Update app.py

app.py

@@ -230,15 +230,24 @@ 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()
+            g_baselines = raw_baselines[raw_baselines['Position'] == 'G']
+            g_baselines = g_baselines.drop_duplicates(subset='Team')
+            max_var = len(g_baselines[g_baselines['Position'] == 'G'])
           
             field_growth_rounded = round(field_growth)
             ranges_dict = {}
             
             if site_var1 == 'Draftkings':
                 # Calculate ranges
-                for df, dict_val, min_val, key in zip(ref_dict['pos_dfs'], ref_dict['pos_dicts'], [10, 10, 20, 10, 30], ['C', 'W', 'D', 'G', 'UTIL']):
+                for df, dict_val, min_val, key in zip(ref_dict['pos_dfs'], ref_dict['pos_dicts'], [10, 10, 20, 30], ['C', 'W', 'D', 'UTIL']):
                     count = create_overall_dfs(pos_players, df, dict_val, key)
                     ranges_dict[f"{key.lower()}_range"] = calculate_range_var(count, min_val, FieldStrength, field_growth_rounded)
+                if max_var <= 10:
+                    ranges_dict['g_range'] = round(max_var)
+                elif max_var > 10 and max_var <= 16:
+                    ranges_dict['dst_range'] = round(max_var)
+                elif max_var > 16:
+                    ranges_dict['dst_range'] = round(max_var)
                 
                 # Generate random portfolios
                 rng = np.random.default_rng()
@@ -251,9 +260,15 @@ def create_random_portfolio(Total_Sample_Size, raw_baselines, field_growth):
                 
             elif site_var1 == 'Fanduel':
                 # Calculate ranges
-                for df, dict_val, min_val, key in zip(ref_dict['pos_dfs'], ref_dict['pos_dicts'], [10, 10, 20, 10, 30], ['C', 'W', 'D', 'G', 'UTIL']):
+                for df, dict_val, min_val, key in zip(ref_dict['pos_dfs'], ref_dict['pos_dicts'], [10, 10, 20, 30], ['C', 'W', 'D', 'UTIL']):
                     count = create_overall_dfs(pos_players, df, dict_val, key)
                     ranges_dict[f"{key.lower()}_range"] = calculate_range_var(count, min_val, FieldStrength, field_growth_rounded)
+                if max_var <= 10:
+                    ranges_dict['g_range'] = round(max_var)
+                elif max_var > 10 and max_var <= 16:
+                    ranges_dict['dst_range'] = round(max_var)
+                elif max_var > 16:
+                    ranges_dict['dst_range'] = round(max_var)
                 
