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Michael’s Programming Bytes Personal 2026-27 NBA Season Predictions

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Hello everyone,

Michael here, and in today’s post, I’m going to give you my personal East/West NBA predicted standings for the 2026-27 NBA season and see how they compare to the Michael Model’s predictions!

But first, a little recap

Before I dive into my personal upcoming NBA season predictions, let’s recap what the Michael Model had to say about both the Eastern and Western conferences.

First off, here’s the Michael Model’s predictions about the Eastern Conference:

Play-OffsPlay-InsMaybe Next Year
1. Boston Celtics (68-14)7. Atlanta Hawks (53-29)11. Detroit Pistons (47-35)
2. Milwaukee Bucks (62-20)8. Miami Heat (52-30)12. Orlando Magic (45-37)
3. Cleveland Cavaliers (59-23)9. Indiana Pacers (50-32)13. Brooklyn Nets (45-37)
4. Toronto Raptors (56-26)10. Chicago Bulls (48-34)14. Washington Wizards (43-39)
5. Philadelphia 76ers (54-28)15. Charlotte Hornets (41-41)
6. New York Knicks (54-28)

Here’s the Michael Model’s predictions about the Western Conference:

Play-OffsPlay-InsMaybe Next Year
1. Denver Nuggets (64-18)7. Minnesota Timberwolves (52-30)
11. Portland Trail Blazers (49-33)
2. Oklahoma City Thunder (61-21)
8. Dallas Mavericks (51-31)		12. Memphis Grizzlies (48-34)
3. LA Clippers (57-25)
9. Utah Jazz (50-32)		13. New Orleans Pelicans (47-35)
4. LA Lakers (55-27)10. San Antonio Spurs (50-32)14. Phoenix Suns (47-35)
5. Golden State Warriors (54-28)
15. Sacramento Kings (45-37)
6. Houston Rockets (53-29)

Now, as I noted in my previous post And Now for a Third Crack at NBA Python Regression Season Predictions (2026-27 season edition), a projected wins range between 41-68 wins for all teams definitely seems unlikely. After all, every NBA season, there’s got to be at least 5 teams that lose more than half their games.

And now, here’s the fun part!

Michael’s Juicy Personal Predictions

Now that we know how the Michael Model thinks the upcoming NBA season will play out, let’s see how I think the upcoming season will play out.

Without further ado, here are my Eastern Conference 2026-27 predictions:

Play-OffsPlay-InsMaybe Next Year
1. New York Knicks (61-21)7. Cleveland Cavaliers (47-35)11. Washington Wizards (36-46)
2. Philadelphia 76ers (60-22)8. Detroit Pistons (44-38)12. Chicago Bulls (30-52)
3. Indiana Pacers (58-24)9. Atlanta Hawks (43-39)13. Orlando Magic (27-55)
4. Toronto Raptors (56-26)10. Charlotte Hornets (41-41)14. Milwaukee Bucks (21-61)
5. Miami Heat (54-28)15. Brooklyn Nets (16-66)
6. Boston Celtics (50-32)

