SportsBall

Chasing Waves 🌊

Sun, 05 Apr 2026 19:00:00 +0000

Happy Sunday! After a five-video sprint last week, we took the foot off the gas a bit, helped by the fact that we spent the week in Hawaii. Drawing graphs for three hours a day doesn’t exactly align with a strict beach and mai tai schedule.

That said, we still managed to get some work done on the page.

Projects we did this week 📅

How pro surfing works - IG, TT, YT
Retirement plans in pro tennis - IG, YT

As always, we have prints of almost every page I draw at our print shop here

Spotlight: How Pro Surfing Works 🌊

Being on an island, it felt only right to focus on the project that involves waves.

I’ve been working with the World Surf League, and what started as a deep dive into brackets and point systems quickly became something much more interesting. An explainer on how professional surfing actually works.

You see, most people haven’t grown up with a coastline on their doorstep. They may have seen a surfing film or two, but the mechanics of the sport are largely a mystery; so I proposed we zoom out and look at pro surfing from a more meta perspective.

How Do Waves Even Work?

Before getting into the tour itself, I had to get my head around the science.

What is a swell? Is the moon involved? How do storms near Brazil turn into six-foot barrels off the coast of Portugal?

I quickly came to learn that the sport is as much a professional weatherman tour as it is a pro surfing tour

The WSL works closely with surf forecasters, companies like Surfline, who can spot promising storm systems weeks or even months in advance. That forecasting is what makes the entire tour possible.

From my very basic college surfing days, I knew winters usually meant better waves. But I never really knew why.

Different regions, even along the same coastline, have completely different seasonal windows. The Southern Hemisphere kicks things off. Australia, New Zealand, and South America get their best swells during their spring and summer. As you move north, the timing shifts to autumn and winter.

It all comes down to where storms are forming and how far the waves have to travel to reach the break.

To visualize this, I built a wave-shaped seasonal diagram, where the peak of each wave represents the best time of year for that region. Then layered the WSL schedule on top to show how the tour moves around the world chasing those conditions.

A line I heard during the process stuck with me:

“We’re trying to put the best surfers in the best waves at the best time.”

That became the backbone of the whole piece.

The Most Unpredictable Calendar in Sport

Unlike almost every other major sport, the WSL doesn’t operate on a fixed schedule.

Each tour stop has a 10-day waiting period. When the swell, wind, and conditions all line up, the green light goes on and surfing happens.

Sometimes that means running multiple heats in a single day. Sometimes it means waiting around for days while the ocean refuses to cooperate.

The ocean floor plays a role too. Sand versus reef creates completely different types of waves. You get hollow, heavy barrels on one end and flatter, more technical waves on the other.

The best analogy I found was bull riding.

The bull gets a score, and so does the rider. If the bull doesn’t perform, neither can the rider.

Same thing here. If the waves are inconsistent for 35 minutes, that’s just bad luck. No judge can fix that.

How a Champion Is Decided

The tour runs from April through the end of the year, spanning 12 events across both hemispheres.

Only a surfer’s best 9 results count toward their total, which rewards consistency without punishing one bad event in poor conditions.

From there, the top surfers advance to the final events of the season, where the world champion is ultimately crowned.

Right now we’re actually in the middle of the first event in Bells Beach if you want to give it a look.

Project Sneak Peeks 🔜

⛳️ A tradition like no other
⚽️ Demotion in soccer

Hope everyone has a great week, and as always, feel free to respond with any feedback. We’re all ears.

More drawings soon.

— Riley & Claire

Baseball's First Robot ⚾️🤖

Sun, 29 Mar 2026 20:35:32 +0000

Happy Sunday! What a week of sports. March Madness paired with the start of Major League Baseball season has been a lethal one-two punch to the productivity department, but we’re powering through.

For the first time ever, I ended up doing a drawing every single day this past week. Very kindergarten core.

Also made it out to Opening Night with MLB, where they had one of the coolest flag features I’ve ever seen.

Projects we did this week 📅

Why March Madness is so Special - IG, TT, YT
Tennis players vs Grand Slams - IG, TT, YT
The next phase of the X Games - IG, TT, YT
MLB ABS explained - IG, TT, YT
How NBA expansion works - IG, TT, YT

As always, we have prints of every page I draw at our print shop here

Spotlight: MLB ABS

A few weeks ago, I got looped into a project with Major League Baseball around something you’re going to start seeing a lot more of this season.

Challenging balls and strikes.

This isn’t brand new. MLB has been testing versions of this for years in the minors and spring training. But this is the first time it’s really being rolled out in a meaningful way at the major league level.

And the reason is pretty simple.

Umpires are really good. But they’re not perfect.

On average, they’re about 94% accurate calling balls and strikes. That sounds great until you realize that’s still 8 to 10 missed calls per game, and some of those can completely change the outcome. We saw that firsthand in the World Baseball Classic, where a missed strike call at the end of a game could have extended things and changed everything.

So the question becomes: how do you fix that without slowing the game down or turning it into a robot-run sport?

The Challenge System

The solution MLB landed on is actually pretty elegant.

Each team gets two challenges per game. If you win the challenge, you keep it. If you lose it, it’s gone. Only the pitcher, catcher, or hitter can initiate it, and they do it instantly by tapping their head. No dugout debates, no replay reviews dragging on.

Building a “Perfect” Strike Zone

The real lift here wasn’t the challenge system itself. It was defining a strike zone precise enough to actually review. In tennis, the ball hits a line you can see. In baseball, the strike zone is… kind of imaginary. So MLB had to build one.

They measured every player and defined the zone based on their height:

Bottom of the zone: 27% of a player’s height
Top of the zone: 53.5% of a player’s height
Width: exactly 17 inches, the width of home plate

Every pitch is judged as it crosses the middle of the plate, not the front.

Which means every single player has a slightly different strike zone. José Altuve’s is compact. Aaron Judge’s is massive. Roughly 20% larger by area, or about seven extra baseballs worth of space.

Same rules, completely different geometry.

The Data Behind It

To enforce that level of precision, MLB uses a system of 12 high-speed cameras tracking every pitch down to less than the width of a grain of rice.

From early data this season:

54% of all challenges are overturned
Pitchers and catchers initiate 53% of challenges
They’re also better at them, winning about 60–61% of the time
Hitters are down around 46%

That actually makes a lot of sense.

Catchers and pitchers have a straight-on view of the zone every pitch. Hitters are making split-second decisions from the side while also trying not to get hit by a 95 mph fastball.

Different vantage points, different results.

If you want to go deeper, MLB has a full Baseball Savant ABS dashboard where you can track all of this in real time. Who’s challenging the most, who’s getting them right, and even how far off certain calls are.

Some of the early ones have been obvious. Pitches 2–3 inches outside the zone getting overturned.

Others are razor thin.

So… Does It Work?

So far, pretty well.

Each review takes about 14 seconds, adding roughly one minute total to a game. And more than 80% of fans say it improves the experience. Which is kind of the whole goal.

What I like about this system is that it doesn’t try to make baseball perfect.

It just tightens the margins.

Umpires still call the game. Players just get a couple chances to say “hey… I don’t think that was right.” And now, for the first time, there’s a system precise enough to actually answer that question.

Project Sneak Peeks 🔜

🌊 Finally some surfing content
🎾 Retirement funds - sports edition

Hope everyone has a great week, and as always, feel free to respond with any feedback. We’re all ears.

More drawings soon.

— Riley & Claire

The Most Complicated Tournament in Sports

Sun, 15 Mar 2026 16:43:01 +0000

Happy Sunday! It’s been a very dynamic week in the sports world, especially on the international front. Champions League games ⚽️, World Baseball Classic in full swing ⚾️, Six Nations’ dramatic finale 🏉, and much more.

One thing I’ve learned in doing this now for a couple of years is that there’s really no lull in the sports calendar, just happy little tournaments sprinkled in.

Projects we did this week 📅

Champions League explained - IG, TT, YT
How Spotify pays artists - IG, TT, YT
Six Nations explained - IG, TT, YT

As always, we have prints of every page I draw at our print shop here

Spotlight: UEFA Champions League

Imagine a map of Europe covering 55 nations, with a pin dropped on every top-tier professional football club, from the Bernabéu to a muddy pitch in the Faroe Islands. You'd end up with more than 700 pins scattered across the continent. Now imagine someone slowly pulling those pins out, one by one, all summer long, until only 36 remain.

That, in essence, is Champions League qualification.

So You Want to Play in Europe?

Of course, for most of those 700 clubs, the Champions League is more fantasy than reality. The realistic pool of potential entrants sits at around 80+ clubs drawn from those 55 countries.

How many spots each country gets depends on UEFA's coefficient rankings, essentially a five-year performance table for national leagues shown in the table below. The better a country's clubs perform in European competitions, the more Champions League spots that league earns.

The heavyweights like England, Spain, Germany, and Italy received multiple automatic places, while smaller leagues may only scrape together one, and that team usually has to fight through qualifying just to reach the main competition.

Number of Champions League teams by nation

Even within the qualifying group, the path isn’t equal.

Clubs from smaller leagues with lower coefficients have to survive up to four separate qualifying rounds just to reach the main stage. Meanwhile, teams from stronger leagues, say a Premier League club like Manchester City, skip all of that and enter directly.

Different starting lines, same finish line.

Turning Brackets Into Bricks 🧱

Once the final 36 teams are set, we reach the league phase. Each club is placed into one of four seeding pots based on their UEFA club coefficient, then plays eight matches competing for 24 spots in the knockout stage.

There were plenty of visual options for showing teams progressing and dropping out, a Sankey diagram being the obvious one.

But I wanted something more literal.

