Additional Time: Spain’s Missing Minutes and Other Findings

The rules of football are very clear.  Law 7, “The Duration of the Match” states that “A match lasts for two equal halves of 45 minutes”.  The rules also make an “Allowance for Time Lost”, in which they list the following scenarios where allowance should be made for all time lost due to:

  • substitutions
  • assessment and/or removal of injured players
  • wasting time
  • disciplinary sanctions
  • stoppages for drinks or other medical reasons
  • any other cause, including any significant delay to a restart (e.g. goal celebrations)

It appears that, according to the rules, no allowance is to be made when the ball goes dead (ie. out for a goal kick, corner, throw-in or for the award of a free kick), unless the referee deems that there has been a significant delay to the restart.  It therefore follows that the rules makers never intended there to be 90 minutes of actual play, but due to the combination of a varying number of dead ball events, differing interpretations of what represents “a significant delay to a restart” as well as the leniency (or otherwise) of the referee with respect to time wasting we are now in a position that the amount of actual playing time from one game to the next can fluctuate wildly.  These fluctuations can be seen between one league and another, but they can also be seen from game to game within the same league.

The image below shows the median time, in minutes, that the ball was in play for in each of the traditional Big 5 European leagues since the 2010/11 season:

Big5Leagues

The first thing to note is that the median Ball In Play (BIP) value across all leagues never exceeds 57 minutes (the minutes values displayed on the chart are rounded to the closest minute).

Almost 90% of games have an effective BIP duration of less than 60 minutes.  That, typically, more than 1/3rd of the allotted playing time per the rules of the game witnesses no action on the pitch isn’t very intuitive.  Is that what the rules makers intended when the laws of the game were laid down?

If we look at the last two or three seasons we see that the Spanish Primera Liga is an outlier in terms of the actual time played when compared to the other leagues.  While the other four leagues have a median observed BIP time of 56 – 57 minutes the Spanish league typically sees 2 – 3 minutes less actual playing time per game.

  How Much Time is Actually Added On?

Although, excluding Spain, there is a certain consistency in the actual minutes of football played between the leagues, the leagues follow a very different path in arriving at their typical 56 – 57 minutes of action.

The chart below shows the median amount of time that is played over and above the mandated 90 minutes.  Note that this is not the time that the fourth official displays on the board, but the actual time that is added on.

AddedMinutes

To give an example: A fictitious game sees the first half end with 46mins 20 secs gone on the clock and the second half end at 94mins exactly.  The amount of additional time actually played in this game was 5mins 20 secs (or 5.33 minutes).

We can see that each of the last 5 seasons follows a similar pattern in terms of additional time played across the leagues.

Games in the English Premier League consistently see much more additional time played than in the other leagues.  It’s interesting that despite, on average, playing one more minute additional time than the other leagues, the English Premier League doesn’t actually see a greater amount of playing time. This additional amount of time added on is merely needed to match the other leagues in achieving a BIP time of 56 minutes.

Following the EPL, we see that Italy consistently has a median value of additional time of approximately 5 minutes per game, with France following slightly behind with about 4.5 minutes.

The amount of additional time added on to Spanish La Liga matches is revealing.

We saw in the first chart that Spanish games consistently see the lowest amounts of time that the ball is actually in play.  So, unlike in England, the Spanish referees are not adding on sufficient amounts of injury time at the end of each half.  In comparison with the other leagues, it would appear that the Spanish arbiters should be playing another couple of minutes additional time per game.

Despite less additional time being played in Germany, fans of the Bundesliga haven’t witnessed (at least for the last 3 or 4 seasons) less minutes of actual playing time than their European peers.  By definition, this can only be due to a lower duration of stoppages during the regular 90 minutes.

  1st Half Additional Time

I wanted to take a look at how the additional time is apportioned over the two halves.

The chart below shows the distribution of time that each league has added on at the end of their first halves over the last 5 seasons.  For display purposes, the amount of additional time has been rounded to the nearest minute in the following charts.

1stHalf

47% of German Bundesliga games had less than 30 seconds added on to the end of the first half; this compares with just 1% of English Premier League games.  Here we can see the first part of Spain’s issue re the lack of additional time;  43% of Spanish games has less than 30 seconds added on at the end of the first half.  In fact, 85% of Spanish games had less than 1.5 minutes of first half additional time; this compares rather unfavourably with England’s equivalent figure of just 38% of games.

The Spanish whistlers also seem loathe to play more than 2.5 minutes of first half additional time.  Only 3.6% of Primera league games seen its clock tick round to 47mins 30secs.  Even the German Bundesliga has a greater percentage of first halves lasting longer than that; and almost 25% of EPL games seen additional time of at least that duration.

No matter how we slice this, it’s clear that playing a fairly short amount of added time is just a thing that the Spanish top flight does.

  2nd Half Additional Time Below is a similar chart, this time looking at the 2nd halves of games: 2ndHalf

I have previously read on my Twitter timeline of the default 3 minutes additional time shown the end of games in Spain.  The analysis undertaken for this article has shown this assertion to have considerable merit as just over 50% of Spanish top flight games had 3 minutes (rounded) of additional time played at the end of the second half.

The German Bundesliga displays similar 2nd half tendencies as we seen earlier when looking at the 1st halves.  I was somewhat surprised to see that 13% of Bundesliga games had less than 30 seconds additional time played at the end of the game.  In fact that we find that almost half of all German Bundesliga games play less than 2.5 minutes of additional time once the 90 minutes are up; compare this with only 1 in 30 EPL games having less than 2.5 minutes 2nd half additional time!

England and, to a lesser extent, Italy are the two leagues where its spectators are most likely to see more than 3.5 minutes of additional time played in the 2nd half.  This analysis would suggest that Spanish fans deserve to see longer periods of additional time played at the end of each half, but for whatever reason they just aren’t getting it.

  English Premier League

Thus far, I have implied that English Premier League referees are doing a pretty good job (compared with the other leagues) with their timekeeping as they generally play the greatest amount of additional time.  This ensures that the typical EPL match sees as much actual ball in play time as the other main European leagues.  I have favourably compared it to Spain where the referees do not seem to be as fastidious in their as their English counterparts in adding on sufficient additional time at the end of each half.

However, even within the EPL there are significant differences in how much actual time is played from game to game.

