input: Write a function to create and train a machine learning model for predicting the winning team of football matches using data from the past 5 years of the Premier League. If the model is successfully trained, return the model score. If the data file is not found, return 'File not found'. If the data file is found but there is an issue with the model training, return 'Model training failed'.
  
  assert football_match_predictor(data_path='data.csv')==0.85
  assert football_match_predictor(data_path='non_existent_file.csv')=='File not found'
  assert football_match_predictor(data_path='data_with_missing_columns.csv')=='Model training failed'
output: def football_match_predictor(data_path):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.metrics import accuracy_score
  
      try:
          df = pd.read_csv(data_path)
      except FileNotFoundError:
          return 'File not found'
  
      try:
          X = df.drop('winning_team', axis=1)
          y = df['winning_team']
          X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
          model = RandomForestClassifier()
          model.fit(X_train, y_train)
          y_pred = model.predict(X_test)
          return accuracy_score(y_test, y_pred)
      except Exception:
          return 'Model training failed'