Add NHL support to ROO build functions and Streamlit display

app.py

@@ -19,8 +19,10 @@ from pandas import DataFrame
 #bring in functions
 from function_hold.NBA_functions import DK_NBA_ROO_Build, FD_NBA_ROO_Build
 from function_hold.MMA_functions import DK_MMA_ROO_Build, FD_MMA_ROO_Build
+from function_hold.NHL_functions import DK_NHL_ROO_Build, FD_NHL_ROO_Build
 
 nba_percentages_format = {'Top_finish': '{:.2%}', 'Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '4x%': '{:.2%}', '5x%': '{:.2%}', '6x%': '{:.2%}', 'GPP%': '{:.2%}'}
+nhl_percentages_format = {'Top_finish': '{:.2%}', 'Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '4x%': '{:.2%}', '5x%': '{:.2%}', '6x%': '{:.2%}', 'GPP%': '{:.2%}'}
 mma_percentages_format = {'Top_finish': '{:.2%}', 'Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '100+%': '{:.2%}', '10x%': '{:.2%}', '11x%': '{:.2%}', '12x%': '{:.2%}', 'GPP%': '{:.2%}'}
 
 def load_file(upload):
@@ -110,6 +112,11 @@ with tab2:
                     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, distribution_type_sb)
+            elif sport_var == "NHL":
+                if site_var_sb == "Draftkings":
+                    disp_file = DK_NHL_ROO_Build(projections, floor_var_sb, ceiling_var_sb, std_var_sb, distribution_type_sb)
+                elif site_var_sb == "Fanduel":
+                    disp_file = FD_NHL_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, distribution_type_sb)
@@ -130,6 +137,8 @@ with tab2:
         if 'disp_file' in locals():
             if sport_var == "NBA":
                 st.dataframe(disp_file.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(nba_percentages_format, precision=2), height=1000, use_container_width = True)
+            elif sport_var == "NHL":
+                st.dataframe(disp_file.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(nhl_percentages_format, precision=2), height=1000, use_container_width = True)
             elif sport_var == "MMA":
                 st.dataframe(disp_file.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(mma_percentages_format, precision=2), height=1000, use_container_width = True)
     except:

function_hold/NHL_functions.py

@@ -0,0 +1,483 @@
+from numpy import nan as np_nan
+from numpy import where as np_where
+from numpy import random as np_random
+from numpy import zeros as np_zeros
+from numpy import array as np_array
+from pandas import concat as pd_concat
+from pandas import merge as pd_merge
+from pandas import DataFrame
+
+def DK_NHL_ROO_Build(projections_file, floor_var, ceiling_var, std_var, distribution_type):
+    total_sims = 1000
+    
+    projects_raw = projections_file.copy()
+    projects_raw = projects_raw.replace("", np_nan)
+    dk_df = projects_raw.sort_values(by='Median', ascending=False)
+    
+    basic_own_df = dk_df.copy()
+    basic_own_df['name_team'] = basic_own_df['Player'] + basic_own_df['Position']
+            
+    def calculate_ownership(df, position):
+        # Filter the dataframe based on the position
+        frame = df[df['Position'].str.contains(position)]
+        
+        # Calculate Small Field Own%
+        frame['Base Own%'] = np_where(
+            (frame['Own'] - frame['Own'].mean() >= 0),
+            frame['Own'] * (5 * (frame['Own'] - (frame['Own'].mean() / 1.5)) / 100) + frame['Own'].mean(),
+            frame['Own']
+        )
+        frame['Base Own%'] = np_where(
+            frame['Base Own%'] > 75,
+            75,
+            frame['Base Own%']
+        )
+        
+        # Calculate Small Field Own%
+        frame['Small Field Own%'] = np_where(
+            (frame['Own'] - frame['Own'].mean() >= 0),
+            frame['Own'] * (6 * (frame['Own'] - frame['Own'].mean()) / 100) + frame['Own'].mean(),
+            frame['Own']
+        )
+        frame['Small Field Own%'] = np_where(
