The aim was to analyse my HYROX performance, which is the fast-growing hybrid fitness event. It combines eight 1-kilometre runs with functional workout stations like sled pushes, burpees, and wall balls. The race’s structure naturally lends itself to analysis—clear segments, repeated runs, and measurable transitions. The goal of the visualisation was to explore how time, effort, and physiology interact across a physically demanding event. What actually transpired was a visualisation that was much more emotive: projecting personal emotion, or how I felt about my performance.

The challenge of condensation

Athletic data is inherently multidimensional. Time, effort, and physiology interact in ways that are complex and deeply human. Condensing all that into a single visual means facing the same challenge every visualisation designer knows too well: what to keep, what to simplify, and what to discard.

This HYROX chart condensed over an hour of physical effort into a few compact panels. Rather than presenting the bars along the conventional layout (x-axis), I shaped the visual to mirror the race’s own rhythm. As the reader moves from left to right, the reader too moves through each run and station. As the viewer follows the visual rhythm of the page and reaches the second chart from the top, they uncover time spent at each station relative to the event average—a clear indication of where momentum built or faded. Green meant faster than average, red meant slower. A cumulative line showed the overall trajectory: moments of acceleration, versus pauses of fatigue relative to the average athlete.

Design-wise, it worked. The streaks of green—for the lunges and sled pull stations—sparked a sense of pride. But as soon as I saw that one bar of deep red—the dreaded wall balls—I didn’t just see inefficiency; I felt disappointment. That’s when I realised how much emotional weight colour can carry in performance visualisation.

When color becomes judgement

It’s clear that colour can convey emotion. Warm hues suggest intensity, fatigue, or struggle, while cool tones evoke calm and control. These associations can subtly influence how athletes perceive their own performance. By using warm reds to mark high heart rate zones and difficult stations, and cool greens to indicate easier segments relative to the average, the visualisation established an intuitive “moral language”: a clear visual distinction between stronger and weaker performance that made the data instantly readable.

This raises a key design question: when visualising personal performance, are we aiming to motivate—or simply to measure? Should a chart make the athlete feel proud, or precise? The answer likely lies somewhere in between. The top chart, rendered in a calm blue gradient, remains neutral: it measures output without judgment. The chart below leans into emotion, using contrast and colour to spotlight effort and highlight moments of struggle.

Rhythm, not just metrics

The bottom half of the visualisation traced my heart rate throughout the race, capturing the ebb and flow of effort across running segments and workout stations. The rising and falling bands of orange and red felt like a heartbeat for the race itself—a pulse that mirrored moments of endurance, bursts of strain, and brief windows of recovery.

It wasn’t just data on a page; it was a rhythm you could feel. Peaks were sudden surges of intensity, while valleys were respites and recovery. Each station became a note in a composition of exertion and relief. In this way, visual structure itself conveyed effort before any labels or numbers were read. As designers, we often obsess over precision, but here, pacing and tempo communicated the human experience of performance more viscerally than any raw statistic ever could.

From metrics to meanings

What I learned from visualising my HYROX race wasn’t just where I was fast or slow, but how visualisation framed that story. Choices of colour, alignment, and context turned raw numbers into something interpretive—something emotional.

For data visualisation practitioners, that’s a valuable reminder: the goal isn’t only to display information, but to mediate understanding. The way we design a visual can shape not only what people learn, but how they feel about what they learn.

The post From Metrics to Mood: The Emotional Story in A HYROX Race appeared first on Nightingale.

I was 14 then, and football was more than a mere sport. I vividly remember my mornings began with practice before school, lacing up my shoes. Yet, in those training sessions, I often felt the weight of being different. Few girls around me cared about sports, but even fewer about football. I envied the boys, who seemed to belong to a world I was desperate to be part of. Hoping one day I’d witness women players in the limelight.

Jump to the year 2025, women’s football is gaining its momentum. The current UEFA  Women’s Euro is  the largest event as the attendance has been record-breaking. Over 600,000 tickets were sold, setting the stage for the most attended Women’s Euro ever.

Before it was Messi, Cristiano, Maradona, now young football enthusiasts can look up to Aitana Bonmatí, Alessia Russo, Sam Kerr, Alexia Putellas, Patri and a whole bunch of amazing women footballers.

Decoding Aitana Bonmatí

Football is a sport that requires immense strategic, physical, and mental endurance. One of the most prominent names in women’s football is Aitana Bonmati, who began her career at the age of 17. Aitana Bonmatí won the Ballon d’Or, which is the most prestigious individual prize in both men’s and women’s football, not one but three times in a row. Aitana Bonmatí grew up in a home where standing up for what she believed in was simply part of life. From the start, her parents made a bold choice to challenge tradition, placing her mother’s surname before her father’s. In doing so, Aitana became one of the first in Spain to carry her mother’s name first—a quiet but powerful statement of equality that would shape the person she was destined to become. She was the lone girl in the boys’ team.

Bonmatí exhibits extraordinary technical finesse, seamlessly maneuvering the ball through tight spaces while maintaining constant awareness of her surroundings. Her vision and spatial intelligence empower her to dictate the tempo of matches. Moreover, her relentless off-the-ball movement and impressive stamina consistently create opportunities for her teammates and drive Spain’s dynamic, high-intensity style of play.

“We need to have women in more powerful positions that are making decisions, so when that 10-year-old girl is looking up and wondering, ‘What can I do and what do I want to be when I get older?’ She has the opportunity to do and be whatever she wants.”

 — Abby Wambach

The debate over gender in sport is nothing new, and football is no exception. For decades, women have been underrepresented, underpaid, and held back by systemic barriers. While the popularity of women’s football has surged in recent years, it still doesn’t rival the global dominance of the men’s game. But on a positive note, female footballers are becoming global superstars and commercialisation is growing in all areas of the game. The 14-year-old girl in me would be overjoyed to see her favorite sport shattering barriers and achieving unprecedented growth.

The post Breaking Stereotypes in Women’s Football appeared first on Nightingale.

Data

We are data scientists with a seasoned football expert on board, so we went for one of the most obvious choices of the field – www.transfermarkt.com. We first wrote a few lines of Python code to scrape the list of participating teams, the list of each team’s players, and the detailed club-level transfer histories of these players arriving at the impressive stats of our intro by comprising the complete transfer history of 800 players, measuring up to 6,600 transfers and dating back to 1995 with the first events.

Club network

The majority of players came from the top five leagues (England, Spain, Italy, Germany, and France) and represented household teams such as Barcelona (with 17 players), Bayern Munich (16), or Manchester City (16). While that was no surprise, one of the many wonders of a World Cup is that players from all around the globe can show their talents. Though not as famous as the ‘big clubs’, Qatari Al Sadd gave 15 players, more than the likes of Real Madrid or Paris Saint-Germain! There are, however, great imbalances when throwing these players’ market values and transfer fees into the mix. To outline these, we decided to visualize the typical ‘migration’ path football players follow – what are the most likely career steps they make one after the other? 

A good (and referenceable) way to capture this, following the prestige analysis of art institutions, is to introduce network science and build a network of football clubs. In this network, every node corresponds to a club, while the network connections encode various relationships between them. These relationships may encode the interplay of different properties of clubs, where looking at the exchange of players (and cash) seems a natural choice. In other words, the directed transfers of players between clubs tie the clubs into a hidden network. Due to its directness, this network also encodes information about the typical pathways of players via the ‘from’ and ‘to’ directions, which eventually capture the different roles of clubs as attractors and sinks.

