)) *
pointsAvg)
return result
def recentPointsModel(preDraftCap, pointsAvg, weightPoints):
result = (np.random.normal(coefs['const'], se['const'], 1) +
(np.random.normal(coefs['weightPoints'],
se['weightPoints'], 1)) * weightPoints)
return result
def allModel(preDraftCap, pointsAvg, weightPoints):
result = (
np.random.normal(coefs['const'], se['const'], 1) +
(np.random.normal(coefs['preDraftCapital'],
se['preDraftCapital'], 1)) * preDraftCap +
(np.random.normal(coefs['donePoints'], se['donePoints'], 1)) *
pointsAvg)
return result
def weeklyResults(seasonMean2, n):
result = (np.random.normal(seasonMean2, pointsSd, n))
return result
X2 = data2[['preDraftCapital', 'donePoints', 'weightPoints']]
Y2 = data2['predictPoints']
X2 = sm.add_constant(X2)
predictData = data.loc[data['winSeason'] == 2018]
for model in models:
if model == 'coin' or (
(model == 'points' or model == 'recentPoints')
and weekRun == 0):
usedModel = randModel
reg = sm.OLS(Y2, X2[['preDraftCapital', 'const']]).fit()
elif model == 'draft' or (model == 'all' and weekRun == 0):
usedModel = draftModel
reg = sm.OLS(Y2, X2[['preDraftCapital', 'const']]).fit()
elif model == 'points':
usedModel = pointsModel
reg = sm.OLS(Y2, X2[['donePoints', 'const']]).fit()
elif model == 'recentPoints':
usedModel = recentPointsModel
reg = sm.OLS(Y2, X2[['weightPoints', 'const']]).fit()
elif model == 'all':
usedModel = allModel
reg = sm.OLS(Y2, X2[['donePoints', 'preDraftCapital',
'const']]).fit()
#print(reg.summary())
summaryData = {}
for index, row in predictData.iterrows():
summaryData[row['winTeam']] = {}
summaryData[row['winTeam']]['wins'] = []
summaryData[row['winTeam']]['losses'] = []
summaryData[row['winTeam']]['ties'] = []
summaryData[row['winTeam']]['points'] = []
summaryData[row['winTeam']]['playoffs'] = []
summaryData[row['winTeam']]['champ'] = []
summaryData[row['winTeam']]['highpoints'] = []
summaryData[row['winTeam']]['lowpoints'] = []
summaryData[row['winTeam']]['firstplace'] = []
summaryData[row['winTeam']]['bye'] = []
summaryData[row['winTeam']]['weekHigh'] = []
summaryData[row['winTeam']]['money'] = []
coefs = reg.params
se = reg.bse
print('start sim:', model)
start = time.time()
for j in range(0, 10000):
teamDict = {}
for index, row in predictData.iterrows():
teamDict[row['winTeam']] = {}
teamDict[row['winTeam']]['pointTotals'] = []
teamDict[row['winTeam']]['matchup'] = []
teamDict[row['winTeam']]['oppPoints'] = []
teamDict[row['winTeam']]['wins'] = []
teamDict[row['winTeam']]['losses'] = []
teamDict[row['winTeam']]['ties'] = []
teamDict[row['winTeam']]['weekHigh'] = []
teamDict[row['winTeam']]['money'] = []
teamDict[row['winTeam']]['seasonMean'] = usedModel(
row['preDraftCapital'], row['donePoints'],
row['weightPoints'])
for key, value in teamDict.items():
value['pointTotals'] = weeklyResults(
value['seasonMean'], 13 - weekStart).tolist()
value['matchup'] = matchups.loc[
matchups['matchTeam'] == key]['matchOpp'].item().split(
',')[weekStart:]
seq = []
for key, value in teamDict.items():
seq.append(value['pointTotals'])
#print(seq)
prior = time.time()
for key, value in teamDict.items():
for i, matchup in enumerate(value['matchup']):
value['oppPoints'].append(
teamDict[matchup]['pointTotals'][i])
value['wins'].append(
value['pointTotals'][i] > value['oppPoints'][i])
value['losses'].append(
value['pointTotals'][i] < value['oppPoints'][i])
value['ties'].append(
value['pointTotals'][i] == value['oppPoints'][i])
if max(map(lambda x: x[i],
seq)) == value['pointTotals'][i]:
value['weekHigh'].append(1)
value['money'].append(20)
else:
value['weekHigh'].append(0)
value['money'].append(0)
if weekRun > 0:
for key, value in teamDict.items():
standTeam = standings.loc[standings['winTeam'] == key]
value['pointTotals'].append(standTeam['points'].item())
