def get_wo():
page = ay.get_pfr_fantasy()
df = ay.get_table(page)
df = df.replace('', 0)
attempts = df['RushAtt'].astype('float64')
targets = df['Tgt'].astype('float64')
games = df['G'].astype('float')
df['WO'] = (attempts * 0.58) + (targets * 1.19)
df['WO/G'] = (df['WO'] / games).round(2)
return df.sort_values(by=['WO/G'], ascending=False)
def load_pfr_scoring():
page = ay.get_pfr_scoring()
df = ay.get_table(page)
refresh_table('pfr_scoring', df)
def load_pfr_fantasy():
page = ay.get_pfr_fantasy()
df = ay.get_table(page)
refresh_table('pfr_fantasy', df)
def get_fp(position):
#Wrangle all data from pfr and airyards.com
f_page = ay.get_pfr_fantasy()
f_df = ay.get_table(f_page)
f_df = f_df.replace('', 0)
s_page = ay.get_pfr_scoring()
s_df = ay.get_table(s_page)
s_df = s_df.replace('', 0)
r_page = ay.get_pfr_rushing()
r_df = ay.get_table(r_page)
r_df = r_df.replace('', 0)
f_df.rename(columns={'FantPos':'Pos'}, inplace=True)
wo_df = wopr.get_wo()
wopr_df = wopr.get_totals()
wopr_g_df = wopr.get_averages()
wo_df.rename(columns={'FantPos':'Pos'}, inplace=True)
wopr_df.rename(columns={'full_name':'Player', 'position':'Pos'}, inplace=True)
wopr_g_df.rename(columns={'full_name':'Player', 'position':'Pos'}, inplace=True)
#Combine data to get full fp dataset
df2 = pd.merge(f_df,r_df[['Fmb', 'Player']],on='Player', how='left')
df3 = pd.merge(df2,s_df[['AllTD', 'Player']],on='Player', how='left').fillna(0)
#print(df3)
#Calculate fantasy points
passyds = df3['PassYds'].astype('float64')
passtds = df3['PassTD'].astype('float64')
ints = df3['Int'].astype('float64')
rushyds = df3['RushYds'].astype('float64')
receptions = df3['Rec'].astype('float64')
recyds = df3['RecYds'].astype('float64')
tds = df3['AllTD'].astype('float64')
twoptmd = df3['2PM'].astype('float64')
twoptcp = df3['2PP'].astype('float64')
fumbles = df3['Fmb_y'].astype('float64')
if position == 'QB':
games = df3['GS'].astype('float64')
else:
games = df3['G'].astype('float64')
fp = (passyds / 30.0) + (passtds * 4.0) - (ints * 2.0) + (rushyds / 10.0) + (receptions * 0.5) + (recyds / 10.0) + (tds * 6.0) + (twoptmd * 2.0) + (twoptcp * 2.0) - (fumbles * 2.0)
#Combine data to get full opportunity dataset
df4 = pd.merge(f_df,wo_df[['WO', 'WO/G', 'Player', 'Pos']],on=['Player', 'Pos'], how='left').fillna(0)
df5 = pd.merge(df4,wopr_g_df[['wopr', 'Player', 'Pos']],on=['Player', 'Pos'], how='left').fillna(0)
df5.rename(columns={'wopr':'WOPR/G'}, inplace=True)
df6 = pd.merge(df5,wopr_df[['wopr', 'Player', 'Pos']],on=['Player', 'Pos'], how='left').fillna(0)
#Combine opportunity with fantasy points and return dataset filtered on position
df = pd.DataFrame()
df['Player'] = df6['Player']
df['Pos'] = df6['Pos']
df['FP'] = fp.round(2)
df['WO'] = df6['WO']
df['FPG'] = (fp / games).round(2)
df['WOG'] = df6['WO/G']
df['WOPRG'] = df6['WOPR/G']
df['WOPR'] = df6['wopr']
#flex handling
if position == 'FLEX':
final_df = df[(df['Pos'] == 'WR') | (df['Pos'] == 'RB') | (df['Pos'] == 'TE')]
else:
final_df = df[(df['Pos'] == position)]
return final_df