Monday, February 23, 2015

Which MLB Teams Have the Most Luck?

The Pythagorean Expectation was developed in the 1980s by legendary baseball statistician Bill James.  The formula is used to determine the number of games a team "should" have won based upon the number of runs scored and allowed.  He originally used an exponent of 2, which has been revised over the years to the current 1.83.

Teams who outperform their Pythagorean expectation are generally perceived as "lucky" and those who under perform are considered "unlucky."  This viz allows you to take a look at which teams are lucky or unlucky over the years.

Tuesday, February 17, 2015

MLB Franchise Performance

I always look forward to this time of year waiting to hear the phrase "Pitchers and catchers are  reporting to Spring Training this week."

I love baseball and especially baseball history, so I wanted to focus a visualization on the past performance of MLB franchise performance.  I have also wanted to try a "small multiples" visualization.  I love the look of the NBA BALLCODE visualization by Peter Gilks over at Paint By Numbers, so I thought I would try something similar.

The viz below shows the performance of MLB franchises using games above/below .500.  You can use the sliders to go back to the 1870's, but I decided to focus primarily on the Expansion Era beginning in 1961.  I felt this was a good starting point since 4 teams were added over 2 years: Angels, Astros, Mets, and the new Senators (after the previous Senators became the Minnesota Twins).  The league also expanded the number of games from 154 to the current 162.

I also wanted to add another dimension to the data, so I decided to change the team color to gold in seasons when the team won the World Series.  This adds a good perspective to teams like the Yankees (especially if you move the slider back to the 1920s) and the Marlins (only 6 winning season, but 2 World Series titles).

Wednesday, February 11, 2015

2014 PGA Tour Average Proximity

I was inspired to tackle this chart after stumbling across a radial bar chart on the InterWorks Tableau blog.  I figured proximity to the hole was the perfect application for this type of chart.

I had to complete the custom SQL from a full version of Tableau, then extract the results into a spreadsheet to upload them to Tableau Public.  Other than that the rest was fairly straightforward using the calculations in the InterWorks blog