Refactor ownership calculations in app.py to streamline dupes logic for FanDuel and DraftKings. Introduced new columns for 'own_product', 'avg_own_rank', and 'dupes_calc' to enhance clarity and accuracy in ownership metrics. This update improves the precision of player ownership analysis and contest lineup simulations.

app.py

@@ -568,35 +568,35 @@ with tab1:
                 # Calculate Dupes column for Fanduel
                 if sim_site_var1 == 'Fanduel':
                     # Calculate ownership product and convert to probability
-                    own_product = (Sim_Winner_Frame[own_columns].product(axis=1)) + 0.0001
+                    Sim_Winner_Frame['own_product'] = (Sim_Winner_Frame[own_columns].product(axis=1)) + 0.0001
                     
                     # Calculate average of ownership percent rank columns
-                    avg_own_rank = Sim_Winner_Frame[dup_count_columns].mean(axis=1)
+                    Sim_Winner_Frame['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)
+                    Sim_Winner_Frame['dupes_calc'] = ((Sim_Winner_Frame['own_product'] * Sim_Winner_Frame['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,
+                        np.round(Sim_Winner_Frame['dupes_calc'], 0) <= 0,
                         0, 
-                        np.round(dupes_calc, 0) - 1
+                        np.round(Sim_Winner_Frame['dupes_calc'], 0) - 1
                     )
                 elif sim_site_var1 == 'Draftkings':
                     # Calculate ownership product and convert to probability
-                    own_product = (Sim_Winner_Frame[own_columns].product(axis=1))
+                    Sim_Winner_Frame['own_product'] = (Sim_Winner_Frame[own_columns].product(axis=1))
                     
                     # Calculate average of ownership percent rank columns
-                    avg_own_rank = Sim_Winner_Frame[dup_count_columns].mean(axis=1)
+                    Sim_Winner_Frame['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)
+                    Sim_Winner_Frame['dupes_calc'] = ((Sim_Winner_Frame['own_product'] * Sim_Winner_Frame['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,
+                        np.round(Sim_Winner_Frame['dupes_calc'], 0) <= 0,
                         0, 
-                        np.round(dupes_calc, 0) - 1
+                        np.round(Sim_Winner_Frame['dupes_calc'], 0) - 1
                     )
                 #Sim_Winner_Frame = Sim_Winner_Frame.drop(columns=dup_count_columns)