r/dataisbeautiful

I pulled the official published size guides for seven UK high-street brands and lined up
the body chest measurement behind each alpha size. Two things stood out: (1) unlike
womenswear, men's alpha sizing converges - a medium centres on ~96–102cm chest across
all seven; but (2) the "medium" window is 6–7cm wide at several brands, and Mango runs
~1.5–2 sizes large, sitting a full size off the consensus.

Free realtime website: https://cupcharts.com

Methodology and code here

Converted our house from a gas furnace to a heat pump in July 2024 (Canada). We still have a gas stove, and we gained central AC for the first time as part of the install.

To compare apples-to-apples, I plotted total monthly energy cost (gas + electricity) against average daily outdoor temperature, and restricted both series to ≤10°C so the comparison stays within heating season for both — no AC cost muddying the "after" data.

Before (gas furnace): y = -8.08x + 230.16
After (heat pump): y = -5.60x + 188.54

At 0°C, that's $230/month before vs. $189/month after — about 18% lower, with the same gas stove cost baked into both, so the difference is attributable to the heating source itself.

The slopes are the other interesting bit: the heat pump line is noticeably flatter (-5.60 vs. -8.08), meaning our costs are less sensitive to how cold it gets outside. The gas furnace had to work (and cost) proportionally more as temps dropped; the heat pump's cost curve is comparatively steadier.

A few notes for the data purists:

• Data pulled directly from utility bills (Enbridge gas + electricity), anomalies normalized (estimated gas usage vs actual reading restatements), matched to average daily temp for each billing period.
• Restricted to ≤10°C specifically to exclude AC-driven costs, since AC is new post-heat-pump and would confound a straight before/after comparison at warmer temps.
• Sample size is modest (roughly 8-10 points per series), but each point represents a full billing cycle (~30 days) of actual metered usage, not a spot reading — so while there are few points, each one is already an aggregate/average over a month, not a noisy single-day sample.

Data: 84,845 guesses from borderline.world, a daily game I made where you find countries on a 3D globe. It covers 166 countries, minimum 184 attempts each, 12 June to 5 July 2026. Microstates and small island nations aren't in the game because they're too small to tap reliably on a globe so no Vatican, Monaco or Maldives here.

Method: a guess counts as correct when the tap lands inside the country's borders. Wrong answers aren't mis-clicks: the median wrong tap lands over 600 km from the target and 87% of them land inside a different country entirely, so the map is measuring where people genuinely think countries are.

Results: the five hardest are Timor-Leste (25%), Liberia (28%), Sierra Leone (31%), Burkina Faso (31%) and Senegal (32%), and West Africa is the hardest region as a block. The easiest are Brazil and Russia (98%), Australia and France (96%) and Canada (95%). One that surprised me Switzerland (63%) is far harder to place than Sweden (85%) even though the name mix-up famously runs both ways.

Tool: d3-geo with a Natural Earth projection, rendered to canvas.

Play it or poke at the data source: borderline.world

Source: https://en.wikipedia.org/wiki/Same-sex_marriage#Timeline

Tools: https://www.mapchart.net and Apple Preview

Original post linked here

everyone assumes hurricane season is good for home improvement stocks. more storms, more damage, more people buying plywood and generators. insurance too, premiums go up right? so I tested it.

turns out it's the opposite.

what I did

took the start of Atlantic hurricane season (June 1) every year from 2010 to 2025. measured how a basket of hurricane-exposed stocks performed vs the S&P 500 around it — Home Depot, Lowe's, Allstate, Travelers. standard event study, CAPM market model, abnormal returns, t-test.

results

the basket underperformed the market by 2.1% on average. statistically significant (p=0.034). positive in only 4 of 16 years. by stock: Allstate -3.1%, Lowe's -3.1%, Home Depot -1.7%, Travelers -0.6%. worst years 2021 (-9.8%) and 2025 (-7.2%).

why (my theory)

insurers sell off because the market prices catastrophe risk before it happens — 5 months of potential billion-dollar payouts starts June 1. home improvement's rebuilding bump is real but localised and shows up AFTER landfall, not at season start. basically the obvious trade is too obvious — if "buy Home Depot before hurricane season" worked, everyone would do it and it would stop working. which is exactly what the data shows.

what should I test next?

queued up: defence stocks during conflicts, silver vs gold as crisis hedge, whether the rebuilding bump shows up if you measure after landfall. what do you want tested? the weirder the better

source: yahoo finance via yfinance. not financial advice, I just like checking whether market folklore is true

Two radial charts comparing monthly UK temperature and sunshine anomalies in 2026 against a 1961-1990 baseline, with 2025 (the UK's hottest and sunniest year on record) shown as the white reference ring on both.

