It’s been far too long since our last Tableau Public Revizited, but time to get back to reviewing some great work of the past from the community we love so dearly. This week we’re looking at a nice viz by Kate Brown that I originally had lined up for Major League Baseball’s opening week. But, while we don’t yet know when we’ll be seeing baseball games again, we do know that this viz, ‘Enter Sandman,’ published by Kate back on March 30, 2019, captures the career of arguably the best closer the game has ever seen; Mariano Rivera. Let’s get to a few things that make this visualization so special.
Three Things I Love!
Clean Design+ Just Enough Context
Kate starts out by doing non-baseball fans a big favor and providing some context. In her title header and again in the top left section of the viz, where readers eyes are drawn to first, she explains a little about Rivera’s career. As we touched on above, he’s one of the best closers the game has ever seen. However, when looking at the charts throughout the visualization, it’s important to know up front that Rivera began his career as a starting pitcher and then transitioned into the closing role after a few seasons.
This explains the high strikeout mark in 1996. In his set-up role, Rivera struck out a career high 130 hitters, but he also threw a career high 107 2/3 innings, nearly 30 more innings than his next highest season. So we would expect his strikeout totals to drop once he transitioned to the bullpen and began throwing closer to 60-80 innings per season. And without the note about his 2002 and 2012 injuries, readers would be wondering why so many of his stats took a dip both of those years. Looking at his stats on baseball-reference.com I can see that in 2002 Rivera pitched just 46 innings and only 8 1/3 in 2012. He threw at least 60 innings every other year he was the Yankees closer. So the context is very important and Kate does a great job of incorporating it into the viz while maintaining a clean design. Well done, Kate!
Use of Color
I really love how Kate uses just two colors (three if you count the white title) in the viz and ties them to team colors, the Yankees navy blue and gray. Also, the way she splits up the title and viz is nice, using the navy as the title background, but the gray as the background throughout the rest of the visualization. There’s no need for any additional colors.
And speaking of a clean design, Kate’s use of bar charts throughout was a sound decision. She could have changed it up with different chart types, but why make it difficult for the reader to understand? Bar charts are easy to understand and the use of the same chart type for all five categories allows for easy comparisons across categories. For instance, Rivera enjoys his first 50 save season in 2001. If we look at the other categories, we can see how his strikeouts by season ramp up from 36 in 1998 to 83 in 2001. He also cut his walks in half, with just 12 walks in 2001 vs. 25 the prior season. More strikeouts and fewer walks is a great recipe for success.
What’s another great recipe for success? Do the things Kate did in this visualization. Clean, clear context, color, consistency. Oh and I also just love the image she chose. Great job Kate, this was a fun visualization to explore!!
History Visually is an Etsy shop dedicated to capturing this history of great sports teams, careers and moments in visual form, printed and proudly displayed on your wall. While our shop currently offers four items, we have plans to add many more. See below for prints set to become available in 2020.
March Madness Greatest Games Collection | 36×12″ prints
1979 National Championship | Michigan State vs. Indiana State
1987 National Championship | Indiana vs. Syracuse
1990 West Region Second Round | Loyola Marymount vs. Michigan
1991 National Semifinal | Duke vs. UNLV
1992 East Regional Final | Duke vs. Kentucky
1994 National Championship | Arkansas vs. Duke
1997 National Championship | Arizona vs. Kentucky
2008 National Championship | Kansas vs. Memphis
2016 National Championship | Villanova vs. North Carolina
2019 South Regional Final | Virginia vs. Purdue
NBA Retired Jerseys Collection | 18×24″ prints
Los Angeles Lakers Retired Jerseys
Boston Celtics Retired Jerseys
…also stay tuned for the Philadelphia 76ers, San Antonio Spurs, New York Knicks and Detroit Pistons.
MLB Retired Jerseys Collection | 18×24″ prints
…also coming in 2020 are the New York Yankees, Boston Red Sox, Atlanta Braves, Cincinnati Reds, St. Louis Cardinals, Los Angeles Dodgers, San Francisco Giants, Pittsburgh Pirates, Minnesota Twins, Chicago White Sox.
