Update app.py

app.py

@@ -889,517 +889,518 @@ with tab2:
             scaling_var = 15
             
     with col2:
-        if st.button("Simulate Contest"):
-            try:
-                del dst_freq
-                del flex_freq
-                del te_freq
-                del wr_freq
-                del rb_freq
-                del qb_freq
-                del player_freq
-                del Sim_Winner_Export
-                del Sim_Winner_Frame
-            except:
-                pass
-            with st.container():
-                st.write('Contest Simulation Starting')
-                seed_depth1 = 10
-                Total_Runs = 1000000
-                if Contest_Size <= 1000:
-                    strength_grow = .01
-                elif Contest_Size > 1000 and Contest_Size <= 2500:
-                    strength_grow = .025
-                elif Contest_Size > 2500 and Contest_Size <= 5000:
-                    strength_grow = .05
-                elif Contest_Size > 5000 and Contest_Size <= 20000:
-                    strength_grow = .075
-                elif Contest_Size > 20000:
-                    strength_grow = .1
-                    
-                field_growth = 100 * strength_grow
-        
-                Sort_function = 'Median'
-                if Sort_function == 'Median':
-                    Sim_function = 'Projection'
-                elif Sort_function == 'Own':
-                    Sim_function = 'Own'
-                
-                if slate_var1 == 'User':
-                    OwnFrame = proj_dataframe
-                    if contest_var1 == 'Small':
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (10 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own'])
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (5 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%'])
-                        OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%'])
-                        OwnFrame['Own'] = OwnFrame['Own%'] * (900 / OwnFrame['Own%'].sum())
-                    if contest_var1 == 'Medium':
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (6 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own'])
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (3 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%'])
-                        OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%'])
-                        OwnFrame['Own'] = OwnFrame['Own%'] * (900 / OwnFrame['Own%'].sum())
-                    if contest_var1 == 'Large':
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (3 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own'])
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (1.5 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%'])
-                        OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%'])
-                        OwnFrame['Own'] = OwnFrame['Own%'] * (900 / OwnFrame['Own%'].sum())
-                    Overall_Proj = OwnFrame[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']]
-                    
-                    del OwnFrame
-                    
-                elif slate_var1 != 'User':
-                    initial_proj = raw_baselines
-                    drop_frame = initial_proj.drop_duplicates(subset = 'Player',keep = 'first')
-                    OwnFrame = drop_frame[['Player', 'Team', 'Position', 'Median', 'Own', 'Floor', 'Ceiling', 'Salary']]
-                    if contest_var1 == 'Small':
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (10 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own'])
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (5 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%'])
-                        OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%'])
-                        OwnFrame['Own'] = OwnFrame['Own%'] * (900 / OwnFrame['Own%'].sum())
-                    if contest_var1 == 'Medium':
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (6 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own'])
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (3 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%'])
-                        OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%'])
-                        OwnFrame['Own'] = OwnFrame['Own%'] * (900 / OwnFrame['Own%'].sum())
-                    if contest_var1 == 'Large':
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (3 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own'])
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (1.5 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%'])
-                        OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%'])
-                        OwnFrame['Own'] = OwnFrame['Own%'] * (900 / OwnFrame['Own%'].sum())
-                    Overall_Proj = OwnFrame[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']]
-                    
-                    del initial_proj
-                    del drop_frame
-                    del OwnFrame
-                
-                if insert_port == 1:
-                    UserPortfolio = portfolio_dataframe[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']]
-                elif insert_port == 0:
-                    UserPortfolio = pd.DataFrame(columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST'])
-        
-                Overall_Proj.replace('', np.nan, inplace=True)
-                Overall_Proj = Overall_Proj.dropna(subset=['Median'])
-                Overall_Proj = Overall_Proj.assign(Value=lambda x: (x.Median / (x.Salary / 1000)))
-                Overall_Proj['Sort_var'] = (Overall_Proj['Median'].rank(ascending=False) + Overall_Proj['Value'].rank(ascending=False)) / 2
-                Overall_Proj = Overall_Proj.sort_values(by='Sort_var', ascending=False)
-                Overall_Proj['Own'] = np.where((Overall_Proj['Median'] > 0) & (Overall_Proj['Own'] == 0), 1, Overall_Proj['Own'])
-                Overall_Proj = Overall_Proj.loc[Overall_Proj['Own'] > 0]
-        
-                Overall_Proj['Floor'] = np.where(Overall_Proj['Position'] == 'QB', Overall_Proj['Median'] * .5, Overall_Proj['Median'] * .25)
-                Overall_Proj['Ceiling'] = np.where(Overall_Proj['Position'] == 'WR', Overall_Proj['Median'] + Overall_Proj['Median'], Overall_Proj['Median'] + Overall_Proj['Floor'])
-                Overall_Proj['STDev'] = Overall_Proj['Median'] / 4
-        
-                Teams_used = Overall_Proj['Team'].drop_duplicates().reset_index(drop=True)
-                Teams_used = Teams_used.reset_index()
-                Teams_used['team_item'] = Teams_used['index'] + 1
-                Teams_used = Teams_used.drop(columns=['index'])
-                Teams_used_dictraw = Teams_used.drop(columns=['team_item'])
-                Teams_used_dict = Teams_used_dictraw.to_dict()
-                
-                del Teams_used_dictraw
-        
-                team_list = Teams_used['Team'].to_list()
-                item_list = Teams_used['team_item'].to_list()
-        
-                FieldStrength_raw = Strength_var + ((30 - len(Teams_used)) * .01)
-                FieldStrength = FieldStrength_raw - (FieldStrength_raw * (20000 / Contest_Size))
-                
-                del FieldStrength_raw
-                
-                if FieldStrength < 0:
-                    FieldStrength = Strength_var
-                field_split = Strength_var
-        
-                for checkVar in range(len(team_list)):
-                                    Overall_Proj['Team'] = Overall_Proj['Team'].replace(team_list, item_list)
-        
-                qbs_raw = Overall_Proj[Overall_Proj.Position == 'QB']
-                qbs_raw.dropna(subset=['Median']).reset_index(drop=True)
-                qbs_raw = qbs_raw.reset_index(drop=True)
