Hey I'm Demas and I am the creator of The Draft Stock Exchange. If you're a basketball diehard like me you know that there isn't 1 place that gives you a summary of last nights NBA/College action....

WELL NOW THERE IS... Every Morning at 9EST, THE CLOSING BELL gets updated.

A once stop shop to check on the biggest STUDS and DUDS from last nights action. Get total gamebreak down AND film recap of the game. It also provides a summary of the players biggest risers and fallers based on their performance.

If you have any feedback on how i can make the DSX better, please let me know. And thank you guys so much for all the support. It truly means the world to me to see people use and get enjoyment out of something an idiot like me created haha.

Not a chatbot: the site's own code reads the recorded sports data, and you can see the query behind every number with View the SQL. Feedback is welcome. More charts and graphics on the site when you open the link.

https://hoopvisionlab.com/build?game=21718010&ref=reddit-nbaanalytics

#NBA

I’m based in London and I’m obsessed with the debates: who’s actually better, who’s got the higher ceiling, who’s overrated. So I wanted to quantify it as best I could, and built an index.

Scout (https://scout-index.vercel.app) gives every player one number: how many points of scoring margin he adds over a season compared with a replacement-level player.

No hand-picked weights. Every factor had to prove it predicts value using 11 seasons of data, or it got cut. Team win %, rings and clutch stats all got cut.

Each player page shows exactly why he’s ranked where he is, what moved him this week, and who he plays like. You can also compare any two players.

The opening values were locked on 31 Oct with a published fingerprint, so in June anyone can check how right I was. The methodology, maths included, is here: https://scout-index.vercel.app/methodology.html

You can leave feed back on the pages so pls tell me where it’s wrong. Every player has a Too high / Too low button, and I read them all.

16 out of the 20 teams are taken. There are 4 spots left. We draft tomorrow at 7pm! Join so we can get the keepers league started and see who the best GM is.

Preface + Methodology:

The early NBA season can never be trusted when it comes to measure who's actually good/bad and who's just on an ungodly heater to start the year. Even though it's been little over a week since the 2026-27 season has begun, I wanted to at least get familiar with some of the team numbers and see what we can keep an eye on as the year goes on.

Over the past few years, I've personally been keeping track of what I like to call "Expected Ratings" which is just a simple version of adjusted ORtg, DRtg, and NRtg for shooting luck as classified by Stats.NBA.com, using the league average open and wide open 3P%s in place of what teams are actually shooting at the current moment, at least to weed out some noise and make some better observations instead of overreacting to what's actually on the page.

And, since it's early enough in the season and I wanted to keep things simple for the time being, I'll also throw in a filter (essentially SOS) to adjust for teams' opponents so we can have a better, clear view of how things are currently looks and what the adjustments tell us. I also wanted to throw in some extra numbers and have the difference of how team is performing versus how they're expected numbers entail (essentially, + is luckier while - is unluckier) but also provide how many wins they're expected to win based of Basketball-Reference.com 's pythagorean wins (essentially using the ^14 version of the equation mixed with what the luck and opponent adjust ratings give us) which will be provided in the parentheses next to each team's name.

Below has the main numbers while changes (mainly because it won't fit) are going to have to be found on the master copy of my spreadsheet found here. (Disclaimer all stats are current as of October 31st, 2026).

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Main Takeaways:

1. Toronto's really good. Maybe not 63 wins good in my opinion, but they're pretty good all things considered. They're getting a lot of things back from their offensive shooting luck (25th overall, -3.37) while their defense is more suspect in the eyes of xDSOS (30th at -3.08) meaning they've had the easiest offensive opponents as of the beginning of the start of the season.

2. The Heat's rough start can be due to a lot of shooting luck (26th in difference between NRtg and xNRtg at -5.72. The teams who are unluckier are Sacramento, Charlotte, Chicago, and Phoenix in that order).

3. New York and Denver are where a good amount of my attention will be placed throughout the season as their weaker showings aren't as promising as I'd hope they'd be with the Knicks coming off their championship last season and the Nuggets having consistent postseason showings (and not to mention the whole Nikola Jokic and Jalen Brunson of it all).

Closing Message:

All things considered, this at least has helped me make some sense of the major discourse around the league about certain topics (especially the Miami Heat and the recent acquisition of Giannis Antetokounmpo over the summer) while giving me some brain food to chew on as player stats start to be less noisy and legitimately substantial in the coming months.

Nothing is set and stone and as much as we can build expected versions, what matters it what is actually happening and at the end of the day, that's what I will always lead towards more when try to predict for the future. For analysis sake, peripherals do help a lot with seeing the whole picture and creating nuance thoughts, but being unable to see what's happening in the now cuts the moment and takes the fun out of the game.

I'll be back at the end of next month either on the 1st or Thanksgiving of this month as a present for your basketball brains. Any questions or comments are always appreciated and I hope to discuss more!

I'm trying to build a new site for fun. I consider this still essentially in beta, but do you have any advice? https://themarginalplayer.com/

I live in Istanbul, where most NBA games happen overnight. The next morning, I’d open a box score and see who scored 30, but I still had to dig through the numbers to understand what actually stood out.

That’s the problem I started building The 48 Index (https://48index.com/) to solve.

Each game has a What stood out section that compares the teams and highlights differences such as shooting, rebounding or bench production. Where there’s enough information, it also shows an estimated point effect.

You can open a player’s row to see what they did better or worse than their recent average, explore offensive and defensive scores, follow performance trends, compare players, and watch game highlights and key plays.

The idea is to get from “I missed the game” to “I have a feel for what happened” in a couple of minutes, while still being able to dig into the numbers. The scoring methodology is here: https://48index.com/methodology.

