Category Archives: Eredivisie

Why I don’t board the PSV bandwagon just yet

On a first glance, things are looking all rosy in Eindhoven. Going into the season, PSV were hoping to challenge Ajax for the title, but four matches into the season it’s been all good news for PSV. They beat Ajax away and hold a six points lead already. Still, I would be hesitant to board the PSV bandwagon just yet.

PSVYes, PSV won four out of four to equal their best league start in 11 years.

Yes, PSV managed to hang on to arguably two of the league’s best players in both Memphis Depay and Georginio Wijnaldum.

Yes, PSV strengthened their squad by re-signing central defender Karim Rekik on a one-year loan from Manchester City, and experienced Mexican international Andres Guardado.

There must be some very compelling arguments not to board the PSV bandwagon right now and start declaring them red hot favourites for the 2014/15 Eredivisie title.


Scoreboard journalism

Co Adriaanse

Well, the main argument is called scoreboard journalism. Back in 2003, this termed was coined by then AZ manager and now prominent TV pundit, Co Adriaanse. He pointed out that, although his team had just lost 5-1 to Roda JC, the play had been quite good, and the pundits judged outcome over process.

In reverse, the same holds true for PSV so far this season. With twelve points from four matches, the outcome has been perfect, yet the process is worrying to say the least. It’s probably easier to fit narratives to PSV’s perfect start, than it is to dive into the underlying numbers and write about the process at hand.

And even if you are smart, but you see your job as filling newspaper space or talk show time, talking PSV up now ensures new stories to write once the current bubble will inevitably burst. There will probably be a player missing through injury, or post Europa League matches, may be even early kick off times to blame. There will be new narratives to fit, new stories to write, everybody happy.

But here at 11tegen11 we don’t have to worry about narratives, and we’re free to take a dive into our beloved stats for a more nuanced opinion.



PSV has played four matches, scored 14 goals, and conceded three. In those four matches, PSV didn’t win the shot count once. Not in their season opener at promoted side Willem II, not away at Ajax when they put a dent in their rival’s early season, not last week beating Vitesse 2-0 at home, and not even in their 6-1 thumping of NAC Breda.

In each of those matches, PSV produces less shots than their opponents. Now, some people would be convinced that this is a good thing. ‘Winning the matches where you play poorly is a sign of champions.’ There’s a lot to say about that statement, but losing the shot count four out of four times is always a bad sign. Shot counts are very well correlated with end of season points, even this early in the season.



Other people would argue that not all shots are equal, and that’s a good point.

PSV has produced shots of higher quality than the shots they have conceded. The average PSV shot this season has an ExpG of 0.131 (5th in the league), while the average shot PSV conceded has an ExpG of 0.094 (3rd in the league). This reflects their present philosophy to try and contain their opponents, and take advantage of quick counter attacks.

Despite a negative shot count, their ExpG count is positive. In four matches, PSV produced 8.8 ExpG and conceded 6.7. Reality check: scoring 14 from 8.8 ExpG won’t last, as will conceding just 3 from 6.7 ExpG.

PSV’s ExpG ratio of 0.549 (8.8 / 8.8 + 6.7) is okay at best, but for a serious title challenge a ratio of 0.625 is a minimum.



Finally, people will argue PSV that have Memphis Depay. He scored five goals already. His finishing alone can help PSV overcome opponents even without producing more shots, or generating more ExpG. Well, it’s definitely true that the eye-test suggests that Depay is the most skilful finisher in the league. Still, scoring more goals than ExpG suggest is hardly a basis for future success. And, for what it’s worth, Depay scored 3.3 goals less than his ExpG of last year suggested. In all likelihood this won’t carry over, but so much for that supposedly superior finishing skill.


In the end

Still standing on board that PSV bandwagon? You may be correct, and I may be wrong. PSV may improve as the season goes on. Football is unpredictable in exact terms. But broadly speaking, PSV will either need to improve big time in their underlying play, or the wheels will quickly start to come off, and you may need to look for another bandwagon before the next international break.

Hint: it may well be red and black, as the discrepancy in Feyenoord’s outcome and process goes exactly the other way.

How to define attacking style!?

Football analytics at the moment is a bit like a toddler. We think we can do quite a decent job, we’ve started talking quite loud with more variety in our vocabulary, and every now and then we start to make some sense too. Oh, and hey, we make people laugh at us at surprising occasions! Yet, most of the time, in hindsight our actions don’t make the most sense. And what we could do a year from now makes our current level of performance laughable at best.



Most of my earlier analytics work has been aimed at performance analysis. Which team is better? And later on, which player does better? However attractive this edge of using stats is, in an environment as highly driven by random occurrences as football, this type of analysis approaches its limits quite soon. In plain English: football is quite hard to predict.


Just a level below predicting, is describing. And a recent promising development on the describing front has been introduced by fellow blogger and analyst Michael Caley. It may well be the describing part where football analytics could win over more souls to support our belief that numbers can add to a better understanding of the game.

Could you to tell me in a few words how your favorite team prefers to attack? Chances are that you’d use words like ‘direct’, ‘patient’, ‘flank play’, ‘through balls’ and ‘crosses’. Now, what Michael has come up with is a simple and easy to use stat to express two key elements of attacking play: pace and style.



Pace is expressed as the number of completed passes per shot taken. Just use raw numbers per team, no complicated formula’s. Here’s what we come up with for the most patient teams in Europe’s top-5 leagues plus the Eredivisie.

Passes per shot - top 10 - Multiple Leagues 05 juni 2014Some of the usual suspects, like Swansea, PSG, Arsenal, Bayern and Barcelona, make this top 10, but the most patient team in Europe are Borussia Mönchengladbach with some 37 passes per shot taken. I haven’t seen them play myself this season, but perhaps some Bundesliga fans are willing to comment here.

The other end of the spectrum will reveal teams playing lightning quick football, preferring to shoot rather than pass around.

Passes per shot - bottom 10 - Multiple Leagues 05 juni 2014That’s interesting! The top four teams are all Eredivisie teams, a league known for high scoring and high shot numbers. At some distance from the rest, relegated side N.E.C. are identified as the most direct team in Europe.

Pace is a descriptive thing, not a performance marker though. Other teams from this top 10 (Levante, Augsburg, Heerenveen) have had decent to good seasons with a very direct style of play.



The second aspect I take from Michael is style of attack. Using two contrasting key elements of constructing offensive schemes, crosses and through balls, we can compute a simple ratio that proves to spread out nicely across different teams. Also, it fits well with the style of play we’ve familiarized ourselves with for certain teams. Here’s the top 10 in terms of the ‘crosses to through balls ratio’.

