GAP (Generalized Attacking Performance) Algorithm

Introduction

The Generalized Attacking Performance (GAP) Algorithm is a statistical model developed to assess the attacking and defensive capabilities of football teams. It was introduced by Edward Wheatcroft at the London School of Economics and Political Science (LSE) as part of his research on predicting Over/Under 2.5 goals outcomes in football matches.

GAP ratings are designed to go beyond traditional goal-based evaluations by incorporating deeper match statistics such as shots, shots on target, and corners to measure team performance. This approach helps provide a more accurate prediction of total goals in a match.

How the GAP Algorithm Works

GAP ratings assign attacking and defensive performance scores to each team based on their match history. These ratings are updated dynamically after every match, ensuring that recent form is reflected in future predictions.

Each team receives four GAP ratings:

Home Attacking Rating (Ha) – Measures a team's attacking strength when playing at home.

Home Defensive Rating (Hd) – Measures a team's defensive capabilities when playing at home.

Away Attacking Rating (Aa) – Measures a team's attacking strength when playing away.

Away Defensive Rating (Ad) – Measures a team's defensive capabilities when playing away.

The GAP formula updates these ratings based on a team’s performance in each match. If a team exceeds expectations in attack, their attacking ratings increase. Conversely, if they perform poorly in defense, their defensive ratings worsen.

Key Insights from GAP Ratings

1. Beyond Goals – Using Match Statistics

Unlike traditional models that rely on the number of goals scored, GAP ratings use match statistics like shots, shots on target, and corners. These metrics provide a more stable measure of a team's attacking and defensive capabilities, as goals can sometimes be influenced by luck or rare events.

2. Adaptability for Different Leagues

The GAP model was tested on 10 major European football leagues, including:

English Premier League

Spanish La Liga

German Bundesliga

Italian Serie A

French Ligue 1

It was found that using shots and corners instead of goals provided better predictive accuracy in estimating Over/Under 2.5 goal probabilities.

3. Performance Trends Over Time

The model demonstrated that teams tend to maintain consistent attacking and defensive trends over time. However, adjustments are needed for factors such as transfers, managerial changes, and form fluctuations.

4. Comparison with Other Models

The GAP Algorithm builds upon previous football rating models like:

Elo Ratings (used for FIFA world rankings)

Pi-Ratings (which estimate goal differences)

Poisson Regression Models (used for match outcome probabilities)

Unlike these models, GAP Ratings separate attacking and defensive capabilities, making it particularly effective for predicting total goals rather than just match outcomes.

Conclusion

The GAP Algorithm provides a data-driven method for assessing team strength based on attacking and defensive performance. By using shots and corners instead of goals, it helps improve predictive accuracy in football analytics. This model continues to evolve and could be applied to other football markets, such as expected goals (xG) and match outcomes.

Full Documentation

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All predictions are based purely on mathematical algorithms and historical data. This website does not encourage gambling in any form and should be used strictly for informational and analytical purposes. I do not take any responsibility for any financial or legal consequences arising from the use of this data.