Refactor Streamlit app to remove redundant name conversion messages and enhance data loading/reset functionality. Added buttons for data export and improved layout with segmented controls for better user experience.

src/streamlit_app.py

@@ -75,7 +75,6 @@ def init_DK_seed_frames(sharp_split):
     raw_display = pd.DataFrame(list(cursor))
     raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
     dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
-    st.write("converting names")
     for col in dict_columns:
         raw_display[col] = raw_display[col].map(names_dict)
     DK_seed = raw_display.to_numpy()
@@ -96,7 +95,6 @@ def init_DK_Secondary_seed_frames(sharp_split):
     raw_display = pd.DataFrame(list(cursor))
     raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
     dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
-    st.write("converting names")
     for col in dict_columns:
         raw_display[col] = raw_display[col].map(names_dict)
     DK_seed = raw_display.to_numpy()
@@ -117,7 +115,6 @@ def init_FD_seed_frames(sharp_split):
     raw_display = pd.DataFrame(list(cursor))
     raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
     dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
-    st.write("converting names")
     for col in dict_columns:
         raw_display[col] = raw_display[col].map(names_dict)
     FD_seed = raw_display.to_numpy()
@@ -138,7 +135,6 @@ def init_FD_Secondary_seed_frames(sharp_split):
     raw_display = pd.DataFrame(list(cursor))
     raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
     dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
-    st.write("converting names")
     for col in dict_columns:
         raw_display[col] = raw_display[col].map(names_dict)
     FD_seed = raw_display.to_numpy()
@@ -219,6 +215,16 @@ def sim_contest(Sim_size, seed_frame, maps_dict, Contest_Size):
         
     return Sim_Winners
 
+if st.button("Load/Reset Data", key='reset2'):
+    st.cache_data.clear()
+    for key in st.session_state.keys():
+        del st.session_state[key]
+    DK_seed = init_DK_seed_frames(10000)
+    FD_seed = init_FD_seed_frames(10000)
+    dk_raw, fd_raw = init_baselines('Main Slate')
+    dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_ID))
+    fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_ID))
+
 selected_tab = st.segmented_control(
     "Select Tab",
     options=["Contest Sims", "Data Export"],
@@ -230,483 +236,481 @@ selected_tab = st.segmented_control(
 )
             
 if selected_tab == "Contest Sims":
-    col1, col2 = st.columns([1, 7])
-    with col1:
-        if st.button("Load/Reset Data", key='reset2'):
-              st.cache_data.clear()
-              for key in st.session_state.keys():
-                  del st.session_state[key]
-              DK_seed = init_DK_seed_frames(10000)
-              FD_seed = init_FD_seed_frames(10000)
-              dk_raw, fd_raw = init_baselines('Main Slate')
-              dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_ID))
-              fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_ID))
-
-        sim_slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate'), key='sim_slate_var1')
-        sim_site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='sim_site_var1')
+    dk_raw, fd_raw = init_baselines('Main Slate')
+    raw_baselines = dk_raw
+    column_names = dk_columns
+    with st.expander("Info and Filters"):
+        slate_data_col, sim_data_col, sim_lock_options, sim_remove_options = st.columns([1, 1, 1, 1])
+        with slate_data_col:
+            sim_slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate'), key='sim_slate_var1')
+            sim_site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='sim_site_var1')
+        with sim_data_col:
+            contest_var1 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large'))
+            if contest_var1 == 'Small':
+                Contest_Size = 1000
+            elif contest_var1 == 'Medium':
+                Contest_Size = 5000
+            elif contest_var1 == 'Large':
+                Contest_Size = 10000
+            elif contest_var1 == 'Custom':
+                Contest_Size = st.number_input("Insert contest size", value=100, placeholder="Type a number under 10,000...")
+            strength_var1 = st.selectbox("How sharp is the field in the contest?", ('Very', 'Above Average', 'Average', 'Below Average', 'Not Very'))
+            if strength_var1 == 'Not Very':
+                sharp_split = 500000
+            elif strength_var1 == 'Below Average':
+                sharp_split = 250000
+            elif strength_var1 == 'Average':
+                sharp_split = 100000
+            elif strength_var1 == 'Above Average':
+                sharp_split = 50000
+            elif strength_var1 == 'Very':
+                sharp_split = 10000
+        with sim_lock_options:
+            player_lock_var1 = st.multiselect("Sim around specific players?", raw_baselines.Player, default=[])
+            team_lock_var1 = st.multiselect("Sim around specific teams?", raw_baselines.Team, default=[])
+        with sim_remove_options:
+            player_remove_var1 = st.multiselect("Remove specific players?", raw_baselines.Player, default=[])
+            team_remove_var1 = st.multiselect("Remove specific teams?", raw_baselines.Team, default=[])
+
+    if st.button("Run Contest Sim"):
             
