Refactor contest length metric calculations in app.py

- Simplified the calculation of contest length metrics by directly multiplying the length of the session state 'Contest' dataframe with percentage values, improving code clarity and accuracy.
- This change enhances the reliability of player count metrics and streamlines the data processing workflow.

app.py

@@ -233,10 +233,10 @@ with tab2:
             players_10per = working_df.head(int(len(working_df) * 0.10))
             players_20per = working_df.head(int(len(working_df) * 0.20))
             contest_len = len(st.session_state['Contest'])
-            len_1per = len(st.session_state['Contest']).head(int(len(st.session_state['Contest']) * 0.01))
-            len_5per = len(st.session_state['Contest']).head(int(len(st.session_state['Contest']) * 0.05))
-            len_10per = len(st.session_state['Contest']).head(int(len(st.session_state['Contest']) * 0.10))
-            len_20per = len(st.session_state['Contest']).head(int(len(st.session_state['Contest']) * 0.20))
+            len_1per = len(st.session_state['Contest']) * 0.01
+            len_5per = len(st.session_state['Contest']) * 0.05
+            len_10per = len(st.session_state['Contest']) * 0.10
+            len_20per = len(st.session_state['Contest']) * 0.20
         player_counts = pd.Series(list(contest_players[player_columns].values.flatten())).value_counts()
         player_1per_counts = pd.Series(list(players_1per[player_columns].values.flatten())).value_counts()
         player_5per_counts = pd.Series(list(players_5per[player_columns].values.flatten())).value_counts()