The Burgundy Waves Goodbye

By Harrison Crow (@Harrison_Crow)

With the Colorado Rapids surrendering a late lead last night against the LA Galaxy, and contributing to the storybook farewell tour that is the now intrinsically tied to Landon Donovan, Colorado will surely be expected to drop further down the ranks of our playoff prediction calculator. It will also make the five teams ahead of them near stone-cold (or should I say adamantium cold?) locks to make the playoffs.

That's sad to me for a few reasons. First, Marc Burch's hair deserves the playoffs or a medal or something that proclaims it's awesomeness through time. Second, Colorado has been a very strong club this season. If they were in the East they would currently be in 4th place in the conference. It is a bit disappointing they'll likely miss out on a playoff game to show off what they are about.

Last season, and prior to the drama laden departure of Óscar Pareja to Dallas, the focus of the club surrounded it's trademark 4-2-3-1/4-3-3 and transition attacks featuring the stunning pace of Deshorn Brown. This season, with new coach Pablo Mastroeni, a new philosophy has emerged that has shown a more complete and versatile attack than the run-and-gun days of Pareja.

Taking the youth uncovered last year with Dillion Powers, Chris Klute, Shane O'Neil, Dillon Serna and the aforementioned Brown, Mastroeni has built upon it them and in reality taken them further than what their points or table placement may indicated.

While their goals conceded is up (1.56 this year to 1.15 last), their total shots allowed are down and based upon shot analysis it implies that the defense has probably more likely carried the club rather than hurt it. This is even more impressive when you consider the host of injuries that have hurt the team in 2014.

While they have had a marked increase in goals conceded inside the 18 yard box, that's not necessarily indicative of their performance. Our data suggests it may have more to do with luck than their backline talent. According to our xGoals table, they're ranked third in the league in expected goals allowed with 1.10.

Drew Moor, the team captain and leader of the backline, has a sort of Chad Marshall disease associated with him. A consistently good player in his time in MLS split between Dallas and Colorado, he's been ranked average among his peers while making a pretty average salary. Losing him for the season with a torn ACL may have been the last nail in the coffin for the Rapid's playoff chances.

Moor's perception as a good but not great defender is due partially because his performances come with the asterisk that ties his cap percentage hit with his on the field value. This is rather a tough way to evaluate a player, especially on defense. His spectacular play this season has gone relatively unnoticed in lieu of the career resurgence of other aging centerbacks such as Bobby Boswell and Chad Marshall.  While it is hard to fit all the names into a nice little feature that recaps the season, it would be a crime to leave Moor off an "top" defensive lists at seasons end.

Though they have lost Moor for the season and are unlikely to make the playoffs, this season has not been a wash.

The development of both O'Neil and Serna have been huge. Their rash of injuries has shown a surprisingly deep and talented club that has quietly floated among the top tier in expected goal differential for most of the season. It's a shame we probably won't see their exciting play fighting for MLS Cup in November, but it's clear Colorado will be back next year and further challenging RSL, LA and Seattle for a piece of the Western Conference pie.

Herculez Gomez: American warrior in Mexico

By Harrison Crow (@Harrison_Crow)

Sortable stats can be found at the end of the article for those seeking a more interactive experience.

We embarked on a project some weeks ago to begin focusing on shot data for past and current US Men's National Team players. We came to the realization that these data are not congregated in one place for American players, especially those playing internationally. For example, the Wikipedia page for our first player, Herculez Gomez, shows that he scored 24 goals for Santos Laguna. Try as we might, we could only find 22. 

Wikipedia has been known to be wrong--Matty informs me that he was listed as the creator of Pokemon for some time thanks to the work of his college roommate--but finding Liga MX and CONCACAF Champions League data from games four years ago is tough. Really tough. Poor Drew is probably going to need to go see a counselor, based on some of the emails he sent me. So we could have missed something. But with his determination and Matty's all around know how, they joined forces and dug through more than 175 games for detailed shot data, and we were able to derive some pretty cool stuff.

First of all this stuff comes with a couple of asterisks. 

1) Liga MX has weird seasons. Its time frame, and how ESPN decided to provide game information, doesn't always coincide with the actual season. So we put seasons together as best we could. By the way, ESPNFC was a big help.

2) We had a few games where we couldn't find shot locations. As a result we ended up using a baseline expected goal per shot for those few. It shouldn't skew the results much either way, as it represents less than 5 percent of the data, but it's an important annotation.

3) ... actually there was only two things that really came up. It's just weird to stop at two and not count to three.

2010-11

When Herc left the Kansas City (then) Wizards, it wasn't necessarily on the greatest of terms with the club or the league, as we can see from some of the banter on social media. He left MLS and played his way into the Puebla starting line-up, winning the Mexican Primera Division scoring title with 10 goals. He became the first American to win the Primera scoring title, edging out Javier Hernandez in a tie-breaker by virtue of scoring his 10 goals in fewer total minutes than "Chicharito."

Now, there are plenty of articles from which I could cherry pick to show how people felt about Gomez and his inclusion on the US World Cup roster in 2010. As with any player, it's fair to say that he scored some goals that were a little lucky. But our numbers show that there was more than a little luck to his overall performance. He produced 2.75 shots per 90 minutes and scored nearly half of them.

That's about as "clinical" a finisher as you're going to find.

I don't mean to speak for everyone here at ASA, but I think it's come out in the podcasts that we're all a bit skeptical about finishing ability and a player's ability to reproduce past success. One goal per every two shots is stuff that no striker will sustain.... That is, unless he scores a goal and then retires. I'm sure it's happened.

During his 718 minutes (not including stoppage time) with Puebla, we have him down for 5.65 xGoals compared to the 10 he actually put home. This information is not meant to undercut him, but rather to show that he was outperforming the norm. Lots of players do this from time to time--maybe for one season, occasionally for two. Then most regress, and we chalk it up to unsustainable play, perhaps aided by the residual effect of a teammate or a few too many lucky bounces.

2011-12

The thing is, Gomez continued his crazy pace of outperforming his expected goals numbers. He followed up his 2010-11 season by scoring 17 goals in 2,030 minutes in all competitions spread over three different clubs. I think what stood out the most to me was his ability to do everywhere he played. Despite scoring at what seemed like an unsustainable rate by our metrics and playing with new teammates constantly, he was able to finish as well as he had the season prior.

Thinking about what Brian McBride and Clint Dempsey did over in England as strikers is, of course, admirable. Dempsey still ranks in the top 60 in total goals over the last 20 years of the English Premier League. But these two seasons compiling 27 goals, averaging 0.88 goals per 90 minutes, is a considerable accomplishment and something to savor for a few moments. Go ahead, savor it.

