Playing with Pressure

Recent debate about Jose Mourinho’s description of Manchester United as the “unluckiest” team in the league has brought out one of the oldest debates within football analytics – is the amount a team over or under performs its expected goals difference solely ‘luck’?

It’s obvious why such a description might be uncomfortable: it relies on the underlying assumption that our models are perfectly capturing what should lead to a goal. To decipher the extent to which teams have been lucky or unlucky, we first must evaluate what we might have expected them to return given their chances.

Due to this, the difference between what our model expects and what happens contains two distinct pieces of information – ‘luck’ and ‘random error’. Teasing the two apart is near impossible, unless our model gets so good that the random error reduces to a satisfactory point.

With current expected goals models, there is a lot that is not being captured. One of the most obvious variables is defensive pressure, the level of pressure on the player taking the shot. Luckily for us, this is something that our data partners Stratagem have begun to pioneer the recording of. This blog is the first of an exploration of what exactly it, and the other variables coded by Stratagem, add to the conversation.

This post will look at the level of defensive pressure faced by teams on their shots taken. Penalties were excluded from the sample, which includes chances that have been marked by Stratagem as low quality or better.


Starting with the average defensive pressure faced by teams per shot taken, Southampton are clear leaders here, followed by Tottenham Hotspur, West Bromwich Albion, and Leicester City. Perhaps more interestingly, Hull City are bottom of the table, facing the least amount of defensive pressure on their shots.

I found that there is relatively low correlation between the amount of defensive pressure a team faces when they are at home and when they are away.


This is not quite as surprising a finding as it may at first seem – clubs may have different strategies for creating chances home and away, and as must be repeatedly be stressed, this is still a relatively small sample of shots to base such analysis on.

The team that stuck out to me most from separating shots taken home and away was West Bromwich Albion, who are top for pressure faced away, but 4th from bottom in shots taken at home.

A simple subtraction of mean away pressure from mean home pressure allows us to see both sides of this effect. Where West Brom face markedly more pressure on shots taken away from home, Stoke City are the opposite, facing much less away than they do at home. What is also interesting here is the even spread of teams.


Taking expected goals numbers from David Sumpter’s recent articles on ‘luck’, I wanted to check briefly for a relationship between how much an attack has been overperforming and the average defensive pressure it faces. As it turns out, there is a faint correlation.


This suggests that at least some of what is missing from expected goals models is defensive pressure, though this is hardly surprising. This variable is likely to be more noticeably important when comparing an xG model without it to one that has it marked on every shot. As Stratagem only started marking it on chances above low quality this June, we’ll have to wait a bit before we can test that out properly.

This blog was written using data from StrataBet, the sports trading platform that covers more than 6000 games from 22 competitions every season.


On the lexicon of expected goals

Apart from an excuse for a pretentious title, I aim here to discuss some of the intricacies in the language used in football analytics. If you are new to the field, it might be worth reading One Short Corner’s fantastic primers

There’s a brilliant video of Richard Keys and Andy Gray discussing Wenger’s use of the term ‘expected goals’. “I’m not buying into that one,” says Keys, before continuing, “please don’t tell me a stat can tell you when a player is going to score.”

It’s nothing new to say that the name is problematic. But as football analytics continues to gradually permeate the mainstream, it is a problem of increasing importance.

It is one best illustrated by an example, where you are the model. On a scale of 1-5, where 5 is absolute certainty of a goal, rate the following chance:


Now it doesn’t really matter what you actually rate it, because the point is you’re digesting the information available to you at this point of action. You have where the shot is being taken from, like a basic expected goals model, and where the defenders are positioned.

What happens if we add more information?


Some more pre-shot information, mainly that the shot is a product of a Suarez-led counter-attack with defenders out of shape and in recovery mode, makes the chance seem a better one. The likelihood of a goal is probably higher than we thought it was before.

What happens if we add where the shot went?

Neymar’s finish is a good one, drilled at the bottom left corner. Regardless of where you started on the scale of 1-5, you should be closer to 5 now.

But there’s a subtle difference in what we’re talking about as soon as the post-shot information (where the shot went) is included. We’re no longer talking about ‘chance quality’ in the footballing sense, but we are (confusingly) still evaluating the ‘chance’ of a goal.

The colloquial definition of ‘expected goals’ is an objective version of when someone sees a striker miss an opportunity and goes “he should have scored there.” At this point, the quality of finish is irrelevant, it is about trying to categorise the chances independent of it.

