Data metrics explained: Expected Assists (xA)

Serie A Femminile Text graphic for The Data Scout by Marc P. Lamberts.

So, tell us. How often do you hear about data in football? I would imagine, quite a bit. Data is often used in the media to enhance a certain narrative. It’s also used to give more authority to a performance, but they do it without giving a proper explanation or context. That’s why we are going to explain a data metric that is often used: Expected assists.

Expected assists (xA)

So, what exactly is the expected assists metrics? The Analyst describes it as follows:

“Expected assists (xA) quantifies the quality of a pass leading up to a scoring chance. It calculates the probability of that pass resulting in a goal, considering various factors such as pass type, pass distance, assist location, and the nature of the attacking move. Just like expected goals, xA is measured on a scale from zero to one, where zero signifies a pass that is unlikely to result in a goal and one represents an assist that should lead to a goal almost every time”

This metric reveals itself often. Player A accumulates an xA of 2.1, while player B boasts an xA of 1.73. Based on the quality of their passes leading to scoring opportunities, Player A had a higher likelihood of creating goal-scoring chances compared to player B. However, it’s crucial to grasp that xA alone doesn’t paint the entire picture. It evaluates the potential of a pass to set up a goal. But it doesn’t predict the final outcome of the game. That’s an entirely different aspect to consider altogether.

It’s not as simple as stating that a player “should have provided an assist” based solely on a high xA value. Similarly, if an assist opportunity is missed, it doesn’t necessarily imply a poor performance by the player. Expected assists take into account a range of variables. This includes the accuracy and effectiveness of the pass, the positioning of the receiving player, the presence of defenders, and the overall execution of the attacking move.

How do we use it?

But how do we effectively utilise expected assists in our analysis? It’s all about weaving together the narrative and understanding the bigger picture. When examining a single game, we can gauge the quality of a player’s passing and their ability to create scoring opportunities. It’s an intriguing observation, yet not an ultimate judgment. On another day, the player may have overperformed or underperformed their xA value.

This perspective becomes particularly valuable when it comes to sports media, be it through writing or audio-visual formats. However, from a coaching or analytical standpoint, relying solely on single match xA doesn’t provide us with comprehensive insights into a players’ long-term performance. The club-level analysis revolves around identifying trends. If a player consistently generates more assists than their xA suggests over a significant number of games, it’s safe to conclude that the passer possesses exceptional vision and execution.

Conversely, struggling teams that underperform their xA can be expected to eventually catch up and create more goal-scoring opportunities in the long run. Another crucial aspect to consider is the volume of passes leading to chances and the xA per pass. It’s not solely about the overall xA value; context matters. A team might have a few key passes that collectively equal 1.80 xA or a multitude of passes that sum up to the same xA value. Understanding the context in which the assists are created is paramount.

Expected goals assisted (xAG)

While xA measures the quality of a pass leading to a scoring chance, xAG takes it a step further. They do that by quantifying the probability of an assist resulting in an expected goal. In other words, xAG focuses specifically on the contribution of an assist towards the likelihood of a goal being scored.

The key distinction between xA and xAG lies in their respective scopes. Expected assists (xA) considers all passes that lead to a scoring chance, regardless of whether the chance is ultimately converted into a goal. On the other hand, expected goals assisted (xAG) solely focuses on the passes that directly contribute to an expected goal. It provides a more refined perspective on the effectiveness of assists, highlighting their influence in the goal-scoring process.

By incorporating xAG into our analysis, we gain a deeper understanding of the impact of each assist. A high xA value might indicate that a player is adept at creating scoring opportunities, but xAG enables us to evaluate the extent to which those opportunities translate into expected goals. It allows us to distinguish between assists that lead to highly probable goals and assists that result in more challenging goal-scoring chances.

Expected goals assisted: how do we use it?

For instance, let’s consider two scenarios. In Scenario A, a player delivers a precise through ball that sets up a one-on-one situation for the striker, resulting in a high xA value. However, if the striker fails to convert the chance, the xAG associated with that assist might be lower, since it was a more difficult opportunity to score. In Scenario B, another player provides a simple square pass to a teammate. That player then unleashes a powerful shot from a favorable position, resulting in a lower xA value. However, the xAG on that assist could be higher since it had a higher probability of resulting in a goal.

By analyzing both xA and xAG together, we can gain a comprehensive understanding of a player’s playmaking abilities. While xA highlights their creativity in generating scoring chances, xAG sheds light on their effectiveness. But look at contributing to the expected goals tally. This nuanced perspective allows us to differentiate between players. This happens between players who excel at creating opportunities but may struggle in delivering high-quality assists that lead to expected goals; and those who consistently contribute to goal-scoring opportunities with their assists

Expected assists: Final thoughts

Expected assists (xA) is an invaluable metric that provides insights into the quality of passes leading to scoring chances. It offers a glimpse into the passes that “should” result in goals. Moreover, it allows assessment of player performance in terms of their ability to create opportunities over an extended period. However, it’s vital to remember that xA shouldn’t be treated as an infallible gospel. It’s a tool that needs to be incorporated within the broader narrative, playing philosophy, and contextual understanding of the game.

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