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.

How to scout a striker?

Scouting strikers should not be that hard, right? Their prime responsibility is putting the ball in the back of the net, and goals are one of the few elements of football where traditional fans and nerdy analysts agree. A goal is a goal, counting goals cannot go wrong. Strikers who score a lot of goals are better than strikers that score less goals. Or not?

In our previous piece on scouting offensive talent, we’ve distinguished two elements that constitute a good striker.

  1. The striker has to get into good scoring positions, and accumulate good shots. This is best measured as Expected Goals (ExpG) per 90 minutes, with exclusion of penalties.
  2. The striker has to convert these chances into goals. This can be measured by comparing ExpG and actual non penalty goals.

The previous post on strikers illustrated how we can measure those two elements and judge strikers separately on both of these qualities. Today we will take it a step further and see what scouting implications come from it. We will show that sometimes it is better to buy a lower scoring striker, and which high scoring strikers to avoid. But first, I want you to meet someone.

Meet our striker!

He plays in a big league, for a good team, where he has taken 160 non penalty shots in the past season. On average, each shot was good for 0.152 ExpG, so over all shots together we could have expected 24.4 goals from him.

The thing is, our striker is pretty good, so instead of 24.4, he scored 43 non penalty goals for an over performance of 18.6 goals. We can stick an ugly acronym to it and say his non penalty goals above replacement (NPGAR) is 18.6.

NPGAR = Non Penalty Goals – Expected Non Penalty Goals

You’ve probably guessed by now that our striker is Lionel Messi. This season, Messi still plays for Barcelona, where he has taken 75 non penalty shots to date. On average the quality of the chances was comparable to last season, with an ExpG per shot of 0.149. Overall, we should expect 11.1 goals.

The thing is, Messi is suddenly not so excellent at finishing, and he has come up with 9 non penalty goals instead of 11. His NPGAR is now -2.14, which indicates that the average player, not even the average striker, would have scored two more goals with the type and number of shots that Messi has taken this season.



A story about Messi is not analysis, it’s anecdote. And anecdotal evidence is no evidence. We could ‘prove’ that finishing does stick with a player by simply picking someone else that happened to follow an excellent finishing season with another excellent finishing season and fire that point home.

It makes more sense to repeat this work for all 479 players of the top-5 leagues who took at least 10 non penalty shots in the baseline 2012/13 season. We take separate looks at the creation of goal scoring chances (ExpG per 90) and at the conversion of chances into goals (Goals minus ExpG). Both parameters will be compared over one season and the next.


ExpG per 90

In the first graph we will look at the repeatability of non penalty Expected Goals per 90 minutes (ExpG NP per90). The horizontal axis shows ExpG NP per 90 for the first season, and the vertical axis shows the same for the next season.

ExpG90 correlationExcellent! It turns out that players with a high ExpG per 90 in one season, are also the players with a high ExpG per 90 in the next season. This is not too surprising, as several factors influencing ExpG per 90 will remain constant over time. Strikers will still be playing as strikers, and most players playing for top team will still be playing for top teams. More work needed here, but we’ll leave that for another post, as there is a far more interesting graph coming up.



The next graph shows the repeatability of non penalty goals above replacement (NPGAR). This represents the conversion of goal scoring chances into actual goals.

NPGAR correlationIt turns out that if you correct for the quality of goal scoring attempts, there is absolutely no connection between conversion in one season and the next. A high or low NPGAR in one season has zero relation with NPGAR in the next season.

Messi is the dot in the lower right hand corner, who had an unworldly 2012/13 season, with an NPGAR of +18.6, followed by the current season of -2.1.



This is a shocking conclusion with huge implications for striker scouting. If a striker bases his goal scoring mainly on conversion, he has a good chance to fail in the next season. If a striker bases his goal scoring mainly on good underlying ExpG numbers, he has a good chance to persist his level of scoring.

Buying strikers who score their goals due to a high NPGAR is something you should always avoid.

We all know these famous examples of one season wonders, who got transferred for big money, only to disappoint at their new clubs. Usually, loads of soft factors like the higher level of competition, language issues, or playing style are used to explain the disappointing results, while the only thing going on is regression of NPGAR.