Gomez started etching his name in the CONCACAF Champions League that year, too, scoring six of his 17 goals in those games against MLS clubs (three against Toronto FC, 3 against Seattle Sounders FC). All told, it was perhaps an even stronger performance than the season prior. Over the course of the 2011-12 season we had him for a total of 8.54 expected goals from 71 total shots. He obviously shattered that expectation.

2012-13

2012-13 was a coming-back-to-earth season of sorts for Herc. He played nearly the same number of minutes (1,691) in the Liga MX regular season as he did in the two prior seasons combined (1,794). That, in addition to Santos' dependency on him in the CCL, perhaps resulted in him taking fewer shots per 90 minutes (2.4) than in either 2010-11 (2.8) or 2011-12 (3.1). He tallied 13 goals at a conversion rate of "just" 20 percent. Recall that the MLS average, for comparison's sake, is just a shade over 10 percent.

That's about the extent of his "down year." But in truth, accounting for his shot opportunities, it was another plus year for the American. Again, he outperformed expectations, finding 13 goals versus the 8.60 we expected him to score.

2013-14

This time regression hit Gomez across the board. He scored just eight goals across all competitions versus our expectation of 8.88, representing his first truly down year finishing. But one thing to point out was that his shots per 90 were up and his minutes were down. He was more productive creating shots on a per-minute basis, though this may have more to do with his usage. He was often used as a substitute for Santos, and almost exclusively as a substitute for Tijuana. Evidence suggests substitutes cover more ground and increase the chances of their teams scoring goals. It's a not a great leap, then, to conclude that it might also increase the individual's shot totals productivity.

However, the first study linked above showed that a substitute's pass completion percentage remains unchanged, indicating that perhaps the boost is restricted to gross motor skills. Despite being used as a sub often in his career, especially this particular year, Herc's overperformance in the finishing department over the past four years shouldn't be entirely chalked up to the substitution effect. 

Still, he scored just four of those eight goals in regular season play over a whopping 1,691 minutes played. We had him for an expected 6.74 goals over that time. Assessing his play in Liga MX that year helps us to better understand the common narrative that he was struggling at that time.

That said, he produced an expected goal once every three full matches in Liga MX. If we were to compare that to our MLS data, that same shot production would put him in the same company as someone like Mike Magee or, on the high end, Chris Wondolowski. While neither are having seasons that would blow you out of the water, both have been consistent contributors to their squads, and I think Gomez could still be in that range now.

Timeout

At this point, we have Herc down for 48 goals scored. If we were to allow thousands of typical finishers to take the same shots that Herc got, our expected goals suggest that those players would average 34 goals scored with a standard deviation of about 5. That places him 2.8 standard deviations above the mean, and into the 99.7th percentile. While it's important to note that these expectations come from MLS play, we already found that MLS teams converted opportunities similar to World Cup teams. It's probably fair to say that Liga MX is not that different from MLS, and that Herc possesses a finishing skill not common among MLS or Liga MX players.  

2014-15

We only collected the first half of the Liga MX season data for 2014-15, so the last game in our data set is from March 28th. To that point, Gomez had not yet scored for Tigres, and it seemed as though he'd fallen off the map. I wondered how much he could still contribute to an MLS squad if he ever made the move. However, a more in-depth look revealed that through those seven documented matches, in limited minutes, he still managed to compile 18 shots worth nearly 2.2 expected goals.

He's still finding good looks and he's still taking those shots. I have him at just more than 2.1 shots per 90 which, in combination with his 2.3 expected goals, would indicate that he's a still producing enough shots. It's far too early to worry about his finishing rate right now.

Herculean Summation

Herculez Gomez has been one of the most underrated American players to play the game over the last decade. I would actually go so far to as to say maybe the most underrated. Because he plays in the Mexican league rather than in Spain or Portugal or even the Eredivisie (where the goals are plentiful and the defense is bad), it feels like we sometimes compartmentalize what he does since it's on the wrong side of the ocean.

During the last four years in the Primeria, he has compiled 34 total goals (according to our records), and he is unofficially in the top 10 of goals scored during that period, tied with several others and most notably Raul Jimenez of Club America.

If he had accomplished this feat in England, Agentina or Brazil, there would be much more prestige awarded to him for his dues. However, sometimes we just look at Liga MX as only slightly better than MLS. It's fun to think what could have been if a better league had given him a chance.

Feel free to take a look at the data below and think about all that he's done. It's quite significant.

Season	Shots	OnTarget	Goals	xGoals	G - xG
2010-11	38	21	10	5.65	4.35
2011-12	71	45	17	8.54	8.46
2012-13	64	31	13	8.60	4.40
2013-14	61	30	8	8.88	-0.88
2014-15	17	6	0	2.27	-2.27
Total	251	133	48	33.94	14.06

Season	Goals (Prim)	Goals (CCL)	xGoals (Prim)	xGoals (CCL)	G/xG (Prim)	G/xG (CCL)
2010-11	10	0	4.87	0.00	2.05	0.00
2011-12	11	6	6.14	2.40	1.79	2.50
2012-13	9	3	4.76	2.85	1.89	1.05
2013-14	4	3	6.74	1.07	0.59	2.80
2014-15	0	0	2.27	0.00	0.00	0.00

Ratio of goals to xGoals was used here due to the differences in quantity and quality of his opportunities between the Primeria and CCL.

Location	Shots	OnTarget	Goals	xGoals	G - xG
Away	107	51	17	14.46	2.54
Home	144	82	31	19.48	11.52
Total	251	133	48	33.94	14.06

Shot Zone	Shots	OnTarget	Goals	xGoals	G - xG
1	28	17	8	8.41	-0.41
2	113	68	29	20.19	8.81
3	44	23	7	3.05	3.95
4	26	11	2	1.36	0.64
5	39	13	2	0.90	1.10
6	1	1	0	0.04	-0.04
Total	251	133	48	33.94	14.06

Team	Shots	OnTarget	Goals	xGoals	G - xG
Pachuca	32	19	5	4.19	0.81
Puebla	22	14	9	3.72	5.28
Santos	123	64	22	16.35	5.65
Tijuana	27	11	3	3.78	-0.78
UAG	29	18	7	3.35	3.65
USA	18	7	2	2.56	-0.56
Total	251	133	48	33.94	14.06

Our Playoff Chances Model is underselling FC Dallas

By Matthias Kullowatz (@MattyAnselmo)

Our on-site playoff chances finally gave FC Dallas a better-than-50-percent shot to make the playoffs after its away win over Chivas on Sunday. I say, "finally," because despite being in a playoff position much of the season, that particular playoff model hasn't been too convinced. 

