Implement comprehensive contest simulation functionality with dynamic sport, site, and slate selection

app.py

@@ -281,6 +281,389 @@ except:
     dk_raw, fd_raw = init_baselines('NBA')
 
 tab1, tab2 = st.tabs(['Contest Sims', 'Data Export'])
+            
+with tab1:
+    with st.expander("Info and Filters"):
+        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_raw, fd_raw = init_baselines('NFL')
+        sim_sport_var1 = st.radio("What sport are you working with?", ('NBA', 'NFL'), key='sim_sport_var1')
+        dk_raw, fd_raw = init_baselines(sim_sport_var1)
+        sim_slate_var1 = st.radio("Which data are you loading?", ('Showdown', 'Secondary Showdown', 'Auxiliary Showdown'), key='sim_slate_var1')
+        sim_site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='sim_site_var1')
+        if sim_site_var1 == 'Draftkings':
+            raw_baselines = dk_raw
+            column_names = dk_columns
+        elif sim_site_var1 == 'Fanduel':
+            raw_baselines = fd_raw
+            column_names = fd_columns
+        contest_var1 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large', 'Custom'))
+        if contest_var1 == 'Small':
+            Contest_Size = 1000
+            st.write("Small field size is 1,000 entrants.")
+            raw_baselines['Own'] = raw_baselines['Small_Field_Own']
+            raw_baselines['CPT_Own'] = raw_baselines['small_CPT_Own']
+        elif contest_var1 == 'Medium':
+            Contest_Size = 5000
+            st.write("Medium field size is 5,000 entrants.")
+        elif contest_var1 == 'Large':
+            Contest_Size = 10000
+            st.write("Large field size is 10,000 entrants.")
+        elif contest_var1 == 'Custom':
+            Contest_Size = st.number_input("Insert contest size", value=100, min_value=1, max_value=100000)
+        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 = 400000
+        elif strength_var1 == 'Average':
+            sharp_split = 300000
+        elif strength_var1 == 'Above Average':
+            sharp_split = 200000
+        elif strength_var1 == 'Very':
+            sharp_split = 100000
+
+    if st.button("Run Contest Sim"):
+        if 'working_seed' in st.session_state:
+            maps_dict = {
+                    'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)),
+                    'cpt_projection_map':dict(zip(raw_baselines.Player,raw_baselines.cpt_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'])),
+                    'cpt_Own_map':dict(zip(raw_baselines.Player,raw_baselines['CPT_Own'])),
+                    'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)),
+                    'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev)),
+                    'cpt_STDev_map':dict(zip(raw_baselines.Player,raw_baselines['CPT_STDev']))
+                    }
+            Sim_Winners = sim_contest(1000, st.session_state.working_seed, 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()
+            
+            # Data Copying
+            st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy()
+            
+        else:
+            if sim_site_var1 == 'Draftkings':
+                if sim_slate_var1 == 'Showdown':
+                    st.session_state.working_seed = init_DK_seed_frames(sim_sport_var1, sharp_split_var)
+                    if sport_var1 == 'NFL':
+                        export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                    elif sport_var1 == 'NBA':
+                        export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                elif sim_slate_var1 == 'Secondary Showdown':
+                    st.session_state.working_seed = init_DK_secondary_seed_frames(sim_sport_var1, sharp_split_var)
+                    if sport_var1 == 'NFL':
+                        export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                    elif sport_var1 == 'NBA':
+                        export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                elif sim_slate_var1 == 'Auxiliary Showdown':
+                    st.session_state.working_seed = init_DK_auxiliary_seed_frames(sim_sport_var1, sharp_split_var)
+                    if sport_var1 == 'NFL':
+                        export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                    elif sport_var1 == 'NBA':
+                        export_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 == 'Showdown':
+                    st.session_state.working_seed = init_FD_seed_frames(sim_sport_var1, sharp_split_var)
+                    if sport_var1 == 'NFL':
+                        export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                    elif sport_var1 == 'NBA':
