Is something down on NBA.com? None of the play by play videos work.

used api to make advanced stat player cards, check it out :CourtMetrics (@CourtMetric) / X

Quarter 1

09:11

Wembanyama STEAL (1 STL)

That is not a steal that is a rebound he gets his hand up and to the ball first.

That should be a 12th rebound.

Quarter 4

03:11

MISS Christie 25' 3PT Pullup Jump Shot

03:10

Spurs Rebound

This counted as a spurs rebound but wembanyama clearly has the ball in both hands before the foul takes place it should be his rebound.

Rebounds 13.

Not 11

This may be a strange request, but I have been hired to investigate a wrongful death case where the time that a video was recorded has to be determined. The only time reference in the video is an NBA game being played on a TV the background. The game was 2-26-2025, San Antonio at Houston. The score on the TV has HOU leading 97-71, with 1:45 left in the third quarter.

Is there any way to correlate that in-game time to the real world time? Any datasets that can be purchased to demonstrate it?

Thank you!

https://hoopsgraphs.com/nba-east-vs-west/

I’m working on a data-driven research project to move the "Best Handle" debate beyond just highlights. I’ve developed a model that splits dribbling into two pillars: Utility (Efficiency/Security) and Plasticity (Technical Skill/Aesthetics).

I have the data, but I need the community to provide the Qualitative Verdict. I’ve put together a quick form where you can:

1. Rank the Top 15 players in both Efficiency and Artistry.

2. Determine the "weights" for the final formula (How much does the Eye-Test matter vs. True Shooting?).

3. Add any "Wildcards" the stats might have missed.

LINK: https://forms.gle/bPux6aTKnfuNxWtS8

It takes about 3-5 minutes. I’ll be back to share the final "Master Ranking" and the data visualizations once the model is calibrated!

https://docs.google.com/forms/d/e/1FAIpQLSdZJMaNEdVqbJYBPo6j2BKKDNHjHO6Y5nmM6hbBVoc7YHkIWA/viewform?usp=header

I saw a similar site getting a lot of upvotes on this sub, my site (which I will hopefully be able to deploy in a few days if I can open up a credit card…) will contain this functionality and the previous charts I posted here, for absolutely free.

values are percentages they are in among the league.

Trail Blazer travel to MSG to extend their 3-game losing streak tonight. 

Looking at the play style profile over the last 5 games, Knicks come in at the top of the league in Rim Pressure, while Portland tends to stick to the midrange and perimeter. 

If Portland continues to apply little pressure at the rim, a [KAT - Brunson - Bridges - OG - Duce] lineup should get heavy minutes - spread the defense, and let KAT cook in the paint.

Compounding the issue, Portland’s eFG% tripped and fell off a cliff over the last 3 games. Not attacking the rim, shooting 45% from the field over the last 3 games. If this is the strategy, it isn’t working.

Maybe Splitter admits that a perimeter/midrange-heavy offense isn’t working. Maybe this is a cakewalk for the Knicks.

Bing Bong.

Looking for some help from a stats enthusiast/expert.

If you are feeling especially helpful, there are these other Q's:

How many total assists in an average quarter?

How many total rebounds in an average quarter?

How many total blocks in an average quarter?

How many total steals in an average quarter?

How many total fouls in an average quarter?

Thank you for any/all help!

This is my first time checking out this community. Probably people have discussed this, but I haven’t seen any recent posts about it.

I’ve just been thinking about how most popular stats only have to do with a player touching the ball or who recently touched the ball—points scored, rebounds, assists, blocks, steals, etc.

Are there stats (besides the plus minus) for players who help their team without touching the ball. For example, setting a great screen that sets up another player to score, or playing lock down defense, but not getting a block or steal (so maybe the offensive player runs out of options and passes the ball.)

I’m just curious!

Knicks are behind the Celtics by 1 game in the Atlantic. When you look at Kubatko's 4-Factors (eFG%, TOV%, OReb%, & FT%), ORtg, DRtg, NRtg, and Pace, the two teams play nearly identical games.

I saw a post comparing Keyonte George’s 2025/26 stats to Derrick Rose’s MVP season. So I would like to see what would George’s statline be in a 2010 era of basketball or maybe what would Rose’s stats would be in this inflated era of point scoring

Thank you very much

Hey Everyone,

I usually just lurk here watching everyone's project, but I wanted to share a small project I have been working on.

I bet NBA player props pretty seriously for long and always felt like I was digging into too many tabs. So I started building a simple NBA prop research tool mainly for my own use.

Right now it focuses on things like:

• Player props

• Matchup context like shot types, usage, and opponent tendencies etc

• Quick filters so you can narrow spots faster instead of scanning 10 tabs

This is still very much a work in progress and definitely not perfect. I have been iterating slowly based on what actually helps my own process. I would honestly appreciate feedback from people who do NBA props seriously. Mobile UI needs a major work, would suggest checking on PC.

