How Our Football Predictions Work
A transparent, plain-English explanation of what we do (and what we don't)
Let's Talk About Football Predictions
Imagine you're trying to predict the weather. You could look outside right now and guess, or you could look at satellite data, pressure systems, historical patterns, and decades of meteorological records. You still won't be right 100% of the time—weather is complex—but you'll be right more often than someone just guessing.
That's what we do with football. We can't tell you with certainty who will win Saturday's match. Football isn't math. A goalkeeper can have the game of their life. A referee can make a controversial decision. A star player can wake up feeling unwell. A lucky deflection can change everything.
But here's what we can do: identify patterns that give you better odds than pure guessing. Like a weather forecast, we won't always be right—but we'll be right more often than chance.
This page explains exactly how we do it, what data we use, what our limitations are, and how you can use our predictions wisely. No jargon. No exaggerated claims. Just transparency.
Why Is Predicting Football So Hard?
Football is beautifully unpredictable. Think about these scenarios that happen regularly:
⚽ The Underdog Victory
Leicester City won the Premier League in 2016 at 5000-1 odds. Greece won Euro 2004. These aren't flukes—they're proof that football has inherent randomness that no model can fully capture.
🎯 The Missed Sitters
A team can dominate, create 20 chances, hit the post three times—and lose 1-0 to a team that had one shot all game. The better team doesn't always win.
👤 The Human Factor
Players get injured in warmups. Managers change tactics unexpectedly. A team fighting relegation plays with desperation. A red card in the 10th minute changes everything. These factors are nearly impossible to predict.
So why bother with predictions at all? Because patterns emerge over time. A team that consistently creates better chances will score more goals over a season. A team with a solid defense will concede less. Form matters. Head-to-head history matters. Home advantage is real.
Our Honest Success Rate
Our success rate varies by strategy: ~72% for 1X2 bets (home win, draw, or away win), and ~91% for Double Chance. That's significantly better than random chance (33% for 1X2, 67% for DC), but it also means we're wrong ~28% of the time for 1X2.
We'll never claim 90% accuracy for 1X2 bets. Those numbers require "cheating" with data (more on that later). These are our real, walk-forward validated performance numbers.
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.
Here's the key insight: If these teams played 10 times with similar performances, Liverpool would win 7-8 of them. The 1-1 draw was against the run of play. Betting on Liverpool in the next match might be smart—the odds probably don't reflect how dominant they were.
This is why we don't just look at final scores. xG is more predictive than actual goals because it measures underlying performance, not luck.
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 friend who watches every match and remembers everything.
Step-by-Step Process
Collect Historical Data
For every match, we gather 3+ 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, ~9 years), with 253 engineered features per match
Train Two Specialized Models
We use machine learning (think: pattern recognition software) 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 prediction: Liverpool vs. Burnley → Home pG: 2.3, Away pG: 0.6
This means Liverpool is expected to create chances worth 2.3 goals, Burnley chances worth 0.6 goals. From this, we can calculate the probability of home win, draw, or away win.
Apply Betting Strategies
Raw predictions aren't enough. We need to decide: should you bet on this?We apply optimized strategies that compare our predictions against bookmaker odds to find value.
More on this in the "Betting Strategies" section below
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 (3 H2H matches vs. 15 H2H matches)
- • Our historical accuracy on similar predictions
The Result
You get: predicted goals for both teams, our recommended bet, confidence score, and transparent reasoning for why we predict what we predict. No black box. Full transparency.
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
Match Context
Context matters. We factor in:
- • Rest days since last match (fatigue matters)
- • Fixture congestion (playing 3 games in 7 days?)
- • Season stage (desperate for points at season end?)