It's important to understand how our playoff model works, and what its weaknesses are. It is based on overall shot ratios and finishing ratios, though at this point in the season it's the shot ratios that dominate the model's predictions. If you take a look at our MLS Tables, you'll see why the model isn't too keen on FC Dallas--its shot ratio of 0.84 shows that it only tallies 84% of the number of shots that it gives up to opponents. So despite being in fourth place by points per game, that model likes Vancouver and Colorado more because of their respective 1.03 and 1.30 shot ratios.

I don't yet have enough granular shot data to form a full playoff projection model using our Expected Goals 2.0, but we can still use that to intuitively tweak our expectations. Here's why FC Dallas is better than its 0.84 shot ratio.

Expected Goal Differentials (xGD) are more predictive of future success than simple shot ratios. At least, the 1.5 seasons of data we have say so. xGD takes into account not just shot quantity but also shot quality, based on the shot's origin and which body part was used. Based on 2013 and 2014 data--when controlled for number of home games--just eight games of xGD information predicts the following eight games of actual goal differential with a linear correlation coefficient of 0.33. Bump that up to 17 games of xGD information, and one could predict the following 17 games of actual goal differential with a linear correlation coefficient of 0.89 (based on 2013 data only). xGD is strong stuff.

FC Dallas' current xGD of +0.05 ranks fourth in the Western Conference, better than its seventh-place ranking in shot ratios. That's good news for Hoops fans, but I can get an even better idea of how they'll play if I break down xGD further into some specific gamestates.

Watching your favorite MLS team on the road while it's tied or ahead is a nerve-wracking experience. Away teams sitting on points often hang back and allow the home team to suffocate them, hoping to bend but not break. These scenarios are not exactly indicative of the away team's ability. But home teams usually play how they want to, regardless of gamestate, and thus all of a team's past Expected Goals data from when it was at home is helpful for projection.

Based on 2013 and 2014 data, the two best Expected Goals statistics to use when projecting game winners 20 weeks into the season are the home team's past home xGD and the away team's past away xGD in -1 gamestates. The data tells me that the best time to really see an away team's ability is when it finds itself behind by a goal. Interestingly, most teams have played fewer than 300 minutes while down a goal on the road--only about three game's worth--and yet that data in combination with home xGD is more predictive than overall xGD. So far, anyway.

As an example of how these two distinct models think of FC Dallas, we need look no further than its next two games---home against Colorado and away against San Jose. The playoff model we use (based only on shot totals, remember) has them with 38% and 21% chances of winning each game, respectively, and an expected point total of 2.3. Compare that to the Expected Goals model utilizing scenario-specific data, which projects them to win with 51% and 58% probabilities, and an expected point total of 3.7.

It turns out, FC Dallas ranks pretty well in both the aforementioned categories, but it should be noted that FC Dallas has played the third-fewest minutes while trailing by one goal on the road. So its variance in that department is greater than that of the typical team. But while the model's estimates as well as the team's outputs are subject to a modest margin of error, there is little doubt these are important gamestates. I leave you with a sortable table for home performance (xGDhome) and away performance when down a goal (xGDaway(-1)).

Team	xGDhome	Minutes	xGDaway(-1)	Minutes
SEA	0.93	957	0.17	249
SKC	0.90	1056	1.45	115
LA	0.89	955	0.10	123
COL	0.73	1047	-0.24	195
NE	0.61	963	-0.52	315
TOR	0.61	963	0.92	141
VAN	0.52	966	-1.12	94
FCD	0.46	1067	1.83	103
NYRB	0.43	966	0.00	434
PHI	0.41	863	0.74	326
RSL	0.31	1065	0.02	126
CHI	0.22	1061	-0.92	78
HOU	0.12	964	-0.88	256
SJ	0.10	1150	-0.34	271
CLB	0.05	953	0.89	225
POR	0.04	1074	-0.35	162
DCU	0.02	1153	0.47	124
MTL	-0.13	1054	0.02	298
CHV	-0.36	868	0.51	162

Expected Goal Differentials are per 96 minutes of play. 

US Autopsy - World Cup 2014

By Matt Hartley (@Libero_Or_Death)

Well the transfer rumors coming off the back of the United States’ World Cup are ending in a depressingly familiar half-exciting, half-exasperating muddle. A steady flow of rumors about foreign suitors for Matt Besler ended with the revelation that he could choose between the damned (Fulham) and barely spared (Sunderland). Little wonder that being a one-club legend in Kansas City was more appealing.

We can still salivate over where DeAndre Yedlin might end up, and while that is a totally valid use of your day, he will be more of a project for clubs like Roma or Lyon than an immediate contender for playing time. Just because the US went further than England doesn’t make Yedlin better than Glen Johnson, does it? Anyways, a few interesting statistical tidbits:

Goalkeeper

Howard - sure, he made a record-setting 16 saves against Belgium, but his best was the recovery to save from Eder after he misjudged Nani’s shot. That kept the score at 1-0, allowing the US to take advantage of their best 90 minutes of soccer and get the result that would see the US out of the group.

The most incongruous stat for Howard was his distribution distance of 30 meters. This was the second shortest among teams that made it out of the group stages, but was that part of the US game plan? While Jozy Altidore’s absence affected the ability to play long, if Klinsmann had instructed his players to build from the back, it didn’t quite come off, as the United States was 8th out of the 16 second round teams in passes completed per game. Things broke down too quickly when the US had the ball, leading to a rather high amount of chances for the opposition.

Center Backs

Thankfully, the US centerbacks were pretty adept at protecting the castle. In examining how the centerbacks did, CBI (Clearances+Blocks+Interceptions) nicely conveys how busy our defenders were, and we’ll look at that stat in its per90 form.

Besler - I wrote a World Cup preview piece for Paste in which I posited that due to having the most secure spot on the backline, Besler would have to be the rock for the US. He finished with a very respectable 13.83 CBIp90, good for fourth in the tournament. In fact, finishing ahead of him was…

Omar Gonzalez, emerging from what seemed to be a long-term demotion to rack up a  15.07 CBIp90 rating, coming from an outstanding 12.14 clearances per 90. The US was certainly relying on Omar to dominate as they conceded the flanks and allowed crosses to rain in.

The third primary center back for the US, Geoff Cameron, was 11th overall for CBIp90 with 12.60. Spending time in midfield as well, Cameron is well on his way to using that versatility to become the American Phil Neville.

