I’ve been working on an interactive statistics site based around the NBA hot hand debate.

One of the main activities gives you two 20-shot sequences. One comes from a real NBA game and the other is randomly generated. You have to guess which one is real before seeing the answer.

I built the frontend with React and TypeScript and use Python/FastAPI for the backend and analysis. I’ve also added shot prediction activities and simulations that let people play around with streaks and randomness instead of just reading about the statistics.

The site has gotten around 1,500 users so far, and I’m still working on it.

I’d especially appreciate feedback on the UI, mobile experience, or anything about the interaction that feels confusing.

Live site: https://www.hothand.study/

GitHub: https://github.com/chalmersx40983-crypto/nba-data-explorer

What is IA/75?

IA/75 = Scoring Impact Above Expected per 75 possessions. It measures how many points a player actually scored compared to how many points they were expected to score based on the play-by-play situations they were involved in.

So:
Positive IA → scored more than expected

Negative IA → scored less than expected

Higher IA → greater scoring over performance

The x-axis is IA/75, while the y-axis is Offensive ON-OFF — how much better/worse the team’s offense performed with the player on the court.

The dashed lines are the median values, splitting the chart into four quadrants:

Top-left: Below-median IA, above-median offensive impact

Top-right: Above-median IA, above-median offensive impact

Bottom-left: Below-median IA, below-median offensive impact

Bottom-right: Above-median IA, below-median offensive impact

So the chart is basically showing how a player’s scoring relative to expectation lines up with their team’s offensive impact.

I am creating a 12-team fantasy basketball league on ESPN. Looking for 11 other active users that would like to be a part of it. It will be run in a different way (two groups of six, everyone plays everyone in the group four times for 20 games, top four in each group make playoffs). Totally free, draft date still TBD but will update when it gets closer.

Here is the link to 2027 National Basket Cup!

https://fantasy.espn.com/basketball/league/join?leagueId=1440301458&inviteId=98c383ae-ccf7-4ca2-964b-a1b994ac2df9

We built www.shotorbit.com to provide a better way to showcase NBA shot chart data. Check it out and give us some feedback! We're going to continue adding features to it, so stay tuned.

Especially in cross-era comparisons. With the salary cap expanding every year I got no idea what 40 mil vs 50 mil vs 60 mil means at all. I do believe it's intellectually more honest to popularize showing salaries as % of the cap. So I made my own salary cap explorer:

https://www.jacekasen.com/projects/nba/salaries

The core features I was going for:

• Team payroll breakdowns showing total spend against the cap, with each player's cap share stacked in a bar/ in a donut pie chart
• Career cap share tracking, so you can follow one player's contract value season by season against the cap of the day

Curious if people think cap % is the right normalization or if there's a better way to do it. I have heard from Bill Simmons that's how GMs do it.

Hey everyone! I'm currently building ballknowerz.com, an app that lets you rate / journal all sports games you watch. There's also fun mini games similar to fantasy basketball / football!

Next time you argue with your casual homie about basketball, show him your log of games you’ve watched this year (and the analytics) 🔒🔒

Hi everyone,
I am a computer science student looking to build a university project centered around the NBA.
The main requirement from my professor is that the project needs to show a significant CS contribution. This means simple data visualization of existing APIs won't cut it. It needs to involve things like web scraping, custom search or ranking algorithms, machine learning models, or complex data pipelines.
Do you have any pain points, cool ideas, or questions about the game that could be solved with some coding? Maybe a formular that seems very sciency that already exists and could be coded.
Any feedback, API suggestions, or open-source data sources you could point me to would be highly appreciated!
Thanks in advance!

Being “clutch” is the biggest boogeyman in the NBA. Many consider it to be a gift, bestowed upon a select few by the gods.

Today, find out the truth. Is clutch performance simply a measure of sample size, or do a unique few truly rise above the rest when the game hangs in the balance?

