Every action a GK performs can be separated into 3 categories:
Save-> here the GK attempts to block the ball going into the goal
Distribution-> here the GK attempts to play the ball to a teammate
Interception-> here the GK attempts to stop the ball from reaching an opponent
Evaluating GK intercepting from public data is difficult as most stats providers split intercepting actions between recoveries, tackles, interceptions, punches, & claims, & the differences between the categories are unclear & not relevant to interception difficulty/importance.
To avoid having this issue I categorise every ball touch which is not a distribution or a save into either an intercepted cross or an intercepted throughball depending on the pass trajectory.
This allows the 2 key intercepting action areas to be analysed completely & separately.
Another issue with public GK intercepting data is the lack of information of whether the action actually prevented a chance occuring or not.
For instance many, but crucially not all, recoveries GKs make are just collecting over hit throughballs which would go out for goal kicks.
In this thread for each intercepting action I utilise pass trajectory & potential recipient location to evaluate whether the GK prevented a chance from occurring.
I define any action which prevents a potential chance occurring as a “pressurised” claim or a “pressurised” sweep.
The final issue with public intercepting data is that as they don’t include info on whether a chance is prevented they also don’t include any info about the quality of the chance prevented & obviously preventing a 15yrd 1v1 > preventing a pressurised possession 30yrd out.
In this thread I will overcome this by (as well as discussing pressurised claims & sweeps separately) utilising various NSExG grids along with each passes trajectory & potential recipient location to estimate the ExG prevented for each interception a GK makes.
I can then look at every cross & throughball a GK intercepts over a season & sum the individual ExG prevented values to reach a total ExG prevented due to intercepting actions.
I can then look at this for various #PremierLeague GKs & compare shot prevention to shot stopping.
In order to present how the model works I will go through a few actions #Alisson made in the 19/20 #PremierLeague & highlight their classification.
Here are 2 crosses:
vs #AFC if he doesn’t come possible free header -> pressurised
vs #WHUFC if he doesn’t come & pinch the ball off #Antonio’s toes #Antonio gets a clear 1v1 shot away -> pressurised
vs #AVFC the throughball is massively over hit & there is no possible recipient hence no chance is prevented -> unpressurised
Here are 2 pressurised claims:
1st: if the GK did not claim it it would have potentially given a free header close to goal between the goal posts -> ExG prevented 0.35
2nd: if the GK did not claim it the trajectory is such that only #Aguero could reach it -> ExG prevented 0.08
Here are 2 pressurised sweeps:
vs #NCFC if the GK did not sweep up this pass there would have been a clear 1v1 ~10yrd out -> ExG prevented 0.45
vs #WWFC if the GK did not sweep, a shot under defensive pressure from just inside the area could have occurred -> ExG prevented 0.05
Hopefully you have a feeling of how the model works so here is a graphic of every cross #Alisson claimed.
circles = unpressurised
stars = pressurised
⚪️ caught
🟡 punched away from danger
🌸 punched into danger
🔴 missed
Note: unpressurised crosses always have ExG prevented=0
A key part of the model I have not yet mentioned is what happens if the GK does not catch.
After every punch I track the punch trajectory & opposition player locations to estimate an ExG provided number.
The total ExG prevented is then the ExG prevented - the ExG provided.
Here #Alisson punches a cross into danger. My model suggests his decision to make contact prevented 0.08 ExG however his poor punch presented #BFC with 0.15 ExG therefore this action net provided #BFC with 0.07 ExG.
This is one of the red -ve ExG prevented stars on the graphic.
Punches into danger can still be net positive actions.
Here #Alisson punches a low cross back into danger however my model finds the initial intervention prevented 0.82 ExG while it only presented 0.22 ExG thus even though it went into danger the action net prevented 0.60 ExG!
Here is a graphic of every throughball #Alisson swept up last year, he’s so active!
circles = unpressurised
stars = pressurised
⚪️ retained possession
🟡 cleared away from danger
🌸 cleared into danger
🔴 missed the ball
(unpressurised throughballs always have 0 ExG prevented)
When throughballs are not retained they can also present ExG
Here #Alisson tried to intercept a ball aimed for #Son but #Son beat him to the ball & rounded him. My model suggests he prevented 0.15ExG but as the goal is open presented 0.32ExG thus net providing #THFC with 0.17ExG
Also poorly cleared throughballs can have net positive effects.
