recent changes

app.py

@@ -1317,7 +1317,7 @@ if selected_tab == 'Manage Portfolio':
                     'Own': 500.0,
                     'Geomean': 500.0,
                     'Weighted Own': 500.0,
-                    'median': 500.0,
+                    'median': 1500.0,
                     'Finish_percentile': 1.0,
                     'Diversity': 1.0
                 }
@@ -1327,14 +1327,14 @@ if selected_tab == 'Manage Portfolio':
                     macro_min_col, macro_max_col = st.columns(2)
                     with macro_min_col:
                         min_salary = st.number_input("Min acceptable salary?", value=0, min_value=0, max_value=salary_max, step=100)
-                        min_proj = st.number_input("Min acceptable projection?", value=0.0, min_value=0.0, max_value=500.0, step=1.0)
+                        min_proj = st.number_input("Min acceptable projection?", value=0.0, min_value=0.0, max_value=1500.0, step=1.0)
                         min_own = st.number_input("Min acceptable ownership?", value=0.0, min_value=0.0, max_value=500.0, step=1.0)
                         min_dupes = st.number_input("Min acceptable dupes?", value=0, min_value=0, max_value=1000, step=1)
                         min_finish_percentile = st.number_input("Min acceptable finish percentile?", value=0.00, min_value=0.00, max_value=1.00, step=.001)
                         min_lineup_edge = st.number_input("Min acceptable Lineup Edge?", value=-1.00, min_value=-1.00, max_value=1.00, step=.001)
                     with macro_max_col:
                         max_salary = st.number_input("Max acceptable salary?", value=salary_max, min_value=0, max_value=salary_max, step=100)
-                        max_proj = st.number_input("Max acceptable projection?", value=500.0, min_value=0.0, max_value=500.0, step=1.0)
+                        max_proj = st.number_input("Max acceptable projection?", value=1500.0, min_value=0.0, max_value=1500.0, step=1.0)
                         max_own = st.number_input("Max acceptable ownership?", value=500.0, min_value=0.0, max_value=500.0, step=1.0)
                         max_dupes = st.number_input("Max acceptable dupes?", value=1000, min_value=1, max_value=1000, step=1)
                         max_finish_percentile = st.number_input("Max acceptable finish percentile?", value=1.00, min_value=0.00, max_value=1.00, step=.001)

global_func/load_dk_fd_file.py

@@ -14,6 +14,7 @@ sport_headers = {
             'SOC' : ['F1', 'F2', 'M1', 'M2', 'D1', 'D2', 'GK', 'UTIL'],
             'TENNIS' : ['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6'],
             'WNBA' : ['G1', 'G2', 'F1', 'F2', 'F3', 'UTIL'],
+            'NBA' : ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX'],
             'NASCAR' : ['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6'],
             'MLB' : ['SP1', 'SP2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3'],
             'NFL' : ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST'],
@@ -31,6 +32,7 @@ sport_headers = {
             'SOC' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],
             'TENNIS' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],
             'WNBA' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],
+            'NBA' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],
             'NASCAR' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],
             'MLB' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],
             'NFL' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],
@@ -50,6 +52,7 @@ sport_headers = {
             'SOC' : ['F1', 'F2', 'M1', 'M2', 'D1', 'D2', 'GK', 'UTIL'],
             'TENNIS' : ['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6'],
             'WNBA' : ['G1', 'G2', 'G3', 'F1', 'F2', 'F3', 'F4'],
+            'NBA' : ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C'],
             'NASCAR' : ['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6'],
             'MLB' : ['P', 'C/1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL'],
             'NFL' : ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST'],
@@ -67,6 +70,7 @@ sport_headers = {
             'SOC' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],
             'TENNIS' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],
             'WNBA' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],
+            'NBA' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],
             'NASCAR' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],
             'MLB' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],
             'NFL' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],

