Update app.py to include 'player_id' in ownership calculations, enhancing data accuracy for player projections. This change improves the integrity of the 'raw_display' DataFrame by ensuring all relevant metrics are accounted for, particularly in relation to contest sizes.

app.py

@@ -163,7 +163,7 @@ def init_baselines(sport):
     
         raw_display = pd.DataFrame(list(cursor))
         raw_display = raw_display[['Player', 'Minutes Proj', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%',
-                                   'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX', 'site', 'version', 'slate', 'timestamp']]
+                                   'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX', 'site', 'version', 'slate', 'timestamp', 'player_id']]
         raw_display['Small_Field_Own'] = raw_display['Large_Own']
         raw_display['small_CPT_Own_raw'] = (raw_display['Small_Field_Own'] / 2) * ((100 - (100-raw_display['Small_Field_Own']))/100)
         small_cpt_own_var = 200 / raw_display['small_CPT_Own_raw'].sum()
@@ -180,7 +180,7 @@ def init_baselines(sport):
     
         raw_display = pd.DataFrame(list(cursor))
         raw_display = raw_display[['Player', 'Minutes Proj', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%',
-                                   'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX', 'site', 'version', 'slate', 'timestamp']]
+                                   'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX', 'site', 'version', 'slate', 'timestamp', 'player_id']]
         raw_display['Small_Field_Own'] = raw_display['Large_Own']
         raw_display['small_CPT_Own'] = raw_display['CPT_Own']
         raw_display['cpt_Median'] = raw_display['Median']