Update app.py

app.py

@@ -176,56 +176,62 @@ with tab7:
 
                 st.write("Initiated")
 
-                sh = gc.open_by_url(NCAAF_model_url)
+                sh = gc.open_by_url(sheet_url)
                 worksheet = sh.worksheet('ATLranks')
                 ranks_df = DataFrame(worksheet.get_all_records())
                 ranks_dict = dict(zip(ranks_df.Team, ranks_df.ATL))
                 conf_dict = dict(zip(ranks_df.Team, ranks_df.Conference))
-
+                
                 time.sleep(.5)
-
+                
+                worksheet = sh.worksheet('Injuries')
+                injuries_df = DataFrame(worksheet.get_all_records())
+                injuries_dict = dict(zip(injuries_df.Team, injuries_df.Team_Modifier))
+                
+                time.sleep(.5)
+                
                 worksheet = sh.worksheet('HFA')
                 hfa_df = DataFrame(worksheet.get_all_records())
                 hfa_dict = dict(zip(hfa_df.Team, hfa_df.HFA))
-
+                
                 time.sleep(.5)
-
+                
                 worksheet = sh.worksheet('Odds')
                 odds_df = DataFrame(worksheet.get_all_records())
                 odds_dict = dict(zip(odds_df.Point_Spread, odds_df.Favorite_Win_Chance))
-
+                
                 time.sleep(.5)
-
+                
                 worksheet = sh.worksheet('Acronyms')
                 acros_df = DataFrame(worksheet.get_all_records())
                 right_acro = acros_df['Team'].tolist()
                 wrong_acro = acros_df['Acro'].tolist()
-
+                
                 time.sleep(.5)
-
+                
                 worksheet = sh.worksheet('Add games')
                 add_games_df = DataFrame(worksheet.get_all_records())
                 add_games_df.replace('', np.nan, inplace=True)
                 neutral_dict = dict(zip(add_games_df.game_id, add_games_df.Neutral))
-
+                
                 time.sleep(.5)
-
+                
                 worksheet = sh.worksheet('Completed games')
                 comp_games_df = DataFrame(worksheet.get_all_records())
                 comp_games_df.replace('', np.nan, inplace=True)
-
+                
                 time.sleep(.5)
-
+                
                 worksheet = sh.worksheet('LY_scoring')
                 lyscore_df = DataFrame(worksheet.get_all_records())
                 for checkVar in range(len(wrong_acro)):
                     lyscore_df['Team'] = lyscore_df['Team'].replace(wrong_acro, right_acro)
-
+                
                 PFA_dict = dict(zip(lyscore_df.Team, lyscore_df.PF_G_adj))
                 PAA_dict = dict(zip(lyscore_df.Team, lyscore_df.PA_G_adj))
 
                 # Send a GET request to the API
-                response = requests.get(pff_url)
+                response = requests.get(url)
 
                 st.write("retreiving PFF data")
 
