Portland Timbers: Comeback Kids?

I watched the Timbers go down 2 – 0 in the first half Wednesday night against FC Dallas before leaving disgusted for my indoor game. At halftime of my game, I noticed that Portland had come back to tie. Two common occurrences for the Timbers this year have been comebacks and ties, so perhaps it shouldn’t have been that surprising.

The Timbers have played nearly 400 minutes this season from behind–a quarter of their time spent on the field–which has given them plenty of time to win back the home crowd after early goals conceded. In all that time spent losing (nearly four game’s worth) Portland has outscored its opponents 13-to-4. That’s like four straight 3 – 1 wins. Even though most teams perform better when playing from behind, that still ranks Portland second in the league behind Vancouver (see chart below).

This begs the question, is Portland actually one of the best teams when facing a deficit, or might this be a product of some random variation? To the stats!

It turns out, Portland also does well by Expected Goals in losing gamestates. In fact, relative to the league, the Timbers are the best at generating quality and quantity of opportunities in these situations with an expected goal differential of +1.4. We know Expected Goals to be more stable, and thus it is probably a truer indication of what to expect in the future. Check out the chart below, scaled on a per 96-minute basis (basically, per game).

xGD When Losing

Team GF GA GD xGF xGA xGD GD Rank xGD Rank
POR 3.1 1.0 2.2 2.5 1.1 1.4 2 1
FCD 2.0 0.9 1.1 1.9 0.8 1.2 6 2
SEA 2.3 1.3 1.0 1.6 0.7 1.0 8 3
LA 1.8 0.0 1.8 1.8 0.9 1.0 3 4
NYRB 2.0 1.0 1.0 1.8 1.0 0.8 9 5
TOR 2.3 1.1 1.1 1.9 1.2 0.7 7 6
SJ 1.6 0.7 0.9 1.6 1.0 0.6 10 7
PHI 1.6 1.6 0.0 1.8 1.3 0.5 14 8
CHI 3.0 1.5 1.5 1.5 1.0 0.5 4 9
SKC 1.3 0.9 0.4 1.7 1.3 0.4 12 10
DCU 2.0 0.7 1.3 1.2 0.9 0.3 5 11
CLB 0.9 0.5 0.5 1.5 1.3 0.2 11 12
COL 2.7 2.3 0.4 1.6 1.5 0.1 13 13
MTL 0.8 1.8 -1.0 1.4 1.3 0.1 16 14
RSL 1.6 2.6 -1.0 1.6 1.5 0.0 17 15
NE 0.5 1.4 -0.9 1.4 1.3 0.0 15 16
CHV 0.6 2.9 -2.3 1.3 1.4 0.0 19 17
VAN 3.1 0.4 2.7 1.3 1.5 -0.1 1 18
HOU 0.8 2.5 -1.7 1.1 1.7 -0.6 18 19
Averages 1.8 1.3 0.5 1.6 1.2 0.4    

But wait! Hold the bus. There is one major confounding factor that we can control for here. Home field advantage. The Timbers have oddly found themselves frequently facing deficits at home, which means that a large portion of their time spent losing is spent in the friendly confines of Providence Park in downtown Portland. In fact, the Timbers lead the league in minutes spent losing at home–a weird stat, to be sure. Here’s the same chart, but for teams losing at home.

xGD When Losing at Home

Team GF GA GD xGF xGA xGD GD Rank xGD Rank
SJ 3.3 0.8 2.5 3.5 0.5 3.0 5 1
NYRB 3.2 1.6 1.6 2.6 0.6 2.1 7 2
POR 3.6 1.0 2.6 3.0 1.0 2.1 4 3
FCD 2.8 0.0 2.8 2.1 0.4 1.7 3 4
COL 3.6 3.6 0.0 2.1 0.8 1.3 14 5
TOR 3.8 0.0 3.8 2.5 1.3 1.3 2 6
SEA 1.6 0.5 1.1 1.6 0.6 1.0 8 7
CHI 2.5 1.6 0.8 1.5 0.6 0.9 10 8
LA 0.9 0.0 0.9 1.8 1.0 0.8 9 9
NE 0.0 1.2 -1.2 1.4 0.6 0.7 16 10
CLB 0.8 0.4 0.4 1.7 1.0 0.7 13 11
PHI 2.4 1.7 0.7 1.9 1.3 0.6 11 12
VAN 5.1 0.0 5.1 1.5 0.9 0.6 1 13
MTL 0.7 1.5 -0.7 1.8 1.4 0.4 15 14
DCU 1.9 1.3 0.6 1.0 0.9 0.1 12 15
SKC 2.1 0.0 2.1 1.3 1.2 0.1 6 16
HOU 1.5 2.9 -1.5 1.7 1.6 0.1 17 17
RSL 0.0 1.8 -1.8 0.5 0.8 -0.3 18 18
CHV 0.0 3.8 -3.8 1.0 2.1 -1.0 19 19
Averages 2.1 1.3 0.8 1.8 1.0 0.8  