+            frame['Small Field Own%'] > 75,
+            75,
+            frame['Small Field Own%']
+        )
+
+        # Calculate Large Field Own%
+        frame['Large Field Own%'] = np_where(
+            (frame['Own'] - frame['Own'].mean() >= 0),
+            frame['Own'] * (2.5 * (frame['Own'] - frame['Own'].mean()) / 100) + frame['Own'].mean(),
+            frame['Own']
+        )
+        frame['Large Field Own%'] = np_where(
+            frame['Large Field Own%'] > 75,
+            75,
+            frame['Large Field Own%']
+        )
+
+        # Calculate Cash Own%
+        frame['Cash Own%'] = np_where(
+            (frame['Own'] - frame['Own'].mean() >= 0),
+            frame['Own'] * (8 * (frame['Own'] - frame['Own'].mean()) / 100) + frame['Own'].mean(),
+            frame['Own']
+        )
+        frame['Cash Own%'] = np_where(
+            frame['Cash Own%'] > 75,
+            75,
+            frame['Cash Own%']
+        )
+
+        return frame
+
+    # Apply the function to each dataframe
+    w_frame = calculate_ownership(basic_own_df, 'W')
+    c_frame = calculate_ownership(basic_own_df, 'C')
+    d_frame = calculate_ownership(basic_own_df, 'D')
+    g_frame = calculate_ownership(basic_own_df, 'G')
+
+    w_reg_norm_var = 330 / w_frame['Base Own%'].sum()
+    w_small_norm_var = 330 / w_frame['Small Field Own%'].sum()
+    w_large_norm_var = 330 / w_frame['Large Field Own%'].sum()
+    w_cash_norm_var = 330 / w_frame['Cash Own%'].sum()
+    w_frame['Own'] = w_frame['Base Own%'] * w_reg_norm_var
+    w_frame['Small Field Own%'] = w_frame['Small Field Own%'] * w_small_norm_var
+    w_frame['Large Field Own%'] = w_frame['Large Field Own%'] * w_large_norm_var
+    w_frame['Cash Own%'] = w_frame['Cash Own%'] * w_cash_norm_var
+
+    c_reg_norm_var = 260 / c_frame['Base Own%'].sum()
+    c_small_norm_var = 260 / c_frame['Small Field Own%'].sum()
+    c_large_norm_var = 260 / c_frame['Large Field Own%'].sum()
+    c_cash_norm_var = 260 / c_frame['Cash Own%'].sum()
+    c_frame['Own'] = c_frame['Base Own%'] * c_reg_norm_var
+    c_frame['Small Field Own%'] = c_frame['Small Field Own%'] * c_small_norm_var
+    c_frame['Large Field Own%'] = c_frame['Large Field Own%'] * c_large_norm_var
+    c_frame['Cash Own%'] = c_frame['Cash Own%'] * c_cash_norm_var
+
+    d_reg_norm_var = 210 / d_frame['Base Own%'].sum()
+    d_small_norm_var = 210 / d_frame['Small Field Own%'].sum()
+    d_large_norm_var = 210 / d_frame['Large Field Own%'].sum()
+    d_cash_norm_var = 210 / d_frame['Cash Own%'].sum()
+    d_frame['Own'] = d_frame['Base Own%'] * d_reg_norm_var
+    d_frame['Small Field Own%'] = d_frame['Small Field Own%'] * d_small_norm_var
+    d_frame['Large Field Own%'] = d_frame['Large Field Own%'] * d_large_norm_var
+    d_frame['Cash Own%'] = d_frame['Cash Own%'] * d_cash_norm_var
+
+    g_reg_norm_var = 100 / g_frame['Base Own%'].sum()
+    g_small_norm_var = 100 / g_frame['Small Field Own%'].sum()
+    g_large_norm_var = 100 / g_frame['Large Field Own%'].sum()
+    g_cash_norm_var = 100 / g_frame['Cash Own%'].sum()
+    g_frame['Own'] = g_frame['Base Own%'] * g_reg_norm_var
+    g_frame['Small Field Own%'] = g_frame['Small Field Own%'] * g_small_norm_var
+    g_frame['Large Field Own%'] = g_frame['Large Field Own%'] * g_large_norm_var
+    g_frame['Cash Own%'] = g_frame['Cash Own%'] * g_cash_norm_var
+
+    basic_own_df = pd_concat([w_frame, c_frame, d_frame, g_frame])
+
+    basic_own_dict = dict(zip(basic_own_df.Player, basic_own_df.Own))
+    small_own_dict = dict(zip(basic_own_df.Player, basic_own_df['Small Field Own%']))
+    large_own_dict = dict(zip(basic_own_df.Player, basic_own_df['Large Field Own%']))
+    cash_own_dict = dict(zip(basic_own_df.Player, basic_own_df['Cash Own%']))
+    basic_team_dict = dict(zip(basic_own_df.name_team, basic_own_df.Team))