-                qbs_raw = qbs_raw.sort_values(by=['Median'], ascending=False)
-        
-                qbs = qbs_raw.head(round(len(qbs_raw)))
-                qbs = qbs.assign(Var = range(0,len(qbs)))
-                qb_dict = pd.Series(qbs.Player.values, index=qbs.Var).to_dict()
-                
-                defs_raw = Overall_Proj[Overall_Proj.Position.str.contains("D")]
-                defs_raw.dropna(subset=['Median']).reset_index(drop=True)
-                defs_raw = defs_raw.reset_index(drop=True)
-                defs_raw = defs_raw.sort_values(by=['Own', 'Value'], ascending=False)
-        
-                defs = defs_raw.head(round(len(defs_raw)))
-                defs = defs.assign(Var = range(0,len(defs)))
-                def_dict = pd.Series(defs.Player.values, index=defs.Var).to_dict()
-                
-                rbs_raw = Overall_Proj[Overall_Proj.Position == 'RB']
-                rbs_raw.dropna(subset=['Median']).reset_index(drop=True)
-                rbs_raw = rbs_raw.reset_index(drop=True)
-                rbs_raw = rbs_raw.sort_values(by=['Own', 'Value'], ascending=False)
-                
-                wrs_raw = Overall_Proj[Overall_Proj.Position == 'WR']
-                wrs_raw.dropna(subset=['Median']).reset_index(drop=True)
-                wrs_raw = wrs_raw.reset_index(drop=True)
-                wrs_raw = wrs_raw.sort_values(by=['Own', 'Median'], ascending=False)
-                
-                tes_raw = Overall_Proj[Overall_Proj.Position == 'TE']
-                tes_raw.dropna(subset=['Median']).reset_index(drop=True)
-                tes_raw = tes_raw.reset_index(drop=True)
-                tes_raw = tes_raw.sort_values(by=['Own', 'Value'], ascending=False)
-        
-                pos_players = pd.concat([rbs_raw, wrs_raw, tes_raw])
-                pos_players.dropna(subset=['Median']).reset_index(drop=True)
-                pos_players = pos_players.reset_index(drop=True)
-                
-                del qbs_raw
-                del defs_raw
-                del rbs_raw
-                del wrs_raw
-                del tes_raw
-        
-                if insert_port == 1:
-                    try:
-                        # Initialize an empty DataFrame for Raw Portfolio
-                        Raw_Portfolio = pd.DataFrame()
-                        
-                        # Loop through each position and split the data accordingly
-                        positions = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
-                        for pos in positions:
-                            temp_df = UserPortfolio[pos].str.split("(", n=1, expand=True)
-                            temp_df.columns = [pos, 'Drop']
-                            Raw_Portfolio = pd.concat([Raw_Portfolio, temp_df], axis=1)
+        with st.conatainer():
+            if st.button("Simulate Contest"):
+                try:
+                    del dst_freq
+                    del flex_freq
+                    del te_freq
+                    del wr_freq
+                    del rb_freq
+                    del qb_freq
+                    del player_freq
+                    del Sim_Winner_Export
+                    del Sim_Winner_Frame
+                except:
+                    pass
+                with st.container():
+                    st.write('Contest Simulation Starting')
+                    seed_depth1 = 10
+                    Total_Runs = 1000000
+                    if Contest_Size <= 1000:
+                        strength_grow = .01
+                    elif Contest_Size > 1000 and Contest_Size <= 2500:
+                        strength_grow = .025
+                    elif Contest_Size > 2500 and Contest_Size <= 5000:
+                        strength_grow = .05
+                    elif Contest_Size > 5000 and Contest_Size <= 20000:
+                        strength_grow = .075
+                    elif Contest_Size > 20000:
+                        strength_grow = .1
                         
-                        # Select only necessary columns and strip white spaces
-                        CleanPortfolio = Raw_Portfolio[positions].apply(lambda x: x.str.strip())
-                        CleanPortfolio.reset_index(inplace=True)
-                        CleanPortfolio['User/Field'] = CleanPortfolio['index'] + 1
-                        CleanPortfolio.drop(columns=['index'], inplace=True)
-                        
-                        CleanPortfolio.replace('', np.nan, inplace=True)
-                        CleanPortfolio.dropna(subset=['QB'], inplace=True)
-                        
-                        # Create frequency table for players
-                        cleaport_players = pd.DataFrame(
-                            np.column_stack(np.unique(CleanPortfolio.iloc[:, 0:9].values, return_counts=True)),
-                            columns=['Player', 'Freq']
-                        ).sort_values('Freq', ascending=False).reset_index(drop=True)
-                        cleaport_players['Freq'] = cleaport_players['Freq'].astype(int)
-                        
-                        # Merge and update nerf_frame
-                        nerf_frame = pd.merge(cleaport_players, Overall_Proj, on='Player', how='left')
-                        for col in ['Median', 'Floor', 'Ceiling', 'STDev']:
-                            nerf_frame[col] *= 0.90
-                        del Raw_Portfolio
-                    except:
-                        CleanPortfolio = UserPortfolio.reset_index()
-                        CleanPortfolio['User/Field'] = CleanPortfolio['index'] + 1
-                        CleanPortfolio.drop(columns=['index'], inplace=True)
+                    field_growth = 100 * strength_grow
+            
+                    Sort_function = 'Median'
+                    if Sort_function == 'Median':
+                        Sim_function = 'Projection'
+                    elif Sort_function == 'Own':
+                        Sim_function = 'Own'
+                    
+                    if slate_var1 == 'User':
+                        OwnFrame = proj_dataframe
+                        if contest_var1 == 'Small':
+                            OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (10 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own'])
+                            OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (5 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%'])
+                            OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%'])
+                            OwnFrame['Own'] = OwnFrame['Own%'] * (900 / OwnFrame['Own%'].sum())
+                        if contest_var1 == 'Medium':
+                            OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (6 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own'])
+                            OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (3 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%'])
+                            OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%'])
+                            OwnFrame['Own'] = OwnFrame['Own%'] * (900 / OwnFrame['Own%'].sum())
+                        if contest_var1 == 'Large':
+                            OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (3 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own'])
+                            OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (1.5 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%'])
+                            OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%'])
+                            OwnFrame['Own'] = OwnFrame['Own%'] * (900 / OwnFrame['Own%'].sum())
+                        Overall_Proj = OwnFrame[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']]
                         
