Expected Goals (xG) — What It Is, How It Works and Why It Matters for Betting
What Expected Goals (xG) Is — in Plain Terms
Expected Goals (xG) is an advanced metric that scores the quality of chances. Each shot gets a value from 0 to 1 reflecting the probability of a goal from that position. It accounts for distance to goal, shot angle, body part (foot, head), attack type (counter, set piece, open play) and the keeper's position.
A penalty has an xG of ≈0.76 (76% historical conversion). A shot from the centre of the box — xG 0.15-0.30. A shot from outside the box — xG 0.03-0.08. The sum of a team's shot xG is the match xG. If xG = 2.3 and they scored 1, they were "unlucky". If they scored 4, "lucky". Over a sample, real goals converge to xG — regression to the mean.
xG vs the Poisson Model — Friends, Not Foes
The Poisson model (the basis of BETSKOP) and xG are complementary. Our model works with historical statistics — average goals per season, home/away. It's predictive: it forecasts the future from stable patterns. xG works with the quality of chances in specific matches — it's descriptive, showing what "really" happened.
The ideal combination: model for the forecast, xG for the correction. If a team consistently outperforms its xG (scores more than it "should"), that may be striker quality — or luck that will end. If it underperforms, the goals "will come".
How xG Helps in Betting — Practical Examples
A team scores 2.0 per match but has an xG of 1.3. The striker is wildly efficient, but regression is inevitable — future xG is nearer 1.3-1.5. The book sees 2.0 and prices a high Over. We see xG 1.3 and know the real potential is lower. Under 2.5 can be a value bet.
The reverse: a team scores 0.8 against an xG of 1.5 — systematic bad luck. Future xG is nearer 1.3-1.5. The book underrates the team, creating value on their goals.
Limitations of xG
xG doesn't know the finisher — Messi and a defender from the same spot get the same xG. Not every league has quality xG data — for the RPL and Süper Lig it's less reliable. The model doesn't capture defensive pressure at the moment of the shot.
How BETSKOP Uses xG
We use xG as an extra correction factor for expected goals in the Poisson model. If a team consistently creates chances above its conversion, we nudge xG up — and vice versa. This sharpens the edge. All workings are on the analysis pages. Signals in the Telegram channel. Results are open.