Update app.py

app.py

@@ -50,79 +50,32 @@ all_dk_player_projections = 'https://docs.google.com/spreadsheets/d/1I_1Ve3F4tft
 @st.cache_resource(ttl=299)
 def init_baselines():
     sh = gc.open_by_url(all_dk_player_projections)
-    worksheet = sh.worksheet('SD_Projections')
+    worksheet = sh.worksheet('DK_SD_ROO')
     load_display = pd.DataFrame(worksheet.get_all_records())
     load_display.replace('', np.nan, inplace=True)
-    raw_display = load_display.dropna(subset=['PPR'])
-    raw_display.rename(columns={"name": "Player", "PPR": "Median"}, inplace = True)
-    raw_display = raw_display[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own', 'rush_yards', 'rec']]
-    dk_roo_raw = raw_display.loc[raw_display['Median'] > 0]
-
-    worksheet = sh.worksheet('FD_SD_Projections')
-    load_display = pd.DataFrame(worksheet.get_all_records())
-    load_display.replace('', np.nan, inplace=True)
-    raw_display = load_display.dropna(subset=['Half_PPR'])
-    raw_display.rename(columns={"name": "Player", "Half_PPR": "Median"}, inplace = True)
-    raw_display = raw_display[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own', 'rush_yards', 'rec']]
-    fd_roo_raw = raw_display.loc[raw_display['Median'] > 0]
-
-    worksheet = sh.worksheet('SD_Projections_2')
-    load_display = pd.DataFrame(worksheet.get_all_records())
-    load_display.replace('', np.nan, inplace=True)
-    raw_display = load_display.dropna(subset=['PPR'])
-    raw_display.rename(columns={"name": "Player", "PPR": "Median"}, inplace = True)
-    raw_display = raw_display[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own', 'rush_yards', 'rec']]
-    dk_roo_raw_2 = raw_display.loc[raw_display['Median'] > 0]
-
-    worksheet = sh.worksheet('FD_SD_Projections_2')
-    load_display = pd.DataFrame(worksheet.get_all_records())
-    load_display.replace('', np.nan, inplace=True)
-    raw_display = load_display.dropna(subset=['Half_PPR'])
-    raw_display.rename(columns={"name": "Player", "Half_PPR": "Median"}, inplace = True)
-    raw_display = raw_display[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own', 'rush_yards', 'rec']]
-    fd_roo_raw_2 = raw_display.loc[raw_display['Median'] > 0]
+    load_display['Salary'] = load_display['Salary'] / 1.5
+    dk_roo_raw = load_display.loc[load_display['Median'] > 0]
     
-    worksheet = sh.worksheet('SD_Projections_3')
-    load_display = pd.DataFrame(worksheet.get_all_records())
-    load_display.replace('', np.nan, inplace=True)
-    raw_display = load_display.dropna(subset=['PPR'])
-    raw_display.rename(columns={"name": "Player", "PPR": "Median"}, inplace = True)
-    raw_display = raw_display[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own', 'rush_yards', 'rec']]
-    dk_roo_raw_3 = raw_display.loc[raw_display['Median'] > 0]
-
-    worksheet = sh.worksheet('FD_SD_Projections_3')
-    load_display = pd.DataFrame(worksheet.get_all_records())
-    load_display.replace('', np.nan, inplace=True)
-    raw_display = load_display.dropna(subset=['Half_PPR'])
-    raw_display.rename(columns={"name": "Player", "Half_PPR": "Median"}, inplace = True)
-    raw_display = raw_display[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own', 'rush_yards', 'rec']]
-    fd_roo_raw_3 = raw_display.loc[raw_display['Median'] > 0]
+    dk_ids = dict(zip(dk_roo_raw['Player'], dk_roo_raw['player_id']))
 