- • Bookmaker odds (market wisdom as a sanity check)
League Position & Form
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)
Our Four Betting Strategies
Having a prediction isn't enough. You need a strategy—a decision framework for how to bet. We offer four approaches, each optimized for different goals:
Best PickRECOMMENDED
The smart selector: Automatically picks the optimal bet
Think of this as having an experienced bettor decide for you. The system analyzes each match and automatically chooses between 1X2 (higher risk, higher reward) or Double Chance (safer bet) based on confidence and odds.
How It Works:
- • High confidence + good odds? → Goes for 1X2 (home/draw/away)
- • Lower confidence or tight odds? → Switches to Double Chance
- • Focus: Maximizing win rate while still getting decent returns
Best for: Most users—especially if you're unsure which strategy to use. Let the system make the call based on the specific match context.
High ROIVALUE HUNTER
The profit maximizer: Picks bets with best odds value
This strategy is all about Return on Investment (ROI). Instead of just picking the most likely outcome, it looks for matches where the bookmaker odds are generous relative to our prediction—where you get the most bang for your buck.
How It Works:
- • Calculates expected value for each bet type (1X2 and DC)
- • Picks the bet with highest ROI, even if less likely to win
- • Example: 55% win chance at 2.50 odds beats 70% chance at 1.30 odds
- • Focus: Long-term profit over short-term win rate
Real Example:
Our model gives Team A a 60% chance to win (1X2 odds: 2.00). It also gives Double Chance 1X a 75% chance (odds: 1.40). Best Pick would choose DC for safety. High ROI picks the 1X2 because (60% × 2.00) = 1.20 expected return vs. (75% × 1.40) = 1.05 expected return.
Best for: Experienced bettors with good bankroll management who understand variance. You'll win less often but earn more per bet. Requires patience and discipline.
1X2: The Classic Bet
Home Win · Draw · Away Win
Traditional three-way betting. Pick exactly who wins or if it's a draw. Higher odds mean better returns, but you need to be exactly right.
Strategy Details:
- • Uses optimized thresholds based on predicted goal difference
- • Predicts "Home Win" if home pG - away pG > threshold
- • Predicts "Away Win" if away pG - home pG > threshold
- • Otherwise predicts "Draw"
Best for: Traditional bettors who want clear predictions and better odds. Accept higher risk for higher reward.
Double Chance: The Safe Bet
1X (Home or Draw) · 12 (Either Wins) · X2 (Draw or Away)
Cover two of the three possible outcomes. Much higher win rate, but lower odds. Great for conservative betting or building accumulators.
Strategy Details:
- • Analyzes predicted goal difference and odds
- • If home team favored → "1X" (home win or draw)
- • If away team favored → "X2" (draw or away win)
- • If very close match → "12" (either team wins, no draw)
Best for: Risk-averse bettors who want consistent wins and steady returns. Lower stress, better sleep.
Quick Decision Guide
→Not sure? Use Best Pick
→Want maximum profit? Use High ROI (requires patience)
→Want better odds? Use 1X2
→Want safer bets? Use Double Chance
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? And more importantly: are the numbers real or inflated?
Walk-Forward Validation
We use a technique called "walk-forward validation"(also called time-series cross-validation). Here's what that means in plain English:
The Honest Way to Test a Model:
Train on old data: Use matches from Jan 2021 - Dec 2022 to train the model.
Test on new data: Predict Jan-Mar 2023 matches (the model has never seen these).
Roll forward: Retrain with Jan 2021 - Mar 2023, then predict Apr-Jun 2023.
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.
Temporal Data Filtering: No Cheating
Here's a dirty secret of the prediction industry: it's easy to get impressive accuracy by accidentally using future data. This is called "data leakage" or "look-ahead bias".
Example of Cheating (We Don't Do This)
Imagine calculating "Team A's average goals per game this season" and using that to predict a match from middle of the season. You're including goals from matches that happened after the one you're predicting. That's cheating—you're using future information.
Many prediction sites do this accidentally (or deliberately) to inflate their accuracy numbers. The model looks great in testing but fails in real-world use.
How We Prevent This
Every single 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.