-                        # Replace empty strings and drop rows with NaN in 'QB' column
-                        CleanPortfolio.replace('', np.nan, inplace=True)
-                        CleanPortfolio.dropna(subset=['QB'], inplace=True)
+                        del OwnFrame
                         
-                        # Create frequency table for players
-                        cleaport_players = pd.DataFrame(
-                            np.column_stack(np.unique(CleanPortfolio.iloc[:, 0:9].values, return_counts=True)),
-                            columns=['Player', 'Freq']
-                        ).sort_values('Freq', ascending=False).reset_index(drop=True)
-                        cleaport_players['Freq'] = cleaport_players['Freq'].astype(int)
+                    elif slate_var1 != 'User':
+                        initial_proj = raw_baselines
+                        drop_frame = initial_proj.drop_duplicates(subset = 'Player',keep = 'first')
+                        OwnFrame = drop_frame[['Player', 'Team', 'Position', 'Median', 'Own', 'Floor', 'Ceiling', 'Salary']]
+                        if contest_var1 == 'Small':
+                            OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (10 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own'])
+                            OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (5 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%'])
+                            OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%'])
+                            OwnFrame['Own'] = OwnFrame['Own%'] * (900 / OwnFrame['Own%'].sum())
+                        if contest_var1 == 'Medium':
+                            OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (6 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own'])
+                            OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (3 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%'])
+                            OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%'])
+                            OwnFrame['Own'] = OwnFrame['Own%'] * (900 / OwnFrame['Own%'].sum())
+                        if contest_var1 == 'Large':
+                            OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (3 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own'])
+                            OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (1.5 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%'])
+                            OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%'])
+                            OwnFrame['Own'] = OwnFrame['Own%'] * (900 / OwnFrame['Own%'].sum())
+                        Overall_Proj = OwnFrame[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']]
                         
-                        # Merge and update nerf_frame
-                        nerf_frame = pd.merge(cleaport_players, Overall_Proj, on='Player', how='left')
-                        for col in ['Median', 'Floor', 'Ceiling', 'STDev']:
-                            nerf_frame[col] *= 0.90
-
-                elif insert_port == 0:
-                    CleanPortfolio = UserPortfolio
-                    cleaport_players = pd.DataFrame(np.column_stack(np.unique(CleanPortfolio.iloc[:,0:9].values, return_counts=True)),
-                                               columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-                    cleaport_players['Freq'] = cleaport_players['Freq'].astype(int)
-                    nerf_frame = Overall_Proj
+                        del initial_proj
+                        del drop_frame
+                        del OwnFrame
                     
-                ref_dict = {
-                    'pos':['RB', 'WR', 'TE', 'FLEX'],
-                    'pos_dfs':['RB_Table', 'WR_Table', 'TE_Table', 'FLEX_Table'],
-                    'pos_dicts':['rb_dict', 'wr_dict', 'te_dict', 'flex_dict']
-                    }
-        
-                maps_dict = {
-                    'Floor_map':dict(zip(Overall_Proj.Player,Overall_Proj.Floor)),
-                    'Projection_map':dict(zip(Overall_Proj.Player,Overall_Proj.Median)),
-                    'Ceiling_map':dict(zip(Overall_Proj.Player,Overall_Proj.Ceiling)),
-                    'Salary_map':dict(zip(Overall_Proj.Player,Overall_Proj.Salary)),
-                    'Pos_map':dict(zip(Overall_Proj.Player,Overall_Proj.Position)),
-                    'Own_map':dict(zip(Overall_Proj.Player,Overall_Proj.Own)),
-                    'Team_map':dict(zip(Overall_Proj.Player,Overall_Proj.Team)),
-                    'STDev_map':dict(zip(Overall_Proj.Player,Overall_Proj.STDev)),
-                    'team_check_map':dict(zip(Overall_Proj.Player,Overall_Proj.Team))
-                    }
-                
-                up_dict = {
-                    'Floor_map':dict(zip(cleaport_players.Player,nerf_frame.Floor)),
-                    'Projection_map':dict(zip(cleaport_players.Player,nerf_frame.Median)),
-                    'Ceiling_map':dict(zip(cleaport_players.Player,nerf_frame.Ceiling)),
-                    'Salary_map':dict(zip(cleaport_players.Player,nerf_frame.Salary)),
-                    'Pos_map':dict(zip(cleaport_players.Player,nerf_frame.Position)),
-                    'Own_map':dict(zip(cleaport_players.Player,nerf_frame.Own)),
-                    'Team_map':dict(zip(cleaport_players.Player,nerf_frame.Team)),
-                    'STDev_map':dict(zip(cleaport_players.Player,nerf_frame.STDev)),
-                    'team_check_map':dict(zip(cleaport_players.Player,nerf_frame.Team))
-                    }
-                
-                del cleaport_players
-                del Overall_Proj
-                del nerf_frame
-                
-                st.write('Seed frame creation')
-                FinalPortfolio, maps_dict = run_seed_frame(seed_depth1, Strength_var, strength_grow, Teams_used, Total_Runs)
-                
-                Sim_size = linenum_var1
-                SimVar = 1
-                Sim_Winners = []
-                fp_array = FinalPortfolio.values
-                
-                if insert_port == 1:
-                    up_array = CleanPortfolio.values
-                
-                # Pre-vectorize functions
-                vec_projection_map = np.vectorize(maps_dict['Projection_map'].__getitem__)
-                vec_stdev_map = np.vectorize(maps_dict['STDev_map'].__getitem__)
-                
-                if insert_port == 1:
-                    vec_up_projection_map = np.vectorize(up_dict['Projection_map'].__getitem__)
-                    vec_up_stdev_map = np.vectorize(up_dict['STDev_map'].__getitem__)
-                
-                st.write('Simulating contest on frames')
-                
-                while SimVar <= Sim_size:
                     if insert_port == 1:
-                        fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size-len(CleanPortfolio))]
+                        UserPortfolio = portfolio_dataframe[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']]
+                    elif insert_port == 0:
+                        UserPortfolio = pd.DataFrame(columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST'])
+            
+                    Overall_Proj.replace('', np.nan, inplace=True)
+                    Overall_Proj = Overall_Proj.dropna(subset=['Median'])
+                    Overall_Proj = Overall_Proj.assign(Value=lambda x: (x.Median / (x.Salary / 1000)))
+                    Overall_Proj['Sort_var'] = (Overall_Proj['Median'].rank(ascending=False) + Overall_Proj['Value'].rank(ascending=False)) / 2
+                    Overall_Proj = Overall_Proj.sort_values(by='Sort_var', ascending=False)
+                    Overall_Proj['Own'] = np.where((Overall_Proj['Median'] > 0) & (Overall_Proj['Own'] == 0), 1, Overall_Proj['Own'])
