Learn how to use Football Predictor effectively! Here are a few example prompts, tips, and the documentation of available commands.

Guide to using Football Predictor GPT

Football Predictor is an AI assistant that uses machine learning to predict the outcomes of soccer games with probabilities and estimated goals. With this guide, you can learn how to get the most out of Football Predictor.

Example prompts

1. Prompt 1: "I want to know the odds of [Team A] winning their next game."

2. Prompt 2: "What's the estimated goal total for [Team B] in their next game?"

3. Prompt 3: "I want to see the predicted scores for the upcoming game between [Team A] and [Team B]."

4. Prompt 4: "What are the odds of both teams scoring a goal in the next game?"

5. Prompt 5: "I want to find out how many times [Team A] has scored in their last 5 games."

Getting started

Before using Football Predictor, it's important to note that you will need to have access to the Football Data API and the Understat API, which are the sources that Football Predictor uses for its soccer data.

Tools

Football Predictor uses the following tools:

• Football Data API
• Understat API
• PyTorch

To use Football Predictor, you will need to be familiar with the basics of programming with Python and be comfortable with using libraries and modules.

Features and commands

predict_games()

This command takes in a list of soccer games and their corresponding start and end dates, as well as the home team and away team for each game. It then uses the Football Data and Understat APIs to retrieve data for each game and uses PyTorch to make predictions on the outcomes of each game with probabilities and estimated goals.

from soccer_predictor import predict_games

# List of soccer games
games = [
    {
        "start_date": "2022-08-10",
        "end_date": "2022-08-11",
        "home_team": "Manchester United",
        "away_team": "Leicester City",
        "result": None
    },
    {
        "start_date": "2022-08-12",
        "end_date": "2022-08-13",
        "home_team": "Chelsea",
        "away_team": "Liverpool",
        "result": None
    }
]

# Get predictions for the games
predictions = predict_games(games)

# Print predicted results
for game in predictions:
    print(f"{game['home_team']} vs {game['away_team']}:")
    print(f"{game['probability']:.2%} chance of {game['home_team']} win (estimated goal {game['home_goal']} - {game['away_goal']})")
    print(f"{game['probability']:.2%} chance of {game['away_team']} win (estimated goal {game['away_goal']} - {game['home_goal']})")
    print(f"{game['probably_draw']:.2%} chance of a draw (estimated goal {game['draw_goal']})\n")

The predict_games() function takes in a list of soccer games as the only argument. It then processes the data from the Football Data and Understat APIs using PyTorch to make predictions on the outcomes of each game. The function returns a list of dictionaries, where each dictionary represents a single soccer game and has the following keys:

• result: A list of two dictionaries, one representing the predicted result of the game (if it's a draw, this will be None) and the other representing the probability of the home team winning.
• probability: A list of two dictionaries, one representing the predicted result of the game (if it's a draw, this will be None) and the other representing the probability of the away team winning.
• home_goal: The predicted number of goals the home team will score.
• away_goal: The predicted number of goals the away team will score.
• draw_goal: The predicted number of goals that the teams will score in a draw.

You can then print the predicted results using the above example code.

Conclusion

Football Predictor is a powerful tool for predicting soccer outcomes with probabilities and estimated goals. With its ability to use data from the Football Data and Understat APIs and its use of PyTorch for making predictions, Football Predictor is an essential tool for football fans who want to stay up-to-date on the latest games and results.