+                        export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                elif sim_slate_var1 == 'Secondary Showdown':
+                    st.session_state.working_seed = init_FD_secondary_seed_frames(sim_sport_var1, sharp_split_var)
+                    if sport_var1 == 'NFL':
+                        export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                    elif sport_var1 == 'NBA':
+                        export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                elif sim_slate_var1 == 'Auxiliary Showdown':
+                    st.session_state.working_seed = init_FD_auxiliary_seed_frames(sim_sport_var1, sharp_split_var)
+                    if sport_var1 == 'NFL':
+                        export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                    elif sport_var1 == 'NBA':
+                        export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                raw_baselines = fd_raw
+                column_names = fd_columns
+            maps_dict = {
+                    'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)),
+                    'cpt_projection_map':dict(zip(raw_baselines.Player,raw_baselines.cpt_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'])),
+                    'cpt_Own_map':dict(zip(raw_baselines.Player,raw_baselines['CPT_Own'])),
+                    'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)),
+                    'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev)),
+                    'cpt_STDev_map':dict(zip(raw_baselines.Player,raw_baselines['CPT_STDev']))
+                    }
+            Sim_Winners = sim_contest(1000, st.session_state.working_seed, 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)
+            # Add percent rank columns for ownership at each roster position
+            # Calculate Dupes column for Fanduel
+            if sim_site_var1 == 'Fanduel':
+                dup_count_columns = ['CPT_Own_percent_rank', 'FLEX1_Own_percent_rank', 'FLEX2_Own_percent_rank', 'FLEX3_Own_percent_rank', 'FLEX4_Own_percent_rank']
+                own_columns = ['CPT_Own', 'FLEX1_Own', 'FLEX2_Own', 'FLEX3_Own', 'FLEX4_Own']
+                calc_columns = ['own_product', 'avg_own_rank', 'dupes_calc']
+                Sim_Winner_Frame['CPT_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,0].map(maps_dict['cpt_Own_map']).rank(pct=True)
+                Sim_Winner_Frame['FLEX1_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,1].map(maps_dict['Own_map']).rank(pct=True)
+                Sim_Winner_Frame['FLEX2_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,2].map(maps_dict['Own_map']).rank(pct=True)
+                Sim_Winner_Frame['FLEX3_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,3].map(maps_dict['Own_map']).rank(pct=True)
+                Sim_Winner_Frame['FLEX4_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,4].map(maps_dict['Own_map']).rank(pct=True)
+                Sim_Winner_Frame['CPT_Own'] = Sim_Winner_Frame.iloc[:,0].map(maps_dict['cpt_Own_map']) / 100
+                Sim_Winner_Frame['FLEX1_Own'] = Sim_Winner_Frame.iloc[:,1].map(maps_dict['Own_map']) / 100
+                Sim_Winner_Frame['FLEX2_Own'] = Sim_Winner_Frame.iloc[:,2].map(maps_dict['Own_map']) / 100
+                Sim_Winner_Frame['FLEX3_Own'] = Sim_Winner_Frame.iloc[:,3].map(maps_dict['Own_map']) / 100
+                Sim_Winner_Frame['FLEX4_Own'] = Sim_Winner_Frame.iloc[:,4].map(maps_dict['Own_map']) / 100
+                
+                # Calculate ownership product and convert to probability
+                Sim_Winner_Frame['own_product'] = (Sim_Winner_Frame[own_columns].product(axis=1)) + 0.0001
+                
+                # Calculate average of ownership percent rank columns
+                Sim_Winner_Frame['avg_own_rank'] = Sim_Winner_Frame[dup_count_columns].mean(axis=1)
+                
+                # Calculate dupes formula
+                Sim_Winner_Frame['dupes_calc'] = ((Sim_Winner_Frame['own_product'] * Sim_Winner_Frame['avg_own_rank']) * (Contest_Size * 1.5)) + ((Sim_Winner_Frame['salary'] - 59800) / 100)
+                
+                # Round and handle negative values
+                Sim_Winner_Frame['Dupes'] = np.where(
+                    np.round(Sim_Winner_Frame['dupes_calc'], 0) <= 0,
+                    0, 
+                    np.round(Sim_Winner_Frame['dupes_calc'], 0) - 1
+                )
+                Sim_Winner_Frame['Dupes'] = (Sim_Winner_Frame['Dupes'] * (500000 / sharp_split)) / 2
+            elif sim_site_var1 == 'Draftkings':
+                dup_count_columns = ['CPT_Own_percent_rank', 'FLEX1_Own_percent_rank', 'FLEX2_Own_percent_rank', 'FLEX3_Own_percent_rank', 'FLEX4_Own_percent_rank', 'FLEX5_Own_percent_rank']