+                    Overall_Proj = Overall_Proj.loc[Overall_Proj['Own'] > 0]
+            
+                    Overall_Proj['Floor'] = np.where(Overall_Proj['Position'] == 'QB', Overall_Proj['Median'] * .5, Overall_Proj['Median'] * .25)
+                    Overall_Proj['Ceiling'] = np.where(Overall_Proj['Position'] == 'WR', Overall_Proj['Median'] + Overall_Proj['Median'], Overall_Proj['Median'] + Overall_Proj['Floor'])
+                    Overall_Proj['STDev'] = Overall_Proj['Median'] / 4
+            
+                    Teams_used = Overall_Proj['Team'].drop_duplicates().reset_index(drop=True)
+                    Teams_used = Teams_used.reset_index()
+                    Teams_used['team_item'] = Teams_used['index'] + 1
+                    Teams_used = Teams_used.drop(columns=['index'])
+                    Teams_used_dictraw = Teams_used.drop(columns=['team_item'])
+                    Teams_used_dict = Teams_used_dictraw.to_dict()
+                    
+                    del Teams_used_dictraw
+            
+                    team_list = Teams_used['Team'].to_list()
+                    item_list = Teams_used['team_item'].to_list()
+            
+                    FieldStrength_raw = Strength_var + ((30 - len(Teams_used)) * .01)
+                    FieldStrength = FieldStrength_raw - (FieldStrength_raw * (20000 / Contest_Size))
+                    
+                    del FieldStrength_raw
+                    
+                    if FieldStrength < 0:
+                        FieldStrength = Strength_var
+                    field_split = Strength_var
+            
+                    for checkVar in range(len(team_list)):
+                                        Overall_Proj['Team'] = Overall_Proj['Team'].replace(team_list, item_list)
+            
+                    qbs_raw = Overall_Proj[Overall_Proj.Position == 'QB']
+                    qbs_raw.dropna(subset=['Median']).reset_index(drop=True)
+                    qbs_raw = qbs_raw.reset_index(drop=True)
+                    qbs_raw = qbs_raw.sort_values(by=['Median'], ascending=False)
+            
+                    qbs = qbs_raw.head(round(len(qbs_raw)))
+                    qbs = qbs.assign(Var = range(0,len(qbs)))
+                    qb_dict = pd.Series(qbs.Player.values, index=qbs.Var).to_dict()
+                    
+                    defs_raw = Overall_Proj[Overall_Proj.Position.str.contains("D")]
+                    defs_raw.dropna(subset=['Median']).reset_index(drop=True)
+                    defs_raw = defs_raw.reset_index(drop=True)
+                    defs_raw = defs_raw.sort_values(by=['Own', 'Value'], ascending=False)
+            
+                    defs = defs_raw.head(round(len(defs_raw)))
+                    defs = defs.assign(Var = range(0,len(defs)))
+                    def_dict = pd.Series(defs.Player.values, index=defs.Var).to_dict()
+                    
+                    rbs_raw = Overall_Proj[Overall_Proj.Position == 'RB']
+                    rbs_raw.dropna(subset=['Median']).reset_index(drop=True)
+                    rbs_raw = rbs_raw.reset_index(drop=True)
+                    rbs_raw = rbs_raw.sort_values(by=['Own', 'Value'], ascending=False)
+                    
+                    wrs_raw = Overall_Proj[Overall_Proj.Position == 'WR']
+                    wrs_raw.dropna(subset=['Median']).reset_index(drop=True)
+                    wrs_raw = wrs_raw.reset_index(drop=True)
+                    wrs_raw = wrs_raw.sort_values(by=['Own', 'Median'], ascending=False)
+                    
+                    tes_raw = Overall_Proj[Overall_Proj.Position == 'TE']
+                    tes_raw.dropna(subset=['Median']).reset_index(drop=True)
+                    tes_raw = tes_raw.reset_index(drop=True)
+                    tes_raw = tes_raw.sort_values(by=['Own', 'Value'], ascending=False)
+            
+                    pos_players = pd.concat([rbs_raw, wrs_raw, tes_raw])
+                    pos_players.dropna(subset=['Median']).reset_index(drop=True)
+                    pos_players = pos_players.reset_index(drop=True)
+                    
+                    del qbs_raw
+                    del defs_raw
+                    del rbs_raw
+                    del wrs_raw
+                    del tes_raw
+            
+                    if insert_port == 1:
+                        try:
+                            # Initialize an empty DataFrame for Raw Portfolio
+                            Raw_Portfolio = pd.DataFrame()
+                            
+                            # Loop through each position and split the data accordingly
+                            positions = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
+                            for pos in positions:
+                                temp_df = UserPortfolio[pos].str.split("(", n=1, expand=True)
+                                temp_df.columns = [pos, 'Drop']
+                                Raw_Portfolio = pd.concat([Raw_Portfolio, temp_df], axis=1)
+                            
+                            # Select only necessary columns and strip white spaces
+                            CleanPortfolio = Raw_Portfolio[positions].apply(lambda x: x.str.strip())
+                            CleanPortfolio.reset_index(inplace=True)
+                            CleanPortfolio['User/Field'] = CleanPortfolio['index'] + 1
+                            CleanPortfolio.drop(columns=['index'], inplace=True)
+                            
+                            CleanPortfolio.replace('', np.nan, inplace=True)
+                            CleanPortfolio.dropna(subset=['QB'], inplace=True)
+                            
+                            # Create frequency table for players
+                            cleaport_players = pd.DataFrame(
+                                np.column_stack(np.unique(CleanPortfolio.iloc[:, 0:9].values, return_counts=True)),
+                                columns=['Player', 'Freq']
+                            ).sort_values('Freq', ascending=False).reset_index(drop=True)
+                            cleaport_players['Freq'] = cleaport_players['Freq'].astype(int)
+                            
+                            # Merge and update nerf_frame
+                            nerf_frame = pd.merge(cleaport_players, Overall_Proj, on='Player', how='left')
+                            for col in ['Median', 'Floor', 'Ceiling', 'STDev']:
+                                nerf_frame[col] *= 0.90
+                            del Raw_Portfolio
+                        except:
+                            CleanPortfolio = UserPortfolio.reset_index()
+                            CleanPortfolio['User/Field'] = CleanPortfolio['index'] + 1
+                            CleanPortfolio.drop(columns=['index'], inplace=True)
+                            
+                            # Replace empty strings and drop rows with NaN in 'QB' column
+                            CleanPortfolio.replace('', np.nan, inplace=True)
+                            CleanPortfolio.dropna(subset=['QB'], inplace=True)
+                            
+                            # Create frequency table for players
+                            cleaport_players = pd.DataFrame(
+                                np.column_stack(np.unique(CleanPortfolio.iloc[:, 0:9].values, return_counts=True)),
+                                columns=['Player', 'Freq']
+                            ).sort_values('Freq', ascending=False).reset_index(drop=True)
+                            cleaport_players['Freq'] = cleaport_players['Freq'].astype(int)
+                            
+                            # Merge and update nerf_frame
+                            nerf_frame = pd.merge(cleaport_players, Overall_Proj, on='Player', how='left')
