Over the weekend I described the process I’m using to simulate MLS seasons, and the odds that a team reaches the playoffs. After using this tool for most of this year, I’ve recently tried extending this work to look at how team projections change during a season.
Over the course of this season I’ve been occasionally running simulations of how the rest of the Major League Soccer season might end up. The feedback I’ve gotten from this work has been generally positive, and a number of people have asked about my methodology. This post is, finally, my attempt to explain the process I’ve been following.
The TL;DR version is this: I’m running a Monte Carlo simulation that randomly assigns a result to each remaining league game.
When Columbus and Portland face off this evening in MLS Cup, it will be a clash between two of the better passing teams in the league. Both feature midfields heavy on ball control, with international-caliber players pulling the strings supported by a back line that likes to get forward.
In preparation for this game, I collected player-by-player passing summaries for each game the two teams played, starting from the last time they faced each other in late September. What I found indicates that fans of all stripes could be in for quite a treat.
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.
Several years ago, I wrote about the importance of continuity in a team’s lineup over the course of the season. The piece has since been taken down (it will soon be republished on this site), but the thrust of the argument was that the most successful teams in Major League Soccer were able to identify a core group of players who played a significant amount of a given season together. Teams that couldn’t, or didn’t, coalesce around such a core were less likely to be successful.
Over the past several weeks, I’ve been re-visiting that thesis using some alternate strategies to see if they continue to hold true.
I started working with a new type of impact plot tonight, looking specifically at playing time compared against team goal difference. Dots representing each player are plotted along two axes: the horizontal axis records how much of the season the player has seen, while the vertical axis indicates the team’s goal difference during the player’s time on the field. Continue reading Plotting individual playing time against goal difference
Just before the start of the World Cup, I posted an exploration of the United States’ roster through its last five qualifying cycles. Now that the tournament has finished, here is an updated version that includes the four games played by Jurgen Klinsmann’s team.
The American roster for the World Cup has been named, and the intense discussion over Donovan’s exclusion (and that of Eddie Johnson before him) has begun to subside. This seems an opportune time to look back over how this World Cup cycle compares to the last few.
Continue reading Examining players used in World Cup qualifying cycles