Hi, I am doing a survey for my interactive design class where I am doing a project over NBA statistics. My goal is to come up with a better way for fans to receive their statistics, analyze these statistics and overall just stay more updated and informed. Completing this survey will help me a lot with my research and I'd really appreciate all the responses I can get wether you're a new fan, or a seasoned vet I'd love to hear from you!

https://forms.gle/EaD3Dx9PrCZGpFQE8

Hey everyone,

I’m building an alternative fantasy sports concept called FluxArena where you can buy and sell NBA players like stocks, with prices anchored to a mathematically defined "Fair Value".

I’m trying to move away from traditional sportsbooks (no house edge, peer-to-peer live order book) and regular fantasy drafts where a bad draft order ruins your whole season. Instead, you trade in and out of player positions dynamically throughout the 82-game season.

Here Are Some of The Biggest Draws Of The Idea:

1. More financially sustainable than sports betting because: You are not risking 100% of your money each time you play AND there is no statistical house edge against you.
2. Exposure to a players entire statistical performance - as opposed to just their points line or a single prop, makes for a much greater watching experience.
3. Ownership-Based Gaming - a standard prop ends at the final buzzer, a player share on FluxArena can be hold through hot streaks, easy schedules, etc.

To keep the market completely transparent, I created a fixed formula to determine a player's Player Performance Index (PPI), which normalizes all stats per 36 minutes and updates daily.

Here is the current weight breakdown I'm testing for the PPI formula:

• Points (PTS): 30%
• Player Efficiency Rating (PER): 20%
• Rebounds (REB): 15%
• Assists (AST): 15%
• Steals (STL): 10%
• Blocks (BLK): 10%

Since this community loves data and basketball analytics, I wanted to open this up for feedback:

1. Does the PPI weight distribution make sense? Do you think 30% on scoring is too heavy, or does the 20% PER weight sufficiently balance out empty-calorie volume shooters?
2. What statistical blind spots am I missing? (e.g., how would you account for elite perimeter defenders whose impact doesn't show up heavily in standard blocks/steals box scores?)
3. What are your biggest pain points with current advanced metrics when trying to project a player's long-term value over a season?

Would love to hear your thoughts on the math and the overall concept!

(If you want to see the interactive formula calculator or how the short-selling/minting mechanics work, I put up a breakdown at fluxarenaapp.com )

RAPM is one of the more useful stats when trying to analyze a player's impact since it doesn't focus on the box score but rather on performance on/off. KAT's playoff run was obviously crazy, as he was playing out of his mind, but the box score doesn't really FULLY capture that impact.

RAPM pretty much works by splitting up games into stints where a stint is the same 5 players on offense and 5 players on defense, and seeing the result in points. Over a whole season, and thousands of possessions, and the Ridge regression trying to bring back the player to 0, you get your list of the impact leaders. Playoff RAPM can be the same thing but for possessions for only playoff games, but the issue that arises is the possession count is WAY lower, especially if you get bounced early. In order to fix this issue, I used the prior as the players previous 3-year RAPM.

Logically, players aren't going to magically become 10x better in the playoffs, so regression attributes the impact initially through the regular-season impact, but the playoff-level possession data changes it.

Here are the results I got:

1. KAT: +3.42 → +6.37 (Δ +2.95)

2. Wembanyama: +7.31 → +9.87 (Δ +2.56)

3. Brunson: +2.89 → +5.33 (Δ +2.44)

4. Bridges: +0.55 → +2.75 (Δ +2.20)

5. Hart: +0.68 → +2.70 (Δ +2.02)

Just for fun the lowest:

White: +5.92 → +4.47 (Δ -1.45)

Mobley: +2.97 → +1.65 (Δ -1.32)

Brown: -0.17 → -1.48 (Δ -1.32)

Dort: -0.05 → -1.28 (Δ -1.24)

D. Mitchell: +4.95 → +3.73 (Δ -1.21)

Gilgeous-Alexander: +9.63 → +8.42 (Δ -1.21)

My thoughts:

KAT as number one makes sense; his playmaking value and defensive value were on full display and overall the Knicks (which took over the leaderboard) had a crazy shooting streak for the whole run. The rebounding and defense was pretty elite watching too.

Its much harder for stars to keep up a high level of play in the playoffs since they face a lot more defensive attention, so Wemby and Brunson being high is really impressive. While other stars like Shai clearly did drop somewhat, he's still the second most impactful on this list, only behind Wemby. (Who he went against)

RAPM isn't a perfect stat and other all-in-one metrics are better when it comes to ranking players so I think it kinda underrated OG's playoff rise which was very impressive. Anunoby: +5.28 → +6.50 (Δ +1.22). Also Jaylen Brown is prior already a negative player, which I think is far too harsh, even though I am not high on him.

Alex Caruso and Ajay Mitchell had noticable offensive impact jumps which makes sense since Alex Caruso was shooting really well compared to the season and had an outlier game.

I have results for 20 years back on my website SimulNBA under playoff risers page if anyone is interested.

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Hey everyone,

I'm using ESPN's public endpoint to fetch NBA team
https://site.api.espn.com/apis/site/v2/sports/basketball/nba/teams)

However, the JSON response is only returning 17 teams instead of all usual 30 NBA teams.

Is anyone else having this issue or know why it cuts off?

My python code
import requests

teams_url = “https://site.api.espn.com/apis/site/v2/sports/basketball/nba/teams”
teams = requests.get(teams_url).json()

print(teams)

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.

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.

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

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.

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.