-        contest_var1 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large', 'Custom'))
-        if contest_var1 == 'Small':
-            Contest_Size = 1000
-        elif contest_var1 == 'Medium':
-            Contest_Size = 5000
-        elif contest_var1 == 'Large':
-            Contest_Size = 10000
-        elif contest_var1 == 'Custom':
-            Contest_Size = st.number_input("Insert contest size", value=100, placeholder="Type a number under 10,000...")
-        strength_var1 = st.selectbox("How sharp is the field in the contest?", ('Very', 'Above Average', 'Average', 'Below Average', 'Not Very'))
-        if strength_var1 == 'Not Very':
-            sharp_split = 500000
-        elif strength_var1 == 'Below Average':
-            sharp_split = 250000
-        elif strength_var1 == 'Average':
-            sharp_split = 100000
-        elif strength_var1 == 'Above Average':
-            sharp_split = 50000
-        elif strength_var1 == 'Very':
-            sharp_split = 10000
-
-    
-    with col2:
-        if st.button("Run Contest Sim"):
-            if 'working_seed' in st.session_state:
-                st.session_state.maps_dict = {
-                        'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)),
-                        'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)),
-                        'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)),
-                        'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own'])),
-                        'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)),
-                        'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev))
-                        }
-                
-                Sim_Winners = sim_contest(1000, st.session_state.working_seed, st.session_state.maps_dict, Contest_Size)
-                Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
-                
-                # Initial setup
-                Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=column_names + ['Fantasy'])
-                Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['proj'] + Sim_Winner_Frame['Fantasy']) / 2
-                Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['proj'].astype(str) + Sim_Winner_Frame['salary'].astype(str) + Sim_Winner_Frame['Team'].astype(str) + Sim_Winner_Frame['Secondary'].astype(str)
-                Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts()))
-                
-                # Type Casting
-                type_cast_dict = {'salary': int, 'proj': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32, 'Own': np.float32}
-                Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict)
-                
-                # Sorting
-                st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by=['win_count', 'GPP_Proj'], ascending= [False, False]).copy().drop_duplicates(subset='unique_id').head(100)
-                st.session_state.Sim_Winner_Frame.drop(columns='unique_id', inplace=True)
-                
-                # Data Copying
-                st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy()
-                
-                # Data Copying
-                st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy()
-                
-            else:
-                if sim_site_var1 == 'Draftkings':
-                    if sim_slate_var1 == 'Main Slate':
-                        st.session_state.working_seed = init_DK_seed_frames(sharp_split)
-                        dk_raw, fd_raw = init_baselines('Main Slate')
-                        dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_ID)) 
-                    elif sim_slate_var1 == 'Secondary Slate':
-                        st.session_state.working_seed = init_DK_Secondary_seed_frames(sharp_split)
-                        dk_raw, fd_raw = init_baselines('Secondary Slate')
-                        dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_ID)) 
-                        
-                    raw_baselines = dk_raw
-                    column_names = dk_columns
-                elif sim_site_var1 == 'Fanduel':
-                    if sim_slate_var1 == 'Main Slate':
-                        st.session_state.working_seed = init_FD_seed_frames(sharp_split)
-                        dk_raw, fd_raw = init_baselines('Main Slate')
-                        fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_ID))
-                    elif sim_slate_var1 == 'Secondary Slate':
-                        st.session_state.working_seed = init_FD_Secondary_seed_frames(sharp_split)
-                        dk_raw, fd_raw = init_baselines('Secondary Slate')
-                        fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_ID))
-                        
-                    raw_baselines = fd_raw
-                    column_names = fd_columns
-                st.session_state.maps_dict = {
-                        'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)),
-                        'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)),
-                        'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)),
-                        'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own'])),
-                        'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)),
-                        'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev))
-                        }
-                
-                Sim_Winners = sim_contest(1000, st.session_state.working_seed, st.session_state.maps_dict, Contest_Size)
-                Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
-                
-                #st.table(Sim_Winner_Frame)
-                            
-                # Initial setup
-                Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=column_names + ['Fantasy'])
-                Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['proj'] + Sim_Winner_Frame['Fantasy']) / 2
-                Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['proj'].astype(str) + Sim_Winner_Frame['salary'].astype(str) + Sim_Winner_Frame['Team'].astype(str) + Sim_Winner_Frame['Secondary'].astype(str)
-                Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts()))
-                
-                # Type Casting
-                type_cast_dict = {'salary': int, 'proj': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32, 'Own': np.float32}
-                Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict)
-                
-                # Sorting
-                st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by=['win_count', 'GPP_Proj'], ascending= [False, False]).copy().drop_duplicates(subset='unique_id').head(100)
-                st.session_state.Sim_Winner_Frame.drop(columns='unique_id', inplace=True)
-                
-               # Data Copying
-                st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy()
-                if sim_site_var1 == 'Draftkings':
-                    for col in st.session_state.Sim_Winner_Export.iloc[:, 0:9].columns:
-                        st.session_state.Sim_Winner_Export[col] = st.session_state.Sim_Winner_Export[col].map(dk_id_dict)
-                elif sim_site_var1 == 'Fanduel':
-                    for col in st.session_state.Sim_Winner_Export.iloc[:, 0:9].columns:
-                        st.session_state.Sim_Winner_Export[col] = st.session_state.Sim_Winner_Export[col].map(fd_id_dict)
-                
-                # Data Copying
-                st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy()
-                st.session_state.freq_copy = st.session_state.Sim_Winner_Display
-                
-            if sim_site_var1 == 'Draftkings':
-                freq_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:9].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            elif sim_site_var1 == 'Fanduel':
-                freq_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:9].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            freq_working['Freq'] = freq_working['Freq'].astype(int)
-            freq_working['Position'] = freq_working['Player'].map(st.session_state.maps_dict['Pos_map'])
-            freq_working['Salary'] = freq_working['Player'].map(st.session_state.maps_dict['Salary_map'])
-            freq_working['Proj Own'] = freq_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
-            freq_working['Exposure'] = freq_working['Freq']/(1000)
-            freq_working['Edge'] = freq_working['Exposure'] - freq_working['Proj Own']
-            freq_working['Team'] = freq_working['Player'].map(st.session_state.maps_dict['Team_map'])
-            st.session_state.player_freq = freq_working.copy()
-
+        if 'working_seed' not in st.session_state:
             if sim_site_var1 == 'Draftkings':
-                qb_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:1].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+                if sim_slate_var1 == 'Main Slate':
+                    st.session_state.working_seed = init_DK_seed_frames(sharp_split)
+                    dk_raw, fd_raw = init_baselines('Main Slate')
+                    dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_ID)) 
+                elif sim_slate_var1 == 'Secondary Slate':
+                    st.session_state.working_seed = init_DK_Secondary_seed_frames(sharp_split)
+                    dk_raw, fd_raw = init_baselines('Secondary Slate')
+                    dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_ID)) 
+                    
+                raw_baselines = dk_raw
+                column_names = dk_columns
             elif sim_site_var1 == 'Fanduel':
-                qb_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:1].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            qb_working['Freq'] = qb_working['Freq'].astype(int)
-            qb_working['Position'] = qb_working['Player'].map(st.session_state.maps_dict['Pos_map'])
-            qb_working['Salary'] = qb_working['Player'].map(st.session_state.maps_dict['Salary_map'])
-            qb_working['Proj Own'] = qb_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
-            qb_working['Exposure'] = qb_working['Freq']/(1000)
-            qb_working['Edge'] = qb_working['Exposure'] - qb_working['Proj Own']
-            qb_working['Team'] = qb_working['Player'].map(st.session_state.maps_dict['Team_map'])
-            st.session_state.qb_freq = qb_working.copy()
+                if sim_slate_var1 == 'Main Slate':
+                    st.session_state.working_seed = init_FD_seed_frames(sharp_split)
+                    dk_raw, fd_raw = init_baselines('Main Slate')
+                    fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_ID))
+                elif sim_slate_var1 == 'Secondary Slate':
+                    st.session_state.working_seed = init_FD_Secondary_seed_frames(sharp_split)
+                    dk_raw, fd_raw = init_baselines('Secondary Slate')
+                    fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_ID))
+                    
+                raw_baselines = fd_raw
+                column_names = fd_columns
+            st.session_state.maps_dict = {
+                    'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)),
+                    'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)),
+                    'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)),
+                    'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own'])),
+                    'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)),
+                    'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev))
+                    }
             
