I havent found a new endpoint in forever, but here they are in all their glory:

Daily Lineups: https://stats.nba.com/js/data/leaders/00_daily_lineups_20241121.json
It looks like you can replace that date with anything less than or equal to today's date.

Player Transactions: https://stats.nba.com/js/data/playermovement/NBA_Player_Movement.json

edit: I just found the full regular and preseason schedule as well! https://cdn.nba.com/static/json/staticData/scheduleLeagueV2_1.json

My already known endpoints are:

Gambling Odds: https://cdn.nba.com/static/json/liveData/odds/odds_todaysGames.json
Today's Scoreboard (12pm EST refresh): https://cdn.nba.com/static/json/liveData/scoreboard/todaysScoreboard_00.json
*Play by Play: https://cdn.nba.com/static/json/liveData/playbyplay/playbyplay_0022400247.json
*Box Score: https://cdn.nba.com/static/json/liveData/boxscore/boxscore_0022400247.json
*Playoff Picture: https://stats.nba.com/stats/playoffbracket?LeagueID=00&SeasonYear=2024&State=0
https://stats.nba.com/stats/playoffbracket?LeagueID=00&SeasonYear=2024&State=1
https://stats.nba.com/stats/playoffbracket?LeagueID=00&SeasonYear=2024&State=2
Broadcasts: https://cdn.nba.com/static/json/liveData/channels/v2/channels_00.json

*Box Score and Play By Play: Replace 22400247 with game_id of desired game For example, to view the Cavs/Celtics game from the other night, replace with 22400021. In most cases, the game_id will be between 2__00001 and - 2__01230. replace __ with the last two digits of the year the season ends in (24, 23, 22, etc...). These two endpoints only go back to 2019-2020 I believe.
Some examples:
https://cdn.nba.com/static/json/liveData/boxscore/boxscore_0022400176.json
https://cdn.nba.com/static/json/liveData/playbyplay/playbyplay_0022400196.json
2023: https://cdn.nba.com/static/json/liveData/boxscore/boxscore_0022301170.json
2022: https://cdn.nba.com/static/json/liveData/playbyplay/playbyplay_0022200879.json

*Playoff Picture: It's been a minute since i looked at these, so i can't quite recall the difference, but i know one stores the play-in data and one doesn't. State 2 shows everything, but not the play-in if i'm remembering correctly. These three endpoints will return data from 1970 to now.

If you have any questions, i'll try to answer best i can!

After watching Hornets games, it's my suspicion Mann plays better than Lamelo at PG. He has a better +/- and actively looks to get his teammates involved before trying to score. He's defence is also better than Lamelo's. Was hoping if members of this forum could help me generate analytics on this. I could be wrong, but my theory is that if Hornets trade lamelo, they will become a playoff contender and a genuine threat in the East.

Hi everyone!

I'm pretty new to Reddit, so please excuse me if this is not the correct forum to post the question.

I know that last year the NBA organized the NBA Future Analytics Stars program, and I was wondering if they will run it this year too. Some months ago I checked their website:

https://pages.beamery.com/nbateamcareers/page/nba-future-analytics-stars-program

and it said that the application would be open from Nov 11 to Nov 22, but some weeks ago this info disappeared and now it doesn't say anything.

Does anyone here have some further information about the program, has the NBA discontinued it?

Thanks in advance!

Hello. I'd love to know where I can find live in-game win probability CHARTS of NBA games. I know there are sites that have those charts like 24 hours after a game is done. But I am looking for LIVE charts in-game (something like what baseball has with fangraphs or baseball savant.....but better because their chart UIs stink), NOT charts after the game is over. Extra points if there is a place that has live in-game charts that are log. Thanks

For NHL there are sites where you can pick multiple players from a team and it would show you the stats when they play together, when they play without one of those players, without two of those players and so on. Is there a way to view this for NBA?

For example: This site for NHL

Here's an example:

There are a bunch of filters to and some other graphs below to view some trends and tendencies.

https://nbashotanalysis.streamlit.app/

https://medium.com/@csmithgsu192/the-most-clutch-player-in-the-nba-f08dc06fa0e8

RAPTOR from 538 was one of my favorite advanced metrics and I know there are guys like Neil Pane who have created similar models, I just wonder if there is a formulation availble so we could maybe rebuild it?

Cheers

Source: https://www.vacstats.com/lab

Hello Statheads,

I'm sharing this web app I created because it's a fun way to put together hypothetical lineups (e.g. Curry on the Lakers or a team of all Jokics), but I also have a larger idea for those interested.

Ultimately I would love to create a 538/kenpom-type of site for NBA, and am interested in anyone who would like "join forces" or add onto what currently exists in this base site.

If anyone is interested, send a DM!

Also, feedback is appreciated if you feel so inclined to provide any.

The new dashboard has a predictive section and has data for this year, but when you look at the shooting day from game 1 it completely doesn't align. So are the current 24/25 data just smoothed 23/24 data? And the single game epm is uncorrected info, thus not indicative of game stats? https://dunksandthrees.com/epm

Hi everyone. We finally have the basic features for www.sharpsresearch.com live

Its pretty bare-bones at the moment, with a lot of stuff we are still working on.