Published on March 7, 2018, this week’s Tableau Public Revizited is Explain Data before Explain Data! ‘Beautiful Billboard Bar Chart’ by Tableau Ambassador Sean Miller is an example of superb analysis and storytelling. Sean found a huge outlier in the data and dug in to find out what the cause was behind it. Let’s take a look at Sean’s work.
What makes this great data viz?
Clear titles and annotations – In exploring Sean’s work, it’s clear to me he has read and taken away many learnings from Cole Nussbaumer Knaflic’sStorytelling with Data. His clear, descriptive title at the top of the page tells the reader exactly what question will be answered in the visualization below. Sean’s titles/text are consistent throughout the entire viz, helping the reader to easily understand what the visualization is telling them. He does an outstanding job in this area.
Simple chart types – Another thing I like about the viz is the use of bar charts, a chart that is easy to understand and quick for readers to consume. In the first chart, the reader’s eyes are instantly drawn to the very tall blue bar at position 20. This is the focal point of the visualization and Sean pulls our attention directly to it. In the second chart, we can also see very quickly the spike in songs that spent exactly 20 weeks on the Billboard Hot 100, beginning in 1991.
Use of Color – While the simple chart types themselves aid the reader in quickly understanding the story Sean is telling, his fantastic use of color helps drive it home. He begins with coloring the words “exactly 20 weeks” in the title with blue text, tying them beautifully to the data below that represents songs spending exactly 20 weeks on the Billboard Hot 100.
I wanted to share this visualization because it is an exceptional job of data storytelling. By combining the techniques covered above, Sean has taken a big outlier in the data and told the story behind it in a way that takes the reader only about 20-30 seconds to consume. Great job, Sean!
As I sat down this afternoon, pondering which viz to feature in this installment of Tableau Public Revizited, my mind began to wander. I peered out the window into the frigid Minnesota temperatures outside, thinking of a place and time much warmer than the current 35-below wind chill. A place with lush green grass, sunshine, water and a warm summer breeze. A place perhaps, just like Chebeague Island, Maine.I’ve loved this viz, by Sue Grist, ever since I laid eyes on it. With its Jonni Walker-esque style it looks like something right out of a travel magazine. Let’s take a look at Sue’s beautiful piece of art.
What makes this great data viz?
Beautiful Design – This map is so beautiful and I love how Sue sort of floats the text that provide more information about Chebeague Island in the waters surrounding the island. The grayed out shape of Maine with the blue dot representing Chebeague Island is a very nice, subtle extension of the title. I wouldn’t have otherwise known where, in Maine, Chebeague Island is, so this not only looks great, but is very helpful to the reader.
Use of Color – The yellow dots on the map, indicating summer rentals, are great. I’m not sure how many colors Sue went through before landing on the yellow, but I played around with the viz a little bit and tried several other colors, none of which looked remotely as nice as the yellow she used. Tying the color of the dots to the text is best practice, so nice work there. Another thing Sue did really well was to set the opacity of the yellow dots to 65%. This lightens them up a bit and looks much more professional than if she had left the opacity at 100%. Just look at the difference in the image below.
Ease of Use – Ok, we’ve covered the pure beauty of the visualization as well as Sue’s great use of color, but my favorite part about the ‘Maine: Visit Chebeague Island’ viz is the fact that I could see myself actually using it to plan a trip to Chebeague Island! It’s just so damn easy to use. In the bottom left-hand corner, Sue added a collapsible container where you can select your ideal summer rental based on numbers of bedrooms, bathrooms and/or how many people the unit sleeps. And then, while hovering over the yellow dots, we get a preview of the rental with the ability to navigate to the rental’s website, where we could book a trip right then and there.
So, which Chebeague Island rental was my favorite? Well, I’m glad you asked. I’d have to go with the Hackel Beach House at 47 Jenks Road. For me, this rental won out for several reasons, including its easy access to the beach and huge yard which is ideal for games like bean bags, croquet, bocce ball, etc. I also love the long deck that overlooks the ocean as well as the tongue and groove interior, which really gives it that cabin feel. Lastly, I definitely saw a fire pit in one of the pictures and you simply cannot have a summer cabin getaway without a bonfire to end the night!! While there’s plenty to do around the Hackel Beach House itself, biking around the island and ending up at the Slow Bell Cafe for lunch sounds like a good time. And when the kids are napping, maybe sneaking in a round of golf at the Great Chebeague Golf Club 🙂
It was a lot of fun exploring this viz in detail, Sue. Great job!
