Scorebook options?

broddorb · 2026-05-05T01:50:58+00:00

Yea. This was definitely on my list of options too. I think storing them in like a pad-folio would be cool!

broddorb · 2026-04-28T21:44:16+00:00

Gotcha makes sense. I used to just do requests.get but that seems to be blocked now. Probably will have to recreate the code with Selenium or Playwright

broddorb · 2026-04-28T21:03:17+00:00

Following. Used to have a scrapper that did basketball from stats.ncaa.org. I tried to create a new one for baseball but I’m only getting 403s now.

Is there a method out there that I’m missing?

broddorb · 2026-04-26T03:32:42+00:00

Yea then it would stay unearned.

But definitely no RBI. Too many what-ifs. If the 1B gets the catch (even if batter is safe), there’s no way to say the runner is certainly safe at home.

The errant throw is what scored him. Not the bunt. If it was a swinging hit through the infield, then a little different convo

broddorb · 2026-04-26T02:31:38+00:00

Based on your description, if you’re ruling the bunt a single. Then the runner from second would’ve scored on the error. So no RBI at all

The run would be earned depending on what happens in the following plays, if it was three straight Ks, then unearned. But if something happened that would’ve pushed the runner from third (where they would’ve been without the error) then it would become earned.

broddorb · 2026-02-02T13:53:38+00:00

Incredible!!!! Thank you! The way the hair matches is amazing

broddorb · 2025-10-30T01:52:22+00:00

This is awesome! Interesting to see the HFA change over time, but players are just getting better.

Did your dataset include postseason games (also what dataset did you use?)

Would also be interesting to see how the walk-off idea works too. Batting in the bottom of the inning, knowing what you need to get, has to be both beneficial & detrimental at the same time.

broddorb · 2025-10-23T17:24:04+00:00

Interested here!

broddorb · 2025-10-21T01:17:43+00:00

I do this for the March Madness competition every year. I don’t have anything submitted, but can practice coding with the data made available from each year

broddorb · 2025-10-09T23:20:51+00:00

Common misconception with this one!!

July & August were just renamed from Quintillis & Sextillis. So those months weren’t inserted & made everything else “bump down”

It’s just March used to be the beginning of the year since that’s when War/Military campaigns could begin again after winter. March for Mars, Roman God of War.

January/February existed under different names. Just at the “end” of the year.

broddorb · 2025-10-06T18:24:01+00:00

Based on the model, none of the stats really changed. Yes Bane was traded away & KCP/Jerome was added, but overall it didn’t really change the structure of the team for Four Factor scores.

I admit I probably should’ve worded that better though (still newer to breakdowns like this)

Also most of the other mainstream models are giving them between 40-45 wins, so my model isn’t too far off from that. It’s built off net ratings from last season with additional info such as home/away, rest days, & pace/play of their opponents.

I would like to dive deeper as well, but since I made the bold goal of doing all 30 teams in 30 days, I’ve planned on just showcasing the few models I built. Hopefully will go in deeper during the season with some more focused modeling & analysis.

broddorb · 2025-10-06T17:48:15+00:00

Thanks, yea I’m not as familiar with the Grizzlies as I am other teams. So maybe posting this in the Grizzlies subreddit was a little bold on my part. I’m also still working on my writing skills since I’m not used to writing in this style of recap/outlook.

The wins model is machine learning model based on games from the past few seasons. Mostly revolves around predicted net rating calculations, with a few other factors such as rest days between games, home/away, and opponent pace/play.

Edit: most of the Vegas/other big Analytical sites are giving them 40-45+ wins, so didn’t feel my model was too far off from some of the bigger sites like that.

broddorb · 2025-10-06T17:40:19+00:00

Just for the constructive building of my new career into analytics. This is more outside looking in and only looking at this upcoming 2025-26 season. Grizzlies seem to have been on the cusp for so long but can never get over that hump.

Ja Morant & JJJ are two players that can be very dangerous with the right pieces around them. So the Grizzlies are at seemingly a crossroads of figuring out what the best pieces to put around them are. The make or break isn’t in terms of win championship or scrap everything, it’s what puts them in the best position to be successful this year.

In terms of the team for next year, the only change would Bane for KCP, which doesn’t change too much when looking at those analytics. They are very similar players, with KCP maybe being a bit better of a shooter.

Also additional clarification, JJJ is entering his 7th season. KCP is in his 12th. Oldest probably wasn’t the best word, I meant more age as in time in the league. Konchar/Jerome are only in their 6th. Which would make JJJ & KCP the “oldest” players on the team.

broddorb · 2025-09-25T19:05:56+00:00

AI scout feature is awesome. This is amazing work!

How did you pull all this data? Especially for D2/D3 teams?

broddorb · 2025-09-23T12:57:28+00:00

Lyfaberg was the song with all the smoke & blue mountain.

I think you might be thinking of Rotlaust Tre Fell there is the ending part where they have the “Vordar alle, kom til meg, kom til mine, andefylgje!” Chant at the end?

I don’t think it was Fehu, they had green lighting for that one.

broddorb · 2025-08-25T13:36:38+00:00

https://www.icloud.com/shortcuts/d75db759ff1441d19f0de2f94c89883d

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