Every team in the video is represented as a single brick, and those same bricks move forward through every stage of the tournament. No switching to percentages, no pie charts halfway through. One team = one brick, the whole way. That consistency (hopefully) made the whole thing much easier to follow.

The Quietest Elimination Tournament in Sport

While working on the visualization, one thing really stood out: the sheer scale of the filtering process.

The 2026 World Cup takes 48 teams from a field of around 200. The Champions League whittles 700+ clubs down to 36. It's a different scale entirely, and that whole filtering process, playing out quietly over the summer before a ball is kicked, is the hidden story barely talked about.

Current Champions League bracket after leg 1 of R16

Glamour shots of the week 📸

Next Project Up 🔜

⚾️ Preparing for opening pitch in MLB
🏎️ Some nuances of the F1 starting line
🏀 The most important basketball trading card ever

Hope everyone has a great week, and as always, feel free to respond with any feedback. We’re all ears.

More drawings soon.

— Riley & Claire

Same Playing Field ⛷️

Sun, 08 Mar 2026 22:59:43 +0000

Happy Sunday. It’s a pretty great moment in the sports calendar right now. The World Baseball Classic is underway, the first race of the Formula One season wrapped up last night, and my Colorado Avalanche just beat the Dallas Stars in a shootout in one of the more intense regular-season hockey games I’ve seen in a while.

If that wasn’t enough, the Winter Paralympics have just begun, which is exactly what we’re diving into below.

Projects we did this week 📅

WBC Explained - IG, TT, YT
Paralympics - IG, TT, YT

Glamour shots of the week 📸

Spotlight: Paralympics ⛷️

One of the questions I’ve always had watching the Paralympics is a simple one.

How do athletes with completely different disabilities actually compete against each other?

My original assumption was that every impairment must have its own event. But once I started looking at the numbers, that idea fell apart quickly. There are roughly 600 athletes across the entire Winter Paralympics, 6 sports, dozens of events, and some sports contain more than a dozen impairment classifications.

If every classification had its own race, you’d end up with events with just a handful or fewer competitors

So instead, the system does something much more interesting.

You see, athletes are grouped into three broad competition categories:

Vision Impaired (B1–B3) - now AS1-AS4
Standing (LW1–LW9)
Sitting (LW10–LW12)

Inside those groups, athletes may have very different impairments. Some standing skiers might be missing a leg, others missing an arm, and others may ski with prosthetics.

Because of this, the finishing times of every skier are normalized using time factors. Think of it like golf handicaps, but for people skiing 70 miles per hour down an icy mountain.

Each classification is assigned a multiplier that reflects how much that specific impairment typically affects performance. After the race, the athlete’s raw finishing time is multiplied by that factor to produce an adjusted time.

In giant slalom, their time factors look something like this:

Class	Description	GS Time Factor
LW2	Single above-knee amputation or similar impairment	0.8581
LW3	Double below-knee amputation or similar impairment	0.9317
LW4	Single below-knee amputation	0.9728

Imagine two athletes in that same race:

An LW4 skier finishes the course in 100 seconds
An LW3 skier finishes in 104 seconds

At first glance, it looks like the LW4 skier wins.

But once the time factors are applied:

LW4 adjusted time
100 × 0.9728 = 97.28 seconds

LW3 adjusted time
104 × 0.9317 = 96.90 seconds

Everyone skis the same mountain, but the stopwatch gets a small calibration afterward.

And like a lot of SportsBall projects, especially during the Olympics, this one started in the often-ignored rules section of an international sports federation’s website.

Buried in the rulebooks from World Para Alpine Skiing are tables listing time factors for every classification across every discipline: slalom, giant slalom, Super-G, and downhill.

Each event tweaks the numbers slightly depending on how the course mechanics affect different impairments which you can see below in the chart with a different colored bar for each event

One classification jumped off the chart for me: LW2.

This class represents athletes skiing with one leg, typically due to an above-knee amputation, using outriggers for balance.

Now, if you scan across the chart, you’ll notice a pattern for most classes. The time factors generally increase as the races get faster, moving from slalom → giant slalom → Super-G → downhill. The logic is: as speed becomes the dominant factor, the relative disadvantage between impairments shrinks.

But LW2 breaks that pattern.

Its slalom factor is the highest of the four, meaning athletes skiing on one leg are actually closest to equal performance in the most technical event.

The likely reason comes down to mechanics. Slalom is all about rapid pivots and tight turns, and skiing on one leg can act almost like a pivot point while the outriggers stabilize the turn.

Once speeds increase in Super-G and downhill, stability matters much more and the disadvantage returns.

It’s a small detail in the chart, but a great example of how the physics of the sport shows up directly in the numbers, and how data visualization can surface insights.

Projects coming soon 🔜

Working on a lot of different stuff at the moment:

⚽️ Champions League explainer
🏉 Rugby explainer
🎵 How Spotify pays artists
⚾️ Automatic balls and strikes in MLB
🏎️ Why qualifying matters so much in F1
🏀 MJ rookie card breakdown

Hope everyone has a great week, and as always, feel free to respond with any feedback. We’re all ears.

More drawings soon.

— Riley & Claire

Standardized Testing 🏈

Sun, 01 Mar 2026 14:49:40 +0000

Happy Sunday. It’s been a quieter week at SportsBall HQ as we’ve been coming down from the Olympics push. A couple of hand massages later, it finally feels like we’re back on the horse.

Projects we did this week 📅

Olympics Recap - IG, TT
NFL Combine Participation - IG, TT, YT

Spotlight Project: NFL Combine 🏈

As many sports fans know, we’re in a bit of a quiet spell right now. The Olympics have wrapped up, and we’re sitting in that awkward gap before baseball, March Madness, or the NHL and NBA playoffs ramp up.

So this felt like the perfect time to dig into one of those under-the-radar annual events that quietly generates a mountain of data: the NFL Combine.

For the uninitiated, think of the Combine as the world’s most intense job interview, except instead of being asked where you see yourself in five years, you’re running sprints in front of thirty NFL scouts. College players trying to make the NFL are put through a battery of physical and mental tests, and the results can make or break where, and whether, they get drafted.

The project plan was to scour 20+ years of data going back to 2000, hunting for superlatives: who’s the fastest, who’s the strongest, how do performances stack up across generations? A clean narrative, nice and simple. But the numbers had other ideas.

What I started to notice were significant participation gaps in recent years. The 40-yard dash still pulls enormous numbers, but something like the 225-pound bench press? Sometimes only a fraction of players bother to record a result. Rather than the performance story I’d planned, the more interesting tale turned out to be about participation itself.

Players have become increasingly strategic about which drills they take part in. With limited upside, real injury risk, and the very real threat that one bad rep could tank your draft stock, many prospects are simply opting out of anything that isn’t essential. The Combine has quietly split into two tiers: a core trio of the 40-yard dash, vertical jump, and broad jump that almost everyone still does, and a second tier of agility drills and strength tests that a growing number of players are skipping entirely.

Which raises a bigger question: what is the Combine actually for anymore?

NFL teams already have years of game film, GPS tracking, and performance data, often going back to high school. Because of that, the Combine has shifted away from being purely about physical testing and toward face-to-face evaluation. It’s closer to a speed dating event for decision makers, where teams sit across from prospects, look them in the eye, ask questions, review full medical imaging, and decide whether they’re comfortable with the person behind the player.

In the end, that’s what made this the most rewarding data dive in a while. I came in looking for the fastest and the strongest and left with something far more interesting: a story about how one of sport’s most famous testing grounds is quietly reinventing itself, one opt-out at a time.

Projects coming soon 🔜

Working on a lot of different stuff at the moment, especially as we transition between seasons. In no particular order, or promises on timing:

⚾️ World Baseball Classic explainer
⚽️ Champions League explainer
🏉 Rugby explainer
🏀 March Madness previews
🏀 MJ rookie card breakdown

Hope everyone has a great week, and as always, feel free to respond with any feedback. We’re all ears.

More drawings soon.

— Riley & Claire

Can I get a medal? 🥇

Mon, 23 Feb 2026 00:28:50 +0000

The past 17 days have been the most intense of my “SportsBall career”.

In 2024 for the Paris Olympics, we started drawing graphs to explain sports that only come around once every four years. It’s what first put SportsBall on the map. So 18 months later, I cleared the calendar and went all in on Winter Olympics coverage, knowing the timing was right for sports education.

Eight videos later, I’m exhausted. But it’s been more fun than I could have imagined, and the thesis was proven, with more than 20 million views on explainer videos.

Back to the normal sports, I guess.

Now for this newsletter 🗞️

This email is the “catchall of the week”. On Sundays, we round up all the things we drew, what you may have missed, and a deep dive of the thinking and process behind one of our favorite projects of the week.

Drawings first. Explanation second. Always with the goal of making sports a little easier to understand.

What we did this week 📅

We’ll extend this to all Olympics videos, which go back to February 6th (all IG links)

Intro to the Winter Games
Figure Skating Explained
Olympic Hockey Revitalized
Biathlon Explained
Curling Explained
Skimo Explained
Aerial Freestyle Skiing Explained
Medal Counts

They say the devil works hard but graph guy works harder

Spotlight: How Figure Skating Works ⛸️

We’re spotlighting the figure skating project because it perfectly captures what SportsBall is about: a sport that’s beautiful to watch and confusing to understand.

Before the Olympics, I got a DM from US Figure Skating hoping to get a breakdown of their sport. I knew nothing of figure skating at the time other than they skate, they jump, they spin, and they get a seemingly arbitrary score for it.

These are the perfect projects, though. Ones where I know the sport but I don’t know it really. Where watching is captivating but simultaneously confusing. Thats our bread and butter.

It’s also one of those projects where I need to learn it all myself before I can explain it. A 90 minute call with the USFS team for initiation to the topic, 463 stupid questions from me, and an amount of information that could have been turned into a documentary let alone a 150 second video.

