Refactor player matching logic in `find_name_mismatches.py` for improved accuracy

- Changed the player list used for matching from a set to a list, ensuring that the closest matches are derived from a consistent data structure.
- Updated the logic for finding closest matches to enhance the accuracy of identifying players missing from projections.

global_func/find_name_mismatches.py

@@ -20,6 +20,7 @@ def find_name_mismatches(contest_df, projections_df):
     for col in name_columns:
         portfolio_players.update(contest_df[col].unique())
     projection_players = set(projections_df['player_names'].unique())
+    portfolio_players_list = list(portfolio_players)
     projection_players_list = list(projection_players)
     
     # Find players in portfolio that are missing from projections
@@ -31,7 +32,7 @@ def find_name_mismatches(contest_df, projections_df):
         if not isinstance(player, str):
             st.warning(f"Skipping non-string value: {player}")
             continue
-        closest_matches = process.extract(player, portfolio_players, limit=1)
+        closest_matches = process.extract(player, portfolio_players_list, limit=1)
         if closest_matches[0][1] == 100:  # If perfect match found
             match_name = closest_matches[0][0]
             # Update all occurrences in contest_df
@@ -59,7 +60,7 @@ def find_name_mismatches(contest_df, projections_df):
             current_player = st.session_state.players_to_process[st.session_state.current_player_index]
             
             # Find the top 3 closest matches
-            closest_matches = process.extract(current_player, projection_players_list, limit=3)
+            closest_matches = process.extract(current_player, portfolio_players_list, limit=3)
             
             st.write(f"**Missing Player {st.session_state.current_player_index + 1} of {len(st.session_state.players_to_process)}:** {current_player}")