+                            for col in ['Median', 'Floor', 'Ceiling', 'STDev']:
+                                nerf_frame[col] *= 0.90
+    
                     elif insert_port == 0:
-                        fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size)]
+                        CleanPortfolio = UserPortfolio
+                        cleaport_players = pd.DataFrame(np.column_stack(np.unique(CleanPortfolio.iloc[:,0:9].values, return_counts=True)),
+                                                   columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+                        cleaport_players['Freq'] = cleaport_players['Freq'].astype(int)
+                        nerf_frame = Overall_Proj
                         
-                    sample_arrays1 = np.c_[
-                        fp_random, 
-                        np.sum(np.random.normal(
-                            loc=vec_projection_map(fp_random[:, :-5]),
-                            scale=vec_stdev_map(fp_random[:, :-5])),
-                        axis=1)
-                    ]
+                    ref_dict = {
+                        'pos':['RB', 'WR', 'TE', 'FLEX'],
+                        'pos_dfs':['RB_Table', 'WR_Table', 'TE_Table', 'FLEX_Table'],
+                        'pos_dicts':['rb_dict', 'wr_dict', 'te_dict', 'flex_dict']
+                        }
             
+                    maps_dict = {
+                        'Floor_map':dict(zip(Overall_Proj.Player,Overall_Proj.Floor)),
+                        'Projection_map':dict(zip(Overall_Proj.Player,Overall_Proj.Median)),
+                        'Ceiling_map':dict(zip(Overall_Proj.Player,Overall_Proj.Ceiling)),
+                        'Salary_map':dict(zip(Overall_Proj.Player,Overall_Proj.Salary)),
+                        'Pos_map':dict(zip(Overall_Proj.Player,Overall_Proj.Position)),
+                        'Own_map':dict(zip(Overall_Proj.Player,Overall_Proj.Own)),
+                        'Team_map':dict(zip(Overall_Proj.Player,Overall_Proj.Team)),
+                        'STDev_map':dict(zip(Overall_Proj.Player,Overall_Proj.STDev)),
+                        'team_check_map':dict(zip(Overall_Proj.Player,Overall_Proj.Team))
+                        }
+                    
+                    up_dict = {
+                        'Floor_map':dict(zip(cleaport_players.Player,nerf_frame.Floor)),
+                        'Projection_map':dict(zip(cleaport_players.Player,nerf_frame.Median)),
+                        'Ceiling_map':dict(zip(cleaport_players.Player,nerf_frame.Ceiling)),
+                        'Salary_map':dict(zip(cleaport_players.Player,nerf_frame.Salary)),
+                        'Pos_map':dict(zip(cleaport_players.Player,nerf_frame.Position)),
+                        'Own_map':dict(zip(cleaport_players.Player,nerf_frame.Own)),
+                        'Team_map':dict(zip(cleaport_players.Player,nerf_frame.Team)),
+                        'STDev_map':dict(zip(cleaport_players.Player,nerf_frame.STDev)),
+                        'team_check_map':dict(zip(cleaport_players.Player,nerf_frame.Team))
+                        }
+                    
+                    del cleaport_players
+                    del Overall_Proj
+                    del nerf_frame
+                    
+                    st.write('Seed frame creation')
+                    FinalPortfolio, maps_dict = run_seed_frame(seed_depth1, Strength_var, strength_grow, Teams_used, Total_Runs)
+                    
+                    Sim_size = linenum_var1
+                    SimVar = 1
+                    Sim_Winners = []
+                    fp_array = FinalPortfolio.values
+                    
+                    if insert_port == 1:
+                        up_array = CleanPortfolio.values
+                    
+                    # Pre-vectorize functions
+                    vec_projection_map = np.vectorize(maps_dict['Projection_map'].__getitem__)
+                    vec_stdev_map = np.vectorize(maps_dict['STDev_map'].__getitem__)
+                    
                     if insert_port == 1:
-                        sample_arrays2 = np.c_[
-                            up_array, 
+                        vec_up_projection_map = np.vectorize(up_dict['Projection_map'].__getitem__)
+                        vec_up_stdev_map = np.vectorize(up_dict['STDev_map'].__getitem__)
+                    
+                    st.write('Simulating contest on frames')
+                    
+                    while SimVar <= Sim_size:
+                        if insert_port == 1:
+                            fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size-len(CleanPortfolio))]
+                        elif insert_port == 0:
+                            fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size)]
+                            
+                        sample_arrays1 = np.c_[
+                            fp_random, 
                             np.sum(np.random.normal(
-                                loc=vec_up_projection_map(up_array[:, :-5]),
-                                scale=vec_up_stdev_map(up_array[:, :-5])),
+                                loc=vec_projection_map(fp_random[:, :-5]),
+                                scale=vec_stdev_map(fp_random[:, :-5])),
                             axis=1)
                         ]
-                        sample_arrays = np.vstack((sample_arrays1, sample_arrays2))
-                    else:
-                        sample_arrays = sample_arrays1
-            
-                    final_array = sample_arrays[sample_arrays[:, 10].argsort()[::-1]]
-                    best_lineup = final_array[final_array[:, -1].argsort(kind='stable')[::-1][:1]]
-                    Sim_Winners.append(best_lineup)
-                    SimVar += 1
-
-                        
-                # del smple_arrays
-                # del smple_arrays1
-                # del smple_arrays2
-                # del final_array
-                # del best_lineup
-                st.write('Contest simulation complete')
-                # Initial setup
-                Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=FinalPortfolio.columns.tolist() + ['Fantasy'])
-                Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['Projection'] + Sim_Winner_Frame['Fantasy']) / 2
-                
-                # Type Casting
-                type_cast_dict = {'Salary': int, 'Projection': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float16}
-                Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict)
-                
-                # Sorting
-                Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by='GPP_Proj', ascending=False)
-                
-                # Data Copying
-                Sim_Winner_Export = Sim_Winner_Frame.copy()
-                
-                # Conditional Replacement
-                columns_to_replace = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
-                
-                if site_var1 == 'Draftkings':
-                    replace_dict = dkid_dict
-                elif site_var1 == 'Fanduel':
-                    replace_dict = fdid_dict
-                
-                for col in columns_to_replace:
-                    Sim_Winner_Export[col].replace(replace_dict, inplace=True)
-
                 
-                player_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,0:9].values, return_counts=True)),
-                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-                player_freq['Freq'] = player_freq['Freq'].astype(int)
-                player_freq['Position'] = player_freq['Player'].map(maps_dict['Pos_map'])
-                player_freq['Salary'] = player_freq['Player'].map(maps_dict['Salary_map'])
-                player_freq['Proj Own'] = player_freq['Player'].map(maps_dict['Own_map']) / 100
