Update app.py to adjust array indexing in frequency calculation functions and stack multiplier logic for Draftkings and Fanduel.

app.py

@@ -234,14 +234,14 @@ def convert_df(array):
 
 @st.cache_data
 def calculate_DK_value_frequencies(np_array):
-    unique, counts = np.unique(np_array[:, :9], return_counts=True)
+    unique, counts = np.unique(np_array[:, :10], return_counts=True)
     frequencies = counts / len(np_array)  # Normalize by the number of rows 
     combined_array = np.column_stack((unique, frequencies))  
     return combined_array 
 
 @st.cache_data
 def calculate_FD_value_frequencies(np_array):
-    unique, counts = np.unique(np_array[:, :8], return_counts=True)
+    unique, counts = np.unique(np_array[:, :9], return_counts=True)
     frequencies = counts / len(np_array)  # Normalize by the number of rows 
     combined_array = np.column_stack((unique, frequencies))  
     return combined_array
@@ -263,13 +263,13 @@ def sim_contest(Sim_size, seed_frame, maps_dict, Contest_Size, teams_playing_cou
         if site == 'Draftkings':    
             # Calculate stack multipliers first
             stack_multiplier = np.ones(fp_random.shape[0])  # Start with no bonus
-            stack_multiplier += np.minimum(0.10, np.where(fp_random[:, 12] == 4, 0.025 * (teams_playing_count - 8), 0))
-            stack_multiplier += np.minimum(0.15, np.where(fp_random[:, 12] >= 5, 0.025 * (teams_playing_count - 12), 0))
+            stack_multiplier += np.minimum(0.10, np.where(fp_random[:, 13] == 4, 0.025 * (teams_playing_count - 8), 0))
+            stack_multiplier += np.minimum(0.15, np.where(fp_random[:, 13] >= 5, 0.025 * (teams_playing_count - 12), 0))
         elif site == 'Fanduel':
              # Calculate stack multipliers first
             stack_multiplier = np.ones(fp_random.shape[0])  # Start with no bonus
-            stack_multiplier += np.minimum(0.10, np.where(fp_random[:, 11] == 4, 0.025 * (teams_playing_count - 8), 0))
-            stack_multiplier += np.minimum(0.15, np.where(fp_random[:, 11] >= 5, 0.025 * (teams_playing_count - 12), 0))
+            stack_multiplier += np.minimum(0.10, np.where(fp_random[:, 12] == 4, 0.025 * (teams_playing_count - 8), 0))
+            stack_multiplier += np.minimum(0.15, np.where(fp_random[:, 12] >= 5, 0.025 * (teams_playing_count - 12), 0))
         
         # Apply multipliers to both loc and scale in the normal distribution
         base_projections = np.sum(np.random.normal(