MLS and Serie A Wages: Closer Than You Might Think, but Structured Very Differently

The four pages in the above dashboard present a few perspectives on the recent MLS and Serie A salary releases (big thanks to Falvio Fusi for gathering the Italian data for his own Serie A visualization, then letting me use them and acting as a sounding board for my viz design). I designed these visualizations to pretty much speak for themselves, but I do want to take just a few hundred words to talk through a few other insights that aren’t obvious in the charts as well as some significant caveats.

As the fourth page above lays out, the differences in roster building in these two leagues goes well beyond total spending. Because of MLS’ salary cap and designated player rule, teams must have unbalanced rosters if they want to spend big, while those with smaller budget, like league-leading New York Red Bulls and FC Dallas, have less variation in pay across their rosters than the only Serie A club who spends anywhere close to their level, Frosinone.

The differences between big spenders in MLS and Serie A are far more dramatic. Between the two leagues, MLS employs 10 of 14 highest-paid players, but only 25 out of the top 200. That top 200 is dominated by Serie A’s richest clubs, which gets to the other big difference between the two leagues. Those seven, Juventus, Milan, Inter, Roma, Lazio, Napoli, and Fiorentina, outspend the dregs of their league more than the big spenders in MLS overpay relative to their more frugal table-mates. Big spending doesn’t buy points nearly as effectively in MLS either, but that’s better discussed with data for wages, points, and goal differential that spans multiple years for both leagues (hopefully coming soon).

Beware of diving into either the MLSPU salary release or La Gazzetta’s listing of Serie A wages on a terribly granular level, though (I’m looking at you, myriad “Most Underpaid/Overpaid Players in MLS” articles). The accuracy of the MLSPU release has been derided by MLS coaches, owners, etc. over the years, and it’s hard to think of anything that comes from a European paper prone to publishing erroneous transfer rumors as God’s honest truth. That’s why I don’t make it terribly easy to look up a specific player’s wage in any of the wage visualizations. Of course, mixing data data from two completely separate sources is risky, too. Sure, there’s still some garbage-in-garbage-out risk in using these data at all, but I’m hopeful that rolling it up to clubs and binning the salaries into histograms points us toward useful insights.

However, that doesn’t mean that this data isn’t worth analyzing. MLS’ stated goal is to become a top league in relatively short order. Depending on your definition of “top league,” this probably means surpassing Serie A, the Eredivisie, or Ligue 1 (let’s just assume the Premier League, Bundesliga, and La Liga are totally out of reach). The impression I’m left with in viewing this data is that MLS is an enormously long way from surpassing Serie A’s big spenders, and would need aggressive improvements to wages on the low end of MLS rosters (filled by players worthy of those salaries) to have rosters that are of a similar quality top-to-bottom of clubs in the lower half of Serie A. That last bit seems attainable, but the length of time and means required to get there are hard to decipher.

2015 MLS Salaries Visualized (& You Can Add in Hypothetical Signings)

Whenever the MLS Players Union releases player salaries, I use Tableau Public to visualize them, augmenting each one with new Tableau tricks that I’ve picked up in the months which separate these releases. Another release came out a couple weeks ago, but various obligations prevented me from visualizing it right away. In the meantime, Didier Drogba was signed by Montreal, Shaun Wright-Phillips joined his brother in New York, Nelson Valdez landed in Seattle, among numerous other signings around the league.

This sparked an idea: I added in the ability for readers to insert these signings or others of their own invention. At the bottom of the dashboard you will find a place to type in first name, last name, guaranteed compensation, and choose an MLS club for Player 1, with Drogba at $4.5m (figure I found from some rumor somewhere) by default, Player 2, and Player 3.

As you’ll notice, the players you insert into the chart will be in black, while the rest or in shades of green based on their base salaries, with the darkest hue reserved for those above the current Designated Player threshold, $436,250. Every time you insert or remove a created player from a club, the chart will automatically re-sort the rosters based on wages.