The chart below shows the distribution of actual in play minutes in the Premier League over the last 5 seasons:

EPLMinutes

It’s no surprise, that we see that 55 and 56 minutes are the BIP minutes that have been most common in the Premier League, but that there have been games with BIP minutes ranging from as little as 43 to as much as 68 amy surprise some readers.  It would seem barely plausible that two games played under the same rules in the same league could differ in their actual playing times by such an order of magnitude.

Swansea in November

Earlier this season, during last November, Swansea played a couple of games at home that could hardly have been more different in terms of actual playing time; and I have taken a look at those two games in some detail.

On November 6th Swansea hosted Man United at the Liberty Stadium, lost 3-1 but the fans at that game gorged on 64 minutes of seeing the ball move around the pitch.  However, just three weeks later the Swansea fans experienced a totally different experience when they beat Crystal Palace in a barnstorming, never to be forgotten, 5-4 thriller.  However, (according to my methodology) that thrilling game experienced just 44 minutes of actual moving footballs!

For the two games mentioned above I went through each Opta action in chronological order and produced a summary showing the approximate number and duration of each stoppage that occurred in the games.

The following charts categorise the stoppages.  Note that there aren’t separate categories for the issuing of cards or making substitutions as those delays are built into the stoppage where the event happened.

SCvCPFC

Remarkably, even though more than 100 minutes of time was played in the match, we can see the stoppages that resulted in less than 45 minutes of actual ball in play time.

In total, I calculated that there were 112 stoppages.  We can see that, unsurprisingly, the length of break in play is largely dependent upon the type of stoppage.  Before we look in depth at the various stoppages, let’s compare Swansea’s game versus Crystal Palace with their game played three weeks earlier at the same venue against Man United:

SCvMUFC

The Man United game had only 95 minutes of playing time, but that still resulted in approximately 64 minutes when the ball was in play.

Across the two games we can see patterns emerging in terms of how much inactivity we can expect to see as a result of a given dead ball situation:

  • Each goal results in approximately 60 seconds of no action
  • On average, each time the ball goes over the end line (either for a corner or goal kick) we see a break in play of about 30 seconds
  • Throw-ins take about 15 seconds on average to restart play

The average duration of breaks in play arising from free kicks differed greatly in these two games.  The average free kick in the Crystal Palace game took 31 seconds out of the game, while the equivalent was 10 seconds less in the Man United game.  I ascertained that a large part of the reason for this is that in the Crystal Palace game Gylfi Sigurdsson had two direct free kick efforts at goal.  The first one was struck more than 2 minutes after the free kick was awarded (some screen caps are included below).  As well as the usual spray painting by the referee there was some messing about in the wall which resulted in Yohann Cabaye being called out from the defence and receiving a lecture.  The second one was struck 50 seconds after the foul.  In contrast, there were no direct shots at goal from free kicks in the Man United game.

In terms of providing a rough ready reckoner for how we can have 64 minutes of play in one game, and then only 44 minutes in another I would reduce the 20 minutes less football played in the Crystal Palace game to:

  • 5 additional goals = +5 minutes
  • 5 fewer throw-ins = -1 minute
  • Additional delays attributable to free kicks = +10 minutes
  • 14 additional corners = +6 minutes

  What should be done in such instances? 

That we can have two games in the same competition where one sees 44 minutes actual play, and the other 64 minutes doesn’t seem equitable.  Obviously the example I have used here includes two fairly extreme cases, one at either end of the scale, but they are real, not illustrative.

The first half of the Swansea v Crystal Palace game had less than 3 minutes additional time.  However, one series of events shows just how inadequate that additional time was.  From the screen cap below we can see that Jack Cork was fouled on the edge of the opposition’s penalty area at 33mins 50secs.

FreeAward

Gylfi Sigurdsson scored directly from the subsequent free kick, which was taken at 35mins 57secs, after a delay of 127 seconds.

FreeTaken

Crystal Palace took the restart following the goal, after another full minute expired to allow the celebrations to pass.  So, over a period of about 3mins and 5secs the only action that took place was Sigurdsson striking the free kick.  That one series of events from the 33rd to the 36th minutes had longer stoppages than the additional time played at the end of the half!

Given the considerable differences in playing time it does seem like one possible solution would be to operate a 60 minute stopped clock, instead of the current “we’ll call it 90 minutes but there is no telling how long we’ll actually play for” durationSuch a change from the status quo would see more football being played in almost 90% of games; but the main advantage would be that all teams would be on a level playing field.  What is there not to like about that idea?

Timewasting would not continue to be rewarded, as the perpetrators would know that every second the ball wasn’t in play we would see an equivalent amount of time being added on at the end.  Right now, this definitely isn’t the case.

I appreciate that such a move to a stopped clock will not be easily made, however the Liga de Fútbol Profesional (LFP), the organisation that runs the Spanish Primera league, have a much easier change to make.  Armed with the information in this analysis they need to have a look at their lack of additional time in comparison to the other big European leagues and instruct their referees to add a little more additional time than they currently play.

By doing that, the Spanish football fans will begin to see as much football as their peers in the other European countries.

Opta_200px

Player Aging: Attacking Players

SterlingYaya End of my Hiatus First things first.  Although I never publicly announced it at the time, I’ve spent the last 12 months consulting for a Premier League football team.  My engagement ended at the end of the 2015/16 season and so now I’m able to pick up my virtual pen and begin writing again.  It’s been about 18 months since I’ve done this so please be gentle…   Player Aging Player aging is a thing.  We know that people get physically stronger as they mature from a teenager into an adult and then some time later they begin to lose some of their physical edge.  That much is a fact, but what is open to some debate is when exactly those transitions happen, what is the extent of the improvement and subsequent decline and also whether players’ increasing tactical knowledge and “game sense” as they gain experience can help offset some of their loss in physical edge. There have been other pieces written on player aging.  Simon Gleave did a presentation at one of the OptaPro forums on this topic, and he wrote this follow up piece.  Michael Caley has also written about this, as he has done with just about everything else to do with football analytics, but while most of the current writings tend to focus on the share of player minutes at each age I wanted to have a more detailed look at how some individual components of players’ performances are impacted as they age.   Data Rules and Explanations As always, Opta is the source of the data that I’m using in this study and I’m looking at the Big 5 Leagues for the 6 seasons from 2010/11 to 2015/16.  I wanted to take a look at each position separately as the skills required for each position may be different. I used the Opta starting formational information and included players who started the games, dividing them into the following positions:

  • Full Backs
  • Centre Backs
  • Midfielders (Central: defensive or attacking)
  • Wingers
  • Forwards

I undertook the analysis at a game by game level, so in the games where, for example, Christian Eriksen started centrally his numbers went into the Midfielder grouping, whereas when he played on the left side of midfield his numbers went into the Wingers grouping.  There may be an amount of arbitrary decision making around the position assigned to the players by Opta, but I think my method should ensure that players are broadly assigned to the correct positional grouping. I then excluded any players that didn’t play at least 540 minutes in a given position and analysed the remaining players through the use of a few summary season metrics.  The hope is that we get an idea of how the individual components of a player’s game are impacted by their aging. I grouped all players together who were younger than 20 (identified in the “Teen” group in the charts below) and at the other end of the scale I grouped all players that were older than 32 in the “Old” group.  At my stage in life, 32 actually seems quite young, but that’s probably a discussion for another day! The player’s age for each season is taken as his age as at the 31st December in the season, and the individual metric value generated for each age group is the median value of its population. As there will likely be some variation across leagues I initially analysed each of the five leagues separately, but there was quite a bit of noise as some of the bins were too small so I decided to combine all the leagues to maximise my data size as I want to be able to identify the general trends. In this first part of my look at Player Aging I will concentrate on the attacking positions, Wingers and Forwards.  Other positions should / may follow this article. OK, so now on to the good stuff………   Wingers – Key Metrics Let’s go straight in and look at the key attacking output of wingers; namely Open Play Shots, Open Play Key Passes (regardless of whether those KPs were converted or not) and Scoring Contribution.  Scoring Contribution is defined as Non-Penalty Goals and Assists, and as you are reading this on StatsBomb all three metrics are shown on a Per90 basis. WingerOutput The secondary axis (the one on the right side of the chart) is the axis for Scoring Contribution, whilst the main axis displays the Shot and Key Pass numbers. Open Play Shots The red line represents Open Play Shots per 90 minutes and there is a very tiny increase in this level until players reach the age of 26 (1.95 at 26yo vs 1.85 at 22yo).  After the age of 26 there is a very clear drop off in shot volume for wingers and by the time they reach 29 years old their shot volume has dropped to about 1.6.  There is then a small uptick at 30, but the pattern is clear; Shot volume for wingers reduces after they reach 26 years old. Open Play Key Passes Immediately we can see that for the blue line (Open Play Key Passes) the change in output for wingers as they age is not as severe as that observed in their change in shooting volumes.  There is a slight increase from teenage years until players reach the age of 23, and then it flattens until 28 when it begins it’s very slow decline. One hypothesis for this almost (but not quite) horizontal line is that there are many different ways to play a Key Pass, for example, they can be created through a burst of speed or through the playing of a well-timed, accurate pass.  The former of these methods is more likely to happen with younger players, whereas the latter may be suited to a more experienced player and so we don’t really see age having much of an impact on how creative wingers are. Scoring Contribution The green line, which represents Scoring Contribution, is the absolute key one in terms of final output for attacking players as it represents how many Non-Penalty goals they either score or directly assist.  The pattern here for wingers is very clear as it steadily increases from their teenage years until they reach 26, at which point it begins its steady decline. In absolute terms, the median Scoring Contribution value for 21 year old wingers is 0.29 Per90, and this increases to 0.34 by the time they reach 26 years old, and then decreases to 0.28 by the time they reach 30.  Those differences may sound small, but over a 38 game season the difference in the output between one 26 year old and one 30 year old winger comes to almost 2.30 goals.   Forwards – Key Metrics We’ll now run the same analysis for Forwards as we did for Wingers. ForwardsOutput It’s probably no great surprise to see that the lines on the Forwards key output charts following similar patterns as those seen in the Wingers’ chart.  Shot volume increases until it peaks at 27 while Key Pass volume broadly remains fairly flat throughout the career of a forward. In terms of the composite metric, the green Scoring Contribution one, there is an anomaly with 32 years and older forwards performing very well (notably in Italy). I assume there will be a large element of survivor bias in this number as any 32 year old (or older) that is playing is more likely be doing so because they are performing whereas the same probably can’t be said for the average 30 year old forward.  However, leaving this wrinkle aside we can see a general increase in Scoring Contribution for forwards until they reach 28 years of age, at which point their numbers can expect to decline. The extent of the decrease in Scoring Contribution between a 28 year old forward and a 24 or a 30 year old forward is similar to what we seen when looking at wingers.  The median 28 year old clocks up 0.43 Scoring Contribution Per90, compared to 0.37 for both a 24 year old and a 30 year old.  This lack of a peak age forward leading the line for a team again equates to an expected shortfall of 2.30 goals over a full season.   Wingers – Other Metrics Apart from the key output metrics I wanted to look at how a few other metrics reacted, across the population as a whole, depending on the age of the winger. WingerOthers Dribbling Two of the metrics on the above chart relate to dribbling.  The yellow line is the traditional Successful Dribbles stat as provided by Opta while the orange line is one of my processed metrics.  The orange line represents the number of metres that the player dribbles the ball closer to the goal than from where they picked it up; so this shows how much progress towards the goal the player makes when carrying the ball. These two dribble metrics follow a very clear and similar pattern, albeit a somewhat surprising one.  On the whole, wingers will dribble the ball less with each passing year.  Unlike the shooting and Key Pass metrics that we read about earlier in this piece, players do not carry the ball further or more often in their mid to late twenties than they do in their younger years. To me, this is really interesting.  We have seen that wingers’ attacking output (as defined by shots and assists) increase from their early twenties until they reach 26 years old yet we see that they are carrying the ball less often and over shorter distances.  The median winger will have 1.1 successful dribbles when they are 26 years old compared to 1.6 when they are 20 or 21 but their decreased ball carrying does not seem to have an adverse impact on ultimately how creative they are. One hypothesis for this is that they simply become smarter footballers as they mature.  They make better choices as perhaps they no longer feel that they have to prove themselves by beating their man like they did when they first broke into the team.  Perhaps they learn to lift their head and look for better options instead of simply carrying the ball for its own sake. The takeaway from this discovery: So while we look at (for example) a 19 year old Raheem Sterling and marvel at his numbers we should bear in mind that, whilst his end product should increase until he reaches his mid-twenties, we should expect his ball carrying numbers to reduce.   Fouls Won The purple line displays the number of fouls won or drawn by the median winger at each age of his professional life.  Although the line looks fairly flat on this chart there is a slight consistent reduction in fouls won from 22 years old (from 1.8 to 1.45 by the time the winger reaches 30).  Despite the existence of this slight decrease in fouls won as the winger ages it’s clear that the pace of decline is nowhere near as sharp as that shown in the main dribble metrics.  Once again, that’s an interesting result. Does this suggest that it demonstrates players becoming cuter or more “game smart” as they develop in years because they can draw a foul comparatively easier (when controlling for how much they carry the ball) than when they were younger?  Or does it mean that fast players can’t or don’t win free kicks as often as we think they should?   Non-Corner Crosses The last remaining line on the chart, the black one, shows us how many non-corner crosses the median winger plays.  There is a blip at 25 that otherwise distorts a fairly clear increase in the number of crosses wingers play until they reach 27 to 29 years old, after which point the output sharply decreases.  As the value of a cross is pretty marginal I’ll not spend any more time on this one but just wanted to mention it as I pulled the data to get to this point.   Forwards – Other Metrics ForwardsOthers No comment required here as the lines for the dribbling metrics and fouls won for forwards are almost a carbon copy of the wingers’ numbers produced earlier.  This in itself is encouraging as the emergence of similar patterns across two totally distinct data sets gives us confidence that there is signal in what we are looking at.   Conclusion For most readers, this won’t be the first time they have read about Player Aging in football, and as a concept it is quite straightforward.  However, the reason that I undertook this research was that I was unable to quantify the impact that playing a 24 year old or a 30 year old player instead of a player at peak age (assuming both have achieved similar percentile achievements in their age bracket) for its position would have on a team’s expected output. Balancing a squad from an age perspective is difficult; buying to improve your chances of immediate success will have a negative impact on your future chances and buying young talent to maximise resale value means that the team won’t be at their absolute peak for the forthcoming challenges.  It’s undoubtedly a tough line to walk successfully but now when teams make decisions around the age structure of their squad (here’s looking at you, Man City) we can be a little more knowledgeable around quantifying the potential impact of the decisions that are made. Although the charts contained in this piece relate to the median number posted by each each group for each metric I also looked at the 80th percentile and, while the curves were obviously higher than the median ones, the drop off from the peak was roughly a similar amount to those displayed here. Based on the data that I have analysed it looks like the peak age for a winger is 26, whereas a forward peaks a year or two later when they reach 27 or 28 and the expected impact of playing the 24 or 30 year old instead of your peak age player on an ongoing basis will shave approximately 2.30 goals from your attacking output over the course of a season.

