I quite like that xG ignores the shot taker. That means it can be used to assess the real quality of the chances.
If your style of play regularly creates seemingly gilt edged chances but they fall to a CB, you'll want a CB who can finish, or you'll realise you need to tweak some patterns to get a more natural finisher into those positions.
If your chances are falling to your striker but they're underperforming on xG, it tells you a better striker is needed.
If xG took the player or the position of the player into account, you'd only be measuring them against their own metric, meaning Rasmus's average would look just as good as Ronaldo's. Lowering the expectation based on the players lowers the accepted standard too.
I loved the game-state angle which forced United to take lower xG shots at 0.09. I recently wrote (and still preparing more) deep dives on xG and xGOT, and for the one that is coming out this Thursday I calculated that the average xG/shot at Europe's top 5 leagues for the past 11 seasons stood at 0.11 (data by Understat).
And a great point for who's really taking those chances. This got me interested, and indeed, Leny took a total of 4 shots at United, 3 of which accumulated 0.45 xG but went off (so 0 xGOT, poor execution) and the fourth one is the one you mentioned at 0.3 xG with 0.19 xGOT again poor execution (we judge striker's ability by xGOT - xG).
So this got me thinking, why aren't providers creating xG models for strikers, midfielders, defenders? I know we have xGOT to take care of that, but having a well calibrated xG model that takes account of the player's fundamental role could be valuable (xG is about chance creation, not pure execution) and will deal with this noisy data. Maybe there's something I am missing, I should explore this more.
I quite like that xG ignores the shot taker. That means it can be used to assess the real quality of the chances.
If your style of play regularly creates seemingly gilt edged chances but they fall to a CB, you'll want a CB who can finish, or you'll realise you need to tweak some patterns to get a more natural finisher into those positions.
If your chances are falling to your striker but they're underperforming on xG, it tells you a better striker is needed.
If xG took the player or the position of the player into account, you'd only be measuring them against their own metric, meaning Rasmus's average would look just as good as Ronaldo's. Lowering the expectation based on the players lowers the accepted standard too.
A great break down of xG blind spots, Pauly 👏
I loved the game-state angle which forced United to take lower xG shots at 0.09. I recently wrote (and still preparing more) deep dives on xG and xGOT, and for the one that is coming out this Thursday I calculated that the average xG/shot at Europe's top 5 leagues for the past 11 seasons stood at 0.11 (data by Understat).
And a great point for who's really taking those chances. This got me interested, and indeed, Leny took a total of 4 shots at United, 3 of which accumulated 0.45 xG but went off (so 0 xGOT, poor execution) and the fourth one is the one you mentioned at 0.3 xG with 0.19 xGOT again poor execution (we judge striker's ability by xGOT - xG).
So this got me thinking, why aren't providers creating xG models for strikers, midfielders, defenders? I know we have xGOT to take care of that, but having a well calibrated xG model that takes account of the player's fundamental role could be valuable (xG is about chance creation, not pure execution) and will deal with this noisy data. Maybe there's something I am missing, I should explore this more.
Anyway, loved the piece!