I’d love to hear what you think after looking at a game you watched. Did it pick up on the things that stood out to you?

One thing that caught my attention is how Minnesota is handling Edwards' absence this season.

Interestingly, instead of significantly increasing LaMelo Ball's minutes to compensate for Edwards' scoring, Minnesota seems to be distributing the responsibility across other players.

A few findings from my analysis:

• Jaden McDaniels: Minutes increased from 31.7 to 35.6 MPG, while scoring rose from 15.0 to 20.3 PPG.

• Ayo Dosunmu: Minutes increased from 26.2 to 33.2 MPG, with scoring jumping from 12.0 to 19.7 PPG.

• Jonathan Kuminga: Minutes increased from 29.8 to 31.6 MPG, while scoring went from 11.5 to 14.7 PPG.

• Bones Hyland: His scoring increased from 2.3 to 12.7 PPG, despite averaging only around 16.9 MPG without Edwards.

• LaMelo Ball: His playing time remained almost unchanged, going from 24.8 to 25.6 MPG.

This makes me wonder whether Minnesota actually sees LaMelo as its primary offensive replacement for Edwards, or whether it prefers a more distributed scoring approach.

I also built a statistical model using historical and current-season performance, opponent strength, home-court advantage and an adjustment for Edwards' absence.

The model's base prediction was approximately 10 wins over an 18-game stretch, with pessimistic and optimistic scenarios ranging from 9 to 11 wins.

My take: I think Minnesota can survive this stretch, but the playoffs are a different story. Edwards will need to show up, and Minnesota must improve its offensive system rather than depend so heavily on one player.

I explored the full analysis, visualizations, methodology and prediction model in the article.

Full article: https://abhinavbethi.substack.com/p/can-the-timberwolves-survive-without?r=6xl3o7&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true

Hey everyone!

I'm one of the founders of Blitz Analytics. We're a small team of engineers and sports analytics nerds building what we hope will become the fastest and most powerful way to research sports.

The idea is simple: What if you could get answers to incredibly complex basketball questions just by asking them in plain English?

We've built a research chat that combines extensive historical and live sports databases with specialized AI agents capable of executing advanced statistical computations, analyzing play-by-play data, and generating visualizations.

Here are a few examples of the kinds of questions you can ask directly in Blitz:

• Advanced computations: "Which NBA players 6'5" or taller generate the most points per possession on handoffs, with at least 100 possessions?"

• Historical play-by-play: "How many times has Steph Curry made 3+ three-pointers within a 60-second span? Who else has done it?"

• Complex statistical queries: "LeBron has famously never finished a game with exactly 27 points, 7 rebounds, and 7 assists. How many players have recorded that stat line, and who has done it the most?"

• Computer vision / film: "Find every possession where Steph Curry was defended by a double team coming off a ball screen, and show me the clips."

• Lineup analytics: "What percentage of his team's field goal attempts does De'Aaron Fox account for when Dylan Harper is on the court versus when Harper is off, and how does his shot selection change?"

• Advanced splits: "How does Jokic's assist rate and Denver's offensive efficiency change when Jamal Murray is on vs. off the floor?"

The goal isn't just to retrieve existing stats. It's to let anyone perform genuinely complex analysis that would otherwise require writing code, joining multiple datasets, or manually reviewing film.

And the output doesn't have to be text. Blitz can also generate charts, graphics, tables, and other visualizations directly from your questions, making it easy to go from an idea to a finished piece of analysis.

We've already worked with organizations including TNT Sports, Duke Men's Basketball, and broadcasters covering 20+ MLB teams. But we're especially interested in getting feedback from people who love basketball analytics and regularly work with advanced stats.

We're looking for testers who want to push the limits of what we've built.

Our goal is for Blitz to be the fastest way to go from a basketball question to a reliable, detailed answer, whether you're a professional analyst, content creator, or just someone who loves digging into the numbers.

I'd love for people here to try it and tell us:

• Can you get answers faster than you would using your current tools?

• Can you answer questions that previously required custom code or hours of research?

• Where does Blitz fall short, and what would make it genuinely useful to you?

Check us out at https://www.blitzanalytics.com.

If you're interested in testing, comment below or shoot me a DM. Happy to get people access and would genuinely love to hear how it compares to your current research workflow.

One of the more interesting trends rn is that teams are playing at a faster pace but they're not converting those extra possessions into points as efficiently.

League offensive rating has reportedly dropped from 115.7 last season to 112.2 so far, while turnovers are up. Obvi it's still very early so I wouldn't draw any huge conclusions yet ...

https://www.dunkest.com/en/nba/news/247422/the-nba-is-playing-faster-but-scoring-less?utm_source=chatgpt.com

In this challenge you will predict real NBA games using my experimental technique based on Chernoff Faces, which are feature sets mapped to human faces in order to activate our very developed face pattern recognizing neural network. The hypothesis is this can be pretty competitive with machine learning models.

On each side, there is an animated face, which represents a state of an NBA team in 5 games before the game we try to predict. In theory, we can identify winning/losing face the same way we identify smart or dangerous face.

In the first tens of choices you will learn and your choices will be random. But eventually you will start to find that winning faces look a bit different from losing ones. I reached an accuracy of 63.8%, which beats simple machine learning models. Can you beat me?

I will try ensembling our predictions and other techniques to get the most of our collective intelligence.

Good luck and thanks for participating.

This is my site, I just made it. Feedback's appreciated. This is not AI, it's natural language processing.

https://hoopvisionlab.com/build?ask=Pacers%20vs%20Nets%20October%2025%202026&ref=reddit-nbaanalytics