Regression does not always occur though, and you can see in the scatter plot that some players do indeed follow a season of high NPGAR with another season with high NPGAR. But just as many players do not, and just as many players with high NPGAR in the second season come off seasons with low NPGAR.



We should use NPGAR as a red flag in striker scouting. A player like Alfred Finnbogason, currently the Eredivisie top scorer with 21 goals in 20 matches, is a nice example. We can put up several red flags.

First, 8 of his 21 goals are penalties. Second, his NPGAR is +2.68, indicating that he is nearly three non penalty goals above expectations. There is no ground at all to assume that he, or any other player, will outperform the ExpG model  next year. All in all, Finnbogason’s non penalty ExpG per 90 is 0.51, which is still a good number, but by no means near the present perception of a striker that scores 1.05 goals per 90.

For next season, 0.51 goals per 90 seems a reasonable estimate. The problem is, next season Finnbogason will not be playing at Heerenveen, as he will make the step up to a bigger league, where he won’t contribute the same number as in the Eredivisie. His true level should then be estimated somewhat lower than  0.51 goals per 90 minutes, and we will all start wondering what is going on with all these high scoring strikers who just don’t cut it outside the Eredivisie.



Inevitably, though, there will be players who seems to disprove the workings of NPGAR. We can assume that half of all players will have a positive NPGAR and half will have a negative NPGAR. A season later, one quarter of players will have two consecutive positive NPGAR seasons. One eighth will have three consecutive seasons where they outperform ExpG, and so on.

In this study among players from top-5 leagues with at least 10 shots, we find 479 players. With such a big group of players, there will inevitably be some players who consistently outperform ExpG to produce season after season of positive NPGAR. This is a misleading situation, as these players will be credited with finishing skills that are basically the product of an unrepeatable effort.


In the end

The message in striker scouting is quite clear. Familiarize yourself with the terms ExpG and NPGAR and these mistakes of flopping striker are generally avoidable. Stay away from strikers with high NPGAR and aim for those with high ExpG numbers, as the latter group will cut it next season, while the first group has every chance of falling back.

Probably, a negative NPGAR in a player with good underlying ExpG numbers is a sign of a bargain buy. The world will see a striker struggling to convert, and it takes some balls to buy him, but the numbers indicate that a return to scoring form is right around the corner.

Putting Expected Goals to the test

After yesterday’s post where Expected Goals was explained in detail, today’s post will put the metric to the test. How good is Expected Goals? And is it better than Total Shots Rate?

We’ll compare ExpG and TSR at several levels as we go along. The dataset used for the first part of this analysis consists of all 98 teams from the 2013/14 season so far, for top-5 leagues. As usual, data comes from Squawka, my go-to-site for OPTA driven football data. All comparisons in this piece are made on team level. We’ll leave the individual player analysis of ExpG for another day.

ExpG is calculated as explained in yesterday’s post, and for comparison with TSR, ExpG ratios (ExpGR) are used. For all behind-the-scenes input in the ExpG formula no data from the 2013/14 season was used. All regression analysis that was needed to determine how to rate different factors that influence ExpG was carried out on earlier data. The risk of over fitting is therefore minimized.

ExpGR = ExpG for / (ExpG for + ExpG against)

TSR = Shots for / (Shots for + Shots against)


TSR and outcome

First up, the relation between TSR and the outcome in terms of points per game (PPG) and goal difference (GD). Click on the graph if needed, for a larger version.

TSR and outcomeTSR is a very good metric. It correlates nicely with the most relevant two performance indicators PPG and GD. The R-squared values of 0.55 and 0.58 indicate that knowing a team’s TSR provides around 75% of knowledge needed for a perfect knowledge of either PPG or GD. For more, and better explanations of R-squared and R, check Phil Birnbaum. The man really knows his stuff.

In general, R-squared values are higher when leagues have a clear separation into two groups. EPL typically has values over 0.6, while Ligue 1, where the dots are one bunch, generally scores below 0.4.