-            if sim_site_var1 == 'Draftkings':
-                rbwrte_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,1:7].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            elif sim_site_var1 == 'Fanduel':
-                rbwrte_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,1:7].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            rbwrte_working['Freq'] = rbwrte_working['Freq'].astype(int)
-            rbwrte_working['Position'] = rbwrte_working['Player'].map(st.session_state.maps_dict['Pos_map'])
-            rbwrte_working['Salary'] = rbwrte_working['Player'].map(st.session_state.maps_dict['Salary_map'])
-            rbwrte_working['Proj Own'] = rbwrte_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
-            rbwrte_working['Exposure'] = rbwrte_working['Freq']/(1000)
-            rbwrte_working['Edge'] = rbwrte_working['Exposure'] - rbwrte_working['Proj Own']
-            rbwrte_working['Team'] = rbwrte_working['Player'].map(st.session_state.maps_dict['Team_map'])
-            st.session_state.rbwrte_freq = rbwrte_working.copy()
+            Sim_Winners = sim_contest(1000, st.session_state.working_seed, st.session_state.maps_dict, Contest_Size)
+            Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
             
-            if sim_site_var1 == 'Draftkings':
-                rb_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,1:3].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            elif sim_site_var1 == 'Fanduel':
-                rb_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,1:3].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            rb_working['Freq'] = rb_working['Freq'].astype(int)
-            rb_working['Position'] = rb_working['Player'].map(st.session_state.maps_dict['Pos_map'])
-            rb_working['Salary'] = rb_working['Player'].map(st.session_state.maps_dict['Salary_map'])
-            rb_working['Proj Own'] = rb_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
-            rb_working['Exposure'] = rb_working['Freq']/(1000)
-            rb_working['Edge'] = rb_working['Exposure'] - rb_working['Proj Own']
-            rb_working['Team'] = rb_working['Player'].map(st.session_state.maps_dict['Team_map'])
-            st.session_state.rb_freq = rb_working.copy()
+            #st.table(Sim_Winner_Frame)
+                        
+            # Initial setup
+            Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=column_names + ['Fantasy'])
+            Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['proj'] + Sim_Winner_Frame['Fantasy']) / 2
+            Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['proj'].astype(str) + Sim_Winner_Frame['salary'].astype(str) + Sim_Winner_Frame['Team'].astype(str) + Sim_Winner_Frame['Secondary'].astype(str)
+            Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts()))
             
-            if sim_site_var1 == 'Draftkings':
-                wr_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,3:6].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            elif sim_site_var1 == 'Fanduel':
-                wr_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,3:6].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            wr_working['Freq'] = wr_working['Freq'].astype(int)
-            wr_working['Position'] = wr_working['Player'].map(st.session_state.maps_dict['Pos_map'])
-            wr_working['Salary'] = wr_working['Player'].map(st.session_state.maps_dict['Salary_map'])
-            wr_working['Proj Own'] = wr_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
-            wr_working['Exposure'] = wr_working['Freq']/(1000)
-            wr_working['Edge'] = wr_working['Exposure'] - wr_working['Proj Own']
-            wr_working['Team'] = wr_working['Player'].map(st.session_state.maps_dict['Team_map'])
-            st.session_state.wr_freq = wr_working.copy()
+            # Type Casting
+            type_cast_dict = {'salary': int, 'proj': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32, 'Own': np.float32}
+            Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict)
             
