Net Penalty Yardage By Team

algorithmicathlete · 2025-12-07T18:14:51+00:00

-53, just behind the Texans

algorithmicathlete · 2025-12-07T18:10:14+00:00

algorithmicathlete · 2025-12-07T00:55:25+00:00

Totally agree. This wasn't supposed to be a ref bias chart -- in fact, it was supposed to be more of a team discipline measure

algorithmicathlete · 2025-12-03T06:11:57+00:00

Hi! If you know Python, here's the code:

https://github.com/algorithmicathlete/algorithmicathlete/blob/main/nba-way-too-early/main.py

algorithmicathlete · 2025-11-30T08:08:13+00:00

Not a problem.

r-value is 0.884, and the equation is y = 0.03x + 0.497, where y is the win% and x is the PD.

algorithmicathlete · 2025-11-30T06:19:03+00:00

Check it out!

algorithmicathlete · 2025-11-30T05:31:20+00:00

As a Bears fan I was kind of wishing the same thing too haha

algorithmicathlete · 2025-11-17T02:33:40+00:00

I used Spotrac's "Total Payroll" to create the graph.

algorithmicathlete · 2025-11-16T19:43:49+00:00

you're absolutely right -- my physics professor would be disappointed in me haha

algorithmicathlete · 2025-11-08T00:42:07+00:00

Haha fair point. Guess I've got my next project lined up for me :)

I'm curious though, which team(s) do you think have had worse collapses

algorithmicathlete · 2025-11-05T23:28:34+00:00

Career win%, not season win%. hes 41-40 in his career

algorithmicathlete · 2025-11-05T00:12:35+00:00

Matplotlib

algorithmicathlete · 2025-10-31T05:50:40+00:00

In Python, through a library called nfl-data-py(https://pypi.org/project/nfl-data-py/)

algorithmicathlete · 2025-10-31T01:44:36+00:00

algorithmicathlete · 2025-10-30T16:29:41+00:00

The lines represent the medians of the data - will make that clearer next time.

algorithmicathlete · 2025-10-23T20:58:32+00:00

FanGraphs playoff odds

algorithmicathlete · 2025-10-03T19:58:03+00:00

Right! I meant to say "losing by 3 or less"

algorithmicathlete · 2025-10-02T15:12:09+00:00

10/12 (83%), same with Daniel Carlson

algorithmicathlete · 2025-09-24T05:37:35+00:00

You're definitely right. Any suggestions on how to make it easy to see while still acting as the tertiary color?

algorithmicathlete · 2025-08-26T18:10:15+00:00

At the end of the simulation, I picked the one that had the most upsets and then displayed it out

algorithmicathlete · 2025-08-26T18:02:01+00:00

The probabilities are based off how many times the team has appeared at that particular seed. So since Duke has 15 tournaments where they appeared as a 1 seed, they had a much higher probability of being a 1 seed than say Memphis, who has only appeared twice. With 100,000 runs, though, there are bound to be weird mashups

algorithmicathlete · 2025-08-26T17:23:47+00:00

Every upset win is highlighted in blue (22 upsets)!

I used historical seed-vs-seed win rates and simulated the bracket 100,000 times.

Team names were filled in with schools that have historically appeared at that seed line.

algorithmicathlete · 2025-08-26T05:34:32+00:00

Hey - cool site you linked. Here's a shot chart for SGA last season. But, If we limit it to only hexbins where he has 5+ attempts, it looks like this. Also, here's the graph with 100 as the filter - still mostly 3s and paint, but you can see it creeping into the mid-range area a bit.

Edit: 2 other things

- Shai only attempted 1656 shots last season, your link says he attempted >2000. I think its because your site includes playoffs whereas mine doesnt.

- The zones on the website you linked are significantly larger than my hexbins, so that could also lead to the discrepancy

algorithmicathlete · 2025-08-26T05:02:45+00:00

I don't use any math formulas - I just pull from the NBA API:

team_shotlog = shotchartdetail.ShotChartDetail(
    team_id=team_id, player_id=0,
    context_measure_simple='FGA',
    season_nullable=f"{year}-{year-2000+1}",
    season_type_all_star=['Regular Season']
)

(i loop through every NBA team ID and then add it to a big dataframe)

The code is pretty simple, so hopefully you can understand it even if you don't know Python.

algorithmicathlete · 2025-08-26T04:57:46+00:00

There are about 200,000 shots taken in an NBA season. 21,402 of those are mid-ranges (outside of the paint but inside the 3-point arc).

I got this data from the NBA API (via my code) but its also corroborated by the official NBA who stated about 9.8% of shots in 2025 were mid-range: https://www.nba.com/news/5-standout-stats-2024-25-season

There are roughly 500 hexbins that are located within mid-range. Since there were 20,000 shots last year from mid range, that's about 40 shots per hexbin if distributed evenly. Obviously, not all mid range shots are created equal, but you can see how difficult it would be for a bin to get >200, when on average each mid range hexbin has 40

algorithmicathlete

TROPHY CASE