-                player_freq['Exposure'] = player_freq['Freq']/(Sim_size)
-                player_freq['Edge'] = player_freq['Exposure'] - player_freq['Proj Own']
-                player_freq['Team'] = player_freq['Player'].map(maps_dict['Team_map'])
-                for checkVar in range(len(team_list)):
-                                    player_freq['Team'] = player_freq['Team'].replace(item_list, team_list)
-
-                player_freq = player_freq[['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
-                
-                qb_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,0:1].values, return_counts=True)),
-                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-                qb_freq['Freq'] = qb_freq['Freq'].astype(int)
-                qb_freq['Position'] = qb_freq['Player'].map(maps_dict['Pos_map'])
-                qb_freq['Salary'] = qb_freq['Player'].map(maps_dict['Salary_map'])
-                qb_freq['Proj Own'] = qb_freq['Player'].map(maps_dict['Own_map']) / 100
-                qb_freq['Exposure'] = qb_freq['Freq']/(Sim_size)
-                qb_freq['Edge'] = qb_freq['Exposure'] - qb_freq['Proj Own']
-                qb_freq['Team'] = qb_freq['Player'].map(maps_dict['Team_map'])
-                for checkVar in range(len(team_list)):
-                                    qb_freq['Team'] = qb_freq['Team'].replace(item_list, team_list)
-
-                qb_freq = qb_freq[['Player', 'Team', 'Position', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
-
-                rb_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,[1, 2]].values, return_counts=True)),
-                                           columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-                rb_freq['Freq'] = rb_freq['Freq'].astype(int)
-                rb_freq['Position'] = rb_freq['Player'].map(maps_dict['Pos_map'])
-                rb_freq['Salary'] = rb_freq['Player'].map(maps_dict['Salary_map'])
-                rb_freq['Proj Own'] = rb_freq['Player'].map(maps_dict['Own_map']) / 100
-                rb_freq['Exposure'] = rb_freq['Freq']/Sim_size
-                rb_freq['Edge'] = rb_freq['Exposure'] - rb_freq['Proj Own']
-                rb_freq['Team'] = rb_freq['Player'].map(maps_dict['Team_map'])
-                for checkVar in range(len(team_list)):
-                                    rb_freq['Team'] = rb_freq['Team'].replace(item_list, team_list)
-
-                rb_freq = rb_freq[['Player', 'Team', 'Position', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
-                
-                wr_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,[3, 4, 5]].values, return_counts=True)),
-                                           columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-                wr_freq['Freq'] = wr_freq['Freq'].astype(int)
-                wr_freq['Position'] = wr_freq['Player'].map(maps_dict['Pos_map'])
-                wr_freq['Salary'] = wr_freq['Player'].map(maps_dict['Salary_map'])
-                wr_freq['Proj Own'] = wr_freq['Player'].map(maps_dict['Own_map']) / 100
-                wr_freq['Exposure'] = wr_freq['Freq']/Sim_size
-                wr_freq['Edge'] = wr_freq['Exposure'] - wr_freq['Proj Own']
-                wr_freq['Team'] = wr_freq['Player'].map(maps_dict['Team_map'])
-                for checkVar in range(len(team_list)):
-                                    wr_freq['Team'] = wr_freq['Team'].replace(item_list, team_list)
-
-                wr_freq = wr_freq[['Player', 'Team', 'Position', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
-                
-                te_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,[6]].values, return_counts=True)),
-                                           columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-                te_freq['Freq'] = te_freq['Freq'].astype(int)
-                te_freq['Position'] = te_freq['Player'].map(maps_dict['Pos_map'])
-                te_freq['Salary'] = te_freq['Player'].map(maps_dict['Salary_map'])
-                te_freq['Proj Own'] = te_freq['Player'].map(maps_dict['Own_map']) / 100
-                te_freq['Exposure'] = te_freq['Freq']/Sim_size
-                te_freq['Edge'] = te_freq['Exposure'] - te_freq['Proj Own']
-                te_freq['Team'] = te_freq['Player'].map(maps_dict['Team_map'])
-                for checkVar in range(len(team_list)):
-                                    te_freq['Team'] = te_freq['Team'].replace(item_list, team_list)
-
-                te_freq = te_freq[['Player', 'Team', 'Position', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
+                        if insert_port == 1:
+                            sample_arrays2 = np.c_[
+                                up_array, 
+                                np.sum(np.random.normal(
+                                    loc=vec_up_projection_map(up_array[:, :-5]),
+                                    scale=vec_up_stdev_map(up_array[:, :-5])),
+                                axis=1)
+                            ]
+                            sample_arrays = np.vstack((sample_arrays1, sample_arrays2))
+                        else:
+                            sample_arrays = sample_arrays1
                 
-                flex_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,[7]].values, return_counts=True)),
-                                           columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-                flex_freq['Freq'] = flex_freq['Freq'].astype(int)
-                flex_freq['Position'] = flex_freq['Player'].map(maps_dict['Pos_map'])
-                flex_freq['Salary'] = flex_freq['Player'].map(maps_dict['Salary_map'])
-                flex_freq['Proj Own'] = flex_freq['Player'].map(maps_dict['Own_map']) / 100
-                flex_freq['Exposure'] = flex_freq['Freq']/Sim_size
-                flex_freq['Edge'] = flex_freq['Exposure'] - flex_freq['Proj Own']
-                flex_freq['Team'] = flex_freq['Player'].map(maps_dict['Team_map'])
-                for checkVar in range(len(team_list)):
-                                    flex_freq['Team'] = flex_freq['Team'].replace(item_list, team_list)
-
-                flex_freq = flex_freq[['Player', 'Team', 'Position', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
-                
-                dst_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,8:9].values, return_counts=True)),
-                                           columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-                dst_freq['Freq'] = dst_freq['Freq'].astype(int)
-                dst_freq['Position'] = dst_freq['Player'].map(maps_dict['Pos_map'])
-                dst_freq['Salary'] = dst_freq['Player'].map(maps_dict['Salary_map'])
-                dst_freq['Proj Own'] = dst_freq['Player'].map(maps_dict['Own_map']) / 100
-                dst_freq['Exposure'] = dst_freq['Freq']/Sim_size
-                dst_freq['Edge'] = dst_freq['Exposure'] - dst_freq['Proj Own']
-                dst_freq['Team'] = dst_freq['Player'].map(maps_dict['Team_map'])
-                for checkVar in range(len(team_list)):
-                                    dst_freq['Team'] = dst_freq['Team'].replace(item_list, team_list)
-
-                dst_freq = dst_freq[['Player', 'Team', 'Position', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
+                        final_array = sample_arrays[sample_arrays[:, 10].argsort()[::-1]]
+                        best_lineup = final_array[final_array[:, -1].argsort(kind='stable')[::-1][:1]]
+                        Sim_Winners.append(best_lineup)
+                        SimVar += 1
+    
+                            
+                    # del smple_arrays
+                    # del smple_arrays1
