Talisman Red's NBA Computer Ratings
Presented here are computer ratings and rankings for NBA and, hopefully, a system that can come somewhat close to predicting final game scores. The Predictions tab includes games scheduled for the next few days.
To calculate a hypothetical margin of victory for any other matchup, just use the "Points" column. The home advantage fluctuates based on games played, but is consistently about 2.5 to 3 points.
For the last couple of years, all overtime games are recorded as "ties" because this helps the score predictions be slightly more accurate. 20 Jan 2026 update: The performance this year has been, well, woof. I noticed an error that was keeping way too many old games in the predictions file. Hopefully things will improve from here forward.
If you need original text files for anything below, check the Description tab. Let me know if you see any issues. Thanks and glad you're here!
Everything is now in one scrollable page, instead of the tabs which required Javascript. Easier for phone users, I hope. Scrapers should not be impacted.
Ratings
NBA Computer Ratings Last updated 03-Mar-2026 07 AM ET Rank Team W L OT Rating Points BCS 1 Detroit 43 13 3 3.11 57.78 1 2 Oklahoma City 43 15 4 3.10 59.46 3 3 San Antonio 42 17 1 2.63 56.32 2 4 Boston 39 20 2 2.52 56.95 4 5 Houston 37 17 6 1.99 54.55 5 6 New York 37 21 3 1.99 55.35 7 7 Cleveland 38 21 3 1.93 54.74 6 8 Minnesota 37 21 3 1.36 53.52 8 9 Denver 35 21 6 1.30 53.79 9 10 Charlotte 28 30 3 0.92 54.02 15 Rank Team W L OT Rating Points BCS 11 Toronto 33 23 4 0.85 51.64 10 12 Phoenix 33 25 2 0.50 50.63 11 13 Orlando 29 26 4 0.44 50.65 13 14 Miami 31 29 1 0.41 52.25 19 15 LA Lakers 36 24 0 0.28 49.71 12 16 LA Clippers 27 31 2 0.27 50.97 16 17 Golden State 30 29 2 0.24 51.10 18 18 Philadelphia 29 24 7 0.22 49.83 14 19 Atlanta 30 31 1 0.04 50.08 17 20 Portland 28 32 2 -0.77 47.08 20 Rank Team W L OT Rating Points BCS 21 Milwaukee 24 32 4 -0.90 46.73 21 22 Chicago 25 35 1 -1.44 45.89 22 23 Dallas 19 36 5 -1.57 46.98 23 24 Memphis 21 35 3 -1.64 46.70 25 25 New Orleans 18 39 5 -1.78 45.89 24 26 Brooklyn 15 42 3 -2.86 42.71 27 27 Utah 15 41 5 -2.98 42.40 28 28 Indiana 14 45 2 -3.08 42.34 29 29 Washington 16 42 2 -3.30 40.16 26 30 Sacramento 12 47 3 -3.79 39.81 30
Ratings by Division
Teams are sorted by computer ranking (in parentheses), not necessarily by win-loss percentage. As on the front page, overtime games get recorded as ties to make the predictions hopefully more accurate. Playoff games between division teams end up getting counted here, too.
NBA Computer Ratings (Last updated 03-Mar-2026 07 AM ET) --- Atlantic CW CL OT W L OT Rating Rank Points NCWNCLNCT Non-Conf Sched 1 Boston 7 5 2 39 20 2 2.52 ( 4) 56.95 32 15 0 -0.31 ( 28) 2 New York 10 3 0 37 21 3 1.99 ( 6) 55.35 27 18 3 -0.10 ( 21) 3 Toronto 3 9 1 33 23 4 0.85 ( 11) 51.64 30 14 3 -0.18 ( 24) 4 Philadelphia 8 6 1 29 24 7 0.22 ( 18) 49.83 21 18 6 -0.25 ( 26) 5 Brooklyn 3 8 2 15 42 3 -2.86 ( 26) 42.71 12 34 1 -0.09 ( 20) --- Central CW CL OT W L OT Rating Rank Points NCWNCLNCT Non-Conf Sched 1 Detroit 9 3 1 43 13 3 3.11 ( 1) 57.78 34 10 2 0.04 ( 14) 2 Cleveland 7 4 1 38 21 3 1.93 ( 7) 54.74 31 17 2 0.17 ( 6) 3 Milwaukee 8 5 0 24 32 4 -0.90 ( 21) 46.73 16 27 4 0.13 ( 8) 4 Chicago 4 10 0 25 35 1 -1.44 ( 22) 45.89 21 25 1 0.01 ( 18) 5 Indiana 3 9 0 14 45 2 -3.08 ( 28) 42.34 11 36 2 0.04 ( 15) --- Southeast CW CL OT W L OT Rating Rank Points NCWNCLNCT Non-Conf Sched 1 Charlotte 9 4 0 28 30 3 0.92 ( 10) 54.02 19 26 3 0.05 ( 13) 2 Orlando 6 5 0 29 26 4 0.44 ( 13) 50.65 23 21 4 0.22 ( 3) 3 Miami 