A House Move

I, along with a couple of other pretty decent analysts, have made the move across to http://www.statsbomb.com/ – so any future articles will be published there.

If you like what you have read here on my blog then I’m sure you’ll like the new place even better, so why don’t you follow me across……..



How valuable are Direct Free Kicks?

I have seen questions asking whether free kicks have been included in certain published shooting statistics.
The inference made by those asking the questions is that the conversion rate that could be expected from a free kick is different to a normal open play shot, hence the direct free kicks (DFKs) should be stripped from the player’s shooting numbers in order to give meaningful analysis.

Invariably I see those sorts of questions as a challenge, and my mind translated the questions asked to “Can I prove one way or t’other whether DFKs are much more dangerous than shots from open play?”.

Thankfully, for anyone who is interested, my stats do give me an answer to the subject at hand, and I’m willing to share at least some of that info in this piece.

The way I have decided to present my findings in this very short article is to show how the percentage chance of scoring a goal from a particular zone is higher for DFKs than regular open play shots from the same location.

Additional Goal Expectation for DFK

Free Kicks

By way of explanation let’s start in the most central zone just outside the penalty area.

Dead Centre
The 1% means that the chance of scoring a goal from a DFK in that zone is just 1% higher than scoring an open play shot. Interestingly, if we continue looking at the very central strip of the pitch we can pretty much conclude that a DFK is no more likely to be scored than an open play shot.
This may well  come as a surprise to some, but it seems to confirm the much quoted commentary phrases of “this free kick is too straight” or “he would be better with a bit more angle on this”.

To The Side
Contrast that with the additional goal expectation that we see as we move slightly to the right or left of centre. We can expect DFKs to find the net at a rate which is somewhere between 3 – 10% more than open play shots hit from the same locations.
Given the conversion rates experienced in these zones this uplift is quite substantial and so when looking at player shooting ratios and goal scoring records it would be helpful to know how many of their shots were DFKs.

A DFK is a strange beast, as it changes where you ideally want to shoot from.
In an open play situation you would choose central positions as your shooting choice as these give you the greatest chance of converting.
However, when faced with a dead ball situation and a wall of 4 or 5 opposition defenders in front of you then shooting the ball from an extremely central location is the one of last places you would choose to tee it up.

Before anyone asks I’d prefer not to disclose the scoring rates for each zone for DFKs and open play shots. Given the time and effort taken to collect this information I’d like to keep that part of my research private for the moment. I trust that the uplift in DFK scoring rates as presented here will still be of benefit to readers.

The Best Goalkeeper in the Premier League

I thought it was about time that I shifted my attention to an area that I have ignored thus far, the humble goalkeeper.  As tends to be my style, I’ll do this through the use of detailed analysis and will attempt to reach the conclusion of which Premier League net minder posted the best performances last year.

I suppose before I go any further I should caveat and explain “best performance”.

I am looking solely at the shot stopping ability of keepers in this article, because that is what I have data for.  Unfortunately, I don’t easily have data to hand which will tell me which keeper was most in command of their area or which one has the best distribution.
So by the end of this article I’ll be making a case for just who was the best shot stopper.

As an aside, as this piece has progressed, I feel that as well as addressing the topic at hand it also serves to remind us just how statistics and analytics can be used in different ways.  Indeed, it also reaffirms the fact that looking at similar stats in a slightly different way can bring the reader to different conclusions.
One such example of this was my previous article where I looked at the difference in scoring rates between shots and headers, where it seemed that headers were scored more often than shots.  But when the attempts were adjusted for pitch location it transpired that the oposite was actually the case.  The data presented today isn’t as profound as the headers article, but there are similar subtle twists.

Data Rules

I have excluded penalties and the tables below only include goalkeepers who made at least 40 saves last season.

1 – Simple Percentage of Saves

The most basic metric we could use to see which keeper was the best shot stopper is the Save Percentage.  This is simply calculated as (saves  / number of shots on target faced):


Swansea’s reserve goalie Gerhard Tremmel is the only keeper to have saved more than 80% of the on target shots that came his way.  The fact that Simon Mignolet saved much more of the shots that he faced in comparison to Pepe Reina is pretty well known by now, hence the acquisition seems to make sense for Liverpool.