Even when I control for home field advantage, we still see the Timbers among the best teams at playing from behind, averaging 2.1 more goals than their opponents per 96 minutes. Is it the coaching? The players’ mentalities? The raucous home turf on West Burnside? Luck? I don’t know, but I know it’s happening.

 

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World Cup Bracket Challenge

MLSsoccer.com has been kind enough to provide us with blank, virtual brackets for the World Cup. We here at ASA have been kind enough to provide prizes to the person that picks best.

Here’s the deal. Like us on Facebook to get info about our group’s name and password, then fill out your bracket via the link above, and you could win stuff for free! The prize includes copies of the books Soccernomics and The Numbers Game. If you win and you already have those books, I’m sure we can find something comparable that you like.

Oh, by the way, the World Cup starts next Thursday. Get your shit together.

Some Facts about Corner Kicks

I find myself getting worked up for corner kicks. Whether it’s anxiety because the opponent is about to whip one into the Timbers’ box, or hope because the Timbers are about to do the same to the opponent. There is a chance that corner kick will result in a goal, so perhaps my feelings are justified. However, statistics don’t find corner kicks nearly as exciting.

In 2013, our data shows that the 3,185 corner kicks taken led to just 1,110 shots, 258 of which were on target and 80 of which found the back of the net. That means that only one-third of corner kicks ever produced shots, and the finishing rate on those shots was just 7.2 percent–compared to the league’s typical finishing rate of about 10 percent in 2013. Though shots from corners tended to be struck closer to goal, they also tend to be taken with the head, which is the least efficient body part for finishing. In the end, just one of 40 corner kicks could be found in the back of the net (2.5%).

For comparison’s sake, let’s take a look at how often other possessions lead to goals. Thanks to Alex at Tempo-Free Soccer, we can estimate that an average team gets about 4,500 possessions in a season. Here’s how those 4,500 possessions end for a league-average team.

End in… Possessions Shots Shots/Poss Goals Goals/Poss Finish%
Corner 170 60 0.353 4.3 0.025 7.2%
Attacking 3rd* 2,030 375 0.180 40.0 0.019 10.7%

We can see that, while corner kicks produced about twice as many shots per possession than typical attacking-third possessions, they only led to about 25% more goals per possession due to packed boxes and low finishing rates. But not all attacking-third possession are equal, and it seems as though many of the possessions that lead to corners come from attacking-third possessions that are deeper in the opponent’s territory. As the attacking-third possessions get closer and closer to goal, they probably become more dangerous than corner kicks. It may be more correct to say that teams don’t earn corners, but rather, they settle for corners.

These numbers aren’t as precise as I’d like, but they still sobered me up a little for corner kicks. But no promises that I can keep my cool if the Timbers are facing a corner in the waning seconds stoppage time.

*Teams typically lose possession on bad passes about 89 times per match, and about 43.5 of those instances occur in the attacking third according to OPTA data. This led to a 48.8-percent estimate of possessions ending in the final third.

Penalty Kicks and Pressure

According to an article on BBC, there is a great difference in penalty kick conversion rates at various points in a World Cup shootout. When a kick would win the shootout, players in the World Cup have converted 93 percent of their opportunities, but when facing elimination with a miss, players have converted just 44 percent of the time.

The article doesn’t cite sample sizes for these situations, but we do know there were 204 penalties taken over 23 shootouts in the data set. Every shootout has to include at least one such chance–either a chance to clinch or a chance to choke–so a conservative estimate would be sample sizes of 10. And in fact, all we’d need for statistical significance are sample sizes of 10. Check!

