def get_predictions_for_stat(stat, startDate, endDate, training=True):
trainRange = ml.getDateRangeArr(
"2015-11-04", "2015-11-06"
) # initial training: first three days when all data is available
retrainRange = ml.getDateRangeArr(startDate, endDate) # last date: 4/18/16
# create & train model for stat type:
if training is True:
createResponse = google.createAndTrainNewModelWithDateRange(
trainRange, stat)
google.waitForModelToRetrain(stat)
# pull predictions for date range & stat type, retain each iteration
getAndPostPredictionsForDates(retrainRange, stat)
return "DONE!"
def getGamesForDateRange(startDate, endDate):
dateArr = ml.getDateRangeArr(startDate, endDate)
allGames = []
for date in dateArr:
games = api.getGamesForDate(date)
allGames.extend(games)
return allGames
salariesForDate = fc.getSalaryDataForDate(date, site, playerData,
driver)
# add salary & missing player data
allData['allSalaries'].extend(salariesForDate['currentPlayerSalaries'])
allData['allNewIds'].extend(salariesForDate['missingPlayerIds'])
newSalariesResponse = api.postSalaries(allData['allSalaries'])
# UNCOMMENT LINE BELOW IF ALSO WANT TO ADD NEW FC IDS
# dedupedNewIds = [i for n, i in enumerate(allData["allNewIds"]) if i not in allData["allNewIds"][n + 1:]]
# newIdsResponse = api.postNewIds(dedupedNewIds)
return allData
salaryDates = ml.getDateRangeArr('2017-03-15', '2017-04-12')
allUpdates = getSalariesForDatesAndPostToApi(salaryDates, "fanduel")
# INCOMPLETE: this fcn used for manual bulk scraping of position changes
def getPositionChangesForDatesAndWriteToCsv(dateArr):
driver = webdriver.PhantomJS()
driver.set_window_size(1124, 850)
playerData = api.getCurrentPlayerData()
site = "fanduel" # fanduel or draftkings?
allData = {'allPositionUpdates': []}
# keeps track of dupes
import nba.ops.mlDataPrep as ml
trainDates = ml.getDateRangeArr('2015-11-04', '2016-01-25')
stat = 'tpt'
ml.pullAzureTrainingDataAndWriteToCsv(trainDates, stat)
# calculate actual pts for team
actualPoints = getActualPointsForTeam(team)
# get threshold for date (just double up for now)
cutoff = cutoffs[dateToTest]["Double Up"]
# inc totalOver count if team is over threshold
if predicted_points > 0 and actualPoints > 0:
print("DATE", dateToTest, "PREDICTED", predicted_points,
"ACTUAL", actualPoints, "SALARY", team_salary, "CUTOFF",
cutoff)
totalTested += 1
if actualPoints > cutoff:
totalOver += 1
except (KeyError, TypeError) as e:
print("ERROR OPTIMIZING", str(dateToTest))
print(e)
pass
return float(totalOver / totalTested)
dates = ml.getDateRangeArr('2017-03-20', '2017-04-12')
source = 'GOOGLE'
cap = 60000
minimum = 285
print(getFinalAnalysisForDates(dates, source, cap, minimum))
def getTournamentAverageScores():
driver = webdriver.PhantomJS()
driver.set_window_size(1124, 850)
baseUrl = "https://www.fantasycruncher.com/lineup-rewind/fanduel/NBA/"
dates = ml.getDateRangeArr('2017-03-15', '2017-04-12')
rowsSelector = "#tournament-links-table-container"
# tournamentTypes = ["Double Up", "Triple Up", "Quintuple Up"]
bigTerm = "BIG"
cutoffs = {}
for date in dates:
fullUrl = baseUrl + date
cutoffsForDate = {
"Double Up": [],
"Double Up BIG": [],
"Triple Up": [],
"Triple Up BIG": [],
"Quintuple Up": [],
"Quintuple Up BIG": []
}
try:
driver.get(fullUrl)
print("Getting..", date)
closeLogin = waits.byClass('close-login-alert', driver)
closeLogin.click()
openTab = waits.byClass("open-tournament-links", driver)
openTab.click()
tableCont = waits.byId("tournament-links-table-container", driver)
rawHtml = driver.page_source
tableRowsSelector = 'table.tournament-links-table tbody tr'
tree = html.fromstring(rawHtml)
rows = tree.cssselect(tableRowsSelector)
for row in rows:
tournamentName = row[0].text_content()
if "Double Up" in tournamentName and bigTerm not in tournamentName:
cutoffsForDate["Double Up"].append(
float(row[8].text_content()))
elif "Double Up" in tournamentName and bigTerm in tournamentName:
cutoffsForDate["Double Up BIG"].append(
float(row[8].text_content()))
elif "Triple Up" in tournamentName and bigTerm not in tournamentName:
cutoffsForDate["Triple Up"].append(
float(row[8].text_content()))
elif "Triple Up" in tournamentName and bigTerm in tournamentName:
cutoffsForDate["Triple Up BIG"].append(
float(row[8].text_content()))
elif "Quintuple Up" in tournamentName and bigTerm not in tournamentName:
cutoffsForDate["Quintuple Up"].append(
float(row[8].text_content()))
elif "Quintuple Up" in tournamentName and bigTerm in tournamentName:
cutoffsForDate["Quintuple Up BIG"].append(
float(row[8].text_content()))
except Exception as e:
continue
cutoffs[date] = {}
for tournamentType, cutoffArr in cutoffsForDate.items():
try:
average = sum(cutoffArr) / float(len(cutoffArr))
except ZeroDivisionError as e:
average = None
cutoffs[date][tournamentType] = average
filename = jsonData.LOCAL_DATA_PATH + "2017_tournament_results.json"
with open(filename, 'w') as fp:
json.dump(cutoffs, fp)
driver.quit()
return "DONE"