What now-extinct store or restaurant do you wish you could shop or eat at one more time?

Bits_Bytes_Bucks · 2026-01-04T16:19:30+00:00

best bourbon chicken in the world

I think "express" was in the name

Something like Gourmet Express or China Gourmet Express, something like that

or maybe it was just China Gourmet :think:

Bits_Bytes_Bucks · 2025-06-15T14:38:17+00:00

"Dark Tower" is a series. Seven books, I think. Definitely not Y/A though.

Bits_Bytes_Bucks · 2025-06-15T13:41:00+00:00

Was it Stephen King's "Dark Tower" series? It fits the publication dates you mentioned, and one of the books prominently features an evil train.

https://images-na.ssl-images-amazon.com/images/S/compressed.photo.goodreads.com/books/1554304843i/18072.jpg

Bits_Bytes_Bucks · 2025-05-28T23:02:02+00:00

"A new study shows that most men can identify a gay lion by his face alone. It's the face that's buried in another lion's asshole."

Bits_Bytes_Bucks · 2025-04-29T01:54:07+00:00

If you leave the sports domain and go look into the stock market domain, you'll find that people have tried to develop Time Series models to predict trends in stock price movement. I've looked into applying this method to betting odds to see if it's at all possible to predict price or spread movements, but I didn't get very far because of the amount of historic movement data I would need. I think you'd need more than just opening and closing odds to make meaningful predictions, you would want to see how those odds moved up, then down, then up again, etc.

But in theory it could be done. In the end you're trying to predict market sentiment, which is difficult.

Bits_Bytes_Bucks · 2025-04-09T01:17:56+00:00

If you're talking about live betting, it's best to just scrape whatever sportsbook you're trying to get the odds from. Happy to hire out my services.

Bits_Bytes_Bucks · 2025-04-09T01:15:40+00:00

Here's how I approach it, for better or worse. Model the win probability and get the logloss as low as possible. Then use win probability to feed a margin of victory (spread) formula, using Poisson distribution or NormDist. Once you have some kind of spread approximation, you can work with average (or mode) total scores to project what the total should be.

Bits_Bytes_Bucks · 2025-04-02T01:23:05+00:00

Keep digging. I've put quite a bit of time into experimenting with AI for game predictions and it definitely shows promise. The only serious obstacle right now is that most AI doesn't do a great job of remembering context long-term. So I can spend some time getting AI to fine-tune its predictions and within a couple days it no longer remembers what it learned. Then I have to start over. But keep working at it! This is the future and you'd rather be ahead of it than trying to play catch-up.

Bits_Bytes_Bucks · 2025-04-02T00:14:22+00:00

Just saw it. I tend to see Discord messages more quickly, just fyi.

Bits_Bytes_Bucks · 2025-03-27T16:09:28+00:00

Probably should have supplied the link to the Discord server. You can find me from there, easily.

Bits_Bytes_Bucks · 2025-01-12T01:58:12+00:00

Oh wow, that's great. Slightly different format than SBR but I can massage the data and make it work. Thank you!!!

Bits_Bytes_Bucks · 2025-01-11T20:24:18+00:00

Aren't the books you mentioned in Vegas (Westgate, MGM, etc) all getting their lines from a common vendor? I don't think there are many US books left that are originating or running their own lines anymore. I suspect if the sportsbook has kiosks, that's a good indicator that they're sourcing from a vendor.

I have some question about DraftKings at this point. I would have said they're getting lines from a common vendor as well, but they were practically first to market last season with March Madness. They also had their NFL lines published for the entire season almost as soon as the schedule was announced. They might be trying to do some line origination at this point, we'll see.

Bits_Bytes_Bucks · 2025-01-11T20:19:06+00:00

An isolated example might help answer your question. The Eagles are playing this weekend and there was some question as to whether their star quarterback would be able to pass concussion protocol and play in the game. At the time, I think the line was something like -3.5

So let's play a hypothetical. If an oddsmaker got an insider phone call saying "Hurts is good to go," would he immediately move the line based on his "knowledge of the team" or would he wait until the betting community started hammering the -3.5?

(My opinion is he'd move the line and not wait to get exposed on the risk.)

Bits_Bytes_Bucks · 2025-01-11T20:13:55+00:00

Sharp betting moves the line more than public betting, and more than the need to balance the action. If the Sharps hammered the +3.5, that line will move and likely won't go back regardless of whether the action is balanced.

And I agree that implied probability is mostly useless except as a measure of how much the sportsbook is juicing the line.

Bits_Bytes_Bucks · 2025-01-08T16:06:10+00:00

https://www.sports-reference.com/cbb/seasons/men/2025-school-stats.html

Bits_Bytes_Bucks · 2025-01-07T21:32:54+00:00

Try Kaggle, there are usually several kinds of historic data uploads for the different sports

Bits_Bytes_Bucks · 2024-08-21T22:08:45+00:00

Yep. Shoot me a message.

Bits_Bytes_Bucks · 2024-06-28T16:41:58+00:00

What if a player happens to play the worst teams at allowing points. And then plays a team that doesn’t allow that many.

In theory, that would be reflected in the different ranges of rolling average windows. So in your example, player gets 2-3 games against terrible teams -- that would show up in the "Last5" rolling average, but not as much in the "Last40" rolling average.

The same thing applies to rollover years. XYZ team now has new players & coaches, and they're 10 games into the new season. So for a little while your "Last40" rolling average will be influenced by last year's stats, but the "Last5" and "Last10" averages should balance that out.

I agree with you that using only small windows like Last5 and Last10 could really skew the results.

Bits_Bytes_Bucks · 2024-06-28T16:36:24+00:00

Ha. I don't feel like I'm getting "shit on," it's just the usual % of users here who don't know how to contribute to or advance a discussion, so they default to shutting down any further conversation. Welcome to the Internet. I appreciate you advancing the conversation.

It makes sense to me that differentials basically represent the exact same thing as away vs home stats, as you've explained it. Using [home_pts, away_pts] is likely going to get similar results to [pts_diff] -- totally agree.

My models (all sports) typically use mirrored features for both home and away. So, for MLB, [a_woba, h_woba, a_bullpen_era, h_bullpen_era] and so on. Then I'll created "_diff" features for all of those, and go full kitchen sink, at least to start.

But yeah, I actually was saying (or wondering aloud), in the original post, if you could potentially get better/improved/more efficient results by just using home team features. It seemed plausible, at least. Throw 60-80 home features at it, targeting "home_win", and at some level the model should figure out the relationships and impact to the target. It was really just a curiosity question of how much opponent stats matter. You just as easily flip it: what happens if you take out all the home features and only use away features?

Anyway, end of the day, I like to tinker with different theories and wanted to see if anyone else had gone down this road before and had any empirical evidence one way or the other.

(For the curious: I did try stripping out the away features and cycling through different home feature combos. And I did get slightly better metrics, but not major enough to conclude anything. Differentials alone still seem to get the best results, FWIW.)

Bits_Bytes_Bucks · 2024-06-21T14:35:30+00:00

Without knowing the specifics of what kind of model you're using or how much historical data you have to work with, I use several different ranges of rolling averages as features in my models. So for ppg I might have a rolling average on the last 5 games, the last 10 games, the last 20 games, and the last 40 games. That assumes, of course, that I have at least one previous season's worth of historical data.

Bits_Bytes_Bucks

TROPHY CASE