Expected goals, or xG as it’s commonly known, is a metric that has become popular in football, and it’s used to determine the quality of chances a team has within a football match. xG is essentially a more in-depth look at the chances a team has to score during a game because, for example, a shot from five yards and a long-range effort from twenty yards are ultimately different.
Therefore, each chance during a game is going to be essentially ranked in terms of its quality, with the scale being between 0.00 and 1.00, which is basically 0%-100%. A shot that registers as 0.01% on the xG scale would mean that a chance of that quality would result in a goal every 1 in 100 times. However, if you’re talking about an opportunity that registers as 0.95 xG, this should be converted into a goal the vast majority of the time.
How Is xG Calculated?
xG is an interesting metric because it can be calculated differently by the various data companies based on their own bespoke models. However, in the main, the basics do remain the same, and there are core principles that are stuck to. The way that models can and will differ is just how a chance’s quality is ranked. Usually, the first step is to take into account hundreds of thousands of previous shots that have taken place.
But this isn’t the full story, and it’s the context that often differs from company to company on the xG front. Everything from the pass that leads to the chance to the body part the shot is taken on with, where opponents, usually the defenders and goalkeeper, are positioned, are taken into account with scoring xG. Penalties slightly differ because it’s a set scenario; they’re scored at a set rate, so the xG is always the same.
Team xG vs Player xG
Football is a team sport, so in most instances, it’s more important how the collective is performing rather than individuals. And there’s xG that’s relevant to teams and players, with the former being the usual go-to for most. Team xG includes xGf, which is Expected Goals Forward, so how many chances a team should have put away. There’s also xGa, Expected Goals Against, which is how many goals a team should have conceded. Then, to sum everything up, xPts, which is Expected Points, takes into account xGf and xGa to determine how many points should have been accrued.
You could say that xG that applies to players such as xG, Expected Goals, xA, Expected Assists and npXG, Non-Penalty Expected Goals, combine together to produce a team’s overall xG stats. And they do. But you’d suggest that a team’s xG is the metric that most will look at to see how a team is performing. If there are issues on this front, it’s possible to look at player xG to find the problems and create solutions.
Using xG For Sports Bets
xG has had a significant impact on many areas of football. But one of the stand-out places where xG has been put to work is in sports betting, and football betting more specifically. Data and metrics have allowed the sport of football to advance, but bettors are now using it to their advantage, too, when analysing the latest football odds.
A simple process employed by bettors is reading the form of teams as they look for who they’re going to back. However, final scores and results don’t always tell the whole story, as a team could be outperforming their xG or underperforming on that front. So, having xG available will naturally allow sports betting punters to uncover value when examining Premier League odds or Championship betting markets, for example.
Of course, using xG is a method that football bettors will use regularly, from match to match, even. But, there are also outright and ante-post markets available, especially for big tournaments, and it can be put to work here, too, before the action begins based on xG seen previously with specific teams or how they’ve been performing domestically. So, a punter could examine Europa League odds before the competition starts armed with xG data, and it could prove a useful tool, not just in spotting value.
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Where Can I See xG?
Of all the metrics out there, xG is by far and away the most popular, and as you will expect, it’s also readily available for anyone and everyone. However, as explained above, while the core foundations of xG will remain the same across the board, the context of xG can change from model to model, so ultimately, it depends on what an individual is looking for in terms of xG.
xG stats are available to explore for free in multiple places on the internet, and they can be accessed online, on mobile devices, and on tablets.
What Are The Limitations Of xG?
As there are with many things, especially with metrics, there are limitations where xG scores are concerned. But this isn’t necessarily a negative, and it’s not strictly based on the data itself, more so on how it’s interpreted and what it can tell the person accessing the xG information and attempting to read into it.
For example, if you were just analysing a team’s xG data for one game, a game where Team A had an xG of 2.05 and Team B an xG of 0.35. It could be determined from this data that Team A dominated and won the game. But Team B could have outscored their xG, and won the game 1-0, and Team A seriously underperformed where their xG is concerned. The limitation of xG is context.
