input: from typing import Tuple
  
  def predict_football_match_winner(data_path: str) -> float:
      """ Loads football match data from the past 5 years of the Premier League
      and trains a GradientBoostingClassifier to predict the winning team.
      The function returns the accuracy score of the model on the test set.
      >>> predict_football_match_winner('data.csv')
      0.85
      """
output:     import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import GradientBoostingClassifier
      from sklearn.metrics import accuracy_score
  
      # Load the data
      df = pd.read_csv(data_path)
  
      # Preprocess the data
      X = df.drop(columns=['winner'])
      y = df['winner']
  
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Train the model
      model = GradientBoostingClassifier()
      model.fit(X_train, y_train)
  
      # Predict and calculate accuracy
      y_pred = model.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
  
      return accuracy