The MLS Playoff, Top 3, and Supporters Shield Races

League tables lie, and MLS’ is among the least honest. With 19 clubs, one has to sit out every weekend, and even without those bye weeks the league is generally averse to scheduling the season such that clubs’ games played stay roughly even, so ranking by raw points is usually quite misleading. Instead, I’m ordering clubs by their points per game (PPG), which has the added benefit of being able to chart the PPG pace they’ll need over the rest of the season on the same axis.

Hover or click on a club’s crest or bar for a writeup of their place in each race.

The colored boxes represent the PPG that club will need over their remaining matches to compete for that particular objective. Each box spreads over 2 points in the final standings, for example, today (I aim to update this table at least on a weekly basis) the left side of the purple Supporters Shield box represents the pace needed to reach 64 points, the midpoint is 65, and the right side is 66. I’m roughly trying to place the boxes such that the left side represents a 50/50 chance for most clubs, and the right side is at least above 75%, all based on SportsClubStats’ Monte Carlo simulations. Note that the East and West have different point targets for the playoffs (blue) and top three (green), since the two conference’s races are basically independent.

The three races outlined are the most important when considering playoff implications. The Supporters Shield holder gets a CONCACAF Champions League (CCL) spot next year, the top three in each conference get byes to the conference semifinals, and fourth place hosts fifth in one wildcard match, with the winner meeting the top seed in the next round. None of the top three really get an advantage over the others, since the conference semifinals and finals are in home and home format, which neuters home field advantage. Sure, fourth is preferable to fifth because of wildcard hosting, but that’s a small factor in comparison to a bye or a CCL spot.

What we’re left with is a clear indication that the Supporters Shield race is between Seattle and LA, with DC or RSL only able to enter it with a huge rally alongside a stumble from the leaders. Essentially, the West has paired off with a Sounders/Galaxy battle for first, Salt Lake/Dallas grappling for third, and Vancouver/Colorado desperately trying to join those four in the playoffs, at least as the visitors in that wildcard match. Meanwhile, the East is one big jumble, with each club seemingly capable of falling or rising by a couple spots when all is said and done. Except Montreal, who can safely set up shop in the cellar.

The admitted blind spots in all this are schedule strength and tiebreakers. I note wins and goal differential in the writeups that pop up when you select a club in the table, but the specific point targets are conference-wide. A team with three more ties than clubs they are close to (prime example, Chicago, who may well set the MLS single season record for draws) would need to aim one point higher. Meanwhile, I’m not including fixture difficulty, but the PPG pace needed is still highly applicable whether the road to get there is rocky or smooth.

Overall, this should be markedly more helpful than the standard league table, but for other advanced views of MLS races, I highly recommend simulations on Sounder at Heart in their series, “State of the MLS Run In,” as well as on American Soccer Analysis. These sites take a more nuanced approach while projecting for each club, while I can update mine quickly and allow for assessment of all the races at a glance.

Google Trends in USA Sports Part 2: Regionality

A while back¹, I looked into American Google Trends scores across 16 sports, both overall and in terms of seasonality. Inspired by FiveThirtyEight’s riffs on Trends, I found that Trends is a gauge of online interest or intrigue, not popularity per se, but the data is quite useful for comparing sports in some contexts. One of Trends’ most interesting features is the way it parses results geographically. While some sports’ fanbases seem likely to use Google to differing degrees (as illustrated by Part 1′s hypothetical baseball and soccer fans), conceptually I felt that Googling bias when comparing states should be smaller², making intra-sport regionality quite instructive. The map in the following Tableau dashboard compares each sport only against itself, state-to-state. Click the dropdown list in the top left to switch sports.

