Update app.py

app.py

@@ -77,7 +77,7 @@ prop_table_options = ['NFL_GAME_PLAYER_RUSHING_YARDS', 'NFL_GAME_PLAYER_RECEIVIN
 prop_format = {'L3 Success': '{:.2%}', 'L6_Success': '{:.2%}', 'L10_success': '{:.2%}', 'Trending Over': '{:.2%}', 'Trending Under': '{:.2%}',
                'Implied Over': '{:.2%}', 'Implied Under': '{:.2%}', 'Over Edge': '{:.2%}', 'Under Edge': '{:.2%}'}
 all_sim_vars = ['NFL_GAME_PLAYER_PASSING_YARDS', 'NFL_GAME_PLAYER_RUSHING_YARDS', 'NFL_GAME_PLAYER_RECEIVING_YARDS', 'NFL_GAME_PLAYER_RECEIVING_RECEPTIONS', 'NFL_GAME_PLAYER_RUSHING_ATTEMPTS', 'NFL_GAME_PLAYER_PASSING_ATTEMPTS']
-sim_all_hold = pd.DataFrame(columns=['Player', 'Team', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge'])
+sim_all_hold = pd.DataFrame(columns=['Player', 'Team', 'Book', 'Prop Type', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge'])
 
 tab1, tab2, tab3, tab4, tab5, tab6 = st.tabs(["Game Betting Model", "QB Projections", "RB/WR/TE Projections", "Player Prop Trends", "Player Prop Simulations", "Stat Specific Simulations"])
 
@@ -389,7 +389,7 @@ with tab6:
                                 for books in ['FANDUEL', 'DRAFTKINGS', 'BET365', 'CONSENSUS']:
                                     prop_df = prop_df_raw.loc[prop_df_raw['book'] == books]
                                     prop_df = prop_df.loc[prop_df['prop_type'] == prop]
-                                    prop_df = prop_df[~((prop_df['over_prop'] < 10) & (prop_df['prop_type'] == 'NFL_GAME_PLAYER_RUSHING_YARDS'))]
+                                    prop_df = prop_df[~((prop_df['over_prop'] < 15) & (prop_df['prop_type'] == 'NFL_GAME_PLAYER_RUSHING_YARDS'))]
                                     prop_df = prop_df[['Player', 'book', 'over_prop', 'over_line', 'under_line']]
                                     prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
                                     prop_df = prop_df.loc[prop_df['Prop'] != 0]
@@ -472,7 +472,7 @@ with tab6:
                                     players_only['Player'] = hold_file[['Player']]
                                     players_only['Team'] = players_only['Player'].map(team_dict)
             
-                                    leg_outcomes = players_only[['Player', 'Team', 'Book', 'Prop type', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge']]
+                                    leg_outcomes = players_only[['Player', 'Team', 'Book', 'Prop Type', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge']]
                                     sim_all_hold = pd.concat([sim_all_hold, leg_outcomes], ignore_index=True)
                                     
                                     final_outcomes = sim_all_hold
@@ -499,7 +499,7 @@ with tab6:
                                     df = pd.merge(overall_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
                                 elif prop_type_var == "rush_yards":
                                     prop_df = prop_df.loc[prop_df['prop_type'] == 'NFL_GAME_PLAYER_RUSHING_YARDS']
-                                    prop_df = prop_df[~((prop_df['over_prop'] < 10) & (prop_df['prop_type'] == 'NFL_GAME_PLAYER_RUSHING_YARDS'))]
+                                    prop_df = prop_df[~((prop_df['over_prop'] < 15) & (prop_df['prop_type'] == 'NFL_GAME_PLAYER_RUSHING_YARDS'))]
                                     prop_df = prop_df[['Player', 'book', 'over_prop', 'over_line', 'under_line']]
                                     prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
                                     prop_df = prop_df.loc[prop_df['Prop'] != 0]
@@ -622,11 +622,12 @@ with tab6:
                                 players_only['Bet_suggest'] = np.where(players_only['Over_diff'] > players_only['Under_diff'], "Over" , "Under")
                                 players_only['Bet?'] = np.where(players_only['Bet_check'] >= players_only['prop_threshold'], players_only['Bet_suggest'], "No Bet")
                                 players_only['Edge'] = players_only['Bet_check']
+                                players_only['Prop type'] = prop
         
                                 players_only['Player'] = hold_file[['Player']]
                                 players_only['Team'] = players_only['Player'].map(team_dict)
         
-                                leg_outcomes = players_only[['Player', 'Team', 'Book', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge']]
+                                leg_outcomes = players_only[['Player', 'Team', 'Book', 'Prop Type', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge']]
                                 sim_all_hold = pd.concat([sim_all_hold, leg_outcomes], ignore_index=True)
                                 
                                 final_outcomes = sim_all_hold