global_func/load_file.py

@@ -17,6 +17,7 @@ sport_headers = {
             'SOC' : ['F1', 'F2', 'M1', 'M2', 'D1', 'D2', 'GK', 'UTIL'],
             'TENNIS' : ['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6'],
             'WNBA' : ['G1', 'G2', 'F1', 'F2', 'F3', 'UTIL'],
+            'NBA' : ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX'],
             'NASCAR' : ['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6'],
             'MLB' : ['SP1', 'SP2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3'],
             'NFL' : ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST'],
@@ -34,6 +35,7 @@ sport_headers = {
             'SOC' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],
             'TENNIS' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],
             'WNBA' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],
+            'NBA' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],
             'NASCAR' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],
             'MLB' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],
             'NFL' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],
@@ -53,6 +55,7 @@ sport_headers = {
             'SOC' : ['F1', 'F2', 'M1', 'M2', 'D1', 'D2', 'GK', 'UTIL'],
             'TENNIS' : ['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6'],
             'WNBA' : ['G1', 'G2', 'G3', 'F1', 'F2', 'F3', 'F4'],
+            'NBA' : ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C'],
             'NASCAR' : ['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6'],
             'MLB' : ['P', 'C/1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL'],
             'NFL' : ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST'],
@@ -70,6 +73,7 @@ sport_headers = {
             'SOC' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],
             'TENNIS' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],
             'WNBA' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],
+            'NBA' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],
             'NASCAR' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],
             'MLB' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],
             'NFL' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],

global_func/load_ss_file.py

@@ -14,6 +14,7 @@ sport_headers = {
             'SOC' : ['F1', 'F2', 'M1', 'M2', 'D1', 'D2', 'GK', 'UTIL'],
             'TENNIS' : ['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6'],
             'WNBA' : ['G1', 'G2', 'F1', 'F2', 'F3', 'UTIL'],
+            'NBA' : ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX'],
             'NASCAR' : ['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6'],
             'MLB' : ['SP1', 'SP2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3'],
             'NFL' : ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST'],
@@ -31,6 +32,7 @@ sport_headers = {
             'SOC' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],
             'TENNIS' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],
             'WNBA' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],
+            'NBA' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],
             'NASCAR' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],
             'MLB' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],
             'NFL' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],
@@ -50,6 +52,7 @@ sport_headers = {
             'SOC' : ['F1', 'F2', 'M1', 'M2', 'D1', 'D2', 'GK', 'UTIL'],
             'TENNIS' : ['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6'],
             'WNBA' : ['G1', 'G2', 'G3', 'F1', 'F2', 'F3', 'F4'],
+            'NBA' : ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C'],
             'NASCAR' : ['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6'],
             'MLB' : ['P', 'C/1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL'],
             'NFL' : ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST'],
@@ -67,6 +70,7 @@ sport_headers = {
             'SOC' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],
             'TENNIS' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],
             'WNBA' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],
+            'NBA' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],
             'NASCAR' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],
             'MLB' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],
             'NFL' : ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'],

global_func/predict_dupes.py

@@ -151,9 +151,25 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
         percentile_multiplier = 1.10
     
     if sport_var == 'NFL':
-        own_baseline = 180
+        if type_var == 'Classic':
+            own_baseline = 150
+        elif type_var == 'Showdown':
+            own_baseline = 200
+    elif sport_var == 'NBA':
+        if type_var == 'Classic':
+            own_baseline = 180
+        elif type_var == 'Showdown':
+            own_baseline = 200
+    elif sport_var == 'NHL':
+        if type_var == 'Classic':
+            own_baseline = 80
+        elif type_var == 'Showdown':
+            own_baseline = 200
     else:
-        own_baseline = 120
+        if type_var == 'Classic':
+            own_baseline = 120
+        elif type_var == 'Showdown':
+            own_baseline = 200
     max_ownership = max(maps_dict['own_map'].values()) / 100
     average_ownership = np.mean(list(maps_dict['own_map'].values())) / 100