There’s also a filter allowing you to flip back and forth between the 2014 and 2015. Clubs re-sort themselves based on wages when you enact this filter, too. If a club, or even players, are selected at the time you change years, the selections will carry over and show you where they were in the other season. Your created players won’t appear in 2014 though, since there isn’t much point in inserting hypotheticals into a year that has already passed.

Feel free to use all of this interactivity, then take screenshots to illustrate points on Twitter, mentioning me @StatHunting if you’d like me to see your particular usage of the dashboard.

How To Do Something Like This in Tableau

A core truth in Tableau is that you can only work with the data available to you. I consider this a strong feature for the most part, because it makes it harder for someone to manipulate data in order to support their particular viewpoint. What about when you want to ask hypotheticals like we did above, though? That is one of the many use cases for parameters in Tableau. Parameters a very powerful way to give users a portal which they can use to input values or at least choose from a list. In this case, I created 4 parameters, then triplicated them, so that users could add three players to MLS clubs of their choosing and assign them a salary. The parameters look like this (I didn’t include Last Name in these images because it’s basically identical to First Name):player parameters

Then, as with all parameters, I needed a way for them to relate to my data source. In this case I needed to replace particular cells in my data source with these parameter values. I could have found an existing row in my data that was otherwise irrelevant (such as a “pool” player that isn’t assigned to a particular club), but in this case I chose to create some extra rows in my Excel data source:

Club Last Name First Name Pos Base Salary Compensation season
Unknown1 Unknown1 Unknown1 Unknown1 -0.001 0 2015
Unknown2 Unknown2 Unknown2 Unknown2 -0.001 0 2015
Unknown3 Unknown3 Unknown3 Unknown3 -0.001 0 2015

Every time you use a parameter you have to plug it into your data via a calculated field. In my case I made 4 different calculated fields with the following format:club calcFirst Name*, Last Name*, and Compensation* where all organized the same way, simply changing the parameters on the right and the last use of the data field after “ELSE.” In english whe’re telling Tableau that when Club is  “Unknown1,” then  replace it with Player 1 Club, the same for “Unknown2” and “Unknown3,” and when that’s not the case, leave it as the value of Club in that row.

The last part is simply using Club*, First Name*, Last Name*, and Compensation* where you would their original fields. If you’ve already built something, use Tableau’s Replace References feature, a great shortcut for situations like this. For example, you right-click on Club in the Measures pane, select “Replace References…”, click “Club*” and OK, then every example of [Club] in calculated fields, shelves, etc. will be replaced with [Club*]. Go back to your calculated fields after doing so, as you don’t want First Name*, Last Name*, and Compensation* to start with “CASE [Club*]” instead of  “CASE [Club].”

To make the parameter players stand out a little more, I set base salary to drive color in a custom diverging, black and green scheme, the mid-point set at $0 and using the full color range. Because the base salaries in our Unknown1-3 rows were all -0.001, they will be the only data points that show up as black, while the others’ hues are driven by how close they are to the 2015 cap maximum (aka DP level), $436,250.


I hope MLS fans enjoy the visual above (please tweet fun uses of it to @StatHunting), and that some other Tableau developers out there can find other use cases for this particular Tableau hack.

The Medal Count of USA Women’s Soccer Across Major Competitions is Nothing Short of Dominant

The Women’s World Cup, which ended last night, has a sister competition in the Summer Olympics, which will take place in Brazil roughly 13 months from now. These two tournaments pull competitors from the same pool of countries, their histories begin roughly at the same time (the inaugural Women’s World Cup was in 1991, while women joined Olympic soccer in 1996), and have very similar structures, so here’s a visualization of all-time medal totals:

As you can clearly see, the US national team has as many gold medals in both tournaments than any other country has total medals.