Some Thoughts On Each Eastern Conference Team

  1. New York Knicks-Aside from losing Mitchell Robinson in free agency, most of the 2026 championship squad is back. They still feel like the complete squad that won the title last year, and even if they don’t pull off back-to-back titles, I think they could be in for another amazing season.
  2. Philadelphia 76ers-Talk about a splash this summer in free agency! Breaking up the Jays duo in Boston, acquiring Kentavoius Caldwell-Pope and of course, signing the great Lebron James at 41 for his likely last chance at a 5th championship. Don’t forget Tyrese Maxey and VJ Edgecombe. The only question mark I have about the 76ers is will Joel Embiid’s health hold up (he only played 38 games last season due to knee issues).
  3. Indiana Pacers-A 3-seed for the Pacers may look overly optimistic to some, but I’m more confident than most that of all the teams that had a rough season last year, the Pacers would have the biggest bounce-back year. After all, most of their 2025 Finals core is back (Tyrese Haliburton, Pascal Siakam, among others). Do I think they’ll go all the way to the Finals like their 2025 playoff run? Probably not. For one, let’s see how Tyrese Haliburton not only readjusts to the court after a season away but also how he meshes with Ivica Zubac.
  4. Toronto Raptors-Kawhi Leonard, take a bow. Now that Leonard got his wish to return to the Raptors (and basically got off scot-free after the salary cap circumvention scandal), let’s see how he fares alongside guys like Scottie Barnes and Jamal Shead on defense. The health of star center Jakob Poeltl seems to be the team’s biggest X-factor, as his defensive production was limited due to a lower-back issue that flared up last season.
  5. Miami Heat-Giannis. Bobby Portis Jr. Tim Hardaway Jr. Klay Thompson. Pat Riley’s still got it if he managed to pull in these four NBA veterans. The Giannis & Bam tandem will be particularly exciting to see on the court, but it’s worth noting that there are some depth questions for the Heat this season, especially after trading away the former core of their depth in Tyler Herro/Kasparas Jakuconis/Jamie Jaquez Jr/Kel’el Ware in the Giannis deal. We’ll see if rookie Ryan Conwell can add some youthful spark to the Heat’s depth (plus Pelle Larsson and Nikola Jovic are still around).
  6. Boston Celtics-Having Jayson Tatum back on the court for (hopefully) a full season could help the Celtics win a bunch of games, but I think trading Jaylen Brown for Paul George was a “two steps forward for Philly, one step back for Boston” situation. Sure, Paul George could be a great asset for Boston (when he’s healthy) and perhaps their new 39-year-old veteran acquisition of Mike Conley Jr still has something left in the tank, but the way the Celtics are currently set up, I don’t see a deep playoff run for them this season.
  7. Cleveland Cavaliers-The Cavs have had a few pretty good seasons these last few years, even if their last two playoff exits were fairly disappointing (a loss-in-5 to the Pacers in 2025 and a sweep to the Knicks in 2026). Running it back with the core three of Donovan Mitchell, James Harden and Payton Watson will surely help the Cavs, but given all the other talent in the East, I think the Cavs might be in play-in territory.
  8. Detroit Pistons-At least the Jalen Duren contract drama is over-for now-but it does make me wonder how much longer Duren will be with the Pistons. Duren drama aside, most of their key rotation that got them the 1-seed in the East last season is coming back, but trading away Isaiah Stewart for John Collins is a bit of a step back for them defensively. Tobias Harris will certainly be missed on the Pistons.
  9. Atlanta Hawks-I think Hawks fans will have a lot to look forward to with the new roster and acquisitions like Lu Dort and Aaron Wiggins (both of whom were on the 2025 championship Thunder squad) along with returning favorites like CJ McCollum and Jalen Johnson. The new defense looks good, the new offense looks good, and even with 2024 #1 pick Zaccharie Risaccher gone, the Hawks look ready for the first full post-Trae Young season (even if the potential competitiveness of the East means they’re in the play-in mix).
  10. Charlotte Hornets-Just when it looks like there’s nowhere but up for the Hornets (coming awfully close to their first playoffs in a decade), LaMelo Ball is gone and traded for Naz Reid. I think both the Timberwolves and the Hornets will benefit from the LaMelo Ball/Naz Reid trade, but in the case of the Hornets, they still have several great shooting options in Coby White, Reid, and of course, Brandon Miller and Kon Kneuppel. Now with LaMelo gone, can Kon Kneuppel and Brandon Miller rise to the occasion? Don’t be surprised if the Hornets wind up in the play-in again.
  11. Washington Wizards-If there’s any rookie that will be interesting to watch this season, it’s 2026 #1 overall pick AJ Dybantsa. I think he had solid numbers during his year at BYU (Brigham Young University in Provo, Utah)-especially in the points-per-game, free throws and field goals categories-so it will be fun to see how he meshes with others on the Wizards’ core like Alex Sarr and Bub Carrington. Having Trae Young healthy will certainly help the Wizards, but given the new Wizards’ core, I have a hunch Anthony Davis’s name might be on the midseason trading block. The Wizards are still rebuilding, so don’t count on a big playoff run, but on the bright side, I don’t think they’ll be the Eastern Conference basement dwellers yet again.
  12. Chicago Bulls-Is the Bulls rebuild complete? In my opinion, not quite. Nic Claxton and Norman Powell are nice offseason additions, but Claxton’s hamstring injury during training camp could be an issue for the Bulls’ defense. Caleb Wilson will be fun to watch, and I’m sure we’ll see plenty of Josh Giddey, but I’m not counting on much from the Bulls this season.
  13. Orlando Magic-I know this seems like a low ranking for a team that nearly-but-not-quite upset the 1-seed Pistons in last year’s Eastern Conference first round, but my hopes aren’t high for the Orlando Magic. The Magic are largely running it back from last year, hoping for the best from guys like Paolo Banchero and Franz Wagner, plus they’ve got a learning curve with new coach Sean Sweeney. Having the Magic’s old friend in Nikola Vucevic back can certainly be a nostalgia boost for Magic fans, but we’ll see if he’s still got the numbers he had during his first stint with the Magic in 2012-21 (and those were pretty solid numbers)
  14. Milwaukee Bucks-I’ll just say it right now, Giannis is gone and this will likely be a rough year for the Bucks. The four “Miami Bucks” (Tyler Herro, Jamie Jaquez Jr, Kel’el Ware and Kasparas Jakuconis)-the players Miami gave away as part of the Giannis deal-could be interesting to watch on their new team. Plus, rookies Brayden Burries and Nate Ament did show potential during Summer League, but I think both still need more time to find their footing. Aside from that, the Bucks kept…Kyle Kuzma and Myles Turner this year (not that exciting, I know). Plus, there’s Gary Trent Jr and the Bucks’ own Kawhi-esque-salary-cap-circumvention scandal. Fun times at Bucks HQ, but I’m sure there will be a mid-season trade list ready to go for the Bucks this year.
  15. Brooklyn Nets-And the winner for “Likeliest to Fall to the Bottom of the East” are the Brooklyn Nets. Sure, the new tandem of Julius Randle and Michael Porter Jr could give them a boost. Aside from those two they’ve got guys like…Egor Demin, Mikel Brown Jr, and a host of other young talent. There are reasons for optimism in Brooklyn…maybe in 2030. As for 2027, perhaps another tank-a-thon year and another draft lottery pick for the Brooklyn Nets.

