input: Write a function to develop a ML model in Python for predicting the winning team of football matches using data from the past 5 years of the Premier League. The function should load the data, select relevant features, split the data into training and test sets, train a GradientBoostingClassifier, make predictions, and evaluate the model.
  
  assert develop_football_match_predictor('data.csv') is not None
  assert develop_football_match_predictor('nonexistent_file.csv') is None
  assert type(develop_football_match_predictor('data.csv')) == float
output: def develop_football_match_predictor(file_path):
      try:
          import pandas as pd
          from sklearn.model_selection import train_test_split
          from sklearn.ensemble import GradientBoostingClassifier
          from sklearn.metrics import accuracy_score
          
          data = pd.read_csv(file_path)
          features = data.drop('winning_team', axis=1)
          target = data['winning_team']
          
          X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)
          
          model = GradientBoostingClassifier()
          model.fit(X_train, y_train)
          
          y_pred = model.predict(X_test)
          accuracy = accuracy_score(y_test, y_pred)
          
          return accuracy
      except FileNotFoundError:
          return None