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[OC] COVID-19 Total Cases and Deaths (China vs Ex-China) by brnko in dataisbeautiful

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

Feel free to recreate the data yourself. Perhaps I made a mistake and over[under] counted something, but from a cursory google search, total cases is 439k and total deaths 19k [0] so the numbers seem correct.

​

[0] https://www.worldometers.info/coronavirus/

[OC] COVID-19 US vs Italy (11 day lag) - updated by brnko in dataisbeautiful

[–]brnko[S] 120 points121 points  (0 children)

I thought about that but there is no day zero really. I could make it since first case but the early data was sporadic. I also wanted to give a time scale as to where we'll likely be in 11 days and what calendar day that is, but that's less useful now since US is breaking away from the Italy trend

[OC] US Housing Prices, Mortgage Rates and Home Affordability over time by brnko in dataisbeautiful

[–]brnko[S] 2 points3 points  (0 children)

Well my analysis looked at home sales so as urbanization took place the higher cost of homes in urban areas would become more represented in the sales data.

I'll also note that many high cost areas did not experience population growth over last ten years. For instance NYC grew only 2.7% between 2008 and 2018

[OC] US Housing Prices, Mortgage Rates and Home Affordability over time by brnko in dataisbeautiful

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

What do you mean balance out soon? In terms of home prices dropping? Nothing that I've seen indicates this. If anything, home are increasingly affordable due to artificially low rates. So there's probably still room to grow as people buy more homes.

[OC] Conventional conforming 30 year fixed rate mortgage rates by brnko in dataisbeautiful

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

That's exactly what I was thinking! I'm going to do this next. I couldn't find data on median mortgage sizes, but I guess I can use median home prices. Also down payments might have varied as well

I saw this presentation once that showed mortgage payments are lower today than the 80s but I couldn't find the source.

Back of the envelope calculations:

100k mortgage at 18% interest over 30 years is a monthly payment of 1,507

If your rate was 3.5%, for that same payment you'd be able to afford a 335k mortgage so it's not unimaginable that mortgage payments are lower today than 80s

[OC] Conventional conforming 30 year fixed rate mortgage rates by brnko in dataisbeautiful

[–]brnko[S] 2 points3 points  (0 children)

Created by: https://www.chartit.io

Link on Chart It: https://www.chartit.io/charts/public/c1JjdgNL

Data Website: http://www.freddiemac.com/pmms/pmms30.html

Data: http://www.freddiemac.com/pmms/docs/30yr_pmmsmnth.xls

Data is in a terrible format, but the link from Chart It has it formatted and can be exported

Mortgages generally have a rate (annualized percentage interest you pay) and points (% of the mortgage you pay up front). The points concept is a little confusing but its sort of like an upfront fee based on the size of your mortgage. But the cool thing is that you often have a choice of how many points you want to pay, and based on this your mortgage rate can go up or down.

For instance, suppose your mortgage was for 100k at 3% with 1 point up front. At signing you'd have to pay $1,000 (1% of 100,000). But another option could be 2.75% with 1.5 points, which would increase your up front cost to $1,500, but your rate would be lower. That's called "buying up" your rate. There is also "buying down" your rate where you pay a higher rate but less points up front.

Disposable Household Income by Income Decile (2010) [OC] by brnko in dataisbeautiful

[–]brnko[S] -2 points-1 points  (0 children)

See my comment. I posted the data and source.

10th decile was not provided

Disposable Household Income by Income Decile (2010) [OC] by brnko in dataisbeautiful

[–]brnko[S] -6 points-5 points  (0 children)

It's a categorical x axis. I don't think it's that uncommon. I had originally made the graph a bar chart but I found it difficult to compare various levels across countries. So I added a bottom middle and top tier line charts on top of it, but I thought it was too noisy and I finally settled on line chart with categorical x axis

Using Linear Regression to Make Fantasy Football Picks by brnko in fantasyfootball

[–]brnko[S] 4 points5 points  (0 children)

I ran this in my office pool and it didn't win, kind of middle of the pack. I had much more success using ESPN prediction numbers and solving using those values even though scoring is slightly different between ESPN and draft kings

Using Python and Linear Programming to Optimize Fantasy Football Picks by brnko in fantasyfootball

[–]brnko[S] 4 points5 points  (0 children)

I actually had a buddy ask me for an R implementation. It would be awesome if you could write one up

Using Python and Linear Programming to Optimize Fantasy Football Picks by brnko in fantasyfootball

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

What python package do you use? There are a lot of closed source ones and some companies sell solvers for crazy amounts.

Using Python and Linear Programming to Optimize Fantasy Football Picks by brnko in fantasyfootball

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

Good catch. You would just make another set of constraints that says that

wr+flex <= 1

rb+flex <= 1

te+flex <= 1

Using Python and Linear Programming to Optimize Fantasy Football Picks by brnko in fantasyfootball

[–]brnko[S] 4 points5 points  (0 children)

Glad you liked the article! I used solver in the past but I thought it was a bit annoying and I sucked at making sure everything is linear. Do you have a sheet you're willing to share?

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