The Ballad of Joey Barton

Even in the Championship, Joey Barton is still saying obnoxious things, so I thought I’d take a look to see what he would do if he was back in the EPL. Similar to last week’s “Zlindex”, I calculated the expected change in points if each EPL team signed Joey Barton. It’s not pretty, and explains why he’s not playing first division football anywhere.

Ballad of Joey Barton

Joey Barton would make all 20 Premier League teams worse. Most remarkable is how badly he would hurt Norwich City, a team that isn’t expected to earn that many points to begin with. The raw calculations predict he’d take them from 45 points down to 32 points, or down from 15th to 19th place (Barton would cost Villa 6 points, but they’re only starting at 36 so they’d be down to 30).

Surprisingly he wouldn’t hurt Arsenal that badly, and even more surprisingly after Jonjo Shelvey’s performance today against Manchester United he would only cost them about a point.  The average loss for an EPL team who signed Joey Barton would be 3.6 points, meaning he’d cost you a little more than one win a season.

So with the deadline fast approaching, West Ham can be glad they dodged that rumored bullet (but it’s still not too late to sign Zlatan).

Everton: Don’t Fire Roberto Martinez

Another big story this season is Everton’s poor start to the season (5 points in 4 games) and early reports are that Roberto Martinez might be the first manager on the chopping block. But are they really underachieving?

Deviation 0830

This week’s deviation chart shows that they’re actually slightly over-achieving compared to expectations. They’ve earned 5 points, but their expected value at this point is only 4.24, meaning they’re about a point over where they should be at this time. This means two things:

  1. Everton  isn’t as good as their fifth place finish two years ago.
  2. They’re doing slightly better than they should be, and if you’re going to draw any conclusion from that it would be that Roberto Martinez has gotten slightly more out of the team than he should have been able to.1

I’m sure Roberto Martinez is proud of the early season results2, and my analysis shows that he has at least some right to be. Everton needs to give him more time because they’re performing as they should with the players they have.

  1. As an American, another explanation would be that Tim Howard’s heroics against Spurs earned them a point they wouldn’t have otherwise earned. Goalkeepers aren’t in my model unfortunately, so that’s a valid alternative hypothesis
  2. ESPNFC Podcast joke for Dan Mason fans

EPL Expected Final Table – What is it?

Every week after the EPL games are completed, I post a couple of graphics – the Expected Final Table and each teams’ Deviation from Expected Points. I realized I’ve never really explained how I do this and why they’re important, so I wanted to take a couple of minutes to post what they are.

At the beginning of the season, I calculated the probability for each outcome for each game.1 So each team has an expected probability of winning, expected probability of losing, and expected probability of a draw.  I calculate the expected points through a simple formula:

3(Prwin) + 1(Prdraw) + 0(Prloss)

 Fairly straightforward  – three points for a win, so each team is expected to earn 3 * probability of a win. 1 point for a draw, so each team is expected to earn 1*probability of a draw, and 0 for a loss. Adding up this number for a single team across all 38 games gives me their expected points total for the season, and doing this across all 20 EPL teams gives me the final table.

The next step in the process is, after each game completes, to replace the expected point total for that game with the actual point total. Then I recalculate the expected points by adding the actual points earned for the games completed and the expected points for the remaining games, re-doing the final table with those values. Here is the most recent table:

Final Table August 30

I like this measure for one major reason – it controls for both strength of schedule so far and remaining strength of schedule. Before each team has played all the other teams, there can be huge discrepancies in how many points they’re expected to have earned, but the regular table doesn’t show that, it only shows how many points they’ve earned. As we get further into the season, this can become more important, especially in the title and relegation fights because we’ll know exactly how each team is expected to do in their remaining games without having to do weird mental gymnastics like “Well Norwich City has games against Chelsea and Spurs left, while Bournemouth is 2 points behind but has West Brom and Sunderland.” This does the all that mental math for us “behind the scenes” so we don’t have to guesstimate whether Bournemouth will catch up to Norwich in this hypothetical situation.

