A Journey Into the Heart of Sports: Data Viz with Daren Willman
By: Senthil Natarajan
Tags: Data Visualization, Sports, Sports Journalists, Design, Sportsviz

An Interview with Daren Willman, Director of Baseball Research & Development for Major League Baseball

The following interview has been lightly edited for clarity. “SN” refers to Senthil Natarajan, and “DW” refers to Daren Willman.

SN: Let’s get some quick context for our readers. Can you tell us a little bit about your background and experience?

DW: Sure. I’m Daren Willman, the Director of Baseball Research & Development for Major League Baseball. My background is in computer science — focused primarily in software development, but most recently, I’ve been extremely interested in data visualization and how it can help sports teams, players, coaches, and front offices better understand the massive amounts of data available to them.

Home Run Derby Visualization on Baseball Savant

I played baseball all through college and always loved the game. After I graduated, I worked in law enforcement for almost 10 years, developing criminal history software. In 2012, I stumbled across a massive baseball dataset called PITCHf/x. In 2008 Major League Baseball had set up cameras in all 30 stadiums to track the metrics (velocity, release point, movement, pitch type) of every pitch thrown during a game. As soon as I saw the data there was definitely a watershed moment of all the cool things that could be done with it. I immediately started scraping all the data and coming up with ideas on how I would want to use it if I was a player so I started developing visuals that I might want to see. They started off fairly simple like a spray chart of where balls in play were hit and the location of where pitches were thrown. Shortly after that I started a website, Baseball Savant, and started adding the tools I was developing. I never really expected to work in sports but as the site got more popular opportunities started to come up with teams and the league. That’s when it started to become more of a reality that I could possibly end up making a move to sports full time.

I spend a lot of time analyzing, developing applications to visualize the data and trying to figure out what we can do with it that baseball fans might enjoy.

My role with MLB is pretty much a dream job for me. I get to blend two of my biggest passions, technology and baseball. Most of my time is spent working with the new player tracking system Statcast. Statcast tracks pretty much everything that happens on the baseball field. Player positioning, pitch tracking and hit tracking. Needless to say, this is a massive dataset, so I spend a lot of time analyzing, developing applications to visualize the data and trying to figure out what we can do with it that baseball fans might enjoy.

SN: So, you post a lot of those visuals on Twitter, and they’re really popular! What’s been some of your favorite public visualizations that you’ve done?

DW: Here are some of my favorite data viz tweets of recent memory…

Every Baseball Stadium Drawn Over Each Other

Every Baseball Stadium Drawn Over Each Other

Every Home Run Hit This Season

A First-Person View of a Dinger

Every MLB Team’s Game-by-Game Run Differential

Pitch Distribution Animations

SN: How do these viz, the ones you post publicly, differ from the ones you create for your job? What is that process like — accounting for the types of stakeholders you have now and the different conditions of satisfaction you deal with when it’s something bigger than a hobby or personal project?

DW: The public visuals I do don’t differ all that much from the ones I create at MLB. I’m given a lot of freedom to decide and iterate on what I find interesting. I like to share the progress of things I’m working on to the baseball community as I work on them and tend to get pretty good feedback from social media. It can be toxic at times because dataviz is similar to art. Some people will really like the visuals, but some people will hate them. I think one of the more challenging aspects of data visualization right now is how hard it can be to develop them. There’s a stigma with data visualization that it’s easy. In order to be really good at data visualization you need to be pretty good at several different disciplines. Scraping or wrangling data, organizing it, designing the visual and actually writing the code to display it are all unique challenges.

There’s a stigma with data visualization that it’s easy.

SN: I really like a few of the concepts you just brought up there, so let’s drill down a little bit more on that. The first is the idea of leveraging a community to help move your work forward. What have you found is the best way to interact with a community or some best practice tips on putting your work out there to get feedback from that community? What’s the best way to make use of such a broad range of feedback? Was it ever kind of intimidating to just put your visuals out there in public when you first started? One of the topics we’ve discussed among the DVS members is how tough it can be for people to sometimes allow themselves to open up their work to a larger audience, for a broad range of reasons (but also the importance of doing so!). What are your thoughts on this?

DW: My typical process when working on new visuals goes something like this: after I’ve designed and collected the data for my idea, I’ll mock something up at blockbuilder.org using d3 (I do about 80% of my work with d3, the rest of my work is typically done in three.js). It’s a great tool that Ian Johnson (@enjalot) made to rapidly develop in d3. After I think it’s polished enough for a first iteration, I’ll tweet it out to a broader audience and see what the community thinks.

The feedback I receive can vary but there are often times I don’t consider certain things when creating visuals, typically color choices that I might have made that don’t work well with color blind people. However, people will ask, “Why didn’t you use X chart instead?” which helps me think of the problem in a different light. It can definitely be intimidating putting yourself out there, especially on a platform such as Twitter that can be toxic, but the more you do it, the more you learn to ignore the people who aren’t giving you good feedback.

Max Scherzer Pitch Types by Location — Pie Charts!

A perfect example of feedback using Twitter is here. I created a scatter pie chart using a concept I saw Elijah Meeks mentioning and, as always, the anti-pie chart crowd came out. I think this is an exceptional way to visualize every pitch a pitcher has thrown since we’re dealing with larger amounts of data. A typical scatter plot would be way too much for this but scatter pies are perfect because they allow the data to be binned to the location and show which types of pitches, how many, and the general location — but someone will always gripe since it’s a pie chart.