Crosses per through ball - top 10 - Multiple Leagues 05 juni 2014Four French teams in the top 6, but the EPL is also nicely represented. Manchester United’s Moyesball indeed makes the top 10 for crossing heavy offensive schemes, but to my surprise Mourinho’s Chelsea is not far off!

One thing: I’ve stripped out NAC, as they simply won’t play any through balls and their ratio is so off the chart that the other teams are dwarfed by it. In time a case study to NAC and manager Gudelj should follow.

In the bottom 10 we find the teams that prefer through balls over crosses. It seems a ratio of around 3 is as low as it gets, and with around 4 you’re still very much a through ball oriented team.

Crosses per through ball - bottom 10 - Multiple Leagues 05 juni 2014

Barcelona are the masters of avoiding crosses and poking central passes into the box. But would you have guessed Newcastle are so through ball heavy? And look at Heerenveen, showing up as a very direct teams just above, and avoiding crosses at the same time!


Pace and Style

Things get even more interesting when we combine both of these metrics in one chart. Teams should broadly fall into one of four categories.

–          Patient and central

o   Barcelona, Mönchengladbach, Roma, PSG, Swansea, Arsenal, Bayern, Toulouse and Ajax


–          Patient and wide

o   Nice, Rennes, Manchester United and Bordeaux


–          Direct and wide

o   Bologna, Sochaux, Lazio and Saint Etienne


–          Direct and central

o   Heerenveen, Newcastle, Real Madrid, Sevilla and Dortmund


In the end

There’s no single preferred mode of attack, and patient is not necessarily better or worse than direct. Also, central doesn’t beat wide. There are multiple ways to construct good offense and the players at hand, the philosophy of the club and the level of execution of the style if perhaps much more important.

But these concepts hand us a tool to describe pace and style, to follow trends within clubs and managerial careers. All of that with a simple tool, brought to you by the bright mind of Michael Caley.

To close off this post, here is a mega chart picturing all teams from the top 5 leagues plus the Eredivisie. Do click on it for the full, downloadable version, and you’ll see that the names above are all taken from the four corners of this chart.

Directness and Team Style - Offense version - multiple leagues

Radar Love – Capturing Players in a Single Picture

Comparing football clubs is one thing, comparing football players is yet another. It lies at the heart of many pub debates, where passionate fans try to convince each other that their beloved star is better, often to settle the subtle disagreement by concluding that the players are different. And indeed, different positions, skills and tactical roles make it hard to rank individual players. The first step should be to picture them correctly, and that is where this post will step in.

A lot has been written recently about football analytics and the use of numbers in the beautiful game in general. Some claim it’s an enrichment, some claim it ruins the magic of the game. I don’t see it as such a clear separation. Whether we want it or not, stats are there.

It’s up to each of us to decide for himself how much of it we prefer to add to our football match experience.  And if the analytics community sees anything as its task to lower the threshold for people to start using stats, it should be making stats more accessible. I’m fairly confident that the addition of radar plots will do just that.



‘Standing on the shoulders of giants’ is an apt way to put what I’m doing right here. The conception of many excellent analytical and visualization ideas lies outside football, and radar plots in sports started with basketball, where they appeared in 2009. A few weeks ago, it was @StatsBomb’s own Ted Knutson who introduced them in football. Unsurprisingly, they were quite well received for the many advantages they have.

I’ve given my own twist to the radar plots and I should perhaps mention that the design of these plots is very much a work in progress. Along the way we may decide some elements are missing and others should better be omitted from the chart. For a start, here it is. Click on it for a full-size version.

Radar chart - Dusan Tadic vs Lucas Piazon Eredivisie 2013-14Which better players to give the honor of the first radar plot on 11tegen11 than the two most creative attacking midfielders of the Eredivisie, Twente’s Dusan Tadic and Vitesse’s (or actually Chelsea’s) Lucas Piazón.

My version of the radar plot has nine axes and I’ve spent a considerable amount of time thinking about which parameters to include, as well as how to order the axes. All parameters are presented as per 90 minutes. The decision not to present any actual numbers is a conscious one, as I felt it would distract from the goal of the plot, which is to compare players. If you wish to see the underlying numbers, I’m fairly sure you’ll be able to find them within minutes. The scales of the axes represent the minimum and maximum values found in the league.

Let’s go over the axes one by one.



On top, on the twelve o’clock position is the amount of passes per match. Players who are more involved score higher. I have not yet corrected for total team passes, as I’m unsure whether it provides a true benefit, and what would be the best way to correct for it. Feel free to voice out, as with this concept, there’s no best design yet.

The passing axis is placed in between ‘Incomplete Passes’ and ‘Expected Goals’. The order of the axes is very important, since they determine the surface created for the player. A high score on two, or even three axes leads to a significant area within the plot, creating the image of a high quality player. In this case, it’s combining lots of passes with a low incomplete passes count and a high ExpG.

Usually, more passes contribute to more incomplete passes, and more passes are the domain of players playing further away from the opposing goal. This should provide a balance that gives our radar plot value.


Incomplete passes

We make a counter clockwise trip to the ‘IP’ axis, that stands for ‘Incomplete Passes’. As with all negative traits, this axis is inverted, so that a better performances, in this case less incomplete passes, gives a bigger area on the plot.

Incomplete Passes is flanked by ‘Passes’ and ‘Interceptions’. This should be the area where defensive midfielders excel. Each position in the field should have an area where they can express themselves, otherwise certain positions on the pitch will be underestimated by the plot.



Interceptions are presented as ‘per 400 opposition passes’, as I’ve found raw interceptions per 90 minutes to give too much bias towards players on poor and defensive sides. This correction allows for players on ball possession teams to have a fair shot.

It’s flanked by ‘Incomplete Passes’ and ‘Dribbled by’. The latter represents how often the player is getting dribbled by, which you’d definitely not want for a defensive player. This allows good defensive players a nice area where tidy passers, with intercepting qualities that stand their ground will shine.


Dribbled by

Dribbled by is another inverted axis, as it’s considered better to have less of it. It is flanked by ‘Interceptions’ and ‘Tackles’ and this lower left side is the defensive player’s domain. Expect central defenders and defensive midfielders in this zone.



This is pretty self explanatory really, other than the fact that, like ‘Interceptions’ I like to express it as per 400 opposition passes. It is flanked by ‘Dribbled by’ and ‘Fouls’, since these are two stats you would not like a defending player to have. The fouls axes should provide another balancing act for players making more tackles.



Another self explanatory, and inverted axis. Less fouls, bigger area. It is flanked by ‘Tackles’ and ‘Dribbles’, as I felt is makes the best switch to the offensive player’s side of the chart.