As I sit down to write this, we’re closing in on one week since boarding the red eye flight in Vegas to head back home to Minneapolis and in that week I’ve read a handful of wonderful and thorough recaps of #data19. I’d like to share one as well. However, I won’t be breaking down sessions, new Tableau features or any of the stuff you can find elsewhere…Instead, it’ll simply consist of my personal conference highlights. With #data19 being just my second Tableau Conference, it was once again an amazing week that lived up to the hype and then some!! Alright, here are my top highlights of #data19.
No. 4 – The Stars of First Avenue
For the second consecutive year, I was fortunate enough to have a viz on display in the Tableau Public Viz Gallery. What an honor to be included among so many amazingly talented individuals from the Tableau community! This year, my viz, called “The Stars of First Avenue,” was being displayed, as it had won (on behalf of the Twin Cities Tableau User Group) the Tableau User Group Summer Viz Contest. The viz shares a little bit about the historic First Avenue music club in Minneapolis and can be found here. A wonderful surprise that came along with this was when Tableau reached out to see if I’d be interested in doing a “Lightning Talk” about the viz. These were new to the conference this year and held in the Data Village. A 15 minute TED style talk, it felt like a great opportunity to get my feet wet presenting at Tableau Conference. If you missed it, the video is available here and in it I share an emotional story about how the viz came to be. I’m extremely thankful to Tableau for the opportunity to share my story and would highly recommend anyone who’s asked to do a “Lightning Talk” next year, jump at it.
No. 3 – Braindates
Perhaps the underdog of the conference, Braindates unexpectedly became one of my favorite parts of the entire week. New to Tableau Conference in New Orleans last year, I didn’t participate in any of these, so this year marked my first time attending these scheduled meetups and they were well worth it. Tableau Conference allows for many conversations throughout the week as you run into people in the hallways, before/after sessions, during meals, etc. But with Braindates, the topic has been decided ahead of time and with a dedicated 30-45 minutes, these meetups become extremely valuable, for not only the conversation alone, but also for connecting with others who may be working in the same industry or facing the same challenges as you. I attended a total of four Braindates this year, hosting two of them, titled “Leveraging Tableau Public to land your dream job.” The two meetups I attended, one hosted by Katie Wagner and the other by Brittany Fong were tremendous, while the two I hosted were fantastic, as well. It felt great to share my Tableau journey and how I’ve leveraged the community and Tableau Public to land a job I absolutely love, while hearing from others who had a wide variety of experience and knowledge of both the community and Tableau Public. I’ll definitely be setting up more of these next year in an attempt to continue spreading the word about the Tableau Community, Tableau Public and community projects such as #MakeoverMonday and #WorkoutWednesday.
No. 2 – The Tableau Community
How unbelievable is the Tableau Community? I mean, where else do you have the opportunity to make tons of new friends from all over the world, who share the same passion as you? It’s so true what they say about Tableau Conference being a family reunion of sorts. However, it’s not only catching up with old friends, but also making new friends along the way. The people in this community are so intelligent, selfless, energetic, kind and fun that it’s flat out contagious and you can’t help but want to be around them as much as possible. Often throughout the conference, I’d find myself thinking “Wow, I’ve met so many amazing people this week!” Then I’d go on Twitter for a few minutes and find 20-30 more people I had wanted to meet, but hadn’t run into yet. The Tableau Community is truly something special and we should all be thankful for being a part of it.
No. 1 – Thank you Andy and Eva!!
One of my biggest regrets from last year’s conference was not sticking around after #MakeoverMonday Live to meet Andy Kriebel and Eva Murray. I assumed there would be another chance, but that opportunity never presented itself over the duration of the conference. I was determined to not let that happen again this year, so before the Thursday morning Keynote, I saw an opportunity to go shake Andy’s hand, give him a hug and tell him thank you for all he has done. I was also lucky enough to find Eva after the Keynote, give her a big hug and tell her thank you, as well. It may not seem like much, but being able to say these words; “thank you” to Andy and Eva, in person, meant SO MUCH to me as they and #MakeoverMonday have played such a important role in me getting to where I am today. So Andy and Eva, again, thank you so much for your tireless efforts and dedication to helping others improve in this space. It is greatly appreciated!!