Tableau Hack: Avoid “*” in Tooltips & Other Interactive Text, Using LOD Calculations

When Tableau 9.0 was released, I looked at Level of Detail calculations and thought, “I’m sure this adds a lot of capabilities to Tableau, but I just can’t wrap my mind around them, yet.” Then I discovered that this new functionality allowed me to avoid what I feel to be one of the least intuitive parts of Tableau for end-users: the tooltip asterisk. When you create interactive text in Tableau, like a tooltip, and the Dimension driving it has more than one response, Tableau simply displays an * in that space.

In the past, I smart dashboard designer would either 1) organize their dashboards or data sources so that they were sure these *s would not pop up, 2) include text explaining what the * meant, or 3) went through very complicated calculations that mimicked LOD functionality that didn’t exist yet. Now in two simple calculated fields you can get Tableau to either display the single value for a field or give you a count of unique values present in the data.

If you’re confused, here are screen capture examples from a dashboard I published earlier this week (full dashboard at the bottom of this post):

tooltip no asteriskIf I had simply put the [Club] dimension into that spot in the Tooltip editor, Nagbe’s would have read “POR,” while Amarikwa’s would have simply displayed an *. Since 160 of the 366 players displayed on this graph played for multiple clubs during that timeframe of my data source (2011-present), this would be far from ideal. Instead I wrote the following calculated fields:

calc fields no asteriskLet’s dive into the first one, since this is the new LOD calculation type. It’s syntax is markedly different from many Tableau calculations, but a simple way to think about the FIXED LOD calculation is that if you want the sum of [A] per [B], then you’re calculation will read: “{ FIXED [B] : SUM([A]) }. In the case above we were looking for the distinct count of clubs per player and so my calculation was: { FIXED [Name.Href] : COUNTD([Club]) }.

True, I could have built this as one longer calculated field, but I thought clubs per player might come in handy outside of tooltips, so I left it on its own then used it in my “no * club field” calculation. This one used three functions in its own right. The uses of ‘+” ‘ are Tableau’s way of requesting concatenation (stringing together different text elements, for those who don’t speak Excel). Tableau concatenation doesn’t allow the mixing of strings and numbers, which is the reason that the second instance of [clubs per player] is wrapped in the STR formula, which changes its data type to string, to match ” Different Clubs” within the if and only if statement here.

This may seem like a lot of trouble for a tooltip element, but keep in mind that you could use this hack for any interactive text element on a dashboard. Since you would rarely want “*” in a text field, this approach opens up some important possibilities. If your data is structured in a certain way, this approach could very well be the most expeditious way to summarize an important dimension in an interactive way, which can be very valuable.

Here’s the full dashboard from the example above:

MLS Foul Magnets on Both Sides of the Ball


Whistles are among the more common occurrences in soccer, but it’s easy for fans to lose track of which players tend to most often be on either end of physical play. Who tends to commit the fouls and who tends to suffer them can be important to understanding the dynamics of a match though, especially if the referee is willing to enforce persistent infringement.

I took data from MLS’ own statistics pages going back to the 2011 season MLS’ own statistics pages using an API service called Kimono. In the resultant dashboards it’s clear to see that different positions have very different foul patterns. Unsurprisingly, keepers grouping is the most distinct, as the commit almost no fouls whatsoever, and they commit only a few. Of course, a keeper committing a foul is more costly than anyone else, as location dictates that many of the rate infractions that they do get called for are likely to result in a penalty kick and/or red card.

