Football match results can be deceptive. A team dominates for 90 minutes, shoots from every position, and loses 0–1 after a single counterattack by the opponent. Every fan has seen such a match – and every bettor knows how frustrating such bets are. That is precisely why more and more people betting on matches are turning to xG (expected goals) statistics, which show more than the dry result on the scoreboard.
What is xG and why does it matter
Expected goals is a metric that assigns each goal-scoring opportunity a probability of resulting in a goal. A shot from five meters after a cross? That might be an xG of around 0.8 (80% chance). An attempt from 25 meters, with two defenders in the line of sight? More like 0.05 (5% chance).
In practice, this means that a team can lose a match 0–1 but generate xG at the level of 2.3 – which suggests that, with a bit of luck, it should have scored two, maybe three goals. Such a team is not weak. It is simply unlucky. And these are exactly the situations that are gold for those who can spot them before the bookmaker does.
Key metrics worth paying attention to
Knowing xG alone is not enough. You need to know what exactly to analyze before placing a bet.
xG (expected goals) – the average number of goals a team should score based on the quality of its chances. If a team regularly generates xG at the level of 1.8 per match but scores an average of 0.9 goals, this is a signal that the form of the strikers may soon turn around.
xGA (expected goals against) – how many goals a team should concede when looking at the opponents’ goal-scoring situations. Low xGA (e.g. 0.7) combined with a high number of goals conceded (e.g. 1.5) may mean that the goalkeeper is going through a crisis or that the defense has been unlucky with deflections.
xG90 – a statistic calculated per full 90 minutes of play. Useful when comparing teams from different leagues or those that play different numbers of minutes (e.g. due to red cards, injuries).
The best results come from analyzing the last 5–10 matches. A single match can be distorted by chance – a red card, a referee’s mistake, weather conditions. But when a trend persists over several rounds, it begins to tell the truth about a team’s form.
Betting strategies using xG
1. Over/Under on the number of goals
If both teams have high average xG (together above 2.5) and at the same time weak defenses (xGA above 1.0), this is a strong signal for an over 2.5 goals bet. Such matches often end with scores like 2–2, 3–1, or 3–2.
On the other hand, if the home team has low xGA (meaning it defends well), and the away team plays defensively and has xG below 1.0, it is worth considering under 2.5. This works especially well in matches between lower-ranked league teams, where both sides are afraid to lose.
2. BTTS (both teams to score)
xG statistics help predict whether both teams will find the net. If each team generates an average of more than 1.2 xG per match and at the same time concedes more than 1.0 xGA, the probability of BTTS increases significantly.
Note: do not base your judgment only on the last match. A team may score three goals from set pieces, which does not reflect its true quality in open play.
3. Picking the winner – finding undervalued teams
This is the most interesting strategy. It involves finding teams that play better than the league table suggests. A classic example:
Team A: average xG 1.8, xGA 0.9, but recent results are three draws and one loss. Odds for its victory are high because the bookmaker looks at the table, not the statistics.
Team B: average xG 0.7, xGA 1.5, but three wins in the last four matches – often lucky, after opponents’ mistakes or random goals.
xG suggests that Team A is undervalued. It creates good chances but has not yet turned them into points. Regression to the mean suggests that its points form should improve. This is the moment to bet on it before the odds are adjusted.
Example comparison in practice
Imagine two different situations that can help with decision-making:
An undervalued team – the team generates high xG at the level of 1.8 while maintaining low xGA of 0.9. This means it creates good scoring chances and rarely allows opponents dangerous shots. Despite this, recent results do not translate into points – perhaps due to bad luck in finishing or excellent form of opposing goalkeepers. Such a team is a candidate for a bet on its victory before the market notices the true quality of its play.
An overvalued team – the team has low xG (0.7) and high xGA (1.5), but has recently been winning matches. Statistics show, however, that these wins are the result of luck – goals after deflections, opponents’ mistakes, controversial penalties. Such a streak rarely lasts long. In the case of such a team, it is worth considering an over 2.5 goals bet in its matches, because weak defense and low quality of offensive play suggest that it will soon start dropping points and matches will become more open.
Where to find xG data?
Access to xG statistics does not require paid subscriptions. Some popular sources include:
- Understat – detailed xG data for top European leagues, broken down by individual matches and players
- FBref – extensive statistics, including xG90, xGA, and season comparisons
- FootyStats – simple xG summaries with the option to filter by leagues and teams
It is worth using several platforms, as xG models may differ slightly. One service may value a shot at 0.25, another at 0.30 – this is normal, as algorithms take different factors into account (goalkeeper position, defender pressure, body part used).
What xG does not account for – important warnings
xG statistics are a powerful tool, but they do not explain everything. Several situations where caution is needed:
- Red cards – xG from a match in which a team played shorthanded for an hour says little about its true quality.
- Injuries to key players – if the top striker is out, historical xG may be misleading.
- Competition specifics – cup matches, where one team defends a result from the first leg, look different from league fixtures.
- Set pieces – not all xG models treat free kicks and corners in the same way. A team effective from set pieces may have lower xG than its number of goals would suggest.
Therefore, xG is best combined with other data: recent form, head-to-head matches (H2H), information about lineups, and team motivation.
When does xG work best?
Expected goals are particularly effective in the medium and long term. In a single match, anything can happen – but after 10 rounds, differences between xG and actual goals usually even out.
This means that the best bets are those based on trends, not single results. If a team consistently generates high xG over five or six matches but earns few points, it is worth keeping an eye on it. The chance of a correction in form increases with each round.
It works similarly in the opposite direction – a team with low xG that wins thanks to luck will sooner or later start dropping points. Bookmakers sometimes react slowly to such situations, which creates room to find valuable odds.
Betting on matches based on xG is not black magic, but a methodical analysis of what happens on the pitch. Instead of relying on emotions or the last result, you look at the quality of play – and that often gives an advantage over the market. Of course, no statistic guarantees a win, but xG helps make decisions based on facts rather than intuition. And in the long run, it is precisely this difference that determines success.