+    basic_opp_dict = dict(zip(basic_own_df.Player, basic_own_df.Opp))
+
+    flex_file = basic_own_df.copy()
+    flex_file['Floor_raw'] = flex_file['Median'] * .25
+    flex_file['Ceiling_raw'] = flex_file['Median'] * 2
+    flex_file['Floor'] = np_where(flex_file['Position'] == 'G', flex_file['Median'] * .5, flex_file['Floor_raw'])
+    flex_file['Floor'] = np_where(flex_file['Position'] == 'D', flex_file['Median'] * .1, flex_file['Floor_raw'])
+    flex_file['Ceiling'] = np_where(flex_file['Position'] == 'G', flex_file['Median'] * 1.75, flex_file['Ceiling_raw'])
+    flex_file['Ceiling'] = np_where(flex_file['Position'] == 'D', flex_file['Median'] * 1.75, flex_file['Ceiling_raw'])
+    flex_file['STD'] = flex_file['Median'] / 3
+    flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
+    flex_file = flex_file.reset_index(drop=True)
+    hold_file = flex_file.copy()
+    overall_file = flex_file.copy()
+    salary_file = flex_file.copy()
+    
+    try:    
+        overall_floor_gpu = np_array(overall_file['Floor'])
+        overall_ceiling_gpu = np_array(overall_file['Ceiling'])
+        overall_median_gpu = np_array(overall_file['Median'])
+        overall_std_gpu = np_array(overall_file['STD'])
+        overall_salary_gpu = np_array(overall_file['Salary'])
+        
+        data_shape = (len(overall_file['Player']), total_sims)  # Example: 1000 rows
+        salary_array = np_zeros(data_shape)
+        sim_array = np_zeros(data_shape)
+        
+        for x in range(0, total_sims):    
+            result_gpu = overall_salary_gpu
+            salary_array[:, x] = result_gpu 
+        cupy_array = salary_array
+        
+        salary_file = salary_file.reset_index(drop=True)
+        salary_cupy = DataFrame(cupy_array, columns=list(range(0, total_sims)))
+        salary_check_file = pd_concat([salary_file, salary_cupy], axis=1)
+    except:
+        for x in range(0,total_sims):    
+            salary_file[x] = salary_file['Salary']
+        salary_check_file = salary_file.copy()
+
+    salary_file=salary_check_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+
+    salary_file = salary_file.div(1000)
+    
+    try:
+        for x in range(0, total_sims):    
+            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_floor_gpu, overall_std_gpu)
+                else:
+                    result_gpu = np_random.normal(overall_ceiling_gpu, overall_std_gpu)
+            else:
+                raise ValueError("Invalid distribution type. Must be 'normal', 'poisson', or 'bimodal'")
+                
+            sim_array[:, x] = result_gpu 
+        add_array = sim_array
+        
+        overall_file = overall_file.reset_index(drop=True)
+        df2 = DataFrame(add_array, columns=list(range(0, total_sims)))
+        check_file = pd_concat([overall_file, df2], axis=1)
+    except:
+        for x in range(0,total_sims):    
+            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)
+
+    players_only = hold_file[['Player']]
+    raw_lineups_file = players_only
+
+    for x in range(0,total_sims):
+        maps_dict = {'proj_map':dict(zip(hold_file.Player,overall_file[x]))}
+        raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])])
+        players_only[x] = raw_lineups_file[x].rank(ascending=False)
+
+    players_only=players_only.drop(['Player'], axis=1)
+    
+    salary_2x_check = (overall_file - (salary_file*2))
+    salary_3x_check = (overall_file - (salary_file*3))
+    salary_4x_check = (overall_file - (salary_file*4))
+
+    players_only['Average_Rank'] = players_only.mean(axis=1)
+    players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
+    players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
+    players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
+    players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims)