To do this in practice, our unit of measure is the individual transfer history of each player, shown in Table 1 for the famous Brazilian player known simply as Neymar. This table visualizes his career trajectory in a datafied format, attaching dates and market values to each occasion he changed teams. His career path looks clean from a data perspective, although football fans will remember that it was anything but – his fee of EUR 222M from Barcelona to PSG still holds the transfer record to this day. These career steps, quantified by the transfers, encode upgrades in the case of Neymar. In less fortunate situations, these prices can go down signaling a downgrade in a player’s career. 

Following this logic in our analysis, we assumed that two clubs, A and B, were linked (the old and new teams of a player), if a player was transferred between them, and the strength of this link corresponded to the total amount of cash associated with that transaction. The more transactions the two clubs had, the stronger their direct connection was (which can go both ways), with a weight equal to the total sum of transfers (in each direction). In the case of Neymar, this definition resulted in a direct network link pointing from Barcelona to Paris SG with a total value of EUR 222M paid for the left winger.

Next, we processed the more than six thousand transfers of the 800+ players and arrived at the network of teams shown in Figure 1. To design the final network, we went for the core of big money transactions and only kept network links that represented transfer deals worth more than EUR 2.5M in total. This network shows about 80 clubs and 160 migration channels of transfers. To accurately represent the two aspects of transfers (spending and earning) we created two versions of the same network. The first version measures node sizes as the total money invested in new players (dubbed as spenders), while the second version scales nodes as the total money acquired by selling players (dubbed as mentors).

Spenders

The first network shows us which clubs spent the most on players competing in the World Cup, with the node sizes corresponding to the total money spent. You can see the usual suspects: PSG, the two clubs from Manchester, United, and City, and the Spanish giants, Barcelona, and Real Madrid. Following closely behind are Chelsea, Juventus, and Liverpool. It’s interesting to see Arsenal, who – under Arteta’s management – can finally spend on players, and Bayern Munich, who spend a lot of money but also make sure to snatch up free agents as much as possible.

Explore these relationships and the network in more detail by looking at Real Madrid! Los Blancos, as they’re called, have multiple strong connections. Their relationship with Tottenham is entirely down to two players who played an integral part in Real Madrid’s incredible 3-year winning spell in the Champions League between 2016 and 2018: Croatian Luka Modric cost 35M, and Welsh Gareth Bale cost an at-the-time record-breaking 101M. While Real Madrid paid 94M for Cristiano Ronaldo in 2009 to Man Utd, in recent years there was a turn in money flow, and United paid a combined 186M for three players: Ángel Di María, Raphael Varane, and Casemiro. They also managed to sell Cristiano Ronaldo with a profit to Juventus for 117M.

One can see other strong connections as well, such as Paris SG paying a fortune to Barcelona for Neymar and Monaco for Kylian Mbappé. There are also a few typical paths players take – Borussia Dortmund to Bayern Munich, Atlético Madrid to Barcelona, or vice versa. It’s also interesting to see how many different edges connect to these giants. Man City has been doing business worth over EUR 1M with 27 different clubs.

Mentors

The second network shows which clubs grow talent instead of buying them and have received a substantial amount of money in return. Node sizes represent the amount of transfer fees received. This paints a very different picture from our first network except for one huge similarity: Real Madrid. In the past, they were considered the biggest spenders. They have since adopted a more business-focused strategy and managed to sell players for high fees as mentioned above.

A striking difference, however, is while the top spenders were all part of the top five leagues, the largest talent pools came from outside this cohort except for Monaco. Benfica, Sporting, and FC Porto from Portugal, and Ajax from the Netherlands are all famous for their young home-grown talents, and used as a stepping stone for players from other continents. Ajax has sold players who competed in this World Cup for over EUR 560M. Their highest received transfer fees include 85.5M for Matthijs de Ligt from Juventus and 86M for Frenkie de Jong from Barcelona. Ajax signed de Jong for a total of EUR 1 from Willem II in 2015 when he was 18, and de Light grew up in Ajax’s famous academy. Not to mention that they recently sold Brazilian Antony to Manchester United for a record fee of 95M. They paid 15.75M for him just 2 years ago – that’s almost 80M in profit. Insane!

Benfica earned close to 500M, most recently selling Uruguayan Darwin Nunez for 80M to Liverpool. The record fee they received is a staggering 127M for Portuguese Joao Félix from Atlético Madrid, who grew up at Benfica. Monaco earned 440M from selling players such as Kylian Mbappé (180M) and Aurélien Tchouameni (80M), Portuguese Bernardo Silva (50M), Brazilian Fabinho and Belgian Youri Tielemans (both for 45M). These clubs have become incredible talent pools for the bigger clubs, therefore really appealing to young players. It’s interesting to see how many edges the nodes for these clubs have, further proving that these teams function as a means for reaching that next level.

Player network

After looking at the club-to-club relationships, zoom in on the network of players binding these top clubs together. Here, we built on the players’ transfer histories again and reconstructed their career timelines. Then we compared these timelines between each pair of World Cup players, noted if they ever played for the same team, and if so, how many years of overlap they had (if any).

To our biggest surprise, we got a rather intertwined network of 830 players connected by about 6,400 former and current teammate relationships, as shown in Figure 2. Additionally, the so-called average path length turned out to be 3 – which means if we pick two players at random, they most likely both have teammates who played together at some point. Node sizes were determined by a player’s current market value, and clusters were colored by the league’s nation where these players play.

It didn’t come as a surprise that current teammates would be closer to each other in our network. You can see some interesting clusters here, with Real Madrid, Barcelona, PSG, and Bayern Munich dominating the lower part of the network and making up its center of gravity. Why is that? The most valuable player of the World Cup was Kylian Mbappé, with a market value of 160M, surrounded by his PSG teammates like Brazilians Marquinhos and Neymar and Argentinian Lionel Messi. Messi played in Barcelona until 2021, with both Neymar and Ousmane Dembélé connecting the two clusters strongly. Kingsley Coman joined Bayern Munich in 2017, but he played for PSG up until 2014, where they were teammates with Marquinhos, thus connecting the two clusters.

You can discover more interesting patterns in this graph, such as how the majority of the most valuable players have played together directly or indirectly. You can also see Englishmen Trent Alexander-Arnold (Liverpool) or Declan Rice (West Ham United) further away from the others. Both of those players only ever played for their childhood clubs. But the tight interconnectedness of this network is also evident with how close Alexander-Arnold actually is to Kylian Mbappé. During the 2017–2018 season, Mbappé played at Monaco with Fabinho behind him in midfield, who signed for Liverpool at the end of the season, making him and Alexander-Arnold teammates.

With the World Cup hosting hundreds of teams’ players from various nations, there are obviously some clusters that won’t connect to these bigger groups. Many nations have players who have only played in their home league, such as this World Cup’s host nation Qatar (maroon cluster in the top left corner). Saudi Arabia (green cluster next to Qatar) beat Argentina, causing one of this year’s biggest surprises. Morocco (red cluster in the top right corner) delivered the best-ever performance by an African nation in the history of the World Cups. Both of those nations join Qatar in this category of home-grown talent. These players will only show connections if they play in the same team – in the case of the Moroccan cluster, that team is Wydad Casablanca. The Hungarian first league’s only representative at the World Cup, Tunisian Aissa Laidouni from Ferencváros hasn’t played with anyone else on a club level who has made it to the World Cup. He became a lone node on our network. That shouldn’t be the case for long, considering how well he played in the group stages.

Conclusion

In conclusion, we saw in our analysis how network science and visualization can uncover and quantify things that experts may have a gut feeling about but lack the hard data. This depth of understanding of internal and team dynamics that is possible through network science can also be critical in designing successful and stable teams and partnerships. Moreover, this understanding can lead to exact applicable insights on transfer and drafting strategies or even spotting and predicting top talent at an early stage. While this example is about soccer, you could very much adapt these methods and principles to other collaborative domains that require complex teamwork and problem solving with well-defined goals, from creative production to IT product management.