5 5 0 31 29 1 0.41 ( 14) 52.25 26 24 1 0.09 ( 12) 4 Atlanta 7 6 0 30 31 1 0.04 ( 19) 50.08 23 25 1 0.22 ( 4) 5 Washington 2 9 0 16 42 2 -3.30 ( 29) 40.16 14 33 2 0.02 ( 16) --- Northwest CW CL OT W L OT Rating Rank Points NCWNCLNCT Non-Conf Sched 1 Oklahoma City 7 3 2 43 15 4 3.10 ( 2) 59.46 36 12 2 -0.11 ( 22) 2 Minnesota 8 4 1 37 21 3 1.36 ( 8) 53.52 29 17 2 -0.50 ( 30) 3 Denver 5 3 2 35 21 6 1.30 ( 9) 53.79 30 18 4 -0.35 ( 29) 4 Portland 5 7 0 28 32 2 -0.77 ( 20) 47.08 23 25 2 0.14 ( 7) 5 Utah 1 9 1 15 41 5 -2.98 ( 27) 42.40 14 32 4 -0.08 ( 19) --- Pacific CW CL OT W L OT Rating Rank Points NCWNCLNCT Non-Conf Sched 1 Phoenix 9 6 0 33 25 2 0.50 ( 12) 50.63 24 19 2 0.32 ( 2) 2 LA Lakers 8 7 0 36 24 0 0.28 ( 15) 49.71 28 17 0 0.09 ( 11) 3 LA Clippers 8 5 0 27 31 2 0.27 ( 16) 50.97 19 26 2 0.18 ( 5) 4 Golden State 6 6 0 30 29 2 0.24 ( 17) 51.10 24 23 2 0.10 ( 10) 5 Sacramento 2 9 0 12 47 3 -3.79 ( 30) 39.81 10 38 3 0.34 ( 1) --- Southeast CW CL OT W L OT Rating Rank Points NCWNCLNCT Non-Conf Sched 1 San Antonio 9 3 1 42 17 1 2.63 ( 3) 56.32 33 14 0 0.12 ( 9) 2 Houston 6 4 1 37 17 6 1.99 ( 5) 54.55 31 13 5 -0.17 ( 23) 3 Dallas 3 9 1 19 36 5 -1.57 ( 23) 46.98 16 27 4 -0.27 ( 27) 4 Memphis 4 6 2 21 35 3 -1.64 ( 24) 46.70 17 29 1 -0.22 ( 25) 5 New Orleans 5 5 3 18 39 5 -1.78 ( 25) 45.89 13 34 2 0.01 ( 17)
Division Rankings
The average computer rating of all teams in a division is shown here.
Rank Conference Mean Rating ---- ------------------------- ----------- 1 Atlantic 0.54 2 Northwest 0.40 3 Southeast -0.07 4 Central -0.08 5 Southeast -0.30 6 Pacific -0.50
Game Predictions
These predictions already include home advantage, if necessary.
PREDICTIONS FOR UPCOMING GAMES Date Away Team Home Team Total Prediction ----------- -------------------- -------------------- ----- -------------- 03-Mar-2026 Dallas Charlotte 226 HOME by 8.14 03-Mar-2026 Detroit Cleveland 232 AWAY by -1.94 03-Mar-2026 Washington Orlando 236 HOME by 11.59 03-Mar-2026 Brooklyn Miami 223 HOME by 10.65 03-Mar-2026 New York Toronto 216 AWAY by -2.61 03-Mar-2026 San Antonio Philadelphia 231 AWAY by -5.38 03-Mar-2026 Oklahoma City Chicago 230 AWAY by -12.47 03-Mar-2026 Memphis Minnesota 224 HOME by 7.93 03-Mar-2026 New Orleans LA Lakers 237 HOME by 4.93 03-Mar-2026 Phoenix Sacramento 226 AWAY by -9.72 04-Mar-2026 Oklahoma City New York 222 AWAY by -3.01 04-Mar-2026 Charlotte Boston 218 HOME by 4.03 04-Mar-2026 Utah Philadelphia 241 HOME by 8.53 04-Mar-2026 Portland Memphis 235 HOME by 0.72 04-Mar-2026 Atlanta Milwaukee 229 AWAY by -2.25 04-Mar-2026 Indiana LA Clippers 225 HOME by 9.74 05-Mar-2026 Dallas Orlando 230 HOME by 4.77 05-Mar-2026 Utah Washington 249 AWAY by -1.14 05-Mar-2026 Brooklyn Miami 223 HOME by 10.65 05-Mar-2026 Golden State Houston 217 HOME by 4.55 05-Mar-2026 Toronto Minnesota 224 HOME by 2.99 05-Mar-2026 Detroit San Antonio 231 AWAY by -0.36 05-Mar-2026 Chicago Phoenix 232 HOME by 5.84 05-Mar-2026 LA Lakers Denver 229 HOME by 5.18 05-Mar-2026 New Orleans Sacramento 233 AWAY by -4.97 06-Mar-2026 Dallas Boston 215 HOME by 11.07 06-Mar-2026 Miami Charlotte 232 HOME by 2.87 06-Mar-2026 Portland Houston 223 HOME by 8.57 06-Mar-2026 New York Denver 221 AWAY by -0.46 06-Mar-2026 New Orleans Phoenix 224 HOME by 5.85 06-Mar-2026 LA Clippers San Antonio 224 HOME by 6.45 06-Mar-2026 Indiana LA Lakers 234 HOME by 8.48 07-Mar-2026 Orlando Minnesota 226 HOME by 3.98 07-Mar-2026 Brooklyn Detroit 202 HOME by 16.18 07-Mar-2026 Philadelphia Atlanta 234 HOME by 1.36 07-Mar-2026 LA Clippers Memphis 226 AWAY by -3.17 07-Mar-2026 Utah Milwaukee 233 HOME by 5.43 07-Mar-2026 Golden State Oklahoma City 211 HOME by 9.47 08-Mar-2026 Boston Cleveland 223 AWAY by -1.11 08-Mar-2026 New York LA Lakers 224 AWAY by -4.53 08-Mar-2026 Detroit Miami 238 AWAY by -4.43 08-Mar-2026 Dallas Toronto 225 HOME by 5.76 08-Mar-2026 Washington New Orleans 242 HOME by 6.84 08-Mar-2026 Orlando Milwaukee 220 AWAY by -2.81 08-Mar-2026 Houston San Antonio 226 HOME by 2.87 08-Mar-2026 Charlotte Phoenix 223 AWAY by -2.28 08-Mar-2026 Indiana Portland 237 HOME by 5.85 08-Mar-2026 Chicago Sacramento 240 AWAY by -4.98