Here are my Western Conference 2026-27 predictions:

Play-OffsPlay-InsMaybe Next Year
1. San Antonio Spurs (65-17)7. Utah Jazz (46-36)11. Portland Trail Blazers (35-47)
2. Oklahoma City Thunder (62-20)8. Dallas Mavericks (43-39)12. Memphis Grizzlies (34-48)
3. Minnesota Timberwolves (56-26)9. Phoenix Suns (41-41)13. LA Clippers (30-52)
4. Denver Nuggets (53-29)10. Golden State Warriors (39-43)14. New Orleans Pelicans (21-61)
5. LA Lakers (51-31)15. Sacramento Kings (17-65)
6. Houston Rockets (49-33)

Some Thoughts On Each Western Conference Team

  1. San Antonio Spurs-Wemby! Wemby! Wemby! OK, sure they made a lot of mistakes in the 2026 NBA Finals (including the infamous blown 29-point lead in Game 4), but to top the 2025 champion Oklahoma City Thunder to even get to last year’s Finals was truly something on the part of the Spurs. Also, I know Wemby’s the big star here, but as with any NBA team, one star can’t carry the whole team on their own-fortunately, Wemby must’ve had confidence to take a little less money than he could’ve made in order to keep the Spurs’ core together. Question is, can they bounce back from last year’s Finals loss to capture the 2027 championship? I like their odds.
  2. Oklahoma City Thunder-Even though their quest for back-to-back championships fell short last season, their stellar championship-winning starting five of Shai Gilgeous-Alexander/Cason Wallace/Jalen Williams/Chet Holmgren/Isaiah Hartenstein are coming back for more this season. Depth-wise, the Thunder are in a great spot, and they’ve also got solid backup guys like rookie Aday Mara that can certainly contribute if/when they’re needed. I wouldn’t be surprised to see a Spurs-Thunder rematch in the Western Conference Finals this year.
  3. Minnesota Timberwolves-As I mentioned in my Hornets blurb, I think the LaMelo Ball-Naz Reid trade will ultimately benefit both teams. However, whether the Timberwolves live up to the hype this season will depend on a lot including Jonathan Kuminga’s fit on the team, Jaden McDaniel’s offensive performance and the defensive performance of Rudy Gobert (along with catching all those lobs). If all works out for the Timberwolves, I can say they might be “dark horse” contenders in the West.
  4. Denver Nuggets-Despite some earlier-than-expected playoff exits in the last few years, I can agree with running back the Nuggets’ starting five of Christian Braun/Jamal Murray/Aaron Gordon/Cameron Johnson/Nikola Jokic (of course). Why would I think the Nuggets should keep things the same? They’re good. It’s that simple. Especially because four of the starting five were around for the Nuggets’ 2023 championship run (save for Johnson who joined the team last season). I also think that DeMar DeRozan is a nice veteran addition and can definitely provide scoring help off the bench, as he was one of the few bright spots on an awful Kings team.
  5. LA Lakers-It’s a brand new era for the LA Lakers after the departure of King James. Luka Doncic is the star of the Lakers Show now, and from the looks of things, I think he’s got a solid supporting cast with guys like Walker Kessler and Sandro Mamukelashvili. Their defense might not be as good as their offense, and they might not be the ones to dethrone the Thunder or Spurs for top of the West, but I see a solid postseason run for the Lake Show. Plus, I can’t help but wonder if the Lakers will keep Bronny James (he’s still on the team as of this writing in October 2026)
  6. Houston Rockets-Great regular seasons but falling short in the Western Conference First Round-that can sum up the Rockets’ last two seasons. For what it’s worth, the Rockets will sure be glad to have Fred VanVleet and Steven Adams back to boost both their offensive and defensive performances. The Rockets’ offense will likely run through Alpern Sengun though, as he put up a stellar performance and a triple-double during the Rockets’ preseason opener in China. Plus, they’ve still got Kevin Durant, who still put up solid numbers for his age-37 season.
  7. Utah Jazz-Breathe a sigh of relief Utah Jazz, because I think your rebuild is complete. OK, I don’t think they’ll all of a sudden be “dark horse” contenders ready to threaten the Spurs-Thunder dominance, but given the talent they have, I’m rooting for them to at least make play-in. One particular player worth watching is Jaren Jackson Jr, who was acquired last midseason from the Memphis Grizzlies. Knee issues limited him to only three games after he was traded, but with him healthy once more, I’m excited to see how he fares in the Jazz Starting 5, especially with Jusuf Nurkic out for the early part of the season due to a toe injury.
  8. Dallas Mavericks-Kyrie is back. Cooper Flagg will hopefully show some more growth in his second NBA season. I like some of the Mavericks’ acquisitions like Santi Aldama and 2024 #1 pick Zaccharie Risaccher, but perhaps the most interesting part of the Mavericks’ season will be to see how well coach Dusty May adjusts to the NBA (he’s only ever coached college basketball). At the very least, let’s hope Dusty May’s stint doesn’t end up like another college-to-pro coach in Urban Meyer (yea I know that’s football, but boy was that a disastrous Jacksonville Jaguars stint).
