Update app.py

app.py

@@ -215,6 +215,8 @@ def get_overall_merged_df():
     for i in range(0,4):
         ref_dict['pos_dfs'][i], ref_dict['pos_dicts'][i] =\
             create_overall_dfs(pos_players, ref_dict['pos_dfs'][i], ref_dict['pos_dicts'][i], ref_dict['pos'][i])
+        
+    df_out = pd.concat(ref_dict['pos_dfs'], ignore_index=True)
     
     return ref_dict
 
@@ -228,7 +230,9 @@ def calculate_range_var(count, min_val, FieldStrength, field_growth):
 def create_random_portfolio(Total_Sample_Size, raw_baselines, field_growth):
     
             full_pos_player_dict = get_overall_merged_df()
-            max_var = len(raw_baselines[raw_baselines['Position'] == 'QB'])
+            qb_baselines = raw_baselines[raw_baselines['Position'] == 'QB']
+            qb_baselines = qb_baselines.drop_duplicates(subset='Team')
+            max_var = len(qb_baselines[qb_baselines['Position'] == 'QB'])
           
             field_growth_rounded = round(field_growth)
             ranges_dict = {}
@@ -701,11 +705,11 @@ with tab2:
             insert_port = 0
         contest_var1 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large'))
         if contest_var1 == 'Small':
-            Contest_Size = 500
+            Contest_Size = 1000
         elif contest_var1 == 'Medium':
-            Contest_Size = 2500
-        elif contest_var1 == 'Large':
             Contest_Size = 5000
+        elif contest_var1 == 'Large':
+            Contest_Size = 10000
         strength_var1 = st.selectbox("How sharp is the field in the contest?", ('Not Very', 'Average', 'Very'))
         if strength_var1 == 'Not Very':
             sharp_split = .33
@@ -744,7 +748,7 @@ with tab2:
                         del st.session_state[key]
                     
                     if slate_var1 == 'User':
-                        initial_proj = proj_dataframe[['Player', 'Team', 'Position', 'Median', 'Own', 'Floor', 'Ceiling', 'Salary']]
+                        initial_proj = proj_dataframe[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']]
                         
                         # Define the calculation to be applied
                         def calculate_own(position, own, mean_own, factor, max_own=75):
@@ -769,7 +773,7 @@ with tab2:
                         
                     elif slate_var1 != 'User':
                         # Copy only the necessary columns
-                        initial_proj = raw_baselines[['Player', 'Team', 'Position', 'Median', 'Own', 'Floor', 'Ceiling', 'Salary']]
+                        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=75):
@@ -957,7 +961,7 @@ with tab2:
                     
                     FinalPortfolio, maps_dict = run_seed_frame(5, Strength_var, strength_grow, Teams_used, 1000000, field_growth)
                     
-                    Sim_Winners = sim_contest(5000, FinalPortfolio, CleanPortfolio, maps_dict, up_dict, insert_port)
+                    Sim_Winners = sim_contest(2500, FinalPortfolio, CleanPortfolio, maps_dict, up_dict, insert_port)
                     
