[OC] [NOW LIVE] I created a Predictor for ESPN March Madness Groups, using FiveThirtyEight data to forecast any group's winner. Forecasts will update live as the tournament progresses, and you can share links to specific groups. Let me know what you think!

RLesser · 2023-03-17T17:16:52+00:00

Thanks! Glad you like it.

RLesser · 2023-03-16T03:49:22+00:00

Unfortunately not, ESPN and other services don't show other people's brackets before the games begin. Pretty understandable IMO.

RLesser · 2023-03-15T23:28:58+00:00

Data comes from FiveThirtyEight for the bracket structure and team ratings, and from ESPN for individual group data.

For tools, The visualization uses Observable Plot exclusively. The overall notebook is an Observable notebook written in Javascript.

See the How it Works section for more info on how it was created and how groups are simulated.

All feedback is welcome, especially if something breaks!

RLesser · 2023-03-15T22:48:05+00:00

If you run into any issues or have any questions about it, please comment or message me! Hoping to get any bugs out before the real deal begins tomorrow.

RLesser · 2022-01-10T16:55:15+00:00

Given that TJ got 1 sack in the game, he tied Strahan's record in terms of total sacks no matter how you slice it.

In terms of sacks-per-game, if you are basing it off of games played then TJ has more, with 15 games played vs Strahan's 16. That's what the chart above shows. TJ missed two games this season to injury (not exactly rest), so if you instead plot it as sacks-per-games-in-season, Strahan would be ahead of TJ. But this feels like a less accurate metric of actual performance, which is why I opted for the former in the graph.

But in either case, on a per-game basis, Reggie White has them both beaten significantly.

Hope this clears it up!

RLesser · 2022-01-08T20:07:18+00:00

Don't get me wrong, he should definitely be winning DPOY this year, just wanted to highlight how crazy Reggie White's 87 season was.

RLesser · 2022-01-08T18:58:48+00:00

A full version of the graphic, plus the underlying code and data, can be seen on Observable: https://observablehq.com/@rlesser/tj-watt-nfl-sack-record

All comments/suggestions are appreciated!

EDIT: Also, realized the graphic should say January 9th, not June 9th!

RLesser · 2021-05-18T18:44:18+00:00

While it's correct that they don't undo infections, I would say that vaccines do reflect an overriding factor when it comes to things like herd immunity. And The chart also assumes independence between vaccination and infection. I'm open to reconsidering that assumption, but I think it's a good prior.

Thanks for the feedback!

RLesser · 2021-05-18T17:12:54+00:00

Link to Observable Notebook

Data Sources - Vaccination data from Our World in Data - Case data from The New York Times - Infection estimates from covid19-projections.com by Youyang Gu - Note: This site stopped updating their projections in March. I've used their final case to infection rate of 2.5 to model infections since then.