ExpG and outcome

These two plots show the relation between ExpGR and outcome.

ExpGR and outcomeFrom face value alone, you can tell that ExpGR has a better correlation with outcome than TSR has. The dots are closer to the red regression line, so the R-squared value is a lot higher. For PPG, the R-squared is 0.73, while for GD it is somewhat higher at 0.79.

This is a magnificent correlation between a metric and outcome, but don’t get carried away yet. We would expect ExpGR to do better here, as it carries more detailed information to rate goal scoring chances. The formula behind it is designed to improve the relation with outcome in terms of PPG and GD. It would be a true shock if ExpG did not do a lot better than TSR here. What’s more important is the second half of this piece, looking at repeatability of the metrics.


TSR and repeatability

From here on, a different data set is used, as we’ll now compare the same metric over two consecutive seasons. Data consists of season 2012/13 and 2013/14 so far for the top-5 leagues, where obviously relegated sides from the first season did not produce a second season for comparison, as promoted sides in the second season did not have a first season to compare with. This left 84 teams with consecutive seasons.

TSR repeatabilityTSR is pretty repeatable, producing an R-square of 0.51. This indicates that TSR in the first season is a moderately good predictor of TSR in the second season. Most teams are roughly in the same ballpark, but deviations of 0.100 are far from rare.


ExpGR and repeatability

The next plot shows ExpGR in the first and second season.

ExpGR repeatability

ExpGR has an even better repeatability than TSR did With an R-squared of 0.67 this metric carries a good signal over multiple seasons. Stripping a few outliers, teams generally don’t deviate more than 0.050.


In the end

This scatter plot heavy piece proves a superior correlation for ExpGR with both outcome and repeatability compared to TSR. To speak with Nate Silver, ExpGR carries more signal and less noise than TSR.

The first part of this post, relating ExpGR and outcome, shows that in measuring team performance, ExpGR show prevail over TSR. This conclusion was probably known intuitively, but is now illustrated and quantified.

The second part of this post is more revolutionary, as it establishes ExpGR as a more reliable parameters to use for predictions. This means not just fancy number heavy predictive models, but also any easy made claims regarding upcoming matches or final league positions.

TSR still holds the quite relevant advantage that counting shots is a lot easier than building an ExpG model. However, with more and more variations of ExpG models around, these numbers will gradually become easier to obtain over time.



I feel like I could have put a dozen links to James Grayson’s amazing site in this TSR heavy post, but I’d rather urge you to just go to his site and check it thoroughly. It is good.

What is ExpG?

This post will look at the latest love child of the football analytics community, Expected Goals, commonly referred to as ExpG or xG. I’ve noticed a lot of questions via Twitter recently, regarding this relatively new concept. Spread across multiple posts, the concept is mentioned and has been explained on 11tegen11 before, but I felt the need for a comprehensive explanatory piece on ExpG to explain this important concept, and to use it for future reference.



ExpG stands for Expected Goals. It measures not how many goals a team has scored, but how many goals an average team would have scored with the amount and quality of shots created.

Each goal scoring attempt is assigned a number based on the chance that this attempt produces a goal. Typical parameters to use are shot location and shot type (shot vs header). Some models, including the one I use on 11tegen11, also use assist information to separate through-balls from crosses.

Teams that produce more ExpG than they concede have the best chances of winning football matches.


Total Shots Rate

ExpG has its roots in another key metric in football, Total Shots Rate, or TSR. Before trying to grasp ExpG, it is important to get familiar with shots rates.

Total Shots Rate = Shots For / (Shots For + Shots Against)

This formula provides TSR on a 0 to 1 scale. If a team takes all shots in a match, or a series of matches, TSR will be 1, and the more shots it has to leave to opponents, the lower TSR gets. On average, over multiple teams in the same league, TSR will always be 0.500, since each shot for is a shot against for another team.

TSR is pretty simple, yet it is a powerful predictor for future performance of football teams. Ever since its introduction to football, by James Grayson, TSR has dominated the analytics community. James has shown TSR to have the two qualities that are essential for a powerful team ranking tool.