The post <strong>FIFA World Cup 2022 – The Network Edition</strong> appeared first on Nightingale.

“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!

The post Meet the Data Visualization King of Basketball Twitter: Todd Whitehead appeared first on Nightingale.

Fencing, handball, and soccer results by using the same chart type

This “timeline triangles” visualization (thanks for the name, Neil Richards!) gives a recap of the match and shows the order in which teams scored. This works for several sport disciplines when teams or individuals win by scoring goals or points. When I used this chart type for different sport events, I always added small modifications to make sure it works with the actual sport discipline.

Handball – A team can score 20-30 goals in a match and the two halves can look very different. I divided the chart by half time and the Norwegian team helped showing the strength of this chart type by turning around this gold medal match in the second half.

Ice Hockey – Sometimes one team is winning the match easily, crushing the other team. As the losing team is falling behind, you can observe that only one side of the visualization is building up, highlighting the difference clearly.

Fortunately, most matches are not so one-sided and the losing party can even make a come back after falling behind by many points. The slope shows which team is currently winning and by how much.

Soccer animations will always seem quite simple compared to a handball match because of the low number of goals (0-3 goals per team is pretty common).

Penalties – When teams score the same number of goals during the match, penalties (soccer) or shootouts (hockey) can happen. I added triangles on the sides to represent these penalty scores. A penalty can be a score or a miss, so basically, it’s a binary true/false value. Since 0-0 is not a rare result in soccer, the penalty triangles create can big unused gap showing the missing goals. See the third image for an example of this case.

Fencing – In most sports teams can’t score in the exact same second. There is always a clear order who scored first. Fencing is an exception to that rule, as the two opponents can hit one another at the same moment in which case both get a point at the same time in the animation as well.

Swimming

During a swimming event athletes swim three times: a heat round, a semifinal, and a final if they are in the top eight.

It’s also less important how the other athletes are doing in the pool; athletes are rather competing against themselves and want to swim their best time. So one athlete could have up to three swim results per event and it helps to compare all these results (heat, semifinal, final) in the same animation.

The dataset contained only the split times, but I did add a little easing, since I wanted the dots to be a little faster as if they would “push” themselves from the side of the pool.

For some swimming events there are no semifinals. The final happens after the heats. 800m Freestyle is one example for this.

Tennis

Tennis scoring is very complicated, it’s also difficult to visualize all details that goes into a set.

This visualization focuses on the sets and break (on the top) and gives a quick glimpse into all points, rallies and lost break points that defines a match.

Roland Garros Final was an easy match for Rafael Nadal: straight sets and Ruud won only four service games during the match.

Wimbledon fourth round match between Goffin and Tiafoe shows a close and exciting match with a few break points, three tie-breaks, and five sets:

Swiatek won in straight sets in Roland Garros final and semifinal too, barely leaving any games for her opponents.

Full galleries

Soccer championships – UEFA Champions League

Copa América – South American Football Championship

Ice Hockey Olympics

References

“Timeline triangles” were inspired by Basketball Tower Charts (Andrew Garcia Phillips, Chartball.com)

Swimming visualizations were inspired by Replay: Ledecky 800m Freestyle created by nytimes.com.

The post SPOTLIGHT: Animated Sports Results appeared first on Nightingale.

The All-American Girls Professional Baseball League (AAGPBL) was the first women’s major league, and thus far the only women’s major baseball league, predating Title IX of the Education Amendments of 1972 by nearly 30 years. Title IX marks its 50th anniversary this year, and with Amazon Prime releasing a new series inspired by the Penny Marshall film A League of Their Own this August, I wanted to see if it was possible to make the league’s statistics come alive visually.

During World War II, Major League Baseball shut down as players answered the military’s call for the defense of democracy, and fuel rationing made any sort of road trip far more difficult than before the war. While women could not at the time serve in combat roles, they took mens’ places in factory floors, and on the field. As Anika Orrock shows in her brilliantly written and illustrated book, The Incredible Women of the All-American Girls Professional Baseball League, the AAGPBL held its first tryouts at Wrigley Field in spring 1943. It stood up four teams that began play that summer: the Racine, Wisconsin Belles; Rockford, Illinois Peaches; the Kenosha, Wisconsin Comets; and the South Bend, Indiana Blue Sox. It played for 12 seasons between 1943 and 1954 and expanded to ten teams, before shrinking again to five teams by its end.

As ahead of its time as it was, the league was rooted in the gender roles of the 1940s. Players were chaperoned and received tutoring in makeup, body carriage, and decorum from Helena Rubenstein’s cosmetics company. They wore dresses–not trousers or jeans–when out in public and their game uniforms were above-the-knee skirts, which were far from ideal for sliding on the basepaths. 

Baseball is relatively unique in the way that it embraces statistics to an even greater degree than other major spectator sports (although other sports like hockey and American football have become more stats-oriented in recent years). I’m a member of the Society for American Baseball Research (SABR), a global organization of baseball fans that conducts historical and quantitative research using historical statistics and biomechanical data, so baseball statistics are something that I think about quite often.

The emerging practice of data visualization hasn’t yet been widely adopted in the baseball research community. While there is very much a tradition of analytics that is deeply rooted in the history of the game, stats have been presented in the same way for decades. The most visible element of baseball statistics – the bubblegum card – has included a simple spreadsheet on the back of the card containing a player’s career statistics which has not changed significantly, in format or content, in more than 70 years. Now, though, is a unique opportunity to expand the use of data visualization, as media become ever more visual and the sport seeks to attract new fans.

Coupled with an emerging environment in the U.S. supporting equal access, spanning beyond race and gender, now is a great time to take a new look at the AAGBPL and to visualize its players’ notable accomplishments. 

The Analytical Process

AAGPBL data presented an interesting challenge. While the league has an active alumni association featuring individual player records, online data services like Stathead have yet to integrate the full set of records. The best resource for league data is a hard copy record book first published in 2000 by W.C. Madden, and compiled from a mix of league records, newspaper accounts, and other contemporary sources.

Madden’s work has inspired some valuable research over the years; for example, Kriss Barnhart examined the relationship between the league’s rule changes, including the size of the ball and the length of the base paths, and player performance.

Making sense of 12 years of data

The greatest challenge for this analysis was constructing a workable data set using the available figures from Madden’s record book. Madden notes that for the first half of its life (1943 to 1948), the league kept accurate individual records, but from 1949 onwards league-wide records became more of a challenge and by 1951, the central league office had been disbanded. 

Madden has compiled an authoritative record, but as with any publication, there are a few misprints or mathematical errors, and I needed to do a significant amount of work–about 40 hours worth–to enter, clean and correct the data. (There was unfortunately no substitute for the tedious task of manually keying in the data, checking every line in the book to make sure that I didn’t miss anything.) While I chose Tableau Public to build the visualization, I created my database in Google Sheets, which I linked to my Tableau workbook. I built two worksheets, one for pitching statistics and another for batting statistics. 

This results in a simple database that still enables time-series analysis by year and player, in order to track the overall evolution of the league. Also, some assumptions had to be made for certain statistics, like on base percentage (OBP), which traditionally includes all the ways that a player can reach base, including on a fielder’s error, but which are not recorded measures in Madden’s record book.