The ‘expected goals’ model, though, one aiming to predict future goals or do so with the least error, would be improved by the post-shot information. It would also be improved by information like the current score, who is home and away, what league the match is in, and so on.

The real problem is we only have one name.

The pre-shot model is useful for universalising opportunities and evaluating the sort of situations teams or players are getting and creating independent of their finishing, which we know varies hugely.

The post-shot model is one more useful for simulating matches that have already happened, assessing likely match outcomes, and perhaps in looking into shot-quality.

Going forwards, it is impossible to propose unanimity in the models used – information varies depending on the data source, as does methodology. But we can loosely agree on the terminology that we use.

I propose clarifying ‘chance quality‘ for models that don’t include post-shot information. I would personally argue that these shouldn’t include game state (the score at the time) or league effects either, but that may be a matter for personal taste.

Then, ‘expected goals‘ can be a more self-explanatory term (unless you are Andy Gray) for other models: an attempt to predict goals as accurately as possible with all of the information available.

It may seem tedious, but slightly altering the terminology is helpful because it a) ties into football lingo, where ‘chance’ is a noun that means opportunity, and b) helps alleviate constant confusion about the inputs of a model. There may also be more efficient ways to do this, and so I’d be interested to hear thoughts from others.

Maybe we should give it a….chance?

Announcement 01/07/2016



It is with great sadness that Analytics FC announce the departure of Tom Worville, Ben Torvaney, and Sam Gregory. We wish Ben, Tom and Sam all the best for the future. Analytics FC started as a blog and has now built itself into a legitimate business, and it is one that will continue to grow from strength to strength.

The company has dual aims: to inspire and promote the use of analytics in football through its media arm, and to provide these services professionally on its consultancy side. Analytics FC has been busy behind the scenes preparing new and innovative products, blog posts and articles. With this in mind we are delighted to announce an official partnership between Analytics FC and Stratagem Technologies Ltd, whose StrataData we will use to continue providing regular and insightful content.

The podcast, too, will continue with some exciting guests lined up. Sam and Tom have graciously passed the metaphorical microphone on to our Director of Analysis, Bobby Gardiner, and we will endeavour to build upon the fantastic platform that they have created.

Last of all, we are pleased to be able to announce Jeremy Steele as the Company’s new Managing Director. Jeremy brings with him a wealth of experience within the Football Industry including coaching and scouting roles at Chelsea, West Ham, Stoke City, Portsmouth and Brentford as well as working in Football Consultancy with industry leaders such as Double PASS Ltd who provide specialist Club and Talent Development services to the Premier League, Bundesliga, MLS and J-League amongst others.

In short, the future is bright for football analytics, and Analytics FC wants to be at the heart of that.


Analytics FC is a football analytics consultancy, blog and podcast aiming to deliver advice and solutions to clubs, federations and agencies as well as media outlets. We tailor services specifically to their needs and objectives to help them find a competitive edge. We provide clients with pure data analysis, data visualisations, recruitment services, tactical analysis, opposition analysis and other bespoke services to help drive success through exploiting inefficiencies and guiding better strategic decision making.

Contact us for information on how Analytics FC can help you –

Help Scout Players at the Euros!

I was spending some time perusing squad lists for the Euros yesterday. What struck me was how many players I knew nothing or next to nothing about. There are a lot of downsides about the twenty-four team format, fewer competitive games, less compelling group stage matches etc. but one of the positives will be an opportunity to discover more new players than we have in the past.

There are some good arguments that teams shouldn’t spend much time scouting players during the Euros: the sample size is ridiculously small, the level of competition is varied and players are playing with unfamiliar teammates. But whatever arguments there are to be made against scouting at the Euros teams are going to be doing it anyways so I think it is worth asking how to do it well. What should we look for from players in a small sample size playing on such a big stage? How do teams avoid becoming the summer’s scapegoat for signing a player who never lives up to his tournament performance?

I floated the question on twitter and got some interesting responses one thing they all had in common was that they were non-data based methods of evaluating players (or at least not strictly data-based). Do they look comfortable? Do they handle the pressure of playing on the big stage well? Do they adapt well to playing with unfamiliar teammates?