-            if sim_site_var1 == 'Draftkings':
-                te_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,6:7].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            elif sim_site_var1 == 'Fanduel':
-                te_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,6:7].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            te_working['Freq'] = te_working['Freq'].astype(int)
-            te_working['Position'] = te_working['Player'].map(st.session_state.maps_dict['Pos_map'])
-            te_working['Salary'] = te_working['Player'].map(st.session_state.maps_dict['Salary_map'])
-            te_working['Proj Own'] = te_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
-            te_working['Exposure'] = te_working['Freq']/(1000)
-            te_working['Edge'] = te_working['Exposure'] - te_working['Proj Own']
-            te_working['Team'] = te_working['Player'].map(st.session_state.maps_dict['Team_map'])
-            st.session_state.te_freq = te_working.copy()
+            # Sorting
+            st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by=['win_count', 'GPP_Proj'], ascending= [False, False]).copy().drop_duplicates(subset='unique_id').head(100)
+            st.session_state.Sim_Winner_Frame.drop(columns='unique_id', inplace=True)
             
+            # Data Copying
+            st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy()
             if sim_site_var1 == 'Draftkings':
-                flex_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,7:8].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+                for col in st.session_state.Sim_Winner_Export.iloc[:, 0:9].columns:
+                    st.session_state.Sim_Winner_Export[col] = st.session_state.Sim_Winner_Export[col].map(dk_id_dict)
             elif sim_site_var1 == 'Fanduel':
-                flex_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,7:8].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            flex_working['Freq'] = flex_working['Freq'].astype(int)
-            flex_working['Position'] = flex_working['Player'].map(st.session_state.maps_dict['Pos_map'])
-            flex_working['Salary'] = flex_working['Player'].map(st.session_state.maps_dict['Salary_map'])
-            flex_working['Proj Own'] = flex_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
-            flex_working['Exposure'] = flex_working['Freq']/(1000)
-            flex_working['Edge'] = flex_working['Exposure'] - flex_working['Proj Own']
-            flex_working['Team'] = flex_working['Player'].map(st.session_state.maps_dict['Team_map'])
-            st.session_state.flex_freq = flex_working.copy()
+                for col in st.session_state.Sim_Winner_Export.iloc[:, 0:9].columns:
+                    st.session_state.Sim_Winner_Export[col] = st.session_state.Sim_Winner_Export[col].map(fd_id_dict)
             
-            if sim_site_var1 == 'Draftkings':
-                dst_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,8:9].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            elif sim_site_var1 == 'Fanduel':
-                dst_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,8:9].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            dst_working['Freq'] = dst_working['Freq'].astype(int)
-            dst_working['Position'] = dst_working['Player'].map(st.session_state.maps_dict['Pos_map'])
-            dst_working['Salary'] = dst_working['Player'].map(st.session_state.maps_dict['Salary_map'])
-            dst_working['Proj Own'] = dst_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
-            dst_working['Exposure'] = dst_working['Freq']/(1000)
-            dst_working['Edge'] = dst_working['Exposure'] - dst_working['Proj Own']
-            dst_working['Team'] = dst_working['Player'].map(st.session_state.maps_dict['Team_map'])
-            st.session_state.dst_freq = dst_working.copy()
-
-            if sim_site_var1 == 'Draftkings':
-                team_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,11:12].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            elif sim_site_var1 == 'Fanduel':
-                team_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,11:12].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            team_working['Freq'] = team_working['Freq'].astype(int)
-            team_working['Exposure'] = team_working['Freq']/(1000)
-            st.session_state.team_freq = team_working.copy()
+            # Data Copying
+            st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy()
+            st.session_state.freq_copy = st.session_state.Sim_Winner_Display
+        else:
+            st.session_state.maps_dict = {
+                    'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)),
+                    'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)),
+                    'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)),
+                    'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own'])),
+                    'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)),
+                    'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev))
+                    }
             
