Update app.py

app.py

@@ -701,7 +701,7 @@ with tab2:
                         # # 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_c if row['Position'] == 'C' else factor_other), axis=1)
                         # initial_proj['Own%'] = initial_proj['Own%'].clip(upper=85)
-                        initial_proj['Own'] = initial_proj['Own%'] * (800 / initial_proj['Own%'].sum())
+                        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']]
@@ -710,23 +710,23 @@ with tab2:
                         # Copy only the necessary columns
                         initial_proj = raw_baselines[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']]
                         
-                        # Define the calculation to be applied
-                        def calculate_own(position, own, mean_own, factor, max_own=85):
-                           return np.where((position == 'C') & (own - mean_own >= 0),
-                                           own * (factor * (own - mean_own) / 100) + mean_own,
-                                           own)
+                        # # Define the calculation to be applied
+                        # def calculate_own(position, own, mean_own, factor, max_own=85):
+                        #    return np.where((position == 'C') & (own - mean_own >= 0),
+                        #                    own * (factor * (own - mean_own) / 100) + mean_own,
+                        #                    own)
                         
-                        # Set the factors based on the contest_var1
-                        factor_c, factor_other = {
-                            'Small': (10, 5),
-                            'Medium': (6, 3),
-                            'Large': (3, 1.5),
-                        }[contest_var1]
+                        # # Set the factors based on the contest_var1
+                        # factor_c, 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_c if row['Position'] == 'C' else factor_other), axis=1)
-                        initial_proj['Own%'] = initial_proj['Own%'].clip(upper=85)
-                        initial_proj['Own'] = initial_proj['Own%'] * (800 / initial_proj['Own%'].sum())
+                        # # 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_c if row['Position'] == 'C' else factor_other), axis=1)
+                        # initial_proj['Own%'] = initial_proj['Own%'].clip(upper=85)
+                        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']]