Enhance VORP calculations in Streamlit app by introducing adjusted rankings and position locks for players, improving accuracy in player evaluations and rankings across positions.

src/streamlit_app.py

@@ -363,13 +363,27 @@ def assign_vorp(frame: pd.DataFrame, halfPpr_rv: dict, custom_rv: dict, league_s
     vorp_frame = pd.DataFrame()
     for positions in ['QB', 'RB', 'WR', 'TE']:
         pos_frame = frame[frame['Pos'] == positions]
-        pos_frame = pos_frame[pos_frame['Rank'] != 0].reset_index(drop=True)
-        pos_frame = pos_frame.sort_values(by='Rank', ascending=True)
+        pos_frame = pos_frame[pos_frame['Rank'] != 0]
+        pos_frame = pos_frame.sort_values(by='Rank', ascending=True).reset_index(drop=True)
+        pos_frame['custom_rank_raw'] = pos_frame[rv_type].rank(method='first', ascending=False)
+        pos_frame['custom_calc'] = ((pos_frame['custom_rank_raw'] +  pos_frame['Rank']) / 2).astype(int)
+        pos_frame['custom_proj_rank'] = pos_frame['custom_calc'].rank(method='first', ascending=True).astype(int)
+
+        pos_frame['max_halfPpr'] = pos_frame['halfPpr'].max()
+        pos_frame['halfPpr_range'] = (pos_frame['max_halfPpr']) / len(pos_frame)
+        pos_frame['adj_halfPpr'] = pos_frame['halfPpr_range'] * (len(pos_frame) - (pos_frame.index))
+
+        pos_frame['max_custom'] = pos_frame[rv_type].max()
+        pos_frame['custom_range'] = (pos_frame['max_custom']) / len(pos_frame)
+        pos_frame['adj_custom'] = pos_frame['custom_range'] * (len(pos_frame) - (pos_frame['custom_proj_rank'] - 1))
+        pos_frame['pos_lock'] = (pos_frame['Pos'].astype(str) + pos_frame['custom_proj_rank'].astype(str))
+
+        print(pos_frame[['Name', 'Rank', 'custom_proj_rank', 'halfPpr', rv_type, 'adj_halfPpr', 'adj_custom', 'pos_lock']].head(20))
         
         pos_frame['halfPpr_rv'] = halfPpr_rv[positions]
         pos_frame['custom_rv'] = custom_rv[positions]
-        pos_frame['halfPpr_VORP'] = pos_frame['halfPpr'] - halfPpr_rv[positions]
-        pos_frame['custom_VORP'] = pos_frame[rv_type] - custom_rv[positions]
+        pos_frame['halfPpr_VORP'] = pos_frame['adj_halfPpr'] - halfPpr_rv[positions]
+        pos_frame['custom_VORP'] = pos_frame['adj_custom'] - custom_rv[positions]
         
         vorp_frame = pd.concat([vorp_frame, pos_frame]).reset_index(drop=True)
 
@@ -378,11 +392,13 @@ def assign_vorp(frame: pd.DataFrame, halfPpr_rv: dict, custom_rv: dict, league_s
     vorp_frame['vorp_diff'] = np.where(vorp_frame['halfPpr_VORP'] == vorp_frame['custom_VORP'], 0, vorp_frame['halfPpr_vorp_rank'] - vorp_frame['custom_vorp_rank'])
     for positions in ['QB', 'RB', 'WR', 'TE']:
         vorp_frame.loc[vorp_frame['Pos'] == positions, 'Rank_Adjust'] = (vorp_frame['Rank'] - (vorp_frame['vorp_diff'] * pos_vorp_limiters[positions])).astype(float)
+    pos_lock_dict = dict(zip(vorp_frame['pos_lock'], vorp_frame['Name']))
+    name_lock_dict = dict(zip(vorp_frame['Name'], vorp_frame['pos_lock']))
     vorp_frame['custom_rank'] = vorp_frame['Rank_Adjust'].rank(method='first', ascending=True)
+    vorp_frame['pos_rank'] = vorp_frame.groupby('Pos')['custom_rank'].rank(method='first', ascending=True).astype(int)
+    vorp_frame['pos_designation'] = vorp_frame['Pos'] + vorp_frame['pos_rank'].astype(str)
 
-    print(vorp_frame.sort_values(by='custom_vorp_rank', ascending=True).head(50))
-
-    return vorp_frame.sort_values(by='custom_rank', ascending=True)
+    return vorp_frame.sort_values(by='custom_rank', ascending=True), pos_lock_dict, name_lock_dict
 
 def main():
     st.title("Fantasy Football VORP Calculator")
@@ -406,7 +422,10 @@ def main():
     custom_rv = create_custom_rv(position_df, custom_pos_reqs, user_league_settings)
     
     # Calculate VORP and rankings
-    final_df = assign_vorp(position_df, halfPpr_rv, custom_rv, user_league_settings, user_pos_vorp_limiters)
+    final_df, pos_lock_dict, name_lock_dict = assign_vorp(position_df, halfPpr_rv, custom_rv, user_league_settings, user_pos_vorp_limiters)
+    final_df = final_df.drop(columns=['SR_ID'], axis=1)
+    final_df['pos_lock_lu'] = final_df['Name'].map(name_lock_dict)
+    final_df['pos_lock_name'] = final_df['pos_designation'].map(pos_lock_dict)
     
     # Display results
     st.header("Player Rankings")