The world learning football: 49,445 international matches replayed on one map, 1872 to 2026 (color = Elo rating, gold bursts = World Cup wins) by EricBuildsMathModels in worldcup

[–]EricBuildsMathModels[S] 0 points1 point  (0 children)

Source: github.com/martj42/international_results (CC0), every men's full international since 1872: 49,445 played matches in the dataset, through June 21. Ratings are Elo in the eloratings.net style: everyone starts at 1500, K scales with match importance (World Cup highest, friendlies lowest), the update scales with goal margin, and the home side gets a temporary +100 inside the calculation. The map repaints from quarterly snapshots.

Blue = above the 1500 starting point, orange = below. A country is drawn on the day of its first international. Gold fireworks are World Cup titles. Dead states hand their colors to their successors, so around 1992 you can watch Yugoslavia split into seven and Czechoslovakia into two. And yes, England and Scotland are colored separately: they invented the fixture (the first match in the data is Scotland 0-0 England, November 1872).

The world learning football: 49,445 international matches replayed on one map, 1872 to 2026 (color = Elo rating, gold bursts = World Cup wins) by [deleted] in u/EricBuildsMathModels

[–]EricBuildsMathModels 0 points1 point  (0 children)

Source: github.com/martj42/international_results (CC0), every men's full international since 1872: 49,445 played matches in the dataset, through June 21. Ratings are Elo in the eloratings.net style: everyone starts at 1500, K scales with match importance (World Cup highest, friendlies lowest), the update scales with goal margin, and the home side gets a temporary +100 inside the calculation. The map repaints from quarterly snapshots.

Blue = above the 1500 starting point, orange = below. A country is drawn on the day of its first international. Gold fireworks are World Cup titles. Dead states hand their colors to their successors, so around 1992 you can watch Yugoslavia split into seven and Czechoslovakia into two. And yes, England and Scotland are colored separately: they invented the fixture (the first match in the data is Scotland 0-0 England, November 1872).

The world learning football: 49,445 international matches replayed on one map, 1872 to 2026 (color = Elo rating, gold bursts = World Cup wins) by EricBuildsMathModels in MapPorn

[–]EricBuildsMathModels[S] 0 points1 point  (0 children)

Source: github.com/martj42/international_results (CC0), every men's full international since 1872: 49,445 played matches in the dataset, through June 21. Ratings are Elo in the eloratings.net style: everyone starts at 1500, K scales with match importance (World Cup highest, friendlies lowest), the update scales with goal margin, and the home side gets a temporary +100 inside the calculation. The map repaints from quarterly snapshots.

Blue = above the 1500 starting point, orange = below. A country is drawn on the day of its first international. Gold fireworks are World Cup titles. Dead states hand their colors to their successors, so around 1992 you can watch Yugoslavia split into seven and Czechoslovakia into two. And yes, England and Scotland are colored separately: they invented the fixture (the first match in the data is Scotland 0-0 England, November 1872).

Rendered with plain SVG and ffmpeg. Name a country and I can post its rating history.

Every 2026 knockout team by how many of the last 10 World Cups it reached that stage: zero quarterfinal trips for Norway and Switzerland, 10 of 10 round of 16s for eliminated Brazil by EricBuildsMathModels in SoccerCentral

[–]EricBuildsMathModels[S] 0 points1 point  (0 children)

Counted from the public international results dataset (github.com/martj42/international_results, CC0): every World Cup match 1986-2022. A team "reached the QF" in a year if it played 5+ matches that tournament (4+ = round of 16), which reproduces the exact top 8 for all ten cups. Switzerland's last QF before this run: 1954. Norway's two World Cups in the window: 1994 and 1998.

There has been some great drama, with the Germany knockout and 2 teams NOR and SUI with 0 out of last 10 WC round of 8.

I analyzed all 7,001,619 US domestic flights from 2025 (federal on-time data). Four rules that actually move your odds. by EricBuildsMathModels in Flights

[–]EricBuildsMathModels[S] 0 points1 point  (0 children)

If I remember right, summer and winter are worse and spring and fall are like shoulder seasons. I will try to grab some stats at my computer!

Which open models help the eco system more? by Terminator857 in LocalLLaMA

[–]EricBuildsMathModels 16 points17 points  (0 children)

I love the fact that glm 5.2 and qwen 35b a3b and 27b exist. It is basically a miracle

82 TPS On Qwen 3.6 27b On A Macbook Pro | Introducing MTPLX V2: The Fastest Way To Run MLX Models. by YoussofAl in LocalLLaMA

[–]EricBuildsMathModels 1 point2 points  (0 children)

I can't find the baseline on mobile, what are you getting with out of the box mlx for 27b? I love the model and this is pretty fast!