Even with statistical significance covered, there could very well be some selection bias here, as perhaps the best PK takers are saved for clinching moments. The combination of small sample sizes and selection bias might explain a lot of the discrepancy in conversion rates, but that’s just not a fun conclusion. So let’s assume there is some effect of pressure.

In my mind, a PK to clinch a shootout should have some pressure associated with it, just as a PK to avoid elimination would. But what this data suggests is that it’s the pressure to avoid elimination that really gets to players.

So I thought I’d check it out in MLS. The only problem is that we don’t have nearly enough shootouts that I can access. So instead I will look at in-game PK conversion rates in scenarios where the shooting team is either down one or tied, controlling for which half and whether or not the kick taker is at home.

The results of a logit binomial regression led me to a few conclusions. First, taking a PK at home doesn’t significantly alter its chances of going in, but there is significant interaction between the gamestate and the half. There are four scenarios that seem to matter for PK conversion: tied in the first half, tied in the second half, down one in the first half, and down one in the second half. Here’s a chart that summarizes those outcomes in MLS:

Gamestate Half Goals Attempts Percentage
0 1 38 43 88.4%
-1 2 19 24 79.2%
0 2 18 24 75.0%
-1 1 5 13 38.5%

From our knowledge of World Cup shootouts it was predictable that the highest conversion rate belongs to the situation with the least pressure. Tied in the first half, a miss still leaves the team with a lot of time to win, and there the PK probabilities are highest. What’s somewhat baffling to me is the rest of the chart. Like for instance the incredibly low conversion rate when down a goal in the first half. Though a sample size of 13 is small, the difference between 88.4 and 38.5 percent is still very statistically significant (p = 0.0002). Or how about why facing a deficit seems to matter in the first half but not the second.

I find it hard to blame selection bias here for our findings. Teams that go down a goal in the first half are likely to be worse teams with potentially worse penalty kick takers, but then that wouldn’t explain why they are able to perform well from the spot in the second half. And teams that are tied in the first half have no reason to be the better teams on average, though it’s that group that has converted 88.4 percent of its penalties. I’m left to wonder if I don’t understand psychology, or if this is all a type-I error. After all, if we ignore deficits in the first half, then there is no statistical significance between the other three scenarios.

Top 50 Total Shots Created: MLS Week 13

I’ve been terrible with trying to keep up with this quantitative metric, but I figured it wouldn’t hurt to throw out an updated version in a vain attempt to try to play catch up with the status quo, being that the league is crawling towards the World Cup break.

Really, the point of this exercise is to try and capture how often players are creating shots–not just for themselves, but for teammates. It’s still pretty simplistic, and by no means the definitive answer to who the most valuable attackers are, but it’s a start in moving away from basing value judgements on goal totals.

To be as clear as possible this is not a metric that measures quality or success of the shot. It’s purely about opportunities to score. Either by way of putting mates* in position to score through passes that lead to shots–key passes–or to create a shot by himself–assisted or not–are the ways I count shots created.

*Editor loves word choice.

One thing I did do to include the best available and least luck-influenced player was to set a threshold of 700 minutes played. That limit was arbitrary and selected merely based upon the results of compiling the list. For that reason, and no other, you won’t see individuals such as Michael Bradley, Gilberto, Brad Davis, Joao Plata, Marco Di Vaio and Kekuta Manneh on this list even though their shot creation rates merited a position in the top 50. I am very high on both Plata and Manneh, and I would love to see both surpass the 600-minute mark and really fly beyond 2,000 minutes this season so we can see what their stable versions look like.

50-33:  The Above Average

Rank Name Club Position Minutes Key Passes Assists Shots ShC ShC/90
50 Blas Perez Dallas FWD 899 6 2 24 32 3.20
49 Nick DeLeon DC MF 1026 12 2 23 37 3.25
48 Vincent Nogueira Philadelphia MF 1348 17 2 30 49 3.27
47 Juninho LA MF 962 9 3 23 35 3.27
46 Benny Feilhaber KC MF 1260 26 3 17 46 3.29
45 Erick Torres Chivas FWD 1186 8 1 37 46 3.49
44 Jack McInernery Montreal FWD 844 11 1 21 33 3.52
43 Baggio Husidić LA MF 761 13 1 16 30 3.55
42 Dillion Powers Colorado MF 825 21 3 9 33 3.60
41 Lamar Neagle Seattle MF 987 10 2 28 40 3.65
40 Teal Bunbury NE FWD 1170 15 3 30 48 3.69
39 Felipe Martins Montreal MF 996 15 2 24 41 3.70
38 Jairo Arrieta Columbus FWD 818 9 0 25 34 3.74
37 Max Urruti Portland FWD 744 5 0 26 31 3.75
36 Justin Mapp Montreal MF 949 17 4 19 40 3.79
35 Travis Ishizaki LA MF 735 20 1 10 31 3.80
34 Andrew Wenger Philadelphia FWD 1012 11 1 31 43 3.82
33 Diego Fagundez NE MF 1086 8 2 37 47 3.90