+                own_columns = ['CPT_Own', 'FLEX1_Own', 'FLEX2_Own', 'FLEX3_Own', 'FLEX4_Own', 'FLEX5_Own']
+                calc_columns = ['own_product', 'avg_own_rank', 'dupes_calc']
+                Sim_Winner_Frame['CPT_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,0].map(maps_dict['cpt_Own_map']).rank(pct=True)
+                Sim_Winner_Frame['FLEX1_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,1].map(maps_dict['Own_map']).rank(pct=True)
+                Sim_Winner_Frame['FLEX2_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,2].map(maps_dict['Own_map']).rank(pct=True)
+                Sim_Winner_Frame['FLEX3_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,3].map(maps_dict['Own_map']).rank(pct=True)
+                Sim_Winner_Frame['FLEX4_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,4].map(maps_dict['Own_map']).rank(pct=True)
+                Sim_Winner_Frame['FLEX5_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,5].map(maps_dict['Own_map']).rank(pct=True)
+                Sim_Winner_Frame['CPT_Own'] = Sim_Winner_Frame.iloc[:,0].map(maps_dict['cpt_Own_map']) / 100
+                Sim_Winner_Frame['FLEX1_Own'] = Sim_Winner_Frame.iloc[:,1].map(maps_dict['Own_map']) / 100
+                Sim_Winner_Frame['FLEX2_Own'] = Sim_Winner_Frame.iloc[:,2].map(maps_dict['Own_map']) / 100
+                Sim_Winner_Frame['FLEX3_Own'] = Sim_Winner_Frame.iloc[:,3].map(maps_dict['Own_map']) / 100
+                Sim_Winner_Frame['FLEX4_Own'] = Sim_Winner_Frame.iloc[:,4].map(maps_dict['Own_map']) / 100
+                Sim_Winner_Frame['FLEX5_Own'] = Sim_Winner_Frame.iloc[:,5].map(maps_dict['Own_map']) / 100
+
+                # Calculate ownership product and convert to probability
+                Sim_Winner_Frame['own_product'] = (Sim_Winner_Frame[own_columns].product(axis=1))
+                
+                # Calculate average of ownership percent rank columns
+                Sim_Winner_Frame['avg_own_rank'] = Sim_Winner_Frame[dup_count_columns].mean(axis=1)
+                
+                # Calculate dupes formula
+                Sim_Winner_Frame['dupes_calc'] = ((Sim_Winner_Frame['own_product'] * Sim_Winner_Frame['avg_own_rank']) * (Contest_Size * 1.5)) + ((Sim_Winner_Frame['salary'] - 49800) / 100)
+                
+                # Round and handle negative values
+                Sim_Winner_Frame['Dupes'] = np.where(
+                    np.round(Sim_Winner_Frame['dupes_calc'], 0) <= 0,
+                    0, 
+                    np.round(Sim_Winner_Frame['dupes_calc'], 0) - 1
+                )
+                Sim_Winner_Frame['Dupes'] = (Sim_Winner_Frame['Dupes'] * (500000 / sharp_split)) / 2
+            Sim_Winner_Frame['Dupes'] = np.round(Sim_Winner_Frame['Dupes'], 0)
+            Sim_Winner_Frame['Dupes'] = np.where(
+                    np.round(Sim_Winner_Frame['dupes_calc'], 0) <= 0,
+                    0, 
+                    np.round(Sim_Winner_Frame['dupes_calc'], 0)
+                )
+            Sim_Winner_Frame = Sim_Winner_Frame.drop(columns=dup_count_columns)
+            Sim_Winner_Frame = Sim_Winner_Frame.drop(columns=own_columns)
+            Sim_Winner_Frame = Sim_Winner_Frame.drop(columns=calc_columns)
+
+            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, 'Dupes': int}
+            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()
+            st.session_state.Sim_Winner_Export.iloc[:, 0:6] = st.session_state.Sim_Winner_Export.iloc[:, 0:6].apply(lambda x: x.map(export_id_dict))
+            
+            # Data Copying
+            st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy()
+            freq_copy = st.session_state.Sim_Winner_Display
+        
+        if sim_site_var1 == 'Draftkings':
+            freq_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:6].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(freq_copy.iloc[:,0:5].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(maps_dict['Pos_map'])
+        if sim_site_var1 == 'Draftkings':
+            if sim_sport_var1 == 'NFL':
+                freq_working['Salary'] = freq_working['Player'].map(maps_dict['Salary_map']) / 1.5
+            elif sim_sport_var1 == 'NBA':
+                freq_working['Salary'] = freq_working['Player'].map(maps_dict['Salary_map'])
+        elif sim_site_var1 == 'Fanduel':
+            freq_working['Salary'] = freq_working['Player'].map(maps_dict['Salary_map'])
+        freq_working['Proj Own'] = freq_working['Player'].map(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(maps_dict['Team_map'])
+        st.session_state.player_freq = freq_working.copy()
+
+        if sim_site_var1 == 'Draftkings':