                     # Initial setup
                     Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=FinalPortfolio.columns.tolist() + ['Fantasy'])
@@ -1000,7 +1004,7 @@ with tab2:
                     st.session_state.player_freq['Position'] = st.session_state.player_freq['Player'].map(maps_dict['Pos_map'])
                     st.session_state.player_freq['Salary'] = st.session_state.player_freq['Player'].map(maps_dict['Salary_map'])
                     st.session_state.player_freq['Proj Own'] = st.session_state.player_freq['Player'].map(maps_dict['Own_map']) / 100
-                    st.session_state.player_freq['Exposure'] = st.session_state.player_freq['Freq']/(5000)
+                    st.session_state.player_freq['Exposure'] = st.session_state.player_freq['Freq']/(2500)
                     st.session_state.player_freq['Edge'] = st.session_state.player_freq['Exposure'] - st.session_state.player_freq['Proj Own']
                     st.session_state.player_freq['Team'] = st.session_state.player_freq['Player'].map(maps_dict['Team_map'])
                     for checkVar in range(len(team_list)):
@@ -1012,7 +1016,7 @@ with tab2:
                     st.session_state.qb_freq['Position'] = st.session_state.qb_freq['Player'].map(maps_dict['Pos_map'])
                     st.session_state.qb_freq['Salary'] = st.session_state.qb_freq['Player'].map(maps_dict['Salary_map'])
                     st.session_state.qb_freq['Proj Own'] = st.session_state.qb_freq['Player'].map(maps_dict['Own_map']) / 100
-                    st.session_state.qb_freq['Exposure'] = st.session_state.qb_freq['Freq']/(5000)
+                    st.session_state.qb_freq['Exposure'] = st.session_state.qb_freq['Freq']/(2500)
                     st.session_state.qb_freq['Edge'] = st.session_state.qb_freq['Exposure'] - st.session_state.qb_freq['Proj Own']
                     st.session_state.qb_freq['Team'] = st.session_state.qb_freq['Player'].map(maps_dict['Team_map'])
                     for checkVar in range(len(team_list)):
@@ -1024,7 +1028,7 @@ with tab2:
                     st.session_state.rb_freq['Position'] = st.session_state.rb_freq['Player'].map(maps_dict['Pos_map'])
                     st.session_state.rb_freq['Salary'] = st.session_state.rb_freq['Player'].map(maps_dict['Salary_map'])
                     st.session_state.rb_freq['Proj Own'] = st.session_state.rb_freq['Player'].map(maps_dict['Own_map']) / 100
-                    st.session_state.rb_freq['Exposure'] = st.session_state.rb_freq['Freq']/5000
+                    st.session_state.rb_freq['Exposure'] = st.session_state.rb_freq['Freq']/2500
                     st.session_state.rb_freq['Edge'] = st.session_state.rb_freq['Exposure'] - st.session_state.rb_freq['Proj Own']
                     st.session_state.rb_freq['Team'] = st.session_state.rb_freq['Player'].map(maps_dict['Team_map'])
                     for checkVar in range(len(team_list)):
@@ -1036,7 +1040,7 @@ with tab2:
                     st.session_state.wr_freq['Position'] = st.session_state.wr_freq['Player'].map(maps_dict['Pos_map'])
                     st.session_state.wr_freq['Salary'] = st.session_state.wr_freq['Player'].map(maps_dict['Salary_map'])
                     st.session_state.wr_freq['Proj Own'] = st.session_state.wr_freq['Player'].map(maps_dict['Own_map']) / 100
-                    st.session_state.wr_freq['Exposure'] = st.session_state.wr_freq['Freq']/5000
+                    st.session_state.wr_freq['Exposure'] = st.session_state.wr_freq['Freq']/2500
                     st.session_state.wr_freq['Edge'] = st.session_state.wr_freq['Exposure'] - st.session_state.wr_freq['Proj Own']
                     st.session_state.wr_freq['Team'] = st.session_state.wr_freq['Player'].map(maps_dict['Team_map'])
                     for checkVar in range(len(team_list)):
@@ -1048,7 +1052,7 @@ with tab2:
                     st.session_state.te_freq['Position'] = st.session_state.te_freq['Player'].map(maps_dict['Pos_map'])
                     st.session_state.te_freq['Salary'] = st.session_state.te_freq['Player'].map(maps_dict['Salary_map'])
                     st.session_state.te_freq['Proj Own'] = st.session_state.te_freq['Player'].map(maps_dict['Own_map']) / 100
-                    st.session_state.te_freq['Exposure'] = st.session_state.te_freq['Freq']/5000
+                    st.session_state.te_freq['Exposure'] = st.session_state.te_freq['Freq']/2500
                     st.session_state.te_freq['Edge'] = st.session_state.te_freq['Exposure'] - st.session_state.te_freq['Proj Own']
                     st.session_state.te_freq['Team'] = st.session_state.te_freq['Player'].map(maps_dict['Team_map'])
                     for checkVar in range(len(team_list)):
@@ -1060,7 +1064,7 @@ with tab2:
                     st.session_state.flex_freq['Position'] = st.session_state.flex_freq['Player'].map(maps_dict['Pos_map'])
                     st.session_state.flex_freq['Salary'] = st.session_state.flex_freq['Player'].map(maps_dict['Salary_map'])
                     st.session_state.flex_freq['Proj Own'] = st.session_state.flex_freq['Player'].map(maps_dict['Own_map']) / 100
-                    st.session_state.flex_freq['Exposure'] = st.session_state.flex_freq['Freq']/5000
+                    st.session_state.flex_freq['Exposure'] = st.session_state.flex_freq['Freq']/2500
                     st.session_state.flex_freq['Edge'] = st.session_state.flex_freq['Exposure'] - st.session_state.flex_freq['Proj Own']
                     st.session_state.flex_freq['Team'] = st.session_state.flex_freq['Player'].map(maps_dict['Team_map'])
                     for checkVar in range(len(team_list)):
@@ -1072,7 +1076,7 @@ with tab2:
                     st.session_state.dst_freq['Position'] = st.session_state.dst_freq['Player'].map(maps_dict['Pos_map'])
                     st.session_state.dst_freq['Salary'] = st.session_state.dst_freq['Player'].map(maps_dict['Salary_map'])
                     st.session_state.dst_freq['Proj Own'] = st.session_state.dst_freq['Player'].map(maps_dict['Own_map']) / 100
-                    st.session_state.dst_freq['Exposure'] = st.session_state.dst_freq['Freq']/5000
+                    st.session_state.dst_freq['Exposure'] = st.session_state.dst_freq['Freq']/2500
                     st.session_state.dst_freq['Edge'] = st.session_state.dst_freq['Exposure'] - st.session_state.dst_freq['Proj Own']
                     st.session_state.dst_freq['Team'] = st.session_state.dst_freq['Player'].map(maps_dict['Team_map'])
                     for checkVar in range(len(team_list)):
@@ -1175,4 +1179,4 @@ del insert_port1, Contest_Size, sharp_split, Strength_var, scaling_var, Sort_fun
 del raw_baselines
 del freq_format
 
-gc.collect()
+gc.collect()