Thanks so much for reading and have a wonderful day!
#Data19 has come and gone, but there are still seven weeks left of 2019, so it’s time to finish strong. This week’s #MakeoverMonday data set, ‘Smartphone Ownership Among Youth Is on the Rise,’ comes to us from Common Sense. Below is a look at the viz we made over this week.
What works with the original viz
Labeling the years directly to the left of each line chart (although not needed as we will discuss later).
The line charts do make it easy to compare 2015 vs. 2019, for each age group. However…
What could be improved
Even though the viz has a label for age on the x-axis, it’s difficult for my brain to not want to think the line charts indicate change over time. Therefore, I would shy away from using a line chart in this situation, as it can cause confusion.
My go to for this type of analysis would typically be a dumbbell chart, like the image below as I feel it’s one of the best ways to show change between two periods. However, I felt the need to try something new, so I saved the dumbbells for another day.
It’s unnecessary to label every mark on the view, as it distracts the reader from focusing on the visualization.
There’s also no need for dots and grid lines at every age increment. A better approach would be to swap the x-axis (age) grid lines and for y-axis (ownership) ones instead.
Changing the title to a shade of gray and color coding the years in the title (2015 blue and 2019 yellow) would remove the need for the year labels in the view.
I wanted the focus to be on the change from 2015 to 2019, so I called that out directly in the title.
As I mentioned earlier, it’s really easy in a situation like this to just go with a dumbbell chart. However, I wanted to try a variation of Jeffrey Shaffer’s progress bars.
Since the values for 2019 are greater, I set 2019 as thin lines in the background of the thick, 2015 gray bars. I then labeled the 2019 bars as the difference in percentage points from 2015 to 2019.
For instance, in 2019 53% of 11 year old children owned a Smartphone vs. just 32% in 2015. That’s a difference of 21 percentage points.
The following blog post takes the reader through the process of building my March Madness Bracket of Champions viz, in Tableau. However, this project involved quite a bit of pre-Tableau work, which I would also like to share, so if you came strictly for the Tableau part, please scroll down to the ‘Building the Viz in Tableau’ section.
Prepping the Viz for Tableau
I first saw data portraits being used in Tableau by Zen Master, Neil Richards, in November of 2018, with his TUG data portraits viz. At the time, I was unaware of their origination, but on Neil’s viz he included that the idea was inspired by Giorgia Lupi, so I did a little research to become more familiar with the concept. It appears Giorgia introduced the idea at TED 2017 in Vancouver, through the creation of buttons for conference attendees, as a way to create connections with other conference goers. Prior to the conference, attendees filled out a series of non-invasive questions that revealed fun facts about them. A design system then turned the answer to each question into a unique set of shapes, colors and symbols. About a month after Neil’s viz, I saw Josh Tapley create a viz of badges as well, his for the Philadelphia Tableau User Group. I loved how creative and beautiful they were, so knew I wanted to try it out, the only question was what to do?
Inspiration from Giorgia Lupi
Inspiration from Neil Richards
I didn’t want to copy Neil and Josh…although it does seem like a really cool thing for the Twin Cities Tableau User Group to try one of these months!! Instead I wanted to try something a little different. Being the sports fan I am, it was only natural that my version of data portraits would somehow tie in sports. My initial thought was to make a data portrait for each of the top players in the upcoming NBA Draft. I thought the data from each player’s scouting report could work perfectly for a data portrait, as you would essentially be answering questions, just like on Giorgia’s buttons. What is the player’s position? How tall is the player? What is their biggest strength, etc? However, it was still only December and with the draft still six months away, I simply could not wait that long! So, sticking with the basketball theme, my next thought was to create a bracket, where each team is represented by a data portrait. So, I filed away the idea and a few months later, with NCAA March Madness looming, tried creating my first badge. The North Carolina Tar Heels are my favorite college basketball team, so I created the below (left), badge, which displayed the following information; the team (logo), the year they won the national championship (1993), their tournament seed that year (#1 seed), the conference they played in (bottom coloring), their win/loss record (34-4), win/loss margin by game (step line chart), and the number of players who would go on to reach the NBA (one star per player). I chose to create a bracket of past champions, as I felt it could be a fun lead up to the actual tournament and because fans are always debating which past teams were better, etc. Why not create an interactive bracket, where people could fill out their bracket of past March Madness champions and share it with others?!
