Author: Colin Trainor

In the article I published on Monday I introduced our (Constantinos Chappas and I) ExpG2 metric as an objective quantitative method to rate the shot saving performances of goalkeepers.  Due to the way that ExpG2 values are calculated it makes an excellent objective measure of how effectively a goalkeeper dealt with the shots he faced.

To recap, the ExpG2 value is the goal expectation that each shot has after it has been struck by the player shooting and it is calculated with reference to all of the data that we know about the shot.  One of the main drivers of the ExpG2 values is the placement of the shots.  Any shot that is blocked or off target has an ExpG2 value of zero.

What information do we know about the shot? We have the information that is provided by Squawka and StatsZone (both of which are powered by Opta).  Unfortunately, we have no defensive pressure data available to us so the ExpG2 values do not take into account defensive pressure.  Still, even with that omission we’re left with what, in my opinion, is the most objective measurement of how a goalkeeper performed from a shot stopping point of view.

My article on Monday looked at EPL ratings for last season, and I promised that I would use the same methodology to rate the goalkeepers for the current season.  I’m going to present the data from the first 8 games in the EPL this season, but due to the lack of games played I need to issue a warning as to the volatility of these results.

You will see that even just 1 more goal conceded or prevented may substantially change the ratings at this early stage of the season.  That’s not the fault of ExpG2, it’s simply reporting mathematically,  what has happened.

2013/14 EPL Season (thru 8 Games)

We’ll start off with looking at all shots for goalkeepers that have faced at least 32 shots (that’s 4 per game).

ExpG2 is the amount of goals that our model expected the goalkeepers to concede based on the type of shot and shot placement

Goals are the number of goals conceded by the respective GKs Save Efficiency is (ExpG2  / Goals), with a higher number signifying less goals than expected were conceded

This table has been sorted by default in descending order of Save Efficiency, but you can sort the table as you wish.

Southampton

We can immediately see part of the reason why Southampton has had such a great defensive record at the start of this season.  The concession of just 3 goals means that their defence has conceded the least so far in the Premier League.  As you would expect with such an excellent defensive performance, it has been achieved as a function of both great goalkeeping and a defence or team that protected him very well.

From the protection point of view, the fact that the shots faced by Boruc has lower ExpG2 values than all the other teams in the Premier League demonstrates that his teammates in front of him have excelled at ensuing Boruc had just the minimum amount of work to do.

The Save Efficiency % suggests that Boruc has massively outperformed as he has saved 4 more goals than his numbers would have suggested.  His value of 230% is excellent, but unless he now gets changed in a phone box before matches and wears his underpants outside his shorts his Save Eff number will inevitably have to reduce. For reference, De Gea was the league leader in this measure last season at 138%.

Although I’m absolutely confident that Boruc’s Save Eff % will regress, it will be really interesting to see if his teammates can keep him as well protected for the remainder of this season.

Here is Boruc’s Shot Chart for shots faced this season.  Saves are the white balls and goals conceded are the red ones.  You can see why he has earned a rating of 230%.

Boruc's Shots Faced

Cech, Lloris, Begovic and Mignolet are all bunched up fairly tight behind Boruc as the best of the rest.  Each of those 4 goalkeepers has saved their teams approximately 2 – 2.5 goals so far this season. Mignolet’s performance reaffirms the decision that Liverpool made to replace Reina with the Belgian.

Eagle eyed readers will see that this table doesn’t average at 100%.  The reason for this is that our model wasn’t fitted using just this dataset.  We used a number of leagues and a longer time period.  The fact that the average sits well above 100% tells us that goals haven’t been scored at the rates that the raw shot data would dictate.  Perhaps this may be due to the defensive pressure in the Premier League that we cannot measure, or it may also be due to short term variance.  Most likely, it’ll be a combination of those two factors.

Fulham

Anyone who follows me on Twitter will have seen the Fulham Shot Chart that I published yesterday morning.  They have conceded a huge amount of shots from what appear to be great locations:

Yet they have only conceded 10 goals.  So, for me, it’s interesting to see that the ability of Fulham to prevent those shots turning into goals on a more regular basis doesnn't seem to be down to the performance of Stockwell in goals as he is actually one of the poorer performing keepers so far on this metric.