-        with st.container():
-            if st.button("Reset Sim", key='reset_sim'):
-                for key in st.session_state.keys():
-                    del st.session_state[key]
-            if 'player_freq' in st.session_state: 
-                player_split_var2 = st.radio("Are you wanting to isolate any lineups with specific players?", ('Full Players', 'Specific Players'), key='player_split_var2')
-                if player_split_var2 == 'Specific Players':
-                          find_var2 = st.multiselect('Which players must be included in the lineups?', options = st.session_state.player_freq['Player'].unique())
-                elif player_split_var2 == 'Full Players':
-                          find_var2 = st.session_state.player_freq.Player.values.tolist()
+            Sim_Winners = sim_contest(1000, st.session_state.working_seed, st.session_state.maps_dict, Contest_Size)
+            Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
+            
+            # Initial setup
+            Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=column_names + ['Fantasy'])
+            Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['proj'] + Sim_Winner_Frame['Fantasy']) / 2
+            Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['proj'].astype(str) + Sim_Winner_Frame['salary'].astype(str) + Sim_Winner_Frame['Team'].astype(str) + Sim_Winner_Frame['Secondary'].astype(str)
+            Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts()))
+            
+            # Type Casting
+            type_cast_dict = {'salary': int, 'proj': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32, 'Own': np.float32}
+            Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict)
+            
+            # Sorting
+            st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by=['win_count', 'GPP_Proj'], ascending= [False, False]).copy().drop_duplicates(subset='unique_id').head(100)
+            st.session_state.Sim_Winner_Frame.drop(columns='unique_id', inplace=True)
+            
+            # Data Copying
+            st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy()
+            
+            # Data Copying
+            st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy()    
+
+        if sim_site_var1 == 'Draftkings':
+            freq_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:9].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        elif sim_site_var1 == 'Fanduel':
+            freq_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:9].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        freq_working['Freq'] = freq_working['Freq'].astype(int)
+        freq_working['Position'] = freq_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+        freq_working['Salary'] = freq_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+        freq_working['Proj Own'] = freq_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+        freq_working['Exposure'] = freq_working['Freq']/(1000)
+        freq_working['Edge'] = freq_working['Exposure'] - freq_working['Proj Own']
+        freq_working['Team'] = freq_working['Player'].map(st.session_state.maps_dict['Team_map'])
+        st.session_state.player_freq = freq_working.copy()
+
+        if sim_site_var1 == 'Draftkings':
+            qb_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:1].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        elif sim_site_var1 == 'Fanduel':
+            qb_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:1].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        qb_working['Freq'] = qb_working['Freq'].astype(int)
+        qb_working['Position'] = qb_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+        qb_working['Salary'] = qb_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+        qb_working['Proj Own'] = qb_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+        qb_working['Exposure'] = qb_working['Freq']/(1000)
+        qb_working['Edge'] = qb_working['Exposure'] - qb_working['Proj Own']
+        qb_working['Team'] = qb_working['Player'].map(st.session_state.maps_dict['Team_map'])
+        st.session_state.qb_freq = qb_working.copy()
+        
+        if sim_site_var1 == 'Draftkings':
+            rbwrte_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,1:7].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        elif sim_site_var1 == 'Fanduel':
+            rbwrte_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,1:7].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        rbwrte_working['Freq'] = rbwrte_working['Freq'].astype(int)
+        rbwrte_working['Position'] = rbwrte_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+        rbwrte_working['Salary'] = rbwrte_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+        rbwrte_working['Proj Own'] = rbwrte_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+        rbwrte_working['Exposure'] = rbwrte_working['Freq']/(1000)
+        rbwrte_working['Edge'] = rbwrte_working['Exposure'] - rbwrte_working['Proj Own']
+        rbwrte_working['Team'] = rbwrte_working['Player'].map(st.session_state.maps_dict['Team_map'])
+        st.session_state.rbwrte_freq = rbwrte_working.copy()
+        
+        if sim_site_var1 == 'Draftkings':
+            rb_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,1:3].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        elif sim_site_var1 == 'Fanduel':
+            rb_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,1:3].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        rb_working['Freq'] = rb_working['Freq'].astype(int)
+        rb_working['Position'] = rb_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+        rb_working['Salary'] = rb_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+        rb_working['Proj Own'] = rb_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+        rb_working['Exposure'] = rb_working['Freq']/(1000)
+        rb_working['Edge'] = rb_working['Exposure'] - rb_working['Proj Own']
+        rb_working['Team'] = rb_working['Player'].map(st.session_state.maps_dict['Team_map'])
+        st.session_state.rb_freq = rb_working.copy()
+        
+        if sim_site_var1 == 'Draftkings':
+            wr_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,3:6].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        elif sim_site_var1 == 'Fanduel':
+            wr_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,3:6].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        wr_working['Freq'] = wr_working['Freq'].astype(int)
+        wr_working['Position'] = wr_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+        wr_working['Salary'] = wr_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+        wr_working['Proj Own'] = wr_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+        wr_working['Exposure'] = wr_working['Freq']/(1000)
+        wr_working['Edge'] = wr_working['Exposure'] - wr_working['Proj Own']
+        wr_working['Team'] = wr_working['Player'].map(st.session_state.maps_dict['Team_map'])
+        st.session_state.wr_freq = wr_working.copy()
+        
+        if sim_site_var1 == 'Draftkings':
+            te_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,6:7].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        elif sim_site_var1 == 'Fanduel':
+            te_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,6:7].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        te_working['Freq'] = te_working['Freq'].astype(int)
+        te_working['Position'] = te_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+        te_working['Salary'] = te_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+        te_working['Proj Own'] = te_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+        te_working['Exposure'] = te_working['Freq']/(1000)
+        te_working['Edge'] = te_working['Exposure'] - te_working['Proj Own']
+        te_working['Team'] = te_working['Player'].map(st.session_state.maps_dict['Team_map'])
+        st.session_state.te_freq = te_working.copy()
+        
+        if sim_site_var1 == 'Draftkings':
+            flex_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,7:8].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        elif sim_site_var1 == 'Fanduel':
+            flex_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,7:8].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        flex_working['Freq'] = flex_working['Freq'].astype(int)
+        flex_working['Position'] = flex_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+        flex_working['Salary'] = flex_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+        flex_working['Proj Own'] = flex_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+        flex_working['Exposure'] = flex_working['Freq']/(1000)
+        flex_working['Edge'] = flex_working['Exposure'] - flex_working['Proj Own']
+        flex_working['Team'] = flex_working['Player'].map(st.session_state.maps_dict['Team_map'])
+        st.session_state.flex_freq = flex_working.copy()
+        
+        if sim_site_var1 == 'Draftkings':
+            dst_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,8:9].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        elif sim_site_var1 == 'Fanduel':
+            dst_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,8:9].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        dst_working['Freq'] = dst_working['Freq'].astype(int)
+        dst_working['Position'] = dst_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+        dst_working['Salary'] = dst_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+        dst_working['Proj Own'] = dst_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+        dst_working['Exposure'] = dst_working['Freq']/(1000)
+        dst_working['Edge'] = dst_working['Exposure'] - dst_working['Proj Own']
+        dst_working['Team'] = dst_working['Player'].map(st.session_state.maps_dict['Team_map'])
+        st.session_state.dst_freq = dst_working.copy()
+
+        if sim_site_var1 == 'Draftkings':
+            team_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,11:12].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        elif sim_site_var1 == 'Fanduel':
+            team_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,11:12].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        team_working['Freq'] = team_working['Freq'].astype(int)
+        team_working['Exposure'] = team_working['Freq']/(1000)
+        st.session_state.team_freq = team_working.copy()
+        
+    with st.container():
+        if st.button("Reset Sim", key='reset_sim'):
+            for key in st.session_state.keys():
+                del st.session_state[key]
+        if 'player_freq' in st.session_state: 
+            player_split_var2 = st.radio("Are you wanting to isolate any lineups with specific players?", ('Full Players', 'Specific Players'), key='player_split_var2')
+            if player_split_var2 == 'Specific Players':
+                        find_var2 = st.multiselect('Which players must be included in the lineups?', options = st.session_state.player_freq['Player'].unique())
+            elif player_split_var2 == 'Full Players':
+                        find_var2 = st.session_state.player_freq.Player.values.tolist()
+
+            if player_split_var2 == 'Specific Players':
+                        st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame[np.equal.outer(st.session_state.Sim_Winner_Frame.to_numpy(), find_var2).any(axis=1).all(axis=1)]
+            if player_split_var2 == 'Full Players':
+                        st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame
+        if 'Sim_Winner_Display' in st.session_state:
+            st.dataframe(st.session_state.Sim_Winner_Display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+        if 'Sim_Winner_Export' in st.session_state:
+            st.download_button(
+                label="Export Full Frame",
+                data=st.session_state.Sim_Winner_Export.to_csv().encode('utf-8'),
+                file_name='MLB_consim_export.csv',
+                mime='text/csv',
+            )  
+    tab1, tab2 = st.tabs(['Winning Frame Statistics', 'Flex Exposure Statistics'])
     