The database is in two separate files: one for batting and another for pitching. Each contains twelve separate sheets, one for each season. In addition to the figures that Madden collected, I calculated five sets of statistics that have become popular in more recent decades. For batters, these include on-base percentage (the percentage of the time that a batter gets on base, whether by getting a hit or reaching base on four pitches out of the strike zone); slugging percentage (a weighted average of singles, doubles, triples, and home runs); and on-base percentage plus slugging percentage; and for pitchers, the ratio between strikeouts (good for a pitcher) and walks (not good for a pitcher) and walks and hits per inning pitched (WHIP). 

The two files are combined in a union, based on the player name and year field, so that the visualization can include both sets of statistics. This is important because in the 1940s and 1950s, pitchers still hit in the batting order instead of a designated hitter, which has been the case since 1973.

Database construction In Tableau

Creating a union for the tables allows them to function as a single table, which makes year-to-year comparisons much easier.

And now, at long last, the data are ready to visualize. The game is so complex, with so many statistical measures collected, that showing them comprehensively on a chart is next to impossible. So these visualizations show statistics that are especially relevant or insightful for the performance of the league.

Scatterplots and sparklines: insights for a unique league

The first chart is an overview of key batting and pitching statistics. Each of these is shown by a simple line graph, with no labels on either axis (called sparklines) and linked to the two matrices in the middle, one plotting batting average against OPS and the other showing ERA against K/BB for pitching. Each dot denotes a player’s season statistics, with the size of the dot related to the number of plate appearances and the color of the dot based on the year. By clicking or hovering on each dot in the visualization, a viewer can find the statistics for a particular player, or move from dot to dot to compare players.

One important note for the charts on this page is that in baseball–as in many fields–there are certain measures that show a positive outcome with a low measure – like earned run average and WHIP for pitchers. For those measures, the axes featuring these measures are reversed so that the best outcomes are shown in the upper right-hand corner of the large matrix–as they usually are–and in the sparklines, where improvement is shown by upward movement along the y axis.

The sparklines reveal the most interesting insight about the AAGPBL: the steady increase in offense–measured by hits, home runs, and OPS–and the corresponding decrease in pitching performance, especially ERA and WHIP. These are average measures across the entire league and don’t reflect individual performances as much as they do the changing dynamics of a league that began using underhand pitching, a ball the size of a softball, and bases 40 feet apart to a sport that was much more like the men’s major leagues, with a ball the size of a baseball, overhand pitching, and bases 55 feet apart.

The second chart, “Key Ranges by Season,” shows how typical performance changed during the life of the league, both for batters and pitchers.

Ranges

This chart shows the same six measures as in the first chart: batting average, hits, and home runs for batting; and WHIP, IP, and wins for pitching. While the sparklines show the average trend, this graphic shows the spread: the median, quartiles, and upper and lower limit for each year. Interestingly, the range for the batting measures (especially for home runs, and especially during the last five years of the league) increased, while for innings pitched and strikeout/walk ratio, both ranges decreased for the last eight years of the league.

The third chart, “Four AAGPBL Luminaries,” is the most accessible for someone new to baseball and to the AAGPBL in particular: a survey of players highlighting four, in particular, that the audience can learn more about on their own. This is where the true power of data visualization lies: not merely in revealing facts, but in empowering audiences to discover facts that are especially relevant for them.

Tree diagrams

This chart uses tree diagrams that go from “hot”–the career leaders in each of the four categories – to “cool” (the bottom part of the top 20–roughly–in each category. The four luminaries include:

• Career home run leader Eleanor Callow (55)
• Season batting average leader Joanne Weaver, who hit .429–higher than the marks that Ted Williams (.406) and Rogers Hornsby (.424) set–at the age of 19
• Career wins leader Helen Nicol (163)
• Career hits leader Dorothy Kamenshek (1,085)

I have only scratched the surface of the accomplishments of the All-American Girls Professional Baseball League, but this brief look at the data highlights a few intriguing insights about the league, both in hitting and pitching, that are worth exploring more deeply. Ultimately, the power of data visualization is creating new ways of seeing data–whether this year’s Major League statistics or the entire lifetime of a professional league such as the AAGPBL. Even though few AAGPBL players remain living, their accomplishments, recorded for posterity and able to tell stories of their own, live on. The best thing that the reader can do to follow up on reading this article is to interact with the visualizations themselves by visiting the Tableau Public dashboard. In the end, the value of data visualization for baseball is the same as it is in any other field: to create a new way of seeing data that are otherwise contained in reams of spreadsheet columns. 

The post Insights Of Their Own: Visualizing Women’s Baseball appeared first on Nightingale.

We need to first define the parameters of what is essential in any bracket design. A single elimination tournament bracket is a tree diagram that tracks the progression of teams across games where the loser is eliminated in head to head matchups. A bracket must show the entire pool of teams and each subsequent round on the same view. Each team shall have some indicator to describe seeding. Seeding is the preliminary ranking for each team to determine their initial opponents. Higher seeds play lower seeds. It should also track winners and depict team advancement. A completed bracket is an artifact that captures the tournament story into a single picture.

Current bracket design is primarily catered to casual participants who enjoy the annual tradition of filling out a bracket. But this base case is severely lacking for serious bettors, basketball enthusiasts, and skilled bracketologists. I think about bracket design in three classes:

1. Research: How can we super charge brackets to include more information about particular matchups beyond seed numbers? 
2. Prediction: What are various methods to improve the act of making predictions? Note that this is not necessarily mutually exclusive from the research phase.
3. Evaluation: And lastly, when the winner is decided, can the bracket inform us not only who won, but how they won? 

A single bracket view cannot necessarily answer all of these questions in one go, but defining the use cases and exploring custom views for each problem will help facilitate a discussion on the future of bracket design.

Research

Researching how to fill out a bracket includes sifting through rankings, scanning team statistics, and even learning about anecdotal stories behind each team. While extensively available, the data is separated and outside of the bracket. Exploring the research phase is how we can integrate external resources and data points into the main bracket design.

For starters, the “NCAA March Madness Men’s Bracket Challenge” sponsored by Capital One makes team statistics readily available on each matchup through an informational icon on the right of the matchup. Clicking this opens a modal containing relevant matchup information between the two teams. Users can even compose a custom analysis by choosing specific stats and weighing them to create a composite score. While all of these features are useful, they are outside of the bracket and a separate experience and product altogether. Integrating matchup information within the bracket design keeps the selector within the same context and enables them to easily compare across multiple matchups, not just one matchup.

In 2013, Sean McDade experimented with the idea of data integration in bracket design in his article “Men’s NCAA Interactive: Redesigning Bracket Slightly Easier than Winning It.” One visualization structure he explored in his brainstorming process was to integrate various team statistics directly into the bracket as means to compare matchups as shown below.

Each region is depicted as a radial pie with 16 teams each. A line for each team is drawn where the length of the line represents the ranking of how that team performs against a particular statistic. Higher ranking teams have lines that extend to the circumference of the circle, while lower ranking teams are shorter. While certainly unique, McDade describes how “It may be adequate in comparing statistics. It’s weak, however, in its principal functionality: to display team advancement and subsequent rounds.”

Positioning relevant data points closer to individual matchups in bracket design provides clarity and transparency behind each selection and decision. Figuring out what data points are important and sifting through the noise is an obstacle for a general participant. However, for the veteran bracket picker, it may be a boon to have data analysis built into the bracket.

Prediction

Moving the bracket selection process online has made it easier to fill out brackets, undo selections, and manually play out scenarios. In its current form, the prediction phase is focused on simple data entry, however. The design does not afford any conveniences on informing the user of any trends or biases made during the selection process. The “ESPN Tournament Challenge Bracket App” makes the prediction process effortless for the casual fan who wants to fill out a bracket. This is useful for someone who doesn’t know anything about college basketball, but may end up being clutter for a basketball enthusiast.