Description
Original Text Files
Feel free to download these; if you use them elsewhere, give credit to talismanred.com.
- Rankings
- Division Standings (but sorted by computer rating)
- Game Predictions
What is this about?
Computer ratings are the starting point for Vegas oddsmakers, and have traditionally done reasonably well at predicting game winners. On this page, I share my own formula, which I've been producing for one sport or another for about 20 years. If you're familiar with ratings you've probably heard of the others: Sagarin, Massey, and more.
So it's just a math formula?
Yep, that's all that computer ratings are. You can do it this way, or you can do like they do for college sports and do a human "poll" (coaches, writers, whoever).
The formula here is one that I developed quite a few years ago to try and "compete" with all the other folks who are doing it. That's where the predictions come from -- incorporating historical games and trying to use those to figure out what will happen next. It includes scores, dates, and locations, but no other information. No information about injuries, pace or speed of the game, shooting percentages, etc. Those things *do* matter, but they aren't factored in here.
Are the predictions any good?
In 2023-24, my NBA ratings predicted 64.2% of the correct game winners. By comparison, Jeff Sagarin's method got 65.8% correct. Over a season of about 850 games, that is a 10-15 game difference. I'll let you decide how much difference there is for 10-15 games out of 850. (I suspect that most of that was early in the season, when it's more difficult to know what a team's true strength is going to be. Jeff's preseason algorithm is probably more sophisticated than mine.)
That said, the Vegas line got 68.0% of winners. You just cannot beat a human touch!
In 2025-26, about mid-January I noticed that this year's predictions were doing terribly (61%, compared to most of the other computers at 65-66%). So I did what any self-respecting programmer would do, I adjusted the formula in hopes of improving accuracy. I'll report back with the results. One of the big factors is how much recent information do you keep when trying to predict a team's performance. A couple weeks? A month? The whole season?
Ratings Description
This rating system uses a formula developed by the owners of talismanred.com. It is explained in more detail over on the college football page, but the bottom line is that it can be used both for ranking teams as well as predicting future scores. In some circles, you'll see that referred to as both "retrodictive" and "predictive."
About the Author
I am a faculty member at Indiana University in Bloomington, with a Ph.D. in Atmospheric Science from the University of Alabama in Huntsville. For my day job, in addition to teaching lots of courses I have research interests in the broad areas of thunderstorms and numerical weather prediction (forecasting the weather using computers). Contact me using this email form if you have questions or non-hateful comments.