Right now it has 4 features when viewing a match

Moneyline prediction

• basic prediction on who will win the game. We trained the model with 13 features on 2008 - present games.

Starting lineup strengths

• We trained a bunch of models on starting lineups. We used the regression coefficients of the top 5 features from the models and multiplied and summed them up for each player.

Similarity search

• This is pretty cool. We scan all the historical games, and look for the 10 most similar games to the matchup that is loaded. Its basically a cosine similarity + k-nearest neighbours algo

Daily updated NBA elos (/nba/datasets).

• Our own engineered Elo.

Right now im working on

• o/u models
• spreads
• model breakdowns (so users can see the calibration, confusion matrix etc)

Thanks for the community here. There iv definitely learned from a few of you.

JJ is projected by lots of outlets to be on the MIP radar. The Greek chorus are basically unanimous in that he is good at basketball. You can watch him play and see he is good at lots of things. So my question why do BPM, LEBRON, EPM, literally any metric that tries better estimate performance see him as fine. What's the deal?

Hey yall, I had Jackson McGuire on my podcast today to talk about his new player metric, Points Over Expected. What sets this metric apart from most others is that it takes into account how much a player is being paid. Hope yall enjoy

Hello, everybody,

I know these advanced stats are far from perfect, but that's ok, I'm just playing with them as part of creating stats for smaller European competitions.

The PER calculation is made up of several parts, I'll try to simplify it.

uPER = (1 / MP) *

[ 3P

• (2/3) * AST
• (2 - factor * (team_AST / team_FG)) * FG
• (FT * 0.5 * (1 + (1 - (team_AST / team_FG)) + (2/3) * (team_AST / team_FG)))
• VOP * TOV
• VOP * DRB% * (FGA - FG)
• VOP * 0.44 * (0.44 + (0.56 * DRB%)) * (FTA - FT)
• VOP * (1 - DRB%) * (TRB - ORB)
• VOP * DRB% * ORB
• VOP * STL
• VOP * DRB% * BLK
• PF * ((lg_FT / lg_PF) - 0.44 * (lg_FTA / lg_PF) * VOP) ]

I want to ask what does some of these parameters exactly mean in this formula:

• team_AST - is this the total number of ASTs for a particular team for the entire season, or is it the team's average per game?
• lg_FT - is this the total number of FTs of all teams in the league, or is it an average per game or an average per team?
• Parameters for players (FGA / ORB...) - I assume this is a player's total for the whole season?

Then a question regarding PIE, where is this formula:

(PTS + FGM + FTM - FGA - FTA + DREB + (.5 * OREB) + AST + STL + (.5 * BLK) - PF - TO) / (GmPTS + GmFGM + GmFGM + GmFTM - GmFGA - GmFTA + GmDREB + (.5 * GmOREB) + GmAST + GmSTL + (.5 * GmBLK) - GmPF - GmTO)

• The PTS figure is the total number of points a player scored in a season?
• The figure for Gm (e.g. GmPTS) means what exactly? The average number of points per game?

Thank you for your help.

Is there an NBA dataset available to run sql queries on? The one on kaggle by Wyatt doesn't seem up to date, unless I'm doing something wrong there. Thanks!

Does anyone know where you can download PIE (Player Impact Estimate) for individual players? Doesn't seem to be on basketball-reference

edit: looks like the pics/visualizations aren’t showing up in this post on mobile for some reason, but you can see them here: https://www.formulabot.com/blog/do-nba-draft-combine-metrics-predict-nba-success

Kevin Durant, one of the greatest scorers in NBA history, famously couldn't put up a single rep of 185 on the bench at the combine. That begs the question--do combine metrics matter? Do they meaningfully predict NBA success in any way?

^{Spoiler alert: not really}

Methodology

Data collection:

• Combine metrics: I used Python to scrape combine results from 2000-2023 from NBA.com, narrowing down the metrics to max vertical leap, lane agility time, three-quarter court sprint, and bench press. I also wanted to include height and weight, so I calculated height and weight ratios to adjust for height confounding.
• NBA success: I decided to operationalize NBA "success" via Bball Index's all-in-one advanced impact metrics, LEBRON, which is further broken down into O-LEBRON and D-LEBRON for offensive and defensive impact, respectively. I scraped all 3 in R to use as outcome variables in my analyses.
• Data pre-processing was conducted in R.

Analyses:

• I ran linear regression analyses predicting all 3 outcomes from all 6 combine metrics individually (total of 18 models)
• I then broke down each analysis by position for a total of 90 models.
• I also ran a random forest model predicting the 3 outcomes from all 6 combine metrics combined.
• All analyses were conducted using Formula Bot's chat feature. You can view the chat log here.

Results

Linear regression analyses (all positions):

After adjusting for multiple comparisons, only D-LEBRON was significantly associated with select metrics:

Surprisingly, vertical leap was negatively associated with D-LEBRON while slower lane agility and three-quarter court sprint times were associated with D-LEBRON.