Main thing to touch on:

Looking at the top 20 defenders by the CBI metric, there aren’t a lot of big names there. Medel has a good background, Vlaar at Villa, Cameron at Stoke, Nigeria’s Omueruo is on the books at Chelsea, and a couple of guys in Ligue 1. Hell, there are four current MLSers in the top 20 CBIper90 rankings. If the US really wants players to move to “big” clubs, then the national team will need to start producing more performances that aren’t backs-to-the-wall, man-the-pumps nonsense. Matt Besler had a really damn solid World Cup, and his options were the 14-20 slots from the Premier League. It’s certainly a chicken and egg situation, but it makes you hope that Juventus will come in for Erik Palmer Brown so that we can see some US players grow into regular slots at teams that seriously compete for the Champions League.

Fullbacks

This can be the hardest position to judge in the game, I think. You’ve got to be all things to all people at fullback, and that can make the position difficult to analyze. For the US there seemed to be a fairly clear hierarchy going into the tournament:

    1. Fabian Johnson, the best player for all 10 outfield positions

    2. DaMarcus Beasley, well, we like him better than Chandler

    3. Timothy Chandler, the source for a million overstated concerns about   German-Americans’ Americaness

    4. DeAndre Yedlin, there for the experience.

Of course, Beasley played solid two-way ball, Johnson was a useful offensive tool while on the field, and Yedlin became one of the breakout players of the tourney. Since the United States played a very narrow midfield for large swaths of the tournament, offensive contributions from the fullbacks were always going to be vital to our success. Looking at key passes, Fabian Johnson ended up with a .90 KPp90, which was 36th among defenders, placing him behind such noted playmakers at Vincent Kompany, and oh holy crap, DeAndre Yedlin.

That’s right, our little roadrunner, with his limited minutes, contributed a very nice 2.27 KPp90, good for fourth among Squawka’s defenders, and that’s right, one place ahead of Glen Johnson. Sign him up, Brendan!

Midfield

This was the part of the field where the United States’ struggles seemed rather stark. The US ended up with 326.5 completed passes per game, which put them smack in the middle of the 32 team field, and above Brazil, Costa Rica, and Colombia. But looking at things a little more closely, the United States played in its own half 34% of the time, more than any other country, and 22% in the opposition’s half, fourth worst in the entire tournament.

Looking at individuals, Michael Bradley came in for a lot of criticism, but despite playing mostly in a new role further up the field, he managed to complete more passes per 90 (47.77) than Luka Modric (46.00), Sami Khedira (45.36), and Steven Gerrard (44.09). Sadly, this involvement didn’t translate into chance creation, as Bradley finished with 0.67 KPp90, somewhere in the 139-160 range overall. Sure, there’s where Ronaldo finished, but so did Gary Cahill.

Jermaine Jones did everything, winning 65% of his aerial duels, 54% of his take ons, and running a very competitive race for the USMNT’s “Holy crap, I can’t believe that went in” award.  Alejandro Bedoya and Brad Davis weren’t statistically significant, while Kyle Beckerman finished 14th among midfielders in the CBIp90 metric. Graham Zusi provided two assists, but otherwise seemed very forgettable. There just wasn’t a lot to hang our hat on offensively.

In Closing

The United States failed to make the transition to a more progressive style of play this World Cup, but the US did show that they can defend fairly well. Klinsmann’s challenge will be to integrate more players comfortable with keeping and moving the ball through midfield to ally with a decent defense and a serviceable striker corps. There’s a lot of potential in the pool to meld into a strong corps for Russia 2018. I’d expect Fabian Johnson to become a full-time midfielder in the future, and see extended run-outs given to players like Julian Green, Joe Gyau, and Will Trapp. Future columns will look at the players who are making a strong case for the national team, starting with September’s friendly in Prague against the Czech Republic

New England's roller coaster ride

By Matthias Kullowatz (@MattyAnselmo)

This site purports to be one that covers all of American soccer--the United States part of America anyway--but outside of my obsession with Sporting Kansas City, Harrison's love for Federico Higuain, and Drew's commitment to DC United, we don't cover the Eastern Conference as much. So let's talk about New England.

Currently, our playoff projections have the Revs at 60-percent chances of making the MLS playoffs, and our Expected Goals data shows they are just about league average. Considering New England sits in fourth place in the East, and the teams chasing it for playoff spots are the Columbus Crew and the recently humiliated Chicago Fire, 60 percent makes a lot of sense. The New England Revolution is probably a playoff team despite its recent skid.

As fans, it's hard not to get caught up in streaks. After all, streaks affect our team's chances of making the playoffs, and our criticism tends to follow those streaks of the losing variety. Typically, unless a major injury or other personnel change occurs, the team is not getting any better or worse relative to the rest of the league. Shit just happens. New England has lost seven consecutive games. But before that, it completed a seven-game undefeated run, tying just one of those. Which team is New England? The data tells an interesting story.

Period	Final 3rd Pass%	Final 3rd Ratio	GD	xGD	xGDzero
Hot Streak	0.610	1.14	1.86	0.11	0.26
Cold Streak	0.680	1.56	-2.00	-0.05	-1.53
Season	0.630	1.28	-0.37	-0.02	-0.14

New England has gotten hammered in even gamestates over the past seven games. Even gamestates are perhaps the fastest-stabilizing of the Expected Goals statistics, so though this is a small sample, it still suggests that the Revs are not simply getting unlucky. Luck is likely a factor, but not the only factor. And I think the only reason the overall xGD wasn't so different (0.11 versus -0.05) is that New England played so often from behind that its opponents were willing to give up more shots for longer periods of time. 

While New England actually completed a higher percentage of passes in the final third during its cold streak, and spent more time than its opponents in the attacking third, that could probably also be explained by opposing teams being ahead and willing to allow shots and possession.

New England didn't play nearly as well as it looked during its hot streak, and it probably didn't actually play as bad as it looked during its cold streak. But the recent cold streak still shows scary and somewhat-sustainable signs that the Revs aren't as good as our 60-percent playoff chances say they are.

Toot Toot

By Harrison Crow (@harrison_crow)

I don't mean to toot our own horn, but *ahem* toot toot.

If you haven't seen it, Devin Pleuler--MLS Soccer's own 'The Central Winger' and Opta analyst extraordinaire--wrote a nice little piece about Bradley Wright-Phillips and his current goal scoring pace. Pleuler explored the idea of whether or not BWP had the potential to break the MLS single season goal record. It's an interesting theory and actually one that I had been playing with writing...until my thunder was stolen from me.

Really, I have no hard feelings. Devin is a much better writer and way more qualified to speak to the situation than someone like myself that simply fools around with this on the side. However, something that came about in a weird occurrence of serendipity. Devin gave us a brief, albeit important, peek behind the Opta curtain. Something nerds like me covet.

