Update app.py

app.py

@@ -768,49 +768,53 @@ with tab2:
             Strength_var = .01
             scaling_var = 15
             
+        Sort_function = 'Median'
+        Sim_function = 'Projection'
+        
+        if Contest_Size <= 1000:
+            strength_grow = .01
+        elif Contest_Size > 1000 and Contest_Size <= 2500:
+            strength_grow = .025
+        elif Contest_Size > 2500 and Contest_Size <= 5000:
+            strength_grow = .05
+        elif Contest_Size > 5000 and Contest_Size <= 20000:
+            strength_grow = .075
+        elif Contest_Size > 20000:
+            strength_grow = .1
+            
+        field_growth = 100 * strength_grow
+            
     with col2:
         if st.button("Simulate Contest"):
             with st.container():
                 st.write('Contest Simulation Starting')
-                seed_depth1 = 10
-                Total_Runs = 1000000
-                if Contest_Size <= 1000:
-                    strength_grow = .01
-                elif Contest_Size > 1000 and Contest_Size <= 2500:
-                    strength_grow = .025
-                elif Contest_Size > 2500 and Contest_Size <= 5000:
-                    strength_grow = .05
-                elif Contest_Size > 5000 and Contest_Size <= 20000:
-                    strength_grow = .075
-                elif Contest_Size > 20000:
-                    strength_grow = .1
-                    
-                field_growth = 100 * strength_grow
         
-                Sort_function = 'Median'
-                if Sort_function == 'Median':
-                    Sim_function = 'Projection'
-                elif Sort_function == 'Own':
-                    Sim_function = 'Own'
-                
+                for key in st.session_state.keys():
+                        del st.session_state[key]
+                    
                 if slate_var1 == 'User':
-                    OwnFrame = proj_dataframe
-                    if contest_var1 == 'Small':
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (10 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own'])
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (5 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%'])
-                        OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%'])
-                        OwnFrame['Own'] = OwnFrame['Own%'] * (800 / OwnFrame['Own%'].sum())
-                    if contest_var1 == 'Medium':
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (6 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own'])
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (3 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%'])
-                        OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%'])
-                        OwnFrame['Own'] = OwnFrame['Own%'] * (800 / OwnFrame['Own%'].sum())
-                    if contest_var1 == 'Large':
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (3 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own'])
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (1.5 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%'])
-                        OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%'])
-                        OwnFrame['Own'] = OwnFrame['Own%'] * (800 / OwnFrame['Own%'].sum())
-                    Overall_Proj = OwnFrame[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']]
+                    initial_proj = proj_dataframe[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']]
+                    
+                    # Define the calculation to be applied
+                    def calculate_own(position, own, mean_own, factor, max_own=75):
+                        return np.where((position == 'QB') & (own - mean_own >= 0),
+                                        own * (factor * (own - mean_own) / 100) + mean_own,
+                                        own)
+                    
+                    # Set the factors based on the contest_var1
+                    factor_qb, factor_other = {
+                        'Small': (10, 5),
+                        'Medium': (6, 3),
+                        'Large': (3, 1.5),
+                    }[contest_var1]
+                    
+                    # Apply the calculation to the DataFrame
+                    initial_proj['Own%'] = initial_proj.apply(lambda row: calculate_own(row['Position'], row['Own'], initial_proj.loc[initial_proj['Position'] == row['Position'], 'Own'].mean(), factor_qb if row['Position'] == 'QB' else factor_other), axis=1)
+                    initial_proj['Own%'] = initial_proj['Own%'].clip(upper=75)
+                    initial_proj['Own'] = initial_proj['Own%'] * (900 / initial_proj['Own%'].sum())
+                    
+                    # Drop unnecessary columns and create the final DataFrame
+                    Overall_Proj = initial_proj[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']]
                 
                 if insert_port == 1:
                     UserPortfolio = portfolio_dataframe[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'FLEX', 'S_FLEX']]
@@ -1078,66 +1082,66 @@ with tab2:
                 for checkVar in range(len(team_list)):
                                     st.session_state.dst_freq['Team'] = st.session_state.dst_freq['Team'].replace(item_list, team_list)
                 
-            with st.container():
-                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()
+        with st.container():
+            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').background_gradient(cmap='RdYlGn_r', subset=['Own']).format(precision=2), use_container_width = True)
+            if 'Sim_Winner_Export' in st.session_state:
+                st.download_button(
+                    label="Export Tables",
+                    data=convert_df_to_csv(st.session_state.Sim_Winner_Export),
+                    file_name='NFL_consim_export.csv',
+                    mime='text/csv',
+                )
         
-                    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').background_gradient(cmap='RdYlGn_r', subset=['Own']).format(precision=2), use_container_width = True)
-                if 'Sim_Winner_Export' in st.session_state:
+        with st.container():
+            tab1, tab2, tab3, tab4 = st.tabs(['Overall Exposures', 'QB Exposures', 'RB Exposures', 'WR 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 Tables",
-                        data=convert_df_to_csv(st.session_state.Sim_Winner_Export),
-                        file_name='NFL_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',
+                    )
+            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_export.csv',
+                        mime='text/csv',
+                    )
+            with tab3:
+                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_export.csv',
+                        mime='text/csv',
+                    )
+            with tab4:
+                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_export.csv',
                         mime='text/csv',
                     )
-            
-            with st.container():
-                tab1, tab2, tab3, tab4 = st.tabs(['Overall Exposures', 'QB Exposures', 'RB Exposures', 'WR 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',
-                        )
-                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_export.csv',
-                            mime='text/csv',
-                        )
-                with tab3:
-                    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_export.csv',
-                            mime='text/csv',
-                        )
-                with tab4:
-                    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_export.csv',
-                            mime='text/csv',
-                        )
 
 
 gc.collect()