Figure Skating Scoring Guide

There’s really one core data element in this video and that’s how they score points visualised in the points chart at the bottom, explaining how each jump carries a base value with a dot then scales up or down on grade of execution or GOE. The data itself came from this official scoring sheet from USFS, which certainly was in need of some visualization, and the video hopefully de-mystifies the entire scoring process.

For those of you who are in the data visualization space, you might know the feeling where a chart fits a data set so perfectly it makes you warm and fuzzy inside. A happy marriage between a downtrodden table and a beautiful graph.

This felt like one of those times.

In the end, the video became our most popular ever on TikTok, and top 3 on Instagram, meaning I’d like to think we helped more a few people watch figure skating more effectively over the past two weeks.

That’s it for this week.

We’re excited to have this newsletter back up and running after a hiatus of sorts, and genuinely grateful to everyone who’s been watching, sharing, replying, and supporting SportsBall along the way. It’s what makes projects like this possible.

If there’s a sport, chart, or concept you’d love to see broken down next, just reply to this email. We (try to) read everything.

More drawings soon.

— Riley & Claire

Not fair! 📝

Riley Martin — Fri, 13 Dec 2024 17:40:00 +0000

Happy Friday! Before we start, just a heads up that we now have shop 🛍️ after hundreds, if not thousands, if not hundreds of thousands of requests. We sell just 3 things - all of which would surely make anyone happy during the holidays.

Shirts ($30)
Prints of our one-pagers ($15-$20)
High-resolution scans of every one-pager. Print yourself ($5)

SportsBall Shop

Shirts, prints, and PDFs

www.chameleonvisualcompany.com/productsforsale

Just imagine the look on a child's face on Christmas morning when they get a PDF of a drawing of how an expansion draft works in the WNBA.

Pure magic. ✨

On Sunday night, I was getting ready for the week, taking inventory of the to-do list, and ready to kick my feet up and take a graph break. Then Juan Soto signed with the New York Mets. ✍️

“Claire do you think I need to make something for SportsBall about this?” I say as I kick back on the couch.

Her: “Well is it a big deal?”

Me: “It’s the largest contract in sports history worth almost a billion dollars”

Her: “Sounds kind of important…”

So here we are! Contracts like this are fascinating, especially when compared across different sports so we decided to zoom out and take a global perspective on what to expect the next time you see a jaw-dropping number like Juan Soto’s $765 million. 🤯

CHARTS 🌁

New Sherriff in town 👮

What could you buy with $765 million? I guess maybe a better question is what couldn’t you buy… 💰

Juan Soto, likely THE best young player in baseball, just signed a 15-year, $765 million deal that will last him until the age of 41. Plus it’s all guaranteed. So if he gets injured or doesn’t play well ever again, he’s still cashing $51M to the bank account every year.

In the chart above, each dot represents one of the top 100 contracts by total value across all sports, with Juan’s deal deservedly sitting at the top. But it’s also the furthest to the right, marking it as the longest contract in baseball history. 👴

Now, let’s revisit that $51 million per year figure. By examining the slope of each dot on this chart, we can determine how much a player earns annually.

You guys remember “rise over run” right? 🧮

Take these two contracts above for example: Jayson Tatum and Yoshinobu Yamamoto, both signed deals at approximately $315M in total 👯 but Tatum’s deal spans five years, while Yamamoto’s stretches over twelve. A simple calculation reveals that Tatum earns $62 million per year—more than double Yamamoto’s meager $25.8 million annually. 🤏

Bet you never thought algebra would come into play this late in life.

With your refreshed y=mx+b knowledge in mind, one thing should immediately stand out here—the soccer contracts. ⚽️ Three of the top six contracts by value belong to soccer stars you’ve probably heard of: Messi, Ronaldo, and Karim Benzema.

Together, these three athletes boast over a billion followers on social media and are among the most recognizable people on the planet—especially outside the United States. 🌎

Now, about these contracts…

Messi ✍️
Leo Messi currently plays in the MLS, but his record-breaking contract dates back to his time with Barcelona. Signed in 2017, it was worth an astonishing $674 million over four years. That’s just shy of $170 million per year and, when broken down further, an incredible $4.4 million per game he played.

Ronaldo and Benzema ✍️
Both megastars were in the later stages of their careers when they signed with Saudi clubs in 2023. Despite the short contracts—2.5 years for Ronaldo and 2 years for Benzema—the numbers are staggering: $536 million and $447 million, respectively. On a per-year basis, that’s $214 million for Ronaldo and $223 million for Benzema—more than four times Juan Soto’s annual figure we discussed earlier.

You may have gathered this by now but there isn’t a salary cap 🙅 in global soccer especially not in Saudi soccer leagues… These teams can spend to their heart’s, wallet’s, or Crown Prince’s desire to bring fans and attention to their teams which we see on display with all 3 of these contracts.

Take a look at our technical grouping mechanism (hand-drawing on a graph), and you’ll notice how these high-value baseball contracts often stretch over an extended period of time. 📅

But why? I’m certainly not the purveyor of absolute truth on this matter but a lot of it comes down to the lack of a salary cap in the MLB. Ownership groups can spend freely and craft more “creative” deals to manage massive expenses—like Shohei Ohtani’s deferred money.

There’s a significant amount of “we’ll deal with it later” in baseball these days. But when you’re worth $21 billion, like the Mets’ owner, it probably doesn’t matter if Juan Soto isn’t the same man he once was in 2034. 👴

Football and basketball contracts are arguably the most "grounded in reality" of any professional sports deals (aside from the NHL, which didn’t have representation in the top 100 😭). With a salary cap in place, front offices must conduct meticulous due diligence to keep a team healthy and winning. 🏆

Through that process, key questions arise like how much is this player truly worth to the team? What value do they bring to the franchise? How much do they contribute to winning?

These questions seem often overlooked in baseball contracts… 🙈

The graph above shows that the highest-value NBA and NFL contracts typically fall within 4 to 6 years. Which makes sense since the CBA strictly limits contract lengths - teams can sign free agents for a maximum of 4 years, extend their own players for up to 5 years, or extend rookies or veterans for up to 6 years.

In the NFL, while there’s no limit on contract length, the league’s performance-driven nature means teams are cautious. Injury risks and performance uncertainties make decade-long commitments rare. 🔎

That said, exceptions exist for players like Patrick Mahomes, a proven franchise star whose 10-year deal reflects his unparalleled value. Such risks aren’t taken lightly on less established players.

OTHER STUFF WE MADE 🎥

Eras Tour x Sports 🎤

Early Signing Day in College Football ✍️

How Expansion Drafts work 📋

Bryson DeChambeau Challenge ⛳️

Our crazy idea for SF 🌉

Thanks for reading and please feel free to reach out with any feedback! Love it or hate it we’re all ears 👍

— Claire and Riley

NBA Scare Tactics 🏀😱

Riley Martin — Fri, 22 Nov 2024 17:30:00 +0000

Happy Friday! This week, we dug into the NBA’s viewership decline.

It’s no secret that fewer people are tuning in to regular season games—but is that really a problem? 🧐 We set out to find the answer.

Or at least give it our best shot… ⬇️

OUR FAVORITE BREAKDOWN 💡

Am I in trouble? 🏀📺

Watch here → Instagram TikTok

CHARTS 🌁

Down and to the right 📉

It’s no longer a secret—traditional NBA viewership is struggling. Simply put, fewer people are tuning in to watch games on TV.

Average viewers per regular season game 📅
2013-14: 1.95M
2023-24: 1.6M
10-year change: -18%

Average viewers during the NBA Finals 🏆
2013-14: 15.54M
2023-24: 11.31M
10-year change: -27%

Even if we treat that Finals number with caution due to its variability, there’s an undeniable decline from the regular season peak we saw about 15 years ago.

Chart Type - Waffle 🧇

Rating ✭✭✭✩✩

I didn’t mean to make this chart so graphic by slashing through people ❌ with a red line, but we needed to get the point across.

Simply put, if 100 people were watching the NBA in 2014, 18 of them have left the party. Since this is just a simple proportion, we could’ve gone with a pie chart 🥧 but waffle charts with symbols like this add more personality—especially when the data represents real people.

Also avoiding pie charts at all costs. 🙅

The reason for the season 🤕

This is the part where we try and uncover why people are watching less basketball. If you follow any sort of sports media you have probably heard the takes of “3 point shots and fake injuries are ruining the game”. Is that true? 🤔

Let’s start with 3-pointers.

If we’re asking if teams are shifting their focus to shooting threes, the answer is yes. 10 years ago, teams shot 22.4 three-pointers per game on average. So far this year that number is up to 37.4, a 67% increase.

3-pointers now account for 42.2% of all shots in a typical NBA game, which some argue has led to a decline in the quality of play on the court. 👎

Now, let’s address the issue of star player absences.

Whether due to scheduled rest or long-term injuries, the number of games missed by star players 🤕 has climbed significantly over the past two decades. According to an NBA study, star players in this decade miss an average of 23.9 games per season—30% of all games—compared to just 10.9 games per season in the 1990s.

This year, the league introduced a “load management” policy that fines teams for resting players excessively without an injury. However, the impact of this policy remains to be seen as this season is already on pace to break missed game records. 🩼

Chart Type - Bespoke Bars ✍️

Rating ✭✭✭✭✭

When I design visualizations, I always try to weave them into the artwork’s story. 🎨 Sure, a basic stacked bar chart can show NBA shot types, but layering it over the court brings the data to life.

The three-point line, being so binary—you’re either behind it or not—makes it the perfect dividing line for stacking bars around it.