Dribbles gets its own axis so that offensive players get enough room to shine. Also, I think it’s an under appreciated domain in stat use in general, where players add a dimension of unpredictability to the team. A good dribbler provides a threat that influences the style of defense of the opposition. It is flanked by ‘Fouls’ and ‘Expected Assists’.

Particularly the link with ExpA is a valuable one, since it allows wide players to express themselves in this lower right hand part of the chart.



On a team basis, this may be one of the most important axes, yet on an individual player basis it should just be one of nine. Expected Assists represents the passes leading to a goal scoring attempt, where each of those attempts is weighed according to the odds to score from it.

ExpA is flanked by ‘Dribbles’ to give attention to players that should be hard to defend against: those players with enough skill to dribble and to deliver the final ball. Also, it is flanked by ‘Expected Goals’, to allow players with multiple offensive dimensions to claim a bigger part of the chart.



Expected Goals is the final axes of our circle. There’s hardly a need for explaining this terms anymore. Suffice to say it represents all goal scoring chances a player takes, which are weighted according to the odds to score from it.

ExpG is flanked by ‘Expected Assists’ and ‘Passes’. The latter connection is very powerful and opposition would never want a goal scorer to see a lot of the ball, so those goal scorers that do just that should be rewarded with a bigger piece of the chart.


In the end

This concludes our trip around the chart. In my view, it provides a fair balance between different elements of the game, and the ordering of the axis makes it difficult to claim a lot of ‘area’ without having serious underlying qualities. This balancing act also ensures that I will use the same chart layout for all players, so that learning to use them is as straightforward as possible.

Some of you may notice that traditional metrics like ‘Goals’ and ‘Assists’ are missing. My recent work on the ‘unrepeatability’ of scoring once the quality of the goal scoring attempt has been corrected for, leads me to believe that both ‘Goals’ and ‘Assists’ are inferior to ‘Expected Goals’ and ‘Expected Assists’. Scoring or assisting without the underlying ExpG or ExpA won’t last, so why credit a player for doing it. Or, to use a quote that is mostly linked to Jonathan Wilson, the writer who inspired me to football blogging in the first place, “goals are overrated.

I’ll leave you with some bonus charts.

Radar chart - Daley Blind vs Felipe Gutierrez Eredivisie 2013-14The two best defensive midfielders of the Eredivisie! You can see Blind gets the nod in the defensive department of tackles, dribbled by and interceptions. Gutierrez is a bit more tidy in his passing, but that’s probably related to making less passes overall. Blind is more of an assisting threat, while Gutierrez gets a tiny advantage in terms of goal scoring.

Radar chart - Jeffrey Bruma vs Joel Veltman Eredivisie 2013-14Two young Dutch center backs. Ajax’ Joël Veltman does better on nearly every single axis compared to PSV’s Jeffrey Bruma. It’s Veltman’s passing accuracy that could be improved on.

Radar chart - Memphis Depay vs Viktor Fischer Eredivisie 2013-14Another Ajax v PSV meeting, with players playing in the same left wing position, but in very different interpretations of that role. Depay is much better in assisting and scoring, whereas Fisher gets the nod in dribbling, passing tidiness and interceptions. Both players add a significant amount of ‘area inside the plot’ with their dribbling skill, which is why I put this chart up. I feel it’s important to recognize that element of the game.

Radar chart - Graziano Pelle vs Luc Castaignos Eredivisie 2013-14Two players who are more similar that I would have expected. Both Pellè and Castaignos do little else both contributing to ExpG and ExpA, where the Feyenoord striker puts in an unreal amount of goal scoring threat. He touches the border of the chart, so no player beats him in the ExpG category.

Winning without playing – Eredivisie Predictions after Match Day 20

The biggest winners of the past Eredivisie weekend did so without any effort other than making the trip up to the snow covered North of the Netherlands. Twente say their match at Groningen chalked off in icy conditions, but still did the best business of the weekend.



Of the four title candidates, Vitesse drew at home to league bottom team N.E.C. and Feyenoord lost 3-2 at ADO. Last week, the model called it an even split between Vitesse and Twente, but now Vitesse dropped 12 percentage points to go to 21% chance of winning the league, while Twente rose the same amount to go to 45%.

Boxplot projected League positions Eredivisie 2013-14 27 januari 2014The same trade-off happened between Ajax, who were only a distant fourth last week at 13%, but the mix of events, including their win at Go Ahead Eagles, made them rise to 24%. With that move, Ajax’s odds rose by the same amount that Feyenoord’s fell, to 22% in that case.


By the way, the rationale behind these numbers has been explained earlier. For the sake of readability, I’m not going to write the same paragraph in each weekly updated predictions piece. Please check it out here if you’d like. And, as always, feel free to comment on either the method or the predictions itself!



The Ajax match was an interesting one where the input to the model differs from the input to the human memory. To me, at least, it appeared that Ajax were on the verge of losing points in Deventer, but as we can see from the match plot, they dominated the game throughout. It’s the late timing of the opening goal that makes this match go into human memory as a huge risk of points dropped, but the model sees an away team create around 2.0 ExpG, while allowing the home side just 0.25 ExpG. The model likes it, the mind doesn’t.

ExpG plot Go Ahead 0 - 1 Ajax

Further down the predictions table, PSV did not change compared to last week. Yes, they won their home match against AZ, but the best part of those three points were to be expected and the performance to go along with it was rather disappointing with both teams creating 1.5 ExpG. You’d expect a lot better from a home team that goes a goal up within five minutes. A truly worrying sign for PSV, who, just like last week, will make the play-offs, but not anything more than that.

ExpG plot PSV 1 - 0 AZ

Groningen did not play and remained rather unchanged. Heerenveen lost to fierce local rivals Cambuur, which only hurt their excellent play-off prospects a little bit. The Frisian side have such an excellent shot at making the play-offs, that they can take a hit or two before they should start worrying.



The battle for the final play-off spot is an interesting one. Last week, we saw that it was mostly between AZ (then 7th in the table) and PEC Zwolle (then 11th in the table). The league table now has them 8th and 9th, exactly where the battle takes place. The third candidate for that spot should be Heracles, who increased their odds from 21% to 35% with a 2-1 home win over RKC. The score line there did not impress, but the 2.5 – 1.0 ExpG score left RKC little room for hope, even more so since their ExpG mainly came when already two goals down.

ExpG plot Heracles 2 - 1 RKC The defeat at Heracles saw RKC move a bit closer to where the model predicts them to finish the season. They are now ranked 17th, but have a 56% chance of finishing even lower.



Things are quite open now for the relegation play-offs. As mentioned last week, Roda are not truly in the mix for those spots, as their underlying performance is just too good for that. Regression will catch up, and move them out of trouble, starting with this weekend’s 1-0 win over Utrecht. Or feel free to believe it’s actually newbie manager John-Dahl Tomasson doing just that.

