input: 
  import pandas as pd
  import numpy as np
  from sklearn.model_selection import train_test_split
  from sklearn.ensemble import GradientBoostingClassifier
  
  def predict_football_match_winner(data: pd.DataFrame) -> float:
      """ This function develops a machine learning model to predict the winning team of football matches.
      It uses data from the past 5 years of the Premier League. The function loads data, selects relevant
      features, trains a Gradient Boosting Classifier, and evaluates the model's performance.
      
      >>> data = pd.DataFrame({
      ...    'home_team_goals': [2, 1, 3, 0],
      ...    'away_team_goals': [1, 2, 2, 1],
      ...    'home_team_possession': [60, 55, 50, 45],
      ...    'away_team_possession': [40, 45, 50, 55],
      ...    'home_team_shots': [10, 8, 12, 5],
      ...    'away_team_shots': [6, 9, 11, 4],
      ...    'home_team_yellow_cards': [2, 1, 0, 1],
      ...    'away_team_yellow_cards': [1, 3, 2, 0],
      ...    'home_team_red_cards': [0, 0, 0, 0],
      ...    'away_team_red_cards': [0, 0, 0, 0],
      ...    'winner': ['home', 'away', 'home', 'away']
      ... })
      >>> predict_football_match_winner(data)
      0.5
      """
      # Select features
      features = data[['home_team_goals', 'away_team_goals', 'home_team_possession', 'away_team_possession',
                       'home_team_shots', 'away_team_shots', 'home_team_yellow_cards', 'away_team_yellow_cards',
                       'home_team_red_cards', 'away_team_red_cards']]
      
      # Select target
      target = data['winner']
      
      # Split data into training and test sets
      X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)
      
      # Train the model
      gbm = GradientBoostingClassifier()
      gbm.fit(X_train, y_train)
      
      # Make predictions
      y_pred = gbm.predict(X_test)
      
      # Evaluate the model
      score = gbm.score(X_test, y_test)
      return score
  
output:     # Select features
      features = data[['home_team_goals', 'away_team_goals', 'home_team_possession', 'away_team_possession',
                       'home_team_shots', 'away_team_shots', 'home_team_yellow_cards', 'away_team_yellow_cards',
                       'home_team_red_cards', 'away_team_red_cards']]
      
      # Select target
      target = data['winner']
      
      # Split data into training and test sets
      X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)
      
      # Train the model
      gbm = GradientBoostingClassifier()
      gbm.fit(X_train, y_train)
      
      # Make predictions
      y_pred = gbm.predict(X_test)
      
      # Evaluate the model
      score = gbm.score(X_test, y_test)
      return score