-    worksheet = sh.worksheet('SD_Projections')
+    worksheet = sh.worksheet('FD_SD_ROO')
     load_display = pd.DataFrame(worksheet.get_all_records())
     load_display.replace('', np.nan, inplace=True)
-    load_display.rename(columns={"PPR": "Median", "name": "Player"}, inplace = True)
-    raw_display = load_display.dropna(subset=['Median'])
-    dk_ids = dict(zip(raw_display['Player'], raw_display['player_id']))
+    fd_roo_raw = load_display.loc[load_display['Median'] > 0]
     
-    worksheet = sh.worksheet('FD_SD_Projections')
-    load_display = pd.DataFrame(worksheet.get_all_records())
-    load_display.replace('', np.nan, inplace=True)
-    load_display.rename(columns={"Half_PPR": "Median", "name": "Player"}, inplace = True)
-    raw_display = load_display.dropna(subset=['Median'])
-    fd_ids = dict(zip(raw_display['Player'], raw_display['player_id']))
+    fd_ids = dict(zip(fd_roo_raw['Player'], fd_roo_raw['player_id']))
 
-    return dk_roo_raw, dk_roo_raw_2, dk_roo_raw_3, fd_roo_raw, fd_roo_raw_2, fd_roo_raw_3, dk_ids, fd_ids
+    return dk_roo_raw, fd_roo_raw, dk_ids, fd_ids
 
-dk_roo_raw, dk_roo_raw_2, dk_roo_raw_3, fd_roo_raw, fd_roo_raw_2, fd_roo_raw_3, dkid_dict, fdid_dict = init_baselines()
+dk_roo_raw, fd_roo_raw, dk_ids, fd_ids = init_baselines()
 
 @st.cache_data
 def convert_df_to_csv(df):
     return df.to_csv().encode('utf-8')
 
-tab1, tab2, tab3 = st.tabs(['Uploads and Info', 'Range of Outcomes', 'Optimizer'])
+tab1, tab2, tab3 = st.tabs(['Range of Outcomes', 'Optimizer', 'Uploads and Info'])
 
-with tab1:
+with tab3:
     st.info("The Projections file can have any columns in any order, but must contain columns explicitly named: 'Player', 'Salary', 'Position', 'Team', 'Opp', 'rush_yards', 'rec', 'Median', and 'Own'. For the purposes of this showdown optimizer, only include FLEX positions, salaries, and medians. The optimizer logic will handle the rest!")
     col1, col2 = st.columns([1, 5])
 
@@ -148,13 +101,13 @@ with tab1:
         if proj_file is not None:  
                   st.dataframe(proj_dataframe.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
 
-with tab2:
+with tab1:
     col1, col2 = st.columns([1, 5])
     with col1:
         if st.button("Load/Reset Data", key='reset2'):
               st.cache_data.clear()
-              dk_roo_raw, dk_roo_raw_2, dk_roo_raw_3, fd_roo_raw, fd_roo_raw_2, fd_roo_raw_3, dkid_dict, fdid_dict = init_baselines()
-        slate_var2 = st.radio("Which data are you loading?", ('Paydirt (Main)', 'Paydirt (Secondary)', 'Paydirt (Third)', 'User'), key='slate_var2')
+              dk_roo_raw, fd_roo_raw, dkid_dict, fdid_dict = init_baselines()
+        slate_var2 = st.radio("Which data are you loading?", ('Paydirt (Main)', 'User'), key='slate_var2')
         site_var2 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var2')
         if slate_var2 == 'User':
             raw_baselines = proj_dataframe
@@ -162,142 +115,44 @@ with tab2:
             if site_var2 == 'Draftkings':
                 if slate_var2 == 'Paydirt (Main)':
                     raw_baselines = dk_roo_raw
-                elif slate_var2 == 'Paydirt (Secondary)':
-                    raw_baselines = dk_roo_raw_2
-                elif slate_var2 == 'Paydirt (Third)':
-                    raw_baselines = dk_roo_raw_3
             elif site_var2 == 'Fanduel':
                 if slate_var2 == 'Paydirt (Main)':
                     raw_baselines = fd_roo_raw
-                elif slate_var2 == 'Paydirt (Secondary)':
-                    raw_baselines = fd_roo_raw_2
-                elif slate_var2 == 'Paydirt (Third)':
-                    raw_baselines = fd_roo_raw_3
     