Utrecht fans have a bit of right to worry. Their team ranked 9th last week, but slid down to 11th after losing at Roda. and according to the model, there is more downside than upside to their perspectives. Most likely, though, they will end up in the safe white area that marks mid-table.


Winning against the odds

ADO’s win moved them up a little bit, but the underlying performance did not inspire much faith. Yes, they earned an emotional victory over Feyenoord, but with an ExpG result of 1.2 – 2.2, winning 3-2 won’t be happening regularly. This also explains why the model still rates Feyenoord as at least an outside shot at the title. Their underlying performance was once again quite good, and sometimes you just lose. It wasn’t much more than that, actually, in Den Haag.

ExpG plot Den Haag 3 - 2 Feyenoord

Last week, the model recognized ADO, N.E.C. and Cambuur as the three most likely relegation play-off teams. Two of those teams won home games, while N.E.C. drew at Vitesse. It makes sense to see all of them improve the odds a bit, and the fight with Go Ahead Eagles and NAC seems very open now.


Next week

Expect fireworks in the title odds, as Feyenoord host Vitesse for the Friday night fixture. That result will teach us which team will stay in the mix with Twente and Ajax.

AZ have a must-win home fixture against Groningen if they are to stay in the mix for the play-offs, even more so since rivals PEC Zwolle and Heracles have winnable home fixtures against Roda and NAC played at the same time.

On Sunday N.E.C. – Go Ahead Eagles is a direct relegation play-off dogfight. And Utrecht may see their orange bar creeping up further with Ajax about to test their troubled defense.

How to scout creative talent?

They may well be the most admired players in the world. Players serving high quality goal scoring chances to their team mates virtually at will. Be it by sliding through crowded defenses, with cutting edge though-balls, by smashing in sharp crosses from the wing, or whatever method works. This post will look at the best way to identify creative talent. Players that allow other players to score goals.

We’ve started off this scouting mini-series by looking at strikers, and should you have missed that one, I urge you to read that first. It makes digesting this one a lot easier. Oh, and just like always, data is collected by OPTA and acessed through Squawka, If it were not for them, no scouting pieces here!


The problem with assists

If you’d ask anyone in football, they would probably tell you that creative talent should be identified by assisted goals. No assists, not good. Lots of assists, good. But just like in striker scouting, this methods only looks at output and it may prove very misleading, and therefore quite costly. Some open doors…

1. An assist is only an assist if the striker finishes the shot, no matter how high the quality of the key pass. Looking at assists underestimates players that provide high quality key passes to poor or unlucky strikers. In the video it’s Twente’s Luc Castaignos that needs a rebound to put the ball past the keeper. No assist for Gutierrez, but oh man, that pass…

2. A poor key pass may count as an assist if the striker somehow turns a goal out of it. In the example video it’s a defensive header that earns Jon Obi Mikel an assist. This video also features on an excellent post by @MacAree on this very point.

3. Assists are less frequent than goals, and therefore much more susceptible to fluctuation. Unfortunately you can’t link a video to that point…


A solution

A workaround to these problems is to apply the Expected Goals idea to key passes. Our Expected Goals (ExpG) formula assigns each shot a chance of finding the back of the net, but we could do likewise with each pass leading to a shot. After all, if your creative midfielder creates three one-on-ones, he should be rewarded higher than a winger creating five headers off crosses, right?

So we take the ExpG for each shot, and if the shot comes off a key pass, the player providing the key pass is rewarded with the Expected Assists, or ExpA. Long-term, this ExpA value should align with the total number of assists. This method has some important advantages, as has also been shown earlier by Colin Trainor in this sublime piece on Stats Bomb.

1. ExpA is not susceptible to fluctuation like assists are, because a player may note 0.2 ExpA per match, rather than the odd assist.

2. ExpA removes the finishing from the equation. Players who regularly provide key passes to top strikers (Dani Alves anyone?) get more assists than players serving poor strikers. ExpA corrects this by assuming an average player to apply the finish.


ExpA in the Eredivisie

So here’s the top creative talent of the Eredivisie. Feel free to click on the chart for the full size version. In line with our intuition, Twente’s Dusan Tadic is the absolute number one creative player around, with over 0.5 ExpA per 90 minutes played.

I’ve limited the sample to all players that have played at least half of all available minutes . The second filter is set at players that provide at least one key pass per 90 minutes, in order to end up with players in creative roles. The green bars represent players with an ExpA over the 95% confidence interval. In other words, these players provide significantly more ExpA than the others

Top ExpA plot Eredivisie 2013-14 21 januari 2014It’s interesting to see some players with low assist numbers high on the ExpA list. Roda’s Mark-Jan Fledderus is 4th on our list, with around 0.4 ExpA per 90 minutes, despite having just two assists. ADO’s winger Jerson Cabral should have around 0.35 assists per full match, but he doesn’t even have a single assist. Convertional measure would have criticized these players, while the ExpA method shows that the issue lies not at their feet.

The reverse is also true for some players on the list. Take AZ’s main creative outlet Maarten Martens. He’s got 9 assists already this season, despite producing ‘just’ 0.28 ExpA per 90 minutes, which should lead to 3 or 4 assists if we assume average finishing. I’m writing ‘just’ because 0.28 is still quite good actually. However, it looks like Martens is at the positive end of some statistical variation with his 9 assists this half season.


The good, the bad and the ugly

If we say ExpA is the good, then wasted passes are the bad and ugly. The ideal player creates loads of ExpA, and scores very low on incomplete passes. Looking at ExpA in isolation may provide the wrong picture, as we would want our creative talent to create a maximum of threat with a minimum of costs. If ExpA is the threat, incomplete passes are the costs.

Here’s the both of them in one plot, where I would encourage you to click on it for the full size version. The horizontal axis has each player’s incomplete passes per 90 mins, while the vertical axis has ExpA per 90 minutes. The dotted black and red lines represent the 95% and 99.9% confidence interval. Players beyond these margins have a significantly different pattern from the others in the graph, and these are the only players labeled by their name, to keep the chart accessible. Where would the best players be?

ExpA and leaking plot  Eredivisie 2013-14The very best players should be in the top left corner of this chart: a maximum of ExpA and no incomplete passes. However, from the slope of the black line you can see that there is a positive relation between ExpA and incomplete passes. Which makes complete sense, offensive players have a more difficult job completing passes, and they are also the players in the position to score high in terms of ExpA.