Likewise, using shaded bench seats to represent injury time missed just felt right. Those empty spots are exactly what you see when players are sidelined, so it’s a natural way to connect the data with the visual feel of a game. 🔗

I love this chart combo because it augments the look and feel of an actual game ⛹️ with data visualizations. 📊

Like father, like son 👨‍👦

This is where we turn a corner in the analysis. Sure, fewer people are watching the NBA on traditional television—but is that the whole story?

Of course not. Over the last decade, the league has intentionally built a robust “media ecosystem” centered on social media. 📱

Take the NBA’s Instagram account, for instance—it boasts 89.1 million followers, nearly triple that of the NFL, and far surpasses other major leagues in the country. On average, NBA teams have 6.1 million followers each, again almost triple that of the NFL.

Every day, league and team accounts flood their social channels with real-time highlights, delivering the game’s most exciting moments directly to fans without requiring them to watch an entire game.

Over the past 30 days alone, the NBA posted 1,350 times—an average of 45 posts per day. 😵‍💫 The Warriors, the league’s most-followed team, post more than a dozen times on gamedays to ensure fans never miss a Steph Curry highlight.

While social media impressions don’t directly translate to full-game viewership, it’s clear the league has strategically used its massive online audience to diversify its media exposure and reduce reliance on traditional television. 📺

Chart Type - Double Dot Plot 🔴···

Rating ✭✭✭✭✩

Big dot, little dot.

This data genuinely surprised me. I’m so used to assuming the NFL dominates every facet of media, but when it comes to social presence, the NBA blows them out of the water.

That said, it’s a double-edged sword. ⚔️ League team accounts operate differently, which is why we introduced the graph with the big dots representing the league first, then followed it with a separate chart showing individual teams.

This approach not only highlights the umbrella account size disparity but also provides granular team data to back up the claim.

Who cares, anyway? 🤷

Of course, the NBA isn’t thrilled about declining viewership—but they’ve got some time to figure it out.

Thanks to the new media rights deal signed this past offseason, the league is guaranteed $6.9 billion per year for the next 11 years from TV networks to broadcast games. Even if viewership drops to zero, they’ll still cash the check.

This gives the NBA a decade to navigate the ever-shifting landscape of big media while doubling down on younger audiences through emerging social platforms.

Yes, there are existential challenges ahead—like introducing new stars and managing the eventual phase-out of icons like LeBron and Steph. But in our view, declining viewership isn’t as big a concern as it might seem.

Chart Type - Stacked bar chart 📚

Rating ✭✭✩✩✩

A boring but necessary chart. 😴

This shows that, even if nothing else changes, the NBA’s revenue will grow by 33% next year solely because of the new media rights deal. Highlighting that slice of the pie—and its growth relative to the league’s overall business—was essential.

It’s not the cleanest or easiest chart to read, which definitely loses us some style points, but hopefully the message still comes through about the health of the basketball business. 💼

OTHER STUFF WE MADE 🎥

Wemby the Alien 👽🏀

Why we created SportsBall ⚽️🏀🏈🏒

Thanks for reading and please feel free to reach out with any feedback! Love it or hate it we’re all ears 👍

— Claire and Riley

It's a Gamble 🎰

Riley Martin — Fri, 15 Nov 2024 16:10:00 +0000

Happy Friday!

This past week we did yet another SportsBall learning series - this time on sports betting in America.

There’s a ton of available data on the topic I found at the American Gaming Association and Covers so feel free to go wild on the data set on your own!

OUR FAVORITE BREAKDOWN 💡

Money Heist 🎰

Watch here → Instagram TikTok

CHARTS 🌁

Buy One Get One 1️⃣

Chart Type - Spire and Line Chart ⛪️

When the federal sports betting ban was lifted in 2018, it kicked off a rapid domino 🁂effect. Now every year, about 5 to 7 states join the legalization bandwagon. That original group of seven ballooned 🎈 to 38 in just five years.

The top chart here captures this explosive growth. Each horizontal line marks a new state jumping in, with the line’s length showing when they legalized. One by one, they added up—and as they did, the critical mass started building.

Of course, the money followed.

More states legalizing gambling obviously means more money in the pot. But we weren’t expecting quite this much:

Year	Handle	Percent Change
2018	$6,608,072,006
2019	$13,073,426,594	+97.8%
2020	$21,476,520,882	+64.3%
2021	$57,525,518,127	+167.9%
2022	$93,726,726,923	+62.9%
2023	$121,064,466,469	+29.2%

In just five years, the sports betting handle—the total amount wagered—has skyrocketed 🚀 nearly 20x, jumping from $6.6 billion to an astonishing $121.1 billion.

For the chart, we wanted to showcase state legalization growth alongside dollar volume growth, so we used a shared x-axis between both graphs. This alignment allows the eye 👁️ to track vertically between the charts seamlessly.

How we rate it ✭✭✭✭✩

This is only the second time we’ve used a “spire chart” in a breakdown, but honestly? I’m loving it.

Cumulative data like this should really hit the reader 💥. You want them to feel how big the shift is even at first glance. This chart delivers, especially for binary values like individual states legalizing sports betting. The finishing value dwarfs the start, just as it should.

And let’s not forget the shared axis. As we said before, it makes this section a BOGO deal—and that “double-up” theme runs through this entire breakdown.

Seasons of Life 🍁

Chart Type - Timeline and… Timeline ⏱️

One of the most fascinating aspects of sports betting is its seasonality.

The sports world revolves around major events, and the gambling industry follows that rhythm. To illustrate this, we overlaid the season lengths of the four major American sports leagues to highlight how overlaps create prime gambling opportunities. 🎰

The goal here is to spotlight 🔦 those moments. By shading the chart areas where the NFL season kicks off and runs through March Madness, the data clearly shows just how dominant football is in the betting world.

The most intriguing part of the chart? The noticeable dip in February.

You might be thinking, “Wait, isn’t the Super Bowl the biggest gambling day of the year? What gives?” The explanation lies in the number of games played each month. While the Super Bowl reigns supreme in sports betting, it’s also the only football game in February.

NFL Games by Month - 2024:

January: 28
February: 1

January is packed with playoff action, creating a gambling frenzy, but by February, it’s all about the Super Bowl—a single, monumental event that carries the entire month. .

Looking at the year as a whole, the stark contrast between “offseasons” and peak periods is striking. For example, in July 2024, the total handle was under $6 billion, whereas March—boosted by March Madness—soared to $14 billion. 📊

It’s further proof that football is, without question, the driving force in the sports betting world.

How we rate it ✭✭✭✩✩

I’m a big fan of annotations.

Shading specific regions or adding notes to a chart ✏️ is like sprinkling Italian seasoning on a dish—it always works. By highlighting the football season here, we can zero in on the massive spikes in the sports betting market during certain times of the year, while summer remains relatively quiet.

My one gripe with this chart is the space constraints at the top. Those season lines were supposed to be a key feature, but they felt a bit too cramped. Ah well— that’s just how it goes. 🤷

This whole breakdown was inspired by a Tableau project I did back in 2022 where we looked at the state of sports gambling. Most of the visual elements were brought in from here. See the live version at this link.

OTHER STUFF WE MADE 🎥

World Series Viewership Sigh of Relief 😮‍💨⚾️

EPL Time Spent Analysis ⚽️

Sports Betting: State by State-by-State Implications 🎰

Missouri Sports Betting Potential Problems 🏈

Thanks for reading and please feel free to reach out with any feedback! Love it or hate it we’re all ears 👍

— Claire and Riley

When the Stars Align ⚾️🏀🏈🏒

Riley Martin — Fri, 01 Nov 2024 15:00:00 +0000

Happy Friday, I hope everyone had a fantastic Halloween!

In case you missed it, this week marked the Sports Equinox on October 28th, and we wrapped up our Equinox Broadcast Game Time analysis 🤓 with some intriguing insights from our 4-sport comparison.

Our big finale was a detailed breakdown between sports, where we ventured into a bit of "sports theory" and shared our picks on which games we enjoyed the most. ⬇️

OUR FAVORITE BREAKDOWN 💡

The Equinox ☀️

Watch here → Instagram TikTok

CHARTS 🌁

Earn Your Stripes 🐅

Chart Type - Stacked Ring Bar Chart 🪐

This project was a grind.

I’m not sure the last time I sat and watched a full sports game, no phone, no standing up for commercials, no chatting. Just pure research 🔬 for the greater good.

As I jammed my buttons for each category, the data came out looking like this - just with 10,000 more rows.

The key to prepping this data was making sure all sports were represented in the same way. We simplified it into three categories: in-play, out-of-play, and commercial time (plus free throws for basketball).

Some of our TikTok commenters didn’t love these categorizations, but the simplicity was designed for casual fans—not necessarily for someone keeping their own scorecard at a baseball game. 📋

With the final data in hand, the next decision was to choose the right type of chart. 📊

We had some limitations: multi-series proportional data can only be represented in a few ways. Traditionally, a stacked bar chart is the go-to option—we even created one above for the data visualization purists out there. ⬆️

But I use those a lot already, so I wanted to change it up.

While the stacked bar chart is certainly cleaner, more representative, and generally more presentable, switching to stacked rings felt like the right move.

Me choosing the proper charts for a visualization

How we rate it ✭✭✩✩✩

There are a few reasons we don’t love this one, partially mentioned above.

At its core, the chart visually misrepresents data. As this response points out, the outer rings occupy more space on the page, which can subconsciously lead readers to place more importance on them. 🧠

It becomes an issue especially when we’re working with a big radius and in this case, the basketball 🏀 ring is more than double the length of the baseball ⚾️ ring.

Here’s that concept visualized if we straightened those rings out. It’s the same problem you get with the Apple Watch fitness rings.

