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Reimagining the league table

The reason for the infrequent posting is my travel schedule. I spent the past week in Seattle at JSM. This is an annual meeting of statisticians. I presented some work on fantasy football data that I started while writing Numbersense.

For my talk, I wanted to present the ubiquitous league table in a more useful way. The league table is a table of results and relevant statistics, at the team level, in a given sports league, usually ordered by the current winning percentage. Here is an example of ESPN's presentation of the NFL end-of-season league table from 2014.

Espn_league_table_nfl_2014

If you want to know weekly results, you have to scroll to each team's section, and look at this format:

Espn_cowboys_2014_team

For the graph that I envisioned for the talk,  I wanted to show the correlation between Points Scored and winning/losing. Needless to say, the existing format is not satisfactory. This format is especially poor if I want my readers to be able to compare across teams.

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The graph that I ended up using is this one:

  All_teams_season_winloss_vs_points

 The teams are sorted by winning percentage. One thing should be pretty clear... the raw Points Scored are only weakly associated with winning percentage. Especially in the middle of the Points distribution, other factors are at play determining if the team wins or loses.

The overlapping dots present a bit of a challenge. I went through a few other drafts before settling on this.

The same chart but with colored dots, and a legend:

Jc_dots_two_layers

Only one line of dots per team instead of two, and also requiring a legend:

Jc_dots_one_line

 Jittering is a popular solution to separating co-located dots but the effect isn't very pleasing to my eye:

Jc_dots_oneline_jittered

Small multiples is another frequently prescribed solution. Here I separated the Wins and Losses in side-by-side panels. The legend can be removed.

Jc_dots_two_panels

 

As usual, sketching is one of the most important skills in data visualization; and you'd want to have a tool that makes sketching painless and quick.

Comments

Dan

These charts don't make much sense to me. NFL teams don't score 150 or 200 points in a game. What are you charting, exactly?

Kaiser

Dan: Those are fantasy football scores. They are based on real football statistics but put through a formula. The graph is for a specific league I was analyzing, and for a specific season.

Chris Pudney

Nice.

An alternative that would be more compact is to use bars: height indicates the score, colour shows win/loss.

Bars could be ordered chronologically if you're interested in how teams fare as the season progresses, or ordered by score to emphasise the score-result (cor)relation.

jlbriggs

can you post the data to play with?

Kaiser

jlb: here is the data: Download ffl_leaguetable_data.dat (4.3K)

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