Everton fans won’t like to see Tim Howard with just 92%.  Mind you, the Man City fans might just wish that Tim Howard was their goalkeeper.

Joe Hart

In Monday’s article I concluded that Joe Hart was below average last season, and this under performance was solely attributable to the long range shots he faced.  As I said at the top of this piece, it’s early in the season and much too early to be drawing too many conclusions from these numbers but the lack of saves made thus far by Joe Hart is alarming.  He has conceded almost 2 goals more than the shots he has faced would suggest.

If I can brielfy have a licence to be melodramatic, we can compare the sublime (Boruc) with the ridiculous (Hart) with Joe Hart’s shot chart:

As England’s Number 1, or even just a Premier League standard goalkeeper, Hart will be disappointed with how poorly he has saved the shots aimed at him during City’s opening 8 games.  In this regard it will be interesting to see how Hart performs over the remainder of the season and just how high he can lift his rating from the current 81%.

Shots from Inside the Penalty Area

We have included goalkeepers that have faced at least 32 shots from inside the penalty area. Unsurprisingly, the general shape of this table is the same as the overall one with a few noticeable differences. Joe Hart has a more respectable 104% Save Efficiency rating for close range shots (this mirror’s last season’s pattern) and Arsenal’s Szczesny goes the other way down the table with what looks to be some poor shot stopping from shots taken inside the area.

Shots from outside the Penalty Area

The final table shows the shots that each keeper faced from outside the area.  Given the relatively few goals that the keepers have conceded from long range shots at this stage of the season these ratings are incredibly volatile and will change quite a lot as more goals are conceded.

So with that in mind, a large dollop of prudence is required when trying to undertake any analysis of these values.  They are provided for information purposes as much as anything else.

Joe Hart’s unfortunate penchant for being beaten from long range has seemed to continue into this new season.  He has conceded 3 goals from outside the penalty area, when our model suggests that he should have been beaten on only one occasion.  It does appear that Hart has an issue with saving long range shots; this pattern has emerged from this data, the piece I looked at on Monday as well as this article by Paul Riley.  Generally, I’m not a fan of long range shooting, but if I was advising any of Man City’s opposition I certainly wouldn’t be discouraging them from trying their luck from long range a little more often than would ordinarily be good for them.

At the top of the table, Szczesny has done really well in producing a mirror image of Hart’s numbers – he conceded just 1 goal when he “should have” conceded 3, whilst two other London based keepers, Cech and Lloris, are yet to be defeated from shots outside the area.

It’s worth noting that Szczesny’s ExpG2 value for shots from outside the area is significantly higher than any of the other keepers in the league.  This is due to the fact that Arsenal have been excellent at forcing teams to shoot from long ranges. Here is the Shots Chart for shots conceded by Arsenal this season:

The Small Print

For those not familiar with my work, perhaps a brief introduction to me may help manage readers’ expectations.

I see myself primarily as a sports bettor and the articles that I write here on Statsbomb are only possible thanks to the huge amount of data that I have collated and analysed for betting purposes.  If I didn’t bet then I’m quite sure that I wouldn’t have spent the time required to collect the necessary data.

So the plus side is that my articles exist as a by-product of my football betting.  The downside is that I always need to be mindful of my betting edge.  This inevitably means that I can’t get into specifics as to how our models work or what’s taken into account in their calculation.   Sorry, but that's the deal I made with myself.

In a previous article which can be found here I did some research on the percentage of scoring shots and headers that a shooting player can expect to achieve given a specific shot placement. As it was my first attempt at looking at shot placements I grouped all shots together but the difficulty with stats and data is that you can never just take the first metric at face value as further analysis can be undertaken, and inevitably this second level of analysis can provide interesting insights that are missed at the higher level of data review.

In order to refresh memories, here is the scoring percentage for each shot placement zone for all shots and headers:

Remember that we are looking at the goal mouth from the point of view of the striker. I now want to undertake some further analysis to see what other information we can learn, and to do this I am going to look at shot placement based on which area on the pitch the shots or headers were struck from. I have divided all unblocked shots and headers into three pitch areas (right, left and central) as laid out in this image below:

The boundaries of the three zones have been deliberately chosen to ensure that approximately 50% of shots in the sample fall within the Central Shots zone, with the other 50% being split almost equally between right and left sides.

