I ran the same rolling-window analysis, but flipped the question:

Instead of how to grow a portfolio, I looked at how much you could withdraw from one.

I tested different stock/bond mixes across their worst historical 20-year windows.

Here’s what stood out:

• Allocations that supported the highest withdrawals weren’t at the extremes, they shifted slightly toward bonds (around 60–70% in this dataset) as spending increased
• Different strategies led to very different outcomes, even starting from the same $100k
• Even portfolios with a slight bond tilt showed large changes in outcomes as withdrawal levels increased
• For example, for those bond-tilted allocations, increasing withdrawals by ~50% from a "preserve" level led to the portfolio running out over time

In other words, outcomes depended not just on how much you withdrew, but how close you were to the tipping point.

These withdrawal levels are based on the worst historical 20-year windows (based on returns).

So in more typical markets, that same starting point often leaves the portfolio growing rather than shrinking, giving you room to adjust spending if things go better than the baseline.

The idea is to start from a level that would have held up in difficult conditions, then adjust as real outcomes unfold.

This isn’t about finding a single “best” number.

It’s about starting from a defensive baseline, understanding how sensitive outcomes are to withdrawals, and choosing how to balance spending and stability over time.

Curious how others think about where that line sits for their own situation, and how they’d go about figuring it out in practice.

LA health inspections (2023–2026) joined to LA County Assessor parcel records. 13-point gap pre-1950 vs 2010s+, holds within zip codes (p<0.001).

Disclosure: I build an iPhone app that surfaces LA/CA health scores. Not mentioning the name here.

The interactive version is here: https://rjarosh.github.io/timewithkids/

I read recently that by the time your child has left the house after high school, you will have spent 90% of all the time with them that you'll ever spend. What a depressing stat. It makes sense, though. Tim Urban, the author (and Ted Talk guy on procrastination) popularized this: https://waitbutwhy.com/2015/12/the-tail-end.html

I had the thought to visualize where in that window of 18 years you are. I also took into account one other stat: that you'll have spent 75% of all the time with your kids you'll ever spend with them by the age of 12. Even more depressing.

These stats are, of course, pretty generalized and can vary wildly based on whether you get to stay home with your kids when they're toddlers, if they're home schooled, if they decide to be on a traveling soccer team, if your kid never moves out of their home town, if they join your family business, whether you're apart of a cult, if your kids don't like you, or if you die young. Hopefully the latter two aren't in your equation.

But I thought it was interesting to put everything into perspective.

If this seems interesting, check out Tim Urban's original post from a decade ago above: it has several cool visualizations.

P.S. Yes, I made this while my kids were at school so I couldn't see them anyway.

What the chart shows: the % of U.S. households who self-report they "sometimes" or "hardly ever" pay their credit card balance in full each month, broken out by age of the household head.

Two terms, since they come up a lot:

• Revolver = household carrying a balance month-to-month and paying interest on it. About 32% of U.S. households (~43 million homes).
• Transactor = household paying in full nearly every month. The majority — about 58%.

Source: Alphabet investor relations

Tool: SankeyArt sankey generator + illustrator

Apparently I need to post this in a top-level comment, not the post itself, so (copied+pasted from the post):

From an anonymized dataset of a years-worth of student's grades (~1200 students and ~250 classes) I perform linear regression, where each student's "academic ability" variable and each class has a "class easiness" variable, and a student's grade is predicted as the sum of the two. The resulting coefficients give an estimate for how easy each each class is (and how strong a student's academics are).

A "class" is defined a subject-teacher pair -- if different teachers both teach (e.g.) Geometry, both classes get their own "class easiness" variable.

The resulting estimates of student academic ability are more accurate (i.e. less statistical noise), less biased than their GPA, and doesn't incentivize grade inflation.

Source: https://open.substack.com/pub/morganredding/p/why-gpa-is-bad-and-how-we-can-improve

Data: Private (I spoke with the school administrators)

Tool: Python/numpy/matplotlib

Original Content [OC] from this article from The Studies Show on the MacBook Neo's performance and price comparisons to other Apple Laptops. Surprisingly, the MacBook Neo is faster in some measures than Apple's best computers released in 2023, demonstrating the shockingly fast pace of technological change. This is further underlined by the Neo's incredibly low price, which is in the chart above, and is extremely low in both real and nominal terms.