Is it a big deal for my doodle videos ✍️ explaining sports? Not really.

Is it a bad practice in general? 🙅 You bet.

Double Dutch ‼️

Chart Type - Timeline and Area Timeline ⏱️

The core of this breakdown was the ring chart, but that doesn’t mean we can’t have some fun in the margins.

As we worked through this analysis, a clear division between the two groups of sports emerged.

Conceptually, baseball ⚾️ and football 🏈 are what we’d call “distinct event sports.” They’re marked by short, highlight-ready plays 🎉 that can stand alone in the media, making them more similar than you might expect. The average play only lasts around 5 seconds in these sports, with long breaks in between—but that’s not necessarily a drawback.

Both sports are highly strategic and rely heavily on individual matchups, like the pitcher versus batter in baseball. This means the time between plays is where teams strategize, aiming to beat their opponents mentally. ♟️

Hockey 🏒 and basketball 🏀, on the other hand, follow a different rhythm.

These sports don’t have mandatory stoppages, allowing for minutes of continuous action. I like to think of these as “flow games,” where you need to watch for extended periods 🧘 to fully experience the movement and rhythm of the game.

How we rate it ✭✭✭✭✩

These charts made great supporting actors. They weren’t the stars of the show, but they played their parts nicely.

The Average Play Time chart captures the idea of paired sports well, showing how basketball and hockey closely align, while baseball and football follow a similar pattern.

The next chart gives insight into game flow—on the left, you see the distinct moments, and on the right, the extended periods of continuous action.

Both charts complement the core ring chart and add new insights beyond just proportional time spent. They’re the Robin to our ring chart’s Batman.

OTHER STUFF WE MADE 🎥

All 4 Sports of our Equinox Series ☀️🌎📺

MLS Season in Review and Playoff Preview ⚽️🏆

Thanks for reading and please feel free to reach out with any feedback! Love it or hate it we’re all ears 👍

— Claire and Riley

Don't Blink 👀📺🏈

Riley Martin — Fri, 25 Oct 2024 18:01:25 +0000

Happy Friday!

It’s been an exciting week as we kicked off our Sports Equinox Broadcast Time Spent Series (trademark pending). For this, we’re doing primary research 🔬 to find out just how much of a sports broadcast is actual gameplay.

You may have already seen two of these - hockey and football - but the goal is to get all 4 major sports leagues done before the actual sports equinox ☀️ on October 28th. Then we’ll make one full breakdown to rule them all.

Anyways. Today’s newsletter explores the NFL-specific broadcast breakdown 🏈⏱️

OUR FAVORITE BREAKDOWN 💡

Time Well Spent ⏲️

Watch here → Instagram TikTok

CHARTS 🌁

Earn Your Stripes 🐅

Chart Type - Categorical Heatmap 🔥

From the moment we chose this topic, we knew this chart would be the key. The overwhelming sea of dark blue and red tells you everything you need to know about the composition of a broadcast.

To measure the game time, we built a timer tool with toggles for each type of play. It looks like this. ⬇️ Also linking it here if anyone wants to do this themselves.

By the end of the game, we had almost 11,000 data points—one for every second, categorized by status. The average NFL play unfolds like this:

🏈 Run the play (~5 seconds)
🗣️ Broadcaster discusses the play (~10 seconds)
🔄 Replay shown (~20 seconds)
🚶 Getting ready for the next play (~10 seconds)

As we reviewed every second, we began to notice how regimented the game really is. While there’s plenty of downtime, the structural flow made the whole experience feel scientifically engaging. 🧑‍🔬

As the experiment went on, I started to feel like a lab rat 🐀. The way the broadcast effortlessly switched tones and nailed every timing mark had me hooked from start to finish. It made me realize just how tightly we’re locked into their rhythm.

How we rate it ✭✭✭✩✩

Leaving a bad review on this chart stings, especially since I’ve been thinking about it since the moment this data came to mind. I mean, drawing 400 lines by hand has to count for something, right? ✍️

For comparison, here’s how the computer-generated version.

I did my best to replicate it, but the lines were so tiny that my hand-drawn version turned into a bit of a mess. I didn’t even have pencils thin enough to mark the 5-second intervals, so I used negative space to represent them instead.

Sure, it still conveyed the story well, but the visual wasn’t as sharp—so we’re docking two stars.

Mountain Range 🏔️

Chart Type - Double Histogram 👯

A rare opportunity to use a histogram properly.

We wanted to show the distributions of play durations on the same plane as stoppage durations. Each play is fairly short (4-7 seconds) and doesn’t have too much variability; even an 80-yard run 🏃 is at most 12 seconds long. But that’s not the case for stoppages.

While there is of course a play clock of 40 seconds ⏱️, so many factors can extend the time between plays. Moving the chains for a first down, flags, injuries, you name it.

In fact, 26% of all time between plays were more than 40 seconds.

How we rate it ✭✭✭✭✩

This chart was well-intentioned and served proper statistics 📊 for once.

When visualizing distributions there aren’t a ton of options with out-of-the-box graphs so this was an easy choice. I especially liked comparing distributions on the same chart. Since every play in football is paired with a stoppage, it’s cool to see how they align.

The only issue was my choice of colors. Using two shades of blue didn’t go over well—at least according to TikTok. 📲

It’s Not a Pie Chart I Swear 🥧

Chart Type - Donut 🍩

The premier dessert-inspired 🎂 graph has arrived. In my opinion, every pie chart should be a donut chart. Donuts are sleek, professional, grown up, and elevated 🎩. Pie charts are elementary, simple, boring, and uninspiring 🧢. Avoid at all costs.

That said, this part of the breakdown was the mic-drop moment. It’s where all the granular data converges into the aggregate metrics we see here. From a storytelling perspective, this approach was really enjoyable.

😈 Tease the finale at the start.
🔢 Dive deep into the weeds of the data.
💣 Wrap up with the big metric everyone’s been waiting for.

And the metric itself was wild… Of a 3+ hour broadcast, 16 minutes were spent playing football, just 9% of the broadcast. 🤯

How we rate it ✭✭✭✭✭

The beauty here lies in its simplicity. You don't need to study this chart to understand what's happening—its low barrier to entry makes it an effective graphic.

When working with proportional data, it can be challenging to get too creative. We’ve tried stacked bars, dot plots, and other options, but visual boundaries are essential to represent the 100% totals accurately.

If anyone has suggestions for better proportion charts, I’d love to hear them!

OTHER STUFF WE MADE 🎥

How much hockey are you actually watching?? 🏒📺

Unrivaled Basketball Club launch 🏀🚀

Thanks for reading and please feel free to reach out with any feedback! Love it or hate it we’re all ears 👍

— Claire and Riley

The Race is On ⛵️

Riley Martin — Fri, 18 Oct 2024 15:30:00 +0000

Happy Friday and welcome to everyone new here!

This is edition #52 and since we write one per week… That means it’s officially been one year of SportsBall. 📅

What started as a wordy newsletter for casual sports fans has blossomed into… a wordy newsletter for casual sports fans (plus some graphs and videos). Claire and I are so grateful for every person who reads this or watches our breakdowns on socials - all the kind words mean a lot 🥰.

And the mean words about my free-hand drawings only help to sharpen the sword 💪⚔️.

Anyways. Today we’re getting into The America’s Cup ⛵️⬇️

OUR FAVORITE BREAKDOWN 💡

Come Sail Away 🌊

Watch here → Instagram TikTok

CHARTS 🌁

It takes a village 🏘️

Chart Type - Dot + Tree Map 🌲

There’s way more to sailing competitions than what you see on TV. Sure, it looks like eight guys chilling on a yacht⛵️, making a few smooth turns—easy stuff, right?

But behind the scenes, these teams are massive. We’re talking 100+ engineers, designers, and technicians pouring over every detail, all with $150 million budgets. 💸Wild to think that one yacht could be worth more than the company I clock in at every day.

It’s just like Formula 1. Sure, the “drivers” get the spotlight, but it’s the genius designers behind the curtain who make or break the race. Just look at how big of a deal Adrian Newey’s team switch was this year—this guy’s the GOAT 🐐 of F1 design and his transition was more important than the drivers’.

In sailing, even the smallest mistake, like a one-degree tilt in the hull, can sink a team’s chances so the precision of the background players is critical.

How we rate it ✭✭✭✭✩

If you’ve been following us for a while, you’ve probably noticed dot plots like this. ⚫️ They work best when you want to convey the sheer scale of something, helping the audience feel the magnitude of the subject.

So, when I say a team has 100 designers, I want everyone to grasp how big of a crew that actually is.

Each one of those dots is a person dedicating their life to making the best boat in the world. 🌎 We did something similar when breaking down the odds of making it to the NBA—plotting 550 dots to represent all 550,000 high school hoopers, just to make it hit home.

Sure, we could have used a pie chart 🥧 to show the same thing—but who really wants that?

Schematic 👷

Chart Type - X-Ray 🩻

Ok you got me, this isn’t actually a graph.

These America’s Cup yachts are engineering wonders, totally unlike anything you’ve ever seen. They hit 60 mph 💨, float above the water on winged foils 🪽, and they’re powered by four cyclists 🚴 tucked away in the hull.

No graph in the world can capture all that. ❌📊

That’s why schematics and tutorials are so powerful. In just 15 seconds, we used zoom features and micro charts to highlight the key elements, giving viewers a glimpse of just how extraordinary these carbon fiber machines really are.

How we rate it ✭✭✩✩✩

This feels a bit like cheating, so we’re docking points. That said, the schematic didn’t just tell a story—it also served as a visual anchor ⚓️ for the entire page.

I’ve been experimenting with different page layouts, and I’ve found that including a central chart or image creates a more polished, professional look and feel.

