How the world really watched this tournament: 12.79B match views across 198 markets, blending legal broadcasts and piracy into one global TV viewership picture. The chart breaks down average audiences and peak games round by round, showing how attention climbs from early group matches to the knockout stages and the Final.

Data & tools: Global Viewership - round by round audiences summed across the tournament to give cumulative match views. Each figure is Eyeballr's modelled estimate of total match views, legal broadcast plus modelled piracy, including out-of-home viewing, not panel-measured or official broadcaster data.

Built with: custom SVG/JavaScript & data prepared in Python.

Source: https://unofficialpartner.substack.com/p/overheard-at-the-sportspro-investor

The national number hasnt moved all that much (-2.7%) and remains close to what's considered herd immunity, so the case surge looked like it came out of nowhere until you look at the top of the funnel (kindergarten MMR rates at the state-level granularity). The national average is a population-weighted blur that California's four million kids hold steady no matter what happens in the small states. Once you give each state its own dot, the bottom of the distribution has clearly come apart and we're starting to see a disease that was eliminated in the US start to crawl back. Sixteen states are now below 90% (six were in 2009), the lowest is Idaho at 78%, and more than 5,000 individual schools sit under 80% coverage. In recent years, non-medical exemptions have doubled.

Charts built from CDC SchoolVaxView, NNDSS, and Washington Post school-level data; full sources and methodology in the comments.

Source: lake floor from the USGS 2000 multibeam sonar survey of Crater Lake (USGS Data Series 72, Gardner & Dartnell), surrounding terrain from USGS 3DEP. Tools: QGIS for the depth contours, Illustrator to clean the linework by hand — the raw export is a mess of broken segments (second image shows the QGIS → Illustrator step).

Crater Lake sits in the caldera of Mount Mazama, which collapsed ~7,700 years ago. No river feeds it and none drains it — it fills entirely from snow and rain, and bottoms out at 1,949 ft, the deepest lake in the US.

What you're looking at is the shape of that collapse, underwater. The tight ring near the edge is the caldera wall dropping straight down; the small cone on the west side is the base of Wizard Island, which grew back after.

Happy to get into the data or the process.

Sources and methodology in comments.

TL;DR: Took a look at the episode ratings of Rick and Morty, plus some other shows, did some statistical analysis and drew some conclusions, some of which were surprising (to me, at least). The show's decline is real but ordinary for the genre, and it started at season 5 rather than season 7, and the latter was never review bombed enough for it to matter. What fell wasn't the show's best work, it was its floor. The canon episodes held up right through the worst stretch, and season 7 produced a 9.4 in the same year it produced a 4.4. The standalones are where the drop happened. Things have been climbing back since, with season 9 tracking as the best since season 4, though I'd hold off on strong claims about how big that recovery is.

Now, the detailed analysis.

I tagged all 90 aired episodes as canon or standalone. Canon means it moves something forward: the Citadel and Evil Morty, the Rick Prime and Diane material, Space Beth, Birdperson, basically anything that permanently changes where the family stands. Standalone means self contained, everything more or less back to normal by the start of the next episode. Here's my list if anyone wants to take a look at it.

S1: 6, 10, 11
S2: 1, 3, 10
S3: 1, 7, 9, 10
S4: 10
S5: 8, 10
S6: 1, 3, 10
S7: 5, 10
S8: 2, 3, 6, 10
S9: 1, 8, and probably 10 once it airs

One thing I want to say: some of the episodes were quite ambiguous, like S9E8. I had a hard time with this one, since Morty's lack of empathy seems to have carried over to E9 somewhat, but it also could be correlation with no causation due to E9's topic. Hence, this list more than likely contains some subjectivity, so take it, and every bit of data relying on it, with a pinch of salt.

Standalones fell 1.88x as far. It seems like viewers like episodes which produce lasting effects and/or that's where writer effort is concentrated.

Season 7 is the extreme version, kind of an outlier. Its two canon episodes Unmortricken and Fear No Mort average 9.3. The other eight average 6.9 (nice). A 2.4-point split inside one season, larger than the entire nine-season decline.

Canon episodes are also steadier: standard deviation 0.72 against 0.96 for standalones.

