Understanding Expected Goals (xG) in Soccer Betting: A Guide to Data-Driven Insights
What is Expected Goals (xG) in Soccer?
Expected Goals, or xG, is a stat that helps us understand the likelihood of a shot turning into a goal. It’s a metric that’s become super popular in soccer analytics and betting because it gives a clearer picture of scoring chances compared to just looking at goals alone. Essentially, xG calculates the probability of a shot being scored based on various factors. If a shot has an xG of 0.4, it should go in about 40% of the time. This article will dive into how expected goals work, why they’re useful for betting, and some of the limitations to keep in mind.
Breaking Down Expected Goals (xG)
What Does Expected Goals Mean?
Expected Goals (xG) is a stat that measures how likely a shot is to result in a goal. It's a number between 0 and 1, with 1 being a guaranteed goal (100% chance). This measure helps analysts understand which shots are high-quality opportunities. For example, a close-range shot from a good angle will have a higher xG than a long-distance shot with defenders blocking the goal. Calculating xG involves factors like shot location, body part used, and sometimes even the speed of the play.
Expected Goals Glossary
- xG (Expected Goals): The number of goals a player or team would be expected to score based on shot quality.
- xG/90 (Expected Goals per 90): Expected goals for a player per 90 minutes, offering a normalized view across players.
- npxG (Non-Penalty Expected Goals): Expected goals, excluding penalties, which gives a clearer view of performance in open play.
- xGf (Expected Goals For): Total expected goals a team is predicted to score in a match.
- xGa (Expected Goals Against): Expected goals a team is likely to concede based on opponent shots.
- xA (Expected Assists): Expected assists based on passes that lead to a shot.
- xPts (Expected Points): The number of points a team could expect to win based on their xG data, helping predict long-term performance.
Why Expected Goals Matter for Soccer Betting
The Advantage of Using xG
Expected Goals is a game-changer because it gives a better picture of a team or player’s actual scoring chances. Soccer is a low-scoring game, so basic goals and shots data don’t always tell the whole story. Total shots don’t differentiate between a low-quality long shot and a high-quality close-range shot. But xG assigns value to each shot based on how likely it is to become a goal, making it a more accurate tool for evaluating performance.
Using xG to Predict Future Performance
xG can help bettors spot teams or players that are due for a change in performance. For example, a team that consistently creates high xG but scores fewer goals than expected might be “unlucky” and could score more in the future. This can help identify potential betting value because the market might not reflect these underlying trends.
Analyzing Player Performance with Expected Goals
xG is especially useful for comparing players with different styles. For example, some players might take fewer shots but from better positions, resulting in a higher xG per shot. Comparing players using xG shows us who’s getting high-quality chances, which is often more revealing than just looking at shot numbers.
Player Comparison Example
| Player | Shots per 90 | npxG/90 | xG per Shot | Goals per 90 |
|---|---|---|---|---|
| Granit Xhaka | 2.08 | 0.06 | 0.02 | 0.07 |
| Yunus Malli | 1.96 | 0.21 | 0.12 | 0.21 |
In this example, although Xhaka takes more shots per game, Malli’s xG and goal rate per 90 minutes are better. Malli takes fewer shots but from better positions, which is why his xG numbers look better despite fewer attempts.
Limitations of Expected Goals Analysis
What xG Can Miss
While xG is useful, it doesn’t capture every variable. It doesn’t account for player skills like elite finishing or top-level goalkeeping. For example, a world-class striker may score from tough chances more often, while a great goalie might save shots that would normally be goals. So when analyzing players like Harry Kane or Lionel Messi, you should also consider their individual skills.
Other Tactical and Defensive Factors
Some teams, like those managed by defensive-minded coaches, consistently concede fewer goals than their xG against would suggest. These teams may have strong defensive tactics that limit high-quality chances. Changes in a team’s style or lineup can also affect xG performance, so it’s good to combine xG data with tactical insights for a fuller picture.
Conclusion
Expected Goals (xG) is a powerful tool in soccer analytics and betting, helping us understand the quality of scoring chances in a way that traditional stats can’t. While it has its limitations, xG offers a solid foundation for evaluating teams and players when combined with other insights. For soccer betting, xG is a great way to uncover underlying performance trends and identify value that may not be obvious at first glance.