So I’ve decided to use this as a tool or a trial run to see how well this type of scouting at a major tournament works. I’ve chosen five young attacking players who I’ve done some very preliminary statistics based research on (basically just minutes, non-penalty goals and assists). Other than being under 21 and attacking – because let’s be honest watching attacking players is just more fun and tournament football is all about enjoying the summer – the only additional requirement was that these be players I have never watched play before. It could be that as soon as I see them at the Euros I realize immediately they aren’t as good as their numbers might suggest. That is the whole point of this to see what kind of insight we can get into a player purely from watching their performances at the Euros.

On top of watching these players myself I want to make this a public thing if you are watching these games involving any of these players let me know what you think.  Did you learn something about the player watching them play? Do you think you know more about them than you did before the match?

So here are the five players I’ve selected.

215598Shani Tarashaj – Switzerland (Everton)

Tarashaj – 21 years old – has played his whole career at Grasshopers and was bought by Everton this past January, but was immediately loaned back. He had 0.46 Non-penalty goals per 90 minutes this season in Switzerland. He plays as an attacking midfielder and second striker.


Oleksandr Zinchenko – Ukraine (Ufa)

Zinchenko is only 19 years old and plays for Ufa in the Russian Premier League. He is an attacking midfielder and had 0.21 assists per 90 minutes popping in with a couple goals as well. He’s played just over 2000 minutes as a full professional, so it will be interesting to see how he does on the big stage.


Mariusz Stepinski – Poland (Ruch Chorzow)

Stepinski – 21 years old – plays mostly as a striker and scored an impressive 14 non-penalty goals last season (0.47 per 90 minutes) playing in the Polish League. He had a spell at Nuremburg earlier in his career, but was sent back to Poland after only one full season.


Emre Mor – Turkey (FC Nordsjaelland) 

At only 18 years old Liverpool target Mor will be an interesting player to watch in France. He’s on here for his age as much as anything else, he has two goals and two assists in 973 minutes this year in Denmark, his first season as a professional. Marcus Rashford is the only player younger than him at the tournament.


Ante Coric – Croatia (Dinamo Zagreb) 

Coric is 19 years old and has already played 3000 league minutes for a side that has won the Croatia League three years in a row. He averaged 0.48 non-penalty goals and assists per 90 minutes last season playing at a fairly high level. The previous season he had 8 assists for an average of 0.625 per 90 minutes.


The main worry with targeting younger players is that they may get less playing time than others at the tournament, but I think it makes for a more interesting case study. So if you are watching any of these players during the tournament let me know what you think, tweet at me (@gregorydsam) or if you feel so inclined write up a more comprehensive scouting report and see if we can’t learn anything new about these relative “unknowns” playing at the Euros.

Is that the Thierry Henry finishing?

Some players have a trademark goal. For Totti, it was the chip, for Juninho (Pernambucano) it was the vicious and deceptive free kick. But one that’s always stuck in my mind is Thierry Henry’s.

You know the one.



Having seen (and spent far too much of my childhood years trying to emulate) this, I have often wondered whether shots struck with the inverse foot (that is the right foot when on the left of the pitch, and vice versa) are more likely to be scored than others.

Using StrataData, we can investigate this empirically for shots classified as a ‘good’ or ‘great’ chance in the wide areas of the pitch (L*/R*).


When we look at the scoring in these areas, an interesting pattern appears:


We can clearly see that over this period of time, shots taken with the left foot from the right hand side of the pitch were scored at almost twice the rate of those from similar positions elsewhere on the pitch.

Moreover, we can drill further down into the data to a finer level of detail. What we find is that this pattern persists in shots classified by Strata Analysts as ‘Great Chances’ (13 scored from 16!) as well as ‘Good Chances’:


Why on earth is this happening?

We can get a clue when we look at the volume of shots in each of these buckets. It is immediately clear that there are far fewer shots being attempted in these situations on the right than on the left:



Given the small number of shots being taken, it may be tempting to blame it on a small subset of elite players (Robben?) skewing conversion with their finishing skill. However, this goes against what we know about finishing skill. What’s more, only five players took more than a single shot with their left from the right and did not contribute enough to skew the sample (Robben, of course, took the most, scoring three from eight shots).

An alternative explanation for this disparity is the relative rarity of left footed players in the population. Because there are more right footed players (it is estimated that between 87 and 92% of the world are right-handed although this effect is obviously diminished at the highest level of football), it may be more likely that players will cut in from the left onto a favoured right foot to shoot, whereas those on the right are more likely to cross the ball or take it on their right.