value['wins'].append(standTeam['win'].item())
value['losses'].append(standTeam['loss'].item())
value['ties'].append(standTeam['ties'].item())
value['weekHigh'].append(
standTeam['highPoints'].item())
value['money'].append(standTeam['highPoints'].item() *
20)
teams = []
winArray = []
lossArray = []
tieArray = []
pointArray = []
for key, value in teamDict.items():
wins = sum(value['wins'])
losses = sum(value['losses'])
ties = sum(value['ties'])
points = sum(value['pointTotals'])
weekHigh = sum(value['weekHigh'])
money = sum(value['money'])
teams.append(key)
winArray.append(wins)
lossArray.append(losses)
tieArray.append(ties)
pointArray.append(points)
summaryData[key]['wins'].append(wins)
summaryData[key]['losses'].append(losses)
summaryData[key]['ties'].append(ties)
summaryData[key]['points'].append(points)
summaryData[key]['weekHigh'].append(weekHigh)
d = {
'team': teams,
'wins': winArray,
'losses': lossArray,
'ties': tieArray,
'points': pointArray
}
df = pd.DataFrame(data=d)
#high and low points
df = df.sort_values(['points'], ascending=[0])
df = df.reset_index(drop=True)
for index, row in df.iterrows():
if index < 1:
highpoints = 1
teamDict[row['team']]['money'].append(225)
else:
highpoints = 0
if index == 13:
lowpoints = 1
else:
lowpoints = 0
summaryData[row['team']]['highpoints'].append(highpoints)
summaryData[row['team']]['lowpoints'].append(lowpoints)
#playoffs
df = df.sort_values(['wins', 'losses', 'points'],
ascending=[0, 1, 0])
df = df.reset_index(drop=True)
df['firstplace'] = df.index < 1
df['bye'] = df.index < 2
df['playoffs'] = df.index < 5
df = df.sort_values(['playoffs', 'points'], ascending=[0, 0])
df = df.reset_index(drop=True)
df['playoffs'] = df.index < 6
for index, row in df.iterrows():
if index < 6:
playoff = 1
else:
playoff = 0
if row['firstplace'] == True:
firstplace = 1
teamDict[row['team']]['money'].append(225)
else:
firstplace = 0
if row['bye'] == True:
bye = 1
else:
bye = 0
summaryData[row['team']]['playoffs'].append(playoff)
summaryData[row['team']]['firstplace'].append(firstplace)
summaryData[row['team']]['bye'].append(bye)
df = df.sort_values(['playoffs', 'wins', 'losses', 'points'],
ascending=[0, 0, 1, 0])
df = df.reset_index(drop=True)
#round 1
scores = []
teams = []
for index, row in df[2:6].iterrows():
teams.append(index)
seasonMean = teamDict[row['team']]['seasonMean']
scores.append(weeklyResults(seasonMean, 1))
advanceTeams = []
if scores[3] > scores[0]:
advanceTeams.append(teams[3])
else:
advanceTeams.append(teams[0])
if scores[2] > scores[1]:
advanceTeams.append(teams[2])
else:
advanceTeams.append(teams[1])
#round 2
scores = []
teams = []
for index, row in df[0:2].iterrows():
teams.append(index)
seasonMean = teamDict[row['team']]['seasonMean']
scores.append(weeklyResults(seasonMean, 1))
for index, row in df[advanceTeams[0]:advanceTeams[0] +
1].iterrows():
teams.append(index)
seasonMean = teamDict[row['team']]['seasonMean']
scores.append(weeklyResults(seasonMean, 1))
for index, row in df[advanceTeams[1]:advanceTeams[1] +
1].iterrows():
teams.append(index)
seasonMean = teamDict[row['team']]['seasonMean']
scores.append(weeklyResults(seasonMean, 1))
advanceTeams = []
thirdPlaceTeams = []
if scores[3] > scores[0]:
advanceTeams.append(teams[3])
thirdPlaceTeams.append(teams[0])
else:
advanceTeams.append(teams[0])
thirdPlaceTeams.append(teams[3])
if scores[2] > scores[1]:
advanceTeams.append(teams[2])
thirdPlaceTeams.append(teams[1])
else:
advanceTeams.append(teams[1])
thirdPlaceTeams.append(teams[2])
#champ
scores = []
teams = []
for index, row in df[advanceTeams[0]:advanceTeams[0] +
1].iterrows():
teams.append(index)
seasonMean = teamDict[row['team']]['seasonMean']
scores.append(weeklyResults(seasonMean, 1))
