Enhance ownership calculations in app.py by introducing 'Small_Field_Own' and 'small_CPT_Own' metrics. Updated calculations to improve accuracy in player ownership projections for both DraftKings and FanDuel, ensuring consistent handling of contest sizes. This refactor streamlines the logic while maintaining clarity in ownership metrics.

app.py

@@ -132,6 +132,10 @@ def init_baselines(sport):
         raw_display = pd.DataFrame(list(cursor))
         raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
                                    'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
+        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 = 100 / raw_display['small_CPT_Own_raw'].sum()
+        raw_display['small_CPT_Own'] = raw_display['small_CPT_Own_raw'] * small_cpt_own_var
         raw_display['cpt_Median'] = raw_display['Median'] * 1.25
         raw_display['STDev'] = raw_display['Median'] / 4
         raw_display['CPT_STDev'] = raw_display['cpt_Median'] / 4
@@ -144,6 +148,8 @@ def init_baselines(sport):
         raw_display = pd.DataFrame(list(cursor))
         raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
                                    'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
+        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']
         raw_display['STDev'] = raw_display['Median'] / 4
         raw_display['CPT_STDev'] = raw_display['cpt_Median'] / 4
@@ -158,6 +164,10 @@ 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']]
+        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 = 100 / raw_display['small_CPT_Own_raw'].sum()
+        raw_display['small_CPT_Own'] = raw_display['small_CPT_Own_raw'] * small_cpt_own_var
         raw_display['cpt_Median'] = raw_display['Median'] * 1.25
         raw_display = raw_display[raw_display['site'] == 'Draftkings']
         raw_display['STDev'] = raw_display['Median'] / 4
@@ -171,6 +181,8 @@ 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']]
+        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']
         raw_display = raw_display[raw_display['site'] == 'Fanduel']
         raw_display['STDev'] = raw_display['Median'] / 4