It may come as somewhat of a surprise to see Hugo Lloris languishing towards the foot of the table.  Tottenham were superb last season in terms of the paltry amount of shots that they conceded, but, in comparision to the rest of the league they allowed many more of those shots to turn into goals.  Perhaps the style of play, principally the high pressing game they employed contributed to this fact.
This is a good time to mention that this analysis doesn’t take into account the amount of defensive protection that the goalkeepers received; it is purely looking at the shots that they faced.

Expected Saves

Another way of presenting this table is to compare the number of saves that the keepers made compared to the number of saves they would have made had they saved at the league average rate:


Tremmel made 55 saves, but had he saved the shots struck at him at just the league average rate he would only have made 49 saves.  The additional amount of saves made of 6 has been described as “Performance”, and the final column, the “Perf %” shows the Performance as a percentage of their actual saves.
The 6 additional saves made by Tremmel’s represented an 11.7% over-performance by him.  On the other side of the coin Artur Boruc underperformed by more than 14% as he made 6 less saves that expected.

In terms of absolute numbers, Jaaskelainen made the most amount of additional saves at 11 and Wigan’s Al-Habsi didn’t help his team’s chances of avoiding relegation as he conceded 11 more goals than the league average would have suggested.

I’m sure that Joe Hart, given his reputation and his position as the England number 1, will be disappointed at the lack of saves that he was able to make.  His performance is actually in negative territory, ie worse than the league average.

One last interesting take away from the above table is that the sum of the Performance for those keepers that made at least 40 saves and thus were generally the starting keeper for their teams is 24.  The league average is 0, which means that the sum of the Performances for the keepers I have omitted from the table (those with less than 40 saves) is -24.
This suggests, that by and large, the managers have made the right choices in terms of which keepers get to play and which tend to keep the bench warm.

2 – What about Headers?

At this stage, those aware of my work will probably have said “but surely you need to look at headers and shots separately due to the fact that they are scored at different rates”.  And they would be correct.


The Expected Saves figure in the above table has been arrived at by looking separately at headers and ground shots and using separate league average save rates for both headers and shots.
As it happens looking separately at headers and shots didn’t really change the ladder that much, the order is roughly the same and the Performance figures are also broadly similar; Lloris rises up the table a little bit and Mignolet sneaks into 4th place.

From this point on, all the Expected Saves quoted in this article will have the headers and ground shots kept separate, for no other reason than it is correct to do so.

3 – What about Shot Location?

Another favourite area of mine has been looking at where shots were taken from, and this variable is important in our quest to hunt out the best shot stopper.
It doesn’t require a person of great IQ to realise that a keeper who faces shots from long distances would be expected to have a higher save rate than a keeper who is being bombarded by shots centrally from 10 or 12 yards out.

This time taking into account the location of where the shots were taken from, my Performance table now looks as follows:


When we take into account shot position a new name is now in contention for the title of “Best Shotstopper”; QPR’s Brazilian custodian Julio Cesar.  It is interesting that by taking shot location into account Cesar has gone up the table, yet Rob Green has gone the other way.  This suggests that Cesar faced shots which were more advantageous to strikers than those faced by his teammate Green.

 4 – The Strike

Apart from the location of the shot, the final piece of the jigsaw that is required in order to be able to decide who the is best goalie is the placement of the actual strike at goal.  Once again, it is obvious that a shot that is struck centrally will be easier to save than one that is arrowed for the corners.
I broke the shot placement locations into 4 sectors, low centre, low sides, high centre and high sides, and this time after adjusting the saves for the locations of the shots faced I arrived at this position:


Once the placement of shots have been adjusted for Tremmel has once again fought his way to the top of the rankings table.

At this point it’s probably worth pausing to have a quick glance back over the previous tables in the article.  The one thing that immediately stands out to me is the fact that the tables haven’t substantially changed.
Adjusting the expected saves for shot placement, location of shot and whether the shots where headed or from the ground has not substantially changed any of the tables; Joe Hart is still broadly neutral, Ali Al-Habsi and Artur Boruc are bringing up the rear and Swansea’s second choice goaltender is making great claims for a move to a starting position somewhere in the league.

