Analyzing Success in the 2026 Winter Olympics

Please forgive the detour from regularly scheduled baseball programming to indulge in some Winter Olympics

The 25th article in this publication’s history fittingly centers around the 25th Winter Olympic Games. All but one of these articles (the first one) have been baseball-related, so hopefully this is a welcome change of pace before the other major international competition this winter (World Baseball Classic) and 2026 season kick into gear.

---

Now that the Milan/Cortina d'Ampezzo Games have concluded, it’s time to look back on the successes of each nation. The star of the Games was predictably Johannes Høsflot Klæbo, the Norwegian skier who broke the record for golds won in a single Winter Olympics (6) and who now has more golds (11) than any Olympian not named Michael Phelps. Klæbo spearheaded Norway’s efforts as the highest-performing nation once again.

No matter how you slice it—gold medals, total medals, or “Medal Points” (3 for gold, 2 for silver, 1 for bronze)—Norway topped everyone else:

This level of success from Norway is expected as they have established themselves as the GOAT Winter Olympic nation over the last century. If we compared how these countries did relative to what we’d expect of them, though, how would the table look then?

Performance vs Expectation

All credit for the following data goes to Neil Paine, whose Expected Medal Points model serves as a great reference point to compare past success to current. Let’s see how each medal-winning nation performed against their own history:

As we can see, Norway met expectations to a T, with only a 0.2 MP difference between actual and expected. Most high-performing nations fluctuated against their expectations, however.

The largest over-performance belonged to the host nation Italy, whose past predicted a 5G/5S/7B showing compared to their actual 10G/6S/14B—third-best by MP. While impressive, Italy’s performance must be understood within the context that they were the host nation, so the benefits that come with that (e.g., facility familiarity, travel ease, crowd support) likely made outperforming expectations easier. Japan and the Netherlands were the other significant over-performers.

Meanwhile, the big under-performers were Canada, Finland, Germany, and the United States. Canada definitely feels the hockey sting, but they underwhelmed across the board, expecting 61 MPs but achieving only 38. Germany and the U.S. also carry histories that indicate they should’ve given Norway a better run for their money, though they still finished top 4. Finland used to be much stronger in the 20th century, but their 0G/1S/5B outing is actually fairly expected by modern standards.

Accounting for Breadth

Valuing each event in every discipline the same is standard both for the IOC and the general public. That is what all of the above analysis assumes, as well. However, if you’d like to account for each discipline equally regardless of number of events, then this section is for you.

Consider the fact that Speed Skating had 14 events whereas Ice Hockey had 2. Raw medal potential is 7 times greater in Speed Skating than in Ice Hockey. If we instead judge countries based on medal proportion in each discipline, then Speed Skating and Ice Hockey (and everything in between) are on equal footing. If one country wins 7/14 Speed Skating golds and another wins 1/2 Ice Hockey golds, they would earn the same value (50% in a discipline). Traditional analysis dictates the former country performed remarkably better than the latter, but they are equal when accounting for breadth.

Here’s how each medal-winning nation would stack up in this case:

Norway might be king by raw Medal Points, but adjusting for breadth reveals that their skiing focus tends to benefit them. When it comes to domination per event, Germany’s sledding success coupled with their podium spread in other events indicates that their overall prowess is significantly underrated by raw medal count. Hoarding two-thirds of the bobsleigh/skeleton medals (as Germans are wont to do) helps them a lot here.

Joining Norway in the specialization camp are the Netherlands, China, and Italy. The Dutch in particular benefited massively from event asymmetry as all of their medals were earned in speed skating, a discipline with a high event count. Adjusting for their lack of breadth shifts them from nearly top 5 by MP to outside the top 10 by bMP. China and Italy weren’t that hyper-specialized, but their bMP suffers from many of their medals coming in high-event disciplines.

Whether or not adjusting for breadth like this is the “right” way to judge how countries perform at the Winter Olympics is up to interpretation. Nevertheless, I believe it is an important perspective in light of how much it correlates with performance vs expectation:

The correlation between breadth and MPOE is moderately strong in the negative direction (r = -.67), with an R^2 of about 45%. This indicates that nearly half (45%) of the variation in how much a country over- or under-performs can be explained by how much that country benefited from success in high-event disciplines. Basically, countries that “under-performed” did so in large part because they didn’t take advantage of high-event disciplines like skiing, opting instead to conquer low-event disciplines like hockey and sledding.

So, even if you prefer to view Olympic success without breadth in mind, recognizing its impact on performance vs expectation is still valuable and arguably necessary.

