Update app.py

app.py

@@ -128,7 +128,7 @@ def sim_contest(Sim_size, FinalPortfolio, CleanPortfolio, maps_dict, up_dict, in
         
     return Sim_Winners
 
-def run_seed_frame(seed_depth1, Strength_var, strength_grow, Teams_used, Total_Runs):
+def run_seed_frame(seed_depth1, Strength_var, strength_grow, Teams_used, Total_Runs, field_growth):
     RunsVar = 1
     seed_depth_def = seed_depth1
     Strength_var_def = Strength_var
@@ -138,31 +138,31 @@ def run_seed_frame(seed_depth1, Strength_var, strength_grow, Teams_used, Total_R
     while RunsVar <= seed_depth_def:
         if RunsVar <= 3:
             FieldStrength = Strength_var_def
-            FinalPortfolio, maps_dict = get_correlated_portfolio_for_sim(Total_Runs_def * .1, sharp_split)
-            FinalPortfolio2, maps_dict2 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .1, sharp_split)
-            FinalPortfolio = pd.concat([FinalPortfolio, FinalPortfolio2], axis=0)
+            FinalPortfolio, maps_dict = get_correlated_portfolio_for_sim(Total_Runs_def * .1, sharp_split, field_growth)
+            FinalPortfolio2, maps_dict2 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .1, sharp_split, field_growth)
+            FinalPortfolio_init = pd.concat([FinalPortfolio, FinalPortfolio2], axis=0)
             maps_dict.update(maps_dict2)
         elif RunsVar > 3 and RunsVar <= 4:
             FieldStrength += (strength_grow_def + ((30 - len(Teams_used_def)) * .001))
-            FinalPortfolio3, maps_dict3 = get_correlated_portfolio_for_sim(Total_Runs_def * .1, sharp_split)
-            FinalPortfolio4, maps_dict4 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .1, sharp_split)
-            FinalPortfolio = pd.concat([FinalPortfolio, FinalPortfolio3], axis=0)
-            FinalPortfolio = pd.concat([FinalPortfolio, FinalPortfolio4], axis=0)
-            FinalPortfolio = FinalPortfolio.drop_duplicates(subset = ['Projection', 'Own'],keep = 'last').reset_index(drop = True)
+            FinalPortfolio3, maps_dict3 = get_correlated_portfolio_for_sim(Total_Runs_def * .1, sharp_split, field_growth)
+            FinalPortfolio4, maps_dict4 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .1, sharp_split, field_growth)
+            FinalPortfolio_merge_3 = pd.concat([FinalPortfolio_init, FinalPortfolio3], axis=0)
+            FinalPortfolio_merge_4 = pd.concat([FinalPortfolio_merge_3, FinalPortfolio4], axis=0)
+            FinalPortfolio_step_2 = FinalPortfolio_merge_4.drop_duplicates(subset = ['Projection', 'Own'],keep = 'last').reset_index(drop = True)
             maps_dict.update(maps_dict3)
             maps_dict.update(maps_dict4)
         elif RunsVar > 4:
             FieldStrength = 1
-            FinalPortfolio3, maps_dict3 = get_correlated_portfolio_for_sim(Total_Runs_def * .1, sharp_split)
-            FinalPortfolio4, maps_dict4 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .1, sharp_split)
-            FinalPortfolio = pd.concat([FinalPortfolio, FinalPortfolio3], axis=0)
-            FinalPortfolio = pd.concat([FinalPortfolio, FinalPortfolio4], axis=0)
-            FinalPortfolio = FinalPortfolio.drop_duplicates(subset = ['Projection', 'Own'],keep = 'last').reset_index(drop = True)
-            maps_dict.update(maps_dict3)
-            maps_dict.update(maps_dict4)
+            FinalPortfolio5, maps_dict5 = get_correlated_portfolio_for_sim(Total_Runs_def * .1, sharp_split, field_growth)
+            FinalPortfolio6, maps_dict6 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .1, sharp_split, field_growth)
+            FinalPortfolio_merge_5 = pd.concat([FinalPortfolio_step_2, FinalPortfolio5], axis=0)
+            FinalPortfolio_merge_6 = pd.concat([FinalPortfolio_merge_5, FinalPortfolio6], axis=0)
+            FinalPortfolio_export = FinalPortfolio_merge_6.drop_duplicates(subset = ['Projection', 'Own'],keep = 'last').reset_index(drop = True)
+            maps_dict.update(maps_dict5)
+            maps_dict.update(maps_dict6)
         RunsVar += 1
     
-    return FinalPortfolio, maps_dict
+    return FinalPortfolio_export, maps_dict
 
 def create_stack_options(player_data, wr_var):
         merged_frame = pd.DataFrame(columns = ['QB', 'Player'])
@@ -218,7 +218,7 @@ def calculate_range_var(count, min_val, FieldStrength, field_growth):
     
     return min(var, len(count[0]))
 