All other positions have a wider spread, but also a much more varied tactical assignments. Lumping all defenders together is certainly suboptimal, as many of them rarely enter the attacking third outside of set pieces, while others regularly overlap midfielders far upfield. Imperfect as these standard, basic positional designations are, they are clearly different foul tendencies between defenders, midfielders, and forwards. Beware drawing conclusions on the in-between positions, as F-M and M-D are the smallest sample size groups. Filtering them in alongside other positions is probably more helpful than isolating those intermediate positions. I also made a small multiples version to make it easy to compare positions side by side:

None of this is to say that foul data absolutely indicates overall player quality, but it must be beneficial for an attack when someone who routinely enters the attacking third is also regularly drawing fouls, as has been the case for Mauro Diaz, Darlington Nagbe, Benny Feilhaber, Javier Morales, and (in smaller samples) Matias Perez Garcia, Sebastian Giovinco, and Tommy Thompson. Outliers among defenders include Damarcus Beasley (who draws fouls more often than any other MLS defender while committed fouls at one of the lowest rates), Abdoulie Mansally (2nd to Beasley in fouls suffered rate for defenders, but also second in fouls committed per 90), and Greg Cochrane (easily the least foul-involved outfield player in MLS).

I’ve included a lot of instruction inside the dashboards, so I’ll keep my writing here brief. Later this week I plan to write some how to guide on a few different Tableau techniques I employed when putting this together. I’m most excited to tell you how I banished the asterisk from tooltips using a Level of Detail calculation. For now, enjoy these fouls dashboards, and please let me know if you have a particular question on how I did certain things in Tableau.

The Online, Interactive World Atlas

Welcome to my Wikipedia-fueled world atlas. I’ve taken ratings of nations from many Wikipedia pages and provided a portal through which you can aggregate them through dividing by population, GDP, and physical size and display the metric’s geographic trends. On the second page you can choose two different metrics to generate a scatterplot illustrating the relationship (or lack thereof) between them.

My aim here is to enable viewers to investigate the data themselves, without having to build their own dashboards. To that end, I’d like to invite anyone to write up an analysis of a map and/or scatterplot they generate using this atlas. If you do, please send such an analysis to me ( Any submissions that I like (I value brevity, so aim for 500-1,000 words) will get published on this site, and I’ll give constructive criticisms to any that don’t make the cut. If you want to post on your own site, please send me an email or a message on Twitter (@StatHunting) and link to this site in your post, so I’ll know to point my followers in your direction.

I’m going to try to make some enhancements of this viz throughout the week as well, I have my eye on an alternate option to the filled map for tomorrow morning, and would be happy to incorporate more data as well. If you can find a Wiki page that you think would fit this project and gives ratings for at least 120 countries, please let me know in the comments or through social media.

Custom Colors, Interactivity, and a Couple Happy Little Charts

Last week some of my Tableau work was featured on, and since I think the charts speak for themselves, I figure I should use this space to explain how I used Tableau to make the dashboard, and then tack on a couple enhancements.

If you want to follow along with this how-to guide, simply download Tableau Public (it’s free) and install on your PC or Mac, then click the Download link in the bottom right of the viz.

A little while ago the Galaxy’s digital media manager, Chris Thomas, reached out and asked me to create something in Tableau that charted LA’s record against other MLS clubs, 2009-2014 (all of Bruce Arena’s full seasons in charge). My initial thoughts were 1) I can grab all the scorelines in that period from MLS’ schedule page, 2) I’d like to in some way use an isolated image of the Galaxy crest’s quasar, simple yet distinctive, 3) I’d need to customize the colors to match LA Galaxy branding, and 4) I wanted a second chart on the dashboard offering a different perspective on this era, and also functioning as a filter.

The data was pretty straightforward in 1), though there were a couple of odd null scorelines listed in 2010, but nothing quick internet research and Excel adjustments couldn’t fix. After loading the resultant csv into Tableau, I simply used calculated fields to list results in terms of the Galaxy and Opponents instead of Home and Away. Then a couple other calculated fields to get figures for wins, losses, draws, points, and goal differential.

Chris quickly gave me the image for 2) and Pantone color codes for 3). I used this site to convert the Pantone codes to RGB, and then popped them into Tableau by choosing More Colors when setting the color of any element and typing the RGB numbers in the Red, Green, and Blue spaces. After I input them once, Tableau held onto the Galaxy’s midnight blue, lighter blue and gold for easy selection on other chart elements.