@@ -240,7 +246,7 @@ with tab7:
                     # Initialize an empty list to store game data
                     games_list = []
                     team_list = []
-
+                
                     # Iterate over each week and its games
                     for week in weeks:
                         week_number = week.get('week')
@@ -280,12 +286,12 @@ with tab7:
                             games_list.append(merged_data)
                             team_list.append(home_data)
                             team_list.append(away_data)
-
+                
                     # Create a DataFrame from the games list
                     df = pd.DataFrame(games_list)
                     team_df = pd.DataFrame(team_list)
                     team_df = team_df.drop_duplicates(subset=['team', 'conf'])
-
+                
                     # Display the DataFrame
                     print(df)
                 else:
@@ -311,19 +317,19 @@ with tab7:
                 df_cleaned = pd.concat([comp_games_merge, df_merge_1])
                 df_cleaned = df_cleaned[['pff_week', 'game_id', 'away_id', 'home_id', 'Away', 'Home', 'point_spread', 'over_under', 'Day', 'CST']]
                 df_cleaned = df_cleaned.drop_duplicates(subset=['game_id'])
-
+                
                 def cond_away_PFA(row, df):
                     mask = (df['Away_ATL'] >= row['Away_ATL'] - 5) & (df['Away_ATL'] <= row['Away_ATL'] + 5)
                     return df.loc[mask, 'Away_PFA'].mean()
-
+                
                 def cond_home_PFA(row, df):
                     mask = (df['Home_ATL'] >= row['Home_ATL'] - 5) & (df['Home_ATL'] <= row['Home_ATL'] + 5)
                     return df.loc[mask, 'Home_PFA'].mean()
-
+                
                 def cond_away_PAA(row, df):
                     mask = (df['Away_ATL'] >= row['Away_ATL'] - 5) & (df['Away_ATL'] <= row['Away_ATL'] + 5)
                     return df.loc[mask, 'Away_PAA'].mean()
-
+                
                 def cond_home_PAA(row, df):
                     mask = (df['Home_ATL'] >= row['Home_ATL'] - 5) & (df['Home_ATL'] <= row['Home_ATL'] + 5)
                     return df.loc[mask, 'Home_PAA'].mean()
@@ -337,27 +343,32 @@ with tab7:
                 df_cleaned['conf_game'] = df_cleaned.apply(lambda row: conf_adj.get(row['game_id'], row['conf_game_var']), axis=1)
                 df_cleaned['Away_ATL'] = df_cleaned['Away'].map(ranks_dict)
                 df_cleaned['Home_ATL'] = df_cleaned['Home'].map(ranks_dict)
+                df_cleaned['Away_inj'] = df_cleaned['Away'].map(injuries_dict)
+                df_cleaned['Home_inj'] = df_cleaned['Home'].map(injuries_dict)
+                df_cleaned['Away_inj'] = df_cleaned['Away_inj'].replace(['', np.nan], 0)
+                df_cleaned['Home_inj'] = df_cleaned['Home_inj'].replace(['', np.nan], 0)
+                df_cleaned['inj_mod'] = df_cleaned['Away_inj'] - df_cleaned['Home_inj']
                 df_cleaned['Away_PFA'] = df_cleaned['Away'].map(PFA_dict)
                 df_cleaned['Home_PFA'] = df_cleaned['Home'].map(PFA_dict)
                 df_cleaned['Away_PAA'] = df_cleaned['Away'].map(PAA_dict)
                 df_cleaned['Home_PAA'] = df_cleaned['Home'].map(PAA_dict)
-
+                
                 # Apply the function to each row in the DataFrame
                 df_cleaned['cond_away_PFA'] = df_cleaned.apply(lambda row: cond_away_PFA(row, df_cleaned), axis=1)
                 df_cleaned['cond_home_PFA'] = df_cleaned.apply(lambda row: cond_home_PFA(row, df_cleaned), axis=1)
                 df_cleaned['cond_away_PAA'] = df_cleaned.apply(lambda row: cond_away_PAA(row, df_cleaned), axis=1)
                 df_cleaned['cond_home_PAA'] = df_cleaned.apply(lambda row: cond_home_PAA(row, df_cleaned), axis=1)
-
+                
                 df_cleaned['cond_away_PFA'] = np.where((df_cleaned['Away_ATL'] <= 0), 18, df_cleaned['cond_away_PFA'])