Screen Shot 2016-04-20 at 05.09.25

Could it be that right footed players cutting in on the left are more shot happy and therefore take more shots from poor locations (c.f. Coutinho, P)? While we have hopefully accounted for some degree of chance quality in the StrataData definitions (‘good’ and ‘great’ chances), this could be having an effect.

Interestingly, if this is the case, it does not clearly manifest itself in the shots’ location. Almost all the wide shots are taken from the L2 and R2 buckets (see above) and if we isolate shots from these equivalent zones, the conversion does not change by more than 1% in any of the four groups identified above (L/R foot; L/R side of the pitch). It is possible that the shot locations could be skewed within these zones; however, it seems unlikely that they could be driving such a large effect.

In a similar way, the effect we are seeing may well be a result of selection bias. On the right of the pitch, players may tend to use their (often weaker) left foot only when presented with a very good opportunity to do so. This seems like the most satisfying explanation so far; however the effect remains very large and I’m open to other ideas.

A final possible explanation that ought to be considered is that of the definitions. Perhaps chances from the right with the left foot are disproportionately considered poor chances (‘Attempts’), leaving fewer unconverted shots in the ‘Good’ and ‘Great’ chance categories. Again, it seems unlikely that such a large effect should come from a bias like this (it should also be noted that each match of StrataData is verified by two additional analysts).

(02/05/16): Recently Paul Riley investigated an alternative explanation investigating the different patterns of goalkeeper positioning between right and left handed ‘keepers. This is a really nice piece and nicely shows how public work can lead to collaboration and development ideas that otherwise would not be possible:

Expected goals and collapsing the wave

Given the way it is generally calculated (at least publicly), it easy easy to think of expected goals (xG) as an attribute that applies just to shots. When we see an xG map or an interactive, it’s easy to fall into this way of thinking. In reality, it makes more sense to think of every action on the pitch having an expected goal value. In fact, to would probably be more accurate to think about the fact that at any point at which the ball is in play, there is a given probability that a goal is scored within the next n minutes of play.

This is in line with recent work done by Thom Lawrence at Deep xG investigating the average time taken for a shot to be produced at different points in a game.

In this way, when we take an xG value at the time of a shot, we’re really just looking at one dimension of a much larger picture (data show is entirely fictional and used for illustrative purposes):


This has repercussions for how we evaluate chances in a broader sense. For instance, when multiple shots happen in a passage of play, how do we evaluate the chance? Perhaps the simplest answer is to sum the xG values. However, if you do this, you can end up with an expected goals total for a single passage of play that is more than one. Clearly, you cannot score more than one goal in one passage of play.

A frequently proposed alternative, then is to take the total probability of a goal being scored from each of the successive shots. In other words, the probability of the first shot being a goal plus the probability of the second shot being a goal multiplied by the probability of the first shot being missed:


However, when we view expected goals as an attribute of shots, as opposed to a continuous variable that’s constantly fluctuating, I can’t help but feel that this seems an improper way of evaluating a series of connected shots or chances, that is ultimately an imperfect way of thinking about goals and probability in football.


Ones to watch: 3 goalscorers

I have a confession to make. I secretly quite like goalscoring stats. Yes, they are noisy and basic, and yes, expected goals is a better predictive and explainatory metric. However, goals are, for the most part, unambiguous. There is either a goal or there is not. Likewise, they are obviously important events in football matches. As a result goals, and goalscorers, are recorded pretty much everywhere, which makes getting broad data on player goalscoring relatively manageable.

In Nate Silver’s The Signal and the Noise, he talks about following baseball player Dustin Pedroia’s career progression because he was highlighted by Silver’s prediction model PECOTA. A bit like when you start following a team you took over on Football Manager in real life. What follows is a collection of players whose goalscoring numbers are good and who may be ones to keep an eye on in the future.

Spalv-is all you need

Lukas Spalvis is a name you may or may not be familiar with. If I explain that he’s a 21 year old forward playing for AaB in Denmark, that may not realy help either .

However, despite being just 21 years old, Spalvis has already scored a 0.69 non-penalty goal (NPG) season in the league (11.6 90s) 13/14, been injured for a year (cruciate), and bounced back recover to hit 0.97 NPG p90 this season (so far).

I can understand why bigger Premier League clubs would be hesitant to use up a roster spot on Spalvis. However, for teams at the lower end of the league, or for teams in the Championship (maybe a certain team willing to blow £9 million on a striker), I think Spalvis could have something to offer. For teams looking to get ahead, a young prolific forward seems exactly the kind of player with a big potential reward, either in goals or in resale value, that they should be after.