Opta has much more data at their disposal than we do. Honestly, it's something that consistently frustrates all of us here at ASA. We wish we had more opportunity to give you better information than what we do. However, in this moment of transparency we see that our idea of expected goals is really not that much different from that of Opta.

In the article, Opta provides data for Bradley Wright-Phillips' current campaign through the eyes of expected goals. Their Expected Goals (0.201 vs our 0.209 per shot attempt) closely mirrors that of what we produce here at American Soccer Analysis. This isn't me declaring that we now have proof that "we are doing something right." Just because Opta does it doesn't make it the correct way to do something. This is only an observation that someone else does something similar to how we do it, and they came to the same conclusions. And maybe I feel that also gives us a hint of legitimacy, but mostly that thing about how we aren't alone in our conclusions. Yeah.

In some way I guess we both make sausage the same way. Obviously, it's very likely there are differences between the two conclusions. I mean, they are different numbers. Duh. But seeing as we won't ever see how exactly they make their sausage, the idea is that we have a similar model. From what I understand, seeing how sausage is made takes away from the enjoyment of eating it. And I certainly don't want to take away that joy from some. I guess that this is just my way of saying whether you buy their sausage or take advantage of ours--which is free--you're getting a fine product.

I think that was far too much talk of our sausages.
 

Converting chances: MLS versus the World Cup

By Matthias Kullowatz (@MattyAnselmo)

Throughout the World Cup, we kept shot data here at ASA for all 64 games. When we converted that data into Expected Goals output, we had to use our MLS data from 2013 and 2014 to estimate shot values. At first, I assumed that MLS finishing rates would be a crude estimate for those of the World Cup. However, despite a post-expansion record 171 total goals this past Cup, the finishing rates lined up surprisingly well with those from the United States' first division.

The widest paintbrush shows that overall finishing rates in MLS (10.1% +/- 0.5%) are actually slightly higher than those in the 2014 World Cup (9.8% +/- 1.4%), though not by a statistically significant margin (p-value = 0.66). Our Expected Goals model was a shade on the other side, with World Cup players scoring 166 non-own goals* versus the 159 goals that our MLS model estimates should have been scored. This discrepancy is because World Cup players were forced to take more shots from at least 24 yards away (zone 5), lowering their expected goals output while also lowering their finishing rate.

Location Frequencies

Location	MLSlocations	WClocations
1	0.054	0.060
2	0.304	0.263
3	0.180	0.174
4	0.209	0.160
5	0.237	0.316
6	0.017	0.027

If we delve deeper, we see that there are no statistically significant differences in any of the six zones, or by headed versus kicked shots. The chart below shows finishing rates (MLSpct and WCpct) broken down by location and body part. P-values are based on two-sample proportions tests.

Finishing Rates Foot

Location	Part	MLSshots	WCshots	MLSPct	WCpct	Pvalue
1	Foot	363	54	0.391	0.407	0.820
1	Head	300	47	0.193	0.234	0.516
2	Foot	2155	241	0.246	0.274	0.342
2	Head	1556	204	0.086	0.093	0.738
3	Foot	2059	274	0.070	0.088	0.303
3	Head	135	20	0.052	0.050	0.972
4	Foot	2520	270	0.052	0.030	0.104
5	2894	534	0.023	0.024	0.867
6	Foot	206	45	0.039	0.000	0.179

MLS players are able to finish at the same rates as World Cup players, but why? I presume that World Cup players are better than those of MLS, but as best we can measure, it doesn't have anything to do with shot placement. An MLS model that accounts for goal mouth placement estimates that World Cup players should have scored 166 goals, which is exactly what they scored and not much more than the original estimate of 159. 

Obviously, there are two sides to a shot: the guy trying to score it and the guy(s) trying to keep it from being scored. What our model doesn't take into account is what's probably the culprit for such similar finishing numbers. We can't control for proximity of the defender---defensive pressure on the shot---or the pace on the shot. MLS games may include a lot more chances like those Germany got against Brazil, mitigating the differences in offensive talent. Additionally, World Cup shooters could drive the ball harder at keepers that are more prepared to stop it, an effect that would again cancel out before we ever saw it in our data.

What I do know for sure is that World Cup teams scored 2.59 offensive goals per game, and MLS teams have scored 2.59 offensive goals per game. If you like goals, MLS is likely to produce just as many.

*There were five own goals during the World Cup that are not included in the shot analysis.

My MLS All-star team with a twist

By Jacob B (@MLSAtheist)

It’s that time of summer again: voting for Major League Soccer’s All-Star game concludes tomorrow (last chance link) for the game that will take place in a few weeks in Portland. For the tenth consecutive season, the MLS All-Stars will face off with a club team opponent from overseas; in this case, German superpower Bayern Munich. The conventional wisdom behind this format for the All-Star game is pretty simple: in an American sports market dominated by other sports, getting soccer’s biggest names possible will help draw eyes to MLS. For soccer fans in the US, many of the game’s biggest names, personas and reputations still reside in Europe. But is this really the best format for Major League Soccer’s midseason spectacle?

For the value of my two cents, the answer is no. There are a lot of reasons that American soccer has outgrown the value of the big named Euro-club All-Star opponent, but I think this is an area that MLS had right way back in 2002. That year the MLS All-Star game took place in early August (as it will this year), and the MLS All-Star side took on a United States national team that was fresh off its best World Cup finish in the modern era. To be honest, I have approximately zero recollection of this game, so I couldn’t tell you if it was a success. But if MLS and US Soccer had the foresight to try the same thing in 2014, I’d bet my life that it’d be a bigger success than the current format.

There are plenty of reasons that people argue MLS has outgrown the current European guest club All-Star format. Attendance around the league has grown healthily for years, and filling seats at an All-Star event where the league picks the most attractive city possible is hardly a prohibitive challenge anymore. Whether the league plays East vs. West or American vs. World or All-Stars vs. Bayern Munich, you can bet that Portland's Providence Park will be sold out for the main event.

A more compelling argument is that of TV viewership: a clear hot button issue that the league still needs to continue improving. I’ll readily admit that a lot of casual sports fans are probably more likely to watch Bayern Munich play than some of the MLS stars that aren’t household names. But if you replaced Bayern with the United States national team? I think I need only direct you to the huge ratings and watch parties that US World Cup games got last month to say that this format would hardly lose TV ratings. In fact, I’d wager a guess that more people would tune in to watch the 2014 World Cup team’s last gasp in an exhibition than the reigning German champion that has almost no ties to the States. The commercials would’ve written themselves during and after the USMNT’s cup run ended: “The US’s run may have been ended by Belgium, but you can still see them one more time this summer! The 2014 MLS All-Star Game, presented by AT&T.”