Adjusting Expectation by Breadth

This is probably not kosher, but adjusting MPOE by breadth (bMPOE) is possible if we scale it according to the negative relationship between them:

Italy and the Netherlands experiencing diminished positive expectation at the top seems correct in light of their foci on high-event disciplines. On the other hand, Germany and Great Britain appear to have performed much better with their high breadth now considered.

Adjusting for Population & GDP

Olympic statisticians tend to like analyzing country performance not only in isolation, but also by contextualized output. The two most common ways of doing this are by adjusting for each country’s population (per capita) and economy (Gross Domestic Product, or GDP)¹. Although adjusting by population is a popular choice (pun intended), going by GDP proves to be more sound in light of how much better it correlates with Olympic success.²

Nevertheless, let’s inspect how some of the above metrics relate to population first, since this angle can still be worthwhile in some respects:

Norway’s relatively small population of 5-6 million means they crush everyone else here, though Slovenia over-performed the most on a per capita basis.

Now let’s adjust these numbers for breadth:

Not much different.

Now for the fun part: adjusting for GDP. Which countries punched above their economic weight the most?

Similar to the per capita numbers, Norway dominates the field when adjusting for economy, while Slovenia exceeds expectations. However, this time Georgia leads all nations in the “over expected” stat thanks to its smaller ~$34B GDP.

Lastly, let’s adjust again for breadth:

Similar again, but this time Norway’s lead shrinks due to their negative breadth, allowing Slovenia, Austria, Georgia, and Latvia to narrow the gap somewhat.

Putting It All Together

That was a lot of tables. Let’s summarize and contextualize what we’re seeing when we use these different angles to measure Olympic success.

• Norway is still the GOAT. Sure, the Norwegians “just” met expectations, but those expectations were sky-high. The only knock against them is that their medal output is a bit inflated by specialization, but when we consider how much the country punches above its weight, it’s hard to argue against them being the most impressive nation at this Winter Olympics.

• The biggest over-performer was probably Japan. Italy over-performed the most by MPOE, but with home-field advantage. I’m wary to give them the crown of “biggest over-performer” because of this. Japan would be next by MPOE, with the Netherlands behind them. The Dutch are hurt by having the worst breadth of any medal-winning nation, though, dampening their bMPOE considerably. Japan is my pick for biggest over-performer, but this is ultimately a matter of preference.

• Pound-for-pound, Slovenia impressed the most. Thanks to 2G/1S/1B—all in ski jumping—Slovenia sports the second-highest (b)MP/$100B and (b)MPOE/$100B. Switzerland edges them slightly per capita, but by the resources available to them, Slovenia was the largest standout. The Prevc family is mostly responsible; older brother Domen took individual gold and team gold, and little sister Nika took team gold, silver, and bronze (brothers Peter and Cene took home some ski jumping hardware four years ago, too). Georgia—larger by population but smaller by economy—bests Slovenia per GDP, but their lack of volume (1 silver, in figure skating) makes their argument flimsier.

• Germany has a fringe case for best nation despite under-performing. The leaders in bMP would have led by even more had they not claimed the third-worst MPOE. The United States has a similar case: objectively one of the best, high breadth, but under-performed. Austria is like Germany and the U.S. in these ways, except smaller, so less total output but among the best per capita/GDP.

• The biggest under-performers were Canada and Finland. Canada was bitten slightly by too much breadth, but that doesn’t explain most of their woes. They expect to be a top 3-5 nation, yet they were only fringe top 10. Meanwhile, Finland continues to live in its own shadow of yesteryear—though they still do objectively well on a rate basis.

• China will always struggle on a rate basis, but they still met expectations. The other global superpower may lack breadth, but their performance this winter was pretty predictable. As for other high-GDP nations, France exceeded expectations to become top 5 in MP, and Great Britain and Australia also over-performed.

• The more stats we analyze, the more weather bias becomes apparent. Just look at the top 10 in bMP/$100B in the last table. No wonder countries like Sweden and Estonia are able to punch above their economic weight; they’re geographically located in the most opportune climate possible. (Some countries like Norway and Switzerland benefit even further with mountains.) This will always be an advantage over countries like Brazil and Spain, whose high GDPs won’t mean much if training is infeasible. Accounting for this bias is outside the scope of this article, but I would be interested to read anything that tries to.

1. Data gathered from medalspercapita.com.

2. Analyzing by GDP per capita is possible, but since more populous nations tend to have more participants by virtue of being more populated, GDP per capita would essentially double-count population.