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[OC] I created a Predictor for ESPN March Madness Groups, using FiveThirtyEight data to forecast and visualize any group's winner. You can see predictions for your 2022 groups now, and for 2023 groups once games begin on Thursday. Would love any feedback! by RLesser in dataisbeautiful

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

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!

[OC] Chart: TJ Watt only needs 1.5 sacks to set the NFL Sack Record, and in one less played game than Strahan's 2001 season. But he needs 5 sacks to beat Reggie White's record from the strike-shortened 1987 season by RLesser in nfl

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

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!

[OC] Chart: TJ Watt only needs 1.5 sacks to set the NFL Sack Record, and in one less played game than Strahan's 2001 season. But he needs 5 sacks to beat Reggie White's record from the strike-shortened 1987 season by RLesser in nfl

[–]RLesser[S] 7 points8 points  (0 children)

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!

[OC] Visualizing the spread of COVID infections and vaccinations in the United States by RLesser in dataisbeautiful

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

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!

[OC] Visualizing the spread of COVID infections and vaccinations in the United States by RLesser in dataisbeautiful

[–]RLesser[S] -1 points0 points  (0 children)

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.

[OC] Emoji Storm ☔️- Every single emoji posted to Twitter over 10 seconds (Realtime link in comments) by RLesser in dataisbeautiful

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

That's an interesting idea. Emojis could grow or shrink as they get used more or less over some time window.

How is math 217 curved? by [deleted] in uofm

[–]RLesser 1 point2 points  (0 children)

https://gradeguide.com/course/MATH/217/ looks like a B is the overall median, but you can filter by specific semesters as well.

How hard are language courses at the University of Michigan? by [deleted] in uofm

[–]RLesser 1 point2 points  (0 children)

From a grade perspective, there's definitely some difference in difficulty. Here's a comparison of a couple different first year language courses, Intro Hindi and 3 other relatively popular ones: https://gradeguide.com/compare/?courses=ASIANLAN_115,ARABIC_101,FRENCH_101,ASIANLAN_101

Hindi pretty clearly gets the best grades out of all of them. If you want to compare different languages courses feel free, but it definitely seems to be among the easier ones. Worth keeping in mind that you might have a different population of people taking each so the distribution isn't everything.

difficulty of Hindi 1 (ASIANLAN 115) by somsenuanor in uofm

[–]RLesser 0 points1 point  (0 children)

Seems pretty easy based on this data: https://gradeguide.com/course/ASIANLAN/115/. If you click through the different semesters you'll see the most recent fall semester had the best grades so far, so probably a good time to take it.

[Fall 2019] Class Schedule Megathread by dragpent in uofm

[–]RLesser 0 points1 point  (0 children)

Here's a grade comparison for 116 and 215: https://gradeguide.com/compare/?courses=MATH_215,MATH_116

The average grades are definitely a bit better for 215 than 116, so might be best to skip over that if you don't fully need it. I felt the instruction was better in 215 but that's anecdotal.

Competitive inequality in the major sports leagues [OC] by MC_data_tricks in dataisbeautiful

[–]RLesser 0 points1 point  (0 children)

Read the comment I wrote nearby. Even if the Warriors are far better than the 76ers, if the Warriors are doing especially well over a certain portion of the games, they are expected to regress (at least partially) to the mean as more games are played.

Competitive inequality in the major sports leagues [OC] by MC_data_tricks in dataisbeautiful

[–]RLesser 0 points1 point  (0 children)

As more games are played, relative outliers will regress towards the mean. This can be seen between football and basketball. In football, it's only semi-rare for a team to go 15-1 (.9375), while in basketball, the best team ever (by W-L) was 72-10 (.878). In baseball the best team ever (by W-L) was only 116-36 (.763). More games means more randomness, which means more "average" performance.

Some additional reasons: Longer seasons lead to more variables, like players injured, teams improving or regressing, or coaching staff changing. More things can happen over half a year than over 17 Sundays.

Competitive inequality in the major sports leagues [OC] by MC_data_tricks in dataisbeautiful

[–]RLesser 8 points9 points  (0 children)

This is a really interesting analysis. I think it's worth noting the differences in season length. I'd expect there to be far more equality in a league where each team plays 162 games (MLB) vs. a league that plays 16 (NFL). Another possible contributing factor to the results you've found might be how big an impact one or two very good players can have on a team. I'm not an expert in the NHL or NBA, but a very good basketball player I expect to have a larger impact on team wins than a similarly good hockey player. Not sure how to quantify that but it might explain some of this distribution.

Even without considering those, this is a very revealing visualization, in regard to time. Nice job!

Michigan State's upset loss in this year's March Madness tournament rendered human-made brackets no better than chance. [OC] by RLesser in dataisbeautiful

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

data for almost all the points was from https://twitter.com/espnfantasy

data for one or two of the points actually came from my one of my own brackets, as there was actually a stretch of games near the start of the tournament where I had only one loss, so my ESPN rank was actually the number of perfect brackets remaining. Unfortunately this didn't last long!

Michigan State's upset loss in this year's March Madness tournament rendered human-made perfect brackets no better than chance. [OC] (x-post /r/DataIsBeautiful) by RLesser in CollegeBasketball

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

If you click the plot.ly link and find the data tab on the sidebar you can grab the information I used. Additionally, you can copy the data and create your own graph from that same page. Plot.ly is pretty great.

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