-                if player_split_var2 == 'Specific Players':
-                          st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame[np.equal.outer(st.session_state.Sim_Winner_Frame.to_numpy(), find_var2).any(axis=1).all(axis=1)]
-                if player_split_var2 == 'Full Players':
-                          st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame
-            if 'Sim_Winner_Display' in st.session_state:
-                st.dataframe(st.session_state.Sim_Winner_Display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
-            if 'Sim_Winner_Export' in st.session_state:
+    with tab1:
+        if 'Sim_Winner_Display' in st.session_state:
+            # Create a new dataframe with summary statistics
+            summary_df = pd.DataFrame({
+                'Metric': ['Min', 'Average', 'Max', 'STDdev'],
+                'Salary': [
+                    st.session_state.Sim_Winner_Display['salary'].min(),
+                    st.session_state.Sim_Winner_Display['salary'].mean(),
+                    st.session_state.Sim_Winner_Display['salary'].max(),
+                    st.session_state.Sim_Winner_Display['salary'].std()
+                ],
+                'Proj': [
+                    st.session_state.Sim_Winner_Display['proj'].min(),
+                    st.session_state.Sim_Winner_Display['proj'].mean(),
+                    st.session_state.Sim_Winner_Display['proj'].max(),
+                    st.session_state.Sim_Winner_Display['proj'].std()
+                ],
+                'Own': [
+                    st.session_state.Sim_Winner_Display['Own'].min(),
+                    st.session_state.Sim_Winner_Display['Own'].mean(),
+                    st.session_state.Sim_Winner_Display['Own'].max(),
+                    st.session_state.Sim_Winner_Display['Own'].std()
+                ],
+                'Fantasy': [
+                    st.session_state.Sim_Winner_Display['Fantasy'].min(),
+                    st.session_state.Sim_Winner_Display['Fantasy'].mean(),
+                    st.session_state.Sim_Winner_Display['Fantasy'].max(),
+                    st.session_state.Sim_Winner_Display['Fantasy'].std()
+                ],
+                'GPP_Proj': [
+                    st.session_state.Sim_Winner_Display['GPP_Proj'].min(),
+                    st.session_state.Sim_Winner_Display['GPP_Proj'].mean(),
+                    st.session_state.Sim_Winner_Display['GPP_Proj'].max(),
+                    st.session_state.Sim_Winner_Display['GPP_Proj'].std()
+                ]
+            })
+
+            # Set the index of the summary dataframe as the "Metric" column
+            summary_df = summary_df.set_index('Metric')
+
+            # Display the summary dataframe
+            st.subheader("Winning Frame Statistics")
+            st.dataframe(summary_df.style.format({
+                'Salary': '{:.2f}',
+                'Proj': '{:.2f}',
+                'Fantasy': '{:.2f}',
+                'GPP_Proj': '{:.2f}'
+            }).background_gradient(cmap='RdYlGn', axis=0, subset=['Salary', 'Proj', 'Own', 'Fantasy', 'GPP_Proj']), use_container_width=True)
+
+    with tab2:
+        if 'Sim_Winner_Display' in st.session_state:
+            # Apply position mapping to FLEX column
+            flex_positions = st.session_state.freq_copy['FLEX'].map(st.session_state.maps_dict['Pos_map'])
+            
+            # Count occurrences of each position in FLEX
+            flex_counts = flex_positions.value_counts()
+            
+            # Calculate average statistics for each FLEX position
+            flex_stats = st.session_state.freq_copy.groupby(flex_positions).agg({
+                'proj': 'mean',
+                'Own': 'mean',
+                'Fantasy': 'mean',
+                'GPP_Proj': 'mean'
+            })
+            
+            # Combine counts and average statistics
+            flex_summary = pd.concat([flex_counts, flex_stats], axis=1)
+            flex_summary.columns = ['Count', 'Avg Proj', 'Avg Own', 'Avg Fantasy', 'Avg GPP_Proj']
+            flex_summary = flex_summary.reset_index()
+            flex_summary.columns = ['Position', 'Count', 'Avg Proj', 'Avg Own', 'Avg Fantasy', 'Avg GPP_Proj']
+            
+            # Display the summary dataframe
+            st.subheader("FLEX Position Statistics")
+            st.dataframe(flex_summary.style.format({
+                'Count': '{:.0f}',
+                'Avg Proj': '{:.2f}',
+                'Avg Fantasy': '{:.2f}',
+                'Avg GPP_Proj': '{:.2f}'
+            }).background_gradient(cmap='RdYlGn', axis=0, subset=['Count', 'Avg Proj', 'Avg Own', 'Avg Fantasy', 'Avg GPP_Proj']), use_container_width=True)
+            
+        else:
+            st.write("Simulation data or position mapping not available.")
+    with st.container():
+        tab1, tab2, tab3, tab4, tab5, tab6, tab7, tab8, tab9 = st.tabs(['Overall Exposures', 'QB Exposures', 'RB-WR-TE Exposures', 'RB Exposures', 'WR Exposures', 'TE Exposures', 'FLEX Exposures', 'DST Exposures', 'Team Exposures'])
+        with tab1:
+            if 'player_freq' in st.session_state:
+                
+                st.dataframe(st.session_state.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 Full Frame",
-                    data=st.session_state.Sim_Winner_Export.to_csv().encode('utf-8'),
-                    file_name='MLB_consim_export.csv',
+                    label="Export Exposures",
+                    data=st.session_state.player_freq.to_csv().encode('utf-8'),
+                    file_name='player_freq_export.csv',
                     mime='text/csv',
-                )  
-        tab1, tab2 = st.tabs(['Winning Frame Statistics', 'Flex Exposure Statistics'])
-        
-        with tab1:
-            if 'Sim_Winner_Display' in st.session_state:
-                # Create a new dataframe with summary statistics
-                summary_df = pd.DataFrame({
-                    'Metric': ['Min', 'Average', 'Max', 'STDdev'],
-                    'Salary': [
-                        st.session_state.Sim_Winner_Display['salary'].min(),
-                        st.session_state.Sim_Winner_Display['salary'].mean(),
-                        st.session_state.Sim_Winner_Display['salary'].max(),
-                        st.session_state.Sim_Winner_Display['salary'].std()
-                    ],
-                    'Proj': [
-                        st.session_state.Sim_Winner_Display['proj'].min(),
-                        st.session_state.Sim_Winner_Display['proj'].mean(),
-                        st.session_state.Sim_Winner_Display['proj'].max(),
-                        st.session_state.Sim_Winner_Display['proj'].std()
-                    ],
-                    'Own': [
-                        st.session_state.Sim_Winner_Display['Own'].min(),
-                        st.session_state.Sim_Winner_Display['Own'].mean(),
-                        st.session_state.Sim_Winner_Display['Own'].max(),
-                        st.session_state.Sim_Winner_Display['Own'].std()
-                    ],
-                    'Fantasy': [
-                        st.session_state.Sim_Winner_Display['Fantasy'].min(),
-                        st.session_state.Sim_Winner_Display['Fantasy'].mean(),
-                        st.session_state.Sim_Winner_Display['Fantasy'].max(),
-                        st.session_state.Sim_Winner_Display['Fantasy'].std()
-                    ],
-                    'GPP_Proj': [
-                        st.session_state.Sim_Winner_Display['GPP_Proj'].min(),
-                        st.session_state.Sim_Winner_Display['GPP_Proj'].mean(),
-                        st.session_state.Sim_Winner_Display['GPP_Proj'].max(),
-                        st.session_state.Sim_Winner_Display['GPP_Proj'].std()
-                    ]
-                })
-
-                # Set the index of the summary dataframe as the "Metric" column
-                summary_df = summary_df.set_index('Metric')
-
-                # Display the summary dataframe
-                st.subheader("Winning Frame Statistics")
-                st.dataframe(summary_df.style.format({
-                    'Salary': '{:.2f}',
-                    'Proj': '{:.2f}',
-                    'Fantasy': '{:.2f}',
-                    'GPP_Proj': '{:.2f}'
-                }).background_gradient(cmap='RdYlGn', axis=0, subset=['Salary', 'Proj', 'Own', 'Fantasy', 'GPP_Proj']), use_container_width=True)
-
+                    key='overall'
+                )
         with tab2:
-            if 'Sim_Winner_Display' in st.session_state:
-                # Apply position mapping to FLEX column
-                flex_positions = st.session_state.freq_copy['FLEX'].map(st.session_state.maps_dict['Pos_map'])
+            if 'qb_freq' in st.session_state:
                 