                 # Generate random portfolios
                 rng = np.random.default_rng()
@@ -279,7 +294,7 @@ def get_correlated_dk_portfolio_for_sim(Total_Sample_Size, sharp_split, field_gr
     RandomPortfolio['W3'] = pd.Series(list(RandomPortfolio['W3'].map(full_pos_player_dict['pos_dicts'][1])), dtype="string[pyarrow]")
     RandomPortfolio['D1'] = pd.Series(list(RandomPortfolio['D1'].map(full_pos_player_dict['pos_dicts'][2])), dtype="string[pyarrow]")
     RandomPortfolio['D2'] = pd.Series(list(RandomPortfolio['D2'].map(full_pos_player_dict['pos_dicts'][2])), dtype="string[pyarrow]")
-    RandomPortfolio['G'] = pd.Series(list(RandomPortfolio['G'].map(full_pos_player_dict['pos_dicts'][4])), dtype="string[pyarrow]")
+    RandomPortfolio['G'] = pd.Series(list(RandomPortfolio['G'].map(gs_dict)), dtype="string[pyarrow]")
     RandomPortfolio['UTIL'] = pd.Series(list(RandomPortfolio['UTIL'].map(full_pos_player_dict['pos_dicts'][3])), dtype="string[pyarrow]")
     RandomPortfolio['plyr_list'] = RandomPortfolio[RandomPortfolio.columns.values.tolist()].values.tolist()
     RandomPortfolio['plyr_count'] = RandomPortfolio['plyr_list'].apply(lambda x: len(set(x)))
@@ -386,7 +401,7 @@ def get_uncorrelated_dk_portfolio_for_sim(Total_Sample_Size, sharp_split, field_
     RandomPortfolio['W3'] = pd.Series(list(RandomPortfolio['W3'].map(full_pos_player_dict['pos_dicts'][1])), dtype="string[pyarrow]")
     RandomPortfolio['D1'] = pd.Series(list(RandomPortfolio['D1'].map(full_pos_player_dict['pos_dicts'][2])), dtype="string[pyarrow]")
     RandomPortfolio['D2'] = pd.Series(list(RandomPortfolio['D2'].map(full_pos_player_dict['pos_dicts'][2])), dtype="string[pyarrow]")
-    RandomPortfolio['G'] = pd.Series(list(RandomPortfolio['G'].map(full_pos_player_dict['pos_dicts'][4])), dtype="string[pyarrow]")
+    RandomPortfolio['G'] = pd.Series(list(RandomPortfolio['G'].map(gs_dict)), dtype="string[pyarrow]")
     RandomPortfolio['UTIL'] = pd.Series(list(RandomPortfolio['UTIL'].map(full_pos_player_dict['pos_dicts'][3])), dtype="string[pyarrow]")
     RandomPortfolio['plyr_list'] = RandomPortfolio[RandomPortfolio.columns.values.tolist()].values.tolist()
     RandomPortfolio['plyr_count'] = RandomPortfolio['plyr_list'].apply(lambda x: len(set(x)))
@@ -494,7 +509,7 @@ def get_correlated_fd_portfolio_for_sim(Total_Sample_Size, sharp_split, field_gr
     RandomPortfolio['D2'] = pd.Series(list(RandomPortfolio['D2'].map(full_pos_player_dict['pos_dicts'][2])), dtype="string[pyarrow]")
     RandomPortfolio['UTIL1'] = pd.Series(list(RandomPortfolio['UTIL1'].map(full_pos_player_dict['pos_dicts'][3])), dtype="string[pyarrow]")
     RandomPortfolio['UTIL2'] = pd.Series(list(RandomPortfolio['UTIL2'].map(full_pos_player_dict['pos_dicts'][3])), dtype="string[pyarrow]")
-    RandomPortfolio['G'] = pd.Series(list(RandomPortfolio['G'].map(full_pos_player_dict['pos_dicts'][4])), dtype="string[pyarrow]")
+    RandomPortfolio['G'] = pd.Series(list(RandomPortfolio['G'].map(gs_dict)), dtype="string[pyarrow]")
     RandomPortfolio['plyr_list'] = RandomPortfolio[RandomPortfolio.columns.values.tolist()].values.tolist()
     RandomPortfolio['plyr_count'] = RandomPortfolio['plyr_list'].apply(lambda x: len(set(x)))
     RandomPortfolio = RandomPortfolio[RandomPortfolio['plyr_count'] == 10].drop(columns=['plyr_list','plyr_count']).\
@@ -601,7 +616,7 @@ def get_uncorrelated_fd_portfolio_for_sim(Total_Sample_Size, sharp_split, field_
     RandomPortfolio['D2'] = pd.Series(list(RandomPortfolio['D2'].map(full_pos_player_dict['pos_dicts'][2])), dtype="string[pyarrow]")
     RandomPortfolio['UTIL1'] = pd.Series(list(RandomPortfolio['UTIL1'].map(full_pos_player_dict['pos_dicts'][3])), dtype="string[pyarrow]")
     RandomPortfolio['UTIL2'] = pd.Series(list(RandomPortfolio['UTIL2'].map(full_pos_player_dict['pos_dicts'][3])), dtype="string[pyarrow]")
-    RandomPortfolio['G'] = pd.Series(list(RandomPortfolio['G'].map(full_pos_player_dict['pos_dicts'][4])), dtype="string[pyarrow]")
+    RandomPortfolio['G'] = pd.Series(list(RandomPortfolio['G'].map(gs_dict)), dtype="string[pyarrow]")
     RandomPortfolio['plyr_list'] = RandomPortfolio[RandomPortfolio.columns.values.tolist()].values.tolist()
     RandomPortfolio['plyr_count'] = RandomPortfolio['plyr_list'].apply(lambda x: len(set(x)))
     RandomPortfolio = RandomPortfolio[RandomPortfolio['plyr_count'] == 10].drop(columns=['plyr_list','plyr_count']).\
@@ -1101,8 +1116,12 @@ with tab2:
                     gs_raw.dropna(subset=['Median']).reset_index(drop=True)
                     gs_raw = gs_raw.reset_index(drop=True)
                     gs_raw = gs_raw.sort_values(by=['Own', 'Median'], ascending=False)
+                    
+                    gs = gs_raw.head(round(len(gs_raw)))
+                    gs = gs.assign(Var = range(0,len(gs)))
+                    gs_dict = pd.Series(gs.Player.values, index=gs.Var).to_dict()
             
-                    pos_players = pd.concat([cs_raw, ws_raw, ds_raw, gs_raw])
+                    pos_players = pd.concat([cs_raw, ws_raw, ds_raw])
                     pos_players.dropna(subset=['Median']).reset_index(drop=True)
                     pos_players = pos_players.reset_index(drop=True)
             
@@ -1423,4 +1442,4 @@ del insert_port1, Contest_Size, sharp_split, Strength_var, scaling_var, Sort_fun
 del raw_baselines
 del freq_format
 
-gc.collect()       
+gc.collect()