Time to Tiki-Taka

Speaking to Bild this week, Giovanni Trapattoni is quoted criticising Pep’s style at Bayern Munich:

For me there’s too much possession. Tick, tack, tick, tack. Tuck, tuck, tuck. To and fro. With too little return. And after 27 minutes they shoot on the goal for the first time – that’s inefficient.

Tobi (@redrobbery on Twitter) posted some passes-per-shot numbers dispelling this myth, showing that FCB are actually pretty decent at getting regular shots on target:

I thought I’d add to this with some time-to-shot-for numbers because they are even more pronounced. Here’s the median TTSF for each team’s passing in the Champions League, that is, the average number of seconds it takes between a pass and a shot:

Real Madrid 119
Bayer 04 Leverkusen 146
FC Bayern München 148
Atlético de Madrid 150
Juventus 167
Lyon 173
Galatasaray 174
Roma 175
Manchester City 181
Barcelona 188
Paris Saint-Germain 192
Olympiakos 193
Manchester United 199
VfL Wolfsburg 199
Benfica 201
FC Astana 210
Chelsea 220
Arsenal 222
FC Porto 223
Sevilla 224
Valencia CF 227
Borussia Mönchengladbach 237
Dinamo Zagreb 238
Shakhtar Donetsk 238
Dynamo Kyiv 253
KAA Gent 261
Zenit St Petersburg 267
CSKA Moscow 277
Maccabi Tel Aviv 284
PSV 315
Malmö FF 399
BATE Borisov 510

Borisov messing with my colour scheme there, but still – there you go: 2 minutes, 28 seconds, a mere factor of 10 out for Trapattoni. It’s a pretty reliable number for Bayern too, if you look here at the distributions of each team’s TTSF values for passes:


Even at their absolute slowest, Pep’s team have only been about 750 seconds from tick, to tack, to shot.

Time to Tiki-Taka

Idle Hands

“He’s had nothing to do all game,” we hear, every single week on Match of the Day, as if we’ve just cut to images of Hugo Lloris in a deck chair with a dog-eared copy of War and Peace, startled as a striker thunders by spilling his mojito.

Do keepers really switch off when they’ve had nothing to do? I thought it would be simple enough to check, so I looked at all the shots I have on record in terms of my save difficulty metric.


By working out the time between every shot on target faced and the previous goalkeeper event (be it another save, or a goal kick or whatever to wake the keeper out of their trance), you have the number of seconds the keeper has been idle before that shot. I limited the data to shots from open play, as you won’t have the element of surprise from dead-ball situations, and reset the clock at half time, so the maximum time a keeper can be idle is a little north of 45 * 60 = 2700 seconds.

Then to measure keeper over- or under-performance, you can work out the saves above expected for that shot: if a shot has a save difficulty of 70%, we expect a statistically average keeper to only save it 30% of the time. So if they do save it, we’ll score that as 0.7 saves above expected – they got 1 whole save, we expected 0.3 saves (which obviously isn’t actually possible on a single shot, but you get the picture), so they got a profit of 0.7. If they don’t save it, they got a big fat zero saves, and we score it as -0.3.

So, we know for every shot whether the keeper over or under performed when attempting a save (to the extent you believe the outputs of an expected saves model, obviously), and we know how long they’ve been idle. Is there any interesting correlation here? Do higher numbers for idleness result in saves under the expected value?


There is no overall correlation between idleness and shot stopping. I looked at the measure above, along with raw save percentage, with saves grouped into buckets by various lengths of idleness. The chart below shows the save percentage as the green area, and the saves above expected as the line.


This shows basically nothing – the saves above expected values are tiny, and dwarfed by the error of any particular xG model you choose to use. You can also safely ignore the big jump towards the end of the half – the sample size is miniscule. So, keepers can rest easy against their goalposts?

On a hunch I filtered the data down to what Opta deem as ‘fast breaks’. If you’re going to catch an idle keeper off guard, maybe you just need to be quick about it. It’s a smallish dataset (just over 4000 shots) but behold this trend:


So there you go, have we found something? By the time we’re in that 1200-1499 second bucket, we’re talking 117 shots, with 72 in the next bucket, so again, small sample. I’ve also chosen the bucket size fairly arbitrarily – at 150 seconds per bucket, things are far more chaotic, and we should be wary of Simpson’s paradox when aggregating data. But it does seem to be a hint that maybe something’s going on. There’s at least a 10 percentage point drop in save percentage as idle time increases, and keepers are also saving fewer shots than we expect, which should account for any shot quality issues above and beyond raw save percentage.

