More MLS 2015 Visual Exploration Tools

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Previously, I created an interactive view just looking at goal breakdowns by Major League Soccer teams overall for the 2015 season.  I’ve added several more frames to look at the breakdown of the same dataset to this new view in a exploratory way to see variables that correspond or not.

MOTIVATION

As per verbatim from my previous post:

I’ve really into soccer in general, especially international play, after my grad school project where I worked at the Annenberg Innovation Lab collaborating with Havas Sports and Entertainment and IBM on a research project studying soccer fans (see Fans.Passions.Brands, Fan Favorites, and Sports Fan Engagement Dashboard). I also did my degree practicum on Marketing to Female Sports Fans.

I’m now in another universe creating data visualization at an advertising agency and am trying to combine the geeky fandom with practical practice related to my daily work.

 

DESIGN

I deliberately tried to use Tableau components and styling that was out of the ordinary, to some mixed level of success.  I put in a custom color palette in the Tableau repository preferences file.  Also, I tried to take advantage of using the context filters (when you click on one bar graph of a team for example, the other charts only show stats about that team you just click on instantly), scale filters, and pivoting the data on the third dimension, using both a color gradient and size of a value in a chart for instance.

TECH

Trying to stretch the design and exploratory strengths of Tableau here.  The one knock I give for Tableau in 2016 is that it doesn’t present data in a sexy enough way compared to Javascript-based visuals.  On the other hand, none of the Javascript based tools democratizes creating views you can explore with some pretty heavy statistical tools, one could some of the basic functions many people use SPSS for could be better left using Tableau as one integrated tool.  In particular, I utilized the r-squared and p-values to show correlations across different metrics that might matter or be interesting to see how they hold to some teams or not.  There’s not much of correlation between Corner Kicks and Goals for teams overall for instance, but there is greater negative correlation for those teams who have more losses.

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