The vertical axis is identical to the bar graph earlier, with Dusan Tadic a class above the rest. The trailing group is spread out horizontally, with Heerenveen’s playmaker Hakim Ziyech being the most wasteful, and Vitesse’s Chelsea loanee Piazón and Feyenoord winger Boëtius the most tidy names. N.E.C. striker Soren Rieks is also in a nice spot, being more tidy than the average player, but still creating an elite ExpA in a poor team.

One Ziyech, or two?

This is not to say it’s not good to have Ziyech in your team. Actually it is quite good to have one of the best creative talents of the Eredivisie, but with his amount of incomplete passes, the rest of the team should play a very tidy game, to prevent the tactic from tipping over. One Ziyech wasting some 12 to 13 passes per match is okay, but put two Ziyechs in your midfield and your team loses 25 possessions there already.

Teams could use concepts like this to balance their first eleven out. It’s a risk and reward analysis, where having too many Ziyechs may prove very costly, while having too little of them won’t work either. It’s this kind of analysis that may help to find the ideal balance, without surrendering yourselves to the dangers of random variation. It may also help in selecting the optimal first eleven, as it is not always advisable to start the 11 best individual players outright, without looking at the mix of qualities needed.



Obviously, this concept could be used as a scouting tool too. I suppose that if you successfully trade players from below the thick black line for players above it, you add more creative talent to your team. And if your supposedly creative midfielders are found below the black line, this indicates that they create too little ExpA for the amount of incomplete passes they use.

Measuring creative output in ExpA helps to quantify what type of players you have, at what costs in terms of incomplete passes they operate and what alternative names you could be looking for. Obviously, in composing the midfield, there’s more than just ExpA and incomplete passes, but we’ll get to the other midfield positions later. For the creative part, it’s ExpA versus incomplete passes!

Predicting the final Eredivisie standings

Despite the early season fuzz about the progressive equalization of the league, the Eredivisie has a clear top-4. It has had that in terms of underlying stats for some time, but presently it also does in the league table. This post will use these underlying stats to identify front runners for the title, the Europa League play-offs, and of course relegation football.


Stats, what stats?

The model used to create these predictions has been explained before, but I’ll explain the most important elements once more.

It’s a shots based model, where each shot is rated by its chance of resulting in a goal. Hence the term ‘Expected Goals’, or ExpG model. To convert a goal scoring attempt to an ExpG we use shot location as the most important factor, but also shot type and assist type have their influence. Game state also plays a role, as it’s easier to score when leading due to the other team taking more risks to come back into the game.

So, with those element we construct an idea of each team’s relative strength, and from there on we simulate each match to get the appropriate spread of chances for a home win, a draw and an away win.

The final step is to simulate the remainder of the season to achieve an estimate for the number of points, and the league placements come the end of the season.

Stats are all collected by OPTA and presented at Squawka. I can’t thank these guys enough, as without their input… no model.



In the following graph, each team’s predicted points are plotted. The boxes are color coded for the relevant league places, with the code explained at the bottom. The box represents half of the simulated results, with the think black line the mean of the simulated results.

Vitesse is expected to win on average some 67 points, but anywhere from 65 to 70 should cover 50% of simulations. On both sides of the box a wide spread can be seen, representing 95%  of the simulations, with some extreme outliers on both sides.

Please take careful note of the wide spread, and if you’re willing to learn one thing from this post, please make it this! Even with the exact same underlying performance, potential outcomes have a very wide spread. Anywhere between 58 and 77 is reasonably possible with the exact same underlying performance that lead Vitesse to their current points tally of 40 points after 19 matches.

Boxplot projected league table Eredivisie 2013-14 20 januari 2014In terms of predicted points, Vitesse has the highest mean, though slightly against intuition this does not automatically mean they have the best chance of winning the league. Their spread is quite small compared to Feyenoord and Twente, who happen to have more open games, which leads to a higher ceiling, but a lower bottom.



Ajax’ fourth place may come as a surprise, but, and I cannot stress this enough, the wide margins predict them to finish anywhere from first to fourth. Ajax currently shares the lead of the table with Vitesse, on 40 points, being trailed by Twente at three and Feyenoord at four points. Therefore, this prediction indicates the Ajax is expected to have the weakest performance over the remaining matches of all four title candidates. How come?

Well, there are two things that are weak in Ajax’ game in terms of ExpG. First of all, they concede 1.11 ExpG per 90 minutes while playing at level score (0-0, 1-1, etc.). For now, they’ve come away with that, but Ajax simply have a bigger chance of going a goal down than their rivals have. Cillessen’s save percentage of 95% sounds fantastic, but there is no defense in the world that is going to keep that going.

The second ‘problem’ at Ajax is defending small leads. While most top teams improve their ExpG when leading by a single goal, this is certainly not the case at Ajax. This problem also existed during the past season, when I wrote about it for ‘De Zestien’. As a consequence, Ajax will allow more teams a comeback goal than their rivals.


Predicted final standings

Besides predicting the final points total, the exact same simulation also predicts league ranks. The next graph shows the spread in predicted positions. Despite the fact that Vitesse produced a higher average predicted points number, they are on equal terms with Feyenoord and Twente due to Vitesse’s narrow spread compared to the other two.

One caveat, though. Ajax is predicted with just 14% chance of winning the title, but during both the past and the present season, they have managed to overachieve in comparison with the model. And they are the only team to do so. This means that either Ajax do something very good that is not part of my model, but in this case they would be the only team to do so, or Ajax has been lucky for quite some time. Only time will tell…

Boxplot projected League positions Eredivisie 2013-14 20 januari 2014

Europa League

The Dutch Cup final will not be contested between two top-4 teams, so places three and four will provide EL qualification to two teams dropping out of the title race. Place 5 to 8 will lead to EL play-off football. PSV, Groningen and Heerenveen are currently up there and are widely expected to stay, while AZ may well drop out as they are predicted about level with PEC Zwolle, with an outside chance for Heracles.


Not much to play for

Each league always has a bunch of teams with not much to play for in the second half of the season, but the Eredivisie is still quite open at this stage. Utrecht can still make the play-offs – just 11% despite currently holding the ninth place – but stand an equal chance of battling relegation. Yes, luck and random effects are very very important in football.

Go Ahead Eagles, NAC and Roda JC still have to worry a bit about relegation, but will in all likelihood stay up.



The R-monster is quickly sneaking up on RKC. The model identifies them as the main relegation candidate, despite their current 15th spot, two points off the bottom. The bottom five teams are just two points apart in the current league table, and it’s an equal battle between ADO, N.E.C. and Cambuur to try and escape the relegation play-offs, while Roda is part of that five-team-group at the bottom in terms of league points, but not in terms of underlying performance.