How does €1 million sound? Pretty good?

Unfortunately for some, he’s just agreed a summer transfer to Sporting Lisbon for an initial fee around that price.

Still, he gives you a reason to pretend to watch Danish league football other than FC Mitdjylland. Moreover, when he’s scoring for fun in Portugal, you can proudly exclaim that you knew him first. After all isn’t that what this is all about?

Kram-ming in goals

Next on the list is someone who’s name will likely ring a bell. Andrej Kramaric is a striker signed to Premier League leaders, Leicester City (nope, still weird to say/write/think).

While playing in Croatia, put up some pretty solid, if unspectacular numbers before transferring to HNK Rijeka and hitting the great 0.71 and 1.10 NPG p90 seasons that secured his move to Leicester (and, if you remember, some pretty strong links with the then league leaders, Chelsea (also weird)).

Kramaric’s NPG p90 in the Prva HNL

Perhaps understandably given Leicester’s form, he hasn’t been able to get a game. Instead, he’s moved on loan to Hoffenheim. Given his output in Croatia, I’m keen to see how he fares in the Bundesliga.

As I mentioned last week, I have reservations about how scoring in Croatia translates into bigger European leagues and this is an extra data point.

If that isn’t enough to tempt you into tuning in, in his last game, he scored one goal and also got sent off.

Sabitz too good for Austria

Marcel Sabitzer is on this list for basically one reason. In 2014/15 he scored 19 NP Goals and assisted 16 times in under 26 90s. In case you hadn’t already whipped out a calculator and done the maths, that’s a touch over 1.35 G+A p90. And he’s still only 21.

Granted, this was while playing with some very talented teammates for RB Salzburg. Likewise, his consistent (and still good) numbers before 2014/15 are probably closer to his true level.


Nonetheless, I don’t think this diminishes the achievement. Goals and assists numbers are noisy and subject to variance. However, in order to hit the heights that Sabitzer did in 14/15, even with luck and good teammates (both of which are required, too), you have to be getting minutes for a good team and contributing enough shots of good enough quality for variance to swing the output that high.

Even without the underlying numbers, we can think about this probabilistically. With a bit of quick bit of back-of-an-envelope stats*, we can say that to have a 5% chance of contributing 35 goals in 2322 minutes, you would need to be contributing around 9.05 shots per 90 (assuming a conversion rate of 11.3 goals per shot). For reference, this year Messi is contributing 7.57 shots + shot assists p90 (Squawka).


Even if we drop that down to a 1% chance, you’d still need to be providing around 8.03 attempts on goal per 90.

Looking at it from a different angle, if we were to assume a (very good) shot contribution of around 6.00 p90, you’d need an xG per shot of around 0.172 to have a 5% chance of 35 goals. For a 1% chance, you’d still need around 0.153 xG per shot, an elite level of shot quality.



So to hit these kinds of heights, you need to be putting in very impressive underlying numbers.

This year, Sabitzer is playing in Germany for RB Leipzig, who sit at the top of the second division and are highly likely to get promoted. On the radar of many top clubs (and the IBWM 100) it’ll be exciting to see what comes next for him.

* This makes the assumption that scoring and assisting from shots and key passes can be modelled as a series of Bernoulli trials. This is an imperfect approximation but suitable for the demonstration. You can explore similar ideas using Danny Page‘s expected goals simulator.

Fire Charles Reep: 10 Things We Learned

Fire Joe Morgan was a blog that used to dissect what they considered to be poor mainstream baseball journalism, often using advanced statistics. It was a lot of fun and written by Michael Schur, Alan Yang and Dave King who you might also know as the showrunners of The Office, Parks and Recreation and Master of None. I’ve decide to put an Analytics FC spin on Fire Joe Morgan with a new series called Fire Charles Reep. Hope you enjoy! 

For the first edition of Fire Charles Reep I thought I’d take a look at one of my biggest pet peeves in football writing, the “10 things we learned” pieces. To be fair to the authors of these pieces I imagine they are incredibly difficult to write and I don’t envy being given them as an assignment. That being said let’s go ahead and pick apart some of the more flimsy narratives in the Daily Mail’s most recent weekend recap “Alex Oxlade-Chamberlain might need to move on from Arsenal while Michail Antonio provides West Ham with another right-back option – 10 THINGS WE LEARNED

2. West Ham boss Slaven Bilic discovered another option within his squad with the performance of winger Michail Antonio as a makeshift right back in the 5-1 romp at Blackburn. … Not only did he win more duels (10) and tackles (4) than any other Hammers player, he also found time to get forward and have four shots on target.