Here’s a picture of an actual America’s Cup yacht if you want to judge my drawing skills. ⬆️

Fancy Timeline Alert 🚨

Chart Type - Time Skip Chart (Riley II) 🐸

I thought long and hard about how to portray this concept. 🤔

The America’s Cup doesn’t follow a fixed schedule. It only takes place when the reigning champion is challenged by another country’s yacht club—typically every three or four years. If the challenger wins, they become the new holder of the Cup until another challenger steps up. If they lose, the champion keeps the title. 🏆

Enter the Riley II.

This custom chart is designed to combine a dynamic timeline with the concept of challenger events. Take 2003, for example—Switzerland challenged New Zealand and won. When New Zealand challenged Switzerland again in 2007, Switzerland defended the Cup, visually leap-frogging 🐸 the challenger in the chart.

As you pan across, you can see how often countries attempt challenges, reflecting their obsession with the Cup.

So New Zealand’s story is a perfect example. They lost the title in 2003 ❌, challenged in 2007 but lost ❌, tried again in 2013 and lost again ❌, but finally reclaimed it in 2017 ✅. Now, having defended their title twice in a row, they’re facing off against Great Britain as we speak.

How we rate it ✭✭✭✭✭

I love this graph. 💙

Even ask Claire, when I finished it I held it up to her smiling like a toddler showing off a frog they caught in the yard. 🐸

It was so simple and clear in what it represented, especially when explaining a complex format like the America’s Cup challenge system. This chart type could work for any situation with a structure like this. Especially in elections across the world, this could be an interesting use case for a graphic.

OTHER STUFF WE MADE 🎥

Just one other video this week… Lots of other stuff in the works coming up though!

Do MLB players get paid overtime in the playoffs? ⚾️💰

Thanks for reading and please feel free to reach back out with any feedback! Love it or hate it we’re all ears 👍

— Claire and Riley

The building blocks of sports 🛠️

Riley Martin — Fri, 11 Oct 2024 18:47:38 +0000

Happy Friday and welcome to everyone new here!

The SportsBall newsletter is simple.

We take our favorite analysis of the week and go behind the scenes on the data visualizations and storytelling process. Today’s breakdown du jour is a piece we’ve wanted to do for a long time - how pro sports leagues make their money. ⬇️

OUR FAVORITE BREAKDOWN 💡

Earnings Report 📋

Watch here → Instagram TikTok

CHARTS 🌁

Level the Playing Field ⚖️

Chart Type - Stacked Percentage Bar Chart 🥞

The first thing to know is that each league makes a very different amount of money. Here are the 2023 estimated raw revenues for the top four leagues:

🏈 NFL: $20B
🏀 NBA: $11B (jumping up to ~$15B this year)
⚾️ MLB: $11B
🏒 NHL: $6B

But that’s not the point.

What we cared about most was how these leagues make their money, not how much. If I were up in the high tower, making the big decisions, what would I focus on? That’s why we zeroed in on proportional revenue and landed on the stacked percentage bar chart.

This approach allowed us to make apples-to-apples 🍎 comaprisons, highlighting their reliance on different income streams.

For example, the NHL still makes most of its revenue from ticket sales. 🎟️ That means fluctuations in attendance can be tough for teams, and they need large enough stadiums to maximize capacity. The Arizona Coyotes (RIP), for instance, played their last two seasons in a 5,000-seat arena. Even though it was temporary, the team suffered financially. 🤒

Then you’ve got the NFL and NBA, both cashing in on huge national media deals that deliver a massive, fixed fee every year. This is that mouth-watering, recurring revenue most execs only dream about 🤤, and it’s a key reason why these two leagues will stay super healthy for a long time.

How we rate it ✭✭✭✭✭

This was a big “stay in your lane” moment.

It’s so easy to go down rabbit holes when talking about money in leagues but a chart like this strips out all other variables and focuses the conversation on the single topic we were discussing.

It’s a prime example of chart-insight alignment. When presenting data, your visuals need to be in sync with your message—otherwise, you get what I call “impact leakage,” and the audience tunes out.

Market Map 🗺️

Chart Type - Map Density Plot 🌎

I’m not usually a fan of map data visualizations.

Most of the time, they overwhelm you with too much data, and your eyes have to do way too much work. But before we dive into the inevitable map overload that comes with election season, I had to sneak this one in.

This map is all about showing the relative size of sports markets when it comes to regional media coverage—and for that, it hits the mark. 🎯

New York, LA, and Dallas pop off the page compared to the smaller Midwest markets. And that’s the point. The huge disparity between these markets is what makes big national media deals in sports like baseball and hockey such a headache. Teams like the Yankees and Dodgers have no interest 🙅 in splitting their cash with the rest of the league, and regional media deals let them keep it that way.

How we rate it ✭✭✭✩✩

I could have made this chart better.

Since it wasn’t driven by real data, I should’ve made the dots more varied in size to really show the differences. A little shading wouldn’t have hurt either—darker colors on the bigger dots would have made them pop.

With map data visualizations, less is more. We don’t need to see every little detail—county lines, rivers, even state borders most of the time. If the data points are obvious spots like LA, New York, and other major cities, you can strip it down, and the reader will still get the point without getting distracted.

And yeah, minus one star for me misplacing Milwaukee, as the IG comments so kindly pointed out. Geography is a work in progress. 🌎

Media Company Pie 🥧

Chart Type - Pie Chart 🥧

A true classic.

I imagine this is the reaction to us actually using a normal graph in our visualizations for once. ⬇️

To some people (Claire), every chart is a pie chart.

It’s so sturdy. So simple. So foundational to the data visualization community. Many say that pie charts walked so SportsBall could eventually run. 🏃

This chart, in particular, highlights how massive media companies pay billions to the NFL and NBA to broadcast games. The revenue is split evenly among the teams, so each NFL team gets a check for more than $400 million annually, even before selling a single ticket. That certainly helps the bottom line. 🤑

It’s also a huge shield 🛡️ for these leagues against unpredictable factors like attendance. Take COVID, for instance—stadiums were empty, but it didn’t hit the NBA as hard because teams still cashed in on their media deals.

How we rate it ✭✭✩✩✩

Good idea, bad execution.

The point was to showcase the equal split between teams and revenue but making perfect pie slices by hand was much harder than I thought it would be.

In fact, I would argue not one of those slices is the same size as another, fueling a contradiction between the story and the visual.

We’ll get em next time.

OTHER STUFF WE MADE 🎥

Our most complicated topic yet… Escrow in the NHL 🏒🏦

Why October is the best month for sports: Sports Equinox ☀️

Have you ever seen a hype video for graphs?

Thanks for reading and please feel free to reach back out with any feedback! Love it or hate it we’re all ears 👍

— Claire and Riley

Dynasties aren't Built Overnight 🏀⛪️

Riley Martin — Fri, 04 Oct 2024 15:30:00 +0000

Happy Friday!

Our favorite video breakdown this week was actually for a client—Duke Women’s Basketball.

The project was created to celebrate the team’s 50th anniversary in 2025, highlighting the storied history of the program. It also happens to be our 50th newsletter anniversary - what are the odds?

This project turned out to be the most creative data visualization we’ve ever produced and worthy of a breakdown. Let’s get into it ⬇️

OUR FAVORITE BREAKDOWN 💡

Cumulative Success 🏀

Watch here → Instagram

CHARTS 🌁

When the Bones Are Good 🔵

Chart Type - Dot Plot Timeline 🔗

You’re looking at the Sharpie bleed ✍️ on the back of the paper, which shows exactly how the dots are laid out without the lines.

Every year since 1975, we marked an achievement with a dot. 🔵 Simple, right? A dot means it happened—no dot means it didn’t.

We narrowed the statistics into eight categories - divided into individual accolades and team awards - and plotted them on a vertical timeline.

Individual Awards

National Players of the Year
AP All Americans
McDonald’s All Americans
WNBA Draft Picks

Team Awards

ACC Championships
NCAA Tournamants
Sweet 16s
Final Fours

The overarching vision of the project was to showcase the cumulative success of the program, so we added a “collection zone” of dots at the bottom that adds them all up. 🧮

That zone is particularly effective when watching the video in real-time, as it reinforces the message by tallying the awards both verbally and visually.

How we rate it ✭✭✭✩✩

By itself, this chart would be pretty dull—just a bunch of dots in vertical lines... gross.

But where it shines is in showcasing Duke’s periods of success. Even if you squint, you can spot where the “action” starts to happen, which leads perfectly into phase 2️⃣ of the graphic.

Tree of Success 🌲

Chart Type - Spire Chart ⛪️ (Riley I)

Innovation alert 🚨

I haven’t personally seen this type of chart before, which means we might have the chance to name it. We could go the Simone Biles route and call it the Riley I, or choose something more generic like Spire Chart, since it’s giving church spires ⛪️.

Here’s how it works:

For each dot on the plot, we drew a line l starting from that point and extending down to the present axis with each new line placed outside the previous ones, causing the base to widen. The zoomed graphic 🔎 above shows how it works in practice.

So, for categories like WNBA draft picks, where Duke Basketball has more awards, the base expands accordingly and gives an effective sense of breadth even at first glance.

How we rate it ✭✭✭✭✭

Just think about the alternative for a moment.

Normally, a “history of the program” graphic would just be numbers on a page—20 ACC titles, 18 Sweet 16s, etc. 🥱

This chart does so much more to visualize what it really means to build a dynasty. In the beginning, the space is empty, but as successes accumulate, no one can take them away.

Cumulative achievement is often overlooked in favor of recent wins, and it’s time we give it the recognition it deserves with proper data visualization. 📈

OTHER STUFF WE MADE 🎥

A few fun breakdowns and partnerships with some big names in sports this week. The NFL and NBA both collaborated on a post which was an awesome boost and it was so fun to work with those leagues, check them out below!

