How to Predict Both Teams to Score: A Statistical Approach

Why BTTS Bets Are Worth Your Attention

Both Teams to Score sits at roughly 50-55% in the Premier League across a full season. That’s close enough to a coin flip that bookmakers price it tightly — which means even a small analytical edge translates into value.

I’ve tracked BTTS outcomes across the top five European leagues for the past three seasons. The market isn’t as efficient as people assume, particularly in the first few weeks of a season (when bookmakers haven’t calibrated to squad changes) and in congested fixture periods (when rotation leads to unpredictable defensive lapses).

Here’s the framework I use.

The Core Numbers: What Actually Predicts BTTS

1. Goals Per Game (Home and Away Split)

Not goals per game overall — the home/away split matters. Some teams are a different animal at home versus away. Wolves in 2023/24 averaged 1.4 goals per game at Molineux but just 0.7 on the road. Using the overall 1.05 average for BTTS predictions would have missed the pattern entirely.

Pull the home and away rates separately. FBref has this data for free.

2. Clean Sheet Rate (the BTTS Killer)

If a team keeps clean sheets in 35%+ of their matches, BTTS can only land in at most 65% of their games — and in practice it’s lower, because they also fail to score occasionally. Clean sheet rate is the single strongest negative predictor.

In 2024/25, Arsenal’s home clean sheet rate sat around 42% through December. Backing BTTS No in Arsenal home games during that window hit at about 55%. Not spectacular, but at average odds of 1.80, that’s profitable territory.

3. Failed to Score (FTS) Rate

The other side of the coin. How often does the team draw a blank? A team with an FTS rate above 30% is a drag on any BTTS Yes selection.

4. xG and xGA — Not Just Actual Goals

This is where it gets more predictive. A team might have scored in 6 straight matches, but if their xG was below 1.0 in four of those games, they were getting lucky with finishing. xG smooths out the noise.

What I look at: xGA above 1.2 on both sides. If both teams are conceding 1.2+ xGA per game, they’re giving up quality chances regularly — regardless of whether the goals have actually gone in yet.

The BTTS Probability Calculation

The basic formula is simpler than people expect:

P(BTTS) = P(Team A scores) × P(Team B scores)

Where P(Team A scores) = 1 – FTS rate, adjusted for venue and opponent strength.

Worked Example

Man City (home) vs Brighton (away), hypothetical:

• Man City home scoring rate: scored in 90% of home games → P = 0.90
• Brighton away scoring rate: scored in 65% of away games → P = 0.65

P(BTTS) = 0.90 × 0.65 = 0.585 (58.5%)

That implies fair odds of around 1.71. If a bookmaker offers BTTS Yes at 1.80, there’s roughly 5% value. Over 100 bets at that edge, expected profit is about +5 units.

The Adjustment I Make That Most Models Don’t

Raw scoring rates treat all opponents as equal. They’re not. Brighton scoring in 65% of away games includes trips to Luton and Bournemouth alongside trips to Arsenal and Liverpool. If the upcoming opponent is top-6, I knock 10-15% off the scoring probability. If the opponent is bottom-half, I add 5-10%.

It’s crude, but it matters. Without this adjustment, my model was overrating BTTS in matches involving elite defences and underrating it when both sides were mid-table.

The Red Flags (When to Back BTTS No)

1. Clean sheet machine at home — Any team keeping 35%+ clean sheets at home. Liverpool under Slot, Arsenal under Arteta.
2. Toothless attack away from home — FTS rate above 40% on the road. Burnley in 2023/24 failed to score away in 58% of matches.
3. Low xGA team — If either side concedes below 0.8 xGA per game, the other team genuinely struggles to create chances against them.
4. Tactical context — Teams fighting relegation at home often sit deep and play for a clean sheet first. The game becomes cagey.
5. Key attacker missing — If the team’s primary goalscorer is out (not a squad player, the main striker), FTS probability jumps.

League-by-League BTTS Rates (2024/25 Season)

Not all leagues behave the same. Your BTTS model needs to account for this.

League	BTTS Rate	Best for
Bundesliga	~58%	BTTS Yes — open, high-pressing football
Premier League	~54%	Both sides — depends heavily on the fixture
La Liga	~51%	More selective — top teams keep clean sheets
Eredivisie	~60%	Highest BTTS rate in Europe — defences are optional
Ligue 1	~48%	BTTS No — PSG dominance creates low-scoring patterns
Serie A	~49%	BTTS No leans — Italian defensive coaching still matters
Championship	~52%	Chaotic league, but BTTS rates spike in final 10 matchweeks

Beyond Yes/No: BTTS Market Variations

• BTTS & Over 2.5 — Both teams score AND 3+ total goals. Higher odds (~2.50 typically), but only works in genuinely open matches. Don’t force this in defensive fixtures.
• BTTS & Under 3.5 — Both score but not too many goals. Think 1-1 or 2-1. Good for tight leagues like Serie A.
• First Half BTTS — Much harder to predict. First-half BTTS lands in only about 20-25% of matches. The odds compensate (often 3.50+), but your edge needs to be large to overcome the variance.
• BTTS in Both Halves — Extremely rare (~5-8% of matches). The odds are huge but the hit rate is brutal. Treat this as entertainment, not a strategy.

Tracking and Adjusting

Keep a spreadsheet. Seriously. Track: date, match, league, your BTTS probability estimate, bookmaker odds, implied probability, result, and profit/loss.

After 50 bets, check your calibration: when you estimate 60% BTTS probability, does it land about 60% of the time? If you’re consistently over-predicting, your scoring rates are too generous. If under-predicting, you’re underweighting offensive capability.

After 100+ bets, you’ll know whether your model adds genuine value or needs revision. Mine went through three significant adjustments before it stabilised. Don’t expect to nail it on the first try.

See our daily BTTS predictions for today’s picks using this methodology.

18+ only. Gambling can be addictive. BeGambleAware.org

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