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 📅
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
