Enhance projection calculations by adding 'cpt_Own_map' to mapping logic in app.py. Updated 'Proj Own' calculations for both 'cpt_working' and 'flex_working' DataFrames to utilize the new mapping, improving accuracy in player ownership projections.

app.py

@@ -415,6 +415,7 @@ with tab1:
                         'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)),
                         'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)),
                         'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own'])),
+                        'cpt_Own_map':dict(zip(raw_baselines.Player,raw_baselines['CPT_Own'])),
                         'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)),
                         'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev))
                         }
@@ -454,6 +455,7 @@ with tab1:
                         'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)),
                         'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)),
                         'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own'])),
+                        'cpt_Own_map':dict(zip(raw_baselines.Player,raw_baselines['CPT_Own'])),
                         'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)),
                         'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev))
                         }
@@ -505,15 +507,13 @@ 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']) / cpt_own_div
+            cpt_working['Proj Own'] = cpt_working['Player'].map(maps_dict['cpt_Own_map'])
             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'])
@@ -533,7 +533,7 @@ with tab1:
                 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']) / cpt_own_div)
+            flex_working['Proj Own'] = (flex_working['Player'].map(maps_dict['Own_map']) / 100) - (flex_working['Player'].map(maps_dict['cpt_Own_map']))
             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'])