-                # Count occurrences of each position in FLEX
-                flex_counts = flex_positions.value_counts()
+                st.dataframe(st.session_state.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=st.session_state.qb_freq.to_csv().encode('utf-8'),
+                    file_name='qb_freq.csv',
+                    mime='text/csv',
+                    key='qb'
+                )
+        with tab3:
+            if 'rbwrte_freq' in st.session_state:
                 
-                # Calculate average statistics for each FLEX position
-                flex_stats = st.session_state.freq_copy.groupby(flex_positions).agg({
-                    'proj': 'mean',
-                    'Own': 'mean',
-                    'Fantasy': 'mean',
-                    'GPP_Proj': 'mean'
-                })
+                st.dataframe(st.session_state.rbwrte_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=st.session_state.rbwrte_freq.to_csv().encode('utf-8'),
+                    file_name='rbwrte_freq.csv',
+                    mime='text/csv',
+                    key='rbwrte'
+                )
+        with tab4:
+            if 'rb_freq' in st.session_state:
                 
-                # Combine counts and average statistics
-                flex_summary = pd.concat([flex_counts, flex_stats], axis=1)
-                flex_summary.columns = ['Count', 'Avg Proj', 'Avg Own', 'Avg Fantasy', 'Avg GPP_Proj']
-                flex_summary = flex_summary.reset_index()
-                flex_summary.columns = ['Position', 'Count', 'Avg Proj', 'Avg Own', 'Avg Fantasy', 'Avg GPP_Proj']
+                st.dataframe(st.session_state.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=st.session_state.rb_freq.to_csv().encode('utf-8'),
+                    file_name='rb_freq.csv',
+                    mime='text/csv',
+                    key='rb'
+                )
+        with tab5:
+            if 'wr_freq' in st.session_state:
                 
