With the final weekend of the season coming up – dramatically branded Decision Day by the league office – this seems an appropriate time to update the Playing Time Evolution plots that I first published about a month ago.
Several months ago I wrote about a number of ways to visualize how often different players appear together. Since that writing I’ve continued to explore this question, and have revisited an older plot design that focuses on a single player and his or her teammates. This focus brings a measure of clarity that I would like to explore here.
The US Open Cup Final will be played tonight in Philadelphia. The game, between the hosting Union and Sporting Kansas City, will hopefully be an exciting finale to the 102nd edition of the tournament. This year’s event was the biggest in recent history, with 91 teams entering – including every American team from the three professional divisions.
For devotees of the tournament, the Open Cup is one of the uniquely attractive elements of soccer. In theory, any group of players could enter and see how they match up all comers. The cinderella runs of underdogs like the San Francisco Bay Seals, Cal FC, and the Rochester Rhinos (champions in 1999) are the sorts of plots that Hollywood screenwriters long for.
For all this magic, however, there is a frequent undertone from the top of the soccer pyramid that also gets dragged out every year. Search for the phrase “take the Open Cup seriously” and you will find a litany of articles from recent years, about almost every team in Major League Soccer.
Doth the writers protest too much? Continue reading Taking it seriously: MLS and the US Open Cup
In my last post, I introduced a visualization that illustrates how playing time evolves as a season progresses. The feedback for that post was compelling enough that I decided to produce similar plots for every team in Major League Soccer.
Columbus Crew SC will play host to FC Dallas this weekend in a game that will be nationally televised on Fox Sports 1. While the game is still several days away, and will feature two clubs in third place in their conferences and fighting for playoff seeding, much of the conversation ahead of the game has instead focused on absences.
There is a narrative about Major League Soccer that describes an emerging financial arms race between its teams. The Designated Player Rule, instituted in 2007 with the arrival of David Beckham, has allowed teams additional flexibility to spend larger sums of money on key players. Every team in the league has taken advantage of this opportunity, and the rule itself has been expanded several times in recent years. Teams can currently have up to three such players on their roster, and a new category of expenditure – “Targeted Allocation Money” – was announced earlier this season. This tactic was used almost immediately by the Los Angeles Galaxy, with the end result being the acquisition of Giovani dos Santos.
Surveying this shifting landscape, columnist Steve Davis recently argued at World Soccer Talk that the teams in MLS will effectively split into two groups:
Now [MLS is] like all the other leagues of haves and have nots. We will now march predictably into every season essentially choosing among a handful of big brand clubs as the real title contenders. Everyone else will fight for the scraps.
Is this narrative of financial inequality accurate? I set out to investigate.
I was inspired the other day by something Steve Fenn (@StatHunting on Twitter) wrote about analytics in soccer:
IMO it’d be better if a club’s analytics 1st made sure metric was predictive, or at least repeatable.
— Steve Fenn (@StatHunting) July 28, 2015
This question of repeatability is something that resonated with me, so I started digging around a bit. While I can’t claim great familiarity with some of the advanced modeling that goes on around the soccer world, my starting point was a fairly simple question:
How repeatable is team success itself?
The week of the All-Star Game is upon us. Most teams in Major League Soccer hit the midway point of their season a few weeks back, the Gold Cup just finished, and the CONCACAF Champions League is starting soon. This seems a decent time to step back from the season, take stock of the trends so far, and begin to anticipate the push to the playoffs.
How do you evaluate soccer players? Is there a way to examine a given player, in the context of his or her team, regardless of their position on the field?
This is an issue that I’ve been somewhat preoccupied with this season, and the question has led me to put together a plot style that attempts to answer those sorts of questions. Continue reading Experiments in plotting player performance
The question of player combinations has fascinated me for some time. The topic is predicated on the belief that a team is, more than anything else, the aggregation of its players. Coaches have an impact, but it is the players on the field. More than individual players, even, I have been looking for ways to understand the results of player combinations.
This post explains a few outputs that address this question, and gives some notes on their application. Continue reading About player combinations