+    players_only['2x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims)
+    players_only['3x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims)
+    players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)
+
+    players_only['Player'] = hold_file[['Player']]
+
+    final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
+
+    final_Proj = pd_merge(hold_file, final_outcomes, on="Player")
+    final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
+    final_Proj['Own'] = final_Proj['Player'].map(basic_own_dict).astype(float)
+    final_Proj['Small Field Own%'] = final_Proj['Player'].map(small_own_dict).astype(float)
+    final_Proj['Large Field Own%'] = final_Proj['Player'].map(large_own_dict).astype(float)
+    final_Proj['Cash Own%'] = final_Proj['Player'].map(cash_own_dict).astype(float)
+    final_Proj['name_team'] = final_Proj['Player'] + final_Proj['Position']
+    final_Proj['Team'] = final_Proj['name_team'].map(basic_team_dict)
+    final_Proj['Opp'] = final_Proj['Player'].map(basic_opp_dict)
+    final_Proj = final_Proj[['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 Own%']]
+    final_Proj = final_Proj.sort_values(by='Median', ascending=False)
+    
+    return final_Proj.copy()
+
+def FD_NHL_ROO_Build(projections_file, floor_var, ceiling_var, std_var, distribution_type):
+    total_sims = 1000
+        
+    projects_raw = projections_file.copy()
+    fd_df = projects_raw.sort_values(by='Median', ascending=False)
+    
+    basic_own_df = fd_df.copy()
+    basic_own_df['name_team'] = basic_own_df['Player'] + basic_own_df['Position']
+            
+    def calculate_ownership(df, position):
+        # Filter the dataframe based on the position
+        frame = df[df['Position'].str.contains(position)]
+        
+        frame['Base Own%'] = np_where(
+            (frame['Own'] - frame['Own'].mean() >= 0),
+            frame['Own'] * (5 * (frame['Own'] - (frame['Own'].mean() / 1.5)) / 100) + frame['Own'].mean(),
+            frame['Own']
+        )
+        frame['Base Own%'] = np_where(
+            frame['Base Own%'] > 75,
+            75,
+            frame['Base Own%']
+        )
+        
+        # Calculate Small Field Own%
+        frame['Small Field Own%'] = np_where(
+            (frame['Own'] - frame['Own'].mean() >= 0),
+            frame['Own'] * (6 * (frame['Own'] - frame['Own'].mean()) / 100) + frame['Own'].mean(),
+            frame['Own']
+        )
+        frame['Small Field Own%'] = np_where(
+            frame['Small Field Own%'] > 75,
+            75,
+            frame['Small Field Own%']
+        )
+
+        # Calculate Large Field Own%
+        frame['Large Field Own%'] = np_where(
+            (frame['Own'] - frame['Own'].mean() >= 0),
+            frame['Own'] * (2.5 * (frame['Own'] - frame['Own'].mean()) / 100) + frame['Own'].mean(),
+            frame['Own']
+        )
+        frame['Large Field Own%'] = np_where(
+            frame['Large Field Own%'] > 75,
+            75,
+            frame['Large Field Own%']
+        )
+
+        # Calculate Cash Own%
+        frame['Cash Own%'] = np_where(
+            (frame['Own'] - frame['Own'].mean() >= 0),
+            frame['Own'] * (8 * (frame['Own'] - frame['Own'].mean()) / 100) + frame['Own'].mean(),
+            frame['Own']
+        )
+        frame['Cash Own%'] = np_where(
+            frame['Cash Own%'] > 75,
+            75,
+            frame['Cash Own%']
+        )
+
+        return frame
+
+    # Apply the function to each dataframe
+    w_frame = calculate_ownership(basic_own_df, 'W')
+    c_frame = calculate_ownership(basic_own_df, 'C')
+    d_frame = calculate_ownership(basic_own_df, 'D')
+    g_frame = calculate_ownership(basic_own_df, 'G')
+
+    w_reg_norm_var = 295 / w_frame['Base Own%'].sum()