For a serious player who does not pick teams on a whim, how do we introduce additional context within the bracket to improve predictions? This may span information about previous picks or even consolidated picks from multiple brackets. Adding in how many upsets are predicted informs the bracket picker of perhaps their own biases towards underdogs. For the expert bracket picker who makes multiple brackets, we can aid the prediction process by transplanting information from completed brackets into ones in progress. For example, before selecting “Duke” to move on in the third round, we could remind the user how they have that same scenario playing out in 15 of their other 20 brackets. This reduces risk and increases exposure to other teams which is important when trying to build the perfect bracket.

Constructing a bracket with win probabilities is something Micah McCurdy does in his Wimbledon brackets to directly convey odds of success. Each player in the tennis tournament has a different chance to advance in each round. The probability is depicted in each matchup and flows over to the final round. Skill between players or teams in a tournament greatly varies. In a traditional bracket, the odds of success are unclear. Micah’s visualization clearly communicates the tournament favorite is and the likelihood for each player to advance. This makes it easier to play out scenarios and make better predictions.

By combining prediction capabilities along with supplanted research information, the bracket picker is well equipped to not only view their predictions but see how they arrived at it. This creates a sense of accountability that is measured in the next phase: evaluation.

Evaluation

As tournament games unfold, participants track winners and scratch losers. The traditional bracket design represents results in binary format. Did the team win or lose? The story of an individual game where both teams face the highest pressure to win is lost on the larger scale of the tournament bracket. How can we incorporate not just what happened, but how it happened?

Chris DeMartini explores how we might visualize results in tournaments across different sports. In collegiate golf two formats are combined: stroke play to determine qualifiers and then a standard elimination tournament between the qualifiers to determine the winner. Chris weaves the results for qualifier and then the eventual tournament into a single visual. The results for each team in each round is represented as a stacked bar chart where cumulative round score is highlighted while cumulative team score is faded. In the playoff graphs, each head to head team is represented with a series of dots where the distance between the dots is the margin of victory. This provides a concise visual on what happened in a single match.

A traditional bracket design would simply depict the tournament side of the competition and show who advanced at each stage; however, Chris’s bracket visualization not only shows the story in each match, but also provides the qualifying background to show how the tournament teams got there. This visualization, available here is full of interaction points to provide details on demand without taking the user out of the bracket.

In the same style, another of Chris’s visualizations depicts the NBA playoffs where you can see the flow of a game. The NBA playoffs is a 16 team tournament over the course of several months to determine the NBA champion. Each team plays in a best of seven game series to determine who advances. A traditional bracket will tell you which team won and who they beat, but Chris’s visual literally depicts the highs and lows for each game in each series across each matchup. This bracket design is engaging because you can track how each team performs in a single game and how they performed in the entire series. This visualization is available here. The road to the finals isn’t easy. Each team has their own path. Evaluating the results and visualizing the journey doesn’t reduce a tournament into just winners and losers, but instead crafts an artifact you can look back on.

From paper brackets to innovative designs on the web, bracket design has evolved because innovators make daring design decisions, technology improves, and more data becomes accessible. Each iteration is a step forward towards creating a better bracket experience as the research, prediction, and evaluation phases become interwoven. The NCAA March Madness Bracket Challenge shows us how we can access matchup data between teams. Sean McDade’s radial design took it further to directly embed data within the bracket. Micah McCurdy combined odds of success with potential matchup advancement to depict the path for each victor. And finally, Chris DeMartini shows how we can use bracket design as an artifact to trace how the winning team won. Each of these iterations explored a different segment in the bracket experience.

I believe future versions of bracket design will strive to combine the different segments into a single integrated view. The future bracket selection experience will integrate critical information into the same context where picks are made to improve the decision making process. Brackets will be redesigned to fit mobile layouts and possibly even take advantage of the interactivity available in the infinite landscape of the metaverse. More interaction points will be exposed to glean insights from all phases before, during, and after a tournament. And at the end, the bracket will transform from a research and prediction tool to a reflective story on what transpired. 

What if we could apply the same principles in bracket design to parts of our lives? Brackets are organized vehicles to represent documented decisions between discrete choices. Outside of the world of sports and tournaments, we could use more data products that explicitly require us to document decisions and prepare us to reflect on choices made. Decisions like determining what house to buy, what major to study in, and even what to eat are all unorganized choices we constantly make despite lack of research and evidence. If we can make the other decisions in our life as organized as a bracket and integrate it with external data points, better prediction capabilities, and retrospective tools we will be able to make better picks in our own lives!

The post Bracket Carousel: Discovering the Next Generation of Bracket Design appeared first on Nightingale.

As someone who considers himself all of the above, I looked forward to reading From Data to Stories, and I certainly wasn’t disappointed. The book is a delightful read, split into two parts. The first section, described as a graphic novel, interweaves data and visualisation with an ongoing story in comic form, documenting the ups and downs of a few days in the life of Ringo and Priya, living in London, along with their friends, Dey and Dev, in Bengaluru, and other characters we are introduced to along the way.

And there’s no getting away from the fact that the subject of the comic is cricket–specifically the 2019 T20 World Cup in England. Cricket is a strong theme to the book, since the human comic story and the data-heavy tournament story run concurrently. If, like me, you know, understand, and enjoy cricket, then I think you will enjoy the book tremendously. Cricket, like baseball and other similar sports, is exceptionally data heavy, with statistics available on every ball, shot, catch, wicket, player, match, and team. The longer nature of individual matches, not to mention the tournament as a whole, offers the opportunity to tell a full and detailed story with data over the course of every game. 

This first section is in the form of a comic in several chapters. Each chapter represents the timing on and around the day of one particular match, which offers an ideal opportunity to tell a story broken down into bite-sized parts. The comic book style allows each chapter to begin with the latest update in the tale of the comic characters, while then moving on to the story of the specific match, accompanied by visual scorecards and other charts to complement and enhance the narrative. A brief data comic tip starts each chapter, with advice on topics such as connecting tables to visualisations with arrows, or showing emotion on characters’ faces aligned to time series graphs to accentuate findings in the data.

Each chapter contains visualised scorecards, and it’s the ball-by-ball nature of the scorecards, forming the bulk of the data visualisation, which allows the story to be told in detail, almost as if you were at the match!

The book itself demonstrates of how gently and seamlessly mixing visual and storytelling techniques can produce a successful data visualisation. Of course, this approach doesn’t have to be restricted to cricket. Although I mentioned above how likely you are to enjoy the book if you enjoy cricket, if you can see past this sport to other potential uses, then it provides the inspiration to create your own data comic on a different theme. I see no reason why it wouldn’t be possible to visualise similar sport tournaments (FIFA World Cup, Olympics, or Paralympics) or a data-heavy TV series, for example. One scenario in the book even shows comic figures reacting to monthly business sales figures. I’m sure there could be a case for introducing data and data visualisation to children, too, if the comic elements were pitched appropriately.

The first section illustrates firsthand the ability of data comics to communicate insight. Regardless of how you feel about the term “storytelling,” the fact remains that it is a method of presenting and communicating the data that adds value, understanding, and even character to the nature of the data itself. After all, who but the most die-hard of cricket nerds (OK, I think I might just have described myself there) would gain the same level of insight purely from a staid, black-and-white scorecard full of numbers? 

The second section is the practical section of the book, offering advice and instruction on how to obtain and use data in Excel. Aimed at beginner level, it’s full of step-by-step screenshots on how to collect, question, clean, and analyse the data in Excel before taking the step to convert to comic form. The data used in the book itself is available for download, allowing you to work through the process hands on. 