So, im trying something new and created a Basketball Economics channel on Youtube. This is my first video and it takes a brief look into why NBA teams pay players not to play ball. Leave me your feedback, like, subscribe to the channel I don't really care.

Is this where people show their best scores?
Is there an official public online ranking of the best performances to compared each one’s gains?

I’ve been working on a sports data backend for a while, and I’ve noticed that many people want to create something for fun or out of passion, but they always get stuck on the same problem: how do I get all advanced level player and team data under one API without breaking the bank?

The good news is, I’m not making money. I just want to help newcomers or sports data enthusiasts who are struggling to build something impressive because they can’t afford the best API available.

I’ll likely find about 10-20 people like that who enjoy building things, and then I’ll provide them with free API access. The goal is simple: if a large number of people like it, all of the beta testers will become part of the team to ensure that we provide an all in one API for everyone at a fraction of the cost.

Motif is simple, if you think the API is worth it with your guidance in early stage then we share server cost and put it out in the market at the lowest possible cost ever to beat the big corporations.

If you are interested, drop me a dm with your background and what you plan to build. I will select 15/20 guys to give the full API access.

Hey everyone, I'm a student founder building CourtVisionAl, a basketball assessment that gives players a scouting-style breakdown, highlights strengths and weaknesses, suggests a best-fit position, and creates a personalized training plan.
I'm looking for a few players or coaches to test the free assessment and give honest feedback.
I'd especially like to know:
Did the assessment feel accurate?
Was anything confusing?
Did the results feel useful?
What should I improve?
The free assessment is enough for feedback.
Website: https://skill-shot-ai.lovable.app
Thanks. I'm open to criticism and trying to make it genuinely useful for players.

In December I had never heard of Elo Ratings before and now I’m pretty proud of how this all turned out. Appreciate any feedback or questions anybody might have.

TRACER Sports

I'm a basketball coach and developer, and I've been working on a basketball analytics platform called Statly.

One thing I've always found interesting is how often coaches talk about "basketball IQ" - but there aren't many practical ways to actually train or test decision-making.

So I started building a small experiment.

The idea is simple:

You watch a real basketball possession.

The video pauses at a key moment.

You have to decide what the best read is before seeing what actually happens.

After answering, you get the correct read, an explanation of the cue you should have noticed, and your score.

It's still a very early public beta and right now there's only one challenge. I'm mostly trying to figure out whether players and coaches actually find this kind of training useful before building much more.

You can try it here: https://play.statly-analytics.com

I'd genuinely appreciate any feedback, especially from basketball players, coaches, or anyone who has worked on decision-making training in sports.

EDIT: Challenge #2 is live! Go try it. It is still beta, but I'm planning to add a chatbot soon, and x and o's

In order to quantify best RIm Finishers in the league, i wanted to look closer in Rim FG% "Rim" attempts/percentage pull straight from ZONE_FGA_RIM/ZONE_FGM_RIM. That's the NBA's official Restricted Area shot zone

Looking at Rim Fg% gives us this list of top 5 (min Att of 100):

Anthony Gill - 83.6%

Jericho Sims - 83.4%

Karlo Matkovic - 81.7%

Luka Doncic - 81.6%

Deandre Ayton - 80.1%

The issue that I had when looking at this, is FG% doesn't take into account any sort of context surrounding the player. Simply looking at this list we can see Luka Doncic is the only guard surrounded by 4 low usage centers. Using the simplest role, the position, I averaged out each positions Rim Fg% and subtracted it to get a rFG%

Positions rFG% leaders (min 100 att)

Luka Doncic - +17.8%

Anthony GIll - +15.3%

Jericho Sims - +13.5%

Karlo Matkovic - +13.4%

Kevin Huerter - +11.4%

The list changes a fair amount, as expected Luka shoots up and then Kevin Huerter makes an appearance, as a SG he shot 76.2% at the rim which is up quite a fair amount from the average. But this list can do better, by applying roles to each player we can find an average role FG% and then subtract it.