The conclusion from this is that the sheer volume of shots faced by goalkeepers over a season ensures that they broadly get an even spread of all types of shot locations and shot placements.  This will be reassuring to those who wish to keep a check on goalkeepers by simply using the Save % stat as posted at the top of this article.

However, as I have my hands on more dranular data I’m not satisfied by using such a simple metric and thus I’ll continue on with my final table.

5 – Final Table


This table is the all singing and dancing table of a goalkeeper’s shot stopping abilities.  The saves have been adjusted for the combination of whether the shots were headed or ground shots, their location and the shot placement.

The end result is that I’m crowning Julio Cesar as the best shot stopper in the Premier League, with an additional 10 saves made in addition to the 86 that the league average keeper would have saved.
Liverpool’s new Belgian keeper looks a really sound acquisition as he finishes as runner up, the Anfield club should notice the difference that Mignolet will bring them as Reina ends the table with 1 less save than the league average.
With an additional 14 saves Mignolet had the greatest absolute Performance, but Julio Cesar tops the table due to the higher Performance %, ie the Brazilian faced less shots and thus had less opportunities to chalk up those additional saves.

Assuming that QPR are keen to reduce their wage bill following their relegation last season I would propose that Julio Cesar could make a nice acquisition for someone.  I have read recently that Arsenal have shown an interest in him, and it appears based on the above table that the Gunners could do with strengthening their goalkeeping options as Szczesny returned a poor Performance of -2.5%.

Swansea emerge with great credit from this analysis as they have two keepers in the Top 9 positions.  In Manchester, David De Gea certainly repaid the confidence that Alex Ferguson showed him this season in making him first choice as he racked up 8% more saves than league average.

Depending on the feedback received it may be possible to monitor goalkeepers as the new season progresses using this methodology.

The Choice of Analysis

In my prelude I had said that a by-product of this article was how a subtle change in the statistics used can change the analysis and any conclusions.  Although the general shape and feel of all the player ranking tables in this article are broadly similar it can be seen that the statistic used does have an impact when we want to get into specific detail.  The top and bottom positions changed depending on whether we adjusted for shot location and / or shot placement.

This should serve as a timely reminder for all users of stats or analysis.

Stoke’s One Direction

This is a very short article which doesn’t contain anything groundbreaking, however I wanted to post a viz which says something amazing about Stoke.

By this stage everyone knows that Stoke’s gameplan revolves around set pieces, long balls and flick ons. However, I was surprised at just how one dimensional they actually are.

This image shows the ground shots (headers and penalties are excluded) that Stoke scored with during the 2012/13 Premier League season:


There is no need to correct your screen, all the white space on that image is correct!!

Stoke scored 21 goals from ground shots, but 20 of them came from what I have termed the Prime Positions, ie the central portion of the penalty area.
They only scored 1 kicked goal all season which was not from this centrally close position!!  This was the thunderbolt scored by Cameron Jerome in the 90th minute versus Southampton which earned his team a point.

Their inability to score from the ground unless the shot was from very close central positions is bound to have left Stoke a fairly easy team to defend against.
Well maybe, “easy to defend against” is not the correct phrase but their lack of variation will certainly have counted against them.
Although he was coming from a different viewpoint,  Mike Goodman pointed out why variety in a team’s attacking attacking play is so important.


In order to give Stoke’s total lack of variety some meaning, here is a table which ranks each team by the proportion of their ground hit goals that  came from the Prime Position.  Before that however, I’m also including an image that defines the boundaries of the Prime Position.


% of Ground Goals from Prime Location


The above table clearly shows just how much more reliant Stoke were on scoring ground shots from close in locations than any other team in the league.
After their 95% figure there is a lot of clear air until we reach the 71%s that were posted by Man United and Southampton.

Tony Pulis

I know I’m not the first person to say that Stoke were correct in dispensing with the services of Tony Pulis at the end of last season, but the figures contained in the above table seem to give great weight to the fact that Stoke City football club just had to do something to try to spark a change in the way that they play.