MUFC vs Liverpool Positional Tracker

Man United 3 vs 0 Liverpool Here is our visualization that shows the smoothed positions of players around the time as indicated. As we don’t have access to detailed tracking data we have tried to be as smart as we can with the “on-the-ball” data collected by Opta; we think we’ve made a decent attempt at trying to understand the flow of the game and the general positional trends of the players within the game. We know it’s not perfect, but we’d need full tracking data to ensure that we have the exact positions of every player correct at all times.  In the absence of full tracking data, hopefully people will find these visualizations helpful. Click to open viz in a larger window. A couple of my thoughts:

  • Game was exceptionally compressed in the middle third during opening half hour
  • Moreno totally neglected his defensive positioning during the first half.  He is shown as further forward than Coutinho during nearly all of the first half
  • In the second half Fellaini dropped right back to offer additional protection to United’s defence

MUFCvLIV

Southampton v MUFC Player Positional Tracker

Southampton 1 vs 2 Man United Here is our visualization that shows the smoothed positions of players around the time as indicated. As we don’t have access to detailed tracking data we have tried to be as smart as we can with the “on-the-ball” data collected by Opta; we think we’ve made a decent attempt at trying to understand the flow of the game and the general positional trends of the players within the game. We know it’s not perfect, but we’d need full tracking data to ensure that we have the exact positions of every player correct at all times.  In the absence of full tracking data, hopefully people will find these visualizations helpful. Click to open viz in a larger window. SOUvMUFC

Newcastle v Chelsea – Player Positional Tracker

Newcastle 2 vs 1 Chelsea Here is our visualisation that shows the smoothed positions of players around the time as indicated. As we don’t have access to detailed tracking data we have tried to be as smart as we can with the “on-the-ball” data collected by Opta; we think we’ve made a decent attempt at trying to understand the flow of the game and the general positional trends of the players within the game. We know it’s not perfect, but we’d need full tracking data to ensure that we have the exact positions of every player correct at all times.  In the absence of full tracking data, hopefully people will find these visualisations helpful.   I don’t have much time this morning (which has been the case for the last month or so) so I am only pointing out a couple of very noticeable features from my watching of the PPT below.

  • In the early stages, Newcastle were quite agressive with Ameobi and Dummet playing high up the left wing.  Was this deliberate to keep Ivanovic in check?
  • Newcastle had a decent spell of possession half way through the first half, with Sissoko especially involved during this time
  • I was surprised at the absolute lack of width displayed by Chelsea; it was virtually non-existent in the opening hour.  At times Chelsea had 5 players in the central attacking part of the pitch; Willian, Hazard, Oscar, Diego Costa and Fabregas

Anyway, let me in the comments know what else you see, and you can click the image below to open in a larger window. NEWvCHE

Man City v Man United Player Positional Tracker

Man City 1 vs 0 Man United Here is our visualisation that shows the smoothed positions of players around the time as indicated. The locations are identified with reference to actions as identified by Opta. Comments from Sam Gregory appear below the PPT.  Click on the gif to open in a larger window: MCFCvMUFC

First Half

  • Rooney’s return to the United starting XI saw him take up an influencial central midfield role playing alongside Fellaini and Blind in the middle of a 5 man midfield.
  • Both teams started playing with one man up top but Aguero was much more effective than Van Persie. Van Persie was marked out of the match by Kompany and received very little service. Aguero on the other hand found plenty of space between Rojo and Smalling and was very mobile moving from side to side. His involvement is noticeably much larger than Van Persie.
  • After Smalling’s sending off Jovetic moved up the pitch and effectively joined Aguero as a second forward.