I had an idea, but what did the data look like, that would support the idea? To be honest, I didn’t need much to get started. My initial data set included only the Year, the Champion, their Seed, their win/loss record and their conference. I grabbed it from sports-reference.com/cbb and dumped it into Google sheets. It looked like this.
From here, I could start building out the team data portraits. Where else would I turn for this step, other than PowerPoint?! For more on combining the powers of Tableau and PowerPoint, be sure to check out this great post from Kevin Flerlage. In his post, Kevin recommends blog posts by Josh Tapley and one by Kevin’s brother and Tableau Zen Master, Ken Flerlage, that introduced him to the concept of mixing Tableau with PowerPoint. The only other data I would end up including was game by game margins of victory/defeat for each team (for the step line chart), as well as statistical leaders for each team, which was a late addition to the tooltips.
The Data Portraits
With the initial data in hand, it was off to PowerPoint to create 32 more data portraits, one for each NCAA Men’s Basketball champion, from 1985 through 2018. Basically, all I did here was make copies of the original North Carolina data portrait and then swap out the elements for each of the other teams. For example, to create this Michigan data portrait, I copied the North Carolina one, switched the year, added/removed the appropriate number of stars, changed the seed number and conference color accordingly and finally swapped the logo and line graph and adjusted the win/loss record. The line graphs were made in Tableau, saved as images and brought into PowerPoint. The logos were saved as images from ESPN.com and brought into PowerPoint and then I added an artistic effect under the formatting tab, to give them a little colored pencil look.
Dean Smith’s 2nd title
The Glen Rice Wolverines
It took some patience, but after several hours, over the course of a few late nights, I had finally completed all 33 of the data portraits and was ready to start building the bracket! One quick note; the 2013 championship won by the Louisville Cardinals was vacated due to team violations, so I omitted them from the viz.
After taking a stab at ranking the teams myself, it dawned on me that maybe someone else, much more qualified, had already done this work. A quick google search and I was delighted to see that, indeed, this had been done and fairly recently. In April 2018, ESPN Insider, John Gasaway had ranked all champions from 1939 to 2018. I compared my rankings against his and although many of mine were within one or two spots of his, a few, most notably 1995 UCLA, were way off. I had that Bruins squad much higher than Gasaway’s ranking of sixteenth. So, to ensure the seedings in the bracket were legitimate, I decided to follow Gasaway’s rankings, with a few very small tweaks, in order to balance out the bracket and avoid having the same school play another version of itself, early on.
Of the 33 teams, there were five instances of Duke, four North Carolina’s, four Connecticut’s, three Kentucky’s and three Villanova’s. So, those five schools accounted for 19 of the 33 teams. With far too much time spent jockeying the teams around, I was finally able to produce a bracket in which none of the above schools would meet until at least the third round. So, with the rankings set, it was time to build the viz.
Building the Viz in Tableau
The Set Up
I wanted the viz to have the look of an actual bracket that you might fill out by hand or online, in your local bracket challenge pool. So, in Tableau, once I had the team data portraits placed on the dashboard, I would leverage ninety-two text boxes to draw out the bracket. Each text box was filled with navy blue and set to be 3 pixels tall or wide, depending on its position. Looking back, this part was pretty tedious, but it allowed me to design the bracket exactly the way I wanted it to look, which was nice. Ok, back to the data portraits.