+            cpt_working = pd.DataFrame(np.column_stack(np.unique(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':
+            cpt_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:1].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        cpt_working['Freq'] = cpt_working['Freq'].astype(int)
+        cpt_working['Position'] = cpt_working['Player'].map(maps_dict['Pos_map'])
+        if sim_sport_var1 == 'NFL':
+            cpt_working['Salary'] = cpt_working['Player'].map(maps_dict['Salary_map'])
+        elif sim_sport_var1 == 'NBA':
+            cpt_working['Salary'] = cpt_working['Player'].map(maps_dict['Salary_map']) * 1.5
+        cpt_working['Proj Own'] = cpt_working['Player'].map(maps_dict['cpt_Own_map']) / 100
+        cpt_working['Exposure'] = cpt_working['Freq']/(1000)
+        cpt_working['Edge'] = cpt_working['Exposure'] - cpt_working['Proj Own']
+        cpt_working['Team'] = cpt_working['Player'].map(maps_dict['Team_map'])
+        st.session_state.sp_freq = cpt_working.copy()
+        
+        if sim_site_var1 == 'Draftkings':
+            flex_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,1:6].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            cpt_own_div = 600
+        elif sim_site_var1 == 'Fanduel':
+            flex_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,1:5].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            cpt_own_div = 500
+        flex_working['Freq'] = flex_working['Freq'].astype(int)
+        flex_working['Position'] = flex_working['Player'].map(maps_dict['Pos_map'])
+        if sim_site_var1 == 'Draftkings':
+            if sim_sport_var1 == 'NFL':
+                flex_working['Salary'] = flex_working['Player'].map(maps_dict['Salary_map']) / 1.5
+            elif sim_sport_var1 == 'NBA':
+                flex_working['Salary'] = flex_working['Player'].map(maps_dict['Salary_map'])
+        elif sim_site_var1 == 'Fanduel':
+            flex_working['Salary'] = flex_working['Player'].map(maps_dict['Salary_map'])
+        flex_working['Proj Own'] = (flex_working['Player'].map(maps_dict['Own_map']) / 100) - (flex_working['Player'].map(maps_dict['cpt_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(maps_dict['Team_map'])
+        st.session_state.flex_freq = flex_working.copy()
+
+        if sim_site_var1 == 'Draftkings':
+            team_working = pd.DataFrame(np.column_stack(np.unique(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':
+            team_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,7:8].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='NFL_SD_consim_export.csv',
+                    mime='text/csv',
+                )  
+                
+        with st.container():
+            tab1, tab2, tab3, tab4 = st.tabs(['Overall Exposures', 'CPT Exposures', 'FLEX 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 'sp_freq' in st.session_state:
+                    
+                    st.dataframe(st.session_state.sp_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.sp_freq.to_csv().encode('utf-8'),
+                        file_name='cpt_freq.csv',
+                        mime='text/csv',
+                        key='sp'
+                    )
+            with tab3:
+                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 tab4:
+                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'
+                    )
+
 with tab2:
     with st.expander("Info and Filters"):
         if st.button("Load/Reset Data", key='reset1'):
@@ -440,389 +823,4 @@ with tab2:
             
     with st.container():
         if 'data_export_display' in st.session_state:
-            st.dataframe(st.session_state.data_export_display.style.format(freq_format, precision=2), use_container_width = True)
-            
-with tab1:
-    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_raw, fd_raw = init_baselines('NFL')
-        sim_sport_var1 = st.radio("What sport are you working with?", ('NBA', 'NFL'), key='sim_sport_var1')
-        dk_raw, fd_raw = init_baselines(sim_sport_var1)
-        sim_slate_var1 = st.radio("Which data are you loading?", ('Showdown', 'Secondary Showdown', 'Auxiliary Showdown'), key='sim_slate_var1')
-        sim_site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='sim_site_var1')
-        if sim_site_var1 == 'Draftkings':
-            raw_baselines = dk_raw
-            column_names = dk_columns
-        elif sim_site_var1 == 'Fanduel':
-            raw_baselines = fd_raw
-            column_names = fd_columns
-        contest_var1 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large', 'Custom'))
-        if contest_var1 == 'Small':
-            Contest_Size = 1000
-            st.write("Small field size is 1,000 entrants.")