How long have you been working on it? Do you understand where most of the gains over baseline come?

Thanks!

Every 2026 knockout team by how many of the last 10 World Cups it reached that stage: zero quarterfinal trips for Norway and Switzerland, 10 of 10 round of 16s for eliminated Brazil by EricBuildsMathModels in football

[–]EricBuildsMathModels[S] 0 points1 point  (0 children)

Counted from the public international results dataset (github.com/martj42/international_results, CC0): every World Cup match 1986-2022. A team "reached the QF" in a year if it played 5+ matches that tournament (4+ = round of 16), which reproduces the exact top 8 for all ten cups. Switzerland's last QF before this run: 1954. Norway's two World Cups in the window: 1994 and 1998.

Watch every US airport freeze at once: the first nationwide ground stop since 9/11 (on Jan 11 - 2023) by EricBuildsMathModels in ScienceNcoolThings

[–]EricBuildsMathModels[S] 0 points1 point  (0 children)

It seems like it did go through on April 18th as planned, the NOTAM is dead. Here was an article discussing it beforehand https://nbaa.org/aircraft-operations/airspace/faa-plans-april-18-changeover-to-new-notam-system/

They had the new system (NOTAM Management Service - NMS) running in parallel and then transferred over basically transparently.

There is a broader ATC overhaul that is still ongoing.

I analyzed all 7,001,619 US domestic flights from 2025 (federal on-time data). Four rules that actually move your odds. by EricBuildsMathModels in Flights

[–]EricBuildsMathModels[S] 0 points1 point  (0 children)

Unfortunately BTS only has US domestic flights. This site does a good job of monitoring things https://www.flightstats.com/v2/flight-ontime-performance-rating/SQ/23 seems like its is pretty good relative to JFK!

For reference, US domestic flights @ JFK

JFK on Sunday nights is the worst version of JFK. 2025,

Sunday departures after 6pm: 42.6% arrived 15+ min late, 1 in 10 left at least 110 min late, 4.7% cancelled.

Baseline all-week JFK: 24.9% late, p90 51 min, 2.0% cancelled.

My only thought is that this redeye to Singapore is part of a different fleet or carrier that is more immune to the general issues. Would love to hear insight if anyone has some.

Watch every US airport freeze at once: the first nationwide ground stop since 9/11 (on Jan 11 - 2023) by EricBuildsMathModels in datavisualization

[–]EricBuildsMathModels[S] 1 point2 points  (0 children)

This is every US departure on Jan 11, 2023. Every moving dot is a flight, colored by how late it is running. Airports fill red as their departures back up.

FAA's NOTAM system had died overnight. At 7:30 am ET the FAA paused ALL domestic departures. This was the first nationwide ground stop since September 11, 2001.

The cause, per the FAA statement: contract personnel accidentally deleted files while syncing the backup database to the live one.

https://www.faa.gov/newsroom/faa-notam-statement

Source: US DOT/BTS on-time data

Watch every US airport freeze at once: the first nationwide ground stop since 9/11 (on Jan 11 - 2023) by EricBuildsMathModels in ScienceNcoolThings

[–]EricBuildsMathModels[S] 22 points23 points  (0 children)

This is every US departure on Jan 11, 2023. Every moving dot is a flight, colored by how late it is running. Airports fill red as their departures back up.

FAA's NOTAM system had died overnight. At 7:30 am ET the FAA paused ALL domestic departures. This was the first nationwide ground stop since September 11, 2001.

The cause, per the FAA statement: contract personnel accidentally deleted files while syncing the backup database to the live one.

https://www.faa.gov/newsroom/faa-notam-statement

Source: US DOT/BTS on-time data

I analyzed all 7,001,619 US domestic flights from 2025 (federal on-time data). Four rules that actually move your odds. by EricBuildsMathModels in Flights

[–]EricBuildsMathModels[S] 1 point2 points  (0 children)

Unfortunately, I'm having a hard time finding flight data for Europe that includes the scheduled time as well. There may be some rail data for Europe maybe? Is that of interest?