NFL London games - tube map visualization 🗺️

Fantasy basketball breakdown - Yahoo and NBA partners post 🏀🔢

How the Oakland Athletics turned their back on fans ⚾️🏟️

What MLB teams spend on their players 📈⚾️

Thanks for reading and please feel free to reach back out with any feedback! Love it or hate it we’re all ears 👍

— Claire and Riley

Look back at it 🏀⛹️‍♀️

Riley Martin — Fri, 27 Sep 2024 15:30:00 +0000

Happy Friday!

Our best video breakdown this week was on the Caitlin Clark Effect in the WNBA this season.

The goal was to isolate Caitlin’s impact on attendance numbers across the league - illuminating stats on who could be the most important athlete in their respective sport since Michael Jordan or Tiger Woods.

Here’s how it went ⬇️

OUR FAVORITE BREAKDOWN 💡

The Caitlin Clark Effect 🏀

Watch here → Instagram TikTok

CHARTS 🌁

Banner Year 📅

Chart Type - Dual Axis Line and Bullet Chart 🚄

The message is total fan attendance in the WNBA jumped almost 50% this year - notated by the orange line. But what drove that jump?

Access to raw game-by-game attendance numbers allowed us to plot the unbelievable increase of games with 16K or more fans on a second axis. This year there were 35 of them in the WNBA with 30 including Caitlin Clark’s Fever. 🤯

The league has never been close to that mark before and the last 10 years combined had just 25 of these “super” games.

How we rate it ✭✭✭✭✭

This graph would not survive alone in the wild. 🌲 The dual axis is an immediate turn-off for readers let alone a bullet chart ingrained within it. If I saw this in a newspaper or article I’d turn the page.

But this is NOT the wild. This is not a newspaper or article (kind of). 😬

Combining voice-over and drawing in the video immediately connected the orange line to the blue bars, providing an answer to the question in everyone’s mind - why did attendance jump so much?

And that’s why I love these hybrid data stories, the ability to spoon-feed 🥣 a viewer through an analysis no matter the complexity.

Everybody Eats 📈

Chart Type - Slope Chart ⛷️

Everybody eats.

All 12 teams in the WNBA had an attendance bump this year - averaging 60% across the league. And it could have been even higher. 😳

That’s because several teams were capacity-constrained in their arenas this year. The Chicago Sky are a good example of an exciting team able to fill bigger stadiums but were limited to Wintrust Arena with only 10K seats. Compare that to the red line of the Indiana Fever who were playing in Gainbridge Fieldhouse which can hold 18,000 fans.

The Fever averaged over 17K fans per game this this year, the highest mark ever in the league and an almost 3X increase from last season.

How we rate it ✭✭✭✭✩

Slope charts are great for two-point comparisons. It’s essentially a short-form line chart where you can display a bunch of series’ at once.

But this chart can be dangerous… ⚠️

If the categories (teams in our case) don’t all move in a similar direction it quickly becomes confusing and looks more like a spider web than a graph.

That’s why you need to pick your spots carefully and highlight the important lines in a chart like this. Since we had a dataset that was uni-directional 🆙 the slope chart really hit home.

Honey, we have visitors! 🏡

Chart Type - Bullet Chart (again) 🚄

It can be difficult to isolate the impact of a single person on an entire league. All season we’ve heard of broken records, never-before-seen moments, and hype around Caitlin’s game.

But could we find one non-esoteric angle that really proved out her stardom? ⭐️

Yes.

With the attendance data set, we could distinguish WNBA fans when Caitlin was in the house vs. when she wasn’t. One caveat is some teams moved arenas to local NBA venues when the Fever came to town, jumping those numbers way up - but that’s the point, right?

On average, hosting the Fever in your city brought in 110% more fans than a non-Caitlin Clark game across the league. Talk about impact.

How we rate it ✭✭✭✭✩

I didn’t expect to use one, let alone two bullet charts for this breakdown but I’m glad I did. They’re great for showcasing a subset of a category within a bar chart which this type of data catered for very well.

OTHER STUFF WE MADE 🎥

A few fun breakdowns and partnerships this week, check them out below on Instagram!

Odds of becoming a professional baseball player ⚾️

Shohei Ohtani 50/50 Club chart ⚾️

Why Sidney Crosby is the best guy in Hockey 🏒💰

The future of NBA contracts 📈🏀

Thanks for reading and please feel free to reach back out with any feedback! Love it or hate it we’re all ears 👍

— Claire and Riley

Our most challenging breakdown yet ⭐️

Riley Martin — Fri, 13 Sep 2024 16:12:39 +0000

Happy Friday to all who celebrate!

Today we’re double-clicking on one of our most challenging data visualizations to date - The Taylor Swift effect on the NFL which we published in conjunction with Sportsish. It was tough for multiple reasons:

😡 Pissing off Swifties is not a good business decision so the facts needed to be ultra-precise.

👯 The type of data we used was totally homogenous so adding variability to the breakdown wasn’t easy.

Here’s how it went ⬇️

OUR FAVORITE BREAKDOWN 💡

The Taylor Swift Effect ⛳️

Watch here → IG TikTok

CHARTS 🌁

Overall Participation 🟪

Chart Type - Waffle Chart 🧇

Yahoo’s survey stated that 22% of all fantasy football participants this year were playing for the first time and half of them were women. This stat might not jump off the page but fantasy football’s participant pool is normally 80% men, meaning the 50/50 split of new participants is highly over-indexed toward women. ⚖️

This single statistic set the tone 🔊 for the breakdown.

How we rate it ✭✭✭✭✩

I love waffle charts. Compared to pie and bar charts this gives a better sense of “part of a whole” data. You can even imagine every square is one survey respondent and the story becomes ultra clear.

The data also catered to a “hierarchy format” where we could embed the women's participation callout within overall participation. This wouldn’t work in a pie or bar chart ❌ but the waffle obliged.

Fantasy Football Strategies 𑗏

Chart Type - Triple Donut 🍩🍩🍩

Close your rings! Inspired by Apple Watch’s fitness visuals, this chart creates a tiered “percent of total” graphic - a perfect candidate for survey data. The metrics were:

❤️ 10% of fantasy football participants are playing because of the Taylor/Travis relationship.

⭐️ 37% think drafting players in celebrity relationships is a strong strategy.

8️⃣7️⃣ 59% think drafting Travis Kelce at all costs is a good strategy.

How we rate it ✭✭✩✩✩

There are three things wrong here…

First, any data professional would tell you that “ring charts” aren’t statistically accurate. Since the radius is different for each ring, outer rings are over-represented in a side-by-side comparison if you straighten out the lines. Apple has even caught a bit of flack from the small but mighty 💪 data visualization community for this… They seem to be doing just fine though.

Second, the first category is not like the other two. Talking about general participation and then shifting to draft strategies was a bad decision on my part and could have lost the readers easily. Shame! 😞

Third, it’s hard to draw and looks a bit ugly.

Work/Life Balance 💼

Chart Type - Stacked Proportion Bars 📚

All roads lead home.

Most surveys will heavily use proportion bars like this in their results but they can be overdone. The data here stated:

💼 47% of respondents said doing well in fantasy football at work would help them get a raise and 42% said it would help with a promotion.

💕 On the relationship side, 10% said they were playing fantasy football to impress a romantic partner and 16% claimed they were managing a team with their significant other.

How we rate it ✭✭✭✩✩

This chart was… fine. While it can be a bit boring, splitting the charts into logical pairs (work and relationships) made the comparisons easier to digest.

Plus nothing will ever beat a good old-fashioned bar chart for side-by-side comparisons.

OTHER STUFF WE MADE 🎥

A few fun breakdowns and partnerships this week, check them out below on Instagram!

Scorigami - Every unique score in the NFL mapped 🏈

Huge F1 paydays - but not for the drivers 🏎️

College football teams spend millions to lose 😵

Sports gambling for the girls 👜

Thanks for reading and please feel free to reach back out with any feedback! Love it or hate it we’re all ears 👍

— Claire and Riley

Today’s Edition	Context
Money and Golf ⛳️💰	Last weekend was the Tour Championship, the finale of the entire PGA golf season with an astronomical payout. We explained how it works, the winners and losers, and why it’s growing. 📈

The Tour Championship ⛳️

Watch here → IG TikTok

CHARTS 🌁

Prize Money 💰

Chart Type - Bar chart with a twist 🔀

There are 38 tournaments that make up the golf season and the goal was only to compare cash prize values of each. 💰 We removed the timeline variable and positioned each tournament right after one another.

We also utilized axis manipulation to emphasize how significant the prize money was for the Tour Championship. The x-axis only extended to $40 million until we added on the $100 million tournament.

This is a fantastic method of emphasizing the outlier value, especially in video format. 🎥

How we rate it ✭✭✭✭✩

This was an almost perfect use case for this type of chart. It handled the vertical layout phenomenally and since 38 bars is a ton, positioning it this way was elegant 💃 - if I do say so myself.

Golfer Earnings 💰

Chart Type - Beeswarm 🐝

One of my very favorite graphs to use, but only if the data is right!

The goal was to emphasize Scottie Scheffler’s dominance this past year as he earned ~50X more than the median golfer. 🤯

When the dots are spread out like this, the reader easily understands where the significant mass lies, helping outlier data points like Scottie pop even more. 💥

How we rate it ✭✭✭✭✭

This graph is ripe for annotation.

Circling individuals, overlaying median values, or introducing benchmarks is very easy and it doesn’t muddy the waters too much. 🔎

We loved it!

Tour Championship Purse 👜

Chart Type - Line with steps 🪜

Line charts are always an option when dealing with time series data.