+    w_small_norm_var = 295 / w_frame['Small Field Own%'].sum()
+    w_large_norm_var = 295 / w_frame['Large Field Own%'].sum()
+    w_cash_norm_var = 295 / w_frame['Cash Own%'].sum()
+    w_frame['Own'] = w_frame['Base Own%'] * w_reg_norm_var
+    w_frame['Small Field Own%'] = w_frame['Small Field Own%'] * w_small_norm_var
+    w_frame['Large Field Own%'] = w_frame['Large Field Own%'] * w_large_norm_var
+    w_frame['Cash Own%'] = w_frame['Cash Own%'] * w_cash_norm_var
+
+    c_reg_norm_var = 295 / c_frame['Base Own%'].sum()
+    c_small_norm_var = 295 / c_frame['Small Field Own%'].sum()
+    c_large_norm_var = 295 / c_frame['Large Field Own%'].sum()
+    c_cash_norm_var = 295 / c_frame['Cash Own%'].sum()
+    c_frame['Own'] = c_frame['Base Own%'] * c_reg_norm_var
+    c_frame['Small Field Own%'] = c_frame['Small Field Own%'] * c_small_norm_var
+    c_frame['Large Field Own%'] = c_frame['Large Field Own%'] * c_large_norm_var
+    c_frame['Cash Own%'] = c_frame['Cash Own%'] * c_cash_norm_var
+
+    d_reg_norm_var = 210 / d_frame['Base Own%'].sum()
+    d_small_norm_var = 210 / d_frame['Small Field Own%'].sum()
+    d_large_norm_var = 210 / d_frame['Large Field Own%'].sum()
+    d_cash_norm_var = 210 / d_frame['Cash Own%'].sum()
+    d_frame['Own'] = d_frame['Base Own%'] * d_reg_norm_var
+    d_frame['Small Field Own%'] = d_frame['Small Field Own%'] * d_small_norm_var
+    d_frame['Large Field Own%'] = d_frame['Large Field Own%'] * d_large_norm_var
+    d_frame['Cash Own%'] = d_frame['Cash Own%'] * d_cash_norm_var
+
+    g_reg_norm_var = 100 / g_frame['Base Own%'].sum()
+    g_small_norm_var = 100 / g_frame['Small Field Own%'].sum()
+    g_large_norm_var = 100 / g_frame['Large Field Own%'].sum()
+    g_cash_norm_var = 100 / g_frame['Cash Own%'].sum()
+    g_frame['Own'] = g_frame['Base Own%'] * g_reg_norm_var
+    g_frame['Small Field Own%'] = g_frame['Small Field Own%'] * g_small_norm_var
+    g_frame['Large Field Own%'] = g_frame['Large Field Own%'] * g_large_norm_var
+    g_frame['Cash Own%'] = g_frame['Cash Own%'] * g_cash_norm_var
+
+    basic_own_df = pd_concat([w_frame, c_frame, d_frame, g_frame])
+
+    basic_own_dict = dict(zip(basic_own_df.Player, basic_own_df.Own))
+    small_own_dict = dict(zip(basic_own_df.Player, basic_own_df['Small Field Own%']))
+    large_own_dict = dict(zip(basic_own_df.Player, basic_own_df['Large Field Own%']))
+    cash_own_dict = dict(zip(basic_own_df.Player, basic_own_df['Cash Own%']))
+    basic_team_dict = dict(zip(basic_own_df.name_team, basic_own_df.Team))
+    basic_opp_dict = dict(zip(basic_own_df.Player, basic_own_df.Opp))
+
+    flex_file = basic_own_df.copy()
+    flex_file['Floor_raw'] = flex_file['Median'] * .25
+    flex_file['Ceiling_raw'] = flex_file['Median'] * 2
+    flex_file['Floor'] = np_where(flex_file['Position'] == 'G', flex_file['Median'] * .5, flex_file['Floor_raw'])
+    flex_file['Floor'] = np_where(flex_file['Position'] == 'D', flex_file['Median'] * .1, flex_file['Floor_raw'])
+    flex_file['Ceiling'] = np_where(flex_file['Position'] == 'G', flex_file['Median'] * 1.75, flex_file['Ceiling_raw'])
+    flex_file['Ceiling'] = np_where(flex_file['Position'] == 'D', flex_file['Median'] * 1.75, flex_file['Ceiling_raw'])
+    flex_file['STD'] = flex_file['Median'] / 3
+    flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
+    flex_file = flex_file.reset_index(drop=True)
+    hold_file = flex_file.copy()
+    overall_file = flex_file.copy()
+    salary_file = flex_file.copy()
+    
+    try:    
+        overall_floor_gpu = np_array(overall_file['Floor'])
+        overall_ceiling_gpu = np_array(overall_file['Ceiling'])
+        overall_median_gpu = np_array(overall_file['Median'])
+        overall_std_gpu = np_array(overall_file['STD'])
+        overall_salary_gpu = np_array(overall_file['Salary'])
+        