Second Half

  • For the first twenty-five minutes of the second half City absolutely dominated the ball. The ten United players left on the pitch had some of the smallest influence dots during this period that I’ve ever seen on a PPT. It wasn’t until the goal that United was able to put anything together in the second half. Yaya Toure and Fernando were particularly dominant to start the second half
  • During the last twenty minutes Di Maria, Rooney and Fellaini played much further up the pitch, with Di Maria playing in a more central position. This gave United something going forward and gave City a bit of a scare during the closing moments.
  • Added by Colin: As was noted on my Twitter feed by @CityAcrossPond, Kompany and Demichelis swapped sides in Man City’s defence for the final 20 minutes of the game.  Presumably this was an attempt by Pellegrini to contain Di Maria as much as possible. EDIT – @evolutionHPcal has suggested it may also have been to help Clichy with Fellaini’s aerial threat.

 

Conclusion

  • City were dominant and fully deserved the three points in a game they should have scored more than one goal. Chris Smalling’s sending off was clearly the turning point, but City deserve credit for capitalizing on the advantage.

Man United v Chelsea Player Positional Tracker

Man United 1 vs 1 Chelsea United grabbed a very late equalizer as Mourinho’s Chelsea just faced to hold on to their second half lead. Here is our visualisation that shows the smoothed positions of players around the time as indicated. The locations are identified with reference to actions as identified by Opta. Comments from Sam Gregory appear below the PPT.  Click on the gif to open in a larger window: MUFCvCHE

1st Half 
  • United lined up with Van Persie in the middle flanked by Januzaj and Di Maria on the wings. It was a fairly straight forward 4-3-3 with Mata floating between the two lines as the link between midfield and forwards. This marked yet another change in formation for Van Gaal who had used a 4-1-4-1 against West Brom.
  • Blind was quite effective in the first half following Fabregas throughout and keeping him off the ball in the middle. Fabregas was forced to drop into positions closer to the Chelsea back four as the half went on.
2nd Half
  • To start the second half Di Maria and Januzaj switched wings and Di Maria was very involved for the first fifteen or twenty minutes testing Ivanovic on the left side.
  • Once Chelsea scored through Drogba they reverted to their typical shut-down football. Cahill and Terry dropped much deeper while Ivanovic and Felipe Luis made fewer attacking runs. Mikel came off the bench to fulfil his usual role of guarding the back four, giving Matic and Fabregas more freedom to disrupt United’s passing game further up the pitch.
  • United’s attacking game completely fell off after the Chelsea goal and it wasn’t until the final five or so minutes when they started to make a few chances.
Conclusion
  • Fabregas probably had his least effective game since coming to Chelsea and a lot of the credit goes to Blind and Fellaini who kept him fairly quiet.
  • A draw was probably a fair result, but Chelsea usually hold onto these results when they are able to slow down and kill off the game so United can take solace in the fact they were able to pull off the draw.

Tottenham v Newcastle Player Positional Tracker

Tottenham 1 vs 2 Newcastle Newcastle upset the odds with a come from behind win at White Hart Lane on Sunday afternoon. Here is our visualisation that shows the smoothed positions of players around the time as indicated. The locations are identified with reference to actions as identified by Opta. Comments from Zubair Arshad appear below the PPT: TOTvNUFC  

  • This was a real “game of two halves”.
  • Main comment for Spurs was their narrowness of Chadli/Lamela occupying the same space as Eriksen, therefore making it a bit easier for Newcastle to defend. This can be seen in the PPT. Lennon was introduced in the 75th minute to address this but Pardew reacted by bringing on Haidara to play in front of Dummett on the left.
  • Newcastle struggle to establish any dominance in the game (especially in first half) without Tiote. Anita is the furthest possible replacement for Tiote that we have. He received 4 passes and made 4 passes in the first half therefore no surprise to see his “dot” very erratic and small. It was difficult to pinpoint NUFC’s structure (mainly because they barely had one), with Sissoko and Colback high up the pitch to try and support Perez but NUFC overall had very little possession in the first half. (21%).
  • The second half was a different story. Clearly the Ameobi goal changed the game, but there was a lot more structure to Newcastle’s midfield. Cabella, Ameobi and Gouffran interchanged between the lines and flanks, with Sissoko and Colback sitting a little deeper frustrating Tottenham in the middle of the pitch.

Where has Liverpool’s Press gone to?

Liverpool’s defensive problems this season have been well documented.  This very brief post isn’t going to address Liverpool’s defensive issues, but will concentrate on one very specific team issue; their lack of a high press this season. Last Season’s Press These are the PPDA values for each team last season in the EPL (the lower the PPDA value the more aggressive the press employed by the team). For anyone unfamiliar with the PPDA metric an introduction can be found in this article. 1314EPL Apart from the fact that Liverpool were ranked in 3rd place on my Pressing metric last season, what is probably more stark is the fact that Liverpool pressed as aggressively when they were leading games as when they were behind or drawing.  Naturally, we would expect a team that is leading to sit back a little and to reduce the intensity of their press.  But Liverpool didn’t do this. All the other top teams exhibited the expected pattern of posting lower PPDA numbers, and thus a more aggressive press, during losing Game States.  Liverpool were the sole exception to this (of the top teams). As to the reason for this we’d only be speculating, but there are a couple of ideas that spring to mind. The first is that Rodgers didn’t trust his team to defend a lead by sitting back.  Perhaps he knew that they were suspect defensively, and to keep them continually on the attacking front foot really was The Reds’ best form of defense. Alternatively, these pressing numbers encapsulate the Spirit of Luis Suarez.  We can all envisage Suarez running around the attacking half of the pitch like one of those Ever Ready Bunnies from the old television adverts.  His work rate was phenomenal and perhaps this PPDA metric quantifies that.   8 Games into the 2014/15 EPL Season 1415EPL Liverpool’s press, their desire to win the ball back in attacking positions, has markedly decreased this season.  They fall from last season’s 3rd position to a mid table ranking.  But what is also really noticeable is that their PPDA values when split across Game States are now beginning to follow the familiar pattern that all the other teams exhibited; they press less when they are in winning positions in games. Has the work ethic in the attacking half dropped off a little?  Is this a planned decision or has it just “happened”? We can obviously only offer guesses and conjecture at this stage, but has the team been forced to drop a little deeper to provide some defensive cover for Gerrard this term?  Never mind the goals that are missing due to the absences of Suarez and Sturridge, but are we also seeing the impact of these absences on the way Liverpool defends when not in possession of the ball. It will be interesting to see what changes Rodgers makes to try to get his team press a little more throughout the remainder of the season.  For I’m sure he will be disappointed in their relative lack of pressing through 8 league games so far this campaign.

Goalkeepers: How repeatable are shot saving performances?