-            raw_baselines['Own'] = raw_baselines['Small_Field_Own']
-            raw_baselines['CPT_Own'] = raw_baselines['small_CPT_Own']
-        elif contest_var1 == 'Medium':
-            Contest_Size = 5000
-            st.write("Medium field size is 5,000 entrants.")
-        elif contest_var1 == 'Large':
-            Contest_Size = 10000
-            st.write("Large field size is 10,000 entrants.")
-        elif contest_var1 == 'Custom':
-            Contest_Size = st.number_input("Insert contest size", value=100, min_value=1, max_value=100000)
-        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 = 400000
-        elif strength_var1 == 'Average':
-            sharp_split = 300000
-        elif strength_var1 == 'Above Average':
-            sharp_split = 200000
-        elif strength_var1 == 'Very':
-            sharp_split = 100000
-
-    
-    with col2:
-        if st.button("Run Contest Sim"):
-            if 'working_seed' in st.session_state:
-                maps_dict = {
-                        'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)),
-                        'cpt_projection_map':dict(zip(raw_baselines.Player,raw_baselines.cpt_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'])),
-                        'cpt_Own_map':dict(zip(raw_baselines.Player,raw_baselines['CPT_Own'])),
-                        'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)),
-                        'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev)),
-                        'cpt_STDev_map':dict(zip(raw_baselines.Player,raw_baselines['CPT_STDev']))
-                        }
-                Sim_Winners = sim_contest(1000, st.session_state.working_seed, 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()
-                
-                # Data Copying
-                st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy()
-                
-            else:
-                if sim_site_var1 == 'Draftkings':
-                    if sim_slate_var1 == 'Showdown':
-                        st.session_state.working_seed = init_DK_seed_frames(sim_sport_var1, sharp_split_var)
-                        if sport_var1 == 'NFL':
-                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
-                        elif sport_var1 == 'NBA':
-                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
-                    elif sim_slate_var1 == 'Secondary Showdown':
-                        st.session_state.working_seed = init_DK_secondary_seed_frames(sim_sport_var1, sharp_split_var)
-                        if sport_var1 == 'NFL':
-                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
-                        elif sport_var1 == 'NBA':
-                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
-                    elif sim_slate_var1 == 'Auxiliary Showdown':
-                        st.session_state.working_seed = init_DK_auxiliary_seed_frames(sim_sport_var1, sharp_split_var)
-                        if sport_var1 == 'NFL':
-                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
-                        elif sport_var1 == 'NBA':
-                            export_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 == 'Showdown':
-                        st.session_state.working_seed = init_FD_seed_frames(sim_sport_var1, sharp_split_var)
-                        if sport_var1 == 'NFL':
-                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
-                        elif sport_var1 == 'NBA':
-                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
-                    elif sim_slate_var1 == 'Secondary Showdown':
-                        st.session_state.working_seed = init_FD_secondary_seed_frames(sim_sport_var1, sharp_split_var)
-                        if sport_var1 == 'NFL':
-                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
-                        elif sport_var1 == 'NBA':
-                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
-                    elif sim_slate_var1 == 'Auxiliary Showdown':
-                        st.session_state.working_seed = init_FD_auxiliary_seed_frames(sim_sport_var1, sharp_split_var)
-                        if sport_var1 == 'NFL':