But they can be boring. 😴

We needed to simply show the increase in prize money for this single tournament and the long stretch of no change followed by the quick step-ups did the trick.

How we rate it ✭✭✩✩✩

This felt lazy and lackluster, even drawing it had me yawning. 🥱

OTHER STUFF WE MADE 🎥

A few fun breakdowns and partnerships this week, check them out below on Instagram!

Taylor Swift's Impact on fantasy football 🏈

Why international NFL games exist 🌎

Private equity in the NFL 💼

Thanks for reading and please feel free to reach back out with any feedback! Love it or hate it we’re all ears 👍

— Claire and Riley

New News 🗞️

Riley Martin — Fri, 30 Aug 2024 15:30:00 +0000

Happy Friday! We have some big-ish updates to share about the newsletter and how it will soon become the foremost location for sports data storytelling… In the world 🌎

THIS PAST WEEK ⏮️

New Eras ⏳

It’s been another busy week over at SportsBall HQ - with changes abound. If you haven’t been chronically online, you likely haven’t noticed our pivot into the handdrawn-sports-data-visualization-breakdown-short form-video niche. 📝

During the back half of the Olympics, we began creating video breakdowns with pen and paper rather than using proper data tools. It allowed us to rapidly develop and bring more creativity to the data storytelling and since then we’ve been pumping out 2-3 a week.

And people loved it!

Pro athletes, middle school teachers, finance professionals, you name it. We quickly went from 400 followers on Instagram to 67k over 20 days and have been trying to keep up with all the excitement flowing from that avenue.

It also has bred some new ideas for this newsletter

For some reason, these short explanations of sports concepts are resonating with people, especially those who are data-curious. 🤓 While 90 seconds of explanation is great for the IG and TikTok crowd, we also want to scratch the itch for those who yearn to learn more about the art of data storytelling.

That’s what this newsletter will become.

Each week we’ll break down the analytics process behind our favorite asset we made that week, diving into the concepts, storyboards, and visualization tools that built the data story. 📊

Anyone and everyone are welcome to engage with us here..

🎓 If you’re in high school and want a unique way to learn analysis concepts, welcome!

💼 If you’re a CFO hoping to use data to explain how your business units are blowing up your budget, welcome!

👨‍👩‍👧‍👦 If you’re our parents who have been reading this newsletter out of obligation for a year now, we love you and please don’t unsubscribe!

SportsBall is a place for anyone who likes sports, numbers, funny references, and engaging storytelling so we cast a pretty wide net. Next week will be our first foray into this new format and we truly can’t wait. We also encourage you all to respond, reach out, or give feedback on how we progress - always looking to improve over here at SportsBall.

POSTS THIS WEEK 📊

⚽️ Cristiano Ronaldo’s YouTube Takeover (IG) (TikTok)

🎾 Attending the US Open on a Budget (IG) (TikTok)

⚽️ How the English Premier League Works (IG) (TikTok)

🦄 Shohei Ohtani’s Historic feat (IG) (TikTok)

🎾 Why the US Open is a Beacon for Womens Sports (IG) (TikTok)

🏈 Private Equity is Weaseling into the NFL (IG) (TikTok)

Thank you all and welcome back to being a casual fan!

— Riley and Claire

What do we do now? 📅

Riley Martin — Fri, 16 Aug 2024 15:30:00 +0000

Happy Friday everyone! It’s been a crazy couple of weeks here at SB HQ, Claire and I have been laser-focused on getting our new sports breakdown videos 🎥 made for the Olympics which has put the email on the backburner, if only for a moment.

Today’s edition has…

🏅 Medal madness
➡️ What do we do now?
🏈 The 8 Letter word some of you have been waiting for

THIS PAST WEEK ⏮️

Hardware Tracker 🥇

Sunday was the grand finale of the Paris Olympics. With the U.S. Men's and Women's basketball 🏀 teams clinching gold and the closing ceremony wrapping things up, we finally got a look at the overall medal count.

Some Interesting things we noticed:

👯 Same same - The top 10 countries by total medals and by gold medals are exactly the same. While the order changes, the inclusion does not.

⭐️ Gold-blooded - Japan, Australia, the Netherlands, and South Korea all ranked higher in the gold medal standings. This means they won more gold medals compared to silver and bronze—certainly not a bad problem to have.

Storylines

This Summer installment was packed with incredible stories and standout athletes (naturally). For the past couple of weeks, we've been rolling out data-driven, hand-drawn breakdowns of different events nearly every day. We'll be linking them all here for you to dive into ⬇️

🏊 Katie Ledecky Dominating in the 1500m race
🏃‍♂️ Noah Lyles photo finish
🏄 Why Olympic surfing isn’t in France
🏃‍♂️ Cole Hocker’s 1500m comeback victory
🧗 Sport climbing - the fastest Olympic event
🏃‍♀️ Sydney Mclaughlin-Levrone shattering records
⛹️ USA basketball nailbiters
🏃‍♂️ Jumping events are no joke
🥇 Medal count breakdown

What’s next?

We watched Olympic coverage from the moment we woke up until the moment we went to bed each day. The withdrawals have been real 🤒, which got us thinking about what's next on this year's sports calendar.

It inspired us to put together another breakdown of the pro sports overlap throughout the year 📅 and see what could pull us out of this hole.

While we're currently in the "dog days of summer" for sports, things are about to pick up quickly. The NFL season kicks off in a couple of weeks, followed closely by the NHL and NBA. By late October, we'll witness the sports equinox 🌓, when all four major U.S. leagues will be in action at the same time, which should cure our sadness.

THIS NEXT WEEK ⏩

Football is so back (almost) 🏈

Although the regular season starts in early September, people are getting their football fix with preseason NFL games. These games are all about preparing teams for the regular season and give coaches a chance to assess which players are the best fit for the team.

Winning is decidedly not important during the preseason so don’t worry if your team struggles on the field. In fact, a Reddit user did a quick statistical analysis 📊 on whether preseason success leads to regular season success. I’m no statistician but this doesn’t smell like a correlation to me.

Mid-Season Reset 🔁

The WNBA's three-week Olympic break has ended, and the season is resuming with just about a month left until the playoffs. Following an exciting Olympic gold medal win, we expect even more attention on the league which has already exploded with the Caitlin Clark effect.

So far this season, the effect has been monumental, as Joe explains how the league has more than doubled almost every performance metric this year. 🆙

We’re excited to see the final tallies roll in, especially since the growth has been undeniably real for months now.

Thank you all for reading, it’s been a busy week and we’re so excited to be back pumping out emails 💙

— Claire and Riley

Carrying the Torch 🔥

Riley Martin — Fri, 02 Aug 2024 15:30:00 +0000

Happy Friday and welcome to the new readers out there. We’re excited to be back from our wedding/honeymoon 💍 making graphs and breaking down the world of sports - it’s been too long!

The focus this week is the Olympics. While summer usually brings a sleepy lull in the sporting world, the Olympics are the smelling salts to wake us back up. Today we’ll have:

📺 Olympic viewership numbers
🥇 Medal count update
⚾️ Complicated MLB trade deadline charts

OLYMPICS CORNER 🥇

We’re so Back 📺

Flashback to 2021. The world’s climbing out of the big sick, social unrest throughout the country, and a new president. The last thing people were thinking about was the Tokyo Olympics. 😴

The first week of those 2021 Summer Games garnered “only” 19 million US viewers on average; people were worried the Olympics were down bad.

Not so fast…

Viewership at the Paris Olympics has come out of the gate extremely hot with first-week viewership up 79% to 34 million as NBC now makes the Games available all day. From when you wake up and start watching live events to the moment you drift off to primetime “re-packaged” Olympic highlights, there’s no shortage of opportunities to get eyeballs 👀 on the event. For context, this 34 million viewers mark is comparable to:

🏈 NFL Wild Card Playoff games: 38M
📺 Barbara Walters interviews Monica Lewinsky: 48M
👔 First 2024 presidential debate: 51M

Not exactly apples to apples but you get the point!

Drivers Seat 🚗

We’ve been posting daily Olympic medal counts on our social accounts here; this is the updated chart as of yesterday at 5 PM.

The Old and the New ⏳

48 events are part of the 2024 Olympics, but that list is very fluid. Only 4 of the events this year have taken place in every single Olympic games since 1896:

🏊 Swimming
🏃 Track and Field
🤺 Fencing
🤸 Gymnastics

The newest events are Breaking (breakdancing), Surfing, Sport Climbing, Skateboarding, BMX Freestyle, and 3×3 Basketball, all of which have been added in the last two Games.

To become an Olympic Sport is no walk in the park; there’s a multi-step process starting with sport “recognition” by the Olympic committee, then application to the Olympics, and finally criteria evaluation. Here’s a thread on how it works and why chess isn’t allowed.

VIDEO CATCHUP ⏮️

Katie Ledecky’s continued domination 🏊

NBA Media Rights Explained 🏀

EXTRAS 📊

MLB Trade Deadline 🤝

The MLB trade deadline was Tuesday, marking the last time teams can transact before the playoff push with a locked-in roster 🔒

We wondered which teams trade together 👯 the most and who the most prolifically trading teams are. We grabbed every trade over the last five years from Spotrac and visualized the connections between teams, pulling out the top partners in each division. Some initial findings:

🐟 The Tampa Bay Rays have made the most trades while the Washington Nationals have made the least

🤝 The most common trade partners are the Mariners/Giants and Dodgers/Rays

The chart itself is ultra confusing (and we know that) but the data is super interesting to understand how “loose” teams are with trades. We have a divisional breakdown here with some more details and team analysis.

Thank you all for reading - we’re looking forward to getting back on the horse with emails and charts after a bit of a break. 🏝️ Have a fantastic weekend!

— Riley and Claire