Remove table display in 'app.py' and adjust dupes calculation in 'predict_dupes.py' to include a new scaling factor for CPT_Own_percent_rank, enhancing prediction accuracy.

app.py

@@ -1175,7 +1175,7 @@ if selected_tab == 'Manage Portfolio':
             st.session_state['working_frame'] = st.session_state['base_frame'].copy()
             # st.session_state['highest_owned_teams'] = st.session_state['projections_df'][~st.session_state['projections_df']['position'].isin(['P', 'SP'])].groupby('team')['ownership'].sum().sort_values(ascending=False).head(3).index.tolist()
             # st.session_state['highest_owned_pitchers'] = st.session_state['projections_df'][st.session_state['projections_df']['position'].isin(['P', 'SP'])]['player_names'].sort_values(by='ownership', ascending=False).head(3).tolist()
-            st.table(check_frame)
+            # st.table(check_frame)
 
             if 'trimming_dict_maxes' not in st.session_state:
                 st.session_state['trimming_dict_maxes'] = {

global_func/predict_dupes.py

@@ -194,6 +194,7 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
         portfolio['dupes_calc'] = ((portfolio['own_product'] + ((portfolio['CPT_Own_percent_rank'] - .50) / 1000) + ((portfolio['Own'] / 6) / (max_salary / 2))) * portfolio['avg_own_rank']) * Contest_Size + ((portfolio['salary'] - (max_salary - portfolio['Own'])) / 100) - ((max_salary - portfolio['salary']) / 100)
         portfolio['dupes_calc'] = portfolio['dupes_calc'] * dupes_multiplier * (portfolio['Own'] / (own_baseline + (Contest_Size / 1000)))
         portfolio['dupes_calc'] = ((((portfolio['salary'] / (max_salary * 0.96)) - 1)*(max_salary / 10000)) + 1) * portfolio['dupes_calc']
+        portfolio['dupes_calc'] = portfolio['dupes_calc'] * ((portfolio['CPT_Own_percent_rank'] + .50) / 2)
         
         # Round and handle negative values
         portfolio['Dupes'] = np.where(