Now that I had my data and palette set, it was time to get charting. Lollipop charts are a nice option for visualizing figures that can land above and below zero, so I made some quasar-tipped lollipops (using this technique, but with custom shapes for good measure) to display the Galaxy’s average goal differential against all of their opponents. Chris asked me to filter out results from the CONCACAF Champions League, US Open Cup, and World Football Challenge because they were a bit off topic (and probably also because the records vs Real Madrid and the Carolina Railhawks are embarrassing sans context). Turns out that over these years and within only an MLS set, the Galaxy haven’t been outscored by anyone! Impressive, even if it negated the lollipops’ strength in charting negatives. I still liked the shooting star aesthetic, though.

For the second chart, I figured that since I had a date dimension for all the Galaxy fixtures, I’d use a line graph to display points over time. Obviously, individual matches’ points would be far to erratic to be useful, so I right-clicked on my calculated field for points, chose a quick table calculation for moving average, then selected “Edit Table Calculation” and set “Previous Values” to 34, the current length of an MLS season. This chart got funky in early 2009 because it didn’t have 34 matches to look back on, so I went back to my data source, added 2008 fixtures to the csv file, and refreshed my data in Tableau. I then had to filter out 2008 in the goal difference table, and forced my date axis in the line graph to start on Jan 1, 2009 so that the 2008 results were only evident in the background calculation for the moving average of points.

After some discussion with Chris and others on the Galaxy web team on design elements (the original had a gold background and a different font) and glamming up the tooltips, text you see when hovering over a data point, I ended up with the dashboard you see above.

I’m always tinkering with these projects though, and after some Twitter feedback I decided to make an alternate version. First I undid the context filter on Competition, making it clearer that LA hasn’t been without their issues in the CONCACAF Champions League (aka CCL), the World Football Challenge, and the US Open Cup. However, the later two can be partially chalked up to Arena often placing less priority on those tournaments to save his best players’ legs for more important competitions. Second, I created parameters and calculated fields (similar to this technique) a couple of times to allow readers to switch between charts based on points or goal differential. Here’s the result:

By the way if any other club would like to look back on their records (full history or a limited window) in a similar manner, it would be very easy for me to replicate this approach, tweaking the color palette, images, etc. to fit the project.

Thierry Henry in MLS: The Grass is Always Greener

The king is hanging up his cleats. Thierry Henry officially retired today, and will be an (assumedly blunt, witty, and insightful) analyst for Sky Sports. Many exclusively remember him for his years with Arsenal and Barcelona, but those in the United States feel privileged to have seen him casually dominate for the last five years in a New York Red Bulls kit. Joining the MLS club at 32 after already scoring 232 goals in 455 professional matches, for many European fans Henry’s stateside accomplishments were always going to be little more than footnotes on a remarkable career. However, despite his nonchalant bearing, Henry never played MLS matches as though they were anything but serious business.

What was truly remarkable about Thierry Henry’s time in MLS was that he aged his game in a smart way that few athletes seem to even attempt. Oftentimes an aging star either tries to recreate his old magic by sacrificing other areas¹ or just takes a walking paid retirement². Thierry Henry took the road less traveled by recognizing the limitations body was thrust on him and adapting himself to still be enormously effective despite being a very different player³.

The blazing footspeed of Henry’s heyday was gone, but without it he more clearly exhibited his beautiful mind which could read the circumstances of the match faster than anyone else, then elegantly position himself and execute a sly pass, run, or shot that turned everything on its ear. His goal and assist numbers below reflect that he remained enormously productive throughout this period.

Some MLS supporters didn’t get to see the show in person though, because Henry selectively sat out matches played on certain artificial surfaces. Every time he was scheduled to play on turf that was tailored for American football, there was always a reason for Henry to take the day off. I’m not sure if “turf distaste” was ever the explicit reason for Henry’s absence, but the last four stadiums in the below graphic lay out the trend pretty clearly.

