An introduction to … Expected assists

What are Expected Assists

Another football metric which is becoming increasingly more used is expected assists or xA. An assist, for those who may not know, is the final pass that directly leads to a goal. Every goal has a pass come before it, except for those which come from direct set pieces, so almost every goal will have another player accredited with an assist. The xA metric works in a similar way to xG, if you are unsure what expected goals are then read the previous article in the blog where we cover it in depth. The best definition of xA comes from Opta, who themselves produce their own model.

“Expected assists measures the likelihood that a pass will be a primary assist. The model is based on the finishing location of the pass, what type of pass it was and a variety of other factors. This model is not reliant on whether a shot was taken from this pass, so credits all passes, regardless of whether they result in a shot.”

Opta

Put simply, expected assists puts a value on passes and is essentially a measure used to assess the creativity of a player or a team. Just how xG shows the quality of chances a team has xA shows the quality of chance created by a pass, regardless of whether it leads to a goal.

To calculate xA, each pass by a player is given a value based on a data set of previously recorded passes and how likely it is that pass leads to a goal. As explained in Opta’s quote above, a variety of factors determine the final xA value of a pass but the most important one is the finishing location of the pass. For example, a sideways pass in the 6-yard box will have a much greater chance of a goal than a sideways pass at the halfway line. Another important factor is the type of pass with through balls, for example, scoring much higher than crosses on the xA metric as statistically crosses are a much less efficient way to score goals.

Why should we use Expected Assists

But why use the fairly complicated method to calculate xA and not just continue to use normal assists? Just like goals scored or conceded, assists can also often be equally misleading. This can simply be seen when analysing Lionel Messi’s goal vs Athletic Bilbao in the 2015 Copa del Rey final.

From that simple halfway line pass, Dani Alves would have registered an assist and scouts at the end of the season may have been oblivious to the magic of Messi to score from that position, skewing Alves’ data. Alves’ final assist numbers could then be fairly misrepresentative of his actual ability in chance creation, not giving scouts or coaches an accurate view of his true performance. However, when plugging this goal into the xA model, it can recognise the magic that must have happened for a goal to be scored from that pass. Unsurprisingly, the xA value of Alves’ pass would be less than 0.01 or put into words, less than 1% of the time a goal will occur due to that pass. However xA can also be used to recognise players that have been unlucky in not recieving assists as well as those who have been fortunate. In this video below the xA value of the final pass into the box is 0.96 however Uche (the player who makes the final pass) is not credited with the assist. You can judge how unfair this is by watching the video below.

The best part of xA is that all passes are registered i.e the pass does not need to result in a shot for it to be considered. This means that it doesn’t penalise a player for the team’s poor finishing, lack of intent to shoot or lack of dribbling skill to create space for a shot from a pass which the top strikers are able to do. If in that video above no one was able to get a shot of from Uche’s pass, the pass would still generate a high xA value due to the quality of that pass and the likelihood of scoring a goal from that pass in another scenario

Opta’s xA data from the 2016/17 Premier League season also shows how useful the metric is. Players such as De Bruyne and Eiksen are able to outperfom their expected assists showing their killer ability to create from tough situations as well as their team mates ability to finish these chances. Other players such as Eden Hazard and Marko Arnautovic are generating decent xA numbers however looking at their actual assists tally suggests taht their team mates finishing ability may be letting them down.

Simply, there is not better metric currently avaliable to assess chance creation than expected assists.

By Anmol Gupta

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