Assessing the skills of goalkeepers is exceptionally difficult, and it’s why I have never attempted to do it. As well as the basic and fundamental skill of shot stopping, the best goalkeepers will be able to effectively assess situations and decide whether to advance or stay on their line. How they deal with a high ball is also important, as is their distribution and their communication and organisational skills. Combine those altogether and you have a range of skills that would be very difficult to measure using conventional statistics. Although I don’t think we are in the position of being able to rate goalkeepers in terms of their entire skillset we are in the position of being able to assess their shot stopping attributes. I’ve been told that the best way to eat an elephant is “one bit at a time”, and so we’ll take the same approach to rating goalkeepers. Let’s start the process by having a look at goalkeepers’ shot stopping numbers. The dataset that I’ll use for this analysis is Opta data for the four complete seasons from 2010/11 to 2013/14 covering the Big 5 leagues (EPL, La Liga, Serie A, Bundesliga and Ligue 1). This gives me a dataset of more than 64,000 on Target shots which were faced by 393 goalkeepers.   Goalkeepers with Best Save Percentage To get our bearings we’ll take an initial glance at the Top 12 goalkeepers from the last four seasons as ranked by Save Percentage (Saved Shots / Total OnTarget shots faced) and I’ve applied a cut-off of a minimum of 300 shots. SavePerc This list seems to make some sense; we have Buffon topping it and it includes other accomplished net minders such as Abbiati, Sirigu, Neuer, Cech, De Gea and Hart. With the possible exception of Victor Valdes, it’s fair to say that this list of Top 12 Shot stoppers (ranked by Save %) includes most of the names that would quickly spring to mind. The football world will be glad to hear that, even when looking solely at numbers, some goalkeepers appear to be better than others. OK, so that isn’t exactly ground breaking; but it’s a starting point. At this point it’s also worth considering whether, just because the best shot stoppers have the highest save percentage, it means that the keepers with the highest save percentage are necessarily the best shot stoppers.   Repeatability In terms of assessing the shot stopping skills of goalkeepers, the question about whether we can tell the great stoppers apart from the average ones is not the most important question for me. What is important is the timescale, or quantity of On Target shots, that we need to observe before we are in a position to be able to soundly judge shot stopping skills. Why is this important? If it takes a very long period of time before we can be confident that a goalkeeper’s save numbers are repeatable then that has to have implications for football teams. How do teams scout for a goalkeeper? How can they possibly tell whether the saves a potential new signing made in the handful of games he was watched is likely to be repeatable going forward? How do they know when to drop a goalkeeper due to a few bad performances? For me, it’s apparent that we need to be able to assess how repeatable shot stopping performances are for a goalkeeper from one period of time to another. To do otherwise means that any decisions or judgements that are based on the outcomes achieved during the first period of time may be built on shaky foundations. This viewpoint seems to be shared by Billy Beane, as in an interview last week with Sean Ingle he was quoted as saying:

“You don’t have a lot of time to be right in football. So ultimately, before you mark on anything quantitative, you have to make sure you have scrutinised the data and have certainty with what you are doing, because the risk is very high.”

