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.
I’ve scouted NBA prospects on my own for 12+ years, and this summer I turned it into a site: The Draft Stock Exchange (thedraftstockexchange.com).
The idea is that every prospect is a stock. Each one gets a ticker, an IPO price, and a prospectus with the bull case, the bear case, and a comparable. The board is built like a terminal, with film, advanced stats, and valuation all in one place.
What’s on it
Class boards: the 2026 class is the main event, and prior classes go back to 2013. Each class has its own board.
Two prices per player on completed classes: a frozen pre-draft “IPO price” built from college and international production, and a post-rookie-year price based on what they actually did in the NBA. You can see who the market got right and who it got wrong.
Analyst coverage: each player shows where the public boards ranked him next to my composite, so you can see who’s a bull or bear versus the consensus.
Compare tool: put 2 to 4 players side by side from any class, including each player’s own first three NBA seasons lined up.
One-Universe Rankings: every player from 2013 to 2025 ranked on one scale by current production.
Film Lab: clips tied to the players.
No paywall, no ads, no signup. I’m not selling anything. I just love this stuff and want to show off the 2026 class in a format I haven’t seen done this way.
I know the “scouting as a stock market” concept has been done in spots. What I haven’t seen is the full terminal: film, stats, methodology, and valuation in one place, across this many classes.
I’d love honest feedback:
Which prices look wrong to you?
What would you want on a prospect page that isn’t there?
Which stats do you trust for translating college production to the NBA?
It's corny but i say all the time because its true... "time is the most valuable resource, and i sincerly appreciate you spending some of yours with me."
I was looking for a fun way to test out random historical draft scenarios and couldn't quite find a lightweight tool that fit what I wanted. So, being a developer and a massive NBA geek, I coded one up over the weekend.
You can check it out here: https://nbarandomdraft.com/ (It’s 100% free, no annoying paywalls or forced sign-ups, just wanted to share with the community).
I’d love to hear your thoughts or ideas for what stats or features I should add next. Hope you guys enjoy playing with it!
Hey everyone, I wanted to share a project I’ve been working on called PointForward.ai
I created it after being frustrated with how awful the coverage of the league had become. Especially when I’d see how insightful the coverage of other sports was, I felt like we needed a platform that could truly educate anybody that has a passion for the sport on what goes into actually assessing wins and losses, contracts, how a player fits on a team, et.
It’s a basketball analytics platform trained on ~80 years of player, team, league, play by play and injury data.
It features a general NBA agent + models on things like injury risk, contradict valuation, shot making, and have recently added fun new pick em style and fantasy games.
Also working on adding the ability to talk to it from anywhere (WhatsApp, Discord, Reddit etc)
Hey everyone! I built a free Chrome extension called Sideline to make checking sports scores easier while browsing. It lets you check live scores, team stats, and player box scores without opening another tab. Hope it helps!
Is there any place that I could get lineup and PBP data to calculate some RAPM and how a player changes team rounding/shooting? just trying to learn more about NBA analytics. Thanks!
After a 2 year break the NBA Almanac is back with the most comprehensive and in-depth preview to the new NBA season. Whether you are a casual fan or a NBA nerd, the NBA Almanac is still the most complete and essential guide you will find for the upcoming NBA season. Carefully curated and compiled over the course of the past 12 months to provide readers with an easy-to-read resource the Almanac features:
Team previews for all 30 NBA teams including team and player stats, efficiency landscapes, advanced player impact metrics, shooting charts, offensive blueprints and more.
A detailed recap of the 2025-2026 NBA season - regular season standings, NBA Cup, play-in tournament, NBA playoffs, NBA Finals, award winners, league leaders and more.
Comprehensive offseason recaps for all 30 teams - every draft pick, free agency signing, contract extension and trade that took place this summer is included in this book. Even the final transactions which took place on the eve of the season are included. This guide will give you the most accurate and up-to-date rosters for the start of the 2026-2027 NBA season.
DunkSim lets you simulate any NBA game with your own insights. Adjust the inputs, run the matchup possession by possession thousands of times, and see how often each team wins: win probability, projected score, the full range of outcomes, and every player's projected line.
Bring your own read of the game. Before you run, you can:
• Adjust each team's offensive rating, defensive rating, and pace
• Tune any player's scoring, playmaking, efficiency, perimeter defense, interior defense, rebounding, and usage
• Sit a player out for an injury or a rest night, or bring one back
• Save your adjustments as a named model and reuse it on future games
Your inputs are layered on top of the engine's own baseline, so you can see exactly how much your insight moves the win probability.
In this walkthrough: pick tonight's game, set the run count, adjust a team and sit a player out, run the simulation, read the projection detail, and save your call as a prediction.