Are we sure we have the right cause though? I checked if it was just that teams create better quality chances later into a half (encouraging teams on to them for the first half hour to create counter attacks, or probing and finding weaknesses, I dunno) but saw no real differences per minutes of the half. Then I thought that perhaps it’s nothing to do with keepers at all, maybe defences are the problem. So I created this chart – it shows the same save percentage area as above, but instead of saves over/under expected, I just put the average chance quality and the average save difficulty. This tells us how good the oppositions chances were, and how hard they were to save, regardless of how the keeper dealt with them.

fast-break-idle-xgThe important thing to note here is that my chance quality model includes almost nothing about the actual shot as taken by a striker – it’s mostly about the position of the shot, and the buildup to it. For that metric to be going up (again only slightly, and again with a small sample size) it’s entirely possible that the fault doesn’t only lie with idle keepers, but with idle defences too, for allowing better chances. It’s also possible that the under-performance of keepers in terms of expected saves (to the extent we believe it exists) is because we have no measure for defensive pressure.

So what do we know? If there is a decline in performance due to idleness, it’s small, hard to prove with confidence, and may in fact be due to defences and not keepers. Not very convincing, I’m sure you’ll agree, but I was recently reminded how important it was to publish low-significance and null results along with everything else (if only to ease the pressure on the wasteland that is my drafts folder). I also googled around a bit and found nothing mentioning this, so I thought it would be good to get it out there for posterity. At the very least, every time you hear the old cliché in commentary, you’ll know there’s probably little reason to worry that keepers who have been idle will suddenly forget to stop shots.


A few notes and avenues for future work if you’re bothered:

  • By all means replicate this any way you like, it’s simple enough even if you have public shot data derived from the StatsZone app or BBC live text commentary. I’d be fascinated to hear if you find any patterns I’ve missed.
  • I’ve not looked at individual keepers – it’s possible there are some particular keepers that switch off, although I doubt it, and it’ll be a small sample size.
  • I didn’t include periods of extra time, just because I wanted to make sure that we were always comparing apple-shaped things.
  • I wasn’t strictly measuring idleness as time between saves, I was assuming that a catch or a goal kick was enough to wake a keeper up, but perhaps that’s an assumption to test.
  • I’m only looking at shot stopping, so I can’t rule out that idle keepers underperform on interceptions or catches in some way.
  • There are other measures one could use for fast breaks, or indeed counters, that may increase the sample size.
Idle Hands

The Case of the Missing Throughball, and Other Mysteries

Ben Torvaney noted last night that the number of throughballs per game looks like it’s been going down. It’s a pretty pronounced trend:

Count Completed Completion %
English Premier League 4450 1717 39%
2012 1655 596 36%
2013 1286 496 39%
2014 1132 460 41%
2015 377 165 44%
French Ligue 1 3738 1534 41%
2012 1605 593 37%
2013 942 378 40%
2014 825 411 50%
2015 366 152 42%
German Bundesliga 2333 1309 56%
2012 1115 550 49%
2013 715 432 60%
2014 401 254 63%
2015 102 73 72%
Italian Serie A 4985 2114 42%
2012 2789 1123 40%
2013 971 478 49%
2014 943 400 42%
2015 282 113 40%
Spanish La Liga 5601 2010 36%
2012 2146 745 35%
2013 1478 549 37%
2014 1559 557 36%
2015 418 159 38%
UEFA Champions League 1913 801 42%
2012 713 270 38%
2013 458 203 44%
2014 479 211 44%
2015 263 117 44%

If you were to take this at face value, it would be a hugely significant result: throughballs create high quality chances, and in the space of three or four years, defences appear to have discovered how to suppress them.

That’s obviously possible, but I strongly suspect that this is an issue with the way the data is being created. This is probably one of those things that you’re not supposed to talk about, and I don’t want to bite the hand that feeds this blog, so I hope the powers that be will consider this a good-faith bug report, and not the whining of an uppity lamprey complaining about the quality of the scraps it feeds off. Either way,  I caution you to look at any conclusions you make about a team or player’s output based on their number of throughballs over the last few years.

