Toronto FC: Road Warriors

By Matthias Kullowatz (@mattyanselmo)

Team	xPoints
TOR	73.8
SEA	52.2
MTL	52.0
SJ	50.6
NYRB	49.1
NE	47.9
COL	47.5
VAN	46.1
CLB	46.1
SKC	44.5
FCD	43.1
LA	42.5
ORL	42.2
POR	41.6
RSL	40.1
DCU	39.2
PHI	38.6
NYC	38.6
CHI	38.0
HOU	37.1

I thought my computer had spit out an error when it told me Toronto FC was the best team in MLS. To the right you can see the power rankings that I was too scared to publish in their typical location without an accompanying article. These are the number of points teams would be expected to earn if the 34-game season started today and each team played a balanced schedule. Toronto may or may not be one of the best teams in MLS, but here's why the computer thinks so.

After last weekend's 1 - 0 win in Philadelphia, Toronto finally completed its seven-game road trip to start the 2015 campaign, a difficult way to start the season which was necessitated by construction to expand BMO Field. That type of road trip typically only happens in MLB or the NBA if the rodeo is in town. The model gives teams bonuses when they have played fewer than half their games at home, assuming that, had they gotten more home games, their expected goals stats would be better. 

While it's a bit crazy to think that Toronto will break the MLS points record with more than 70, it's not crazy to think that maybe they're even better than you, our readers, thought when you ranked them second in the East. Toronto is, after all, fifth in the league in expected goal differential (xGD) despite the fact that--as mentioned before--it hasn't played a single home game. 

Let's play around with some more-intuitive math. In the past five seasons, home teams have outscored away teams by an average of 0.41 expected goals, and this season Toronto has outscored its opponents by an average of 0.18 expected goals per game. If we give Toronto a 0.82 xGD swing, weighted over 3.5 games, then their xGD jumps to 0.59. That would rank them first this season, and either first or second in each of the previous four seasons. 

Toronto is an outlier in both not having played any home games, and having played fewer games than most teams overall. This tends to break regression models. You might notice that the Montreal Impact is also toward the top of the rankings, and not surprisingly, they have played just one home game (25%) and only four total games. Small sample sizes, relative to the rest of the league, are more likely to create outlying results, and that's why the computer is insanely high on those two Canadian clubs. That said, Toronto has put together a very impressive season thus far, even if it doesn't look like it in the standings, and I think it justifies our readers' beliefs that Toronto would be good in 2015. 

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.

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.

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.

Player Acquisition: The Tweeners

There is a thing that constantly steals my interest when it comes Major League Soccer. It's how teams choose to scout and evaluate talent that is already in the league. One thing that has been made quite clear with the financial constraints is that it is difficult to hold on to those players that hover around the $200,000 salary threshold, and yet aren't stars or obviously consistent difference makers. Player makers such as Chris Rolfe, Mauro Rosales and Bobby Convey have found new homes in MLS, either in the few months leading up to this season or since the first kick. The names themselves aren't specific references of importance, but rather examples of what happens in the off-season concerning players in the aforementioned pay range that are just casualties of cap situations in today's era.

These players we understand to a degree. They are interesting talents with a fair amount of room for critiquing, whether that be due to personality, problems with injuries or just inconsistent displays of performance from week to week. There are always one or two or even three (in this case) of these players that are available come the off-season.

Two of the three players went to clubs with the ability to take chances.

Chivas USA was obviously getting a steal in adding Rosales. Super Mauro, since being added to the roster, has accrued 17 key passes and 3 assists while producing 12 shots on his own. He leads the club in Total Shots Created.

DC United needed anything to help save their season and jump start their offense. The arrival of Rolfe in return for a bit of allocation money was seemingly a worthwhile risk--and his influence on Ben Olsen's chances of keeping the head coaching job can probably be debated to some extent. Prior to the trade, Olsen and DC United had only produced 1 point through 3 matches. Since the addition of Rolfe, they're now rolling at nearly 2 points per match.

Now, I'm not saying that Rolfe is truly responsible for the turn around. That idea would represent lazy analysis. In fact, DC United generated 34 shot attempts to its opponents' 36 in the first three games, and 108 to 112 since, so it's not like Rolfe's presence has indicated a stable improvement yet. Frankly, since MLS week 4, it's been the Fabian Espindola show at RFK, and that is a completely different discussion.

On to Convey, who didn't go to a team that had to take on a lot of risk. Instead he went to the defending Supporters' Shield-winning New York Red Bulls. He has been somewhat of middling attacking influence in his time on the pitch for the Bulls, adding 9 key passes and 2 shots in just under 700 minutes over his initial tenure this season.

WhoScored isn't exactly impressed. They have graded his performance so far by issuing him a 6.39 rating which is well below their league average rating for a player---which sits near 6.7. Squawka ranks him 16th on the  roster depth chart which mostly follows up that thinking being that WhoScored placed him 15th overall.

These three players represent teams that have taken advantage of a system available to them in an effort to improve their club. What is intriguing to me at this juncture isn't necessarily the impact they've made upon their current club but how their current clubs targeted them as being upgrades and financially worth their investments.

I'm sure that MLS teams have personnel that help front office types make decisions and help discern player talent and ability that make them right for the acquisition. I am familiar enough with certain clubs to be aware of the individuals that are involved in that process, and much of it seems archaic and awkward in method.

Mauro Rosales may have been less of a risk when it comes to Chivas. In fact it was kind of "duh" type moment that perfectly fell in their lap. The other side of the coin is that Rolfe and Convey were both risks, and heavy ones at that considering their price tags (before New York lapped Convey up, that is).

I would certainly concede that all are substantial talents within the US first division. But how they fit the rosters to which they were added to is a bit interesting.

Some could point to Convey's addition to New York as an attempt to add competition to the left side and some wide play making, Convey has instead shifted to the back line in the form of a full back. Which begs the question, was that the idea before he was added?

I, as well as many, had thought Luis Silva would be taking over the role of central play maker in Washington after the departure of Dwayne De Rosario. After the stumbles by Silva early on, I thought that Rolfe would take over that role, but instead he looks to be pushed out wide with Nick DeLeon, being featured more frequently in the central attacking role. Was this a decision made before acquiring him, and did the club think he could fill that role any better than some of the more natural wide midfielders who have moved clubs since?

Results-based analysis is often unhelpful, and in these cases, don't truly tell the story we're seeking in how MLS teams are valuing these types of players. I'm curious if there are any specific statistical values that teams could point to as to why they made this move--and please, I hope it's more than the assists or goals totals, or the fact that they're "winners." For all the talk about transparency in details for the league, it would be nice to see some of the true thought processes involved in analyzing these talents beyond tired cliches. Especially considering that all these clubs they have access to far better gauges and methods than what most of us have at our disposal.