Add ownership percent rank calculations and dupes logic for FanDuel and DraftKings in app.py

This commit introduces new columns for percent rank ownership at each roster position and implements a dupes calculation based on ownership metrics for both FanDuel and DraftKings. The changes enhance the simulation's accuracy by providing a more detailed analysis of player ownership and potential duplicates in contest lineups, improving overall data handling for contest simulations.

app.py

@@ -550,6 +550,49 @@ with tab1:
                 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
+                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']
+                Sim_Winner_Frame['CPT_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,0].map(maps_dict['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)
+                # Calculate Dupes column for Fanduel
+                if sim_site_var1 == 'Fanduel':
+                    # Calculate ownership product and convert to probability
+                    own_product = (Sim_Winner_Frame['Own'].product() / 100) + 0.0001
+                    
+                    # Calculate average of ownership percent rank columns
+                    avg_own_rank = Sim_Winner_Frame[dup_count_columns].mean(axis=1)
+                    
+                    # Calculate dupes formula
+                    dupes_calc = (own_product * avg_own_rank * Contest_Size) + ((Sim_Winner_Frame['salary'] - 59800) / 100)
+                    
+                    # Round and handle negative values
+                    Sim_Winner_Frame['Dupes'] = np.where(
+                        np.round(dupes_calc, 0) <= 0,
+                        0, 
+                        np.round(dupes_calc, 0) - 1
+                    )
+                elif sim_site_var1 == 'Draftkings':
+                    # Calculate ownership product and convert to probability
+                    own_product = (Sim_Winner_Frame['Own'].product() / 100)
+                    
+                    # Calculate average of ownership percent rank columns
+                    avg_own_rank = Sim_Winner_Frame[dup_count_columns].mean(axis=1)
+                    
+                    # Calculate dupes formula
+                    dupes_calc = (own_product * avg_own_rank * Contest_Size) + ((Sim_Winner_Frame['salary'] - 49800) / 100)
+                    
+                    # Round and handle negative values
+                    Sim_Winner_Frame['Dupes'] = np.where(
+                        np.round(dupes_calc, 0) <= 0,
+                        0, 
+                        np.round(dupes_calc, 0) - 1
+                    )
+                Sim_Winner_Frame = Sim_Winner_Frame.drop(columns=dup_count_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