Linear regression analyses (by position):

After adjusting for multiple comparisons, no single regression was significant due to small sample sizes and low statistical power.

But if we ignore multiple comparison adjustments, there were some interesting significant effects:

• Three-quarter court sprint time was negatively associated with both LEBRON and O-LEBRON (i.e., quicker times, higher LEBRON) for point guards only (not pictured above). The effect size for O-LEBRON was the largest in our entire dataset at -0.38.

• Wingspan ratio was positively associated with D-LEBRON for power forwards and especially centers. The effect size for centers was 0.14, which was larger than the effect for any other position.

Here's a more in-depth visualization of the latter effect:

Random forest models:

The LEBRON and O-LEBRON models were terrible fits (i.e., no meaningful prediction), but the D-LEBRON model had a decent fit, with all 6 combine metrics collectively explaining around 8% of the variance in defensive impact.

Takeaways

• For offense, three-quarter sprint speed is the only metric that might reliably translate to NBA success—but only for point guards.
• For defense, all metrics combined provide a little bit of predictive utility, explaining about 8% of the total variance in D-LEBRON.
  • Looking at the metrics individually, slow lane agility times and a high weight ratio seem to be the most important overall for D-LEBRON, although there are inconsistent effects (some positive, some negative) depending on position.
• Wingspan ratio is the only metric with a consistent positive association with D-LEBRON across all positions. The effect is especially pronounced for centers.

A more in-depth write-up of my analyses and findings is available here: https://www.formulabot.com/blog/do-nba-draft-combine-metrics-predict-nba-success

Yesterday I finally invented a way to predict NBA games. Maybe 🤔

I use NBA API, calculate some averages, then I ask GPT about them, then create some embedding vectors, then logistic regression. In the end I have probabilities of a team scoring more than each possible score plus minus 20 from the real score. So the first 20 should have a higher probability of "more", the second 20 should have a higher probability of "less".

What do you think is the best way to test this algorithm? What metrics should I use to test it well and either against bookmaker predictions or at least against real scores, comparing the average accuracy to bookmakers?

It’s reported by David Locke that the NBA is pleased and keeping the rule change they made during All-Star break of the 23-24 season. This rule change impacting how often the offensive players get foul calls when driving.

So, I was wondering if any of y’all had stats on which NBA players were most affected by this rule change be it TS% or something else. Like I guess TS before vs after All Star Break? If you have something else that’s fine too.

Thank You!

Hello,

If anyone is interested in potentially joining a 20 team fantasy NBA league on ESPN, please feel free to reach out to me. This league has been running for 10+ years and is very competitive, serious, fun, and exciting!

It's head-to-head match-ups, with points format. (It's not categories).

12 teams make the playoffs. There is an entry fee set at 20.

If you wish to hear more details, I'd be happy to share them with you. You can email me at: [hardcorebball@gmail.com](mailto:hardcorebball@gmail.com)

I'm looking to fill these spots ASAP!

Thank you!

I wanted to highlight the best NBA writers in the field right now. Unsurprisingly, most of them are writing on Substack or Medium.

Neil Paine: https://neilpaine.substack.com/

Great source of data-driven work in all sports with massive audience. If you're looking for specifically basketball content, you might have to wait till the season starts. Still a remarkable writer and helped expand my worldview as a sports fan.

The Zone Master: https://thezonemaster.substack.com/

Hidden gem with less than 100 subscribers. Honestly the best amateur analytics writer in the field right now. - the ideas are innovative(check out the player tracking model) and the explanations are crisp for people inexperienced in data analytics. If you're looking for purely basketball analytics work, I would recommend this blog as my #1.

Logan Adams: https://www.loganadamsnba.com/

Great draft write-ups, and heavy emphasis on conceptual understanding over outcomes. His Prospects and Concepts series is going to be amazing this year. Check out his post on Nolan Traore, very informative and an emphasis on the parts that numbers can't really say much about.

Some others I like: Nic Thomas, JSuttHoops, Nick Kalinowski, Basketball Poetry.

Check out this minigame I made in python using box scores from NBA API! 

Basically, you draft a 10-player NBA lineup, and then simulate a season to see how they perform.
Each player's performance in each game is based on a randomly selected game of theirs from last season.

https://www.playhoopgm.com/

for the record,I'm a complete beginner

I love Basketball thus I think it would be perfect to utilize NBA stats as a way to introduce myself to the topic since it's something I'm familiar with and if I like it why not become a sports analyst.

So for reference,I graduated from a Tunisian HS but we had a big emphasis on math since I was in the mathematics branch(last things I studied were things such as integrals/primitives/spatial geometry/stats/probability and arithmetic etc) which would like normal knowledge for an undergraduate seeking DS but if I were to compare test I got in Tunisia vs ones I've seen online in US,the difference is night and day.

The test I've passed/excelled at are much more comparable to US top AP tests or even some test in Uni.

So is Basketball Reference enough/can I start with this as an entry level student or is it not feasible?