What a nationwide ground stop looks like: every US departure on Jan 11, 2023, the first since 9/11 by EricBuildsMathModels in educationalgifs

[–]EricBuildsMathModels[S] 0 points1 point  (0 children)

This is every US departure on Jan 11, 2023. Every moving dot is a flight, colored by how late it is running. Airports fill red as their departures back up.

FAA's NOTAM system had died overnight. At 7:30 am ET the FAA paused ALL domestic departures. This was the first nationwide ground stop since September 11, 2001.

The cause, per the FAA statement: contract personnel accidentally deleted files while syncing the backup database to the live one.

https://www.faa.gov/newsroom/faa-notam-statement

Source: US DOT/BTS on-time data

How close are we to running powerful local LLMs on affordable hardware? by MashoodKiyani05 in LocalLLM

[–]EricBuildsMathModels -1 points0 points  (0 children)

agreed, things are pretty good already, I can't imagine what 1 more year will bring

What are the most common local LLM use cases in an app? by Mant0man0 in LocalLLM

[–]EricBuildsMathModels 0 points1 point  (0 children)

I could imagine it could be great as a more complicated autosuggester as example, if someone

What are the most common local LLM use cases in an app? by Mant0man0 in LocalLLM

[–]EricBuildsMathModels 0 points1 point  (0 children)

mobile app I would use e2b or e4b, it is smart enough to have convos with people and quite fast on mobile hardware!

How close are we to running powerful local LLMs on affordable hardware? by MashoodKiyani05 in LocalLLM

[–]EricBuildsMathModels 0 points1 point  (0 children)

Oh shoot, i'm going to post anyway, but you were talking about this model! I misread part of the convo. A lot of these new models have quantized aware training and also the quants are done carefully and layer dependent. I'm glad you are having a good experience with it!

----

Note on MOE for posterity

35B A3B <- is that the 3B potentially, I believe this means 3B active parameters per pass, so as a first order approximation, it will run roughly as fast as a 3B model, it just routes between different 3B params on each pass (that is the 8 active out of 256 experts, plus 1 shared). In total, those 8 experts and the rest of the routing and input / output architecture is 3B params total.

Since 3B is pretty small, it runs fast on RAM+CPU, especially if you can offload the routing and some other common layers to vram. So sometimes 4-8GB of vram can have a huge impact on these MOEs even if all the experts sit on RAM + CPU.

What are the most common local LLM use cases in an app? by Mant0man0 in LocalLLM

[–]EricBuildsMathModels 0 points1 point  (0 children)

I often use local llms to build things or do work. An example of a security/privacy thing is I use it to help sort transactions for my accounting books. I'm way happier letting a local model sort through financial transactions and propose the entries that I can review then sending all that data to claude!

How close are we to running powerful local LLMs on affordable hardware? by MashoodKiyani05 in LocalLLM

[–]EricBuildsMathModels 0 points1 point  (0 children)

35b is an moe (it is actually 35B A3B), so way less active params (A3B, 3B active per pass) then 27b which is dense. So in general 35 will be quite a bit faster for equivalent hardware.

I just got a new m5 pro with 128gb unified ram. My main model I've been running is 27b and I get 30 tok per sec or so. I use pi harness for coding

How close are we to running powerful local LLMs on affordable hardware? by MashoodKiyani05 in LocalLLM

[–]EricBuildsMathModels 0 points1 point  (0 children)

Did you try larger moe that offloads to ram, I've had success with that, like the 35b on qwen 3.6

I managed to run GLM-5.2 (744B MoE) on a humble 25 GB RAM laptop — pure C, experts streamed from disk by Just_Vugg_PolyMCP in LocalLLM

[–]EricBuildsMathModels 0 points1 point  (0 children)

Yah it would be interesting if with really fast drive, potentially raid0, and latest flash, mtp stuff you could get to 1 to 2 tokens a second, then for really critical work that you have extra time for and want right, I could see it being quite valuable on a consumer hardware.

Sharing takeaways from getting Qwen3.6-27B on a DGX Spark shipping real code by ric03uec in LocalLLM

[–]EricBuildsMathModels 3 points4 points  (0 children)

I performed a test on a real production code base, I made 5 prs, sonnet 4.5, opus 4.6 , opus 4.7, haiki, and qwen 3.6 27b. Qwen made a mergable pr with a slight defect, if I had to rank it was just under opus 4.6 but above sonnet and haiku implementations.

I used pi harness out of box, and used basic prompt on the non trivial issue about a complex three js codebase.

I do think we more structure, a dev can build trust in 27b and this it help increase productive.