Team Analysis

Trapp Game: How Caleb Porter has changed the Crew by Cheuk Hei Ho & Eliot McKinley

The year 2019 brought a new Crew. Anthony Precourt got his wish and slithered away to Austin, while head coach and sporting director Gregg Berhalter began his rehabilitation of the US national team, leaving behind five years of solid results in Columbus. Replacing them was a new ownership group and front office led by the Haslam and Edwards families. Toronto FC’s Tim Bezbatchenko came in to lead the Crew’s soccer operations and former MLS Champion Caleb Porter took over as the head coach. With the smallest offseason turnover of a perennial playoff team, expectations were that Porter would provide a steady hand for continued success this season. However, that hasn’t happened and Columbus remains third to last in the Eastern Conference with only a game left in the season. Did the transition fail to succeed because Porter couldn’t implement his vision, or because that vision just didn’t work?

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How the Quakes Dominated the Cali Classico... Again by Anay Patel

In addition to being one of the most storied rivalries in MLS history, the California Classico has an extra flair to it in 2019. New San Jose manager Matias Almeyda played for and managed Argentinian giant River Plate, and new LA Galaxy manager Guillermo Barros Schelotto played for and managed their hated rival Boca Juniors. In addition to that, Almeyda managed Banfield for a period, the rival of Lanús, where Schelotto managed his first side. So on paper the coaching matchup should be about equal. In reality, it hasn’t been.

Following San Jose’s 3-0 win in the first edition of the 2019 California Clasico, LA Galaxy manager Guillermo Barros Schelotto and captain Zlatan Ibrahimovic dismissed the win, claiming that the scoreline was not reflective of the close nature of the match. After all, the Galaxy were missing key players Jonathan dos Santos and Uriel Antuna, who were away on Gold Cup duty. Earthquakes homegrown player Tommy Thompson was dismissive of the comments, remarking that “there’s always a scoreboard, after the game and it said 3-0.” For the rematch only two weeks later, the table was set for a very interesting tactical matchup between two new managers trying to implement their philosophy into their clubs. In actuality, Almeda’s side came out on top again, this time by a score of 3-1.

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Loons Calling: How Minnesota United is Exceeding Expectations by Carl Carpenter

Making the step up from the NASL into Major League Soccer can be extremely difficult (commiserations Cincinnati fans), and Minnesota’s first two seasons in MLS are an excellent example of this. Defensively, the Loons struggled to keep the ball out of the net consistently (Statistically the worst defense in the league in 2017, and tied for third worst in 2018). Adrian Heath’s insistence on playing a high-risk/high-reward brand of soccer was seen as extremely foolhardy considering the construction of his roster, and his history of “brand over results” which ultimately cost him his job at Orlando City. 

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Roster Consistency Part Two: Do Consistent Lineups Lead to Better Results? by Dustin Nation

In a previous article, I looked at the effect of roster consistency on overall team performance. There were enough interesting trends in the data that I wanted to look a little closer and try to see if there is a “right” number of changes that teams should make on a week-to-week basis.

After looking at each squad’s rotation and how it affected their performance over the past three years, it makes sense to look at how changing lineups from one week to the next effected team’s performances in that week. That is to say, given a team’s roster changes from the previous week, how likely were they to perform well?

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Where the Ball Was Won: Using Passing Data as an Indicator of Defensive Pressure Points by Jamon Moore

I’m a die-hard San Jose Earthquakes fan. Please don’t leave yet. In case you aren’t paying attention to MLS much this year, the Quakes have been…underperforming, even by their less-than-lofty standards. I was preparing data for an article about the Quakes troubles with defending the opposition Zone 14 (or are you #TeamZone5?) discussing why they have given up a league-high 6 goals there so far this season, when – you may be aware – Matt Doyle (@MattDoyle76) and Bobby Warshaw (@bwarshaw14) publicly blasted the Quakes for the very same issue back on May 27.

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Marcos Ureña, the unsung Hero of LAFC by Kristan Henage

Bob Bradley is precise with his words. "We knew when we picked him [Marcos Ureña] up that we had a player that, around the goal, is sharp," Bradley said in pre-season. "His qualities are valued and he feels comfortable.”

On first viewing, Bradley’s words sound like anything you’d expect from a head coach, especially one trying to motivate a forward with a career record of one goal in five games (apart from internationally where he’s at one in four).

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Sizing up the Silva for Saborio swap by Jason Poon

Last week, the Alvaro Saborio for Luis Silva trade kind of took the league by surprise. Nobody saw this coming, but after the dust settled this trade makes perfect sense for both parties involved. For DC, they give up a promising youngster for a proven goal scorer they badly need. For Real Salt Lake, they pick up an up and coming midfielder who can help rebuild an aging RSL side.

But for United, this is a move to win now and to take advantage of their favorable table positioning to make a serious run for the Supporter's Shield in a weaker Eastern Conference and a possible deep run for the MLS Cup too. It's a "win now or never" kind of mentality and it's one that will most likely pay off.

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The effect of shot limiting in MLS: Part Two by Sean Steffen

This is part two of a two part series. Click here for part one.

In part one of this study I demonstrated that from a shot limiting standpoint, there is a right way and a wrong way to play possession soccer, and there is right way and a wrong way to sit back. But what distinguishes the efficient from the inefficient? Ultimately I believe it to be a matter of tactics and team spacing.

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Toronto FC: Road Warriors by Matthias Kullowatz

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

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.