The engine's accuracy is public: on a replay of all 1,230 games of the 2025-26 regular season it named the correct winner 69.3% of the time, against 67.0% for simply choosing the team with the better record. Method and limits:
DunkSim is a simulation and prediction game for entertainment. There is no real-money wagering of any kind. For users 18 and over. DunkSim is not affiliated with or endorsed by the NBA or any NBA team.
I play in a Yahoo salary cap league, and the draft room's projected salaries always drove me nuts. They don't change with your budget or categories, and they ignore the room. Once Joker and Wemby go for $15 over, everyone left gets cheaper, but most people can’t do that type of math on a 30-second clock.
-Enter your budget, teams, roster spots and categories, and every player gets a dollar value. The values add up to exactly your league's total money.
-Log each sale (player, price, team) and the board re-prices instantly, showing the room's inflation.
-Bid to is the most you should pay: the live price, adjusted for the categories your roster is short in, and capped at what you can actually bid.
-It shows every team's money left and max bid, so you can see who can still afford a player.
It's free, there's no sign-up, and your draft stays in your browser. It runs best in a second window next to the draft room. Values come from 2025-26 per-game stats (9-cat z-scores over the players who actually get drafted). If you have your own projections, you can upload a CSV.
Limits: you log picks by hand for now. Yahoo sync is built but waiting on Yahoo's API approval. Rookies show up without values, since there are no NBA stats to price them on. There's also no positional scarcity yet.
If you use it in your draft, I'd love to hear what broke or felt off. There's a feedback link at the bottom of the page.
I’m in the process of creating a website which will have a predictive model and things like a what if Luka plays on the 2012 grizzlies and the model would predict their wins and how far they might expect to go in the playoffs. Or you could drop a player from any era into today's NBA environment and it adjusts their stats based on modern pace, three point volume, and defensive rules. Any other ideas you guys might have that you want to see on a website? If you woudln't mind filling out this form too i'd really appreciate it! https://docs.google.com/forms/d/e/1FAIpQLSfrcUUi0n_-93C4A8N5WoXYprtUa4SV20E6XLxbkrp2WouoUg/viewform?usp=publish-editor
With the NBA preseason underway and the regular season around the corner, I’d like to introduce BoxScore, a Letterboxd-inspired site where you can log, rate and review NBA games, current or classic.
Features:
Recent NBA games with box scores, shot charts and highlights from the NBA’s YouTube channel
Ratings and reviews
Game search
G.O.A.T Pyramid
Custom lists
Matchups: head-to-head player comparisons
BoxScore is a fan-made portfolio project, and I’m sharing it to build a small community around it. Older games load slowly the first time someone opens them, then instantly for everyone after, so every classic game you look up and log helps make the site faster for the next user.
Everyone says Luka’s defense is bad, so I pulled the NBA.com tracking data (2025-26, 279 players with 1,000+ minutes) to see whether the numbers agree.
TL;DR: The eye test is mostly right. Luka covers the 2nd-least ground on defense in the NBA, faces the most rim shots of any guard, and is the most-isolated non-center, even though he rarely guards the other team’s best scorer.
Where the eye test is right:
He barely moves. 1.63 km covered on defense per 36 minutes, 2nd least in the league (only KD covers less - he’s been in the league since 2007). A typical player covers 2.01 km.
Teams get to the rim on him. 4.5 shots a game at the rim with Luka as the closest defender, the most of any guard (typical guard: 2.3). They make 72% of them, when 64% would be expected from those same shooters. For comparison: LeBron 64%, Edwards 62%, SGA 56%.
Teams target him on purpose. He gets isolated 1.73 times a game (5th in the NBA, 1st Among non centers), even though only 10% of his defense is spent on the other team’s best scorer. Dort, Daniels and McDaniels each spend 25–30% on the best scorer and get isolated about half as often.
It got worse over time. As a rookie he was the 30th slowest defender. and this season he’s 2nd.
Stat (2025-26)
Luka
Typical guard
Elite defender*
Ground covered on D (km per 36 min)
1.63
2.01
2.22 (Caruso)
Rim shots faced per game
4.5
2.3
—
Opponents' FG% at the rim
72% (exp. 64%)
—
56% (McDaniels)
Isolated per game
1.73
0.82
0.53 (Dort)
% of matchups on opponent's best scorer
10%
12%
30% (Dort)
*Typical = league median. Expected FG% = what those shooters usually make.
Where it’s not that simple:
When teams isolate him, he allows 0.95 points per play, about league average. That stat only counts the attacker’s own shot, though. If Luka gets beaten and the ball is kicked out, the basket goes on someone else’s record.
He’s legitimately good at a few things: 3rd among guards in defensive rebounding, 7th best at limiting threes (Again a lot of other factos included here), and among the best against pick-and-roll ball handlers (having a big body usually helps).
My take: smart hands, strong body, slow feet, and teams know it.
Curious what you think. Is there a stat I’m missing that would change the picture?
