cleaing up some prints

src/sports/nhl_functions.py

@@ -729,8 +729,6 @@ def build_dk_player_level_basic_outcomes(slate_info, dk_player_hold, fd_player_h
         feature_cols = ['Salary', 'Actual', 'actual_adv', 'value', 'value_adv', 'contest_size', 'base_ownership', 'value_play', 'value_density', 'strong_play', 'punt_play']
         X_current = basic_own_df[feature_cols]
 
-        st.write(X_current)
-
         # Use simplified ownership prediction (faster than ML models)
         # Base prediction on value and salary
         basic_own_df['Combo'] = (
@@ -1316,7 +1314,6 @@ def build_dk_stack_matrix_basic_outcomes(slate_info, dk_stacks_hold, own_dict):
         stacks_final_Proj_line_dk_basic = stacks_final_Proj.sort_values(by='Median', ascending=False)
         dk_stacks_hold = pd_concat([dk_stacks_hold, stacks_final_Proj_line_dk_basic])
         st.write(f'finished {slates} DK basic stack matrix')
-        st.write(stacks_final_Proj.head(10))
     st.write(f'finished DK basic stack matrix')
     return dk_stacks_hold
 
@@ -1600,7 +1597,6 @@ def build_fd_stack_matrix_basic_outcomes(slate_info, fd_stacks_hold):
         stacks_final_Proj_line_fd_basic = stacks_final_Proj.sort_values(by='Median', ascending=False)
         fd_stacks_hold = pd_concat([fd_stacks_hold, stacks_final_Proj_line_fd_basic])
         st.write(f'finished {slates} FD basic stack matrix')
-        st.write(stacks_final_Proj.head(10))
     st.write(f'finished FD basic stack matrix')
 
     return fd_stacks_hold