-                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
-                        elif sport_var1 == 'NBA':
-                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
-                    raw_baselines = fd_raw
-                    column_names = fd_columns
-                maps_dict = {
-                        'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)),
-                        'cpt_projection_map':dict(zip(raw_baselines.Player,raw_baselines.cpt_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'])),
-                        'cpt_Own_map':dict(zip(raw_baselines.Player,raw_baselines['CPT_Own'])),
-                        'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)),
-                        'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev)),
-                        'cpt_STDev_map':dict(zip(raw_baselines.Player,raw_baselines['CPT_STDev']))
-                        }
-                Sim_Winners = sim_contest(1000, st.session_state.working_seed, 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)
-                # Add percent rank columns for ownership at each roster position
-                # Calculate Dupes column for Fanduel
-                if sim_site_var1 == 'Fanduel':
-                    dup_count_columns = ['CPT_Own_percent_rank', 'FLEX1_Own_percent_rank', 'FLEX2_Own_percent_rank', 'FLEX3_Own_percent_rank', 'FLEX4_Own_percent_rank']
-                    own_columns = ['CPT_Own', 'FLEX1_Own', 'FLEX2_Own', 'FLEX3_Own', 'FLEX4_Own']
-                    calc_columns = ['own_product', 'avg_own_rank', 'dupes_calc']
-                    Sim_Winner_Frame['CPT_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,0].map(maps_dict['cpt_Own_map']).rank(pct=True)
-                    Sim_Winner_Frame['FLEX1_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,1].map(maps_dict['Own_map']).rank(pct=True)
-                    Sim_Winner_Frame['FLEX2_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,2].map(maps_dict['Own_map']).rank(pct=True)
-                    Sim_Winner_Frame['FLEX3_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,3].map(maps_dict['Own_map']).rank(pct=True)
-                    Sim_Winner_Frame['FLEX4_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,4].map(maps_dict['Own_map']).rank(pct=True)
-                    Sim_Winner_Frame['CPT_Own'] = Sim_Winner_Frame.iloc[:,0].map(maps_dict['cpt_Own_map']) / 100
-                    Sim_Winner_Frame['FLEX1_Own'] = Sim_Winner_Frame.iloc[:,1].map(maps_dict['Own_map']) / 100
-                    Sim_Winner_Frame['FLEX2_Own'] = Sim_Winner_Frame.iloc[:,2].map(maps_dict['Own_map']) / 100
-                    Sim_Winner_Frame['FLEX3_Own'] = Sim_Winner_Frame.iloc[:,3].map(maps_dict['Own_map']) / 100
-                    Sim_Winner_Frame['FLEX4_Own'] = Sim_Winner_Frame.iloc[:,4].map(maps_dict['Own_map']) / 100
-                    
-                    # Calculate ownership product and convert to probability
-                    Sim_Winner_Frame['own_product'] = (Sim_Winner_Frame[own_columns].product(axis=1)) + 0.0001
-                    
-                    # Calculate average of ownership percent rank columns
-                    Sim_Winner_Frame['avg_own_rank'] = Sim_Winner_Frame[dup_count_columns].mean(axis=1)
-                    
-                    # Calculate dupes formula
-                    Sim_Winner_Frame['dupes_calc'] = ((Sim_Winner_Frame['own_product'] * Sim_Winner_Frame['avg_own_rank']) * (Contest_Size * 1.5)) + ((Sim_Winner_Frame['salary'] - 59800) / 100)
-                    
-                    # Round and handle negative values
-                    Sim_Winner_Frame['Dupes'] = np.where(
-                        np.round(Sim_Winner_Frame['dupes_calc'], 0) <= 0,
-                        0, 
-                        np.round(Sim_Winner_Frame['dupes_calc'], 0) - 1
-                    )
-                    Sim_Winner_Frame['Dupes'] = (Sim_Winner_Frame['Dupes'] * (500000 / sharp_split)) / 2
-                elif sim_site_var1 == 'Draftkings':
-                    dup_count_columns = ['CPT_Own_percent_rank', 'FLEX1_Own_percent_rank', 'FLEX2_Own_percent_rank', 'FLEX3_Own_percent_rank', 'FLEX4_Own_percent_rank', 'FLEX5_Own_percent_rank']