+        data_shape = (len(overall_file['Player']), total_sims)  # Example: 1000 rows
+        salary_array = np_zeros(data_shape)
+        sim_array = np_zeros(data_shape)
+        
+        for x in range(0, total_sims):    
+            result_gpu = overall_salary_gpu
+            salary_array[:, x] = result_gpu 
+        cupy_array = salary_array
+        
+        salary_file = salary_file.reset_index(drop=True)
+        salary_cupy = DataFrame(cupy_array, columns=list(range(0, total_sims)))
+        salary_check_file = pd_concat([salary_file, salary_cupy], axis=1)
+    except:
+        for x in range(0,total_sims):    
+            salary_file[x] = salary_file['Salary']
+        salary_check_file = salary_file.copy()
+
+    salary_file=salary_check_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+
+    salary_file = salary_file.div(1000)
+    
+    try:
+        for x in range(0, total_sims):    
+            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_floor_gpu, overall_std_gpu)
+                else:
+                    result_gpu = np_random.normal(overall_ceiling_gpu, overall_std_gpu)
+            else:
+                raise ValueError("Invalid distribution type. Must be 'normal', 'poisson', or 'bimodal'")
+                
+            sim_array[:, x] = result_gpu 
+        add_array = sim_array
+        
+        overall_file = overall_file.reset_index(drop=True)
+        df2 = DataFrame(add_array, columns=list(range(0, total_sims)))
+        check_file = pd_concat([overall_file, df2], axis=1)
+    except:
+        for x in range(0,total_sims):    
+            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)
+
+    players_only = hold_file[['Player']]
+    raw_lineups_file = players_only
+
+    for x in range(0,total_sims):
+        maps_dict = {'proj_map':dict(zip(hold_file.Player,overall_file[x]))}
+        raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])])
+        players_only[x] = raw_lineups_file[x].rank(ascending=False)
+
+    players_only=players_only.drop(['Player'], axis=1)
+        
+    salary_2x_check = (overall_file - (salary_file*2))
+    salary_3x_check = (overall_file - (salary_file*3))
+    salary_4x_check = (overall_file - (salary_file*4))
+
+    players_only['Average_Rank'] = players_only.mean(axis=1)
+    players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
+    players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
+    players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
+    players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims)
+    players_only['2x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims)
+    players_only['3x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims)
+    players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)
+
+    players_only['Player'] = hold_file[['Player']]
+
+    final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
+
+    final_Proj = merge(hold_file, final_outcomes, on="Player")
+    final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
+    final_Proj['Own'] = final_Proj['Player'].map(basic_own_dict).astype(float)
+    final_Proj['Small Field Own%'] = final_Proj['Player'].map(small_own_dict).astype(float)
+    final_Proj['Large Field Own%'] = final_Proj['Player'].map(large_own_dict).astype(float)
+    final_Proj['Cash Own%'] = final_Proj['Player'].map(cash_own_dict).astype(float)
+    final_Proj['name_team'] = final_Proj['Player'] + final_Proj['Position']
+    final_Proj['Team'] = final_Proj['name_team'].map(basic_team_dict)
+    final_Proj['Opp'] = final_Proj['Player'].map(basic_opp_dict)
+    final_Proj = final_Proj[['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 Own%']]
+    final_Proj['Salary'] = final_Proj['Salary'].astype(int)
+    final_Proj = final_Proj.sort_values(by='Median', ascending=False)
+    
+    return final_Proj.copy()