Note that a state’s Trends scores are all relative to the state in which that sport created the most interest, which always has a score of 100. For example, every other state’s score for tackle football is essentially a percentage of the Google searches made per Google use for that sport in Alabama. Soccer is a pretty steady presence from sea to shining sea, though its consistency is not as pronounced as its no-offseason nature lent it in Part 1′s seasonality study. Virginia’s top spot is interesting, as the state’s direct soccer legacy can be traced mainly to college soccer, which gets almost zero media coverage, and D.C. United, which plays its matches on the other side of the Potomac. The nation’s capital does seem to drive the results, though, as all of the most soccer-Trending Virginia cities reside relatively close to it, as illustrated in this map taken straight from Google Trends:

This map does not tell us whether it is driven by D.C. United, University of Virginia soccer, general demographics, or foreign-born ambassadors/lobbyists/etc living in the suburbs and searching for the beautiful game there, but it is notable that UVA is in Charlottesville, a couple hours away from the state’s soccer Trends epicenter.

Generally, soccer seems to be at its strongest in high-population states, as the top seven states by population all land 65 or above on Trends’ 100-point scale. The sport struggles most in sparsely-populated northern states, like North Dakota, Montana, Wyoming, and Alaska, with the 49th state setting the beautiful game’s floor (a relatively high floor at that) for American interest.

In some other sports we start to see a bias toward hotbeds of that particular sport in NCAA competitions. Alabama leads tackle football in Trends, with the many of the NCAA’s most storied football programs (and very few states that house NFL teams) scattered through the top 12. Basketball’s top 10 states contain only three NBA franchises, the Indiana Pacers, Charlotte Bobcats, and the Cleveland Cavaliers, and all three of those states have hugely popular college teams as well.The most striking example is Louisiana topping baseball scores. LSU baseball is legendary in the niche world of college baseball, having been to 16 College World Series with six championships in the last 28 years. The Tigers have had the NCAA’s #1 baseball attendance for 19 straight years, averaging 10,754 per game when no other college drew more than 7,700. Similarly, other non-MLB states like Mississippi, Nebraska, South Carolina, Alabama, and Arkansas are in the baseball top 10.

Meanwhile, ice hockey unsurprisingly has a decidedly northern thrust. Only eight states had Trends scores above 40, and the furthest south of them was Massachusetts. Outside of Alaska (scoring 47), the most Western hockey-interested state is North Dakota. At the other end of the spectrum, 23 states had hockey Trends scores below 15. This means that in all of these states (mostly South and West) hockey searches occurred at most 15% as often as they did in the sport’s standard-bearing state, Minnesota. Of course, the severity of this geographic trend could very well be exaggerated by college hockey programs, which are far more regional than the NHL.

Based on standard deviations of state Trends scores, cricket and lacrosse join hockey in drawing the most sporadic interest in this country. The cricket Trends seems to somewhat mirror the map of Indian immigration in this country, while lacrosse, for reasons unknown to me, seems to have elicited heaviest interest in Wisconsin, Maryland, Connecticut, and some other Northeastern states. If that list makes sense to you, please clue me in on lacrosse patterns in the comments. (Note: Jason Kuenle commented below that Wisonsin has a town named “La Crosse,” which unfortunately means that the lacrosse Trends scores for that state and some of its neighbors are decidedly suspect.)

Hawaii sticks out as the state with the most unique sports profile, popping up as a top three state for niche sports like rugby, ultimate fighting, volleyball, and swimming. If you are one of the few Americans with a passion for those competitions, good news; you may be best off living on a tropical island. Meanwhile, Hawaii has scores below fifty for each of the top four Trends sports: tackle football, soccer, basketball, and baseball.

If someone is deeply convinced that a certain sport will never be big in America, it could well be that they simply haven’t visited one of its domestic hotbeds³. While the apparent college sports bias does raise questions about the actual meaning of Google Trends scores, I feel that they are still a nice way to map online interest. You just have to realize that those driving online interest are likely far younger than the national average and their googling tendencies may well be skewed by which schools they attend. All of the above is a bit fragmented, but the biggest lesson here is that so are American sports preferences.