for index, row in df[advanceTeams[1]:advanceTeams[1] +
1].iterrows():
teams.append(index)
seasonMean = teamDict[row['team']]['seasonMean']
scores.append(weeklyResults(seasonMean, 1))
if scores[0] > scores[1]:
champ = teams[0]
second = teams[1]
else:
champ = teams[1]
second = teams[0]
#3rd place
scores = []
teams = []
for index, row in df[thirdPlaceTeams[0]:thirdPlaceTeams[0] +
1].iterrows():
teams.append(index)
seasonMean = teamDict[row['team']]['seasonMean']
scores.append(weeklyResults(seasonMean, 1))
for index, row in df[thirdPlaceTeams[1]:thirdPlaceTeams[1] +
1].iterrows():
teams.append(index)
seasonMean = teamDict[row['team']]['seasonMean']
scores.append(weeklyResults(seasonMean, 1))
if scores[0] > scores[1]:
third = teams[0]
else:
third = teams[1]
for index, row in df.iterrows():
if index == champ:
summaryData[row['team']]['champ'].append(1)
teamDict[row['team']]['money'].append(900)
elif index == second:
teamDict[row['team']]['money'].append(340)
summaryData[row['team']]['champ'].append(0)
elif index == third:
teamDict[row['team']]['money'].append(150)
summaryData[row['team']]['champ'].append(0)
else:
summaryData[row['team']]['champ'].append(0)
summaryData[row['team']]['money'].append(
sum(teamDict[row['team']]['money']))
print(j, time.time() - start, model)
print(time.time() - start)
for index, row in predictData.iterrows():
if weekStart > 0:
standTeam = standings.loc[standings['winTeam'] ==
row['winTeam']]
currentWins = standTeam['win'].item()
currentLosses = standTeam['loss'].item()
currentTies = standTeam['ties'].item()
currentPoints = standTeam['points'].item()
currentHighPoints = standTeam['highPoints'].item()
else:
currentWins = 0
currentLosses = 0
currentTies = 0
currentPoints = 0
currentHighPoints = 0
wins = np.mean(summaryData[row['winTeam']]['wins'])
losses = np.mean(summaryData[row['winTeam']]['losses'])
ties = np.mean(summaryData[row['winTeam']]['ties'])
points = np.mean(summaryData[row['winTeam']]['points'])
playoffs = np.mean(summaryData[row['winTeam']]['playoffs'])
highpoints = np.mean(summaryData[row['winTeam']]['highpoints'])
lowpoints = np.mean(summaryData[row['winTeam']]['lowpoints'])
champ = np.mean(summaryData[row['winTeam']]['champ'])
firstplace = np.mean(summaryData[row['winTeam']]['firstplace'])
bye = np.mean(summaryData[row['winTeam']]['bye'])
exWeekHigh = np.mean(summaryData[row['winTeam']]['weekHigh'])
exMoney = np.mean(summaryData[row['winTeam']]['money'])
print(row['winTeam'], wins, losses, ties, points, playoffs,
highpoints, lowpoints, champ, firstplace, bye,
exWeekHigh, exMoney)
sql = """insert into analysis.standings
(standWeek, standType, standTeam, wins, losses, tie, weekHigh,
pointsScored, exPointAverage,
exWins, playoffsOdds, champOdds, highpoints, lowpoints,firstplace,bye,
exWeekHigh, exMoney)
values (%s)
on duplicate key update
wins = values(wins),
losses = values(losses),
tie = values(tie),
weekHigh = values(weekHigh),
pointsScored = values(pointsScored),
exPointAverage = values(exPointAverage),
exWins = values(exWins),
playoffsOdds = values(playoffsOdds),
champOdds = values(champOdds),
highpoints = values(highpoints),
lowpoints = values(lowpoints),
firstplace = values(firstplace),
bye = values(bye),
exWeekHigh = values(exWeekHigh),
exMoney = values(exMoney);"""
sqlString = (str(weekStart) + "," + "'" + model + "'," + "'" +
row['winTeam'] + "'," + str(currentWins) + "," +
str(currentLosses) + "," + str(currentTies) +
"," + str(currentHighPoints) + "," +
str(currentPoints) + "," + str(points) + "," +
str(wins) + "," + str(playoffs) + "," +
str(champ) + "," + str(highpoints) + "," +
str(lowpoints) + "," + str(firstplace) + "," +
str(bye) + "," + str(exWeekHigh) + "," +
str(exMoney))
c.execute(sql % sqlString)
conn.commit()
conn.close()