Final words

“Prediction is very difficult, especially when it’s about the future”

These famous Niels Bohr words should probably be in a piece like this. And the 1922 Physics Nobel Prize winner makes a point that is often quickly interpreted as an open door. Yet, it’s fundamental to understand that predicting the future is estimating chances of events occurring. I don’t know if Vitesse will make exactly 67 points, just that 67 points would be my best estimate. And that 70, 65, or even 60 is quite possible too. Don’t believe exact predictions, they will disappoint you.

The Eredivisie weekend in nine graphs

Logo_EredivisieThe Eredivisie is back! Thirty goals in nine matches and just three clean sheets in an eighteen team league, how we’ve missed it…

In this post I will run over all nine matches with the use of ExpG match plots. ExpG stand for Expected Goals, which means that the model assigns each goal scoring attempt a different value estimating the quality of that attempt. Attempts are judged on shot location as the most important variable, but also shot type (shot vs header), assist type (through-balls rule), and a few other parameters go into it. Overall, teams are expected to score the ExpG amount of goals with the efforts they created. Anything more can be aspired to be excellent finishing, but unless you have Luis Suarez or Lionel Messi among your ranks, you can safely assume that any over performance in comparison with ExpG is mostly luck. And luck will run out long term.


ExpG plot Twente 3 - 1 Heracles The weekend kicked off with the traditional Friday night fixture, where Twente crushed nearby Heracles 3-1. The plot shows that Twente limited their opponents to well below 0.5 ExpG, while only creating decent output themselves in the second half. This is very much the type of match that illustrates what happens after an opening goal. Heracles did a decent job stopping Twente up until that time, but had to chase the game for 70 minutes. To Twente’s credit, this did not produce any decent efforts by Heracles, but it did allow Twente more and more ExpG as the match went on.


ExpG plot AZ 3 - 0 NAC BredaAZ beat NAC 3-0 in a match they dominated from start to finish, as the match plot illustrates. NAC never put any offense together and this must have been an inspiring win for the home side, who may hold some decent hopes of making the Europa League play-offs. In the model’s league predictions, which I will write on in a few days, AZ are fighting it out with PEC Zwolle for the final spot in the play-offs, with both teams around 40% chance of making it. However, depending on the outcome of the Cup matches, they could both earn it if the play-offs shift to places 5 to 9.


ExpG plot Zwolle 1 - 2 VitesseTitle candidates Vitesse beat PEC Zwolle 1-2 in the 90th minute. This is exactly the type of match where a match plot can tell you a lot, since there are very different 1-2’s around. In this case, Vitesse did have the best claims to a victory, but this was mainly based on their first fifteen minutes. Since then, they did not create anything substantial until that late winner.


ExpG plot RKC 1 - 1 Groningen

RKC earned a point in a 1-1 draw with Groningen. The away side did a good job, until they conceded a penalty near the end of the game. Besides that single moment, RKC failed to create decent offense from open play. The model will recognize Groningen’s positive effort, as doing this regularly will win you away matches long term. RKC on the other hand, should better not rely on this kind of ‘ deus ex machina’ moments.



ExpG plot Heerenveen 2 - 2 Roda JCThe final Saturday night match was Heerenveen’s 2-2 draw with Roda. This was most definitely a disappointing final score for the home side, as they could have put the match away judging by their impressive offensive effort between the 30th and 70th minute. Was it coincidence that Heerenveen did not convert while besides the penalty only a single open play attempt fell to Finnbogason? This is also the type of effort that Roda should not take too much from. Earning your points this way is not going to hold.


ExpG plot N.E.C. 3 - 1 Den HaagSunday kicked off with N.E.C. 3-1 ADO Den Haag, a fine example of a misleading final score line. In fact, ADO put together a very decent offensive effort, and judging by the match plot, a high scoring draw would have been a fair result.


ExpG plot Cambuur 2 - 0 Go Ahead

Another crushing was found in the battle of promoted teams, where Cambuur beat Go Ahead Eagles 2-0. Most people will simply think along the line ‘ Cambuur is newly promoted – promoted teams are usually not so  good – I expect them to concede goals’. That’s wrong, plain wrong. Cambuur may have serious offensive issues (by far the lowest ExpG at just 1.02 per match), their defensive side of the game is very impressive. Cambuur comes in fifth place in ExpG conceded, with just 1.42 ExpG conceded per game. That’s around the level of Feyenoord and Vitesse. In plain English: scoring against them is not easy, as Go Ahead also experienced here.


ExpG plot FC Utrecht 2 - 5 FeyenoordThe highest scoring game of the weekend was Utrecht’s 2-5 home loss to Feyenoord. In the most impressive performance of the season, Feyenoord confirmed their title aspirations in creating 30 shots, and setting the league record for ExpG at over 5. Needless to say, Utrecht’s problems are in defense and the model even assigns them around 10% chance of having to play relegation play-offs at the end of this bleak season.


ExpG plot Ajax 1 - 0 PSVThe final match of the weekend was also the most anticipated. Ajax beat PSV 1-0 to stay right up there with Vitesse at the top of the table. However, the match plot illustrates just how much of our memory is formed by the score line. PSV had more shots, and more ExpG, but all in the first thirty minutes. If I were employed at either side, I would carefully study this match as this implies a tactical issue or switch around the 30th minute. From that moment on, Ajax went on to create nearly 1 ExpG, which indeed resulted in a single goal. However, what we tend to forget is that PSV could easily have won this match. The model is less forgiving for Ajax though, as conceding this amount of ExpG at a level score line seriously hurts. In the long term, this match is not a good sign for Ajax, and their title odds have even decreased a bit during the weekend. To how much? We’ll read that in the model’s league predictions later this week.

How to scout goal scoring talent?

Strikers are the most sought after commodity in football. Having a player who can put the ball in the back of the net more than others can is a highly valuable asset to a football team. So, how to find one? The easiest and most applied way would be to list names and goals, pick a top name, and bingo!

Now, while semantically it is true without a doubt, that a top scorer is the guy who scorers the most goals, counting goals seems a poor way to identify goal scoring talent. Let’s walk along some simple improvements to do it better.



We start with this well-known format of player names and goals scored. Easy, right?

Top scorers - Traditional - Eredivisie 2013-14

This table will always do a good job at the top, since players like Finnbogason and Pellè take a ton of shots, and would never show up that high if they did not have true goal scoring skill. But what about players a little lower down the table? Is a player with 7 goals to his name at this half way point of the season doing a good job, or not?


Per 90 (G90)

Time to make our first, and very simple adjustment: a correction for playing time. Just like the smart people at Statsbomb – do check that site out, it’s amazing – I prefer my goal scoring information as per 90. Just divide goals by playing time to arrive at that stat. Here’s the table again. I’ve excluded players who’ve played less than half of the season, to prevent Jaïro Riedewald – 2 goals in an 11 minutes sub appearance – from skewing the chart.

