Add distribution type selection for ROO simulations in NBA functions

app.py

@@ -84,6 +84,7 @@ with tab2:
 
     with st.sidebar:
         site_var_sb = st.selectbox("Select Site", ["Draftkings", "Fanduel"])
+        distribution_type_sb = st.selectbox("Select Distribution Type", ['normal', 'poisson', 'bimodal'])
         floor_var_sb = st.number_input("Floor (low end multiplier)", min_value=0.00, max_value=.50, value=.25, step=.01)
         ceiling_var_sb = st.number_input("Ceiling (high end multiplier)", min_value=1.50, max_value=3.00, value=2.00, step=.01)
         std_var_sb = st.number_input("Standard Deviation (variance within distribution)", min_value=1.00, max_value=5.00, value=4.00, step=.01)
@@ -92,19 +93,19 @@ with tab2:
         if st.button('Build ROO'):
             if sport_var == "NBA":
                 if site_var_sb == "Draftkings":
-                    disp_file = DK_NBA_ROO_Build(projections, floor_var_sb, ceiling_var_sb, std_var_sb)
+                    disp_file = DK_NBA_ROO_Build(projections, floor_var_sb, ceiling_var_sb, std_var_sb, distribution_type_sb)
                 elif site_var_sb == "Fanduel":
-                    disp_file = FD_NBA_ROO_Build(projections, floor_var_sb, ceiling_var_sb, std_var_sb)
+                    disp_file = FD_NBA_ROO_Build(projections, floor_var_sb, ceiling_var_sb, std_var_sb, distribution_type_sb)
             elif sport_var == "NFL":
                 if site_var_sb == "Draftkings":
-                    disp_file = DK_NFL_ROO_Build(projections, floor_var_sb, ceiling_var_sb, std_var_sb)
+                    disp_file = DK_NFL_ROO_Build(projections, floor_var_sb, ceiling_var_sb, std_var_sb, distribution_type_sb)
                 elif site_var_sb == "Fanduel":
-                    disp_file = FD_NFL_ROO_Build(projections, floor_var_sb, ceiling_var_sb, std_var_sb)
+                    disp_file = FD_NFL_ROO_Build(projections, floor_var_sb, ceiling_var_sb, std_var_sb, distribution_type_sb)
             elif sport_var == "MLB":
                 if site_var_sb == "Draftkings":
-                    disp_file = DK_MLB_ROO_Build(projections, floor_var_sb, ceiling_var_sb, std_var_sb)
+                    disp_file = DK_MLB_ROO_Build(projections, floor_var_sb, ceiling_var_sb, std_var_sb, distribution_type_sb)
                 elif site_var_sb == "Fanduel":
-                    disp_file = FD_MLB_ROO_Build(projections, floor_var_sb, ceiling_var_sb, std_var_sb)
+                    disp_file = FD_MLB_ROO_Build(projections, floor_var_sb, ceiling_var_sb, std_var_sb, distribution_type_sb)
 
     if disp_file is not None:
         st.dataframe(disp_file.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height=1000, use_container_width = True)

function_hold/NBA_functions.py

@@ -7,7 +7,7 @@ from pandas import concat as pd_concat
 from pandas import merge as pd_merge
 from pandas import DataFrame
 
-def DK_NBA_ROO_Build(projections_file, floor_var, ceiling_var, std_var):
+def DK_NBA_ROO_Build(projections_file, floor_var, ceiling_var, std_var, distribution_type):
     total_sims = 1000
     
     projects_raw = projections_file.copy()
@@ -144,7 +144,22 @@ def DK_NBA_ROO_Build(projections_file, floor_var, ceiling_var, std_var):
     
     try:
         for x in range(0, total_sims):    
-            result_gpu = np_random.normal(overall_median_gpu, overall_std_gpu)
+            if distribution_type == 'normal':
+                # Normal distribution (existing logic)
+                result_gpu = np_random.normal(overall_median_gpu, overall_std_gpu)
+            elif distribution_type == 'poisson':
+                # Poisson distribution - using median as lambda
+                result_gpu = np_random.poisson(overall_median_gpu)
+            elif distribution_type == 'bimodal':
+                # Bimodal distribution - mixture of two normal distributions
+                # First peak centered at 80% of median, second at 120% of median
+                if np_random.random() < 0.5:
+                    result_gpu = np_random.normal(overall_median_gpu * 0.8, overall_std_gpu)
+                else:
+                    result_gpu = np_random.normal(overall_median_gpu * 1.2, overall_std_gpu)
+            else:
+                raise ValueError("Invalid distribution type. Must be 'normal', 'poisson', or 'bimodal'")
+                
             sim_array[:, x] = result_gpu 
         add_array = sim_array
         