Central Shots Zone

Let’s have a look first of all at shots which were struck from the Central zone.

No surprise to see the general “shape” of the scoring rates heatmap pretty similar to the one at the top of the article which is for all shots.  The main difference is that the scoring rates are higher across the board, hence the increased level of "redness" in this plot.  As we are looking at the shots from the best positions (straight in front of goal) this would be in line with our expectation

Shots from the Right

We'll now cast our eyes at shots which came from the right side of the pitch as defined in the image above.

Now it gets interesting!!!!!

The above image shows the scoring rates for shots taken from the right hand side of the pitch and immediately a clear pattern jumps out.  As expected there is considerably more blue and less red on this image than the previous heatmap due to the fact that we are now looking at attempts from less attractive shooting locations. However, that's not what is so intriguing about this heatmap.

The heatmap is extremely unbalanced, with all the red and orange zones concentrated onto the left side. The imbalance is so great that if we divide the plane of the goal into thirds, the average conversion rate for shots that hit the target in the left most third (Far Post) is 32%, the central third 7% and the right third (Near Post) is 14%.

As seen in my previous piece and logic would dictate, it would be expected that shots struck towards the centre of the goal would have the lowest scoring rate; but a conversion rate of 2.25 times higher for on-target shots aimed towards the Far Post than than those aimed for the Near Post appears hugely significant to these eyes.

Shots from the Left

And what about for shots from the other side of the pitch, the left?

The exact same pattern, only in reverse, emerges.

From this left side, the Far Post third (right) has an on target conversion rate of 30%, 8% for central third and 15% for the left third. This means that Far Post on-target shots from the left hand side of the pitch are converted at twice the rate of Near Post on target shots.  This ties in pretty neatly with the finding from the other side of the pitch.

At this stage I think it’s safe to conclude that on target shots towards the far post (third of the goal) has twice the success rate of near post shots.  Even without going any further, that strikes me as a pretty darn important piece of information.

Point of Order: For the rest of this piece, Far Post is defined as any shot where the ball would cross the plane of the goal line in the Far Post third of the goalmouth or wider.  Whereas Near Post is the opposite, it would cross the goal line either in the Near Post third of the goalmouth or wider. Also the remainder of this piece will concentrate on just the shots taken from the right and left sides of the pitch as I want to investigate in greater detail the apparent Near and Far Posts phenomenon.

Far Post is Superior

So what does this mean?  My first thoughts are that the Andy Gray cliché of “he should have went across the keeper there” is correct. However, I’m only going to give him half marks as I believe his assertion was based on the fact that, if missed, a shot across the keeper has a chance of being parried, allowing the attacking team to pick up the rebound and have another strike at goal.  A shot missed on the narrow side does not have this luxury. Not for one second do I think that Andy Gray was aware that on target shots to the far post are scored at rates of 2 to 2.25 times more than those shot towards the near post.  At least if he was aware of that fact then he, along with everyone else in football, kept that particular nugget very quiet.

Possible Reasons for Discrepancy

1 - The first possible explanation for this difference is that I’m only looking at shots that are on target, ie in this analysis I have ignored shots that were wide or high of the target. Perhaps looking at goals as a percentage of all unblocked shots is required as it may be more difficult to hit the target with cross shots than near post shots.

After investigation, it turns out that this was indeed the case as 68% of all Far Post shots missed the target, compared with 64% of Near Post shots.   However, that small difference isn’t anywhere near sufficient to explain the difference in goals scored as a percentage of unblocked shots.

After including missed shots, 9.9% of unblocked Far Post shots are scored, whereas the rate substantially falls to 5.3% for Near Post unblocked shots.  This means we end up with a final ratio of unblocked Far Post shots being scored at 1.8 times the rate of Near Post shots. So after ruling out the difference being attributable to off target shots we are still left with a significant unexplained difference in terms of the scoring rate for Far and Near Post shots.

2 - Could it be that goalkeepers are overly concerned with getting beaten at their near posts?

There is no doubt that it looks bad for a keeper if he is beaten at his near post, but perhaps they are trying to guard the near post at the detriment of the cross shot? At this point (with no access to goalkeeper positioning at the time of the shot) I don’t have any way to either prove or disprove this possible explanation, so unfortunately I have no other option than moving on to my next possibility.