-def create_random_portfolio(Total_Sample_Size, raw_baselines):
+def create_random_portfolio(Total_Sample_Size, raw_baselines, field_growth):
     
             O_merge, full_pos_player_dict = get_overall_merged_df()
             max_var = len(raw_baselines[raw_baselines['Position'] == 'QB'])
@@ -251,11 +251,11 @@ def create_random_portfolio(Total_Sample_Size, raw_baselines):
           
             return RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict
 
-def get_correlated_portfolio_for_sim(Total_Sample_Size, sharp_split):
+def get_correlated_portfolio_for_sim(Total_Sample_Size, sharp_split, field_growth):
     
     sizesplit = round(Total_Sample_Size * sharp_split)
     
-    RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict = create_random_portfolio(sizesplit, raw_baselines)
+    RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict = create_random_portfolio(sizesplit, raw_baselines, field_growth)
     stack_num = random.randint(1, 3)
     stacking_dict = create_stack_options(raw_baselines, stack_num)
     
@@ -360,11 +360,11 @@ def get_correlated_portfolio_for_sim(Total_Sample_Size, sharp_split):
     
     return RandomPortfolio, maps_dict
           
-def get_uncorrelated_portfolio_for_sim(Total_Sample_Size, sharp_split):
+def get_uncorrelated_portfolio_for_sim(Total_Sample_Size, sharp_split, field_growth):
     
     sizesplit = round(Total_Sample_Size * (1-sharp_split))
 
-    RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict = create_random_portfolio(sizesplit, raw_baselines)
+    RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict = create_random_portfolio(sizesplit, raw_baselines, field_growth)
           
     RandomPortfolio['QB'] = pd.Series(list(RandomPortfolio['QB'].map(qb_dict)), dtype="string[pyarrow]")
     RandomPortfolio['RB1'] = pd.Series(list(RandomPortfolio['RB1'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]")
@@ -467,7 +467,6 @@ def get_uncorrelated_portfolio_for_sim(Total_Sample_Size, sharp_split):
     
     return RandomPortfolio, maps_dict
 
-
 dk_roo_raw = load_dk_player_projections()
 fd_roo_raw = load_fd_player_projections()
 t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
@@ -834,6 +833,22 @@ with tab2:
             sharp_split = .75
             Strength_var = .01
             scaling_var = 15
+        
+        Sort_function = 'Median'
+        Sim_function = 'Projection'
+        
+        if Contest_Size <= 1000:
+            strength_grow = .01
+        elif Contest_Size > 1000 and Contest_Size <= 2500:
+            strength_grow = .025
+        elif Contest_Size > 2500 and Contest_Size <= 5000:
+            strength_grow = .05
+        elif Contest_Size > 5000 and Contest_Size <= 20000:
+            strength_grow = .075
+        elif Contest_Size > 20000:
+            strength_grow = .1
+            
+        field_growth = 100 * strength_grow
 
     with col2:
         with st.container():
@@ -842,24 +857,6 @@ with tab2:
                     st.write('Contest Simulation Starting')
                     for key in st.session_state.keys():
                         del st.session_state[key]
-                    if Contest_Size <= 1000:
-                        strength_grow = .01
-                    elif Contest_Size > 1000 and Contest_Size <= 2500:
-                        strength_grow = .025
-                    elif Contest_Size > 2500 and Contest_Size <= 5000:
-                        strength_grow = .05
-                    elif Contest_Size > 5000 and Contest_Size <= 20000:
-                        strength_grow = .075
-                    elif Contest_Size > 20000:
-                        strength_grow = .1
-                        
-                    field_growth = 100 * strength_grow
-            
-                    Sort_function = 'Median'
-                    if Sort_function == 'Median':
-                        Sim_function = 'Projection'
-                    elif Sort_function == 'Own':
-                        Sim_function = 'Own'
                     
                     if slate_var1 == 'User':
                         initial_proj = proj_dataframe[['Player', 'Team', 'Position', 'Median', 'Own', 'Floor', 'Ceiling', 'Salary']].copy()
@@ -1074,7 +1071,7 @@ with tab2:
                         }
                     
                     st.write('Seed frame creation')
-                    FinalPortfolio, maps_dict = run_seed_frame(10, Strength_var, strength_grow, Teams_used, 1000000)
+                    FinalPortfolio, maps_dict = run_seed_frame(10, Strength_var, strength_grow, Teams_used, 1000000, field_growth)
                     
                     Sim_Winners = sim_contest(2500, FinalPortfolio, CleanPortfolio, maps_dict, up_dict, insert_port)
                         
@@ -1297,5 +1294,5 @@ del dk_roo_raw, fd_roo_raw
 del t_stamp
 del dkid_dict, fdid_dict
 del static_exposure, overall_exposure
-del insert_port1, Contest_Size, sharp_split, Strength_var, scaling_var
+del insert_port1, Contest_Size, sharp_split, Strength_var, scaling_var, Sort_function, Sim_function, strength_grow, field_growth
 del raw_baselines