I’ll admit there is quite a bit of disparity between Diego Fagundez (#33) and Nick DeLeon (#49). This group does however hold a few names seems that, to my mind, seem to fit together. Blas Perez (#50), Erick Torres (#45), Jack McInerney (#44) and Andrew Wenger (#34) all are viewed a bit differently in terms of success, but, again, this isn’t about results-based productivity so much as process-based productivity. We’re merely looking at how much they’re involved in creating goal scoring chances, regardless of the quality of those chances or where they are located. In that context it makes more sense.

The lone surprise for me in this tier is Justin Mapp. I would have assumed he’d be much higher on this list being that he’s been on the few bright spots for Montreal a long with JackMac.

 

32-10:  The Good.

Rank Name Club Position Minutes Key Passes Assists Shots ShC ShC/90
32 Chris Wondolowski San Jose FWD 810 6 0 30 36 4.00
31 Obafemi Martins Seattle FWD 1246 19 6 31 56 4.04
30 Michel Dallas MF 740 14 2 18 34 4.14
29 Lee Nguyen NE MF 1032 24 0 24 48 4.19
28 B. Wright-Phillips NYRB FWD 1051 8 0 41 49 4.20
27 Edson Buddle Colorado FWD 707 10 1 22 33 4.20
26 Shea Salinas San Jose MF 916 32 4 7 43 4.22
25 Sabastian Fernandez Vancouver FWD 654 10 0 21 31 4.27
24 Will Bruin Houston FWD 1221 20 1 37 58 4.28
23 Graham Zusi KC FWD 794 24 3 11 38 4.31
22 Alvaro Saborio Real Salt Lake FWD 869 5 2 35 42 4.35
21 Leonardo Fernandez Philadelphia FWD 701 13 1 20 34 4.37
20 Giles Barnes Houston FWD 1335 12 2 51 65 4.38
19 Gaston Fernandez Portland FWD 757 19 0 18 37 4.40
18 Mike Magee Chicago FWD 714 9 2 24 35 4.41
17 Harry Shipp Chicago FWD 894 23 4 17 44 4.43
16 Marco Pappa Seattle MF 751 12 1 24 37 4.43
15 Mauro Diaz Dallas MF 646 16 2 14 32 4.46
14 Bernando Anor Columbus MF 718 11 0 25 36 4.51
13 Cristian Maidana Philadelphia MF 871 23 2 20 45 4.65
12 Quincy Amarikwa Chicago FWD 880 15 4 28 47 4.81
11 Dom Dwyer KC FWD 1050 7 0 50 57 4.89
10 Deshorn Brown Colorado FWD 902 6 0 43 49 4.89

Two other names that are notable here. Edson Buddle (#27)–whom everyone thought was done two years ago when he was traded to Colorado–and Marco Pappa (#16), who was kind of a last minute signing before the start of the season, and who was a serious question mark considering his lack of playing time in the Netherlands.  Now both of these individuals that were stamped as likely non-essentials are two of most involved in the creation of their clubs attack. Lee Nguyen (29) coming in higher than Obafemi Martins (31) makes me laugh, simply because Martins is second in the league in assists and most people still hold that to being the truest or, perhaps, the most obvious sign of team goal contributions. Yet Nguyen has been a catalyst for New England and is simply their most valuable player when it comes to finding the ability to create chances. This is the meat and potatoes of the list.

9-4: The Elite.

