Refactor dupes calculation in predict_dupes function to incorporate ownership ratio adjustment based on Contest_Size, enhancing the accuracy of duplicate predictions. Additionally, modify the calculation to use a salary ratio of 50 for improved precision.

app.py

@@ -271,20 +271,36 @@ with tab1:
 
             if sport_var in stacking_sports:
                 team_dict = dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['team']))
-                st.session_state['portfolio']['Stack'] = st.session_state['portfolio'].apply(
-                    lambda row: Counter(
-                        team_dict.get(player, '') for player in row[2:]
-                        if team_dict.get(player, '') != ''
-                    ).most_common(1)[0][0] if any(team_dict.get(player, '') for player in row[2:]) else '',
-                    axis=1
-                )
-                st.session_state['portfolio']['Size'] = st.session_state['portfolio'].apply(
-                    lambda row: Counter(
-                        team_dict.get(player, '') for player in row[2:]
-                        if team_dict.get(player, '') != ''
-                    ).most_common(1)[0][1] if any(team_dict.get(player, '') for player in row[2:]) else 0,
-                    axis=1
-                )
+                if sport_var == 'LOL':
+                    st.session_state['portfolio']['Stack'] = st.session_state['portfolio'].apply(
+                        lambda row: Counter(
+                            team_dict.get(player, '') for player in row
+                            if team_dict.get(player, '') != ''
+                        ).most_common(1)[0][0] if any(team_dict.get(player, '') for player in row) else '',
+                        axis=1
+                    )
+                    st.session_state['portfolio']['Size'] = st.session_state['portfolio'].apply(
+                        lambda row: Counter(
+                            team_dict.get(player, '') for player in row
+                            if team_dict.get(player, '') != ''
+                        ).most_common(1)[0][1] if any(team_dict.get(player, '') for player in row) else 0,
+                        axis=1
+                    )
+                else:
+                    st.session_state['portfolio']['Stack'] = st.session_state['portfolio'].apply(
+                        lambda row: Counter(
+                            team_dict.get(player, '') for player in row[2:]
+                            if team_dict.get(player, '') != ''
+                        ).most_common(1)[0][0] if any(team_dict.get(player, '') for player in row[2:]) else '',
+                        axis=1
+                    )
+                    st.session_state['portfolio']['Size'] = st.session_state['portfolio'].apply(
+                        lambda row: Counter(
+                            team_dict.get(player, '') for player in row[2:]
+                            if team_dict.get(player, '') != ''
+                        ).most_common(1)[0][1] if any(team_dict.get(player, '') for player in row[2:]) else 0,
+                        axis=1
+                    )
                 st.session_state['stack_dict'] = dict(zip(st.session_state['portfolio'].index, st.session_state['portfolio']['Stack']))
                 st.session_state['size_dict'] = dict(zip(st.session_state['portfolio'].index, st.session_state['portfolio']['Size']))
 

global_func/predict_dupes.py

@@ -329,7 +329,7 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
             
             # Calculate dupes formula
             portfolio['dupes_calc'] = (portfolio['own_product'] * portfolio['avg_own_rank']) * (portfolio['Own'] / 100) * Contest_Size + ((portfolio['salary'] - (60000 - portfolio['Own'])) / 100) - ((60000 - portfolio['salary']) / 100)
-            portfolio['dupes_calc'] = portfolio['dupes_calc'] * dupes_multiplier
+            portfolio['dupes_calc'] = portfolio['dupes_calc'] * dupes_multiplier * (portfolio['Own'] / (100 + (Contest_Size / 1000)))
             
             # Round and handle negative values
             portfolio['Dupes'] = np.where(
@@ -355,7 +355,7 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
             portfolio['avg_own_rank'] = portfolio[dup_count_columns].mean(axis=1)
             
             portfolio['dupes_calc'] = (portfolio['own_product'] * portfolio['avg_own_rank']) * Contest_Size + ((portfolio['salary'] - (60000 - portfolio['Own'])) / 100) - ((60000 - portfolio['salary']) / 100)
-            portfolio['dupes_calc'] = portfolio['dupes_calc'] * dupes_multiplier
+            portfolio['dupes_calc'] = portfolio['dupes_calc'] * dupes_multiplier * (portfolio['Own'] / (100 + (Contest_Size / 1000)))
             # Round and handle negative values
             portfolio['Dupes'] = np.where(
                 np.round(portfolio['dupes_calc'], 0) <= 0,
@@ -430,7 +430,7 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
             
             # Calculate dupes formula
             portfolio['dupes_calc'] = (portfolio['own_product'] * portfolio['avg_own_rank']) * Contest_Size + ((portfolio['salary'] - (50000 - portfolio['Own'])) / 100) - ((50000 - portfolio['salary']) / 100)
-            portfolio['dupes_calc'] = portfolio['dupes_calc'] * dupes_multiplier
+            portfolio['dupes_calc'] = portfolio['dupes_calc'] * dupes_multiplier * (portfolio['Own'] / (100 + (Contest_Size / 1000)))
 
             # Round and handle negative values
             portfolio['Dupes'] = np.where(
@@ -476,8 +476,8 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
                 portfolio['avg_own_rank'] = portfolio[dup_count_columns].mean(axis=1)
                 
                 # Calculate dupes formula
-                portfolio['dupes_calc'] = (portfolio['own_product'] * portfolio['avg_own_rank']) * Contest_Size + ((portfolio['salary'] - (50000 - portfolio['Own'])) / 100) - ((50000 - portfolio['salary']) / 100)
-                portfolio['dupes_calc'] = portfolio['dupes_calc'] * dupes_multiplier
+                portfolio['dupes_calc'] = ((portfolio['own_product'] * 10) * portfolio['avg_own_rank']) * Contest_Size + ((portfolio['salary'] - (50000 - portfolio['Own'])) / 50) - ((50000 - portfolio['salary']) / 50)
+                portfolio['dupes_calc'] = portfolio['dupes_calc'] * dupes_multiplier * (portfolio['Own'] / (100 + (Contest_Size / 1000)))
 
                 # Round and handle negative values
                 portfolio['Dupes'] = np.where(
@@ -523,8 +523,6 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
                 portfolio['own_average'] = (portfolio['Own'].max() * .33) / 100
                 portfolio['own_sum'] = portfolio[own_columns].sum(axis=1)
                 portfolio['avg_own_rank'] = portfolio[dup_count_columns].mean(axis=1)
-
-                print(portfolio[['Own', 'own_product', 'own_average', 'own_sum', 'avg_own_rank']].head(10))
                 
                 # Calculate dupes formula
                 portfolio['dupes_calc'] = ((portfolio['own_product'] * 10) * portfolio['avg_own_rank']) * Contest_Size + ((portfolio['salary'] - (50000 - portfolio['Own'])) / 50) - ((50000 - portfolio['salary']) / 50)
@@ -554,7 +552,7 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
                 portfolio['avg_own_rank'] = portfolio[dup_count_columns].mean(axis=1)
                 
                 portfolio['dupes_calc'] = (portfolio['own_product'] * portfolio['avg_own_rank']) * Contest_Size + ((portfolio['salary'] - (50000 - portfolio['Own'])) / 100) - ((50000 - portfolio['salary']) / 100)
-                portfolio['dupes_calc'] = portfolio['dupes_calc'] * dupes_multiplier
+                portfolio['dupes_calc'] = portfolio['dupes_calc'] * dupes_multiplier * (portfolio['Own'] / (100 + (Contest_Size / 1000)))
                 # Round and handle negative values
                 portfolio['Dupes'] = np.where(
                     np.round(portfolio['dupes_calc'], 0) <= 0,