SN: I am one of the people who gripes about a pie chart! But I will agree with you that I can’t really think of a better way to bin the data appropriately for that specific purpose. This brings up an interesting point, which is designing for accuracy/precision versus impact. I recently wrote a little bit about how graphs with radial form factors really tend to bring out this tension. How do you straddle that line and balance those considerations? And have you found the arena of sports, which tends to be sometimes a very traditionalist space with a lot of inertia, to be more or less difficult to get buy-in on new or more niche/unique types of dataviz?

DW: I think it really just depends on the circumstances. You need to know that you’ll never make everyone happy. Going back to what we were discussing earlier about dataviz being similar to art, artists can’t expect everyone to love their work. I think as long as you make a conscious effort to ensure the numbers or data you are trying to convey are shown clearly and not in a deceptive way, then using something like a radial, pie chart, etc. are fine. Another visual that seems to bring out this tension is radar charts but I think when those are done properly, they can in fact be very insightful [author’s note: score 1 for #TeamRadar].

People seem very receptive to new ways to view data in sports.

I think sports is actually great when it comes to new data viz. From my experience, people seem very receptive to new ways to view data in sports. Sports, in general, has so many statistics and data now that it’s perfect for creating and developing new types of ways for fans to look at it. I think the younger generation of sports fans especially enjoys good ways of viewing the sports that they like. I’ve definitely found that the visuals that do exceptionally well on social media are ones which are quickly digestible and convey a clear message.

Pitcher Visualization from a Catcher’s Perspective on Baseball Savant

SN: As part of the dataviz process, you mentioned “designing” the visual. Can you elaborate a little bit on your design thinking process? How does an idea take shape? And how does that idea become reality?

DW: My design process isn’t very scientific. Typically, I’ll come across a really cool dataviz and think to myself how can I do something similar with the data I have. I really like going to Andy Kirk’s website. He writes a “Best of” blog every month, and I’ll go there for some inspiration when I’m in the mood to create something. Also, with the recent addition of the DVS there’s so many awesome ideas flying around, it’s an awesome place to get inspired. It’s great practice to see something you like and re-create it using something you’re interested in from scratch.

SN: Let’s continue that train of thought on the dataviz process. Being from a data analytics background myself, I keep going back to your point that dataviz is a very complex, multi-faceted process. I always find in data science projects that I spend a very large proportion of the time on just finding and preparing data, so it’s encouraging to hear this from another perspective as well. Can you go a little bit into how people can attune themselves to that mentality of developing a well-rounded skillset in order to be good dataviz practitioners? How did you develop that multi-disciplinary, end-to-end approach? Does the data step get any easier now that you’ve got the full force of the MLB data infrastructure behind you?

DW: I was fortunate to come from a more data-centric background. I have been dealing with relatively large datasets since I graduated college, so wrangling data came pretty natural to me when I began dealing with visualizations. I think keeping focus on what I’m trying to do has always helped me. If you have an idea of visualization you want to create, it always helps me to think of exactly what data I need to create that idea and how can I get the data. Typically for my visualizations, all the data I need is in a SQL database so I can query it and extract it pretty easily, however, sometimes I have to write a quick script to scrape a site or an internal json feed. There are so many toolsets out there now to scrape data; it’s gotten pretty easy to do. I actually find the data wrangling process to be almost therapeutic. It’s like trying to solve a puzzle.

Also, when just starting out with dataviz, it’s very easy to get discouraged. The process can be tedious and difficult, but there are so many skills you learn end-to-end that even if the project doesn’t turn out exactly the way you want, it’s great practice for the next time, and every time it gets a little bit easier. I think grabbing the data I need has gotten a bit easier since I’ve joined MLB. My colleagues and I have spent many hours developing an internal data warehouse specifically for grabbing data in an easy fashion. There are always tricky situations writing queries when dealing with rolling windows and condensing millions of data points down to a concise dataviz, but that’s part of the fun.

DW: Yes, I noticed animations tend to do better on social media. I think by adding animations to certain visualizations, it helps catch the eye and exaggerate certain points I’m trying to tell. Having a static scatter plot of a pitcher’s strikeouts can help paint a picture, but when you show the same scatterplot point by point sequentially in an animation, it really helps drive home the fact that the player has a whole bunch of strike outs, base hits, or whatever metric I’m trying to convey. Also, I think transitions just look cool and are fun to play with; I use them a lot to test out new ideas I’ve been thinking about.

SN: Let’s talk about that for a second, the concept of how to really drive home an idea. You’ve obviously talked about animation here, but what are some other techniques or ways that you experiment with or utilize to optimize your data viz? Colors, reframed perspectives, etc.…

DW: That’s definitely a great question. Animation certainly helps draw the attention in especially on a medium such as Twitter but using color is a great way to highlight certain points on a visual. Recently, I’ve been experimenting a bit more with opacities to highlight certain points of animations and then slowly fading out the colors as the next bit of data is flashed on the visual. I still consider myself a novice when it comes to most visualization practices and I’m always researching and looking for inspiration on this very topic. I’m extremely thankful for the dataviz community because that’s where so many of my ideas and experiments initially come from.