It also lets us avoid “streak story-telling” where we overextrapolate from a few early results. Right now one of the big stories is that Chelsea is struggling – they’re currently in 13th place and everyone is worrying that the sky is falling. We all know they’re not going to finish there, but we don’t know what this early-season slump has done to their chances just by looking at the table. This measure lets us see exactly what we’d expect to happen by the end of the season and what would have to happen for them to catch up.

The next is the deviation from expectation. As an example, week 1 Arsenal was expected to earn 82 points2. They were big favorites  week 1  home against West Ham, expected to earn 2.63 points. They lost, meaning they earned 0 points. So their expected points at the end of the season went from 82 to 79.373 with a deviation of -2.63 points.

This measure does a couple of things: first, it lets me diagnose how well the model is doing so far4. Second, it lets us see whether teams are exceeding, meeting, or falling below expectations. We expected Chelsea to perform at a much higher level than they have so far, and this model can quantify exactly how poor they’ve been compared to expectations. Similarly, we know Leicester City has been exceeding expectations, but this lets us quantify that. And despite a relatively slow start from Everton, they’re actually performing almost even with what we’d expect. Finally, we can see that Manchester City’s strong start has them performing significantly above expectations, and we can likely expect a little slump at some point as they regress to the mean.

Here is this week’s chart for an example:

Deviation 0830

Hopefully this explained the method a little more clearly for anyone who is interested – check my twitter (@Soccermetric) or this webpage for weekly updates as the season progresses.

  1. The method is available at
  2. I don’t remember exactly how many points it was, but this is close enough for demonstration purposes.
  3. 82-2.63=79.37
  4. Right now the expected value correlates at 0.48, which isn’t bad this early in the season but I’d like to see higher. I’ll write a blog post about proper hypothesis testing later because I think that’s important for the analytics community

Kevin de Bruyne Would Upset City’s Balance

I wrote the other day about the importance of balance, and I think Manchester City signing Kevin de Bruyne is a great example of this concept. De Bruyne is a great player who seems to have a huge upside and no one seems to balk at his huge price tag (and the huge profit they’re sending to Wolfsburg after just one year). My prediction model doesn’t like him – making him a couple point decrease in expected value over both Navas and Nasri.

Right Wing Options City

I’m in the minority, but I think a big part of the problem is that Navas and Nasri are better creators, stay out on the right wing, and most importantly stay out of David Silva’s (and to a lesser extent, Yaya Toure when he comes forward) way. They give City needed width, and create more than they shoot. They give City the balance they need and fit the system, and I think my model is picking up on that in a lot of ways. He’s a couple point loss so it’s a small shift in any given game, but over the course of the season, if City wants to compete for the title, those points could be huge.

Spurs Have Done Great Transfer Business

I was looking at Spurs’ signings this fall, and individually they’re a mixed bag (N’Jie is both good cover for Kane and decent replacement for Chadli, Son is decent cover but I’m less than impressed by him). Together though, with a little shift in philosophy to a 4-3-3 with Eriksen moving deeper they could make a serious difference in Spurs’ fortunes (roughly a 5 point gain). Not bad at all for Spurs, and the rumors are they might still be in the market for another striker in Berahino.

Spurs Strikers

EPL Week 4 Predictions

Here are my week 4 predictions: the model’s pretty confident in most of the outcomes this week which means it could either be a strong week or a disastrous one if we see a lot of upsets. A lot of home teams in the top 6 are heavily favored, although the model thinks Swansea has a really good chance at some points vs. Manchester United. Will be interesting to see what happens.

EPL Week 4 Predictions

Zlatan Ibrahimovic and the Importance of Balance: The Zlindex~!

With my transfer evaluator finished, and no real interesting transfer rumors the last few days, I wanted to play with the algorithm and see how many points each EPL team would gain by signing Zlatan Ibrahimovic as a replacement for their main striker. Borrowing from Dirty Tackle‘s love of Zlatan, I named it the Zlindex~!

