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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Major League Soccer 2015 Team Goal Stats

 

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First go at this dataset.  I created an interactive dashboard-like view just looking at goal breakdowns by Major League Soccer teams overall for the 2015 season.  Planning to add several views to this panel with the data.

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.

On that note, most of the statistics and visuals I’ve found through a just a cursory look are about wins and losses.  I’m trying to show goal data by team in the MLS in a way that looks at performance based on other factors such as number of attempts and assists and not just the win-loss-draw type metrics I found in most of the soccer sites I saw.

Design

I deliberately tried to use Tableau components and styling that was out of the out-of-the box template for the platform to emphasize the ability to size values on an additional data dimension. For instance, sizing each bubble based on number of goals or customizing labels out of the default. I notice a lot of users of Tableau don’t deviate much from the standard template, and I’m trying to train myself to go beyond that and also get better at the aesthetic piece of data viz.

Tech

I use Tableau at work on a daily basis. I personally think where Tableau shines is its exploratory data capabilities if you know how to prepare data in a form usable in Tableau. A few years ago, its explanatory data visualization capabilities were second to none in this space, but the desktop tool has lost some flash factor to D3 and HTML5 visuals, but definitely not substance in my opinion. Plan to expand on this with win-loss figures as well as analysis of kicks as the high number of goals by team didn’t necessarily line up with number of matches won.