Baseball analysis for the GM
1. Baseball analysis for the GM
Clustered bar charts are great for showing a variety of data over a single period of time. In this case, we will look at the number of outs played by player. This gives us a listing of some of the players with the longest careers in professional baseball, but I’d like to filter down to see only third basemen in the year 2005. To do that, I’ll add a pair of slicers. The first slicer will allow us to narrow down the players by year. After adding the slicer, we will set the year range. Then, let’s add another slicer and filter by position, including only third basemen. These visuals are functional, but we can help users understand things even better if we formatted the visuals to include more appropriate titles and axis values. Let’s drill down into Title and change it to read “Outs Played by Position and Year.” We’ll make the X axis label “Outs Played.” And I’m going to remove the Y axis title altogether to save a little bit of space. For the two slicers, let’s drill down into Slicer header and change the name of this to Position. We’ll do the same with the other slicer, changing it to Year. We can also navigate to the Analytics tab and add lines to help visibility. I’d like to add a constant line which represents a full-time starter. Let’s suppose that 3800 outs played is normal for a full-time starter. To make it easier to see the line, we’ll change the color to Black, 20% lighter. How many outs did the 80th percentile of third baseman play in 2005? We can learn this by adding a Percentile line. I’ll set its color to Black and transparency to 20% to make the difference more obvious. We can see that the 80th percentile third baseman played just under 1500 outs in 2005. Now let’s look at stacked charts on a new page. This time, I will add page-level filters to set the year to 2005 and focus on the National League. Add a 100% stacked bar chart. On this, we’ll make position the legend and choose number of errors for the values. This gives us the percent of errors by position across the entire National League in 2005. Third base was the most error-prone position in 2005’s National League, but was that true across all teams? We can add franchise ID to the Small multiples field to find out. Small multiples let us break out our chart by some additional field, making this analysis easy. Let’s format this to include 6 rows, 3 columns, and no gridlines. This packs a lot of information on the screen, but can be very difficult to compare. To make that easier, right-click on the visual and show it as a table. Now we have a tabular view of the data and we can see that not every team had its most errors committed at third base. The last thing we will do is switch the visual to a stacked bar chart. The 100% stacked chart lets you compare proportions more easily, so you can see that the Chicago Cubs had a lower percentage of errors at Catcher than the Florida Marlins. In the following exercises, you’ll take the reins and build out reports for the general manager of a team.2. Let's practice!
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