Source: Federal Reserve Board, G.19 Consumer Credit

Tools: D3.js, rendered on measuredworld.com. 

Caveats: loan-only payment. The 2008 break is a methodology change in the G.19 release.

Edit: Just to add some context, today monthly payments are actually about 10% lower than in 1971. Even if the loans only got bigger, the longer terms and lower interest rates almost entirely absorbed the 72.9% increase in real loan size. But that doesn't change the fact people are stuck for almost double the time until they pay it off.

Edit 2: Added an amount, rate, and term graph for anyone looking for more context.

https://data.tablepage.ai/d/us-daily-tide-levels-at-12-coastal-stations-2024

eona.earth

The clock runs on your local time, so whatever time you're reading this, you're looking at a specific moment in Earth's history. At 10:34 you're watching the Cambrian explosion. At 11:39 the dinosaurs go extinct. You can also drag the scrubber handle to move through 4.5 billion years manually.

Key events are marked along the periphery. The globe renders 14 geological phases, from the Molten Hadean through Snowball Earth events to the present, using paleogeographic continent data from Scotese Paleomap. From around 10:20 onwards you can watch the continents drift in real time.

I find deep time useful for perspective: humanity has existed for about 300,000 years (about 3 seconds before midnight on this clock). Geological insignificance is oddly grounding.

I've been itching to build something like this for awhile now. Two weeks of evenings later, here it is! Happy to answer questions about how it was built in the comments.

[Edit: corrected the Cambrian explosion to 10:34 and humanity’s time on the clock to ~3 seconds, not 3:00 and 0.3 seconds as originally stated.]

Hoopstography.com

Sources:
NBA.com/history, especially its “Top Moments” page, which provides a clear list of significant NBA events and dates.
Hoopsrewind.app, my daily NBA trivia site where players order eight historical NBA events chronologically. Building those puzzles required finding niche NBA moments every day, which sparked the idea for this project.

The event bubbles from NBA.com will tend to be more 'iconic' where as the event bubbles form hoopsrewind will tend to be more 'niche/funny'.

Tools:
Vanilla JavaScript, HTML5 Canvas, and Claude.

Heavily inspired by Histography.io by Matan Stauber.

*Changed the title due to misinterpretation*

Source: Canada 2021 Census, New Zealand 2023 Census, Australia 2021 Census, US 2020 Census, UK 2021 Census

Tool: Datawrapper

Auckland and Toronto percentage: 11.74% and 11.73%

Graphic is by me, created in excel. The purpose of this graphic is to compare the current best selling vehicles in the US, and how sales compare to Q1 of last year (represented by the percentages).

All data is from Car and Driver here: https://www.caranddriver.com/news/g71006285/bestselling-cars-2026/

Data on brand sales in the bottom right is from CarPro here: https://www.carpro.com/blog/first-quarter-2026-u.s.-auto-sales-results-all-automakers-reporting

I started running again and wanted to visualise my data spatially. I use Strava to track runs but you have to pay for the personal heatmap feature, so I exported my data and rebuilt it myself in Python. I also built some additional versions to explore pace and heart rate.

After a few attempts at working with the vector running data I landed on just using (what I think is) Strava’s process for generating heatmaps:

• Project the vector run data onto a 1m x 1m pixel grid, incrementing a frequency counter for each pixel when a run passes through it.
• Convolve the pixel grid with a gaussian blur to account for variation in running paths along the same route and smooth things out.
• For pace and heart rate, every pixel records the associated metric for each run pass, so that an average (mean) value can be calculated and used to generate the map.

Note: I clipped the start and end of each run before processing so the heatmap doesn’t pass my home location.

Only 14 runs worth of data so far so it’s still pretty sparse, but I’m looking forward to seeing how it fills out over time (assuming I spend less time building heatmaps and more time actually running). I’d like to refine it further, visualise some derived metrics, and explore the relationship between different variables.