3 - Another possible explanation for the difference is that I have so far excluded blocked shots from this analysis (as we never know where they will cross the plane of the goal).

Due to the fact that a cross shot has to travel through the central area of the pitch it certainly seems likely that shots aimed towards the Far Post have a greater chance of being blocked than those targeted towards the near post.  But is the difference in the rates that Near and Far Post shots are blocked enough to explain the near twice as often conversion differential?

This could be quite a difficult question to answer as we have no way of knowing where the shots would have crossed the goal line had they not been blocked.  However, I have been spared some potentially impossible mental gymnastics as even if EVERY blocked shot was a Far Post shot (so none of the blocked shots were destined for Central or Near Post!!) the scoring rate for all Far Post shots would still exceed that of Near Post shots. That really is something. So although that is good news, as a numbers man I have an innate desire to quantify effects and so I’m going to try to make an educated guess at the location in the goal where blocked shots were destined for.

First up, what’s our split of non-blocked shots:

As stated above, I would assume that Near Post shots are likely to get blocked less than Far Post shots, but I would assume it would be reasonable for Central shots to be blocked at the same rate as Far Post shots.

Having established this, let’s then assume that Far Post and Central shots are blocked at twice the rate of Near Post shots (this is only a guess, but seems reasonable to me and I need to pick a number).

This blocked shots weighting combined with the volume of non blocked shots results in an assumed distribution of the Blocked Shots as follows:

Far Post               53%

Central                 21%

Near Post            26%

Total                     100%

I will therefore split the Blocked shots in my data sample as being destined for Far Post, Near Post and Centrally in the ratios of 53%, 26% and 21% respectively.

At this stage, I want to point out that the only purpose of the preceding couple of paragraphs is to approximate the number of blocked shots for each of the goal zones (Far and Near Posts and Central) as the analysis cannot be properly completed with some attempt at apportioning blocked shots.

Yes, some of my assumption can be challenged but I don’t think that I can be that far out in the approximations I have used; and importantly certainly not enough to change the core findings of this analysis piece.

Conversion % of All Shots           

Armed with an approximation of blocked shots for each goal zone we can now reach a conclusion which takes into account the percentage of all shots which are scored from the sides of the pitch (the areas denoted in the second image in this piece) depending on whether the ball would have ended up Near Post, Far Post or centrally in the goal.

Remarkably, 6.8% of Far Post shots were scored, this compares with just 4.4% of Near Post shots.  As raw numbers, both of those conversion rates appear fairly small, but don’t forget that we are dealing with shots that are struck from less attractive locations on the pitch (ie away from the central strip of the pitch).

Conclusion

What I have laid out in this article appears to be quite fundamental. When shooting from less attractive positions, the player shooting has a conversion rate which is more than 1.5 times better for Far Post attempts than for Near Post attempts.

If this fact wasn’t impressive in its own right, when this is parlayed with the chance of a Far Post shot being parried and the rebound scored from then the advantage is even greater than the basic 1.5 multiple as calculated above.

Why?

The question I haven’t been able to answer properly is why this phenomenon exists in professional football when clubs have access to both better data and bigger brains than mine?

I don’t think it can be due to variance as my sample has a huge amount of shots, it contains every shot taken in the Big 5 Leagues during the 2012/13 season – that’s almost 50,000 shots.

After undertaking the work for this article the only conclusion I can arrive at is that it’s due to Goalkeeper positioning.  I have taken account of most other things, ie the difficulty of hitting the target and the apportionment of blocked shots. Could it really be that keepers are so conscious about the “Pride of the Near Post” that they over compensate?  I am unable to coherently put forward any other possible reasons.

In order to gauge reaction to this piece I sent a draft to David Sally and Chris Anderson, the co-authors of "The Numbers Game".  David made the point that a higher success rate for Far Post shots could be indicative of another aspect of the way goalkeepers play.  If they were slow to come off the line then due to basic geometry they would be more exposed to Far Post shots than Near Post efforts.