I applied my SVM to each of the 20 EPL teams, figuring how many points they would be expected to earn over the EPL season. Then I removed their main striker’s stats 1 and substituted  Zlatan’s statistics. I added his stats into the team stats, and re-calculated the results, Finally, I subtracted the expected points from the regular striker from the expected points if Zlatan was the team’s striker to calculate his added value.

Expected Points(Zlatan) – Expected Points(Regular Striker)

The unsurprising news is that Zlatan would improve 16/20 EPL teams, and would improve West Ham by somewhere around 7-8 points (enough to let them challenge for a Europa League spot according to my predictions). He’d also be a fairly significant upgrade for most of the top 6 teams.2 The full table is in the figure below.



However, as Brooks Peck pointed out, it doesn’t make sense that he doesn’t improve the teams at the bottom.3 My first hypothesis is that these are teams that would suffer if they had too many karate kicks and ponytail related assaults, but the model doesn’t account for those so I’m probably wrong there. I did some quick exploration of the data, and found a consistent pattern for three of the four teams (Man City, Norwich City, and Crystal Palace). He doesn’t tackle as much as their current striker, he takes too many shots outside the area and too few inside the area.4

Zlatan Comparison


The two that stood out to me the most were “tackles” and “Shots inside the area”, and those are the two that seem to correlate most highly with points lost. Interestingly, this also fits what I see as a bigger pattern for Zlatan, having watched him a lot when he was with Milan5: he’s often lazy and uninterested on defense, and takes a lot of odd shots outside the area. To his credit, he can make those long distance shots work as well as anyone, but most teams prefer their striker operating a little closer to goal.

Newcastle United still remains a mystery to me – looking at the data for Papiss Demba Cisse, the big area where Zlatan differs is in passing: he passes the ball quite a lot more than most of the strikers in this list, and that may make the model think negatively of him. He might look more like a #10 than a #9, which fits the deep-lying forward style he was used in at Milan (holding up the ball, transitioning from defense to attack). This may be a latent variable the numbers are measuring, and that his style doesn’t fit the few teams he wouldn’t improve.

The important lesson here I think is balance: not every player’s style improves every team. Zlatan is one of the best pure strikers on the planet, but he’s a tall, strong, physical striker who can wear down defenders as good as anyone out there. This doesn’t necessarily fit with what teams are looking for, and even some mid-table teams wouldn’t benefit from his addition to the squad.6

  1. In cases where teams play with two strikers, I picked one at random
  2. Re: Chelsea, he’s basically breakeven with Costa, but is a big upgrade over Pedro
  3. Brooks pointed out that it’s not necessarily surprising Zlatan is a downgrade over Sergio Aguero for Man City, and I’d agree. I’ve been a fan of his since before he was at City because he won several Golden Boots for me in an FM2012 save
  4. The method here is fairly simple: I took the team’s current striker’s stats and subtracted Zlatan’s stats to see the difference between the two
  5. Forza Milan~!
  6. Someone mentioned on Twitter that teams can change styles based on new players, which is a real possibility the model can’t account for, but that leads to other issues in terms of team chemistry and whatnot so I’m not too concerned about that honestly

ELO Challenge Week 2 (And Update from Week 1)

I tweeted about how the soccer analytics community doesn’t seem to spend enough time on diagnostics, so I’m doing an ongoing series of posts comparing my model to one of the more common (and more elegant) models out there: Club ELO. For more info on the measure and what I’m working on, check my opening post on the ELO challenge:

First, some overall diagnostics: Week 1, the actual outcome was the most likely outcome 6.5/10 times (one had a tie between two choices so I give the SVM half credit there), week 2 I had 4/10 matches. Overall, the model’s better than 50% which is pretty strong in a complex model like a soccer game.

Time for this week’s update, and ELO won week 2 by about the same margin my SVM won week 1. It also won 6/10, while the SVM won 4/10. Last week, this week, and overall are in the graph below.