Ending with information and advice on story structure, you are then ready to try the ComicGen open source comic maker library (from Gramener) to generate your own data comic. I was drawn to this book, and this review, as a fan of data, storytelling, and cricket, but remain a complete non-artist with limited experience of comics. But the book gives you enough grounding to potentially persuade even the least artistically inclined to consider a data comic for a future project. I haven’t personally tried the ComicGen comic maker yet, but with more international sports competitions always just around the corner, watch this space!

As Andy Kirk puts it, more evocatively, in the foreword: “Through the charming visual treatment, it transforms potentially dry statistical facts into a visceral form that stirs the emotions of joy and excitement, hope and despair.” If you ignore all of my review with all that stuff about cricket and comics, and focus only on Andy’s comment, you’ll get a great synopsis and understanding of why this book is recommended.

The post REVIEW: How Can We Use Comics to Tell Data Stories? appeared first on Nightingale.

If you follow basketball (and maybe even if you don’t), you’ve probably heard of him: Anthony Davis is a superstar. He was a first overall pick in the NBA draft in 2012. He’s a seven-time All-Star. And as of 2020, he’s got a ring.

You probably haven’t heard of her, but Natasha Howard is also a superstar. She was 2019’s WNBA Defensive Player of the Year. She was second in the league in blocks. She too played (and dominated) in the 2019 WNBA all-star game. And as of 2020, she’s got a second ring.

In fact, stat for stat, Natasha Howard plays — and dominates her respective league — in an eerily similar way to Anthony Davis. And actually, there are a number of statistical doppelgängers between the two leagues (I generated 30,000 player comparisons; check out the data or read the full analysis). The Kawhi Leonard of the WNBA might well be DeWanna Bonner. The Kristi Toliver of the NBA? You could make a strong case for Lebron James. Donovan Mitchell and Arike Ogunbowale play the game nearly identically. The list goes on.

Despite dominating in similar ways, they receive astronomically dissimilar compensation. This isn’t likely to surprise most readers. What is surprising is the size of the gap.

In America, on average, a woman makes 89 percent of what a man makes, despite having the same amount of experience and holding the same position.

The average salary of an NBA player is $7.7M. In the WNBA, it’s closer to $75,000, so the female athletes are making about 1% of the salaries of their male counterparts. Anthony Davis makes $27M a year; one of his closest comparators statistically and in terms of dominance — Natasha Howard — makes $117,000 a year. In the case of Davis and Howard, she’s making 0.43% of what he makes.

But this, of course, is just the surface and far from the whole story.

What constitutes “appropriate” compensation?

Are WNBA players paid appropriately considering the size and commercial success of the game? Can we compare them to the MLS, NHL, MLB — or even a professional eSports league — to see if there is a wage gap based on gender alone? Is the size and financial prowess of a league proportional to the amount it pays its players?

To begin to answer these questions, we can compare the WNBA with other professional sports leagues and factor in things like total revenue, aggregate players, average salary and number of teams. We can start by charting how much each league is paying their players relative to the amount of money they make.

Range plot that highlights each league’s average player salary and how much revenue they give to each player (or total league revenue divided by total number of league players). Note: There is another major female sports league — the National Women’s Soccer League — but their maximum salary is so low ($50,000) and financial figures inaccessible so they were omitted from this data.

You’ll notice that as a league makes more revenue (dark blue), it can pay its players more money (light blue). But it’s not linear — it’s exponential. Every league listed above falls on this curve.

According to this data set, WNBA players make just as much as you would expect athletes to make based on total league revenue; in essence, this data point has been used to argue that their wages are “fair.” Other female athletes have seen this explanation used to justify their unequal pay: when the US Women’s National Soccer Team (ranked number 1 in the world) filed a class action lawsuit against the US Soccer Federation alleging that they’re paid and supported unfairly, the federation blamed economics:

The soccer federation denied the claims in the women’s lawsuit, arguing in a May court filing that the pay differential between the men and women players is “based on differences in aggregate revenue generated by the different teams and/or any other factor other than sex” and that the two teams are “physically and functionally separate organizations.”

Yes, revenue matters, but it’s also starting to become clear that how we perceive and consume women’s sports contributes to the size of the overall pie, and thereby the size of the pay gap.

Professional basketball and professional chess have more in common than you think.

Is it really true that there’s just less interest? Are male basketball players just more entertaining? Do they simply possess more talent that draws larger crowds with deeper pockets? After all, professional female and male basketball players are both playing basketball (albeit, with different approaches). So how much does gender factor into the gaps in revenue and respect?

Fans of Netflix miniseries The Queen’s Gambit might appreciate this next bit.

In the world of professional chess, there exists a bias that male players are far superior. China’s Hou Yifan is ranked 83rd globally. She is the highest ranked female among all professional chess players. The highest ranked male (number one in the world) is Norway’s Magnus Carlsen. Like in professional basketball, most fans of chess know and idolize Carlsen. They’re less likely to do the same for Yifan, considering there are 82 chess players ahead of her. All male.

But according to NYU Professor Wei Ji Ma in his piece titled, “What gender gap in chess?”:

…there is no evidence that the “achievement gap” is anything but a participation gap. Statistically, there is nothing to suggest that top female players are underperforming given the overall ratio of female to male players. In fact, taking into account the systemic injustices and biases that they had to overcome to get where they are, they are likely over-performing.

The same might be true for the WNBA, which is three times smaller than the NBA. If, hypothetically, there were the same number of women as men playing professional basketball, then the perceived differences in talent could be less apparent and the revenue disparity could shrink significantly.

However, while chess serves as a helpful introduction to participation gaps there’s one obvious caveat: women are playing men and women in chess. Women are only playing women in basketball.Still, I believe this is worth mentioning. The key takeaway from the chess study for me was that increasing the overall talent pool for female basketball players potentially serves to lessen the perception of a massive talent disparity as well as elevating the overall level of play in the WNBA. A revenue or pay gap stems in large part from a participation gap. Men and women may not play together, but more (and more good) players correlates with more teams, more sponsorship revenue, more attention and higher salaries.

Table breakdown for major American sports leagues’ and the English Premier League (EPL) team and player totals, how much money they make, and how much of that money is allocated to their players.

It’s worth discussing how an up-and-coming league attains prosperity. It cannot simply be explained by the volume of the talent pool, though I believe it’s a major factor.

Putting the participation gap aside, if a female league and a male league had similar inaugural seasons — that is, both leagues exist roughly the same number of years — would one perform better than the other? Would one make more money or produce more fans?

Is viewership to blame for the revenue disparity?

Consider this: the WNBA’s inaugural season was in 1997. There are leagues in the above datasets that have been around for about as long, or are even newer, that already make exponentially more, despite very little difference in viewership (I understand viewership is not representative of all revenue — lest we forget ticket and merchandise sales, licensing, broadcasting deals, etc — but it’s a good gauge for interest, and serves as a good directional indicator especially when there’s a disproportionate difference between interest and revenue).

Consider the MLS, which has been around only 4 more years than the WNBA. Despite nominal differences in television viewership, salaries and payment are — like nearly all professional sports leagues — disproportionally favoring men’s bottom lines.

And one of eSports’ most notable properties, Overwatch League, was started in 2016. The average pro Overwatch gamer makes $39,000 more per year than the average WNBA player. In 2020, each match entertained an average audience of only 65,000.

Viewership is only growing in the WNBA. They have two decades on Overwatch, who is seeing declines in viewership over the last three years. So if the realities of viewership don’t fully explain the gap, perhaps we need to follow the money.

Is it a coincidence that the only major female sports team is dead last in revenue?