Portland is the exception on which he played every available minute, and it is noteworthy that their artificial turf is the only such surface in MLS that doesn’t occasionally host professional tackle football games. Turf can be setup for soccer or football, and whether that makes a difference can be debated, but Henry clearly acted as though Portland’s approach to artificial turf was the only acceptable non-grass solution.

Montreal has played the vast majority of their MLS matches on grass in Stade Saputo, but of the Red Bulls’ five visits to Montreal, only two were played on grass, including, on July 28, 2012, Henry’s only regular season visit to Seattle, Vancouver, New England, or Montreal that involved kicking a ball. If Montreal did purposefully dodge Henry visits through disproportionately staging them on Olympic Stadium turf, it was a smart strategy, as he was positively merciless when he did play against the Impact, to the tune of 1.8 goals per 90 minutes and 0.8 assists per 90, both the highest marks Henry posted against MLS opponents.

In the above graphic you can explore Henry’s output across 10 different categories, organized by MLS club, city in which the match was played, or even by month. In all cases, the results will be sorted highest to lowest⁴. All of three of the dashboards above are driven simply by the game log on Henry’s MLS profile page, and I should re-iterate that this resource only covers the regular season. Ironically, his final professional appearance was a playoff appearance on New England’s turf.

In no way is this meant to imply that Thierry Henry’s contributions can be fully rendered in graphs. His smooth brilliance was best witnessed by keeping your eyes on him for 90 minutes so you could try to catch as many of the great moments, subtle and/or sublime, that he would churn out regularly while flowing through the beautiful game. The charts just reflect that all of that elegance was still enormously productive.


1. NBA fans are probably thinking of Kobe Bryant hurting the chemistry and defense of the Lakers right now, distilling his game to little more than inefficient scoring and lots of it.

2. Ironically, New York also paid huge sums to two of the most unashamedly lazy aging stars in MLS history, Lothar Matthäus and Rafa Marquez.

3. Another NBA reference: one of the best corralaries to Henry’s evolving approach was the last few years of Jason Kidd’s career, in which his speed was gone, but his brain helped him manipulate circumstances, getting many easy points and steals for his team.

4. Admittedly, this order takes some getting used to when looking at monthly stats, but I figured that some would enjoy the feature nonetheless. 

Create Your Own Home & Home, Away Goals Chart

After the first leg of a home-&-home series, some supporters are always left scratching their heads as to what their club needs to accomplish in order to win the aggregate series. Since most home-&-home series use away goals as their primary tiebreaker, I decided to create a tool by which readers can enter team names, first leg scoreline, and then be shown the necessities for those sides in the second leg.

In short, you just have to replace “Team A” and “Team B” with club or national team names, abbreviations, or even nicknames (complimentary or not), then use the slider to select the first leg scoreline. You can use this for matchups in the Champions League, MLS, Liga MX, certain stages of some confederations’ national team qualifying for major tournaments, or even a competition between tiny clubs that very few care about.

I used the current state of the MLS conference finals for tabs illustrating the use of this tool. The New England Revolution obviously has a substantial advantage heading into the second leg, having struck twice in Red Bull Arena and conceding only once. Meanwhile, LA Galaxy have to be pretty happy with a 1-0 win in their home, though the Seattle Sounders seem quite capable of doing enough to take the series in the second leg.

In all of this, keep in mind that certain scorelines are more likely than others, so simply counting cells in the chart isn’t very useful. Hover over any one cell and a pop-up will note the percentage of soccer matches that generally end in that scoreline, based on this piece from the Soccer By the Numbers blog, which cataloged outcomes from the EPL, La Liga, Bundesliga, and Serie seasons from 2005/06 through 2009/10. I simply averaged the distributions for those four leagues, so don’t consider this the be-all-end-all of scoreline distribution. The figures are also problematic predictors of particular home & home fixtures, especially since objectives are so different within a playoff structure, but they do help us set expectations on a basic level.