 


¹ OK, a lot earlier. This article got delayed because I have been quite busy personally and professionally of late. The delay is kind of a blessing in disguise, though, as it gave me time to mull the results of this study a bit more and come out the other side with what I think is an improved analysis
² Especially given that each state’s Trends score is scaled based on overall Google searches in that state.
³ Also worth noting that there are fluctuations within states, though Trends’ publicly-available data only details the most popular cities for a particular search, meaning it would have been more difficult for me to map them below the state level.

Just For Fun, an Interactive Tableau Doodle

Ever since I first charted a cosine curve on my Casio graphing calculator in high school pre-cal, I’ve been a fan of data visualization. The vast majority of the time, I employ it to learn things, but sometimes I just doodle for the aesthetic appeal. When this is the objective, I often use that trusty cosine curve as my basis.

Tableau has a contest this month for using their product artistically, and I decided to make a cosine graph, its vertical mirror image, and their trend lines with parameters that users could play with to change amplitude, wavelength, color, etc. of the waves. I’m not defining what each of the four parameter controls do at this point (if anyone cares, I’ll gladly post the formula that drives it), I just hope that someone enjoys tinkering with it like I do.

Update: There are some absolutely brilliant finalists for Tableau’s Viz as Art contest, and mine is justifiably not one of them. Highlights include Matthew Bennett’s Pure Data Harmonographs, Robert Mundigi’s adaptions on of Curtis Steiner’s 1000 Blocks sculptures, and George Gorczynski’s “Tendrils”. Mine is just a silly little tool, those pieces are gorgeous.

Charting the Career of a Young, Exciting, Inconsistent Player

Yesterday I wrote about Fabian Castillo’s first 100 MLS matches on Big D Soccer. Click that link for a breakdown of the young career of a speedy young player (who I’ve written about before) that can be frustrating and exciting, often at the same time. Here’s the chart I made chronicling his averages over 10 match periods thusfar:

http://cdn2.vox-cdn.com/assets/4912936/fabian_100.png

This graph was only supposed to be a little exercise in descriptive stats, but after I made it, I realized that I could drum up something similar in Tableau, which could easily include some predictive analytics and that would be easily repeatable for others in MLS, whose player pages all include a game log dating back to 2010. I’m just not sure if there’s a market for that, so here I’ll just post the graphic alone and ask for your feedback for now.

Landon Donovan’s MLS Greatness Went Well Beyond Longevity

Landon Donovan announced his retirement yesterday, effective at the end of the current season. Currently Donovan has the most goals (138) and the 2nd most assists (124) in MLS history. Some have smartly pointed out that these figures are inflated by both his longevity (4th all-time among field players with 27,423 minutes) and his penalty kick goals (2nd most with 28). Thankfully a common fancy stat accounts for both these issues. Non-penalty goals plus assists per 90 is a pretty self explanatory term. Subtract PKs from a player’s goal total, add in assists, divide by minutes, then multiply by 90. I applied this calculation to MLS’ all-time top 25 for both goals and assists, and here’s the leaderboard, active players in green, retirees in blue:

The exclusion of penalty kick goals deserves a quick note. PKs are inherently a different skill than goals in every other situation of the game. Put simply, in the run of play, corner kicks, free kicks, etc. you will never see a single player get an unimpeded run up to a shot 11 yards away from a keeper that’s anchored to the goalline. That situation immensely favors the shooter, scoring 70-80% of the time, while the average shot in other situations goes in only 11%.

Landon’s still #1, but his lead on Preki and Taylor Twellman is pretty slim. A slim lead over MLS originals is even more impressive than you might think, because the standard for assists was very lax in the olden days. You can read about it here, but the basics are that the last two players to touch the ball before their teammate scored were usually credited with assists, even if a lot of things happened between their pass and the strike. Players used to even get an assist even if they took a shot and their teammate scored of a rebound.

The chart above is a bit noisy, and I hope to build an database of MLS players with each year of their production, rather than raw career totals. For now, we have even more reason to be in awe of the face of Major League Soccer who will be hanging up his cleats in a few months.