1. Performance over time
In our upcoming exercises, we will provide our baseball historian persona the information necessary to discuss two key topics.
First, how has offensive productivity changed over time, and how can we see this change?
Second, over the last decade, batters have emphasized hitting for power. We want to see if this emphasis on power hitting has a negative effect on other parts of offensive performance.
Our first goal is to look at historical trends in putouts by year by third basemen. To do this, we first create a slicer and filter on position, where the position is third base. Next, create another slicer and add the year so that we get a range.
A putout is when a player on defense is directly responsible for an out--they catch the ball in the air, they tag the runner, or they step on a base. We’ll track that using a line chart, like so. Add Putouts to the Values field and year in the Axis field.
We can see a range of a number of putouts over time. In the early era of baseball, third basemen were responsible for a lot of putouts. But then there’s a long stretch where they weren’t and in about the 1960s, it started picking up again, except for three years: 1981, 1994, and 2020.
To understand why, let’s add a new line and clustered column chart. The Shared axis will be the year and Line values will be putouts. You can see at this point that we have the same line chart visual.
Now we can add inning-outs--that is, how many outs were played over time by third basemen in the league.
Zooming in on years from about 1950 on, those three years which had big drops actually had a lot fewer inning-outs played. The reason is that 1981 and 1994 were strike years and 2020 was the year of COVID.
Let’s replace the line chart with our line and column chart, as it gives us additional valuable context.
Next up, I want to talk about the 2016 St. Louis Cardinals and to reduce cognitive load, we’ll do this on another page. On this page, we will add a filter where the yearAndTeam is 2016_SLN.
What I’d like to do is create a radar chart of third basemen who played for the Cardinals in that year and look at a couple of fielding measures. Let’s add nameAndTeam to the Category field and then double plays and errors to the Y Axis.
We have too many players on the radar chart, making it a mess. If we filter on the visual to include just third basemen, we narrow it down to six.
Now let’s do two more things. First, I want to format this so that the colors are not the same. Now I can easily distinguish errors versus double plays.
I’m also resizing it so that you can see the player names more clearly. Now we can see the six third basemen for the Cardinals in the year 2016.
The next thing that I’d like to do is compare putouts and assists for all players who had at least 1000 inning-outs for the Cardinals in 2016. I can easily do that with a tornado chart. The tornado chart requires a Group and some Values.
I already have yearAndTeam as a filter for the entire page, and now I want a filter on the visual, where innOuts is greater than or equal to 1000. That way, we’re only looking at players who were on the field for a large percentage of the season.
We will then add nameAndTeam to the Group field. For each group, we will compare two measures: number of putouts and number of assists.
An assist happens when you throw the ball to another defender, who then records a putout. Some players, like Yadier Molina, have many more putouts than assists. Others, like Kolten Wong, have more assists than putouts. This is a function of what position they play.
Now it’s your turn to try this out.
2. Let's practice!