50% of those matches ended with the home side scoring between zero and two goals and the visitors either getting shut out or scoring once. That makes the six cells in the top left of each chart immensely more important than less-likely scorelines on the periphery. Expanding up to three home goals and two away tallies brings the total above 80%, then by five and four almost 99% of outcomes are covered, which is why those are the limits of the home-&-home away goals charts.

Speaking of those home-&-home maps, I just hope that they help some folks understand the away goals tiebreaker (regardless of my opinion that they are a poor fit for MLS playoffs). They are pretty easy to understand when you look at them from the right perspective, even if away goals are a thoroughly arbitrary way to lessen the likelihood of extra time, and cumulative home field advantage within a series.

Effect ofAway Goals on 2nd Legs of MLS Conference Semifinals

For the first time this year, MLS is using away goals as the first tiebreaker in their playoff home and home matchups. Before we get to the pros and (mostly) cons of this rule, here’s a guide to the 2nd legs of the conference semifinals, taking away goals into effect: semis

Cells with white bars above and below represent aggregate ties, and are the scenarios in which the rule matters. In previous MLS home-and-home matchups, the 2nd leg would have gone to extra time, but now you only get OT if the two legs’ scorelines are palindromic. It is important to note that away goals will only count in regulation, so on rare instances where a matchup reaches overtime, the home side will get a glimmer of an edge. But if they can’t take advantage in 30 minutes, the edge evaporates, as studies have shown that penalty kick shootouts are home field neutral.

In this year’s matchups, this means that Los Angeles & Seattle will be incentivized to clamp down the match and keep it low scoring, so that even an aggregate tie would land in their favor, or at least give them 30 minutes of home overtime. Same for New England, but all they have to do is hold Columbus to a couple of goals or less. The other top seed, D.C. United, has the trickiest path, as they need two goals, but if New York scores once, that number doubles.

Which brings us to the major problem with this setup. Home-and-home is designed to negate home field advantage with 90 minutes played in both settings, and the only way for there to be an advantage for the higher seed is for them to get an extra 30 minutes of home field in that second leg. An away goals tiebreaker makes that outcome even less likely than it was before. I’m not the only one who sees the issue this way, and you can read Brian Straus’ smart critique upon the rule’s introduction here.

The issue goes beyond the basic logic of the setup, though. A study performed by the University of Munich’s Department of Statistics concluded in 2010 that

It is shown that the observed differences in frequencies of winning between teams first playing away and those which are first playing at home can be completely explained by their performances on the group stage and – more importantly – by the teams’ general strength.

That’s what you want in a champions league competition where seeding is far from straightforward or trustworthy, but in a season-culminating playoff system? If MLS wants the MLS Cup to feel like the legitimate ultimate trophy for each season, they need a playoff system in which regular season excellence is rewarded, not neutered. They had that, though it was cumbersome, before 2003 in their best of three format, but they’ve been adrift ever since in home and home murkiness, and away goals is taking them even further from shore.

Champions of away goals in MLS, such as MLS’ Technical Director of Competition Jeff Agoos, point to various rationales like the rule promoting attacking play, drama, or being an “authentic” Europe-bred standard. Some very smart people in Europe have problems with it, though, and I have yet to see proof of any off these defenses of away goals.

Thankfully, MLS only have to peer south of the border for a clear, simple upgrade. Mexico’s Liga MX has playoffs in which the home-and-home tiebreaker is regular season record. Underdogs have to win outright, which makes far more sense. As things stand in MLS, there is effectively no difference between the 2nd and 3rd seeds in each conference, who meet each other to start their playoff runs, and the only advantage footer the top seeds is the hope that their opponent is wounded from their play-in wild card match. Shouldn’t 34 matches carry much more weight than that?