2026 so far (Jan-Jun) is the UK's hottest first half of a year on record, and its temperature line sits close to or outside the 2025 ring on most months. But on the sunshine chart, 2026 sits well inside the 2025 ring every month, meaning consistently less sunshine.

The temperature story is largely driven by January and February, which are driving most of the gap versus 2025 rather than June's record breaking heatwave, which is a distinct effect worth separating out.

Likely candidates include cloud cover and humidity suppressing overnight cooling (raising minimums even on lower sunshine days).

Note: the legend includes additional reference lines (1900-2026 range, 2016-2025 average) that are hidden in this static export for visual clarity. They are visible and toggleable on the live interactive version linked below ...

https://4billionyearson.org/climate/helix?region=uk#climate-spiral

A few things that stood out building this:

The top-1000 line sits at 100% until about 1950 — before then, essentially every baby got a name common enough to rank in the top 1000. Today more than a quarter of babies get names outside it entirely.

The dashed line marks ~1950, roughly when broadcast TV went mass-market. It lines up with where top-1000 dominance first breaks — though this is a coinciding inflection, not proven causation. Plenty of other things changed mid-century too (immigration patterns, cultural shifts, rising individualism).

Curious whether people read the post-1990 acceleration as an internet effect or just a continuation of the longer trend.

Title Edit: I can't fix the title unfortunately. For those reading, FP&A stands for Financial Planning and Analysis and it is the basically the finance department in a company that isn't a Tax or Accounting/bookkeeping job.

For those of you that are in the job hunt or are obsessed with data as much as I am, this post is for you. I know how frustrating it feels to be in your position, trust me. Even though the following is from blind applying to job boards I suggest you still spend most of your energy networking and getting referrals.

In this post you'll find a Sankey diagram that summarizes my search. Take a look!

Since I started my job search on August 23, 2025 I spent 313 days or about 45 weeks of applying to positions into what felt like a void, including over 1,100+ emails and endless calls and texts in that time.

From August 23, 2025 until July 1, 2026, I applied to a total of 806 jobs across the three major job boards; LinkedIn, Indeed, and Handshake, resulting in 29 interviews, 294 rejections, and 483 employers that didn't send updates.

To breakdown each major job board, I summarized the results as follows:

I applied to 450 jobs on LinkedIn: this led to 20 interviews, 180 denials, and 250 ghosts.
I applied to 211 jobs on Indeed: this led to 7 interviews, 106 denials, and 98 ghosts.
I applied to 145 jobs on Handshake: this led to 2 interviews, 8 denials, and 135 ghosts.

LinkedIn Application to Response Yield Ratio of 44%.
Indeed Application to Response Yield Ratio of 54%.
Handshake Application to Response Yield Ratio of only 7%.

If I were to start again I suggest most of your focus on Indeed and LinkedIn if you want to stick to the major three. Though not in my data set, I also found and heard major success from peers that use HiringCafe and Bandana.

Perseverances and networking are what tests your resilience and pays off, I promise you!

Sankey created with Sankeymatic. Data compiled manually with an Excel spreadsheet.

Updated version (R16) of the Sankey flow showing how men’s national teams narrow from FIFA ranking to WC 2026 phases (Part 1 here). I have added some pixels, not sue it's enough, tho.
Data: FIFA men’s ranking (Dec 2025), WC 2026 group-stage, R32, R16 outcomes, grouped by confederation.)
Processed in Excel; visualized with Python/pandas/matplotlib.

Source: U.S. Census Bureau. "Language Spoken at Home by Ability to Speak English for the Population 5 Years and Over." American Community Survey, ACS 1-Year Estimates Detailed Tables, Table B16001, https://data.census.gov/table/ACSDT1Y2024.B16001?q=B16001&g=010XX0OUS Accessed on 2 Jul 2026.

Tools: https://sankeymatic.com/build/ and Apple Preview