Putting aside the fact I’d be hesitant to judge any player’s defensive abilities in a 5-1 win, Antonio did put up quite impressive numbers. Numbers that are incredibly atypical of Antonio’s average performances this season in which he’s put up 0.83 shots on target per 90 minutes and 1.1 successful tackles per 90 minutes. You can decided whether these stats are meaningful or not, but clearly his numbers against Championship side Blackburn certainly aren’t indicative of the type of player he is.

3. James McCarthy’s return from the troublesome groin injury that forced him to miss two months of Everton’s season could be good timing for boss Roberto Martinez. … Everton have won four of the five games since McCarthy returned to the starting side – the same number as in the 13 that he missed.

The last five matches Everton have played in which McCarthy has been in the starting XI: @ Carlisle, vs. Newcastle, @ Stoke City, vs. West Brom and @ Bournemouth.

The previous thirteen: @ Man City, vs. Swansea, @ Chelsea, vs. Dagenham and Redbridge, vs. Man City, vs. Tottenham, vs. Stoke City, @ Newcastle, vs. Leicester, @ Norwich, vs. Crystal Palace, @Middlesbrough, @Bournemouth.

Enough said.

4. Craig Cathcart has been one of the unsung heroes of Watford’s outstanding season and must be close to forcing his way into Northern Ireland’s first-choice defence for Euro 2016. … He can play a bit as well as defend – he gave the ball away only five times in the 90 minutes.

Among the twelve outfield players to have played over 1000 minutes for Watford this season only Britos and Deeney have a worse pass completion rate than Cathcart. Pass completion is a statistic devoid of context and fraught with potential for misuse, but maybe highlighting Cathcart’s few giveaways in an FA Cup game versus a struggling Championship side shouldn’t be extrapolated into “something we’ve learned” when his pass completion rate so clearly shows this isn’t the norm.

6. They say that sometimes you get considered to be a better player by not playing, and that’s certainly true of Tottenham’s Toby Alderweireld. The Belgian defender was kept on the bench as an unused substitute as Spurs went down 1-0 to Crystal Palace. Alderweireld has sat out only two matches against Premier League opposition all season – the other was the Capital One Cup clash with Arsenal – and Tottenham have lost both of them.

First off this is a really weird use of the term Premier League opposition, because the only times he’s sat out against Premier League opposition have been in Cup games. Secondly, against Crystal Palace – according to Michael Caley’s Expected Goal model – Spurs held Palace to only 0.5 xG, and in the League Cup game against Arsenal the Gunners had 1.4 xG. This gives an average of 0.95 xG conceded when Alderweireld is not in the side, which isn’t significantly different from Spurs’ season average of 0.86 xG conceded. It’s also important to note that the 1.4 xG Arsenal generated in that League Cup game versus Tottenham is below their season average of 1.98 xG per game. So without Alderweireld we have: a) a small sample size, b) almost certainly a bias sample and c) no significant difference.

7. Guus Hiddink’s faith in the potential of 20-year-old Burkino Faso starlet Bertrand Traore was borne out as the youngster scored his third goal in three recent appearances as a sub.

I’ve included this just to show not all of these 10 things are necessarily off base because Traore put up some great numbers in the Netherlands and it is nice to finally see him pick up some playing time with Chelsea.

So I’ve highlighted five of the ten “Things We Learned” this week from the Daily Mail. The five I haven’t touched on were either opinions that were impossible to falsify or were more facts than opinions. But on the five I covered the author went a solid 1 for 5.

Fire Charles Reep.

Project Pep: Bayern Munich and Modular Design


If you paid any attention to mobile technology news in the past twelve months, you might have heard of “Project Ara“. Project Ara is a Google run initiative that is aiming to create a modular smartphone, which allows the user to build up a device that is specced up to suit their wants and needs.

The phone consists of a frame to hold the different parts together, with the different parts being “modules”. The modules are the components of the phone, like the screen, battery, processor etc.

Continue reading Project Pep: Bayern Munich and Modular Design

Tube strikes, experimentation and Coquelin

In February 2014, London experienced strikes on their underground network. However, it has been suggested that this enabled around one in 20 commuters to find better routes and actually produced a net economic benefit. In the same way, I believe decision making in football can be too risk-averse and that more should be done to promote voluntary experimentation.