As alluded to in my preamble to this post, you can look at a facet of the game using just the headline measurements (conversion % for all shots in this instance), we can then go one level deeper into the data (slice the data by pitch sides) but even this may not be enough.  Chris Anderson made the point that I should probably further divide the data into shooting distances.  This would involve going yet another level deeper into the data. Perhaps I might further subdivide the data in a future article so that I can see the impact of shot distance on this Near and Far post phenomenon.  However, for my money that lack of further slicing of the data doesn't diminish the importance of the findings laid out here.

As an aside, this clearly demonstrates why the basic match stats information is so lacking in detail to give fans a proper understanding of what has happened in a game.  Despite using data in a format that I hadn't seen before (placement success rates), then going one level deeper, I find myself in the position where I could go another level deeper to try to complete our understanding of this quirk.

Whatever the reason, there is no getting away from the fact that that shooting Far Post seems to have a significantly increased higher goal expectation than shooting Near Post. In a game of such small margins were teams try to gain from any advantage where possible let’s see if clubs and players learn from this and we begin to see either a greater proportion of shots being fired towards the Far Post or keepers minding their Near Post just a little less in this coming season.

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.

Summary 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.

To be honest, I’m not sure if I’m a great fan of shot placement work or not.

It’s reasonable to assume that when a player is shooting he is aiming for the target, however more than 50% of non blocked shots miss the target.  This means that in the majority of cases the player isn’t successful in actually putting the ball where he intended. With than in mind, I wondered what there is to gain in attempting to undertake meaningful analysis due to the apparent difficulty for players to have as much influence on the accuracy of their shooting as they would like. However, I realised it would be useful to create a reference source for what actually happens a shot or header once it reaches the plane of the goal line.

It’s worth noting that at this stage I am looking at all shots and headers.  I wanted to have a large enough data sample so meaningful high level patterns would emerge.

Expectation

It would certainly be expected that a shot hit down the goalkeeper’s throat would be a relatively simple save to make for the keeper, whereas a shot arrowed right into any of the corners would be substantially more difficult to save. But just how more likely is the latter shot to score than the former? Presently I can’t put my finger on a source that will give me that answer, hence this point of reference article. As an aside, in my opinion this was part of the reason why this site was created in the first place.  Up until now there just hadn’t been a stats based site that can deliver articulate articles and data in user-friendly formats.  Hopefully that will change.

Probability of a Shot being scored

Here is a front-on view of the goalmouth showing the percentage of shots that resulted in a goal.  The plane of the goal has been divided into zones according to where the ball did or would have crossed the plane of the goal line had it not been saved:

We can see that a ball hit dead centre at low to medium height will result in a goal 12% - 13% of the time (1 shot in every 8).

At the other extreme, a shot that is set to nestle in the corners will result in a goal approx  50% - 60% of the time (1 shot in every 2). For the shots in between, we can see that there is a definite pattern where the percentage success gradually increases the further that the ball is struck away from the centre of the goal.

It is also clear to see that shots struck towards the higher parts of the goal are scored at higher rates than those that keep low to the ground.

Cheat Sheet I have changed the magnitude of the above graph to create an “easy to remember Cheat Sheet”:

The scale has been modified so that the figures in the chart now reflect how likely a shot is to be scored compared to a shot that is hit dead central at mid / low height.

The rate in the centre of the goal is 1, whereas shots into the corners are scored 4 or 5 times as often.  Shots that the keepers have to scamper across the goals to cover are scored 3 times as often as those hit straight at them.

Who knows, perhaps some time in the future we may hear a knowing pundit inform their viewers how much more likely the shot they have just witnessed was to score than one hit straight down the middle........

Significance

Not sure really, but the main aim was to create a point of reference that could be referred to in the future.

We know that players don’t miss the target on purpose, yet most shots fail to trouble the goalkeeper.  It would therefore be a tall order for managers or coaches to instruct players to deliberately shoot for the corners and there is certainly comfort for strikers (especially those short on confidence) to ensure that they hit the target. I’d wager that the next time you watch a match on television you won’t have to wait too long until you hear the commentator extol the virtues of “making the keeper work”, ie whatever you do ensure that the shot is on target.

However, if the striker had known the relative successes of shots hit towards different areas of the goal, and indeed that the success factor can increase by as much as 5 fold, would his thought process be any different?

Well, we can but hope.   Article written by Colin Trainor.  Colin can be followed on Twitter here.