Here is an example, #Alisson only clears the ball to the edge of the box presenting 0.08ExG however as mentioned earlier he stops a clear 1v1 & prevents 0.45ExG thus this action it is still net 0.37ExG prevented!
So that’s the model!
What all does this mean for the importance of prevention?
The range in shot prevention numbers I’ve found = ~10 ExG per season
The range in shot stopping numbers = ~20 PSExG per season
So the best stopper & worst preventer > worst stopper & best preventer
In the extreme case stopping is more important but for individual cases this isn’t always true:
#DeGea saved 0.08ExG/90 more than #Alisson last year. But #Alisson prevented 0.16ExG/90 more than #DeGea thus looking at stopping alone would not indicate which GK had the better year
CONCLUSION
-I made an ExGprevented model which takes into account pass trajectory & player location
-The model evaluates how useful/effective intercepting actions are
-The range in ExG prevented is ~10ExG per year
-Prevention is important but not as important as stopping
EXTRAS:
If you are interested here are #Alisson’s top 3 & bottom 3 throughball sweeps + crosses claimed last year according to my model.
Top 3 throughball sweeps
vs #Brighton +0.44 ExG prevented
vs #Norwich +0.37 ExG prevented
vs #Villa +0.35 ExG prevented
#Alisson noticeable struggled with crosses far more since the restart potentially it was just due to rustyness, luckily VVD was on hand to bail him out.
That’s everything folks!
If you have any further questions or what to know anything else about my cross claiming & throughball sweeping model just ask away!
Has #Ederson turned a corner regarding his issues with long range 1v1s?
Yesterday he positioned himself perfectly inside his 6yrd box during this long range 1v1 thus allowing his defender to pressure the ball while maximising the finish difficulty for the striker!
#SOUMCI #MCFC
#Ederson highlighted how waiting deep is not a passive strategy!
As GKs should only wait deep until the CF gets close enough that the GK will not have enough reaction time to make a save, which is what #Ederson did as he rushed & smothered once the CF was within 14yrd of goal!
#Ederson has had huge problems with 1v1 situations like these in the past as he has rushed out too soon & found himself at the PK spot (rather than the 6yrd box) which gifts the opposition CF simpler finishes like chips & sidefoots while also not allowing his CBs to get back!
If #DeGea had rushed out to engage this touch he would’ve turned a 38% goal probability chance into a 61% goal probability chance as MA could’ve easily rounded him or chipped him!
By waiting he dictated the 1v1 to MA & made him make a decision, MA chose to take another touch…
Rather than continuing to stay deep #DeGea realised that now MA was close enough that his reaction time may not be enough to save the shot so he rushed & formed the premeditated block barrier reducing the goal probability to 34%!
#Karius made 8 saves vs #MUFC, but GKing is about quality not quantity!
5 of the saves had xSave>99% thus #PremierLeague GKs save them every time!
The 2 interesting & difficult saves were the 1v1 vs #Bruno & #Weghorst’s long range shot! Which beat #PL GKs 41% & 29% of the time!
It was these saves whose difficulty outweighed the 2 goals #Karius conceded meaning #Karius saved an above expected amount of shots in the #LeagueCupFinal
#Karius could do nothing with the #Casemiro goal (xSave probability<5%) but the #Rashford goal was a little more interesting
#DeGea made the save look easy & made the save far easier for himself due to his top class decision to hold deep & then use his top hand. This made a difficult situation comfortable!
The GK xG model shown above looks at every shot faced, pass received, cross faced & through ball faced & calculates the probability of a goal occurring for & against the GK’s team before the event & after the event occurs!
Thus it measures & evaluates every action a GKs makes!
This allows all GKs to be given a single number measured in goals called
“Total Value in Goals”
Which describes the GK’s value to their side vs having a league average GK & takes into account everything they do meaning GKs of vastly different styles can be fairly compared!
When receiving the ball under pressure, both #Chelsea GKs have been below the #PremierLeague standard!
#Mendy has cost #CFC 0.52 xG more than the avg #PL GK would be expected to if they received the ball in the same situations (this is heavily influenced by his mistake vs Leeds)
Similarly #Kepa has cost #CFC 0.59 xG more than the avg #PL GK would be expected to if they received the ball in the same situations as he did in his last 15 #PremierLeague games, #Kepa’s numbers here are heavily influenced by his mistake vs #Liverpool which #Mane capitalised on!