  1. TSR shows a strong correlation with both points per game, and goal difference.
  2. TSR in one time period shows a strong correlation with TSR in the next time period.

If only the first condition is met, the metric would be strong in telling what has happened, but does not translate into the future. Goal keeper saves percentage is a nice example of a stat that helps explaining what has happened, but holds no power for matches still to come.

If only the second condition is met, the metric would be strong in translating into the future, but not correlated to performance. Team shirt color is a nice example, where translation into the future is easy, but a relation to performance does not exist.


The problem with TSR

The problem with TSR is that it treats all shots equal, which does not fit the fluency of football, where shots are not equal. Shots may come through a crowd of defenders from 40 yards out, or from the penalty spot in optimal circumstances. For TSR, both shots count as one, and both influence TSR equally.

This induces errors and probably also bias.

Errors arise because some shots are worth more than others. Sometimes a team creating 20 shots did a powerful job, but other days the team was just trigger happy and produced weak quality output. It may sound weird, but errors are not too much of a problem in a predictive model.

Bias is much worse.

If all teams produce and concede an equal case mix of poor and high quality shots, TSR would, despite its errors, be a perfect tool. However, there is plenty of evidence around that this is not the case. Some teams produce high quality shots, like Barcelona, and other teams produces low quality shots, like Laudrup’s Swansea.


Shot quality

Shot quality definitely meets condition one. It is related to performance in terms of points per game and goal difference. However, the clear cut evidence that it meets condition two is less clear. Data to measure shot quality is around since the 2012/13 season, so we don’t have high quality season-to-season correlation measurements. In other words, was Swansea’s recent struggle to produce decent shot quality just a flurry that would fix itself, or does it indicate an underlying reason that will cause the team to produce below average quality shots in the near future?


In the end

ExpG is hot, and if you’d ask me now, I’d say ExpG is the next big step that is being taken now in football analytics. Intuitively it makes a lot of sense to separate goal scoring attempts by the odds of scoring from it. However, for a new metric to be adopted for truth, a bit more work is needed. ExpG is a lot more complex than just counting shots. To show that this effort is worthwhile, we should first do a better job to illustrate its supremacy over TSR.

Never judge a goal keeper by his saves

Sometimes analysis and football intuition fit nicely together, and in those situations writing analysis pieces is easy. Sometimes they don’t, and writing gets tougher.

I’ve been thinking for most of the past weeks on goal keeper analysis. A topic that seems as simple as it gets, but as we’ll find out in this post, is actually a difficult one to get your head around and do properly.

GK saveAccording to the all-knowing Wikipedia, a goal keeper is “a designated player charged with directly preventing the opposing team from scoring by intercepting shots at goal”.

So, what could be more difficult than assessing how many of those shots end up as goals and, voilà, here’s our goal keeper analysis?

Let me start with a poll question. No need to fill out the answer, just take a little bit of time to make up which answer you think is correct.

The best way to identify goal keeping talent is…

  1. Percentage of shots saved
  2. Percentage of shots on target saved
  3. Percentage of shots saved with a correction for shot quality
  4. Other


In my personal history in football analysis I’ve gone from A to B, back to A, to C.

At C I’ve spent most of this season, but some background work I’ve done these past weeks have moved me further down, to D.

Yes, in my view, goal keeper analysis cannot reasonably be done on the basis of analyzing saves.

Now, that statement requires a bit of back up, so here we go. In the remaining part of this article we’ll analyze goal keepers in the top-5 leagues (England, Spain, Italy, Germany and France), who have faced at least 100 shots in two consecutive seasons (2012-13 and 2013-14), with the same club. To my idea, this is the best sample to use, to prevent keepers switching teams from screwing up the sample, and to prevent keepers with low numbers from doing the same.

Percentage of shots saved

We’ll start with raw saves percentage. This is the easiest parameter to collect, and probably the most used tool to evaluate goal keepers. It also ties in nicely with our intuition that good goal keepers stop a higher proportion of shots than bad goal keepers.

