def getCoachRating(team, year):
df = pd.read_csv('./csv_Data/Coaching/1960-2019.csv', encoding = "ISO-8859-1")
winPctDiff = []
teams = []
g = 0
fName = "Error"
lName = "Error"
for index, row in df.iterrows():
if (int(row["year"]) > year):
break
if (standardizeTeamName(row["school"],False) == team and year == int(row["year"])):
if (int(row["games"]) > g):
fName = row["first_name"]
lName = row["last_name"]
g = int(row["games"])
for index, row in df.iterrows():
if (int(row["year"]) == year):
break
if (fName == row["first_name"] and lName == row["last_name"]):
if (standardizeTeamName(row["school"],False) not in teams):
teams.append(standardizeTeamName(row["school"],False))
for a in teams:
winPctHist = []
winPct = []
for index, row in df.iterrows():
if (int(row["year"]) == year):
break
if (standardizeTeamName(row["school"],False) == a and (fName != row["first_name"] or lName != row["last_name"])):
winPctHist.append(int(row["wins"])/(int(row["losses"])+int(row["wins"])))
elif (standardizeTeamName(row["school"],False) == a and (fName == row["first_name"] and lName == row["last_name"])):
winPct.append(int(row["wins"])/(int(row["losses"])+int(row["wins"])))
if (len(winPctHist) < 5):
continue
for bbb in winPct:
winPctDiff.append(bbb - np.average(winPctHist))
if (len(winPctDiff) > 0):
return (np.average(winPctDiff))
else:
return 0
df = pd.read_csv('./new_csv_data/2021/StatsWeek1.csv', encoding = "ISO-8859-1")
for w in range(2, weekMain):
df = df.append(pd.read_csv('./new_csv_data/2021/StatsWeek' + str(w) + '.csv', encoding = "ISO-8859-1"), ignore_index=True)
dropRows = []
for index, row in df.iterrows():
if (index > 0 and row["gameId"] == df.at[index-1,"gameId"]):
dropRows.append(index)
df = df.drop(dropRows)
dict = {"Week":[],"Team":[],"Offense":{},"Defense":{}}
subDictOff = {"Ppa":[],"Sr":[],"Exp":[],"PwrS":[],"Stuff":[],"SecLevel":[],"OpenField":[],"StDownPpa":[],"StDownSr":[],"StDownExp":[],"PassDownPpa":[],"PassDownSr":[],"PassDownExp":[],"RushPpa":[],"RushSr":[],"RushExp":[],"PassPpa":[],"PassSr":[],"PassExp":[]}
subDictDef = {"Ppa":[],"Sr":[],"Exp":[],"PwrS":[],"Stuff":[],"SecLevel":[],"OpenField":[],"StDownPpa":[],"StDownSr":[],"StDownExp":[],"PassDownPpa":[],"PassDownSr":[],"PassDownExp":[],"RushPpa":[],"RushSr":[],"RushExp":[],"PassPpa":[],"PassSr":[],"PassExp":[]}
for index, row in df.iterrows():
dict["Week"].append(row["week"])
dict["Team"].append(standardizeTeamName(row["team"], False))
dict["Week"].append(row["week"])
dict["Team"].append(standardizeTeamName(row["opponent"], False))
#Offense
subDictOff["Ppa"].append(row["offense.ppa"])
subDictDef["Ppa"].append(row["defense.ppa"])
subDictOff["Ppa"].append(row["defense.ppa"])
subDictDef["Ppa"].append(row["offense.ppa"])
subDictOff["Sr"].append(row["offense.successRate"])
subDictDef["Sr"].append(row["defense.successRate"])
subDictOff["Sr"].append(row["defense.successRate"])
subDictDef["Sr"].append(row["offense.successRate"])
subDictOff["Exp"].append(row["offense.explosiveness"])
subDictDef["Exp"].append(row["defense.explosiveness"])
import pandas as pd
import numpy as np
import operator
from cfbFcns import standardizeTeamName
curElo = {}
teamDf = pd.read_csv('./csv_Data/majorDivTeams.csv', encoding="ISO-8859-1")
for index, row in teamDf.iterrows():
curElo[standardizeTeamName(row["school"], False)] = {"Elo": 1500, "G": 0}
#Adding Idaho Manually since they are no longer FBS
curElo["Idaho"] = {"Elo": 1500, "G": 0}
#{Spread : Prob of Fav winning that spread}
pDict = {}
pChart = pd.read_csv('./csv_data/spreadProbChart.csv', encoding="ISO-8859-1")
for index, row in pChart.iterrows():
pDict[float(row["spread"])] = float(row["fav win"].split("%")[0]) / 100