(I make these NBA data breakdowns regularly, more on IG, handle: weilbros)
I've been building a free college basketball world on the BYTHERIM site, mainly for people who play MyNBA and want college prospects with some history behind them.
It runs on its own. Each week the season moves forward: players develop, rotations change, and the stats get dealt game by game. Then comes recruiting, the transfer portal, and a draft where prospects enter, withdraw, or go pro, including international players. You don't have to make any choices. You just watch the story unfold, and the prospects you follow have real seasons behind them.
The stats follow Barttorvik's BPM, so the numbers should look familiar if you already use advanced stats for your MyNBA rosters.
If you check it out, I'd love to hear what you think. There's a rating button in the footer of the RP pages, and I read every note.
NBA Rewind lets you pick a famous playoff moment (Ray Allen’s corner three, Kawhi’s four-bouncer, the Block), swap any player from any season since 1996–97 onto the floor, and see how often history changes.
Every moment starts from its real game state, rebuilt from official play-by-play: score, clock, possession, and the ten players on the floor. With no swap, you watch the real ending replayed on a top-down court. With a swap, the engine plays the moment out thousands of times in your browser, through overtime, and compares the result to the real lineup.
How it’s built:
Python pipeline pulls every player-season and the play-by-play for each moment from stats.nba.com
Possession-level Monte Carlo engine in TypeScript, running in a Web Worker
Player profiles blend real playoff stats with a regular-season projection, weighted by sample size
Next.js static export on Cloudflare Pages, so there’s no backend
I've spent the past couple of weeks working on this personal project of mine. I built a skill wheel that grades 54 different aspects of basketball. I tried breaking down each big skill (such playmaking) into smaller skills and use that to sort of evaluate a player as a whole. Now, of course the data available to the public is limited so a lot of niche skills are pretty hard to quantify but I think I got a sort of good estimate of the skills. If you guys have any ideas for skill grading/skills to add or anything, just let me know I am very open to feedback.
I can't really explain each grade because it will be too long, but if you have any questions, you can ask me and I'll respond but I will explain certain ones.
Three Pointers
For Three Point grading I spilt them up into Catch and Shoot 3s (0 dribbles), Quick 3s (1-2 dribbles) and Off Dribble 3s (3+ dribbles), Deep 3s (27ft+). For the whole project I used NBA play by play data a LOT so that data comes in lines like this MISS Herro 27' 3PT Step Back Jump Shot,Missed Shot,Step Back Jump shot,1,3,260,0022501055,2026-03-25,2025-26
Parsing the distance for Deep 3s was easy. For the dribble 3s, the NBA API carries data for shots off certain number of dribbles, but you can also mix this with defender distance so for example there is data on each players 3-6 Dribble 3FGM/FGA with a defender 2-4 feet. So I parsed through all that and each player's attempts and FGs are separated by dribbles and defender distance. Each group also has a league-average rate, so a player's expected 3P% for lets say Catch and Shoot is just the league average 3p% on their defender distance (so for Curry if he takes way tighter catch and shoots his expected 3p% on that is skewed for the tight defender catch and shoot league average). So then a player is compared by their volume and efficiency. Each player gets some league average since if you make lets say like 15/30 Catch and Shoot 3s that is a pretty low sample over like 150/350. The volume is a per 75 basis so not a minutes thing, and then score is bascially a volume * effieciency metric that is Z scored across the league so each skill has the same scale.
Here is Curry 2025-26 for example
Catch-and-Shoot 3s 97th of 388 Att 247 3P% 37.7% x3P% 36.2% +0.3
Quick 3s 1st of 388 Att 87 FG% 48.3% xFG% 33.4% +3.1
Off-Dribble 3s 14th of 388 Att 236 3P% 40.7% x3P% 33.1% +1.1
Deep 3s 1st of 388Att 261 FG% 37.2% xFG% 31.9%+4.8
Finishing
Layups only, from the play-by-play: driving layups, finger rolls, reverse layups. Dunks, cuts, transition, alley-oops and putbacks are left out, because they're graded elsewhere. The efficiency is graded against the same position in this case.
And then again adding some of the league-average shots: volume x efficiency with volume being per 75 and then Z scored again the league.
Rim Deterrence
Rim deterrence is pretty much like a RAPM style regression, a matrix in which the opposing teams Rim Rate is taken into account. Transition shots are removed because then the best Rim Deterers would just be the best point guards that prevent turnovers not the actual best defenders. It is a RAPM regression so it adjusts for teammates but this one requires more possesions so some low usage role players might not have the grade.
A lot of these defense ones are self explanatory
Positional Versatility
This one uses the matchup API to see who he guarded 1-on-1 and then the spilt and compare the percentage
GroupListed positions
G (guard) W (wing G-F, F-G) F (forward) B(big F-C, C-F) C (center)