Liverpool’s Defensive Weakness

When compiling the recent article that I posted where I looked at where teams conceded shots from I came across some interesting findings in relation to Liverpool.

We’ll go for a slight recap and start with the good news for Liverpool; as a defensive unit they were extremely successful at forcing teams to take shots from poor shooting locations:


So good were Liverpool in forcing teams to shoot from non optimum positions that they only permitted 18% of shots to come from what I have defined as the Prime location.  As you can see, this value ensured that they led the league quite comfortably in terms of this particular metric.  Of course this fact raises potential questions around how Reina performed in keeping out shots given the great job that his defence did in front of him.  That question can remain unanswered until another day.

I want to look at another feature of Liverpool’s defending that caught my eye; which side of the pitch did the conceded shots originate from.


It surprised me a little to see that 47% of all the shots (excluding headers and penalties) in the Big 5 European leagues last season came from left of centre, 19% were straight on and just 34% came from an area to the right of centre.  I can only presume that the large number of shots from the left side of the pitch is due to the predominate use of the right foot when shooting.  It is obviously much more natural for right footed players to shoot from the left hand side.

From a defence viewpoint the image below shows the areas of the shooting zone that I am defining as right, left and centre of defence:


Which side of the pitch did the Premier League teams concede shots from last season?


Remember, this table is from a defensive viewpoint, and has been sorted in descending order on the Right hand side.

Although there is a heavy bias to all teams conceding shots from their right hand side (this is obviously the other side of the coin to more shots being struck from the attacker’s left side), Liverpool allow a greater proportion of shots to come from their right hand side than any other team in the league.  The league average for conceding shots from the right hand side was 47% but Liverpool allowed in excess of 54% of their shots to be struck from their right hand side.

Their figure conceded down the right of 54.4% is more than 2.5 standard deviations from the league mean which is significant at a level of 1%, or in layman’s terms “there is definitely something happening that causes so many shots to come from Liverpool’s right hand side”.

Is Glen Johnson at fault?

The first possible explanation for this fact that jumped into my head was “Glen Johnson”.  Although Glen Johnson offers Liverpool great attacking potential, in fact he averaged 1.5 shots per game last season which put him just behind David Luiz in terms of attempts by defenders; perhaps these attacking forays come at a price to Liverpool.
I am aware that Johnson didn’t play every game last season and I haven’t yet went to the trouble to separate the shooting statistics for the games that he did play versus the games that he missed.

Whatever the explanation it certainly seems that Liverpool is much more open on their right hand side than any other team in the Premier League.  It’s safe to assume that this fact won’t have gone unnoticed by Premier League managers.

I’m interested to hear any other possible explanations for the heavy bias of shots conceded down Liverpool’s defensive right side.

Where did teams concede shots from?

All the shooting location analysis work I have done so far has concentrated on the attacking sides, so I thought it about time that I looked at the other side of the coin; how teams defended.  In my previous article I concluded that headers are a lot less dangerous to concede than shots.
With that in mind, and for the purposes of this article, I thought I would strip out headers (as well as penalties), and thus we are left with just non-headed attempts on goal.

As before, in order to aid analysis, I have divided the shots conceded by each team into four different areas which are colour coded as per the legend below.


Here is the summary of shots conceded by each team in the 2012/13 Premier League season:


I have listed the number of shots that each team conceded, and then showed the percentage of shots that each team allowed in each of the four areas.  The last two columns show the combined proportion and number of shots that each team allowed in the two best areas for shooting, the Prime and Secondary areas.
The table has been sorted by Prime and Secondary Percentage in ascending order.


The team that sits atop of this table is Liverpool.  They permitted less than 55% of the shots they conceded to be struck from the Prime and Secondary areas, and they were the only team in the league to allow less than 20% of their shots conceded to come from the Prime area (18%).
There is no getting away from the fact that Liverpool put in a tremendous defensive performance during the season just ended. For this they should receive a lot of credit.


The appearance of Stoke or “PulisBall” as described by MarkTaylor in second position will come as no surprise to analytics aficionados.  Pulis puts (or should that be “put”) great store on his team taking shots from good locations and preventing the opposition from doing likewise.