My goal in building this viz was to create a fun March Madness bracket, that would become interactive through the use of Tableau Set Actions. If you remember from above, the placement of the teams into the bracket had been determined, so Step 1 was to essentially create a bracket that had not yet been filled out. To place each team into their respective position in the bracket, I created a worksheet, that looked like the one below, for each of the sixteen first round match-ups and then floated (don’t hate me Team Tiled!!) each worksheet on the dashboard. Side note: this dashboard is literally a Team Tiled member’s worst nightmare, as there are somewhere in the neighborhood of 150 floating objects on the dashboard.
Setting up the bracket
Calc to separate out ’93 UNC from ’05 UNC, etc
I used the ‘Bracket’ field to filter each worksheet to its appropriate bracket and then the ‘Seed 1’ field to filter to the correct match-up. To account for schools with multiple championships, I then created a calculated field called ‘Year+Team’ which combined the ‘Year’ and ‘Champion’ fields. Pulled onto the shapes card, this would allow me to assign one data portrait per champion. Once this part was complete, I was left with eighteen sheets (originally seventeen) to float onto the dashboard. Why eighteen and originally eighteen? The original viz was built prior to the 2019 tournament and featured one “play-in” game. The play-in game was built using two sheets instead of one, so that’s how we get to seventeen sheets. Also, I updated the viz after the 2019 tournament, to include the 2019 champion Virginia Cavaliers, after their miracle run to the title; the last two games of which I was fortunate enough to have seen in person, at the Final Four in Minneapolis. What an amazing sports experience!! Anyway, adding Virginia led to the need for another play-in game, thus adding another sheet and getting us to eighteen. Alright, the bracket was set up, next up was to add the interactivity.
The interactivity was set up with a few simple steps, which were repeated for each game throughout the tournament.
Create a Set for each game in the bracket. Each Set looked identical to the one pictured below. The set was created using the Year+Team field and I left all boxes unchecked to ensure the worksheets that would later be dropped onto the dashboard were blank until the addition of the Set Actions.
2. I then created a Boolean (T/F) calculation for each game like the one shown below, created a sheet for each game in the tournament and dragged the Boolean calculations for each game onto the Filters shelf of their respective sheets, setting them all to True. This would ensure that once the Set Actions were in place, the blank sheets would populate with the expected data portrait.
3. Next, the sheets needed to be placed (floated) onto the dashboard, into their positions within the bracket. I floated them on the bracket as shown in the picture below.
4. Lastly, we needed to add in the Set Actions. Once again, there are 31 game so we need 31 Set Actions. In the example below, we’re using the Source Sheet 2.1, which contains the 1995 UCLA Bruins and the 2016 Villanova Wildcats. We tell the Set Action to target the Game 2 Set, which was set to True on the blank sheet named 2.2. And then we click ok and back on the dashboard, if we click the UCLA data portrait on Sheet 2.1, we see them advance into the second round of the tournament, onto Sheet 2.2. Every other Set Action is set up just like this and together, they provide the dashboard interactivity.
Source Sheet 2.1 | Target Set Game 2 Set which is on Sheet 2.2
The Viz in Tooltips
Lastly, while I felt the data portraits provided great high level information about each champion, what they lacked was any type of information regarding the players. So, I pulled some more data from sports-reference.com/cbb and added a tooltip that, on the left-hand side, provided a zoomed in view of the data portrait and on the right-hand side, provided the user with each team’s statistical leaders in three main categories; points, rebounds and assists. The ’94 Arkansas Razorbacks were one of my all-time favorite college teams…and it didn’t hurt that they also beat Duke in the title game!!
Before wrapping up, I want to give a shout out to Kevin Flerlage for some fantastic feedback throughout the whole process of building this viz. Kevin helped me with some decisions regarding the tooltips and a nice clean way of executing a “clear bracket” option, among other great input. Also, when I was in the early stages of building out the viz, I thought it was a pretty cool idea. But after getting it to a point where it could be shared with others, for feedback, Kevin’s reaction and genuine excitement for the viz made me that much more motivated to get this thing across the finish line. Also, a big thanks to my co-workers Jim Van Sistine and Tom Coyer for providing their feedback as well and last, but not least, my friend Jason Underdahl, who said of the initial data portrait “why do you have the logo grayed out? You can barely even see it!” That’s tough love, but he had a good point! Adding the color back to the logos really made them pop!!
Thanks for reading, I hope you enjoyed this post and found it useful.