Top scorers - G90 - Eredivisie 2013-14

It won’t make too much of a change at the top, as those players play nearly every possible minute, but still, subtle changes do occur. Behind the identical top-6, new names appear. Lower down the list, we can expect a bigger impact, since here we find players that may be successful impact subs, have been injured, or youngster who are not the focal point in their teams yet.


Non penalty goals per 90 (NPG90)

With the next improvement also comes the next acronym. In the age of quick, twitter centered communication, football analytics can’t do without it’s acronyms. It is not that we don’t value accessibility, since we really do, but acronyms makes talking about these metrics possible.

So, we’ll strip out penalties and then look at the goals per 90 again. A second, simple adjustment that corrects for the fact that not all players take an equal amount of penalties, or even take penalties at all. Penalties are one of the best goal scoring opportunities around, but they are very unevenly distributed among the players. So it makes intuitive sense to strip them out when looking for goal scoring talent.

Top scorers - NPG90 - Eredivisie 2013-14 

Piazon drops a bit, from 0.72 to 0.52, but the most remarkable drop is Aron Jóhansson, who drops out of the top-10 while holding the fourth spot on the G90 table. Four of his 11 goals are penalties. But, AZ fans, don’t worry, Jóhansson will be back later in this piece.

We can take it a step further, and this may be where things may look more complicated. Don’t worry, because it isn’t complicated and I’ll walk you through the next level.

The main thing that is wrong with the NPG90 table is that not all players have had an equal amount of goal scoring opportunities.


Classroom exam

Imagine yourself sitting in a classroom, taking an important exam. On this exam, only correct answers will be counted, no penalty for wrong answers, and you get a paper filled with just ten questions. A slight look around tells you that other people have been handed more questions, some even got multiple papers to fit all the questions in. That doesn’t feel right, does it? How could you show your qualifications if they don’t ask you enough questions in the first place.

Now, in football, strikers are at least partly responsible for creating their own goal scoring opportunities, so the metaphor does not hold 100%, but I guess you get the point. And not only do shot numbers differ between players, each shot also has a unique chance of being converted to a goal. In our metaphor each question is on a unique level of difficulty.

So, you may have been handed just ten questions, if they were all easy peasy no-brainers, you would still have a good shot at making a good grade. In football, it’s the same. Goal scoring opportunities all have their own different level of quality and should be evaluated as such. Raw conversion rates are useless in a game where some people shoot from 30 yards out and others have a style that relies on short range tap-ins.


Expected Goals

This is where the Expected Goals, or ExpG, concept comes in. Based on shot location, shot type, assist information and some other factors, we can assign each goal scoring opportunity the correct odds of being scored if an average player was taking the shot. This brings us two separate qualities to evaluate with respect to goal scoring.

1. Which player creates the most goal scoring threat? Obviously, each players’ ExpG is a combined product of striker skill and team mate skill, and on top of that, playing for a top team will bring you more ExpG, just like it is with the traditional method of counting goals.

2. Which player makes the most of his ExpG? Which player scores more goals than his goal scoring opportunities would have brought at the feet of an average player?

In the following diagrams, just like above, penalties have been stripped out to create a fair picture.


Goal scoring threat

In terms of goal scoring threat, Graziano Pellè equals over 0.8 goals per game. He is the spearhead of Feyenoord’s offense and we learn here that an average Eredivisie player should expect nearly a goal per game with the goal scoring opportunities that Pellè and his team mates create for Pellè.

Heerenveen’s Alfred Finnbogason, who leads the traditional chart with 17 goals, comes in at fourth. Twente striker Castaignos and Vitesse striker Havenaar complete the top-3 behind Pellè, which feeds the theory that playing on a big team, and therefore having good team mates around, is obviously of influence here. Remember, this metric stands for creating goal scoring opportunities, which is a combined effort of both the striker himself and his team.

Top ExpG plot Eredivisie 2013-14


The second aspect of scoring goals is converting ExpG into goals.

In terms of finishing, the Eredivisie currently holds no better player than Heerenveen’s Alfred Finnbogason. The Icelandic striker manages 4 more goals than an average player would score with his chances. Finnbogason is closely trailed by Vitesse’s Chelsea loanee Piazón and a bit further by AZ’s American striker Aron Jóhansson.

Top scorers plot Eredivisie 2013-14Graziano Pellè paints a completely different picture here. The Feyenoord striker does best in terms of fashioning out chances, but finishing them is a different picture. Even an average player would have scored over three more goals than he did. Pellè is not the worst finisher, though. Imagine what Vitesse could have done with a decent finisher on Havenaar’s position.

The green and red bars represent players whose finishing is more than two standard deviations away from the average.


In the end

In this post, we’ve come from a traditional list of names and goals scored, to a sophisticated metric to judge goal scoring talent in its most honest way. It seems creating chances for yourself, or allowing team mates to do so, is a different skill from finishing those chances. Only the true top strikers blend these skills.

This metric may also help explain why Graziano Pellè was disappointingly average at AZ and Cesena, but is now seen as a real top scorer. Feyenoord has developed a playing style that runs its offense for a huge part through him, and uses his skills to create goal scoring threat to its maximum. But finishing chances is not one of Graziano’s skills.

Another nice individual to single out is AZ’s Aron Jóhansson. He is fourth in the G90 list, but drops out of the top-10 if we strip his four penalties. The combined ExpG graphs learn us that he is way too low in terms of goal scoring threat, but what he gets thrown at him, he finishes with elite skill for this league. He is like a reverse-Havenaar, who gets in the mix of the third most ExpG, but is the worst finisher identified here.

The Eredivisie weekend in nine graphs

This post will quickly pass over all nine Eredivisie matches of the past weekend, using my latest creation, the ExpG graphs to tell the match stories. ExpG stands for Expected Goals, which is the result of using shot location, shot type and several other factors to assign each shot a probability of being scored. For more details behind this method, read the introductory post here.

This is probably also the point where I should admit that, as a kind of test, I will write this post without having seen any of the matches. I guess that by the end of the post we will find out if the ExpG graphs do indeed recreate the story of a match then.

The weekend kicked off with title contenders Twente playing away at relegation threatened RKC.

ExpG plot RKC - TwenteResult-wise, 1-1 was an obvious deception for Twente, as in terms of chance creation they should have won. Judging by the continuous slope of the ExpG line, Twente created chances throughout the entire match and must have been surprised by the RKC opening goal, as they hardly gave anything away during the first half. After a slight spell during the first ten minutes of the second half, RKC defended the single goal lead for most of the second half.

