Not long ago, I had the opportunity sit down with Todd Whitehead to discuss how he creates visualizations that dominate the discourse. Please note that the following interview has been edited and abbreviated for clarity. All charts and visualizations in this article are the works of Todd Whitehead.
“Such. A cool. Analysis. 👏” -Brittni Donaldson, Director of Coaching Analytics for the Detroit Pistons
“Nobody, and I mean nobody, makes better charts than Todd.” -Ian Levy, Creative Editorial Director for Fansided
“This is a fantastic, fantastic way to display a graphic.” -Chris Herring, Sports Illustrated Senior Writer and NYT Best Selling Author of Blood in the Garden: The Flagrant History of the 1990s New York Knicks
The above quotes aren’t referencing anything that you may see on television during a marquee NBA matchup or a splash page on ESPN. No, they’re just a smattering of the hundreds and thousands of retweets, quote tweets, and mentions racked up by Todd Whitehead (a.k.a. @crumpledjumper on Twitter) of Synergy Sports and former writer at Nylon Calculus, a flagship NBA Analytics blog. Just imagine the level of polish and verve someone’s work needs to be able to force a pause on any Lebron versus Jordan internet debates in their tracks. And then imagine the skill and versatility needed to repeat that feat, over and over again, whether it’s in advance for the NBA Finals, during the NBA Draft, or ahead of a pivotal WNBA matchup.
The use of analytics in sports has now been around for a few decades, and it takes a better historian than myself to track it in its entirety, but it’s safe to say that it hit an inflection point in the public consciousness with Michael Lewis’ seminal 2003 book Moneyball (not to mention the ensuing Brad Pitt and Jonah Hill motion picture vehicle of the same name). With baseball as a sort of torch-bearer for how the use of data could be transformative to a team’s ability to construct a roster and win games, the analytics era had arrived. It is not uncommon today to see professional baseball teams employ entire 20+ persons analytics research groups, resembling something that is usually seen at a hedge fund or tech company. Basketball’s own analytics movement is often, or even popularly referred to as “Moreyball,” a moniker that credits both Moneyball as well as Daryl Morey, current President of the Philadelphia 76ers, who is seen as a sort of figurehead for analytics in the NBA from his time at the Houston Rockets.
Whether fans realize it or accept it, analytics has (to borrow a perhaps tired phrase) changed the game. Many of the most successful teams in the league invest in their sports analytics group. The spread out offensive style of play that we see today is a consequence of analytically-derived insights. However, with more data comes more responsibility (thanks, Uncle Ben). Basketball analytics is, at the end of the day, in service to the game of basketball, which means the translation of analytical concepts into actionable basketball insights is the premium skill. As Seth Partnow, Director of North American Sports at StatsBomb and former Director of Basketball Research with the Milwaukee Bucks, frames it in his book, The Midrange Theory, “the goal is a distillation of basketball concepts from what can at first appear to be a jumble of data.”
That’s where data visualization comes in. What better bridge between the data and the consumer, whether they be a front office, a media outlet, or a fan. The best practitioners of data visualization understand how to communicate advanced concepts to diverse audiences, and optimize for the right balance between engagement and conveying new, unique insights. Undoubtedly, the most ubiquitous form of data visualization in basketball is the shot chart, a mapping of a player or team’s shot attempts onto the layout of the basketball court. Running the gamut from point by point recordings to heat maps, they’ve fast become an invaluable way to summarize some of the most critical patterns of a player or a game for analysts, media, and fans alike.
But data visualization in basketball, and all sports more broadly, encompasses so much more than shot charts (in fact, around the launch of Nightingale several years ago, we profiled another celebrated dataviz practitioner in sports, Daren Willman — you can find that article right here). Folks like Peter Beshai, Andrew Patton, and Todd Whitehead (among many others) are elevating the scope and possibilities of dataviz in basketball. Done right, basketball data visualization in the public sphere can become itself a vessel for growing the sport’s audience and engagement, as opposed to some opaque barrier of ones and zeros. And few are doing it better right now than Todd Whitehead. So dear reader, let’s pick his brains and introduce a heavyweight champion of basketball data visualization to the rest of the dataviz world.
Senthil Natarajan (SN): Hey Todd! Let’s get some quick context for readers. Can you tell us a little bit about yourself and your background? How did you get started doing data visualization, and more pertinently in basketball dataviz?
Todd Whitehead (TW): Until recently, I was working in the School of Public Health at Cal-Berkeley, first as a PhD student then as a postdoc and ultimately as an assistant researcher. Academic work calls for writing scientific papers and making conference abstracts, most of which tend to contain a graph or two, so I’ve had some dataviz practice along those lines. At the same time, I was making freelance contributions to Nylon Calculus, a basketball analytics site for nerdy NBA fans.
That experience gave me a chance to bounce dataviz ideas off the likes of yourself, Bo Schwartz-Madsen, and Seth Partnow in an environment that encouraged creativity and experimentation under Ian Levy’s guidance. It also gave me a platform to share my early dataviz work with the public to get feedback so I could iterate and improve. Then, last summer, I started a new career at Synergy Sports, a division of Sportradar, where I am helping coaches and scouts to develop winning team strategies as part of the Analytics and Insights Team. I’m still using my dataviz skills to surface actionable insight but now it’s in the field of sports instead of the field of public health.
SN: Of course now with your job at Synergy, there’s a lot more, let’s call it “standard”, types of data visualization that you have to do for your job. But we’re not here to focus on that. You’re really popular for doing very creative dataviz on Twitter. What are some of the favorite visualizations you’ve done?