GK save percentage 03 februari 2014

The horizontal axis shows save percentage in the first year, and the vertical axis shows save percentage in the subsequent year. Remember, these are all goal keepers playing two seasons for the same club.

The connection is not very strong, but it’s not totally absent either. Generally, goal keepers who noted good saves percentages in the first year, noted better saves percentages in the second year, but the spread is huge. This makes it unreliable to estimate the second year’s saves percentages on the basis of the first year’s saves percentages. The repeatability of goal keepers saves percentage is poor. In general, if your stat has a poor repeatability, it’s useful to describe what has happened, but very misleading to assume that things will happen along the same lines in the future.

These numbers correspond with the excellent and far under viewed work by James Grayson, who found a similarly poor relation in a much larger set, matching teams in one season and the next.


Percentage of shots on target saved

Let’s move a little step forward and isolate shots on target. Some people advocate to use this over raw saves percentage, since goal keepers are hardly responsible for off target shots. In theory, though, keepers may take responsibility for some off target shots. By approaching a striker they could disrupt shot placement, or by reputation alone they could force strikers to try and find more difficult corners of the goal. Just raising a few hypotheses here.

GK save percentage SoT 03 februari 2014Again, first year performance is plotted on the horizontal axis, with second year performance on the vertical axis. The connection is even weaker for saves percentage of on target shots than it is for saves percentage of all shots conceded. Let’s save the debate until after the next plot.


Percentage of shots saved with a correction for shot quality

The third analysis uses shot quality. Based on our Expected Goals (ExpG) model, each shot is assigned a chance of ending up in goal, based on shot location, shot type and several other factors. This helps to control for the difficulty goal keepers have to make the save. In theory, this analysis is the best test for shot stopping quality, since it removes the fact that some keepers face tougher shots than others.

Goals conceded above replacement identifies how many goals a keeper conceded above or below the value of Expected Goals per 100 shots faced.

GK CAR 03 februari 2014After correcting for shot quality, all connection between first year performance and second year performance is lost. A goal keeper who over performed in the first year, has an equal chance of over performing in the second year as a goal keeper who under performed in the first year.

The most intriguing part of this rather shocking conclusion is that this knowledge is already out there, yet people continue to analyze goal keepers on the basis of saves. Again, I’m pointing you towards James Grayson, who, with smaller numbers taken from a Paul Riley post, found no correlation between goal keeper saves percentages in one season and the next after correction for shot location.


Shot quality

Please allow me to add one more scatter plot. This time, I’ve linked saves percentage and ExpG per shot, to show the strong link between those two.

GK save percentage and ExpG 03 februari 2014No goal keepers that faced shots higher than 0.11 ExpG noted a saves percentage over 92%, and no goal keepers that faced shots lower than 0.09 ExpG noted a saves percentage below 90%.


In the end

Putting all four plots together, this is compelling evidence to ignore each and every analysis using goal keeper saves percentage. The only, weak, link between goal keeper saves percentages (first graph) is driven by the quality of shots allowed. Some teams tend to face higher quality shots than others, therefore some goal keepers tend to have higher saves percentages than others. Nothing more, nothing less. On top of that, there’s going to be a huge amount of variance in performance.

This does not mean that shot stopping is not a skill. It most definitely is. It just indicates that among all factors that dictate a goal keeper’s saves percentage, the spread of skill level in shot stopping among top level goal keepers is very close. Other factors that influence goal keeper saves percentage completely overshadow the effect of skill, most notably shot quality, as indicated by ExpG.


Goal keeping talent

GK save 2So, how to scout for goal keeping talent? Start by ignoring saves percentage and you’ll leave most of the scouting world behind. Scouts will be aiming at goal keepers who’ve had random high saves percentages in some season, but those goal keepers stand an equal chance next season compared to all other goal keepers. Goal keepers who’ve had the bad luck of noting a low saves percentage season will probably be undervalued by the market.

What signs to look for, if not saves percentage? This piece shows compelling evidence against saves percentages, but it does not say that all goal keepers are equal. Far from that. It may well be that better goal keepers face less shots or shots with a lower ExpG. Better goal keepers will give up less rebound chances, less corners, claim more crosses, distribute balls better or sweep up nicely behind the defense.