     with col2:
         hold_container = st.empty()
-        if st.button('Create Range of Outcomes for Slate'):
-            with hold_container:
-                working_roo = raw_baselines
-                working_roo = working_roo.loc[working_roo['Median'] > 0]
-                if site_var2 == 'Draftkings':
-                    working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "Median": "Fantasy"}, inplace = True)
-                elif site_var2 == 'Fanduel':
-                    working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "Median": "Fantasy"}, inplace = True)
-                working_roo.replace('', 0, inplace=True)
-                own_dict = dict(zip(working_roo.Player, working_roo.Own))
-                team_dict = dict(zip(working_roo.Player, working_roo.Team))
-                opp_dict = dict(zip(working_roo.Player, working_roo.Opp))
-                total_sims = 1000
-  
-                flex_file = working_roo[['Player', 'Position', 'Salary', 'Fantasy', 'Rush Yards', 'Receptions']]
-                flex_file.rename(columns={"Fantasy": "Median", "Pos": "Position"}, inplace = True)
-                flex_file['Floor'] = np.where(flex_file['Position'] == 'QB',(flex_file['Median']*.25) + (flex_file['Rush Yards']*.01),flex_file['Median']*.25)
-                flex_file['Ceiling'] = np.where(flex_file['Position'] == 'QB',(flex_file['Median'] + flex_file['Floor']) + (flex_file['Rush Yards']*.01), flex_file['Median'] + flex_file['Floor'] + flex_file['Receptions'])
-                flex_file['Ceiling'] = flex_file['Ceiling'].fillna(15)
-                flex_file['STD'] = np.where(flex_file['Position'] != 'QB', (flex_file['Median']/4) + flex_file['Receptions'], (flex_file['Median']/4))
-                flex_file['STD'] = flex_file['Ceiling'].fillna(5)
-                flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
-                hold_file = flex_file
-                overall_file = flex_file
-                salary_file = flex_file
-  
-                overall_players = overall_file[['Player']]
-  
-                for x in range(0,total_sims):    
-                    salary_file[x] = salary_file['Salary']
-  
-                salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
-                salary_file.astype('int').dtypes
-  
-                salary_file = salary_file.div(1000)
-  
-                for x in range(0,total_sims):    
-                    overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])
-  
-                overall_file=overall_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
-                overall_file.astype('int').dtypes
-  
-                players_only = hold_file[['Player']]
-                raw_lineups_file = players_only
-  
-                for x in range(0,total_sims):
-                    maps_dict = {'proj_map':dict(zip(hold_file.Player,hold_file[x]))}
-                    raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])])
-                    players_only[x] = raw_lineups_file[x].rank(ascending=False)
-  
-                players_only=players_only.drop(['Player'], axis=1)
-                players_only.astype('int').dtypes
-  
-                salary_2x_check = (overall_file - (salary_file*2))
-                salary_3x_check = (overall_file - (salary_file*3))
-                salary_4x_check = (overall_file - (salary_file*4))
-  
-                players_only['Average_Rank'] = players_only.mean(axis=1)
-                players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
-                players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
-                players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
-                players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims)
-                players_only['2x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims)
-                players_only['3x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims)
-                players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)
-  
-                players_only['Player'] = hold_file[['Player']]
-  
-                final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
-  
-                final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
-                final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
-                final_Proj['Own'] = final_Proj['Player'].map(own_dict)
-                final_Proj['Team'] = final_Proj['Player'].map(team_dict)
-                final_Proj['Opp'] = final_Proj['Player'].map(opp_dict)
-                final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own']]
-                final_Proj['Projection Rank'] = final_Proj.Median.rank(pct = True)
-                final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True)
-                final_Proj['LevX'] = 0
-                final_Proj['LevX'] = final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank']
-                final_Proj['CPT_Own'] = final_Proj['Own'] / 4
-                final_Proj['CPT_Proj'] = final_Proj['Median'] * 1.5 
-                final_Proj['CPT_Salary'] = final_Proj['Salary'] * 1.5       
-
-                export_final_proj = final_Proj
-                export_final_proj['ID'] = export_final_proj['Player'].map(dkid_dict)
-  
-                display_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']]
-                display_Proj = display_Proj.set_index('Player')
-                display_Proj = display_Proj.sort_values(by='Median', ascending=False)
+        
+        display_Proj = raw_baselines[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']]
+        display_Proj = display_Proj.set_index('Player')
+        display_Proj = display_Proj.sort_values(by='Median', ascending=False)
   
