Opta is the gold standard of football data. Understanding what it tracks and how to interpret it gives you a significant analytical edge over bettors relying on basic statistics.
What Opta Tracks
Every Premier League match generates approximately 2,000 tagged events. Opta's analysts classify each event by type, location, and outcome. Key categories include:
Attacking Metrics
- Expected Goals (xG): Probability of each shot resulting in a goal, based on historical data from similar shots
- Shot-Creating Actions (SCA): Passes, dribbles, or actions leading directly to a shot
- Progressive Passes: Forward passes that move the ball at least 10 metres toward the opponent's goal
Defensive Metrics
- PPDA (Passes Per Defensive Action): Measures pressing intensity — lower PPDA means more aggressive pressing
- High Press Sequences: Possession recoveries in the attacking third
- Interceptions and Blocks: Detailed defensive event tracking
Possession Metrics
- Progressive Carries: Dribbles moving the ball significantly forward
- Touches in Penalty Area: Indicator of attacking threat
- Build-up Play Metrics: Passes in various pitch zones
How to Access Opta Data for Free
You do not need a commercial Opta licence. These platforms provide Opta-derived metrics:
- FBref.com: The most comprehensive free source. Advanced stats including xG, xAG, progressive passes, and defensive actions for all major leagues
- WhoScored.com: Match ratings, heatmaps, and detailed team/player statistics
- Squawka.com: Comparison tools and pass maps
- Understat.com: xG data with shot maps for top five European leagues
Applying Opta Data to Betting
Step 1: Build a Team Profile
For each team, track rolling 10-match averages of: xG for, xGA against, PPDA, and shot-creating actions. This gives you a stable picture of their true attacking and defensive quality.
Step 2: Compare to Actual Results
Calculate the gap between xG and actual goals (both for and against). Teams with large positive gaps (scoring more than xG) are overperforming. Teams with large negative gaps are underperforming.
Step 3: Identify Market Inefficiencies
Bookmaker odds reflect actual results more than underlying metrics. When a team's odds are too short (based on inflated results) or too long (based on unlucky results), Opta data helps you identify the true value.
Building a Simple Opta-Based Model
Download FBref data for your target league. Create a spreadsheet with columns for: team, matches played, xG, xGA, actual goals for, actual goals against, xG difference, and actual goal difference. Sort by xG difference to see which teams are genuinely strongest — this often differs significantly from the actual league table.