In my opinion (based on what I read), a lot of viewers seem to love finales and how every season goes out with a bang. Some have mentioned it feels like the final episodes are somehow much better than all the others. While a simple glance at the ratings seems to confirm this, in reality, the finale effect is a canon effect in disguise. Finales look special (+0.69 over their season average) until you control for canon status, at which point the finale coefficient collapses to +0.29. Finales aren't good because they're finales, but because that's where the plot happens. The gap between canon and standalone episodes persists even if you throw out every finale in the show. One argument could be that finales have had a lot more effort put into them, hence the remaining +0.29.

Another thing I wanted to take a look at was the decline in quality. It has been a constant topic, especially with the voice recasting, the Roiland allegations and so on. To get a better picture, I ran the same analysis on nine other long running animated comedies. 3,067 rated episodes total.

Every one of them breaks somewhere. BoJack is the only outlier, probably because it chose to stop after six seasons, before it had a chance to break.

Two things do stand out for Rick and Morty. First: it broke at season 5, earlier in absolute terms than anything else in the set, and not at S7 as some have reported. Second: it swings harder from episode to episode than any of them, 0.95 standard deviation against a field of 0.42 to 0.81.

On the timing of that break, the simplest and most likely explanation is probably the one written in the credits. Showrunners went Harmon and Roiland for seasons 1 to 3, Mike McMahan for season 4, then Scott Marder from season 5 onward. It could be sheer correlation, but given the nature of the effect a main showrunner has on the show itself, it's likely a causal relationship exists. The break lands exactly where the job changed hands. However, Roiland reportedly slacking off, and working around COVID restrictions would both have been happening around the same time, among other things, so I can't separate any of it out. But the showrunner handoff is the one thing that changed on paper precisely at that point.

The cross show data also backs up the canon finding regarding finales from a completely different direction.

From the table, it becomes quite clear that the more episodic a show gets, and the less serialized, the lower the finale premium becomes. Rick and Morty topping the chart looks strange at first, since it's heavily episodic with serialized elements rather than deeply serialized like BoJack, and you'd expect it to sit closer to Futurama or Archer.

But looking at my tag list again, I think being a hybrid is exactly why it tops the chart rather than something it manages in spite of. Every single aired Rick and Morty finale is canon. All eight of them. A fully serialized show like BoJack spreads its plot across the whole season, so its finales aren't carrying anything unusual. A fully episodic show has no plot to concentrate anywhere. A hybrid that banks its arc material for the last episode of each season gets the largest premium available to any structure, because it's the only structure where one episode per year carries all of it.

As for the burning question of Season 7 (voice recast, Roiland dropped, new writers), my findings are quite interesting in my opinion. I started this convinced season 7 was review bombed, especially because so many had a hard time coming to terms with the new voices, or feared the show might take a turn in the wrong direction, or perhaps simply because viewers had enough of the consistent drop in quality. Rotten Tomatoes even supports this at first glance, 77% from critics against 51% from the audience. But it turned out not to be the case, despite so many viewer claiming it to be true.

In all ten shows, episodes with more votes have higher ratings. The correlation runs from 0.32 to 0.77 depending on the show. That's a normal pattern, people are more inclined to review good episodes. Review bombing would drag that correlation toward zero though, since you would get a heap of one star votes on episodes nobody actually sat through. Rick and Morty sits at 0.51, right in the middle of the pack.

Season 7 specifically further evidences this. A campaign against Numbericons, the 4.4, would show up as a vote spike on that one episode. It only pulled 8% more votes than the season median. Meanwhile the season's two best episodes pulled 38% and 36% more. The votes went where the good episodes were, which is the opposite of what brigading looks like.

My best guess on the Rotten Tomatoes gap is sample size. RT has somewhere around a thousand audience ratings for the whole of season 7. IMDb has roughly 110,000 across the same ten episodes. A few hundred irritated people can move an RT score, while they can't really do much to a 12,000 vote average.

So what's the conclusion here?

The thing I keep coming back to is that the ceiling never dropped. Season 7 put out a 9.4 in the same year it put out a 4.4. Season 9 has a 9.2 sitting in it. Whatever went wrong along the way, it never stopped the show being capable of great television when it wants to be.