A paper published back in September titled “The Benefits of Forced Experimentation: Striking Evidence from the London Underground Network” analysed the effect that the London tube strikes had on commuter journeys. They suggested that “a significant fraction of commuters in London fails to find their optimal route” due in part to distortions in the tube map. As a result, having tried out new ways to get to and from work, some commuters discovered better routes than the ones they had been using before. However, it required the disruption of the tube strike for these people to experiment.

I think it’s fair to suggest that we can see similar reticence in football. For instance, Gabriel Marcotti suggested on the Analytics FC podcast that Herrera’s absence from Manchester United’s lineup for stretches last year was due to van Gaal’s reluctance to chop and change. The cliché “if it ain’t broke, don’t fix it” comes to mind. But what this study hints at, is that even if it ain’t broke, there may be ways to make it better.

For instance, Coquelin is now considered to be an important part of Arsenal’s squad. Nonetheless, his emergence only came about when Arsene Wenger was forced to experiment during an injury crisis. It is not too uncommon to see young players get given a chance like this and go one to be important players (Bellerin, anyone?). This begs the question: why don’t we see this experimentation happen more?

For one thing, there is a cost associated with experimentation. You might not want your manager to be swapping out key players for under-21 debutants during a title-run in, for example. Nonetheless, I don’t think this fully justifies a lack of experimentation. You have friendlies, cup games and potentially dead-rubbers at the tail end of the season in which to rotate.

Another fact to consider is that managers are judged more on outcome than process. If a manager rotates and loses, they will more often than not be criticised for it, regardless of whether that was a decision that improved the chances of winning at the time. Therefore it is important for clubs, particularly at board level, to help alleviate this pressure where possible in order to reap the rewards of experimentation and creativity in the long run.

Part of this could be simply understanding the benefits of voluntary experimentation rather than waiting for circumstance to force your hand. In the same way, buying into analytics can be considered another beneficial experimentation. Relative to the cost of a transfer, investing in analytics is cheap. However, it can provide huge benefits, whether that means avoiding the Falcao signing, or making sure you’re looking at the right performance indicators when evaluating players.

Other goalscoring bits and pieces

How has no-one signed Jonathan Soriano, yet?

He’s been scoring consistently for years. At some point, it stops being an even remotely analytics-based decision and just becomes common sense: A
At 30, it may be too late for him to get a chance truly shine at the top level, which would be a great shame.

Is Ángelo Henríquez good, after all?

It seems fair to say his time at Manchester United didn’t quite go as planned, but he’s been solid at Dynamo Zagreb.

Last season he hit 0.96 Non-Penalty Goals per 90 in the league and he’s currently hitting 0.50 this season. These are not necessarilu world-beating numbers given the league he’s playing in, but they’re certainly enough to mark him out from the rest.

There is the question of how this skill transfers to other leagues (Duje Cop, for instance has not matched his impressive Zagreb output from last season at new club Malaga) but could he be worth another look?

Blasts(ish) from the past(ish)

  • Former future talent Valeri Bojinov is contributing 1.03 NPG+A p90 at Partizan Belgrade. I’m not sure whether this makes me happy or sad. Maybe both.
  • Samuel Eto’o continues to exist and is scoring 0.48 NP Goals p90 for Antalyaspor while assisting another 0.21 p90.
  • Robin van Persie is scoring 0.73 NPGp90 despite earlier posing as one of the most miserable people in existence. Knees are temporary; class is permanent.

Four-Four-Two with Etienne Capoue

From relegation favourites to the Premier League’s 4-4-2 darlings Watford have had quite an eventful first half of the 2015-16 season.  It’s not necessarily about them being an incredible team or even a team nailed on for a top half finish – Mohamed Mohamed actually looked at how their numbers have been just about perfectly average in the Premier League – it’s about how much fun they’ve been to follow.

In an era where playing with two strikers is seen as arcane and the 4-4-2 a throwback to English football at it’s most unimaginative Watford have managed to both pick up results and entertain while playing a fairly standard 4-4-2. Bobby Gardiner wrote about how we should approach formational choices with a degree of tactical relativism, and that idea that one formation isn’t a priori better than another. A much more important question to ask is how a formation is being used.

So what makes Watford’s 4-4-2 work?

Continue reading Four-Four-Two with Etienne Capoue