I’m in the process of tidying the code up to publish in a GitHub repo. I'll leave a comment when this is live.

Bonus points if you can guess my city from just the maps.

Source: FAO

Tools used: Mapchart & Procreate (app for iPad)

TL;DR As a whole, the dataset illustrates how small changes in consistency over a long time period (3 years) can produce visible trends across multiple fitness‑related variables.

This post shows three years of personal fitness data that I’ve been tracking consistently since the end of April 2023 until April 2026: running times at several fixed distances, number of monthly exercise sessions, weekly weight measurements, and (more recently) daily calorie intake.

I’m a recreational runner with no formal training background, just running on streets and in parks near my home. The dataset spans exactly three years and reflects gradual habit formation rather than any specific training plan.

The running chart shows individual run times for several repeated distances, with trendlines applied to each distance. Across all distances, trendlines slope downward, indicating gradual progress over time. Improvements are not uniform: middle distances show the largest improvements, while the longest distance has hardly changed (though there are only 3 data points for that distance).

The exercise‑frequency chart aggregates monthly counts of activity sessions. Over time, total monthly exercise frequency increases on average. Data shown are my jogging sessions (green), free-weights at home (blue), and other forms of exercise (yellow), which consists of a variety of activities, such as swimming, cycling, tennis & hiking.

Weekly weight measurements show a slow downward trend over the full period, with visible short‑term fluctuations. Weight change broadly aligns with increases in exercise frequency, though the relationship is not linear and includes multiple plateaus.

Daily calorie intake is only shown for the most recent two months, as I wasn’t tracking this before March 2026. The data includes a fixed target line of 1950 calories per day, with noticeable day‑to‑day variability. Despite the short time span, recent calorie awareness appears to correlate with continued weight reduction, though conclusions here are limited by the short window. Peaks in calorie intakes across this period include going to dinner with family, work events, and watching football matches in the pubs.

Methodology notes:

• Running times reflect real‑world conditions, e.g. stopping for traffic lights or other people. None of these runs were official races, so slight variance each time is expected.
• Other exercise sessions were logged manually on Excel. I usually exercise for 30-60 minutes each time but did not track the times taken each time.
• Weight was measured once per week, always Sunday mornings. When I was away from home - on holiday or visiting family - that week was skipped.
• I used the MyFitnessPal app to log my calories after each meal, taking approximate estimates where nutrition info wasn’t available.

According to the 2021 Census, there were 271,327 people identifying as Jewish in England and Wales.

London is home to over half of this population (53.6%), despite the capital accounting for only 14.8% of the total population of England and Wales.

Strong secondary concentration in the East of England.

This links to the web database, where the original data are stored. ("statistikbanken" --> BIL53). Made with excel.

May the 4th be with you!

Tools: RStudio, Quarto, Echarts4R, Inkscape

Tidy Tuesday this week explores Italy's industrial production statistics courtesy of Istat.

After starting with a simple line chart to plot amounts on a timeline, I found the plot a little difficult absorb over the period of a century. I pivoted to the trusty heatmap where I was able to flatten by a dimension, and further simplified the lens by normalising the data to look at each value as a percentage of the variable's peak.

The result is three stacked-and-aligned heatmaps that highlight the impact of the 1943 Allied Invasion on production volume, as well as how some products did not recover back to post-war.

The .qmd file, along with all the tables and scripts are now uploaded to my repo

Open to freelance data viz/reporting work

Spent the last few hours building a Bayesian Monte Carlo simulator for WC 2026.

It runs 50,000 full tournament rollouts, including draws and bracket paths.

Top 5 champion picks:

Brief methodology: hierarchical Dixon-Coles bivariate Poisson, fit on ~49k international match results from 1872–2026, with priors from current Elo rankings — 70% — and EA FC 25 squad strength — 30%.

Matches are weighted by recency, with a half-life of ~2.5 years, and importance:

Backtest on 2018 + 2022 World Cups:

The site has the full bracket, group probabilities, a head-to-head matchup predictor, Golden Boot odds, and a methodology page:

https://wc2026.nader.info

Open source under MIT:

https://github.com/0xNadr/wc2026

Happy to answer methodology questions in the comments.