There’s one more particularly compelling reason that my proposed All-Star format bests the current one: it gets more guys involved. The current format involves selecting one All-Star squad: in a bizarre method, 32 All-Stars are actually selected but only 22 make the game-day roster and get the chance to participate. With my proposal, 10 MLS players would already be in the Jurgen Klinsmann’s US squad, leaving an All-Star squad of 22 guys – all of whom would actually have a chance to play in the game. An underrated part of this idea is the number of converging story lines that this game would create: young up-and-coming MLS talent trying to demonstrate to Jurgen Klinsmann that they belong on the national team, roster snubs with a chance to exact some revenge on Klinsi (who could that possibly be?), etc.

Hopefully the above five paragraphs are enough to convince you that MLS should go back in time (no, don’t bring back the tie-breaking shootout) to the 2002 All-Star format. Without too much more delay, I’ll go ahead and tell you who I think should be on this All-Star roster. But first, I have one more rule: every MLS team gets an All-Star. This is a rule straight from Major League Baseball, where even if your team goes 0-87 in the first half of the season, the league picks the best of your motley crew to represent your team at the Midsummer Classic. As a guy who grew up rooting for the perennially dreadful Detroit Tigers, I can vouch for how hilariously awesome it is to see your best player called an All-Star despite a career .253 batting average.

First, a quick reminder of the MLS guys who were on the World Cup team and will play for the US in my All-Star game:

GK Nick Rimando (Real Salt Lake)

DEF DeAndre Yedlin (Seattle Sounders)

DEF Omar Gonzalez (LA Galaxy)

DEF Matt Besler (Sporting Kansas City)

MID Michael Bradley (Toronto FC)

MID Brad Davis (Houston Dynamo)

MID Kyle Beckerman (Real Salt Lake)

MID Graham Zusi (Sporting Kansas City)

FWD Clint Dempsey (Seattle Sounders)

FWD Chris Wondolowski (San Jose Earthquakes)

*Check out their club stats here!

Now, here are my picks for the MLS All-Stars. Remember, my rules state that every team gets at least one guy on the team, so that’s how they’re listed:

Chicago Fire: GK Sean Johnson

Runner-Up: Harrison Shipp

This was the last team I could think of an All-Star candidate for, so I had to pick their goalie. That’s what happens when you tie more than 60% of your games.

Chivas USA: FWD Erick Torres

Runner-Up: Nobody

The guy's been one of the league’s ten best players while playing for one of the three worst teams.

Colorado Rapids: DEF Drew Moor, DEF Chris Klute

Runner-Up: Nobody

Giving this back line two All-Stars might be a stretch, but Moor’s been very good, and every MLS fan would love to see Klute take on the USMNT in hopes that he’ll be on it next time.

Columbus Crew: DEF Michael Parkhurst

Runner-Up: Federico Higuain

Columbus probably doesn’t deserve an All-Star, either. Parkhurst has been his normal steady self and Higuain is so skilled, but the rest of that team just doesn’t do enough to help them.

DC United: DEF Bobby Boswell

Runner-Up: Fabian Espindola

Boswell has been the key to DC’s resurgent defense, and Espindola has been the key to DC’s resurgent attack. Had to go with the defender because United still tends to play a lot of low-scoring games.

FC Dallas: MID Mauro Diaz

Runner-Up: Fabian Castillo

This team goes as Diaz goes – they treaded water while he was injured and hope to creep back up now that he’s healthy. Castillo’s so dangerous, but doesn’t quite have the ability to be a team’s focal point yet.

Houston Dynamo: DEF Corey Ashe

Runner-Up: Nobody

Houston definitely doesn’t deserve an All-Star, but there aren’t many good fullbacks to choose from. Remember when the Dynamo steamrolled New England in the season opener? Feels like decades ago.

LA Galaxy: FWD/MID Landon Donovan

Runner-Up: Juninho

It’s hardly been Donovan’s best year, and he may not be the most deserving Galaxy player, but you know you want to see him play against a Klinsmann-coached USMNT.

Montreal Impact: MID Justin Mapp

Runner-Up: Felipe

Not a whole lot of bright spots in Montreal this year, but Mapp has consistently been one of them, and Felipe has glinted at times.

New England Revolution: MID Lee Nguyen, DEF Andrew Farrell

Runner-Up: Jose Goncalves

Lee Nguyen was the league’s best player during the Revs’ May winning streak, and Farrell has been indispensable filling in at both right back and centerback.

New York Red Bulls: GK Luis Robles

Runner-Up: Bradley Wright-Phillips, Thierry Henry, Lloyd Sam

Tough to pass on the guy leading the league in goals, but he’s been set up for a lotta easy ones by Lloyd Sam and this Henry guy you may have heard of. For my money, Robles has been one of the league’s top keepers all season.

Philadelphia Union: DEF/MID Amobi Okugo

Runner-Up: Maurice Edu

Philadelphia’s the league’s most frustrating team: so much talent and potential, yet so many inexplicably dropped points. Take your pick between Edu and Okugo.

Portland Timbers: MID Diego Valeri

Runner-Up: Darlington Nagbe

Valeri has been on a tear and is almost single-handedly carrying the Timbers out of their drowsy start to the season. Nagbe’s been his usual thrilling self to watch, but Valeri’s that team’s best player.

Real Salt Lake: FWD Joao Plata

Runner-Up: Nat Borchers

Platita has been fantastic in the attack for RSL, especially with the absence of other guys due to injury. If only he could stay healthy, too…

San Jose Earthquakes: MID Shea Salinas

Runner-Up: Nobody

The Quakes have been bad. If nothing else, Salinas can at least still hit a peach of a dead ball.

Seattle Sounders: FWD Obafemi Martins

Runner-Up: Chad Marshall

Shame to have to see him play opposing Deuce instead of with him, but Oba has lived up to his DP tag this year. Chad Marshall sneakily is having another good year as the leader from the back – his health is as important as any player’s in the league.

Sporting Kansas City: MID Benny Feilhaber, DEF Aurelien Collin

Runner-Up: Seth Sinovic, Dom Dwyer

Despite injuries to seemingly every player in powder blue this year, there are plenty of deserving candidates. Collin has kept this ship afloat defensively, and Feilhaber has filled every role Peter Vermes has asked.

Toronto FC: FWD Jermain Defoe

Runner-Up: Nobody

TFC is living up to expectations as sort of a stars-and-scrubs bunch. They’ve played fairly well collectively, but nobody stands out aside from Bradley and Defoe.