-                    own_columns = ['CPT_Own', 'FLEX1_Own', 'FLEX2_Own', 'FLEX3_Own', 'FLEX4_Own', 'FLEX5_Own']
-                    calc_columns = ['own_product', 'avg_own_rank', 'dupes_calc']
-                    Sim_Winner_Frame['CPT_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,0].map(maps_dict['cpt_Own_map']).rank(pct=True)
-                    Sim_Winner_Frame['FLEX1_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,1].map(maps_dict['Own_map']).rank(pct=True)
-                    Sim_Winner_Frame['FLEX2_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,2].map(maps_dict['Own_map']).rank(pct=True)
-                    Sim_Winner_Frame['FLEX3_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,3].map(maps_dict['Own_map']).rank(pct=True)
-                    Sim_Winner_Frame['FLEX4_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,4].map(maps_dict['Own_map']).rank(pct=True)
-                    Sim_Winner_Frame['FLEX5_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,5].map(maps_dict['Own_map']).rank(pct=True)
-                    Sim_Winner_Frame['CPT_Own'] = Sim_Winner_Frame.iloc[:,0].map(maps_dict['cpt_Own_map']) / 100
-                    Sim_Winner_Frame['FLEX1_Own'] = Sim_Winner_Frame.iloc[:,1].map(maps_dict['Own_map']) / 100
-                    Sim_Winner_Frame['FLEX2_Own'] = Sim_Winner_Frame.iloc[:,2].map(maps_dict['Own_map']) / 100
-                    Sim_Winner_Frame['FLEX3_Own'] = Sim_Winner_Frame.iloc[:,3].map(maps_dict['Own_map']) / 100
-                    Sim_Winner_Frame['FLEX4_Own'] = Sim_Winner_Frame.iloc[:,4].map(maps_dict['Own_map']) / 100
-                    Sim_Winner_Frame['FLEX5_Own'] = Sim_Winner_Frame.iloc[:,5].map(maps_dict['Own_map']) / 100
-
-                    # Calculate ownership product and convert to probability
-                    Sim_Winner_Frame['own_product'] = (Sim_Winner_Frame[own_columns].product(axis=1))
-                    
-                    # Calculate average of ownership percent rank columns
-                    Sim_Winner_Frame['avg_own_rank'] = Sim_Winner_Frame[dup_count_columns].mean(axis=1)
-                    
-                    # Calculate dupes formula
-                    Sim_Winner_Frame['dupes_calc'] = ((Sim_Winner_Frame['own_product'] * Sim_Winner_Frame['avg_own_rank']) * (Contest_Size * 1.5)) + ((Sim_Winner_Frame['salary'] - 49800) / 100)
-                    
-                    # Round and handle negative values
-                    Sim_Winner_Frame['Dupes'] = np.where(
-                        np.round(Sim_Winner_Frame['dupes_calc'], 0) <= 0,
-                        0, 
-                        np.round(Sim_Winner_Frame['dupes_calc'], 0) - 1
-                    )
-                    Sim_Winner_Frame['Dupes'] = (Sim_Winner_Frame['Dupes'] * (500000 / sharp_split)) / 2
-                Sim_Winner_Frame['Dupes'] = np.round(Sim_Winner_Frame['Dupes'], 0)
-                Sim_Winner_Frame['Dupes'] = np.where(
-                        np.round(Sim_Winner_Frame['dupes_calc'], 0) <= 0,
-                        0, 
-                        np.round(Sim_Winner_Frame['dupes_calc'], 0)
-                    )
-                Sim_Winner_Frame = Sim_Winner_Frame.drop(columns=dup_count_columns)
-                Sim_Winner_Frame = Sim_Winner_Frame.drop(columns=own_columns)
-                Sim_Winner_Frame = Sim_Winner_Frame.drop(columns=calc_columns)
-
-                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, 'Dupes': int}
-                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()
-                st.session_state.Sim_Winner_Export.iloc[:, 0:6] = st.session_state.Sim_Winner_Export.iloc[:, 0:6].apply(lambda x: x.map(export_id_dict))
-                
-                # Data Copying
-                st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy()
-                freq_copy = st.session_state.Sim_Winner_Display
-            
-            if sim_site_var1 == 'Draftkings':
-                freq_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:6].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(freq_copy.iloc[:,0:5].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(maps_dict['Pos_map'])
-            if sim_site_var1 == 'Draftkings':
-                if sim_sport_var1 == 'NFL':