#k function is a complete gut call
years = []
zzz = 1999
while (zzz <= 2019):
years.append(zzz)
zzz += 1
for year in years:
print(year)
incEloHome = []
incEloRoad = []
resEloHome = []
resEloRoad = []
eloLoss = 0
if (year >= 2002):
encoding="ISO-8859-1").drop(columns=["Unnamed: 0"])
for gamesIndex, gamesRow in games.iterrows():
if (gamesRow["Week"] == "Bowl"
or int(gamesRow["Week"]) > week):
break
#Case that aggregates later weeks in season
if (week == 999):
if (int(gamesRow["Week"]) > 10):
for col in games.columns:
statsDict[col].append(gamesRow[col])
for statsIndex, statsRow in stats.iterrows():
if (statsRow["Week"] == "Year End" or int(
statsRow["Week"]) > int(gamesRow["Week"])):
break
if (standardizeTeamName(
gamesRow["Home Team"],
False) == standardizeTeamName(
statsRow["Team"], False)):
if (not appendedHome):
for col in stats.columns:
if (col != "Week" and col != "Team"):
statsDict["Home " + col].append(
statsRow[col])
appendedHome = True
else:
for col in stats.columns:
if (col != "Week" and col != "Team"):
del statsDict["Home " + col][-1]
statsDict["Home " + col].append(
statsRow[col])
if (standardizeTeamName(
return sum(arr)/len(arr)
return np.nan
for year in years:
df = pd.read_csv('./new_csv_data/advStatsGame/' + year + '.csv', encoding = "ISO-8859-1")
dropRows = []
for index, row in df.iterrows():
if (index > 0 and row["gameId"] == df.at[index-1,"gameId"]):
dropRows.append(index)
df = df.drop(dropRows)
dict = {"Week":[],"Team":[],"Offense":{},"Defense":{}}
subDictOff = {"Ppa":[],"Sr":[],"Exp":[],"PwrS":[],"Stuff":[],"SecLevel":[],"OpenField":[],"StDownPpa":[],"StDownSr":[],"StDownExp":[],"PassDownPpa":[],"PassDownSr":[],"PassDownExp":[],"RushPpa":[],"RushSr":[],"RushExp":[],"PassPpa":[],"PassSr":[],"PassExp":[]}
subDictDef = {"Ppa":[],"Sr":[],"Exp":[],"PwrS":[],"Stuff":[],"SecLevel":[],"OpenField":[],"StDownPpa":[],"StDownSr":[],"StDownExp":[],"PassDownPpa":[],"PassDownSr":[],"PassDownExp":[],"RushPpa":[],"RushSr":[],"RushExp":[],"PassPpa":[],"PassSr":[],"PassExp":[]}
for index, row in df.iterrows():
dict["Week"].append(row["week"])
dict["Team"].append(standardizeTeamName(row["team"], False))
dict["Week"].append(row["week"])
dict["Team"].append(standardizeTeamName(row["opponent"], False))
#Offense
subDictOff["Ppa"].append(row["offense.ppa"])
subDictDef["Ppa"].append(row["defense.ppa"])
subDictOff["Ppa"].append(row["defense.ppa"])
subDictDef["Ppa"].append(row["offense.ppa"])
subDictOff["Sr"].append(row["offense.successRate"])
subDictDef["Sr"].append(row["defense.successRate"])
subDictOff["Sr"].append(row["defense.successRate"])
subDictDef["Sr"].append(row["offense.successRate"])
subDictOff["Exp"].append(row["offense.explosiveness"])
subDictDef["Exp"].append(row["defense.explosiveness"])
ouNum = 0
spreadVol = 0
ouVol = 0
configuration = cfbd.Configuration()
configuration.api_key[
'Authorization'] = 'XBWTTfw3Jo8o/r/jmDnRA6SsnoHp0MKKPBEE0UGID/hPKqzKLV/+0Ljn06dCbQRS'
configuration.api_key_prefix['Authorization'] = 'Bearer'
api_instance = cfbd.GamesApi(cfbd.ApiClient(configuration))
year = 2021
api_response = api_instance.get_games(year=year,
week=week,
season_type="regular")
for index, row in bets.iterrows():
for x in api_response:
if (standardizeTeamName(x.home_team, False) == standardizeTeamName(
row["Home Team"], False)
and standardizeTeamName(x.away_team, False)
== standardizeTeamName(row["Road Team"], False)):
if (not pd.isnull(row["Spread Bet"])):
spreadNum += 1
spreadVol += float(row["Spread Amt"])
if ('+' in row["Spread Bet"]):
team = row["Spread Bet"].split(" +")[0]
points = float(row["Spread Bet"].split(" +")[1])
if (team == standardizeTeamName(x.home_team, False)):