  9. Pheonix Suns-Devin Booker is a Suns great, no matter how many subpar seasons they have. Last season was hit-or-miss for the Suns, but they did get a play-off spot through the play-ins. Miles Bridges is a solid acquisition, but let’s see if he can improve his 3-point shooting. I think the Suns’ record will be perfectly even at 41-41, neither stellar enough to be a contender nor awful enough to warrant a years-long rebuild.
  10. Golden State Warriors-What more do Stephen Curry and Draymond Green have left to prove? I mean, however this season plays out, Curry and Green will certainly have their jerseys hanging in the Chase Center’s rafters once they retire. However, between a bunch of guys in the twilights of their playing careers like Green (36), Curry (38), Jimmy Butler (37) and Al Horford (yes he’s still around at 40) who’ve each had their share of recent injuries and a lack of exciting additions to the team aside from rookie Yaxel Lendeborg, I don’t have a ton of optimism for the Warriors this season. But maybe, just maybe, Jimmy Butler will recover from his torn ACL in the second half of the season and give the Warriors just enough of a boost to eke into the 10-seed play-in spot.
  11. Portland Trail Blazers-It’ll be good to see Dame Time (Damian Lillard) back on the court for the Portland Trail Blazers. In relation to Lillard, it’ll be interesting to see the Trail Blazers’ new Lillard & Ja Morant back-court tandem. Now the questions with the Ja Morant acquisition are A) Can Ja stay healthy for more than half the season and B) Can Ja stay out of trouble-assuming the answer to both questions is yes, the Damian Lillard/Ja Morant duo will be fun to watch. Scoot Henderson and Deni Avidja have also shown their potential last season-but can new coach Micah Nori make all the pieces fit? I know I placed the Trail Blazers at the 11-seed in the West, but that’s only because I have so many questions about how they will fare with the new coach, new superstar in Ja, and Damian Lillard’s second stint with the team.
  12. Memphis Grizzlies-The Grizzlies finally decided that Ja wasn’t worth the trouble and instead decided to pivot towards their hopefully new superstar in Cam Boozer. I like what the Grizzlies are doing with Boozer and Zack Edey and Cedric Coward in their front-court-plus they have other solid role players like Ty Jerome. However, given some of the Western Conference competition this upcoming season, I don’t see them winning tons of games but rather focusing on developing Cam Boozer’s potential.
  13. LA Clippers-Five years of no-first round draft picks (from 2029-2033), a $30 million fine for the team, a ban from team activity participation for owner Steve Ballmer for 1 year, and Kawhi getting off with basically a slap on the wrist ($700,000 fine, but that’s way less harsh compared to what the Clippers got). However you slice it, the Aspiration scandal was rough for the team. Scandal aside, it looks like the injury bug is hitting the team hard already as guys like Brandon Ingram (Achilles), Max Strus (foot) and Jordan Miller (shoulder) won’t be available come opening night (and Ingram and Miller are in jeopardy of missing the entire season). And Bradley Beal is still not ready to play after the hip fracture and subsequent surgery that limited him to just SIX games last season, but the Clippers gave him a 2-year/$13.2 million deal. Oof.
  14. New Orleans Pelicans-After a 26-56 season, the Pelicans are out making big, big moves. I mean, they signed 38-year-old DeAndre Jordan to a 2-year/$7.9 million contract! That’s pretty much it. Other than that, it seems the Pelicans are running it back and hoping for the best with new coach Jamahl Mosley. Really? I mean, they still have Zion Williamson after 7 seasons, and he had one of his healthier seasons last year, but the rest of the team’s performance looked uninspired. It’s going to be a long season for the Pelicans.
  15. Sacramento Kings-Unlike the Pelicans, I see a silver lining for the Kings, namely in rookie Darius Acuff Jr who looked promising in preseason. I’d say the Kings should focus on developing Acuff Jr and overhaul most of their roster, as I don’t see too many other bright spots for the Kings (and I can’t help but wonder if they’ll finally release Domantis Sabonis). I mean, managing a 22-60 record last season with DeMar DeRozan and Russell Westbrook (in his farewell season) doesn’t give me high confidence in the Kings this season. The Ben Simmons project will be interesting to watch though given that he wasn’t signed by a team at all last season, but his numbers weren’t all that spectacular during his 2024-25 season-especially when compared to his Sixers numbers.