  How to Measure Shot Stopping ability For this analysis I am going to use two forms of measurement for assessing the quality of shot stopping performances. The first measure is the simple Save %, and this was the metric that the first table in this article was ranked by. The second measure is based on our (created with Constantinos Chappas) Expected Goals model, or specifically our ExpG2 component of the model. This ExpG2 value is the expected value of the shot AFTER it has been struck. This means it takes into account all of the factors that existed at the point the shot was struck (ie location, shot and movement type etc) but it also includes the shot placement, but doesn’t include the location of the GK at the time of the shot. As expected, the shot placement is a huge driver of the ExpG2 value. A shot arrowed for the top corner will have a much higher ExpG2 value than a shot which was taken from the same location, but which was placed centrally in the goal. ExpG2 can be used to measure the placement skill of the shooter, but it also has great use in measuring the shot stopping performance of goalkeepers. It is only right that a ball that hits the net in the very top corner reflects less badly on the keeper than one which squirms through his body in the middle of the goal; the ExpG2 metric achieves this. This analysis will use ExpG2 Ratio, which is calculated as: ExpG2 / Actual Goals Conceded An example: 12.34 ExpG2, but the GK conceded 14 goals. The ExpG2 ratio in this case is 0.88. A value of 1.00 means that saves have been in line with expectation, a value greater than 1 suggests the keeper has performed better than the average keeper would have done for the shots he faced, and a value of less than 1 means he allowed more goals than the average keeper would have done.   Repeatability (yes, that word again) The key point in this analysis is not to measure the shot stopping performance of any goalkeeper, but to instead look at how repeatable the shot stopping performances are from one period to another. After all if they aren’t repeatable, be that due to variance, luck or something else we aren’t currently measuring, decisions and actions taken by teams shouldn’t be the same as those that they would take if they were known to be repeatable.  Isn’t that right football industry? In March this year, Sander Ijtsma published a piece where he suggested that you could practically ignore save percentages.  I wanted to expand on that concept a little.   Method of Analysis I sorted all the On Target shots by date and sequentially numbered each shot faced by each goalkeeper. I created a variable, n. This allowed me to divide the shots faced by each keeper into sets of n size. I then calculated a single correlation value for each level of n by plotting all the individual save performances achieved in Set 1 (which will be of size n) on the x asis against the individual save performances achieved in Set 2 (which will be of size n) on the y axis. It would probably help to use an example; let’s say n = 50. For each GK I measured the save performance for their shots numbered 1 – 50, 51 – 100, 101 – 150, 151 – 200 etc. I then plotted the relationshion between shots 1 – 50 and 51 – 100. I did this as I wanted to see how repeatable the save performances were for goalkeepers from one set to the next. I also plotted the relationships between shots 51 – 100 and 101 – 150, as well as between 101 – 150 and 151 – 200 etc. I continued on using this logic to plot the relationship values for each consecutive set for each goalkeeper until I couldn’t compare any more sets of n on target shots for them.  A single correlation value was then calculated for each level of n. I made n a variable so it could be changed to assess the level of correlation (repeatability) between consecutive sets of On Target shots faced by goalkeepers based on the number of shots in each sample. The table below sets out the extent of the correlations for varying sizes of n.   Correlation The values in the two columns on the right of the table show the correlation for the save performance of a goalkeeper over two consecutive sets of facing n On Target shots. The third column shows the correlation for the simple Save % metric, while the final column shows the correlation for the ExpG2 Ratio measurement. A brief reminder that a value of 1 means the save performance of the two consecutive sets are perfectly positively correlated, while a value of 0 indicates that no correlation exists whatsoever. I don’t want to make this piece any more technical than it already is but I’ll simply state that the correlation coefficients themselves have confidence intervals around them, these are primarily driven by the number of pairs for each level of n.   Let’s walk through one of the lines in the above table, we’ll use n = 100. There were 305 instances (pairs) in my data set where goalkeepers faced 100 on target shots followed by another 100 on target shots. The correlation in their save performance when measured by the simple save ratio between the first and second sets of 100 shots was just 0.127. When we instead measure the keepers’ save performances by the ExpG2 metric the correlation increases to 0.232. A correlation of 0.232 is a weak correlation as it means that just 5% (0.232^2) of the variability in the second set of 100 on target shots is explained by the first set of 100 on target shots. That is startling. Even when we use the advanced ExpG2 metric to assess how well a goalkeeper performed over a series of 100 on target shots we can still only expect it to explain just 5% of their performance over the next 100 on target shots he faces. As an average goalkeeper faces approximately 4 on target shots per game this means we need to assess a keeper over about 25 games to only get a 5% steer towards how he will perform over the next 25 games. Pause and think of the implications of that. Even a sample size of 250 shots (n = 250) has a correlation, using the more detailed ExpG2 metric, of just 0.405, this level of correlation is generally described as a moderate correlation as it gives an r2 (variance) value of just 0.16. At this point I cannot calculate correlations for n greater than 250 as I do not have enough data in my dataset. To give you an idea of the amount of dispersion of save numbers from one set to the next I have produced below the plot for the 31 consecutive sets of 250 shots (which means the goalkeeper has had to face at least 500 on target shots): n250   Does the shot order matter? Daniel Altman read a draft of this piece and he suggested I should look at whether I am introducing some bias into the correlations due to the way that I populate the two groups of n shots. You will recall that I carried out the above analysis by splitting the shots into groups based on the sequential order the keeper faced those shots, and doing this inevitably builds a time and age factor into the makeup of the groups.  This is fine as I initially only wanted to measure sequential relibality (as this is what happens in the real world when it comes to assessing a goalkeeper’s performances at a certain point in time), but this may cause difficulties if we attempt to measure innate talent. To address Altman’s prescient point I randomised the order of the shots and conducted the same analysis as before.  The correlation values between consecutive sets of n shots (with the shot groupings decided on a random basis) are as follows: Correlation when randomised As expected, because we chose a different basis on which to create our sets of n, there are some differences between the correlation values in this table and the one that appeared earlier in this article.  However, at n = 250 the correlation value of 0.467 still means that we would only expect 22% of variability in the second set of 250 on target shots to be explained by the first set of 250.  This is still quite a low value and suggests that even when we strip away any impact of age / time bias there doesn’t seem to be a great level of repeatability in terms of goalkeeper save performances.   Conclusion Of course there are goalkeepers that save shots better than others. But for every goalkeeper such as David de Gea that have consistently over performed (1.23 and 1.21 is the ExpG2 Ratio for his two sets of 250 shots) we have a Stephane Ruffier who notched up ExpG2 ratios of 1.15 and 0.98 in his two sets of 250 shots. If those two players had been assessed after their first batch of 250 on target shots (which would have taken almost two full seasons to amass) they would both have been assumed to be well above average shot stoppers. However, only one of them went on to repeat it again after they faced another batch of 250 on target shots. Imagine the analyst that recommended signing Ruffier on the strength of his save performances using an advanced metric over a “large” dataset of 250 shots or 60 games. This is a good time to recall Billy Beane’s assertion that we need to make very sure that we know what we are doing with our data as the stakes will be high for any club that truly embraces the use of data in their decision making process. When this level of variance exists after 250 shots, it is easy to see how Simon Mignolet went from being one of the best shot stoppers in the EPL in the 2012/13 season (ExpG2 ratio of 1.25) to being one of the worst in 2013/14 (ExpG2 ratio of 0.88). Very simply, trying to judge how good a goalkeeper is at saving shots based on one season’s worth of data is little more than a craps shoot such is the divergence on performance over 150 shots. Just 11% of his performance next season can be explained by his performance in the season just passed. It looks to me that the very fact a player is good enough to be a goalkeeper for a top tier club means that he has achieved a level of performance that is difficult for even advanced numbers to distinguish, at least not until he has faced a very large number of shots. I’m just not sure yet how large that number needs to be. Remind me never to advise a club when they are looking at buying a goalkeeper as the shot stopping facet of the goalkeeper’s skillset was supposed to be one of the easier ones to measure and interpret.     Thanks to Constantinos Chappas and Daniel Altman for allowing me to bounce off them some of the more stat heavy angles of this piece. Cover picture by Steve Bardens. Opta_200px        

Arsenal v Hull Player Positional Tracker

In a game that Arsenal dominated, they had to settle with a point thanks to their late equalizer from Danny Welbeck.  Arsenal’s territorial and possession dominance can be clearly seen on the Player Positional Tracker. ThatsWengerBall gave me his thoughts on the game via the lens of the PPT, and his comments appear below the gif. (Click on the image to open in a larger window) ARSvHUL That’sWengerBall’s comments:

  • Arsenal started the match in a 4-3-3 formation however spent much of the game in a 3-4-3 shape, with Flamini dropping between the centre backs whilst Gibbs and Bellerin pushed up the pitch.

 

  • Hull City, meanwhile, chose defensive solidarity by starting in a 3-5-1-1 formation and packing the central areas. Both Hernandez and Ben Arfa were continually dropping deep, helping to squeeze out any space.

 

  • There was very little play in Arsenal’s half of the pitch as the Gunners proved adept at keeping the ball in the final third. However the centre of the pitch became over-congested from around the 25th minute onwards when Arsenal’s wingers, Chamberlain and Alexis, both started to play very narrow.

 

  • Arsene Wenger made some changes after the 60th minute. Ramsey and a few minutes later, Campbell came on as Arsenal pushed even higher up the pitch searching for an equaliser. Hull stayed very deep with Ben Arfa effectively acting as a left back for much of the game.

 

  • This game was a classic example of one side playing a low block against another team that dominates possession. When looking at this PPT and the stats after the match many Arsenal fans will be scratching their head as to how they didn’t get the three points. Hull were a little lucky but their defensive resolve matched with their efficiency up front meant they earned a valuable point.