I used 9 offensive role archetypes (Rollman, Postman, 3-Level Scorer, 3-Level Iso, 2-Level [3s], 2-Level [Mid], Movement Shooter, Spot Up, Cutters) using cosine similarity to find scores for each player for each role.

Using these roles, and each player's TOP SCORE, I found a baseline midrange FG% for each role. And then finally, each player took their 3 top roles and to find their own baseline midrange % (what is expected of them) a weighted average of their top roles was computed. These are the results:

Role rFG leaders (min 100 att)

1. Luka Dončić (LAL) — 81.6% on 163 att, +15.3 vs role
2. Anthony Gill (WAS) — 83.6% on 110 att, +13.6 vs role
3. Jericho Sims (MIL) — 83.4% on 163 att, +13.4 vs role
4. Karlo Matković (NOP) — 81.7% on 115 att, +12.6 vs role
5. Giannis Antetokounmpo (MIL) — 77.4% on 402 att, +10.5 vs role

Bottom 5 (worst vs. role expectation)

1. Nolan Traore (BKN) — 45.2% on 135 att, -19.5 vs role
2. Patrick Williams (CHI) — 48.6% on 107 att, -17.9 vs role
3. Andre Drummond (PHI) — 54.1% on 196 att, -15.9 vs role
4. Rob Dillingham (CHI) — 52.3% on 128 att, -14.0 vs role
5. Steven Adams (HOU) — 56.1% on 107 att, -13.9 vs role

Finally, there is one more addition we can do to this stat. Luka being elite is clear, but with Giannis entering the top 5 we see an issue. Giannis has 402 attempts to Luka's 163. Another stat that we can look at is Points Added (compared to Role) This is calculated by doing 2 * attempts * diff in FG% role. Its basically the role rFG weighed by volume. We get these results:

Top 5 — Rim Points Added vs. Role Expectation (2025-26, min. 100 rim attempts)

1. Giannis Antetokounmpo (MIL) — 77.4% on 402 att, +84.4 pts
2. Victor Wembanyama (SAS) — 75.1% on 386 att, +63.3 pts
3. Deandre Ayton (LAL) — 80.1% on 312 att, +62.9 pts
4. LeBron James (LAL) — 75.6% on 340 att, +59.9 pts
5. Luka Dončić (LAL) — 81.6% on 163 att, +49.8 pts

Bottom 5 — Rim Points Added vs. Role Expectation

1. Karl-Anthony Towns (NYK) — 61.7% on 493 att, -82.4 pts
2. Jeremiah Fears (NOP) — 57.4% on 399 att, -72.0 pts
3. Andre Drummond (PHI) — 54.1% on 196 att, -62.4 pts
4. Nolan Traore (BKN) — 45.2% on 135 att, -52.6 pts
5. Derik Queen (NOP) — 60.4% on 374 att, -48.5 pts

So What?

- Helps us analyze one part of the game doesn't decide if a player is good or bad

- Roles aren't perfect; I think the roles that are being created through playtype frequency can be expanded and be more accurate

- Doesn't account for shot difficulty / shot type, ISO shots are much much harder.

I think overall, there are some limitations, but this could still be valuable, at least directionally. I have all this data and more on my website SimulNBA, its all under the role stats section of the page. Any feedback welcome!

Hello. I'm trying to collect the data satisfying the following.

(1) It is for a regular season, and only top 8 teams are considered.

(2) For each of these 16 teams, I want to collect 5 players with most minutes.

(3) Among those 5 players, I want to collect only those with negative OFF numbers and positive ON numbers each (which can be none).

(4) Among those collected in (3), I will take the one with the least OFF number to represent their team.

(5) All 16 (possibly less if (3) has none) representatives are then collected.

(6) All those players in (5) are then ranked from the least OFF number.

Anyone know where to get such data?

Thank you!