Enhance scoring percentage calculations in app.py by implementing slate-specific logic for DraftKings and FanDuel, ensuring accurate ranking and representation of top scores across different slate types.

app.py

@@ -88,7 +88,8 @@ def init_baselines():
     team_frame = pd.DataFrame(cursor)
     scoring_percentages = team_frame.drop(columns=['_id'])
     scoring_percentages = scoring_percentages[['Names', 'Avg First Inning', 'First Inning Lead Percentage', 'Avg Fifth Inning', 'Fifth Inning Lead Percentage', 'Avg Score', '8+ runs', 'Win Percentage',
-    'DK Main Slate', 'DK Secondary Slate', 'DK Turbo Slate', 'FD Main Slate', 'FD Secondary Slate', 'FD Turbo Slate', 'Top Score']]
+    'DK Main Slate', 'DK Secondary Slate', 'DK Turbo Slate', 'FD Main Slate', 'FD Secondary Slate', 'FD Turbo Slate', 'DK Main Top Score', 'FD Main Top Score', 'DK Secondary Top Score', 'FD Secondary Top Score',
+    'DK Turbo Top Score', 'FD Turbo Top Score']]
     scoring_percentages['8+ runs'] = scoring_percentages['8+ runs'].replace('%', '', regex=True).astype(float)
     scoring_percentages['Win Percentage'] = scoring_percentages['Win Percentage'].replace('%', '', regex=True).astype(float)
     scoring_percentages['Top Score'] = scoring_percentages['Top Score'].replace('', np.nan).astype(float)
@@ -315,8 +316,21 @@ with tab1:
     scoring_percentages = scoring_percentages.merge(fd_team_ownership, left_on='Names', right_on='Team', how='left')
     scoring_percentages.rename(columns={'Own%': 'FD Own%'}, inplace=True)
     scoring_percentages.drop('Team', axis=1, inplace=True)
-    scoring_percentages['DK LevX'] = scoring_percentages['Top Score'].rank(pct=True).astype(float) - scoring_percentages['DK Own%'].rank(pct=True).astype(float)
-    scoring_percentages['FD LevX'] = scoring_percentages['Top Score'].rank(pct=True).astype(float) - scoring_percentages['FD Own%'].rank(pct=True).astype(float)
+    if site_var == 'Draftkings':
+        if slate_var1 == 'Main Slate':
+            scoring_percentages['DK LevX'] = scoring_percentages['DK Main Top Score'].rank(pct=True).astype(float) - scoring_percentages['DK Own%'].rank(pct=True).astype(float)
+            scoring_percentages = scoring_percentages.rename(columns={'DK Main Top Score': 'Top Score'})
+        elif slate_var1 == 'Secondary Slate':
+            scoring_percentages['DK LevX'] = scoring_percentages['DK Secondary Top Score'].rank(pct=True).astype(float) - scoring_percentages['DK Own%'].rank(pct=True).astype(float)
+        elif slate_var1 == 'Turbo Slate':
+            scoring_percentages['DK LevX'] = scoring_percentages['DK Turbo Top Score'].rank(pct=True).astype(float) - scoring_percentages['DK Own%'].rank(pct=True).astype(float)
+    elif site_var == 'Fanduel':
+        if slate_var1 == 'Main Slate':
+            scoring_percentages['FD LevX'] = scoring_percentages['FD Main Top Score'].rank(pct=True).astype(float) - scoring_percentages['FD Own%'].rank(pct=True).astype(float)
+        elif slate_var1 == 'Secondary Slate':
+            scoring_percentages['FD LevX'] = scoring_percentages['FD Secondary Top Score'].rank(pct=True).astype(float) - scoring_percentages['FD Own%'].rank(pct=True).astype(float)
+        elif slate_var1 == 'Turbo Slate':
+            scoring_percentages['FD LevX'] = scoring_percentages['FD Turbo Top Score'].rank(pct=True).astype(float) - scoring_percentages['FD Own%'].rank(pct=True).astype(float)
 
     scoring_percentages = scoring_percentages.sort_values(by='8+ runs', ascending=False)
     scoring_percentages = scoring_percentages.drop(['DK Main Slate', 'DK Secondary Slate', 'DK Turbo Slate', 'FD Main Slate', 'FD Secondary Slate', 'FD Turbo Slate'], axis=1)