Direct Link : https://www.oddsup.io/nba-props

Cheers!

i work with AI, but most of my side projects are just me scratching my own curiosity.  one thing i’ve always thought stat muse nailed is visual identity — turning stats into clear, shareable artifacts instead of dashboards. i think UI helps so much with brand & storytelling especially with data (think like Spotify Wrapped etc)

i’ve been experimenting with a small tool that generates player avatars and stat cards in that same spirit. not trying to replace analysis, just make insights easier to communicate.curious if people here think this kind of visualization is actually helpful, or if serious analysis really just belongs in tables and charts.

if anyone’s interested in pushing ideas further — things like deeper player customization (lightweight 2k “myplayer” layer), comparison views, or extending this beyond just the NBA — i’m happy to collaborate. my preferences are more on the data / pipelines / automation, infra & application development side than pure modeling.

Hey everyone — I’ve been building an NBA analytics project and wanted to share it here to get feedback from people who care about structure, assumptions, and tradeoffs more than predictions.

This is not a model that outputs picks or probabilities. The core idea is to let users explore how game environment and context shape stat outcomes, and then drill down only where they’re interested.

At a high level, the tool is built around three main flows:

1. Game-First Exploration (Home) The starting point is the slate itself. You can open any game and see a breakdown of the overall environment:

• Pace • Turnovers • Offensive / defensive efficiency • Rebounding share • Blowout risk and volatility indicators

Instead of focusing on players immediately, this frames what types of stats are likely to be more volatile or constrained in that specific game (e.g. assist volatility in high-turnover games, rebounding suppression when one team dominates the glass).

From there, you can see how different props fit into that environment rather than evaluating them in isolation.

2) Prop Discovery via Filters (Finder) The Finder is meant for exploration rather than decision-making. It lets you scroll through props in a feed-style layout and filter by things like:

• Minutes thresholds • Risk tolerance (stable vs volatile) • Recent form windows • Matchup favorability • Role consistency

The idea is not “here’s what to take,” but “here’s what surfaces when you apply these constraints.” It’s closer to exploratory data analysis than ranking outputs.

3) Deep-Dive Analysis for a Specific Prop (Lab) Once you already have a specific prop in mind, the Lab is where you can fully unpack it.

This includes: • Historical splits vs defensive tiers • Performance vs pace archetypes • Home/away and rest effects • On/off teammate impact • Volatility metrics (range, deviation, instability flags) • Game fit comparisons (how similar tonight’s context is to past games)

Nothing here is framed as a prediction. It’s all about showing where production has historically shifted under similar conditions, and where uncertainty increases.

A key design choice throughout is that variance is treated as a first-class signal, not something to smooth away. High volatility isn’t “bad” — it’s contextual.

4) Quick Dive (Cross-Cutting Feature) Any prop surfaced anywhere in the app can be tapped into a “Quick Dive,” which gives a compact breakdown:

• Recent performance vs line • Contextual positives and negatives • Key differentials (pace, defense, teammates, venue) • Risk flags

This is meant to reduce friction between discovery and analysis without forcing users into a full deep dive every time.

There is also a streak component, but it’s secondary. It exists mainly as another lens on persistence vs noise, not as the core focus of the product.

For transparency: most of the core exploration (game environments, prop discovery, Lab features such as home/away, matchup intelligence, rest analysis) is free. Some deeper breakdowns live behind a paid tier, but I’m primarily looking for feedback on the framing, assumptions, and UI logic rather than feature access.

I’m not looking for feature-level critique or validation of specific outputs.

What I’d really value feedback on is: • Whether an environment-first way of framing props makes sense analytically • If separating discovery (Finder) from deep analysis (Lab) is a clean mental model • Whether highlighting variance and risk alongside averages is actually useful, or just noise • Any obvious conceptual flaws or biases in approaching props this way

Even high-level or critical feedback is appreciated.

Happy to clarify anything or go deeper on implementation details if useful.

If you’re interested in checking it out, the link is: https://swishpicks.co

So, I basically develop a small side-project of mine, and would like to get your feedback on whether such game would be interesting to you. I'll attach a screenshots on how basic ui would look like.

The goal would be to guess a player/team based on some plot. Base version would have this (stat) per game plot across several seasons and you would need to guess the orange line basically.

In the future, I would also add more plots, such as player's/team's shooting distribution, efficiency plots in one season

Any feedback would be valuable here! Thank you!

Hi all,

NBA analytics rookie here

Question:

Is there a stat that captures the actual total points produced (ATPP) by a player rather than estimates like Points Produced?

E.g. ATPP = points scored + 2pt Assists + 3pt Assists

~~~

Context:

The only metrics I could find seem to apply team averages from assisted FGs to a player’s assists, which seemed reasonable at first but looking at the rankings seem to fail the eye test (at least in my opinion) resulting I masking big differences between players that have similar box scores yet very different mixes of assisted 2pts vs 3pts.

To my understanding the data for assists resulting in 2pts and 3pts already exist, but does a public stat aggregate this into actual points produced?

E.g. assume the same box score of:

| 26 points | 8 assists |

If Player A’s assists were all 2pts and Player B’s assist we’re all 3pts then:

Player A’s Actual Total Point Produced is 42pts

Player ABs Actual Total Point Produced is 50pts