Just so we’re all on the same page, here’s the official definition of a throughball, which by all accounts has remained constant:

A throughball is a pass event which splits the defensive line, creating an attacking opportunity.

It’s difficult for us to confirm one way or another that a pass ‘splits the defensive line’ without watching every game. One thing we can notice from the table above is that conversion rates in some leagues seem to be going up. Perhaps that’s our first clue – is the ‘creating an attacking opportunity’ part being more strictly enforced? Perhaps failed throughballs are less likely to be throughballs.

Another clue is if you look at the next match event after a pass tagged as a throughball:

Season Clearance Interception Keeper Pass Shot
2012 10.13% 14.64% 22.58% 23.06% 13.81%
2013 7.79% 12.37% 26.83% 19.76% 17.25%
2014 8.71% 12.72% 27.17% 18.05% 18.20%
2015 6.80% 13.77% 27.27% 17.87% 20.13%

I’ve included only the types of events that seem to show a change. There are some interesting trends here:

  • Clearances have dropped as a proportion of next events. This backs up the theory that unsuccessful throughballs aren’t as likely to be tagged as such.
  • That said, interceptions have remained steady as a next event, however.
  • Balls that make it through to the keeper have increased somewhat as a proportion, up 5 percentage points from 2012-2014.
  • Throughballs that then set up a another pass have seen a big decline. Perhaps the interpretation of ‘creating an attacking opportunity’ doesn’t cover moves that aren’t as direct.
  • Shots have seen the biggest proportional rise from 2012-2014, which backs up the previous statement – the definition of throughballs seems to be increasingly focused on direct attacks.

There are possible footballing explanations for each of these trends. Maybe the Manuel Neuer effect has taken hold on goalkeeping across the leagues, keepers are pushing up and claiming the ball more, and that explains the increase in keeper touches after throughballs, for example. But overall, taking the absolute numbers, and examining some of the wider context, I’m suspicious.

If anyone can shed any light on the numbers, or has a genuinely persuasive argument that tactics have changed over the last few years, I’m all ears.

The Case of the Missing Throughball, and Other Mysteries

Vardy’s Scoring Likelihood

Two articles this morning looked at Jamie Vardy’s excellent start to the season to see if it might be sustainable. Adam Bate at SkySports concludes:

Vardy’s start to the season may have been extraordinary but the evidence is overwhelming — it has not been a fluke. Don’t be too shocked if the goals continue to go in.

Mark Thompson is slightly cooler on Vardy, writing at EastBridge:

Is Vardy genuinely good then? Kinda, but not as good as his current total suggests. If I were a betting man, I might even look up the odds of him scoring fewer than 15 in the league this season.

Building on my chance quality and save difficulty models from yesterday, I would take the over on 15 goals, but I do agree Vardy is over-performing. To analyse this, I’ve taken each player in the Premier League with more than 20 non-penalty shots, and simulated their likelihood to score at least their current number of goals given:

  1. They total shot number and their average chance quality.
  2. The number of shots on target and their average save difficulty.

This should show if they’re doing better or worse than you would expect for the chances coming their way, and also whether they’re getting lucky or unlucky with their actual shots on goal. Looking at it this way, there is only one striker luckier than Vardy this year:

Player Shots On Target Goals SoTR Conv % Scoring % SD CQ Likelihood (SD) Likelihood (CQ) Expected (SD) Expected (CQ)
Graziano Pellè 38 11 5 29% 13% 45% 43% 13% 54% 53% 5 5
Sergio Agüero 33 14 6 42% 18% 43% 42% 12% 56% 22% 6 4
Olivier Giroud 23 12 4 52% 17% 33% 39% 14% 76% 43% 5 3
Alexis Sánchez 45 15 6 33% 13% 40% 37% 12% 49% 47% 5 5
Harry Kane 33 12 2 36% 6% 17% 34% 9% 95% 83% 4 3
Jamie Vardy 36 15 7 42% 19% 47% 35% 12% 24% 15% 5 4
Romelu Lukaku 28 12 5 43% 18% 42% 34% 13% 39% 27% 4 4
Diafra Sakho 29 10 3 34% 10% 30% 34% 13% 72% 76% 3 4
Sadio Mané 26 10 2 38% 8% 20% 30% 9% 86% 72% 3 2
Bafétimbi Gomis 22 10 3 45% 14% 30% 32% 11% 66% 46% 3 3
Ross Barkley 28 9 2 32% 7% 22% 25% 5% 69% 44% 2 1
Odion Ighalo 26 9 5 35% 19% 56% 28% 10% 7% 10% 2 2
Theo Walcott 26 12 2 46% 8% 17% 33% 15% 94% 91% 4 4
Rudy Gestede 22 7 3 32% 14% 43% 25% 11% 25% 44% 2 2
Memphis Depay 25 8 1 32% 4% 13% 22% 8% 87% 88% 2 2
Riyad Mahrez 23 9 3 39% 13% 33% 21% 8% 30% 27% 2 2
Aaron Ramsey 27 8 1 30% 4% 13% 23% 10% 88% 94% 2 3
Philippe Coutinho 39 11 1 28% 3% 9% 20% 7% 92% 95% 2 3
Yaya Touré 26 8 1 31% 4% 13% 21% 9% 85% 92% 2 2
Jonjo Shelvey 21 7 0 33% 0% 0% 9% 5% 100% 100% 1 1
Troy Deeney 23 4 0 17% 0% 0% 9% 8% 100% 100% 0 2

Vardy’s 24% chance of 7 goals from his shots shows he’s probably getting lucky (only Odion Ighalo at Watford is luckier, and by quite a margin). But his most likely haul is still somewhere between 4-5 goals, which isn’t bad at all. The question of whether he and more importantly whether Leicester can continue their scoring shenanigans remains to be seen – if they suddenly learn how to defend, are we still going to see these weird Vardy-inspired fightbacks?

Vardy’s Scoring Likelihood

The 100% Club

Newcastle’s Georginio Wijnaldum-inspired thrashing of Norwich at the weekend surfaced this tidbit:

6 – Newcastle United scored with all six of their shots on target. Unremitting.

— OptaJoe (@OptaJoe) October 18, 2015

Who are the other members of this 100% on-target conversion club (big-5 leagues, 2012 onwards)?

Date Home Away Shots On Target Penalty Goals Goals
2013‑09‑01 VfB Stuttgart 1899 Hoffenheim 13 6 0 6
2014‑10‑18 FC Bayern München SV Werder Bremen 10 6 1 6
2015‑10‑18 Newcastle United Norwich City 11 6 0 6
2013‑03‑30 Fortuna Düsseldorf Bayer 04 Leverkusen 15 5 1 5
2013‑04‑20 Hannover 96 FC Bayern München 14 5 0 5
2013‑08‑25 Atlético de Madrid Rayo Vallecano 14 5 0 5
2014‑09‑21 Leicester City Manchester United 15 5 2 5

And a whole bunch on 4 or fewer. Something of a Bundesliga speciality it appears, with Newcastle part of an exclusive club to do it without penalties. I had a really good Grexit/currency conversion/conversion rate joke, so I nearly included Costa Rica vs Greece from the last World Cup, as Costa Rica converted one normal time goal and all their penalties to join the 6/6 club, but that felt like cheating.

The 100% Club

Tim Sherwood: “85% of the teams who concede first lose”

Miguel Delaney picked up on a nugget of Tim Sherwood gold after Aston Villa’s defeat to Chelsea today:

Sherwood: “First goal is the most important, 85% of the teams who concede first lose.”

— Miguel Delaney (@MiguelDelaney) October 17, 2015

I thought I’d run the numbers for various leagues I have available:

First to Concede Games Losses Draws Wins
Home 2731 1704 (62.4%) 595 432
Away 3696 2738 (74.1%) 645 313
Total 6427 4442 (69.1%) 1240 745

In the Premier League alone since 2011, away teams have trailed first 946 times and gone on to lose 682 times, 72.1% of time. So he’s not enormously far off, but far enough that it’s clear he’s making it up.

Tim Sherwood, 85% of the time, he’s right every time.

Tim Sherwood: “85% of the teams who concede first lose”