if (float(x.home_points) + points > float(
x.away_points)):
spreadSum += float(row["Spread Amt"]) * (
float(row["Spread Odds"]) - 1)
elif (float(x.home_points) + points < float(
Hadded = False
Radded = False
dict = {}
for year in years:
print(year)
a = pd.read_csv('./csv_Data/advStatsSeason/' + str(year - 1) + '.csv',
encoding="ISO-8859-1")
for index, row in bigboy.iterrows():
Hadded = False
Radded = False
if (int(row["Year"]) > year):
break
elif (int(row["Year"]) < year):
continue
for i, r in a.iterrows():
if (row["HTeam"] == standardizeTeamName(r["team"], False)):
Hadded = True
for col in a.columns:
if (col != "team" and col != "season"
and col != "conference"):
if ("offense" in col):
letter = "o"
else:
letter = "d"
if ("H" + letter + "_lastYear_" + col.split("ense.")[1]
not in dict):
dict["H" + letter + "_lastYear_" +
col.split("ense.")[1]] = []
dict["H" + letter + "_lastYear_" +
col.split("ense.")[1]].append(r[col])
elif (row["RTeam"] == standardizeTeamName(r["team"], False)):
import pandas as pd
import numpy as np
import operator
from cfbFcns import standardizeTeamName
import cfbd
from cfbd.rest import ApiException
curWeek = 5
curElo = {}
teamDf = pd.read_csv('./csv_Data/majorDivTeams.csv', encoding = "ISO-8859-1")
for index, row in teamDf.iterrows():
curElo[standardizeTeamName(row["school"],False)] = {"Elo":1500,"G":0}
#Adding Idaho Manually since they are no longer FBS
curElo["Idaho"] = {"Elo":1500,"G":0}
#{Spread : Prob of Fav winning that spread}
pDict = {}
pChart = pd.read_csv('./csv_data/spreadProbChart.csv', encoding = "ISO-8859-1")
for index, row in pChart.iterrows():
pDict[float(row["spread"])] = float(row["fav win"].split("%")[0])/100
#k function is a complete gut call
years = []
zzz = 1999
while (zzz <= 2020):
years.append(zzz)
zzz += 1
for year in years:
eloLoss = 0
if (year >= 2002 and year <= 2019):
curYearDf = pd.read_csv('./csv_Data/BettingResults/' + str(year) + '.csv', encoding = "ISO-8859-1")
browser = webdriver.Chrome(executable_path='chromedriver.exe')
browser.get("https://www.pinnacle.com/en/football/ncaa/matchups/#period:0")
browser.maximize_window()
time.sleep(5)
for i in range(25):
soup = BeautifulSoup(browser.page_source, 'html.parser')
main = soup.find(class_="contentBlock square")
games = main.find_all(class_="style_row__3q4g_ style_row__3hCMX")
for game in games:
dict["Week"].append(week)
dict["Neutral Field"].append(0)
dict["Home Team"].append(
standardizeTeamName(
game.find_all(
class_=
"ellipsis event-row-participant style_participant__H8-ku")
[1].text, True))
dict["Road Team"].append(
standardizeTeamName(
game.find_all(
class_=
"ellipsis event-row-participant style_participant__H8-ku")
[0].text, True))
spread = game.find(class_="style_buttons__XEQem")
try:
if (float(spread.find(class_="style_label__2KJur").text) < 0):
dict["Favorite"].append(
standardizeTeamName(
game.find_all(
class_=
"Neutral Field": [],
"Night Game": [],
"Postseason Game": [],
"Favorite": [],
"Spread": [],
"O/U": [],
"Home Score": [],
"Road Score": [],
"Spread Winner": [],
"O/U Outcome": []
}
idsUsed = []
for index, row in bets.iterrows():
if (row["id"] not in idsUsed):
dict["Home Team"].append(standardizeTeamName((row["homeTeam"]), False))
dict["Road Team"].append(standardizeTeamName((row["awayTeam"]), False))
dict["Home Score"].append(row["homeScore"])
dict["Road Score"].append(row["awayScore"])
k = 0
curGame = row["id"]
consensusFound = False
while (k + index < len(bets.index)
and bets.iat[k + index, 0] == curGame):
if (bets.iat[k + index, 5] == "consensus"):
consensusFound = True
dict["O/U"].append(bets.iat[k + index, 6])
