Showing posts with label baseballsavant. Show all posts
October 31, 2018
Analyzing Pitcher Performance With Density Heatmaps
baseballsavant
,
clayton kershaw
,
density
,
heatmap
,
justin verlander
,
major league baseball
,
MLB
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With Tableau 2018.3 comes density heatmaps, a feature I've been playing with quite a bit and love it for when I have a dense concentration of points and a regular scatterplot doesn't work well. Transparency can help with dense dots, but I think the heatmaps work much better.
To give it a test, I downloaded every pitch for Clayton Kershaw and Justin Verlander (two of the best pitchers in Major League Baseball) from 2008-2018 from the great stats website Baseball Savant. Every time I look at baseball data, I'm amazed at the detail of the stats covered; the data far exceeds anything that is covered in other sports.
After downloading the data, I built the small multiples view below for each pitcher so that I could see their progression through the years. Click on the images for the interactive versions. I love how the data shows me how each pitcher has gotten better with their "misses" through their careers. For example, when they throw sliders for balls, they now tend to miss below the strike zone. This is a great sign that they have command of their pitches and are less likely to miss in an area where the batter can take advantage.
The density heatmap feature will most likely be used by most people on maps, which makes sense, but consider looking at it as an alternative whenever you need to plot x/y coordinates and have lots of points to display.
To give it a test, I downloaded every pitch for Clayton Kershaw and Justin Verlander (two of the best pitchers in Major League Baseball) from 2008-2018 from the great stats website Baseball Savant. Every time I look at baseball data, I'm amazed at the detail of the stats covered; the data far exceeds anything that is covered in other sports.
After downloading the data, I built the small multiples view below for each pitcher so that I could see their progression through the years. Click on the images for the interactive versions. I love how the data shows me how each pitcher has gotten better with their "misses" through their careers. For example, when they throw sliders for balls, they now tend to miss below the strike zone. This is a great sign that they have command of their pitches and are less likely to miss in an area where the batter can take advantage.
The density heatmap feature will most likely be used by most people on maps, which makes sense, but consider looking at it as an alternative whenever you need to plot x/y coordinates and have lots of points to display.
September 13, 2018
Clayton Kershaw & My Learning Process
baseball
,
baseballsavant
,
clayton kershaw
,
density
,
heatmap
,
learning
,
major league baseball
,
pitcher
,
process
,
spatial
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What is Learning?
According to UC Berkley, learning is a process that:
- is active - process of engaging and manipulating objects, experiences, and conversations in order to build mental models of the world (Dewey, 1938; Piaget, 1964; Vygotsky, 1986). Learners build knowledge as they explore the world around them, observe and interact with phenomena, converse and engage with others, and make connections between new ideas and prior understandings.
- builds on prior knowledge - and involves enriching, building on, and changing existing understanding, where “one’s knowledge base is a scaffold that supports the construction of all future learning” (Alexander, 1996, p. 89).
- is situated in an authentic context - provides learners with the opportunity to engage with specific ideas and concepts on a need-to-know or want-to-know basis (Greeno, 2006; Kolodner, 2006).
- requires learners’ motivation and cognitive engagement to be sustained when learning complex ideas, because considerable mental effort and persistence are necessary.
I've left a couple bits out that aren't relevant to learning in the context of data visualization, but all of the others should resonate with you if you approach learning with the correct mindset.
As an example, I am actively look for reasons to practice features in the Tableau 2018.3 beta, especially around density mapping. I was reading an article this morning about Clayton Kershaw, whom many consider the best pitcher in Major League Baseball. He also has highest base salary at $33M for 2018.
Most of the density maps I've seen have had a mapping component. In the case of baseball, and pitching in particular, the spatial zone is the strike zone. Data is easily accessible to get the coordinates of every pitch as it crosses home plate.
For this project, the learning process:
- is active in that I am building my knowledge as I explore the data set and learn the new features.
- builds on my prior knowledge of how the feature works and my knowledge of the game of baseball. However, I had never done a scatterplot of pitching before, so I had to learn new terminology in the data. This knowledge will help me be more productive and learn faster in the future.
- is situated in the authentic context of engaging with the ideas and visual concepts that I saw online and drew on paper.
- required my motivation and engagement to see the project through to fruition and the persistent to make the display visually accurate.
I hope my thought process helps you focus your learning. I love helping people get better at what they do and if I can help you speed up your learning, then we'll all be better for it.
With that in mind, here are two images I created for this project. The first is all pitches by Kershaw and the second is of his curveballs, which is known to be his most potent pitch. Once Tableau Public supports Tableau 2018.3, I'll publish them and include links on the images.
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