-                # Display the summary dataframe
-                st.subheader("FLEX Position Statistics")
-                st.dataframe(flex_summary.style.format({
-                    'Count': '{:.0f}',
-                    'Avg Proj': '{:.2f}',
-                    'Avg Fantasy': '{:.2f}',
-                    'Avg GPP_Proj': '{:.2f}'
-                }).background_gradient(cmap='RdYlGn', axis=0, subset=['Count', 'Avg Proj', 'Avg Own', 'Avg Fantasy', 'Avg GPP_Proj']), use_container_width=True)
+                st.dataframe(st.session_state.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=st.session_state.wr_freq.to_csv().encode('utf-8'),
+                    file_name='wr_freq.csv',
+                    mime='text/csv',
+                    key='wr'
+                )
+        with tab6:
+            if 'te_freq' in st.session_state:
                 
-            else:
-                st.write("Simulation data or position mapping not available.")
-        with st.container():
-            tab1, tab2, tab3, tab4, tab5, tab6, tab7, tab8, tab9 = st.tabs(['Overall Exposures', 'QB Exposures', 'RB-WR-TE Exposures', 'RB Exposures', 'WR Exposures', 'TE Exposures', 'FLEX Exposures', 'DST Exposures', 'Team Exposures'])
-            with tab1:
-                if 'player_freq' in st.session_state:
-                    
-                    st.dataframe(st.session_state.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=st.session_state.player_freq.to_csv().encode('utf-8'),
-                        file_name='player_freq_export.csv',
-                        mime='text/csv',
-                        key='overall'
-                    )
-            with tab2:
-                if 'qb_freq' in st.session_state:
-                    
-                    st.dataframe(st.session_state.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=st.session_state.qb_freq.to_csv().encode('utf-8'),
-                        file_name='qb_freq.csv',
-                        mime='text/csv',
-                        key='qb'
-                    )
-            with tab3:
-                if 'rbwrte_freq' in st.session_state:
-                    
-                    st.dataframe(st.session_state.rbwrte_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=st.session_state.rbwrte_freq.to_csv().encode('utf-8'),
-                        file_name='rbwrte_freq.csv',
-                        mime='text/csv',
-                        key='rbwrte'
-                    )
-            with tab4:
-                if 'rb_freq' in st.session_state:
-                    
-                    st.dataframe(st.session_state.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=st.session_state.rb_freq.to_csv().encode('utf-8'),
-                        file_name='rb_freq.csv',
-                        mime='text/csv',
-                        key='rb'
-                    )
-            with tab5:
-                if 'wr_freq' in st.session_state:
-                    
-                    st.dataframe(st.session_state.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=st.session_state.wr_freq.to_csv().encode('utf-8'),
-                        file_name='wr_freq.csv',
-                        mime='text/csv',
-                        key='wr'
-                    )
-            with tab6:
-                if 'te_freq' in st.session_state:
-                    
-                    st.dataframe(st.session_state.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=st.session_state.te_freq.to_csv().encode('utf-8'),
-                        file_name='te_freq.csv',
-                        mime='text/csv',
-                        key='te'
-                    )
-            with tab7:
-                if 'flex_freq' in st.session_state:
-                    
-                    st.dataframe(st.session_state.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=st.session_state.flex_freq.to_csv().encode('utf-8'),
-                        file_name='flex_freq.csv',
-                        mime='text/csv',
-                        key='flex'
-                    )
-            with tab8:
-                if 'dst_freq' in st.session_state:
-                    
-                    st.dataframe(st.session_state.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=st.session_state.dst_freq.to_csv().encode('utf-8'),
-                        file_name='dst_freq.csv',
-                        mime='text/csv',
-                        key='dst'
-                    )
-            with tab9:
-                if 'team_freq' in st.session_state:
-                    
-                    st.dataframe(st.session_state.team_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(percentages_format, precision=2), use_container_width = True)
-                    st.download_button(
-                        label="Export Exposures",
-                        data=st.session_state.team_freq.to_csv().encode('utf-8'),
-                        file_name='team_freq.csv',
-                        mime='text/csv',
-                        key='team'
-                    )
+                st.dataframe(st.session_state.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=st.session_state.te_freq.to_csv().encode('utf-8'),
+                    file_name='te_freq.csv',
+                    mime='text/csv',
+                    key='te'
+                )
+        with tab7:
+            if 'flex_freq' in st.session_state:
+                
+                st.dataframe(st.session_state.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=st.session_state.flex_freq.to_csv().encode('utf-8'),
+                    file_name='flex_freq.csv',
+                    mime='text/csv',
+                    key='flex'
+                )
+        with tab8:
+            if 'dst_freq' in st.session_state:
+                
+                st.dataframe(st.session_state.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=st.session_state.dst_freq.to_csv().encode('utf-8'),
+                    file_name='dst_freq.csv',
+                    mime='text/csv',
+                    key='dst'
+                )
+        with tab9:
+            if 'team_freq' in st.session_state:
+                
+                st.dataframe(st.session_state.team_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(percentages_format, precision=2), use_container_width = True)
+                st.download_button(
+                    label="Export Exposures",
+                    data=st.session_state.team_freq.to_csv().encode('utf-8'),
+                    file_name='team_freq.csv',
+                    mime='text/csv',
+                    key='team'
+                )
 
 if selected_tab == "Data Export":
     col1, col2 = st.columns([1, 7])