QPR are interesting.  For a team that struggled so badly I was surprised to see them sitting as high as 5th in this table with just over 62% of shots conceded from the Prime and Secondary areas.  Of course, the quantity of shots they allowed at 489 would somewhat counteract that last statement.


Although Tottenham appear towards the bottom of this table with a relatively poor 66% of shots permitted to be taken from the Prime & Secondary positions the fact that they conceded a league low 297 shots resulted in them conceding the fewest number of shots from the two most attractive shooting areas.


From a defensive viewpoint Reading stank the place out.  At 591 they conceded the highest number of shots and the positions they allowed those shots to be taken from were just as horrendous.

Hopefully the above table will serve as a useful point of reference for anyone that wants to see how a particular team did in terms of the number, or more importantly, location of shots they permitted last season.
I’m also that sure this won’t be the last time that I look at the type of shots that teams permitted.

How do Headers compare to Shots?

In performing some of my shooting analysis work I have struggled with how best to deal with headers.

It is obvious that, on average, headers are taken from locations closer to goal than non-headed attempts on goal (these non-headed attempts will be defined as “shots” during the rest of this piece).  That alone would be enough to ensure that we shouldn’t group together headers and shots when undertaking any aggregate analysis.
The combining together of shots and headers is even more problematic when we consider that they may have different outcome profiles even when taken from similar locations.  This realisation has had some recent airings on Twitter, so I thought I would use the data that I have collected from the Big 5 leagues last season to put on record just how a header compares against a shot.

Summary Numbers


When looking at all shots and all headers we can see that there is only a negligible difference in the amount of each type that are on target (34% of headers vs 33% of shots).  However of those on target attempts, a header is more likely to be scored than a shot (12% v 9%).  It is no surprise to see that headers are blocked much more infrequently than shots; shots are blocked approximately three times as often as headers.

So, if headers are scored at a higher rate than shots, does that mean that, given the choice we would prefer our team to be having a headed attempt at goal rather than a shot struck with the foot?

I would suggest that the answer to that question would be “no”.  The main driver of why headers are converted more frequently than shots is due to the location of where the attempts originate.

Location of Attempts


Almost 95% of all headers are taken from the central portion (within the width of the 6yd box) of the penalty area, this compares with just 25% of shots.  Virtually no headers are taken from outside the penalty area; whereas more than 54% of shots originate from these longer distances.
At this stage, it’s now easy to see why headers are converted with greater frequencies than shots.

Direct Comparisons

How would the conversion rates for shots and headers compare if we looked at like for like, ie removed the location basis that is inherent with headers?

Inside 6yd box


Shots taken from inside the 6 yard box are converted at 40%, compared to less than 25% for headers.  So within these close range locations headers were scored only 62% as often as shots were.
One other takeaway from this grouping of shots is that less than 40% of headers from this extremely close location were put on target.  Presumably this is indicative of the pressure that is applied to headers that are attempted from such close range.

Other Central Locations Inside Penalty Area


This time we are looking at shots within the central portion of the penalty area, but beyond the 6 yard line.
Once again, shots are converted at vastly superior rates to headers.  This time the conversion rate for shots is almost double that of headers at 20% and 10% respectively.  As before, we can see the difficulty that headers have in even just hitting the target.

Sides of Penalty Area


Now turning our attention to shots / headers that were struck from the sides of the penalty areas (outside the width of the 6 yard box) we can see the familiar pattern continuing as yet again shots are converted at twice the efficiency of headers.


In writing this article I set out to determine how much less likely a header was to score than a shot.  Without adjusting for shot location headers are scored at a greater rate to those of shots.  However, in respect to this particular topic the devil is in the detail as we determined that when shots and headers that were struck from similar places were compared the conversion rate for headers was only approximately half of that for shots.
This is a fact that should be remembered by anyone interested in the analytical side of football

Perhaps I could go as far to suggest that with shots and headers having such vast differences in conversion rates, perhaps the time has come for shots and headers to be disclosed separately in post match statistics instead of them being aggregated together as is the current norm.