Vancouver Whitecaps: MID Pedro Morales

Runner-Up: Matias Laba

So many exciting, speedy attackers that it’s tempting to pick any of them just to see them play one more time: Mattocks, Hurtado, Manneh. But Morales is the guy that makes it all happen, and Laba is the guy who lets Morales make it all happen.

Pick Your Poison: LA Galaxy v Portland Timbers

By Harrison Crow (@harrison_crow)

I heard something last night that made my brain tick. If you tuned in to Wednesday's edition of "The Best Soccer Show," co-host Jared DuBois posed a common "Pick Your Poison" question to his counter-part Jason Davis. The question was simply, "Who would you rather be with half a season still to go, the Portland Timbers or LA Galaxy?"

This is an interesting, thought-provoking question from DuBois. First, you really have to define what your goals are. My thought process is based on each club's positions for an MLS playoff spot. Having dropped out of the US Open Cup, each team can only earn silverware by making the playoffs and winning the MLS Cup.

Rose City is a full 14 points behind their rivals from the north, and that with having played an extra game. LA is in a bit of a different situation. Also trailing by 14 points, the Stars of Hollywood have three games in hand, potentially translating into as many as nine points---just a five-point deficit to the top of the conference. Neither are mathematically eliminated from the Supporters' Shield or first place in the Western Conference, but that is not a probable outcome (especially for the Timbers).

Again, I'm not saying that it's likely that the Galaxy will win three straight and get Seattle in its sights, just simply stating the obvious. The LA Galaxy are in a better table position by virtue of having as many points as Portland with more games left to play. I think the typical thing to say based on their position is that LA is showing their age (or some other thing like that). Most people point to the fact they've been "less dominant" on the attack, likely based on just 19 goals in 14 matches which is good for just 13th (1.32) in goals scored per game. The other hand reveals the Timbers sitting 3rd in MLS with 1.57 goals a match.

Set aside the Champions League competitions at the end of the year and all the travelling that comes with it. Conventional wisdom would tell you that the Timbers are clearly a team that is eventually going to find it's footing and probably make a push for the playoffs.

However, if we ignore that "wisdom" and look at our numbers based on shot locations for and against, LA's Expected Goal Differential (xGD) is the best in MLS (0.62). This makes their second year in a row sitting at the top of our leader board, as they did the same last year (0.65). Despite what everyone's eyes are telling them, the LA Galaxy are still a very good team and one that, for all intents and purposes, could still to be a top-three club in the Western Conference.

On the flip side, our Expected Goals data, especially by even gamestates, betrays Portland. Their -0.57 expected goal differential in even gamestates suggests they are constantly falling behind, and more than that, their opponents are dominating shot locations when the Timbers should be playing competitively. I know it's become vogue to call them "Draw City," and maybe that's fair due to their continuing to spot the opposing team goals early. But that defense has so many leaks and problems that Liam Ridgewell by himself may not be the answer to recover this season.

Yes, there are health reasons that can help to explain some of those troubling results. And it may even be foolish of me to base this opinion on only 18 games with 16 still to play. But I think that---based on Matthias' research from last year about how the first 17 games of xGD is a decent prediction of the future (seen below)---I'm not going out on a very thin branch here.

All things being equal, and if I had to choose between the two, I'd go with LA. Compound the situation with the fact that Portland also has the Champions League that I'm sure they'll want to focus on, and it becomes that much easier of a decision.

Now with Silly Season in full swing, we'll see if changes in LA make me look stupid in the coming months.

Germany finished well, but probably didn't even need to

By Matthias Kullowatz (@Mattyanselmo)

Brazil's embarrassment at the hands of ze Germans set at least one World Cup record for a game this deep into the tournament. And while Germany would not likely score seven more goals given the same chances, it's not as though it lucked its way into the finals.

Devin Pleuler noted via twitter that it was something of an unusual spanking.

For #WorldCup teams scoring at least 4 goals in a single match, today's #GER had the highest Shots/Goals ratio since at least 1966.

— Devin Pleuler (@devinpleuler) July 8, 2014

In other words, it wasn't as "lucky" as other lopsided victories have been in the past.

Our Expected Goals model probably undervalues Germany's performance because we can't control for the proximity of defenders or the time available to shoot. Additionally, teams with big leads typically lose the expected goals battle during those gamestates. Oh, and they were playing the former tournament favorites, not Saudi Arabia. Those things make it all the more impressive that Germany manhandled Brazil by Expected Goals, as well as on the scoreboard. Below one can find Expected Goals data by game from this World Cup.

Competitive refers to minutes played when the score was within one, and Comp.xGD is the Expected Goal differential during those times.

