diff --git a/.DS_Store b/.DS_Store
index 4b2dabf..b06a9fd 100644
Binary files a/.DS_Store and b/.DS_Store differ
diff --git a/app/src/flask-server/modele/modele.py b/app/src/flask-server/modele/modele.py
index f9d1884..db1c82e 100644
--- a/app/src/flask-server/modele/modele.py
+++ b/app/src/flask-server/modele/modele.py
@@ -99,7 +99,7 @@ def xgboost_predict_proba_v2(shooter,goalkeeper,teamMatesList,opponentsList, min
        'shot_open_goal','shot_follows_dribble', 'shot_redirect',
        'x1', 'y1','number_of_players_opponents',
        'number_of_players_teammates','angle', 'distance', 
-       'x_player_opponent_Goalkeeper',
+       'x_player_opponent_Goalkeeper', 'x_player_opponent_8',
        'x_player_opponent_1', 'x_player_opponent_2','x_player_opponent_3',
        'x_player_opponent_4','x_player_opponent_5', 'x_player_opponent_6',
        'x_player_teammate_2','x_player_opponent_9', 'x_player_opponent_10',
@@ -156,7 +156,7 @@ def xgboost_predict_proba_v2(shooter,goalkeeper,teamMatesList,opponentsList, min
        shot_open_goal,shot_follows_dribble, shot_redirect, 
        shooter[0], shooter[1],number_of_players_opponents, 
        number_of_players_teammates,angle, distance,
-       x_player_opponent_Goalkeeper,
+       x_player_opponent_Goalkeeper, opponentsList[8][0],
        opponentsList[0][0], opponentsList[1][0], opponentsList[2][0],
        opponentsList[3][0], opponentsList[4][0], opponentsList[5][0],
        opponentsList[6][0], opponentsList[7][0], teamMatesList[8][0],
@@ -176,7 +176,10 @@ def xgboost_predict_proba_v2(shooter,goalkeeper,teamMatesList,opponentsList, min
        x_player_teammate_Goalkeeper, y_player_teammate_Goalkeeper,
        shot_kick_off]
    
-
+   categorical_columns = ['position_name', 'shot_technique_name', 'shot_type_name', 'number_of_players_opponents', 'number_of_players_teammates', 'shot_body_part_name']  # list all your object columns here
+   # X_new = pd.get_dummies(X_new, columns=categorical_columns)
+   X_new[categorical_columns] = X_new[categorical_columns].astype('category')
+   
    return model.predict_proba(X_new)[0][1].round(2)