+                    # del smple_arrays2
+                    # del final_array
+                    # del best_lineup
+                    st.write('Contest simulation complete')
+                    # Initial setup
+                    Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=FinalPortfolio.columns.tolist() + ['Fantasy'])
+                    Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['Projection'] + Sim_Winner_Frame['Fantasy']) / 2
+                    
+                    # Type Casting
+                    type_cast_dict = {'Salary': int, 'Projection': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float16}
+                    Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict)
+                    
+                    # Sorting
+                    Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by='GPP_Proj', ascending=False)
+                    
+                    # Data Copying
+                    Sim_Winner_Export = Sim_Winner_Frame.copy()
+                    
+                    # Conditional Replacement
+                    columns_to_replace = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
+                    
+                    if site_var1 == 'Draftkings':
+                        replace_dict = dkid_dict
+                    elif site_var1 == 'Fanduel':
+                        replace_dict = fdid_dict
+                    
+                    for col in columns_to_replace:
+                        Sim_Winner_Export[col].replace(replace_dict, inplace=True)
+    
+                    
+                    player_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,0:9].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+                    player_freq['Freq'] = player_freq['Freq'].astype(int)
+                    player_freq['Position'] = player_freq['Player'].map(maps_dict['Pos_map'])
+                    player_freq['Salary'] = player_freq['Player'].map(maps_dict['Salary_map'])
+                    player_freq['Proj Own'] = player_freq['Player'].map(maps_dict['Own_map']) / 100
+                    player_freq['Exposure'] = player_freq['Freq']/(Sim_size)
+                    player_freq['Edge'] = player_freq['Exposure'] - player_freq['Proj Own']
+                    player_freq['Team'] = player_freq['Player'].map(maps_dict['Team_map'])
+                    for checkVar in range(len(team_list)):
+                                        player_freq['Team'] = player_freq['Team'].replace(item_list, team_list)
+    
+                    player_freq = player_freq[['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
+                    
+                    qb_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,0:1].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+                    qb_freq['Freq'] = qb_freq['Freq'].astype(int)
+                    qb_freq['Position'] = qb_freq['Player'].map(maps_dict['Pos_map'])
+                    qb_freq['Salary'] = qb_freq['Player'].map(maps_dict['Salary_map'])
+                    qb_freq['Proj Own'] = qb_freq['Player'].map(maps_dict['Own_map']) / 100
+                    qb_freq['Exposure'] = qb_freq['Freq']/(Sim_size)
+                    qb_freq['Edge'] = qb_freq['Exposure'] - qb_freq['Proj Own']
+                    qb_freq['Team'] = qb_freq['Player'].map(maps_dict['Team_map'])
+                    for checkVar in range(len(team_list)):
+                                        qb_freq['Team'] = qb_freq['Team'].replace(item_list, team_list)
+    
+                    qb_freq = qb_freq[['Player', 'Team', 'Position', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
+    
+                    rb_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,[1, 2]].values, return_counts=True)),
+                                               columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+                    rb_freq['Freq'] = rb_freq['Freq'].astype(int)
+                    rb_freq['Position'] = rb_freq['Player'].map(maps_dict['Pos_map'])
+                    rb_freq['Salary'] = rb_freq['Player'].map(maps_dict['Salary_map'])
+                    rb_freq['Proj Own'] = rb_freq['Player'].map(maps_dict['Own_map']) / 100
+                    rb_freq['Exposure'] = rb_freq['Freq']/Sim_size
+                    rb_freq['Edge'] = rb_freq['Exposure'] - rb_freq['Proj Own']
+                    rb_freq['Team'] = rb_freq['Player'].map(maps_dict['Team_map'])
+                    for checkVar in range(len(team_list)):
+                                        rb_freq['Team'] = rb_freq['Team'].replace(item_list, team_list)
+    
+                    rb_freq = rb_freq[['Player', 'Team', 'Position', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
+                    
+                    wr_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,[3, 4, 5]].values, return_counts=True)),
+                                               columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+                    wr_freq['Freq'] = wr_freq['Freq'].astype(int)
+                    wr_freq['Position'] = wr_freq['Player'].map(maps_dict['Pos_map'])
+                    wr_freq['Salary'] = wr_freq['Player'].map(maps_dict['Salary_map'])
+                    wr_freq['Proj Own'] = wr_freq['Player'].map(maps_dict['Own_map']) / 100
+                    wr_freq['Exposure'] = wr_freq['Freq']/Sim_size
+                    wr_freq['Edge'] = wr_freq['Exposure'] - wr_freq['Proj Own']
+                    wr_freq['Team'] = wr_freq['Player'].map(maps_dict['Team_map'])
+                    for checkVar in range(len(team_list)):
+                                        wr_freq['Team'] = wr_freq['Team'].replace(item_list, team_list)
+    
+                    wr_freq = wr_freq[['Player', 'Team', 'Position', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
+                    
+                    te_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,[6]].values, return_counts=True)),
+                                               columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+                    te_freq['Freq'] = te_freq['Freq'].astype(int)
+                    te_freq['Position'] = te_freq['Player'].map(maps_dict['Pos_map'])
+                    te_freq['Salary'] = te_freq['Player'].map(maps_dict['Salary_map'])
+                    te_freq['Proj Own'] = te_freq['Player'].map(maps_dict['Own_map']) / 100
+                    te_freq['Exposure'] = te_freq['Freq']/Sim_size
+                    te_freq['Edge'] = te_freq['Exposure'] - te_freq['Proj Own']
+                    te_freq['Team'] = te_freq['Player'].map(maps_dict['Team_map'])
+                    for checkVar in range(len(team_list)):
+                                        te_freq['Team'] = te_freq['Team'].replace(item_list, team_list)
+    
+                    te_freq = te_freq[['Player', 'Team', 'Position', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
+                    
+                    flex_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,[7]].values, return_counts=True)),
+                                               columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+                    flex_freq['Freq'] = flex_freq['Freq'].astype(int)
+                    flex_freq['Position'] = flex_freq['Player'].map(maps_dict['Pos_map'])
+                    flex_freq['Salary'] = flex_freq['Player'].map(maps_dict['Salary_map'])
+                    flex_freq['Proj Own'] = flex_freq['Player'].map(maps_dict['Own_map']) / 100
+                    flex_freq['Exposure'] = flex_freq['Freq']/Sim_size
+                    flex_freq['Edge'] = flex_freq['Exposure'] - flex_freq['Proj Own']
+                    flex_freq['Team'] = flex_freq['Player'].map(maps_dict['Team_map'])
+                    for checkVar in range(len(team_list)):
