You’re in a pub on a Saturday night, and you’ve just finished your drink.
You make your way to the bar, which is stacked with people.
The left side of the bar is serving far quicker than the right, so you decide to go there.
In that moment, you’ve done some sort of calculation in your mind and decided:
“go to the place which serves quickest”. You
didn’t do a literal calculation but you’ve subconsciously recognised that the left side
is more efficient, more valuable than the other, to get served quickly.
That, roughly, is what Expected Threat is about:
the fact that certain spaces are more valuable than others.
The same is true on the football pitch. Having the ball here in your own half,
is not as valuable as having it on the edge of your opponent’s box.
We know those two things intuitively, but how do we measure them?
For a couple of reasons, this is why xG is the wrong model to use here,
and why expected threat (xT for short) is a good solution. Not all attacking actions are connected
to a shot occurring, and xT is a way of zooming out a little further to see more of the game.
The current analytical metrics are descriptive at telling us who is attacking the opponent’s goal
or helping to provide those chances to do so but looking at how the ball gets into those dangerous
positions in the first place is missing. And that’s the crux of what expected threat can
tell us: what’s the chance of scoring during the current possession based on where the ball is now?
This graphic visually illustrates xT, with a model built using three seasons of Premier League data.
The numbers in each grid correspond to the xT from that position, which can be interpreted
as the percentage chance of scoring from the possession when the ball is in that position.
Having possession closer to goal means you’re more likely to score,
and having the ball deep in your own half means that you’re less likely to do so.
Everyone knows that already, but this grid can be used in multiple ways, with the main output being
how players increase their team’s chances of scoring from their carrying or passing.
In fact, any movement of the ball between the different zones can be used to calculate the xT
a player adds through their actions, such as crossing or even looking at passes received
that pull the team further upfield.
For now, though, these are removed as they skew heavily towards target
men getting on the end of crosses and cross-heavy wingers and full-backs.
To start, we want to measure who’s consistently progressing the ball upfield, rather than
providing a general level of danger through their play. It’s worth a note here to say that this
model is built on event data — passes, tackles, shots etc — but has no idea about the location
of the other players on the pitch. Looking at individual examples like the above may prompt a
“that doesn’t look right?” reaction, but in the aggregate, the numbers are far more reliable.
So, here’s how the model can be applied, Looking again at Arsenal’s final game of last season at
home to Brighton, we see Granit Xhaka fire the ball into Martin Odegaard’s feet in the
final third. That pass increased Arsenal’s chances of scoring by just over 1 per cent.
A little under a minute later in the same game, there was a wonderful run by Brighton’s Jakub
Moder. The Poland international picks up the ball following an incisive pass from
centre-back Adam Webster, completely slicing through Arsenal’s press…
…which Moder collects, runs through Arsenal’s midfield before offloading to Leandro Trossard.
By looking at all of the xT increases thanks to a player’s completed passes and carries,
we can illuminate the best ball progressors in the league, some of whom are predictable, but others
perhaps a little underrated or overlooked in their ability to move the ball forwards.
This chart shows the xT per 90 that a player adds from their passes and carries
in open play. Again, crosses and pass receptions are removed at this time,
and only the positive xT is taken into account, as there are some actions – most
backwards and sideways passes, for instance - that reduce a team’s chances of scoring.
Jack Grealish, by some distance, was the most threatening ball progressor in the
league last season by this measure. Elsewhere, Mohamed Salah’s ability
to run with the ball on the break made him Liverpool’s most dangerous player, slightly
ahead of team-mate Trent Alexander-Arnold. Callum Hudson-Odoi is fourth on the list,
perhaps showing just how underrated he is as both a passer and a carrier. Bruno
Fernandes and Kevin De Bruyne, two high-risk, high-reward passers, both make the top 10.
There are plenty of other applications of xT.
Looking at the performance of players and teams over the course of games, adjusting
the numbers for possession and considering how to quantify the defensive side of the game (or
whether that’s even possible) are all interesting avenues to explore throughout the season during
which – at The Athletic – Expected Threat will be a regular part of the analysts’ toolbox.