@@ -153,7 +168,16 @@ def DK_NBA_ROO_Build(projections_file, floor_var, ceiling_var, std_var):
         check_file = pd_concat([overall_file, df2], axis=1)
     except:
         for x in range(0,total_sims):    
-            overall_file[x] = np_random.normal(overall_file['Median'],overall_file['STD'])
+            if distribution_type == 'normal':
+                overall_file[x] = np_random.normal(overall_file['Median'], overall_file['STD'])
+            elif distribution_type == 'poisson':
+                overall_file[x] = np_random.poisson(overall_file['Median'])
+            elif distribution_type == 'bimodal':
+                # Bimodal distribution fallback
+                if np_random.random() < 0.5:
+                    overall_file[x] = np_random.normal(overall_file['Median'] * 0.8, overall_file['STD'])
+                else:
+                    overall_file[x] = np_random.normal(overall_file['Median'] * 1.2, overall_file['STD'])
         check_file = overall_file.copy()
         
     overall_file=check_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
@@ -207,7 +231,7 @@ def DK_NBA_ROO_Build(projections_file, floor_var, ceiling_var, std_var):
     
     return final_Proj.copy()
 
-def FD_NBA_ROO_Build(projections_file, floor_var, ceiling_var, std_var):
+def FD_NBA_ROO_Build(projections_file, floor_var, ceiling_var, std_var, distribution_type):
     total_sims = 1000
         
     projects_raw = projections_file.copy()
@@ -343,7 +367,22 @@ def FD_NBA_ROO_Build(projections_file, floor_var, ceiling_var, std_var):
     
     try:
         for x in range(0, total_sims):    
-            result_gpu = np_random.normal(overall_median_gpu, overall_std_gpu)
+            if distribution_type == 'normal':
+                # Normal distribution (existing logic)
+                result_gpu = np_random.normal(overall_median_gpu, overall_std_gpu)
+            elif distribution_type == 'poisson':
+                # Poisson distribution - using median as lambda
+                result_gpu = np_random.poisson(overall_median_gpu)
+            elif distribution_type == 'bimodal':
+                # Bimodal distribution - mixture of two normal distributions
+                # First peak centered at 80% of median, second at 120% of median
+                if np_random.random() < 0.5:
+                    result_gpu = np_random.normal(overall_median_gpu * 0.8, overall_std_gpu)
+                else:
+                    result_gpu = np_random.normal(overall_median_gpu * 1.2, overall_std_gpu)
+            else:
+                raise ValueError("Invalid distribution type. Must be 'normal', 'poisson', or 'bimodal'")
+                
             sim_array[:, x] = result_gpu 
         add_array = sim_array
         
@@ -352,7 +391,16 @@ def FD_NBA_ROO_Build(projections_file, floor_var, ceiling_var, std_var):
         check_file = pd_concat([overall_file, df2], axis=1)
     except:
         for x in range(0,total_sims):    
-            overall_file[x] = np_random.normal(overall_file['Median'],overall_file['STD'])
+            if distribution_type == 'normal':
+                overall_file[x] = np_random.normal(overall_file['Median'], overall_file['STD'])
+            elif distribution_type == 'poisson':
+                overall_file[x] = np_random.poisson(overall_file['Median'])
+            elif distribution_type == 'bimodal':
+                # Bimodal distribution fallback
+                if np_random.random() < 0.5:
+                    overall_file[x] = np_random.normal(overall_file['Median'] * 0.8, overall_file['STD'])
+                else:
+                    overall_file[x] = np_random.normal(overall_file['Median'] * 1.2, overall_file['STD'])
         check_file = overall_file.copy()
         
     overall_file=check_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)