For most teams and leagues, sponsors are a critical—if not the primary—source of revenue. Are sponsors biased in who they invest in? After all, the WNBA and MLS have nearly identical cable viewership numbers.

The number of viewers any given league captures helps to gauge audience interest. Corporate sponsorships help to gauge financial interest. According to the LA Times:

For every dollar that corporate America spends on sports sponsorship, less than a penny goes to women’s professional sports. The WNBA in particular gets a fraction of that fraction.

Perhaps the MLS has more because sponsors give more. While the NBA makes nearly 100 times what the WNBA makes in sponsorship revenue, differences in cable viewership and in-game attendance (used as representatives of audience interest) are slightly slimmer.

League share of total professional basketball sponsorship revenue, total per-game cable viewership, and total in-game attendance. The NBA‘s sponsorship revenue is about 100x the WNBA’s; the NBA’s cable viewership is 88x the WNBA’s; and the NBA’s in-game attendance is 62x the WNBA’s. Corporations broker deals with each team. These sponsorship, licensing, and broadcast deals then go through each team’s front office, and as with many things in basketball, those front offices and executive suites are made up mostly of men.

Basketball is a game designed by and for men.

Now, that’s a transition which certainly appears to implicate gender bias as the reason for the “fraction of a fraction” corporate America gives to the WNBA. This may not tell the entire story, but here are a few things we do know:

• Over 99 percent of corporate sponsorship dollars go toward male leagues.
• Women occupy only 10 percent of top management positions in S&P 1500 companies.
• Of the 12 WNBA teams, only three are owned by women — the Atlanta Dream (? Kelly Loeffler), the Seattle Storm and (half of) the Chicago Sky.

It’s interesting to see the rapid growth, sponsorship, and financial success of male leagues owned by males, especially in their earlier years, as with the MLS and Overwatch. The same is not true of the one female league owned almost entirely by males.

Intrinsic gender bias at the leadership level is important to be aware of. Systemic gender bias — baked into the rules and regulations themselves — is arguably more important.

While men and women have different physical builds, there is evidence that perceived differences in athleticism are due to systemic differences. For example, a WNBA basketball rim is 10ft off the ground, which is the same as in the NBA. Men can throw windmills and alley oops all game long on a 10ft basket. Women can’t because the WNBA was fundamentally designed around a men’s game (something that goes ignored by dozens of YouTube videos mocking the Candace Parker and Brittney Griner dunks). Some of the best female basketball players in the world are trying to change this.

It’s almost never explicitly verbalized, but sports fans (largely male, myself included) will secretly think, “it’s less entertaining” or “men are just better athletes.” They perceive a difference in talent, but what they’re really seeing is a participation gap and a game designed around male physicality. There’s plenty of literature (just one example here) that proves this theme goes well beyond basketball.For these reasons, WNBA teams found different ways to win. And their approach—as I’ll argue—is fundamentally different from the NBA’s (and worth paying attention to).

The same game played with an entirely different strategy: equitable ball distribution.

If you took every player in each league and lined them up in order from least points per game to most, the player in the middle of both lines would be scoring about 10 points per game.

But the players to the left and right of that middle player differ. That is, in the NBA line up, there are a number of players who score slightly less (about 8 points per game). That’s likely due to the disproportional number of players who score way more — between 23 and 30 points per game. These are the James Harden’s, LeBron James’, and Giannis Antetokounmpo’s of the world.

The WNBA, by contrast, is more evenly distributed. In other words, the majority of players are within 5 points of the median (5–15 points per game), creating a more real middle class of players.This goes well beyond simply points per game, and bleeds into all offensive and defensive stats. In most statistical categories, there is a higher volume of players who are at or above league average as compared to the NBA. For example, in the NBA, 36% of players are above the average number of assists per game (AST). In contrast, 41% of WNBA players are above league average. More players in the WNBA are sharing shot opportunities.

Arrow plot revealing per-stat differences in percentage of players who are above the league average in that given stat category. The right side of the line is the WNBA; the left is the NBA. If an arrow faces right, the WNBA has more players than the NBA that are above average. Left facing arrows mean the opposite.

You can catch a glimpse of this in action just by watching the Lakers and the Storm in their respective championships in 2020.

You’ll notice that fewer names (namely LeBron James and Anthony Davis) are color commentated during big plays. The ball is largely possessed by one or two players and plays take slightly longer to develop.

Compare that to the Seattle Storm. In the first couple minutes of the video below, you hear Lloyd, Stewart, Russell, Bird, and Howard all have their names called. The ball feels like it’s constantly in motion.

In the last 30 years, the NBA has adopted a “heliocentric” style of play coinciding with the rise of singularly dominant playmakers like LeBron James and James Harden. As shown above, this is evident by the disproportional number of players who dominate the ball or get significantly more playing time.

Of course, there are counter-examples on both sides in the NBA and WNBA. The 2013 San Antonio Spurs and prime Steph Curry-era Warriors were exemplars of ball movement, and we showed at the opening tip that the WNBA is not devoid of dominant players who can take over entire games.

Is the WNBA’s divergent approach a function of their environment or is it a chosen strategy, drawn up by coaches and a history of proven success?

It actually doesn’t matter which is true because it’s not really the point. The point is this: the NBA (who owns the WNBA) and the arbiters of the WNBA’s success should pay more attention to the growing interest in the sport, and the thing that makes it great: their unique approach to the game.

Don’t get me wrong. The NBA is incredible. I’m a diehard Wizards fan (?). Watching Brad Beal highlights is a pastime. I’m not trying to say they’re the same sport, or that one is inherently better than the other, I’m saying they are fundamentally different approaches to the same sport with inherently different qualities.

Of course, this is not to paint either league in black and white strokes, but rather to offer up some evidence as to the different styles embraced by the NBA and WNBA in order to best fit their players. Both leagues have dominant superstars, but each league also approaches the game in fundamentally different ways, a difference that I believe is worth celebrating.

Our metrics for what constitutes good entertainment — dunks, 50-point performances, “hero ball” plays— have been crafted over time by mostly male fans, coaches, commentators and players. It’s incredible to watch, but we should try changing our perspective. Perhaps there are alternative ways to watch and appreciate the game. It’s cool to be nerdy now. Cigarettes used to be good for you. No one knew or appreciated Samuel L. Jackson until he performed in Pulp Fiction at the ripe age of 46. Perspectives change.

In my opinion, we should be celebrating and advertising the WNBA’s style of basketball. While they win as a collective, the NBA feels like a couple heroic protagonists with a cast of unremarkable supporting characters. In other words, the Storm play like the Avengers. The Lakers play like two Batmans and a dozen Alfreds.

In conclusion: get involved.

As of January 2020, the WNBA is making some very significant changes. Most notably, players will receive a 53 percent increase in total cash compensation. While this shows a dramatic improvement, and it does lock players into these figures for years to come; there are still considerable disparities compared to male leagues beyond the NBA.

Ultimately, the growth of any sport comes down to viewership and interest. And while there is disproportionate growth between female and male leagues, and there are numerous systemic factors at play, popularity is growing. Agreements are being made. Things are trending upward.

As for you, add a WNBA team to your ESPN notification list. Buy the merch. Mark your calendars for the end of May when the season starts. Check out this master spreadsheet (of my own design) comparing NBA and WNBA players, find your favorite NBA player, find their “stat match” equivalent, and learn about her. Hell, ask me to find your favorite player’s stat match comparator for you. It’d be an honor.

Watch the game. Notice the nuances in style. See the differences. It’s like watching your favorite game, but from a parallel universe.