                 df_cleaned['cond_away_PAA'] = np.where((df_cleaned['Away_ATL'] <= 0), 36, df_cleaned['cond_away_PAA'])
                 df_cleaned['cond_home_PFA'] = np.where((df_cleaned['Home_ATL'] <= 0), 18, df_cleaned['cond_home_PFA'])
                 df_cleaned['cond_home_PAA'] = np.where((df_cleaned['Home_ATL'] <= 0), 36, df_cleaned['cond_home_PAA'])
-
+                
                 df_cleaned['Away_PFA'] = df_cleaned['Away_PFA'].fillna(df_cleaned['cond_away_PFA'])
                 df_cleaned['Away_PAA'] = df_cleaned['Away_PAA'].fillna(df_cleaned['cond_away_PAA'])
                 df_cleaned['Home_PFA'] = df_cleaned['Home_PFA'].fillna(df_cleaned['cond_home_PFA'])
                 df_cleaned['Home_PAA'] = df_cleaned['Home_PAA'].fillna(df_cleaned['cond_home_PAA'])
-
+                
                 df_cleaned['Away_PFA_adj'] = (df_cleaned['Away_PFA'] * .75 + df_cleaned['Home_PAA'] * .25)
                 df_cleaned['Home_PFA_adj'] = (df_cleaned['Home_PFA'] * .75 + df_cleaned['Away_PAA'] * .25)
                 df_cleaned['Away_PFA_cond'] = (df_cleaned['cond_away_PFA'] * .75 + df_cleaned['cond_home_PAA'] * .25)
@@ -366,7 +377,7 @@ with tab7:
                 df_cleaned['Neutral'] = df_cleaned['game_id'].map(neutral_dict)
                 df_cleaned['HFA'] = np.where(df_cleaned['Neutral'] == 1, 0, df_cleaned['Home'].map(hfa_dict))
                 df_cleaned['Neutral'] = np.nan
-                df_cleaned['Home Spread'] = ((df_cleaned['Home_ATL'] - df_cleaned['Away_ATL']) + df_cleaned['HFA']) * -1
+                df_cleaned['Home Spread'] = (((df_cleaned['Home_ATL'] - df_cleaned['Away_ATL']) + df_cleaned['HFA']) * -1) + df_cleaned['inj_mod']
                 df_cleaned['Win Prob'] = df_cleaned['Home Spread'].map(odds_dict)
                 df_cleaned['Spread Adj'] = np.nan
                 df_cleaned['Final Spread'] = np.nan
@@ -376,32 +387,29 @@ with tab7:
                 df_cleaned['Total Adj'] = np.nan
                 df_cleaned['Final Total'] = np.nan
                 df_cleaned['Notes'] = np.nan
-
+                
                 export_df_1 = df_cleaned[['pff_week', 'game_id', 'away_id', 'home_id', 'Away', 'Home', 'conf_game', 'Away_ATL', 'Home_ATL', 'point_spread', 'Home Spread',
                                         'over_under', 'Proj Total (adj)', 'Day', 'CST', 'Neutral', 'Notes']]
-
-
+                
+                
                 export_df_1.rename(columns={"pff_week": "week", "point_spread": "Vegas Spread", "over_under": "Vegas Total", "Proj Total (adj)": "Proj Total"}, inplace = True)
                 export_df_2 = add_games_df
                 export_df = export_df_1
                 export_df['week'] = pd.to_numeric(export_df['week'], errors='coerce')
                 export_df = export_df.drop_duplicates(subset=['week', 'Away', 'Home'])
                 export_df = export_df.sort_values(by='week', ascending=True)
-
+                
                 export_df['Vegas Spread'] = pd.to_numeric(export_df['Vegas Spread'], errors='coerce')
                 export_df['Vegas Total'] = pd.to_numeric(export_df['Vegas Total'], errors='coerce')
                 export_df['Proj Total'] = pd.to_numeric(export_df['Proj Total'], errors='coerce')
                 export_df['Home Spread'] = pd.to_numeric(export_df['Home Spread'], errors='coerce')
                 export_df.replace([np.nan, np.inf, -np.inf], '', inplace=True)
                 export_df = export_df.drop_duplicates(subset=['week', 'away_id', 'home_id'])
-
-                sh = gc.open_by_url(NCAAF_model_url)
+                
+                sh = gc.open_by_url(sheet_url)
                 worksheet = sh.worksheet('Master_sched')
                 worksheet.batch_clear(['A:P'])
                 worksheet.update([export_df.columns.values.tolist()] + export_df.values.tolist())
-
-
                 st.write("Uploaded Master Schedule")
                 
-                
                 st.write("Finished NCAAF Script!")