Club ELO Week 2

As you can see, we’re basically tied. The SVM won week 1, ELO won week 2, and overall we’re about even (a 0.26 point game means ELO is, on average, 1.3% better than the SVM). It’s really interesting how two very different measures come to such similar conclusions, both on many of the individual games and in the aggregate.

The SVM had a couple of REALLY bad misses this week: Everton and Leicester City both won in what my model considered significant upsets. I tweeted about my qualitative asssessment that Everton should have been higher probability this week, but Leicester could go either way. The SVM really likes their overall odds for the season (picking them to compete for a Europa League place), but it didn’t like the odds against West Ham for some reason. I’ll do some diagnostics at some point, but this week’s going to be about putting together a new interactive tool and getting Serie A data up (as well as class prep because school starts next week for me), but I definitely want to see why the model missed so badly on Everton and Leicester. My best guess is that the game stats diverged from the actual stats, but I have no way of knowing until I check. So far I’m happy that I’m doing about as well as the dominant model.

EPL Transfer News for August 17: Manchester Edition and the Value of John Stones

Today’s big news seems to be that Pedro to Manchester United is official, and that they’re still in for Thomas Muller as well, while Manchester City should be announcing the signing of Nicolas Otamendi today. The SVM seems to think that Pedro will be good cover in United’s attacking 4 (a ~1 point loss), while it likes Thomas Muller’s potential a little less (~4 points down).

The other news is disappointing to me for a couple of reasons. Nicolas Otamendi anchored my Mantova back line for a few seasons in FM 2014, and while he was always a little disappointing I became a big fan. The SVM seems to agree with Football Manager – Otamendi replacing Mangala is the biggest gain or loss I’ve seen so far in a player, with a ~14 point drop. That seems really high to me, but after watching Kompany and Mangala against Chelsea yesterday I’m less excited about a replacement. I’m also a big City supported, and surely Otamendi isn’t the long-term replacement for Kompany when he retires. Take a look at the graph to see the effects of the three players on their respective teams, but Chelsea should consider themselves lucky to have missed out on Otamendi (and to have bought Zouma last year who I’ve been a fan of for a couple of years).

Transfer Rumors 0817

My big point from today’s transfer round-up is that when you get up into the level that Man United, Man City, Chelsea, Arsenal, etc. are, there aren’t a lot of players that can significantly improve the team, especially given how even good players can upset the balance of the stats in a team. You see this a lot with Barcelona – they don’t buy a ton of players, but when they do, they buy elite players who are an improvement over the already elite team they have (Suarez and Neymar over Pedro and David Villa for example, or ter Stegen over Victor Valdes). The EPL has so much money that they panic/hype buy, and the teams are often worse off because of it.

The one guy we’ve been hearing rumblings about is John Stones from Everton. He’s 21 years old, so he has plenty of room to grow (especially if he’s mentored by a veteran like Vincent Kompany or John Terry), and had a world class season last year. I know Everton isn’t looking to sell him, but you think they could be convinced to sell for the prices we’re hearing for Otamendi (Otamendi’s listed at $25 million, Stones is only at $13 million). He’s a small improvement for City, Chelsea, and Arsenal, and is a significant (~4 points) for United.

John Stones Transfer Value 0817

The moral of the story is to improve where you’re weakest (instead of buying all the attackers), and be more selective in your buys. Too many EPL teams do the opposite, and it’s one reason why they’re struggling in Europe despite having an embarrassment of riches.




Transfer Roundup 8/14

There isn’t anything too earth-shattering in today’s transfer roundup – the biggest news was Leicester City making a move for Napoli’s Gokhan Inler, but my model thinks that’s a slight net loss for Leicester City. The other news was out of Sunderland with rumors of Man United’s Adnan Januzaj and former striker Fabio Borini potentially moving back to the Stadium of Light. Borini would be a decent upgrade, while Januzaj would basically be break-even this year (obviously a potential signing for the future).

Check back later for Transfer Rumors – Manchester Rivals edition~!

Transfer Rumors 0814