Expected Goals explained: What are xG and xA and why are they a good measure of player performances
Numbers are everything, aren't they? Any player in any sport can lay a claim to be the best amongst his compatriots if he has accumulated better statistics than his or her competitors. When it comes to football, players are judged on the basis of the number of goals and assists they amass during games.
However, as the times have passed by, the footballing fraternity has longed for a better understanding of a player's performance with the help of numbers.
Sports analytics company Opta has created a set of performance metrics, the Expected Goals (xG) and Expected Assists (xA), which caters to the need of a better comprehension of football statistics and achieving high accuracy in the judgement of a particular player or a team. The xG and xA figures have also gained media attention after the BBC and Monday Night Football extensively use them for measuring the performances of a team or a player
So, what are the xG and xA figures and how are they calculated? How do they differ from normal statistics? How do they help in judging a player or a team better than before? Let's delve in to figure it out.
Expected Goals (xG)
Opta defines the 'Expected Goals' metric as follows:
Expected goals (xG) measures the quality of a shot based on several variables such as assist type, shot angle and distance from goal, whether it was a headed shot and whether it was defined as a big chance.
Adding up a player or team’s expected goals can give us an indication of how many goals a player or team should have scored on average, given the shots they have taken.
To put it in simple words, xG is a value that tells us the probability of a shot likely to be converted into a goal. The closer the shot is to the goal, the higher is the xG. The wider the angle from goal, the lower the xG.
For example, A shot taken from the 6-yard box will have a high xG value of 0.9 since it is more likely to go into the net than a shot that is taken from outside the penalty box, which might have an xG value of 0.5.
An xG value of 1 means a guaranteed goal, which does not exist. Some xG models like the one of Stratabet also consider the presence of defenders in front of the goal when a shot is taken, but Opta does not. The presence of defenders also affects the probability of a shot being converted into a goal.
The xG value helps a lot in assessing the qualities of a striker. Top-class forwards like Lionel Messi, Cristiano Ronaldo and Harry Kane always score at a rate above their xG values.
Expected Assists (xA)
Opta defines the 'Expected Assists' metric as follows:
Expected assists (xA) measures the likelihood that a given pass will become a goal assist. It considers several factors including the type of pass, pass end-point and length of the pass.
Adding up a player or team's expected assists gives us an indication of how many assists a player of a team should have had based on their build-up and attacking play.
While the xG value signifies the quality of a chance, the xA value signifies the quality of a pass. Simply put, the xA value puts a crucial emphasis on how likely a pass can be converted into an assist, regardless of whether that pass is actually converted into a shot and eventually a goal.
Usually, every pass made by a player is assigned some xA value, albeit most of the times it is low since not all passes made by a player can generate chances. For example, a pass made by a player in the 6-yard box is more likely to be scored and will have a higher xA value, in contrast to a pass that is made outside the penalty area.
The xA value is also affected by a number of other factors such as the finishing location of the pass and the type of pass. A player who scores more assists than his xA value has better creativity than his counterparts. For example in the Premier League this season, Eden Hazard has notched 10 assists, albeit his xA value is 7.20.
The importance of the xG and xA metrics
The xG and xA values are crucial in changing the perspective of the way we look at footballers on the basis of the numbers they notch on the pitch. These metrics allow us to judge the quality and assess the difficulty level in achieving a particular statistic, something that the usual statistics do not.
Consider two players A and B who have netted 5 goals out of 5 shots each. The fact that they have the same number of goals makes them look equally good. However, suppose if player A has scored all goals through set-pieces while player B has just scored tap-ins. Who then is the better of the two?
Player A, of course, will win the battle since his tally of goals will be higher than his xG value, knowing that he has scored from difficult positions and tighter angles. This also proves that these benchmarks can help us accurately judge the best amongst a certain pool of players.
In case of assists, the xA metric helps in quantifying how creative a player is, regardless of the assists he bags in a measured number of games. A mere number of total assists do not always tell the whole story because getting an assist majorly depends on a player delivering the finishing product, something that the assist-maker can do nothing about.
Let's look at an example to clarify this point. David Silva has bagged an xA value of 6.24 but has only 2 assists to his credit this season. This shows that the Spaniard has been let down by his teammates when it comes to notching assists.
The xG and xA metrics are doing something that was never done before; they are attempting to quantify the qualitative aspects of a footballer. There's little doubt that these magnificent models from Opta have added a new dimension to the analysis and judgement of a player, culminating into a better understanding of football statistics.
All Stats Courtesy: Understat