The cost? Our accuracy dropped from 80%+ to ~72% when we fixed this. But ~72% is the real number—what you'll actually get.
Our Real, Validated Performance
Based on walk-forward validation on 2,033 Premier League matches (2016-2025)
Combining strategies (hedging with DC) can push success rates higher but reduces ROI
What We're NOT Capturing Yet (And Why That's Exciting)
Our ~72% accuracy is good—but there's room to improve. Here's what we're notanalyzing yet, and what that means for the future:
Player-Level Context
Currently missing: Individual player injuries, suspensions, form, and availability.
Why it matters: If Liverpool's best striker is injured, that dramatically affects their expected goals. Right now, we model team-level performance but miss player-specific impacts.
Tactical Matchups
Currently missing: Manager tactics, formations, playing styles, and how they interact.
Why it matters: Some formations counter others. A high-pressing team struggles against a counter-attacking team. We don't model this yet.
Weather & Pitch Conditions
Currently missing: Rain, wind, temperature, pitch quality.
Why it matters: Heavy rain favors defensive teams. Some teams perform worse in cold weather. Small effects, but they add up.
Motivation & Psychology
Currently missing: Team motivation, rivalry intensity, relegation battles, championship pressure.
Why it matters: A team fighting relegation in the last match of the season will play differently than one with nothing to play for. Hard to quantify but very real.
In-Game Events
Currently missing: Live updates for red cards, injuries during the match, tactical changes.
Why it matters: A red card in the 10th minute completely changes the match. We could offer live-adjusted predictions, but don't yet.
Why This Is Actually Good News
We're already achieving ~72% accuracy withoutmodeling player injuries, tactics, or motivation. These are known factors that influence outcomes—which means there's significant room for improvement as we add them.
Many prediction models already tap into these factors and still struggle to beat 65%. The fact that we're hitting ~72% with a relatively simple feature set suggests our foundation is solid.
Realistic Improvement Path
Adding the missing pieces won't take us to 100% accuracy (impossible in football). But getting to 75-80% success rate is realistic with player-level data. That would be exceptional performance.
- →Short term: Add injury/suspension data (estimated +2-3% accuracy)
- →Medium term: Model tactical matchups (estimated +1-2% accuracy)
- →Long term: Advanced features like referee analysis, psychological factors
The Bottom Line: What You Need to Know
Let's be direct about what we offer and what we don't:
✓What We Do Well
- •Significantly better than random guessing (72-91% depending on strategy vs 33-67% chance)
- •Full transparency—real numbers, not inflated claims
- •Based on proven metrics (xG, validated by academics)
- •Multiple strategies for different risk preferences
- •Explainable—you see exactly why we predict what we predict
✗What We Don't Claim
- •We're not 80-90% accurate (those are fake numbers)
- •We don't guarantee profits (variance is real)
- •We can't predict referee decisions or freak events
- •We don't have insider information or "fixed matches"
- •We won't make you rich quick (this isn't a casino)
Our Goal: Give You an Edge, Not a Guarantee
Think of us as your research assistant. We spend hours analyzing data so you don't have to. We identify patterns, calculate probabilities, and highlight value opportunities. But the final decision is always yours.
Use our predictions as one input in your decision-making, not the only input. Watch the teams. Check team news. Consider your own knowledge. Bet responsibly.
Responsible Betting Reminder
Sports betting should be entertainment, not a way to make money. Even with a ~72% success rate, you will lose bets. Variance and bad luck happen.
- • Only bet what you can afford to lose
- • Set a budget and stick to it (never chase losses)
- • Don't bet when emotional or desperate
- • Take breaks and maintain perspective
- • If gambling stops being fun, stop gambling
Need help? Visit BeGambleAware.org or call the National Gambling Helpline
Why Trust Us?
Ready to start? Check out our latest predictions for upcoming matches.
All predictions updated daily. New matches added automatically.