-            with hold_container:
-                hold_container = st.empty()
-                display_Proj = display_Proj
-                st.dataframe(display_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
+        with hold_container:
+            hold_container = st.empty()
+            display_Proj = display_Proj
+            st.dataframe(display_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
   
-            st.download_button(
-                    label="Export Tables",
-                    data=convert_df_to_csv(export_final_proj),
-                    file_name='Custom_NFL_overall_export.csv',
-                    mime='text/csv',
-            )
+        st.download_button(
+                label="Export Tables",
+                data=convert_df_to_csv(raw_baselines),
+                file_name='NFL_SD_export.csv',
+                mime='text/csv',
+        )
 
-with tab3:
+with tab2:
     col1, col2 = st.columns([1, 5])
     with col1:
         if st.button("Load/Reset Data", key='reset1'):
               st.cache_data.clear()
-              dk_roo_raw, dk_roo_raw_2, dk_roo_raw_3, fd_roo_raw, fd_roo_raw_2, fd_roo_raw_3, dkid_dict, fdid_dict = init_baselines()
+              dk_roo_raw, fd_roo_raw, dkid_dict, fdid_dict = init_baselines()
               for key in st.session_state.keys():
                   del st.session_state[key]
-        slate_var1 = st.radio("Which data are you loading?", ('Paydirt (Main)', 'Paydirt (Secondary)', 'Paydirt (Third)', 'User'), key='slate_var1')
+        slate_var1 = st.radio("Which data are you loading?", ('Paydirt (Main)', 'User'), key='slate_var1')
         site_var1 = st.selectbox("What site is the showdown on?", ('Draftkings', 'Fanduel'), key='site_var1')
         if site_var1 == 'Draftkings':
               if slate_var1 == 'User':
                   raw_baselines = proj_dataframe
               elif slate_var1 == 'Paydirt (Main)':
                   raw_baselines = dk_roo_raw
-              elif slate_var1 == 'Paydirt (Secondary)':
-                  raw_baselines = dk_roo_raw_2
-              elif slate_var1 == 'Paydirt (Third)':
-                  raw_baselines = dk_roo_raw_3
         elif site_var1 == 'Fanduel':
               if slate_var1 == 'User':
                   st.info("Showdown on Fanduel sucks, you should not do that, but I understand degen's gotta degen")
@@ -305,12 +160,7 @@ with tab3:
               elif slate_var1 == 'Paydirt (Main)':
                   st.info("Showdown on Fanduel sucks, you should not do that, but I understand degen's gotta degen")
                   raw_baselines = fd_roo_raw
-              elif slate_var1 == 'Paydirt (Secondary)':
-                  st.info("Showdown on Fanduel sucks, you should not do that, but I understand degen's gotta degen")
-                  raw_baselines = fd_roo_raw_2
-              elif slate_var1 == 'Paydirt (Third)':
-                  st.info("Showdown on Fanduel sucks, you should not do that, but I understand degen's gotta degen")
-                  raw_baselines = fd_roo_raw_3
+                  
         contest_var1 = st.selectbox("What contest type are you optimizing for?", ('Cash', 'Small Field GPP', 'Large Field GPP'), key='contest_var1')
         lock_var1 = st.multiselect("Are there any players you want to use in all lineups in the CAPTAIN (Lock Button)?", options = raw_baselines['Player'].unique(), key='lock_var1')
         lock_var2 = st.multiselect("Are there any players you want to use in all lineups in the FLEX (Lock Button)?", options = raw_baselines['Player'].unique(), key='lock_var2')
@@ -334,39 +184,39 @@ with tab3:
                 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['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%'] > 85, 85, ownframe['Own%'])
-                ownframe['Own'] = ownframe['Own%'] * (500 / ownframe['Own%'].sum())
+                ownframe['Own'] = ownframe['Own%'] * (600 / ownframe['Own%'].sum())
             elif site_var1 == 'Fanduel':
                 ownframe = raw_baselines.copy()
                 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())/50) + 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())/150) + ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean(), ownframe['Own%'])
                 ownframe['Own%'] = np.where(ownframe['Own%'] > 75, 75, ownframe['Own%'])
-                ownframe['Own'] = ownframe['Own%'] * (400 / ownframe['Own%'].sum())
+                ownframe['Own'] = ownframe['Own%'] * (500 / ownframe['Own%'].sum())
         elif contest_var1 == 'Large Field GPP':
             if site_var1 == 'Draftkings':
                 ownframe = raw_baselines.copy()
                 ownframe['Own%'] = np.where((ownframe['Position'] == 'QB') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean() >= 0), ownframe['Own'] * (2.5 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean(), ownframe['Own'])
                 ownframe['Own%'] = np.where((ownframe['Position'] != 'QB') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean() >= 0), ownframe['Own'] * (2.5 * (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%'] * (500 / ownframe['Own%'].sum())
+                ownframe['Own'] = ownframe['Own%'] * (600 / ownframe['Own%'].sum())
             elif site_var1 == 'Fanduel':
                 ownframe = raw_baselines.copy()
                 ownframe['Own%'] = np.where((ownframe['Position'] == 'QB') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean() >= 0), ownframe['Own'] * (2.5 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean())/50) + 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'] * (2.5 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean())/150) + ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean(), ownframe['Own%'])
                 ownframe['Own%'] = np.where(ownframe['Own%'] > 75, 75, ownframe['Own%'])
-                ownframe['Own'] = ownframe['Own%'] * (400 / ownframe['Own%'].sum())
+                ownframe['Own'] = ownframe['Own%'] * (500 / ownframe['Own%'].sum())
         elif contest_var1 == 'Cash':
             if site_var1 == 'Draftkings':
                 ownframe = raw_baselines.copy()
                 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'] * (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['Own%'] > 90, 90, ownframe['Own%'])
-                ownframe['Own'] = ownframe['Own%'] * (500 / ownframe['Own%'].sum())
+                ownframe['Own'] = ownframe['Own%'] * (600 / ownframe['Own%'].sum())
             elif site_var1 == 'Fanduel':
                 ownframe = raw_baselines.copy()
                 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())/50) + 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'] * (6 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean())/150) + ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean(), ownframe['Own%'])
                 ownframe['Own%'] = np.where(ownframe['Own%'] > 75, 75, ownframe['Own%'])
-                ownframe['Own'] = ownframe['Own%'] * (400 / ownframe['Own%'].sum())
+                ownframe['Own'] = ownframe['Own%'] * (500 / ownframe['Own%'].sum())
         export_baselines = ownframe[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own']]
         export_baselines['CPT_Proj'] = export_baselines['Median'] * 1.5
         export_baselines['CPT_Salary'] = export_baselines['Salary'] * 1.5