TW: Last year, I had a lot of fun experimenting with physical dataviz. One of my favorites was a 3-D model of Steph Curry’s record-breaking 2974 career three-pointers, inspired by the shot charts Nathan Yau created with a 3-D printer on his Flowing Data blog. I stacked up one tiny plastic tray for each trey Curry had hit in his career to break the NBA record. I wanted to pay a proper tribute to his achievement because I’ve had so much fun watching him play over the years. It was Curry and the Warriors that pulled me back into following the NBA with their joyful style of play and that led me to Nylon Calculus and eventually to a job in sports! The project ended up being the most time-consuming dataviz I’ve ever tackled — I was trimming wire hangers for rebar, laying down a model basketball court, and lovingly arranging each tray in its precise spot — it took me weeks to get the whole thing set up and I was worried about it all toppling over the whole time!
SN: Do these public dataviz differ from ones you do more privately for your job? Or are they similar? How does your thought process differ when considering possibly different audiences?
TW: Definitely. I think knowing your audience is key. I try to organize my dataviz process along two axes, from practical to artistic, and from simple-to-read to intricate. Viz that was meant to be practical but ended up being intricate is what I consider the stuff of first drafts — a project that needs to be improved before it’s finalized. Projects that are artistic and simple-to-read, like the 3-D Curry shot chart was meant to be, are perfect for Twitter. They’re fun to look at and they will catch a viewer’s attention as they scroll through their feed.
Projects that are artistic and intricate also have a time and place, but they ask a lot of the viewer. So, chances are you’ll be left with a smaller, niche audience that is passionate about the topic you’re covering. It may take a second to unpack a more intricate viz like this but there’s ultimately potential for a bigger payoff (these projects can be rewarding for the viewer and the creator). But the practical and simple-to-read space is the place I want to live at Synergy. I want to make visuals that provide actionable insight to coaches which are engaging and intuitive. At work, it’s less about pushing the boundaries and being eccentric, and more about making something that is reproducibly useful.
SN: How do you push yourself to new frontiers of creativity or visual communication? What spurs the continuous process of growth and learning as a dataviz practitioner for you?
TW: I really like to tinker and I get a kick out of trying new things with my charts. Sometimes the tinkering is bad and I make what could have been a simple chart into an overwrought, self-indulgent mess. That’s always a bummer. But, hopefully, sometimes the tinkering can be good or, at least, point in the direction of something that could be good. And that’s a really invigorating feeling! I find it a drag to make the exact same chart over and over again. So I chase that buzz you get from creating something good that is a little bit distinct from what has been tried before.
SN: The theme of this edition is “inspiration.” I recall you having a series of physical visualizations, made by stacking coins, cutting neck ties, etc. What inspires you to these ideas? What’s your muse?
TW: I really enjoy collecting my own data to visualize, so my 40 Years of Draft Fits viz is another of my favorites. For that project, I watched every NBA draft selection show since 1982 and recorded the features of each lottery pick’s outfit: the color and trim of his jacket, the number of buttons, and what type of tie he was wearing. Then I used fabric and buttons to create charts that showed the NBA fashion trends over the last 40 years.
That particular series of charts was inspired by Mona Chalabi. She has this one fantastic visual that stuck in my brain titled For every $1 a white man earns… which was a bar chart made out of folded dollar bills. It’s a physical representation of data that is so unique and fresh. In general, I love her hand drawn aesthetic as a way to draw a viewer in, and make the experience more personal. I wanted to see if I could capture some of that feeling with physical viz on my favorite topic — basketball!
SN: Speaking of sources of inspiration, are there other people or resources you look towards or have been able to learn from? Do you sometimes try to incorporate their various styles into your own work?
TW: Definitely. I have had the good fortune to write a few articles for the FiveThirtyEight sports section. They have a great graphics team and a style that I really appreciate. Their fonts and layouts are always super clean looking and when you go through the editorial process with them you begin to notice the consistency of their format, like their headlines and subheadings. That’s something that I borrowed from them. I know that sounds really basic but it’s actually a super helpful practice!
I like to follow what people are doing in other sports and bring back my favorite pieces of their charts into the basketball world. I’ve also taken inspiration from a few particularly great books with fun visualizations: FreeDarko Presents – The Undisputed Guide to Pro Basketball History has an awesome mix of playful data viz-slash-illustration put together by Jacob Weistein. Dear Data is a lovely data viz postcard correspondence between Stefanie Posavec and Giorgia Lupi that experiments with data collection and data viz in creative ways. I always like flipping through both of those books to look for ideas. Generally, I just try to be open to visual inspiration. I think, once you start making a lot of charts, you sort of get tuned into finding elements that could go into future charts, whether that’s at a trip to the museum or just in watching a TV commercial or reading a magazine ad or whatever. You just start paying more attention, and it becomes a habit.
SN: Let’s close out with a more philosophical consideration of data visualization in sports. What do you think is the next frontier for data visualization in sports (or basketball, if you want to be more specific) media?
TW: I think there is a lot of exciting stuff on the horizon for dataviz in sports media. I think when you look at the popularity and ubiquitousness of baseball’s launch angle and exit velocity graphics to characterize home runs, you can see there is an appetite for new types of data among sports fans, assuming that the information is interesting and that it adds something to the viewing experience.
Oracle’s Ariel Kelman talked at the Sloan Sports Analytics Conference about how the future of media is personalized fan experiences where viewers can decide for themselves how much data they want to see on their screens. I think you can already see that type of personalization happening in the NBA coverage, whether it’s Danny Leroux and Nate Duncan creating their own simultaneous version of the broadcast or Nekias Duncan and Steve Smith with theirs. In the future, people will be able to control how they watch the game, and some of those people will want the most in-depth, data-heavy version of a broadcast possible. I think player tracking data (and in a few years, joint-tracking data) will create a ton of fun content for that type of fan. I’m excited to see what sort of viz we can make with that new data!