-                    freq_working['Salary'] = freq_working['Player'].map(maps_dict['Salary_map']) / 1.5
-                elif sim_sport_var1 == 'NBA':
-                    freq_working['Salary'] = freq_working['Player'].map(maps_dict['Salary_map'])
-            elif sim_site_var1 == 'Fanduel':
-                freq_working['Salary'] = freq_working['Player'].map(maps_dict['Salary_map'])
-            freq_working['Proj Own'] = freq_working['Player'].map(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(maps_dict['Team_map'])
-            st.session_state.player_freq = freq_working.copy()
-
-            if sim_site_var1 == 'Draftkings':
-                cpt_working = pd.DataFrame(np.column_stack(np.unique(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':
-                cpt_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:1].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            cpt_working['Freq'] = cpt_working['Freq'].astype(int)
-            cpt_working['Position'] = cpt_working['Player'].map(maps_dict['Pos_map'])
-            if sim_sport_var1 == 'NFL':
-                cpt_working['Salary'] = cpt_working['Player'].map(maps_dict['Salary_map'])
-            elif sim_sport_var1 == 'NBA':
-                cpt_working['Salary'] = cpt_working['Player'].map(maps_dict['Salary_map']) * 1.5
-            cpt_working['Proj Own'] = cpt_working['Player'].map(maps_dict['cpt_Own_map']) / 100
-            cpt_working['Exposure'] = cpt_working['Freq']/(1000)
-            cpt_working['Edge'] = cpt_working['Exposure'] - cpt_working['Proj Own']
-            cpt_working['Team'] = cpt_working['Player'].map(maps_dict['Team_map'])
-            st.session_state.sp_freq = cpt_working.copy()
-            
-            if sim_site_var1 == 'Draftkings':
-                flex_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,1:6].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-                cpt_own_div = 600
-            elif sim_site_var1 == 'Fanduel':
-                flex_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,1:5].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-                cpt_own_div = 500
-            flex_working['Freq'] = flex_working['Freq'].astype(int)
-            flex_working['Position'] = flex_working['Player'].map(maps_dict['Pos_map'])
-            if sim_site_var1 == 'Draftkings':
-                if sim_sport_var1 == 'NFL':
-                    flex_working['Salary'] = flex_working['Player'].map(maps_dict['Salary_map']) / 1.5
-                elif sim_sport_var1 == 'NBA':
-                    flex_working['Salary'] = flex_working['Player'].map(maps_dict['Salary_map'])
-            elif sim_site_var1 == 'Fanduel':
-                flex_working['Salary'] = flex_working['Player'].map(maps_dict['Salary_map'])
-            flex_working['Proj Own'] = (flex_working['Player'].map(maps_dict['Own_map']) / 100) - (flex_working['Player'].map(maps_dict['cpt_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(maps_dict['Team_map'])
-            st.session_state.flex_freq = flex_working.copy()
-
-            if sim_site_var1 == 'Draftkings':
-                team_working = pd.DataFrame(np.column_stack(np.unique(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':
-                team_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,7:8].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='NFL_SD_consim_export.csv',
-                    mime='text/csv',
-                )  
-                
-        with st.container():
-            tab1, tab2, tab3, tab4 = st.tabs(['Overall Exposures', 'CPT Exposures', 'FLEX 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 'sp_freq' in st.session_state:
-                    
-                    st.dataframe(st.session_state.sp_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.sp_freq.to_csv().encode('utf-8'),
-                        file_name='cpt_freq.csv',
-                        mime='text/csv',
-                        key='sp'
-                    )
-            with tab3:
-                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 tab4:
-                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.data_export_display.style.format(freq_format, precision=2), use_container_width = True)