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.
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.
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
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
How do team rosters change over time? Do players of a certain age tend to play more or less often? These are some of the questions I’ve pondered over the years. And they are the subject of a brief experiment I’ve worked on the last few days.
Continue reading Visualizing minutes played by player age
Two weeks ago, I published an article that analysed the distribution of playing time for Columbus Crew teams over the past 14 years. One of the apparent conclusions of that investigation was that, for a team to be successful, it is necessary to have a relatively stable core of players. The underlying charts identified that the more successful Crew teams had a group of 6-7 players who played at least 80% of the season, and a relatively small number of players who played more than bit roles, usually 15 players who appeared for at least 25% of the time.
With the end of the year quickly approaching, and the 2009 season starting to recede into memories both good and bad, this seems to be a good time to look back and begin to consider how the Crew’s performance this season compares to previous years. One way to do this is to examine how playing time was distributed in 2009, compared with past seasons. Continue reading An analysis of playing time for the 2009 Columbus Crew