+                                        flex_freq['Team'] = flex_freq['Team'].replace(item_list, team_list)
+    
+                    flex_freq = flex_freq[['Player', 'Team', 'Position', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
+                    
+                    dst_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,8:9].values, return_counts=True)),
+                                               columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+                    dst_freq['Freq'] = dst_freq['Freq'].astype(int)
+                    dst_freq['Position'] = dst_freq['Player'].map(maps_dict['Pos_map'])
+                    dst_freq['Salary'] = dst_freq['Player'].map(maps_dict['Salary_map'])
+                    dst_freq['Proj Own'] = dst_freq['Player'].map(maps_dict['Own_map']) / 100
+                    dst_freq['Exposure'] = dst_freq['Freq']/Sim_size
+                    dst_freq['Edge'] = dst_freq['Exposure'] - dst_freq['Proj Own']
+                    dst_freq['Team'] = dst_freq['Player'].map(maps_dict['Team_map'])
+                    for checkVar in range(len(team_list)):
+                                        dst_freq['Team'] = dst_freq['Team'].replace(item_list, team_list)
+    
+                    dst_freq = dst_freq[['Player', 'Team', 'Position', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
                 
-            with st.container():
-                simulate_container = st.empty()
-                st.dataframe(Sim_Winner_Frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Own']).format(precision=2), use_container_width = True)
-            
+        with st.container():
+            simulate_container = st.empty()
+            st.dataframe(Sim_Winner_Frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Own']).format(precision=2), use_container_width = True)
+        
             st.download_button(
                 label="Export Tables",
                 data=convert_df_to_csv(Sim_Winner_Export),
                 file_name='NFL_consim_export.csv',
                 mime='text/csv',
             )
-            
-            with st.container():
-                freq_container = st.empty()
-                tab1, tab2, tab3, tab4, tab5, tab6, tab7 = st.tabs(['Overall Exposures', 'QB Exposures', 'RB Exposures', 'WR Exposures', 'TE Exposures', 'FLEX Exposures', 'DST Exposures'])
-                with tab1:
-                    st.dataframe(player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
-                    st.download_button(
-                        label="Export Exposures",
-                        data=convert_df_to_csv(player_freq),
-                        file_name='player_freq_export.csv',
-                        mime='text/csv',
-                    )
-                with tab2:
-                    st.dataframe(qb_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
-                    st.download_button(
-                        label="Export Exposures",
-                        data=convert_df_to_csv(qb_freq),
-                        file_name='qb_freq_export.csv',
-                        mime='text/csv',
-                    )
-                with tab3:
-                    st.dataframe(rb_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
-                    st.download_button(
-                        label="Export Exposures",
-                        data=convert_df_to_csv(rb_freq),
-                        file_name='rb_freq_export.csv',
-                        mime='text/csv',
-                    )
-                with tab4:
-                    st.dataframe(wr_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
-                    st.download_button(
-                        label="Export Exposures",
-                        data=convert_df_to_csv(wr_freq),
-                        file_name='wr_freq_export.csv',
-                        mime='text/csv',
-                    )
-                with tab5:
-                    st.dataframe(te_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
-                    st.download_button(
-                        label="Export Exposures",
-                        data=convert_df_to_csv(te_freq),
-                        file_name='te_freq_export.csv',
-                        mime='text/csv',
-                    )
-                with tab6:
-                    st.dataframe(flex_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
-                    st.download_button(
-                        label="Export Exposures",
-                        data=convert_df_to_csv(flex_freq),
-                        file_name='flex_freq_export.csv',
-                        mime='text/csv',
-                    )
-                with tab7:
-                    st.dataframe(dst_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
-                    st.download_button(
-                        label="Export Exposures",
-                        data=convert_df_to_csv(dst_freq),
-                        file_name='dst_freq_export.csv',
-                        mime='text/csv',
-                    )
+        
+        with st.container():
+            freq_container = st.empty()
+            tab1, tab2, tab3, tab4, tab5, tab6, tab7 = st.tabs(['Overall Exposures', 'QB Exposures', 'RB Exposures', 'WR Exposures', 'TE Exposures', 'FLEX Exposures', 'DST Exposures'])
+            with tab1:
+                st.dataframe(player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                st.download_button(
+                    label="Export Exposures",
+                    data=convert_df_to_csv(player_freq),
+                    file_name='player_freq_export.csv',
+                    mime='text/csv',
+                )
+            with tab2:
+                st.dataframe(qb_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                st.download_button(
+                    label="Export Exposures",
+                    data=convert_df_to_csv(qb_freq),
+                    file_name='qb_freq_export.csv',
+                    mime='text/csv',
+                )
+            with tab3:
+                st.dataframe(rb_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                st.download_button(
+                    label="Export Exposures",
+                    data=convert_df_to_csv(rb_freq),
+                    file_name='rb_freq_export.csv',
+                    mime='text/csv',
+                )
+            with tab4:
+                st.dataframe(wr_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                st.download_button(
+                    label="Export Exposures",
+                    data=convert_df_to_csv(wr_freq),
+                    file_name='wr_freq_export.csv',
+                    mime='text/csv',
+                )
+            with tab5:
+                st.dataframe(te_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                st.download_button(
+                    label="Export Exposures",
+                    data=convert_df_to_csv(te_freq),
+                    file_name='te_freq_export.csv',
+                    mime='text/csv',
+                )
+            with tab6:
+                st.dataframe(flex_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                st.download_button(
+                    label="Export Exposures",
+                    data=convert_df_to_csv(flex_freq),
+                    file_name='flex_freq_export.csv',
+                    mime='text/csv',
+                )
+            with tab7:
+                st.dataframe(dst_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                st.download_button(
+                    label="Export Exposures",
+                    data=convert_df_to_csv(dst_freq),
+                    file_name='dst_freq_export.csv',
+                    mime='text/csv',
+                )