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How Our Football Predictions Work
A transparent look at how we predict match outcomes using data and machine learning
What Is ScoresAhead?
ScoresAhead is a football prediction tool that uses machine learning to forecast how many goals each team is likely to score in upcoming matches. We call these predictions pG (predicted Goals).
After matches finish, you can compare our predictions against what actually happened. Did we expect a 2-1 home win but it ended 0-0? That's the kind of insight our Results page provides.
Our goal is simple: help you understand football matches better by showing what the data suggests should happen, then letting you see how reality compared.
This page explains exactly how we generate predictions, what data we use, and what our limitations are. No jargon. No exaggerated claims. Just transparency.
What We Predict
For every upcoming match, we generate two numbers:
Home pG
The predicted number of goals the home team will score. Based on their attacking strength vs the away team's defensive ability.
Away pG
The predicted number of goals the away team will score. Based on their attacking strength vs the home team's defensive ability.
Example Prediction
This prediction suggests Liverpool should create chances worth ~2.1 goals while United creates chances worth ~0.9 goals. The implied scoreline is a 2-1 home win.
After the match, you can see the actual score and compare. Maybe it ended 2-0, or 1-1, or 3-2. Over time, you can see how well our predictions track reality.
The Foundation: Expected Goals (xG)
Before we explain our model, you need to understand Expected Goals (xG)—the metric that modern football analytics is built on.
Think of xG Like This:
Imagine you have 100 identical matches where a striker gets the exact same chance: same distance from goal, same angle, same pass type. If they score 80 times out of 100, that chance has an xG of 0.8. If they only score 15 times out of 100, that chance has an xG of 0.15.
xG measures the quality of chances, not just the quantity of goals.
Real Example: The Misleading 1-1 Draw
Looking at just the scoreline, you'd think it was a close match. Maybe Burnley played well and deserved the point.
The xG tells the real story: Liverpool created chances worth 3.2 goals. Burnley's chances were only worth 0.4 goals. Liverpool dominated but had poor finishing. Burnley got lucky.
The key insight: xG is more predictive than actual goals because it measures underlying performance, not luck. A team that consistently out-xGs opponents will win more over a season.
What Factors Determine xG?
How Our Prediction System Works
Now that you understand xG, here's how we use it to make predictions. Think of our system as a very informed analyst who watches every match and remembers everything.
Step-by-Step Process
Collect Historical Data
For every match, we gather years of historical data. Not just final scores, but the full story: how many quality chances each team created, defensive performance, home/away splits, head-to-head history, and more.
Currently analyzing: 2,000+ matches per league (2016-2025), with 360+ engineered features per match
Train Two Specialized Models
We use machine learning to build two separate prediction models:
Home Goals Model
Learns patterns like: "When Team A plays at home against teams with weak defenses, they typically create chances worth 2.3 goals"
Away Goals Model
Learns patterns like: "When Team B plays away against strong pressing teams, they typically create chances worth 0.9 goals"
Generate Predictions
For an upcoming match, both models analyze the matchup and output Predicted Goals (pG)—how many goals' worth of chances we expect each team to create.
Example: Liverpool vs Burnley → Home pG: 2.3, Away pG: 0.6
This means Liverpool is expected to create chances worth 2.3 goals, while Burnley is expected to create chances worth 0.6 goals.
Calculate Confidence
Every prediction gets a confidence score (0-100%) based on:
- • How certain the model is (clear favorite vs. 50-50 match)
- • How much quality data we have for these teams
- • Head-to-head history availability
The Result
You get: predicted goals for both teams, a confidence score, and after the match, the actual result so you can see how we did.
What Data We Actually Analyze
When you hear "machine learning model," you might picture magic. It's not. It's pattern recognition based on specific features. Here's exactly what we analyze:
Head-to-Head History
Some teams just have other teams' number. We analyze their last 10+ meetings:
- • Historical xG when these teams face each other
- • Home/away splits in this specific matchup
- • Recent results (weighted more than old results)
- • Goal scoring patterns in this rivalry
Team Performance Metrics
How good is each team? We measure:
xG per match, shot quality, conversion rate
xG conceded, clean sheets, pressing intensity
Performance difference at home vs. away
Do they perform steadily or erratically?
Recent Form & Momentum
A team's last 5-10 matches tell us a lot:
- • xG trends (improving or declining?)
- • Win/draw/loss streak
- • Goals scored and conceded lately
- • Performance against strong vs. weak opponents
League Position & Standings
Where teams sit in the table provides context:
- • Overall league position
- • Home table position (some teams are fortress at home)
- • Away table position (some teams struggle on the road)
- • Form table (last 5-6 matches)
How Do We Know It Works?
Anyone can build a model that looks good on historical data. The real question is: does it work on new, unseen matches?
Walk-Forward Validation
We use a technique called "walk-forward validation". Here's what that means in plain English:
The Honest Way to Test a Model:
Train on old data: Use matches from 2021-2022 to train the model.
Test on new data: Predict 2023 matches (the model has never seen these).
Roll forward: Retrain with 2021-2023, then predict 2024.
Repeat: Keep rolling forward through time, always testing on data the model hasn't seen.
Why this matters: This mimics real-world usage. In reality, you can only use past data to predict future matches. Walk-forward validation ensures we're not cheating by "peeking" at future results.
No Data Leakage
A common problem in prediction systems is accidentally using future data. We're very careful to avoid this:
How We Prevent Cheating
Every metric we calculate uses ONLY data from before the match date.
- ✓Predicting a match on Jan 15? We calculate form using matches up to Jan 14.
- ✓League standings use only matches played before the prediction date.
- ✓H2H stats only include past meetings, never future ones.
What We Can't Predict
Football is beautifully unpredictable. Here's what our model cannot account for:
Tactical Matchups
Not modeled: Formation interactions, manager tactical adjustments, or playing style matchups.
How a 4-3-3 performs against a 3-5-2, or how teams adjust mid-game—these tactical nuances aren't captured.
Motivation & Psychology
Not modeled: Team motivation, rivalry intensity, relegation battles, title race pressure.
A team fighting relegation in the last match of the season will play differently than one with nothing to play for.
External Factors
Not modeled: Weather, referee assignments, pitch conditions.
Heavy rain favors defensive teams. Certain referees give more cards. Small effects, but they're not in our model.
Random Events
Unpredictable: Own goals, deflections, goalkeeping errors, early red cards.
Football has inherent randomness. Leicester won the league at 5000-1 odds. Greece won Euro 2004. These things happen.
Why This Matters
Our predictions represent what should happen on averagegiven the historical patterns. But individual matches can deviate significantly from expectations. That's what makes football exciting—and prediction imperfect.
The Bottom Line
Here's what ScoresAhead offers:
✓What We Provide
- •Data-driven predictions for upcoming matches
- •Comparison of predictions vs actual results
- •Full transparency about our methodology
- •Context with team stats, form, and H2H history
- •Confidence scores for each prediction
○What We Don't Claim
- •Perfect accuracy (football is unpredictable)
- •Knowledge of team news or injuries
- •Insider information
- •Guaranteed results
Our Goal: Help You Understand Matches Better
Think of our predictions as informed analysis—what the historical data suggests should happen. Compare our pG predictions against actual results over time and form your own view of how well the model performs.
Why Use ScoresAhead?
Check out our latest predictions for upcoming matches.