All this stuff can be counted, but it’ll be hard to separate it from the effort of defenders. We’ll get to that in time. In the meantime, don’t let yourself be fooled by saves percentages.

Predictions for the English Premier League – A midweek title shift

This will be a rather short post where I’ll run the numbers for my league prediction model again. Most of the workings behind the model are explained in detail in the introductory post, back when the model still held Arsenal in marginally higher regard than City. Oh, wait, that was actually only just over two weeks ago.



“How can someone reasonably have thought that Arsenal was going to win the title? I just knew it was always going to be City. Any decent football watcher could see that. All those models are just crap” (anonymous fictional reply)

Eeuhm, no. This is probably the most frustrating part about going public with predictions in football. You will always be wrong at some point. It’s just the unpredictable nature of the sport. And I could take knowledge of the past two weeks out of the data, re-run the model and confirm that, based on all information at that very point, the model rated Arsenal and City very close. I can’t do that with any human mind.

It’s a form of bias that influences our memory, so that we think we’ve always rated City higher than Arsenal. But if results would have taken another turn, we may just have focused more on that brilliant Özil stuff and Giroud finally picking up on his finishing. Once again, we would have confirm what “we [would] have already known for a long time”.



This is exactly the reason why I like to go public with these models from time to time. Let me just put the results of the model out there and see what happens. How do the odds shift upon certain events. In hindsight, we can talk openly about when decisive trends were picked up and why certain teams were over or underrated. That way we can learn, I can learn, and next year, the model will have learned. But if you think ‘I knew it all along’, please just put it out there before events take place and we’ll see. The more models and estimations out there, the more we all learn.



So, with this ramble over and done with, here we go with the predictions for the league table. The format may start to look familiar now. Boxes correspond to a spread of 50% of the outcomes of simulations around the mean, indicated by the think vertical black line. The other edges mark the 95% interval and dots are true outliers.

The outliers teach us that in extremely unlucky cases a team like Liverpool may even finish below 60 points (they have 46 already, they have Suarez and they have 16 matches left to play), with the same underlying performance they show now. Guess we’ll have a hard time convincing the conservative and trigger happy football world to accept just that, don’t we?

Boxplot projected league table English Premier League 2013-14 30 januari 2014Unsurprisingly, City lead the way after their crushing of Tottenham last night. The model has City finishing around 83 to 84 points, with a margin of just over four points to Arsenal. Both Arsenal and Chelsea have cooled off a bit, after their draws. In all likelihood, Liverpool will finish no lower than fourth and the reds may still hope for more.


No battle

Spurs are quite unaffected by the loss, since they had quite a margin to Everton, who lost to Liverpool, and to United, who had still some ground to make up from the start of the season. I’m sorry to disappoint the crowd of football journalists, but the battle for top-4 is just not happening. No team that is presently outside the top 4 holds more than 10% chance of finishing inside that top 4.

Everton and United are by now quite equal, and both have about a one in five chance of making the Europa League. Newcastle, Southampton, Villa and West Brom should probably already be thinking about next season.



The relegation battle has seen some interesting developments. The most important match was of course Sunderland’s narrow home win over Stoke, which sees the Black Cats reduce their odds to below 50%. Things look pretty dreadful for mr. Tan and mr. Solksjaer, who hold the bottom spot and the model thinks quite firmly that they will go down.

Fulham’s underlying numbers are quite terrible and this fuels the model to give them a 4 out of five chance of relegation. I’m talking most shots and ExpG conceded and 17th in shots and ExpG for, while most of their better production came in the stints against Palace and Villa when they were already two goals up.

I do realize, however, that both teams have new managers, and it’d be interesting to see if this will correspond to a shift in underlying numbers. Obviously, the model will need a bit of time to pick that up, as it also did with Palace under Pulis. But in all honesty, “the firing of the manager has to be explained in relation to other reasons rather than for the expected improvement in team performance”.

Boxplot projected League positions English Premier League 2013-14 30 januari 2014

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.