In a New York Times opinion piece titled, “Why the W.N.B.A. Loved Kobe Bryant,” the author highlights something he once said:

“There’s no better way to learn than to watch the pros do it,” Kobe said after he took his daughter’s club basketball team to watch the Los Angeles Sparks play the Las Vegas Aces in May. “The W.N.B.A. is a beautiful game to watch.”

The WNBA is a beautiful game to watch. You should try it out. Because there’s more at stake here than entertainment.

This article was written by me, Josh Strupp — product designer at Taoti Creative by day, data hobbyist and writer the rest of the time. A million thank yous to the folks that made it possible:

• Emily Linton ?‍?— Brilliant educator and editor; my sister who I admire entirely too much; it’s eclectic, not electric, stupid… also, it’s latter, not ladder, stupid.
• Edward Linton ?‍ — Ophthalmologist; math/science/data generalist; best brother in law ever; future owner and proprietor of large marge’s party barge.
• Eliot Goldfarb  — Chicago Bulls analyst; self-diagnosed sports addict; best friend since day one; put a towel down, will ya?
• Scott Donaldson ?‍? — Founder of Open Set; digital wizard and mentor; aka Scottly!
• Colin Kelly? — Director at Bully Pulpit; dynamite friend; I’ll hang up and listen.
• Max Hedgepeth — ? marketing at Capital One; a friendship forged at a terrible conference; flossy Wiz floors seats only.
• Maggie Gaudaen ? & Zach Goodwin ? — creative mentors; DC-based CDs at January Third; creators of the Lyft Luck Machine, the Air Canada Poutinerie and the brand pizza.
• Mark Goldfarb ? & Beth Levine —ski senseis; creators of Jock Doc and Jock Talk (go read it); the reason this essay isn’t 12,000 words.
• Senthil Natarajan ?‍? —my Nightingale in shining armor; killer editor; nicest person I’ve never met.

The post Visualizing the Gap appeared first on Nightingale.

In 1858, Florence Nightingale published a study on the conditions of army hospitals, her seminal Notes on Matters Affecting the Health, Efficiency, and Hospital Administration of the British Army. Her Diagram of the Causes of Mortality had a singular goal: to vividly demonstrate that the lack of proper sanitary caretaking facilities was a far more severe, but also far more avoidable, cause of death for soldiers than injuries suffered in battle. It’s one thing to simply state that the disease killed a lot of soldiers. It’s another thing entirely to effectively and actionably juxtapose it against the casualties encountered at the hands of the opposing army.

Nightingale could have just taken a cue from her predecessors in the data visualization realm and drawn a bunch of bar charts (maybe throw in a line graph if she wanted to get edgy). It would have been the most precise way of capturing the data she spent so much time recording. But, my goodness, what a boring world we’d live in today if she had. And also, probably more soldiers would have continued to die for a bit longer from preventable causes. But mainly, data viz would be more boring; that’s what’s important here, for this article at least.

Pac-man shape of former Ole Miss receiver D.K. Metcalf’s NFL Draft Combine performance). Radial form factors are then optimized if the visualization naturally encodes traditionally radial or cyclical concepts (like launch angles in baseball, directionality of weather patterns, or time/seasons like with Nightingale’s original diagram).

For how it combines impact with enduring popularity, Florence Nightingale’s Diagram of the Causes of Mortality is an objectively cool piece of data visualization (in my absolutely subjective opinion). And over the past year or so, I’ve been tracking down cool, funky, or otherwise interesting works of data visualization. There’s a lot to be learned from deconstructing historical works from pioneers such as Nightingale or W.E.B. Dubois, especially when that work goes against (at least on a surface level) a staid and uninspiring grain. The exercise of discovering, internalizing, then reverse-engineering those same principles that the original authors meticulously considered (this isn’t a Jackson Pollack splatter-paint job; every detail is carefully planned out here) is an exercise that fundamentally improves one’s capacity for creating effective data visualization.

I do data analytics for a bank as a day job, and when I get home in the evening, I do data analytics for basketball. Learning how to effectively weave creative data visualizations into the cases that I present and being able to borrow from inspiration across diverse fields (the wonders of cross-pollination learning!) has been critical to my growth as an analyst and story-teller. Borrowing inspiration from pioneers in the field has been key to spurring that growth. (Oh how far I‘d like to think I’ve come; when I showed Elijah Meeks a network diagram I’d rendered just three years ago, his first reaction was, “Gosh, that’s ugly.”)

Which is why, as a learning exercise, I wanted to recreate historical data visualization but with data sets that interested me. The Nightingale piece was one of the first ones to catch my eye. I turned her exploration of sanitation in army care facilities to something a shade more frivolous: the Diagram of Causes of Mortality for the GOAT in the East, a comparison of LeBron James’ best NBA teammates in the playoffs versus Michael Jordan in his stints with the Chicago Bulls. After all, as I ask in the article, “what is basketball if not a war of attrition?”

Let’s take a look at LeBron’s Miami years from 2011–2013 for an example of how to read this graphic. The blue wedges represent his statistical playoff impact, whereas the pink and dark wedges represent the playoff impacts of his second and third best teammates, respectively. It quickly becomes apparent how the disparity in skill and stature between James and his next best teammates only widened over time (as Dwyane Wade and Chris Bosh aged), leading to his eventual departure back to Cleveland.

Just as with Nightingale’s original work, the radial form factor’s ability to amplify disparities takes a reasonable statement (“Michael Jordan had teammates who were more his equal in his playoff runs than LeBron James has mostly ever had”) and outfits it with flashing neon lights and a blaring Ozzy Osbourne background track. As was the case with its spiritual predecessor, I kept each wedge in my recreation as a temporal snapshot, except instead of months of the year, I mapped each wedge to an NBA season, helping to track more easily the evolution of quantities over time. On the flip side, in order to further make clear the difference between Michael Jordan’s teammates and LeBron James’ teammates, I “preserved” the order of the stacking. When Jordan’s statistics were eclipsed by Scottie Pippen, Jordan’s bar was no longer visible. I took the liberty of using the stacking order to make more profound the overlapping effects. Nowhere in James’ section does a teammate ever eclipse his stats; nowhere in James’ section do you see his blue wedge go missing altogether.

As for the technical milieu of how exactly I created (or recreated, rather) the piece, I used Plotly in Python to build out the base rose diagrams, then did the post-processing to capture the exact texture and style of the original with graphic design software. Plotly is an interesting tool/package because it’s not exclusive to one specific language. It doesn’t occupy the ggplot or matplotlib realm of singular, native associations with programming languages. But it is undeniably versatile, and I’ve found it a more than capable match for a variety of projects now.

However, if you thought I stopped at Florence Nightingale, you would be mistaken. No historical figure is safe from my pillaging. Not Burtin, and certainly not Minard. In fact, they’re all now going into a book that I’m working on, remixing historical data visualizations to tell basketball stories!

It’s the storytelling part that I keep coming back to in my data visualization journey. I’m roughly paraphrasing, but Elijah Meeks wrote that “data visualization is not a technical problem. It’s a communication problem.” At the end of the day, it’s about conveying information, finding and communicating patterns within data. It’s a two-way street, a shared experience between the creator and the end-user. So rather than beating those end-users repeatedly with a blunt object, let’s all together strive for a little bit more creativity and verve. And if you’re stuck, in need of inspiration on just how to go about doing so, well… I know a certain Lady of the Lamp who could provide a few pointers.

Senthil Natarajan is combining data science and engineering with creative storytelling, working across Fortune 500 companies to the NBA. He can be found musing about sports on Twitter or working on startups out of his apartment. Both his pickup basketball style and his board games style has been compared to J.R. Smith, which may or may not be a compliment.

The post From the Battlefield to Basketball: A Data Visualization Journey with Florence Nightingale appeared first on Nightingale.