if (int(bets.iat[k + index, 2]) + int(bets.iat[k + index, 4]) >
float(bets.iat[k + index, 6])):
dict["O/U Outcome"].append("Over")
elif (int(bets.iat[k + index, 2]) +
import CFBScrapy as cfb
from cfbFcns import standardizeTeamName
import pandas
thisYear = 2015
t = cfb.get_team_talent(year=thisYear)
t = t.drop(columns=["year"])
pwrRate = []
for i in range(len(t.index)):
pwrRate.append(float(t.at[i,"talent"])/float(t.at[0,"talent"]))
t["pwrRating"] = pwrRate
dropRows = []
for i in range(len(t.index)):
if (len(standardizeTeamName(t.at[i,"school"]).split("Error")) > 1):
dropRows.append(i)
else:
t.at[i,"school"] = standardizeTeamName(t.at[i,"school"])
t = t.drop(dropRows)
dropRows = []
a = pandas.read_csv("./csv_Data/AdvancedStatsSeason/2014.csv", encoding = "ISO-8859-1")
for i in range(len(a.index)):
if (len(standardizeTeamName(a.at[i,"team"]).split("Error")) > 1):
dropRows.append(i)
else:
a.at[i,"team"] = standardizeTeamName(a.at[i,"team"])
a = a.drop(dropRows)
print (t)
print (a)
for i in range(len(t.index)):
for j in range(len(a.index)):
if (t.at[i,"school"] == )
for year in years:
dict[str(year)] = {}
for w in weeks:
try:
a = pd.read_csv('./csv_Data/teamGameStats/' + str(year) + '_' +
str(w) + '.csv',
encoding="ISO-8859-1")
except FileNotFoundError:
continue
print(year, w)
if (path.exists('./csv_Data/teamGameStats/' + str(year) + '_' +
str(w) + '.csv')):
for index, row in a.iterrows():
#print (row, index)
if (standardizeTeamName(row["school"], False)
not in dict[str(year)]):
dict[str(year)][standardizeTeamName(row["school"],
False)] = {}
if ("Week " + str(w)
not in dict[str(year)][standardizeTeamName(
row["school"], False)]):
dict[str(year)][standardizeTeamName(
row["school"], False)]["Week " + str(w)] = {
"Points For": 0,
"Points Against": 0,
"Completions": 0,
"Attempts": 0,
"Opponent Completions": 0,
"Opponent Attempts": 0,
"TOP": 0,
0].split("-6ot")[0].split("-7ot")[0]
else:
teamString = teamString + " " + row[k].split(
"-ot")[0].split("-OT")[0].split("#")[0].split(
"-2ot")[0].split("-3ot")[0].split(
"-4ot")[0].split("-5ot")[0].split(
"-6ot")[0].split("-7ot")[0]
k = k + 1
if (k == len(row)):
getOut = True
break
if (getOut):
j += 1
continue
if (len(
standardizeTeamName(teamString,
True).split("Error: ")) > 1):
print(standardizeTeamName(teamString, True))
if (len(
standardizeTeamName(
lines[i -
1].split(" (AT)")[0].split(" (P.A.T.)")
[0].split(" (FT)")[0].split("\n")[0],
True).split("Error: ")) > 1):
print(
standardizeTeamName(
lines[i -
1].split(" (AT)")[0].split(" (P.A.T.)")
[0].split(" (FT)")[0].split("\n")[0], True))
if (len(row[0].split("*")) == 2):
NG = True
else:
from cfbFcns import standardizeTeamName
import pandas as pd
print(standardizeTeamName("San José State", True))
str = "San José State"
print(str.lower())
stats = pd.read_csv('./csv_Data/advStatsFwdLooking/2016.csv',
encoding="ISO-8859-1").drop(columns=["Unnamed: 0"])
print(stats.iat[70, 1].lower())
bigboy = pd.read_csv('./new_csv_Data/bigboy.csv', encoding="ISO-8859-1")
Fresh = []
Soph = []
Jun = []
Sen = []
added = False
for index, row in bigboy.iterrows():
print(index)
df = pd.read_csv('./csv_Data/Recruiting/' + str(row["Year"]) + '.csv',
encoding="ISO-8859-1")
added = False
for indexx, roww in df.iterrows():
if (row["Team"] == standardizeTeamName(roww["team"], False)):
Fresh.append(float(roww["points"]))
added = True
break
if (not added):
Fresh.append(0)
df = pd.read_csv('./csv_Data/Recruiting/' + str(int(row["Year"]) - 1) +
'.csv',
encoding="ISO-8859-1")
added = False
for indexx, roww in df.iterrows():
if (row["Team"] == standardizeTeamName(roww["team"], False)):
Soph.append(float(roww["points"]))
added = True
break
if (not added):