Update app.py

app.py

@@ -54,7 +54,7 @@ freq_format = {'Exposure': '{:.2%}', 'Proj Own': '{:.2%}', 'Edge': '{:.2%}'}
 dk_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
 fd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
 
-@st.cache_data(ttl = 600)
+@st.cache_data(ttl = 599)
 def init_DK_seed_frames():  
     
         collection = db["DK_NFL_SD_seed_frame"] 
@@ -379,13 +379,15 @@ with tab1:
             if sim_site_var1 == 'Draftkings':
                 cpt_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:1].values, return_counts=True)),
                                                 columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+                cpt_own_div = 600
             elif sim_site_var1 == 'Fanduel':
                 cpt_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:1].values, return_counts=True)),
                                                 columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+                cpt_own_div = 500
             cpt_working['Freq'] = cpt_working['Freq'].astype(int)
             cpt_working['Position'] = cpt_working['Player'].map(maps_dict['Pos_map'])
             cpt_working['Salary'] = cpt_working['Player'].map(maps_dict['Salary_map'])
-            cpt_working['Proj Own'] = cpt_working['Player'].map(maps_dict['Own_map']) / 400
+            cpt_working['Proj Own'] = cpt_working['Player'].map(maps_dict['Own_map']) / cpt_own_div
             cpt_working['Exposure'] = cpt_working['Freq']/(1000)
             cpt_working['Edge'] = cpt_working['Exposure'] - cpt_working['Proj Own']
             cpt_working['Team'] = cpt_working['Player'].map(maps_dict['Team_map'])
@@ -394,16 +396,18 @@ with tab1:
             if sim_site_var1 == 'Draftkings':
                 flex_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,1:6].values, return_counts=True)),
                                                 columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+                cpt_own_div = 600
             elif sim_site_var1 == 'Fanduel':
                 flex_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,1:5].values, return_counts=True)),
                                                 columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+                cpt_own_div = 500
             flex_working['Freq'] = flex_working['Freq'].astype(int)
             flex_working['Position'] = flex_working['Player'].map(maps_dict['Pos_map'])
             if sim_site_var1 == 'Draftkings':
                 flex_working['Salary'] = flex_working['Player'].map(maps_dict['Salary_map']) / 1.5
             elif sim_site_var1 == 'Fanduel':
                 flex_working['Salary'] = flex_working['Player'].map(maps_dict['Salary_map'])
-            flex_working['Proj Own'] = (flex_working['Player'].map(maps_dict['Own_map']) / 100) - (flex_working['Player'].map(maps_dict['Own_map']) / 400)
+            flex_working['Proj Own'] = (flex_working['Player'].map(maps_dict['Own_map']) / 100) - (flex_working['Player'].map(maps_dict['Own_map']) / cpt_own_div)
             flex_working['Exposure'] = flex_working['Freq']/(1000)
             flex_working['Edge'] = flex_working['Exposure'] - flex_working['Proj Own']
             flex_working['Team'] = flex_working['Player'].map(maps_dict['Team_map'])