Rank Name Club Position Minutes Key Passes Assists Shots ShC ShC/90
9 Javier Morales Real Salt Lake MF 1154 41 5 21 67 5.23
8 Fabian Espindola DC FWD 1086 30 4 30 64 5.30
7 Diego Valeri Portland MF 1117 28 5 37 70 5.64
6 Landon Donovan LA MF 802 24 2 25 51 5.72
5 Thierry Henry NYRB FWD 1170 23 4 49 76 5.85
4 Federico Higuain Columbus FWD 1080 39 5 27 71 5.92

So there that is. There shouldn’t be any argument here with any of these names. Fabian Espindola (#8) is the sole reason DC even has a shot at the playoffs. He is going to get every opportunity to be ‘the man’ in black and red. Landon Donovan (#6) despite his uncanny snubbery from the US National Team is still clearly a major factor for the Galaxy and their attack. Sticking with the theme of decline in skills, Thierry Henry (#5) is still one of the greatest to ever play in MLS.

Oh, and I’m just biding my time for Higuian to get past this “slump” and jet into the MVP Candidate category… because that’s simply where he belongs. More on that down the road.

3-1:  The MVP Candidates.

Rank Name Club Position Minutes Key Passes Assists Shots ShC ShC/90
3 Robbie Keane LA FWD 990 19 2 45 66 6.00
2 Clint Dempsey Seattle MF 751 14 2 43 59 7.07
1 Pedro Morales Whitecaps MF 821 31 4 38 73 8.00

Clint Dempsey (#2) has had the kind of year that is simply bananas. It’s been so crazy that it’s somehow eclipsed the Pedro Morales (#1) show that is going on just a few short hours north of him. Sure, these guys take penalty kicks, but that’s only a small fraction of their shots generated. If these two take this same show into the later stages of the season I can’t think there would be much reason to consider anyone else for MVP.

Oh, I guess you could probably throw Robbie Keane‘s (#3) name in that list, too. People forget about ol’ faithful, but even without his P.I.C. (read: ‘Partner in Crime’ for those that aren’t as hip as I am) for a game or two here and there, he’s still been incredible. Currently he ranks third in individual expected goals, proving that he also finds dangerous places to take his shots and doesn’t hesitate to pull the trigger. Oh, and despite the angry looks and words AND finger wags, he gets his teammates those same opportunities.

And here’s the Excel File for the top 50.

Possession with Purpose… an update of sorts.

All,

Over the last six months my efforts in Possession with Purpose have led to far more interesting avenues than at first expected.

With that, my volume of articles has increased to the point where I needed to create a new Domain site; www.possessionwithpurpose.com

This isn’t an end to me visiting and writing articles on ASA; but it does mean a reduction in the volume of articles I offer here.

My sincere thanks to Matthias Kullowatz and Harrison Crow for their kind words and great support when introducing me to ASA in January of this year.

Best, Chris

Sporting KC still has edge in the capital

If you come in from a certain angle, you can hype this evening’s DC United-Sporting KC game as the Eastern Conference’s clash of the week. The two teams enter this game tied for the second seed with two of the best goal differentials in the conference. With DCU playing at home, and Sporting missing half its team, the edge would appear to go to United. But not so fast.

Despite being inseparable by points, DCU and Sporting are about as far apart as two teams can be by Expected Goal Differential. Sporting sits atop the league at +0.62 per game,* while DCU is ahead of only San Jose with -0.33. If we look to even gamestates—during only those times when the score was tied and the teams were playing 11-on-11—the chasm between them grows even wider. Sporting’s advantage over DCU in Even xGD is more than 1.5 goals per game.*

To this point, as early as it is in the season, I have found that winners are best predicted by Even xGD, rather than overall goal differential. Though the sample size of shots is smaller for each team in these scenarios, the information is less clouded by the various tactics that are employed when one team goes ahead, or when one team loses a player.

Of course, Sporting will be missing the likes of Graham Zusi, Matt Besler, and Lawrence Olum, as they have for the past three games. The loss of those key players has mostly coincided with their current four-game winless stretch, and it would be tempting to argue that they are not in form. However, over those last three games, Sporting overall xGD is +0.27 per game,* and its Even xGD is +0.68.*

Making predictions in sports is generally just setting oneself up for failure—especially in a sport where there are three outcomes—but I will say this. Sporting is likely better than the +180 betting line I’m seeing this morning.

*I use the phrase “per game” for simplicity, but xGD is actually calculated on a per-minute basis in our season charts. Per game implies per 96 minutes, which is the average length of an MLS game.