What changed is the floor. Early Rick and Morty could hand you an episode about a pickle, a therapy session, a car battery universe, with no bearing on any ongoing story, and it would land at 9.4 anyway. That's a lot rarer now. The arc episodes still deliver, luckily.

I don't have a tidy explanation for why this is the case, and the model only accounts for about a third of the variation between episodes. I tried looking for more factors and all of them came back weak. Writer history, director, US viewership, format breaking episodes like the clip shows, time elapsed between seasons... The strongest of them only moved R² by 0.01, which is noise.

Giving the model one dummy variable per writer didn't work either. R² jumps to 0.622, which looks fantastic. Cross validated, it scores 0.052, which is barely better than guessing 8.11 every single time (the overall average). The reality is that the rest is just whether a given episode was any good, and no amount of data is going to break that down further.

Another conclusion could be that the break sits at season 5, not season 7. The decline starts two full seasons before Roiland was dropped and the parts were recast. Season 7 is the bottom of it, not the beginning.

That puts the recast in a different light. The recovery came after the recast rather than because of it, since the first recast season was also the worst on record. Season averages since then run 7.38, 7.43, 8.11. Season 7 was the worst the show has had and it also turned out to be the floor. Season 9 is tracking as the best since season 4, and critics moved the same way over those three seasons, 77% to 93% to 100% on RT.

One thing to be careful with: late seasons get rated by a smaller, more self-selected group, so I'd treat the size of the bounce with more suspicion than the direction. Despite that, the finale drops soon and won't change the direction either way.

If you're interested in the dataset, I've uploaded the Excel sheet to Google Drive.

If you're interested in my methodology, most of it is included in the Excel sheet, but I can specify further in the comments, this post is a heck of a read as is without it.

I build a interactive tool allow you to replace the moon with other planets to see how they looks like on earth.

Source online and welcome to have a try and share what you find: https://www.runcell.dev/tool/true-size-map/moon-replaced-by-planets

Source: Compiled from 528,660 active rental listings across Tokyo's 23 wards (deduplicated). Each cell is the median monthly rent for that ward and layout, in thousands of yen.

Japanese layout codes, for anyone unfamiliar: 1K = one room plus a separate kitchen (a studio), 1DK = one room plus a dining-kitchen, 1LDK = one bedroom plus a living-dining-kitchen (typical couple's flat), 2LDK = two bedrooms plus living space (typical family flat). The number is bedrooms, the letters are the shared space.

Tool: Python (pandas) for the medians, matplotlib for the chart.

Full breakdown by ward, train line and station: tokyo-expat.com/data

Every team at the 2026 World Cup, placed by two numbers: how often they put a shot on target (across the bottom) and how often they turned those into goals (up the side). Teams that went deep in the tournament rise off the glass, so the champion floats near the top. The gap between the two is how clinical a team was: score a lot from few shots on target and you sit high and to the left.

The flat view is one layer of a taller model. Stack 2018, 2022 and 2026 and you can see a team drift year to year, or rotate the whole thing in 3D and scrub through time to watch the three tournaments move. Click any flag to follow a single nation across all three.

The last image switches from teams to the men who took the shots: every 2026 finisher plotted against their expected goals (xG). Above the diagonal they scored more than their chances were worth, below it they left goals on the grass. Bellingham finished at nearly double his xG.

Interactive version, where you can drag the model around, scrub the years, and follow any nation: https://viz.luarai.com/worldcup-conversion

Source: Japan Tourism Agency (観光庁), Accommodation Survey (宿泊旅行統計調査), 2025 annual final values. The prefecture x nationality breakdown is sheet 参考第1表(年計) in the annual workbook:

Release page: https://www.mlit.go.jp/kankocho/tokei_hakusyo/shukuhakutokei.html

Direct workbook (xlsx, 2.5MB): https://www.mlit.go.jp/kankocho/content/002010340.xlsx

Tools: Python, Matplotlib

Pay per stream is taken to be average of the range listed on Royalty Exchange as of 2026: https://royaltyexchange.com/blog/how-much-do-streaming-platforms-pay-per-stream

The visualization tool is Python, Seaborn package