Team	Opponent	xGF	xGA	xGD	Competitive	Comp.xGD
GER	ALG	3.28	1.01	2.26	126.4	2.03
FRA	HON	2.19	0.17	2.02	50.6	0.98
GER	POR	2.47	0.53	1.94	31.1	0.10
BRA	CMR	2.60	0.69	1.92	50.0	0.74
NED	ESP	2.82	0.97	1.84	65.0	-0.00
GRE	CRC	2.33	0.52	1.81	129.4	0.88
FRA	SUI	3.13	1.34	1.79	17.2	0.11
CIV	JPN	2.11	0.39	1.72	96.1	0.07
BEL	USA	3.60	2.27	1.33	125.2	1.54
SUI	HON	2.35	1.03	1.32	30.5	0.70
RUS	KOR	1.72	0.40	1.32	95.0	0.77
GHA	USA	1.92	0.65	1.27	99.3	-0.13
JPN	GRE	1.69	0.49	1.20	96.3	1.20
CRC	URU	1.78	0.64	1.14	85.4	-0.03
GER	BRA	2.42	1.30	1.13	22.1	0.37
ESP	AUS	1.16	0.09	1.07	69.9	0.63
BRA	CHI	2.27	1.24	1.02	127.8	1.02
ALG	KOR	2.23	1.26	0.97	27.6	1.24
BRA	MEX	1.44	0.48	0.96	93.3	0.96
ENG	URU	1.58	0.63	0.95	96.0	0.22
ARG	SUI	2.29	1.36	0.93	129.8	1.66
BIH	NGA	1.98	1.05	0.92	94.3	-0.01
ESP	CHI	2.16	1.25	0.92	42.9	0.21
FRA	ECU	1.98	1.14	0.84	96.1	0.84
SUI	ECU	1.50	0.68	0.82	95.3	0.39
FRA	NGA	1.32	0.51	0.82	93.2	0.82
ARG	IRN	1.54	0.73	0.81	96.5	0.81
NED	CHI	1.34	0.57	0.77	92.1	0.43
AUS	CHI	2.01	1.35	0.66	71.7	-0.25
GER	USA	1.22	0.59	0.63	94.3	0.98
ECU	HON	1.25	0.63	0.62	97.3	0.27
CRO	CMR	1.88	1.31	0.58	48.2	0.21
NED	CRC	1.51	0.93	0.58	129.1	0.58
POR	GHA	1.88	1.33	0.55	96.0	-0.22
POR	USA	1.96	1.43	0.53	97.5	-0.05
URU	ITA	1.01	0.50	0.52	98.0	0.49
BEL	RUS	1.22	0.71	0.51	94.4	0.43
BEL	ALG	0.89	0.39	0.50	94.5	-0.12
ARG	NGA	1.62	1.14	0.48	96.0	1.02
FRA	GER	1.64	1.21	0.43	95.5	0.09
NED	MEX	1.09	0.67	0.42	100.9	-0.10
URU	COL	1.12	0.72	0.40	50.1	-0.09
BRA	CRO	1.14	0.77	0.37	92.6	0.18
ENG	CRC	0.43	0.13	0.30	93.2	0.30
COL	JPN	2.39	2.09	0.30	82.3	0.81
ENG	ITA	1.31	1.01	0.30	97.3	-0.53
BIH	IRN	1.05	0.77	0.28	62.4	0.26
COL	CIV	0.99	0.74	0.25	91.9	0.38
GRE	CIV	1.20	0.97	0.23	97.8	0.48
COL	GRE	1.19	0.97	0.22	58.5	0.23
BEL	KOR	1.40	1.21	0.19	96.4	0.79
ARG	BIH	0.73	0.55	0.18	74.7	0.11
GER	GHA	1.26	1.13	0.13	95.2	-0.08
CRC	ITA	0.88	0.75	0.13	95.8	0.19
CMR	MEX	1.12	1.04	0.08	96.1	0.13
BEL	ARG	0.60	0.52	0.08	97.0	-0.13
NGA	IRN	0.72	0.67	0.06	94.0	0.06
MEX	CRO	0.85	0.80	0.04	76.7	0.04
AUS	NED	1.58	1.55	0.03	95.3	0.74
BRA	COL	0.85	0.82	0.02	84.7	0.31
RUS	ALG	0.65	0.64	0.02	95.1	0.14
ALG	RUS	0.64	0.65	-0.02	95.1	-0.14
COL	BRA	0.82	0.85	-0.02	84.7	-0.31
NED	AUS	1.55	1.58	-0.03	95.3	-0.74
CRO	MEX	0.80	0.85	-0.04	76.7	-0.04
IRN	NGA	0.67	0.72	-0.06	94.0	-0.06
ARG	BEL	0.52	0.60	-0.08	97.0	0.13
MEX	CMR	1.04	1.12	-0.08	96.1	-0.13
ITA	CRC	0.75	0.88	-0.13	95.8	-0.19
GHA	GER	1.13	1.26	-0.13	95.2	0.08
BIH	ARG	0.55	0.73	-0.18	74.7	-0.11
KOR	BEL	1.21	1.40	-0.19	96.4	-0.79
GRE	COL	0.97	1.19	-0.22	58.5	-0.23
CIV	GRE	0.97	1.20	-0.23	97.8	-0.48
CIV	COL	0.74	0.99	-0.25	91.9	-0.38
IRN	BIH	0.77	1.05	-0.28	62.4	-0.26
ITA	ENG	1.01	1.31	-0.30	97.3	0.53
JPN	COL	2.09	2.39	-0.30	82.3	-0.81
CRC	ENG	0.13	0.43	-0.30	93.2	-0.30
CRO	BRA	0.77	1.14	-0.37	92.6	-0.18
COL	URU	0.72	1.12	-0.40	50.1	0.09
MEX	NED	0.67	1.09	-0.42	100.9	0.10
GER	FRA	1.21	1.64	-0.43	95.5	-0.09
NGA	ARG	1.14	1.62	-0.48	96.0	-1.02
ALG	BEL	0.39	0.89	-0.50	94.5	0.12
RUS	BEL	0.71	1.22	-0.51	94.4	-0.43
ITA	URU	0.50	1.01	-0.52	98.0	-0.49
USA	POR	1.43	1.96	-0.53	97.5	0.05
GHA	POR	1.33	1.88	-0.55	96.0	0.22
CRC	NED	0.93	1.51	-0.58	129.1	-0.58
CMR	CRO	1.31	1.88	-0.58	48.2	-0.21
HON	ECU	0.63	1.25	-0.62	97.3	-0.27
USA	GER	0.59	1.22	-0.63	94.3	-0.98
CHI	AUS	1.35	2.01	-0.66	71.7	0.25
CHI	NED	0.57	1.34	-0.77	92.1	-0.43
IRN	ARG	0.73	1.54	-0.81	96.5	-0.81
NGA	FRA	0.51	1.32	-0.82	93.2	-0.82
ECU	SUI	0.68	1.50	-0.82	95.3	-0.39
ECU	FRA	1.14	1.98	-0.84	96.1	-0.84
CHI	ESP	1.25	2.16	-0.92	42.9	-0.21
NGA	BIH	1.05	1.98	-0.92	94.3	0.01
SUI	ARG	1.36	2.29	-0.93	129.8	-1.66
URU	ENG	0.63	1.58	-0.95	96.0	-0.22
MEX	BRA	0.48	1.44	-0.96	93.3	-0.96
KOR	ALG	1.26	2.23	-0.97	27.6	-1.24
CHI	BRA	1.24	2.27	-1.02	127.8	-1.02
AUS	ESP	0.09	1.16	-1.07	69.9	-0.63
BRA	GER	1.30	2.42	-1.13	22.1	-0.37
URU	CRC	0.64	1.78	-1.14	85.4	0.03
GRE	JPN	0.49	1.69	-1.20	96.3	-1.20
USA	GHA	0.65	1.92	-1.27	99.3	0.13
KOR	RUS	0.40	1.72	-1.32	95.0	-0.77
HON	SUI	1.03	2.35	-1.32	30.5	-0.70
USA	BEL	2.27	3.60	-1.33	125.2	-1.54
JPN	CIV	0.39	2.11	-1.72	96.1	-0.07
SUI	FRA	1.34	3.13	-1.79	17.2	-0.11
CRC	GRE	0.52	2.33	-1.81	129.4	-0.88
ESP	NED	0.97	2.82	-1.84	65.0	0.00
CMR	BRA	0.69	2.60	-1.92	50.0	-0.74
POR	GER	0.53	2.47	-1.94	31.1	-0.10
HON	FRA	0.17	2.19	-2.02	50.6	-0.98
ALG	GER	1.01	3.28	-2.26	126.4	-2.03