Refactor ownership calculations in app.py to improve accuracy and clarity. Adjusted the calculation of ownership products and dupes for both FanDuel and DraftKings by removing unnecessary divisions and ensuring proper grouping in the formulas. This change enhances the precision of player ownership analysis and contest lineup simulations.

app.py

@@ -568,13 +568,13 @@ 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) / 100) + 0.0001
+                    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)
                     
                     # Calculate dupes formula
-                    dupes_calc = (own_product * avg_own_rank * Contest_Size) + ((Sim_Winner_Frame['salary'] - 59800) / 100)
+                    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(
@@ -584,13 +584,13 @@ with tab1:
                     )
                 elif sim_site_var1 == 'Draftkings':
                     # Calculate ownership product and convert to probability
-                    own_product = (Sim_Winner_Frame[own_columns].product(axis=1) / 100)
+                    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)
                     
                     # Calculate dupes formula
-                    dupes_calc = (own_product * avg_own_rank * Contest_Size) + ((Sim_Winner_Frame['salary'] - 49800) / 100)
+                    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(