Saturday opened with Heerenveen crushing AZ 5-1 away, in a re-match of the Cup game that saw AZ through on penalties. Managed by Dick Advocaat, AZ now have four consecutive losses and the points-per-game tally is lower under the new manager than under Verbeek.

ExpG plot AZ - HeerenveenHeerenveen may have trashed AZ in terms of the score, up until the 55th minute the match was rather equal in terms of chances. In the final half hour it seems AZ gave up a significant amount of chances, while chasing a two goal Heerenveen lead. Yes, it’s good to win matches 5-1, but with ‘only’ 2.0 ExpG to back this up and a level ExpG until the 60th minute mark, Heerenveen fans should not get carried away by this score line.

Next up: Peter Bosz and Vitesse at his old club Heracles. Vitesse lost precious points in a 2-2 draw, as they could have claimed the ‘winter title’ here.

ExpG plot Heracles - VitesseA very low scoring game in terms of ExpG, where both teams did not pass the 0.5 mark before half time. By then, though, Vitesse led 0-2, despite both teams being equal in terms of ExpG. In the second half, Heracles stormed to a 75th minute equalizer with their best chance of the game, while during the entire second half, Vitesse surprisingly, did not create any offense of note.

NAC beat Cambuur 1-0 with a late opening goal to win three important points off the relegation candidates.

ExpG plot NAC Breda - CambuurJudging from the curves, a 1-0 score line seems fitting. Cambuur hardly put together any offense, perhaps more occupied with holding on to the away point. It took NAC some 20 minutes to get their offense going, and even then, they managed only just of 1.2 ExpG at home against Cambuur. In the end it proved enough, but NAC could just as easily have dropped two points here.

Here’s the most one-sided affair of the weekend, and perhaps for a long time to come.

ExpG plot Feyenoord - ZwolleA Lex Immers brace put Feyenoord 2-0 up by half time, which neatly fitted the ExpG by then. Feyenoord created a lot of chance and some quality ones too. Zwolle put in one of the worst offense performances of the season, which is weird considering the fact that they needed at least a goal for almost an hour.

Early on Sunday, Ajax pulled off a miraculous escape at Roda.

ExpG plot Roda JC - AjaxThe lines start late, as both teams simply did not create any offense before that time. Roda had the fortune to open the score at a point where 0-0 would still have been reasonable. Ajax could perhaps have scored earlier, but in the final minutes they created two very high quality chances to double their ExpG tally for the match, and more importantly to allow 17-year old Jairo Riedewald to score a match winning brace on his debut for the club.

Go Ahead Eagles suffered from an early Utrecht goal, but deserved their win on the basis of three quality chances.

ExpG plot Go Ahead - FC UtrechtUtrecht did not create anything substantial after their early goal, and Go Ahead Eagles scored two from three excellent chances, one of which was a penalty. Simply a deserved home win.

Groningen beat N.E.C. 5-2 in a match that generates the most intriguing curve of the weekend.

ExpG plot Groningen - N.E.C.Yes, that’s six goals in 60 minutes, while both teams had created just about 1.0 ExpG by that time. A flurry of excellent Groningen chances followed, with just one goal from them, and in the end a big Groningen win seems justified. This match may also serve to remember that neither the score line (5-2), nor the ExpG’s at 90 minutes (3.7 – 0.9) tell the full story.

Finally, a troubled PSV side beat ADO 2-0 at home.

ExpG plot PSV - Den HaagPSV played for 45 minutes, held a two goal lead that could easily have been more, and then simply quit. Or, better said, they shut down the game, as ADO did not generate any offense to find a way back into the match. The curves don’t (yet) show red cards, as it must be said that the 31st minute penalty and the dismissal of ADO goal keeper Coutinho killed the game for ADO, and the neutral spectator it seems.


In the end

Nine matches, nine curves, nine stories. This is not to say that one could make a habit out of reporting on matches unseen. But my weekend away from football is a nice chance to write out my interpretations of the matches, based solely on the curves, and basic information like penalties and red cards.

The story of a football match in one picture

Football matches are not just football matches. They are stories, they have identities and each match is a unique experience. No two football matches are the same. Yet, it the end, results of football matches are not all that different from each other. We’ve only got three outcomes: a home win, a draw and an away win. And we’ve got a fairly limited set of score lines to go with that.

But one 2-0 home win may be vastly different from another 2-0 home win, and we all know that some 0-0’s are a genuine waste of precious time, while others are the outcome of an intriguing battle back and forth. And then you have score lines like a 1-2, which may have seen a comeback, or not.

You get the point: the final score line does not suffice.


Match report

Last weekend, Eredivisie Champions Ajax beat newly promoted side Cambuur 1-2.  If I were to tell you about that match, I could say that Ajax created next to nothing offensively, while Cambuur had their share of low quality chances, up until Ajax opened the score shortly before half-time with their first shot of the match.

In the second half, Ajax put on an abominable offensive show, but on the other hand, Cambuur could not put anything together either. Until the final fifteen minutes, that is. Cambuur managed to equalize with their best chance of the match. In the end, Ajax was lucky to get away with a dying seconds winning goal, as their total offensive output did not even match their underdog opponents.

Now, if I’d use this as a base for my match report, add in a little background info, some smart language, specific match details, player names and a tactical twist here and there, I’d say I have a decent match report.

Well, what if I did not say anything, and just presented this image?

ExpG stands for Expected Goals, where each attempt to score is assigned a probability of resulting in a goal, based on location, type of shot, assist details and some other factors.

ExpG plot Cambuur - Ajax

Please, take a moment and re-read the paragraph above with this image in mind, if needed, just click to enlarge. Indeed, it’s all in there: the opening goal being Ajax’ first shot of the game, the low quality of shots created, the long dull phase after the opening goal, Cambuur scoring with their best chance of the game, and the fact that Cambuur created more than Ajax.

I think this is as close to a box score as we can get in football, but please don’t hesitate if you’ve got any improvements in mind. A short summary of the ebb and flow of a football match. In the era of short chunks information, I think this will prove a useful visualization and I hope to use it a lot in the future.

Oh, and there is another advantage, as this picture tell you exactly how a match went. Not how another observer thought it went, no room for shaping up a match report to fit the narrative. Just data in, a little automated computing and graph out, that’s it…

So while we’re at it, here are the other Eredivisie matches of the past weekend, so you can just pick your team… Click the picture for a full size version.

ExpG plot Zwolle - HeerenveenExpG plot N.E.C. - Roda JC ExpG plot Twente - Go AheadExpG plot Den Haag - RKCExpG plot Feyenoord - GroningenExpG plot Heracles - AZExpG plot Vitesse - NAC BredaExpG plot FC Utrecht - PSV

Data comes from various OPTA based sources.