Does van Persie still merit a starting place for MUFC?

  A chart created by Christoffer Johansen made its way into my Twitter timeline last week. This chart was fairly stark in that showed a steady and perceptible decline in the output of Man United’s Robin van Persie over the last 4 or 5 seasons. Christoffer’s chart was as follows: BojoShots That chart doesn’t need much commentary, so I’ll give it none. Johansen then went on to show that van Persie’s decline extended to more than just the rate that he shot over the last five seasons. He showed that the year on year provision of assists is another category that has seen a decline from the Dutch captain: BojoAssists   Wider Attacking Contribution On a team with as much attacking talent as this current Man United side possess, it is obvious that both the shots and the headlines will be shared around. Not everyone can take the final shot, or even play the assist for the shot; this is especially true when the attacking talent includes all of Falcao, Rooney, Di Maria, Mata and van Persie. This desire to award attacking players the recognition that their involvement deserves is what motivated me to create the Attacking Contribution metric . Previously, unless they played the final pass or had the shot, their part in attacking moves would have gone unnoticed by the statistics that are currently reported on. An introduction to this metric can be found in an article published last week on Statsbomb.  In summary, it records the number of times that a player was involved in the final four events of an attacking move that culminated in a shot. Due to the various ways that forwards play it can be difficult to compare their outputs. Some forwards excel in holding up play and linking with others, while some are simply there to score the goals. I decided to include the final four events in the calculation of the Attacking Contribution metric as this will, generally, capture all the players that were integral to the attacking move. If attacking players are regularly failing to be involved in the final four events of their teams’ attacking moves I think that questions should be asked of them. What exactly is their role in the team? What does the coaching staff want them to achieve? And, most importantly, are they taking the position of a player that has more to give to the team than they themselves are?   Robin van Persie’s Attacking Contribution From Johansson’s charts we can see that RVP’s shot numbers have declined and that he’s also providing a minimal level of assists. This in itself might not be a problem.  With all the attacking talent at Louis van Gaal’s disposal it is possible that van Persie is being involved earlier in the moves. Such an earlier involvement would not see him gaining recognition under the two categories of stats that Christoffer Johansen covered in his charts. If he was central to United’s attacking moves, moves which were being finished by the likes of Di Maria, Rooney or Falcao then supporters of RVP could rightly say that the 2014 version of the Dutch forward is about more than just scoring goals. But is this actually the case? The Attacking Contribution Metric can help us answer this question: RVP I only have data for games played from the start of the 2010/11 season. Robin van Persie was remarkably consistent during the spell from 2010/11 to 2012/13, during these three seasons he was involved in almost 50% of the shots his teams took while he was on the pitch. RVP’s productivity numbers noticeably tail off last season (2013/14) as he is involved in only 39% of United’s shots. Although the table above doesn’t include his playing minutes, I can tell you that he played less than 1700 minutes last season compared to the 3500 and 3350 minutes that he clocked up respectively in each of the two preceding seasons. The Dutchman obviously struggled with his fitness last season; he missed plenty of game time, and when he did play he wasn’t as productive as in previous terms. If last season was disappointing for van Persie, then this current one has started off very badly. His involvement in just 28% of United’s shots is an extremely poor individual return for a front line attacker and represents a serious decline from the exceptionally high numbers we have grown used to seeing van Persie deliver, first at Arsenal and then in Ferguson’s final season at Old Trafford.   Man United’s Individual Attacking Contributors The table below shows the attacking involvement of United’s attacking players this season: MUFCReliance I know that the season is young, but we can see that six other United players have had a greater attacking input that van Persie has so far. How can Anders Herrera have had a greater influence (in terms of the percentage of attacking moves he has been involved in) than RVP has had? United’s attacks are passing van Persie by, this is a trend that I picked up few times this season in the commentaries that I made on some of Man United’s Player Positional Trackers, an example of which is United’s defeat against Leicester. Given the attacking firepower that currently resides inside Old Trafford I don’t think, in his current form, that van Persie is deserving of a start in United’s line up. With Mata, Falcao, Rooney, Di Maria, and Herrera real possibilities for the five available attacking spots (and that’s even before we consider Januzaj or Valencia), Robin van Persie should no longer expect to be one of first names on the Man United team sheet. Maybe the injuries have finally taken their toll. The fact that he has recently turned 31 will not help him either, but there is no doubt that his ability to influence games is clearly waning, and United will need more from all of their attacking players if they are to successfully secure a Top 4 league position this season.   Tale of Two Dutch Strikers 404630_gallery In a brief Twitter conversation with Simon Gleave on Saturday, Simon mentioned that there was quite a bit of chatter in the Dutch media around van Persie and Huntelaar. Comparisons were being made between the two, presumably around which of the players should receive the nod to start up front as the Oranje played Kazakhstan on Friday night. Van Persie led the line whilst the Schalke striker had to be content with a place on the bench, although Huntelaar did come on in the 56th minute and he grabbed the Dutch equalising goal just six minutes later. This article is mainly concerned with van Persie, but given the circumstances I decided to widen it out to briefly include some of Klaas-Jan Huntelaar’s numbers.   Van Persie or Huntelaar When van Persie was in his prime there was no contest around which of the two were the more productive player. However, is this still the case now in late 2014? It’s impossible to answer this question with the use of just one metric but let’s take a look at Huntelaar’s Attacking Contribution metric over the last four and a bit seasons: Huntelaar A few seasons back (2010 – 2012) we can see that at with an attacking involvement of approximately 35% in Schalke’s shots he was considerably less involved than van Persie was at Arsenal and Man United. However, as Father Time has quickly caught up with RVP it looks as though Huntelaar’s attacking performances haven’t yet taken the very noticeable decline that van Persie’s has; this despite the fact that just six days separated their birth. I’d expect the 42% contribution rate that Huntelaar has posted so far this season to reduce a little, but at this stage he still looks to be a player that will contribute to about one third of Schalke’s attacks. I’m conscious that this Attacking Contribution metric isn’t all encompassing. It doesn’t assess the quality of chances, nor the rate at which they convert their chances and we also need to be aware that we are looking at players that play in two different leagues. But, even being mindful of all of those caveats, it could be argued that van Persie has regressed to the point that he and Huntelaar could be expected to have a similar attacking contribution for the Netherlands.