And now, for Michael’s Postseason Predictions

That’s right! Not only will I give you guys my predictions for the regular season, I’ll also throw in Michael’s Postseason Predictions as well to make things interesting!

But before we get into that, let’s check out Michael’s Play-in Predictions. Here are my predictions for the Eastern Conference play-ins.

In the East, we have the Cavaliers, Pistons, Hawks and Hornets vying for a play-off spot via the play-in tournament. While I think the Cavaliers will breeze through the play-ins, the 7/8 loser-9/10 winner game of Pistons-Hornets will be close, but I think the Hornets will finally end their playoff drought (they came close in 2026 and haven’t made the playoffs since 2016).

Here are my predictions for the Western Conference play-ins.

As for the Western Conference play-in, it appears the Utah Jazz’s rebuild will appear to finally pay off by getting them a 7-seed in the playoffs. The Dallas Mavericks’ luck might finally change as well after the rough post-Luka-trade period with a healthy Kyrie Irving and a second-year Cooper Flagg.

And now for the juicy part-Michael’s Complete 2027 NBA Playoff Predictions Bracket!!!

As you can see from this bracket, I think we’re in for a pretty competitive 2027 postseason, complete with what I think will be a legendary Sixers-Spurs finals (which I think will be quite competitive going 6 or 7 games). However, I think the Spurs might’ve learned their lessons from their mistakes in last year’s Finals loss to the Knicks and come out as the 2027 NBA champion. Plus, the core of Victor Wembanyama, Stephon Castle and Dylan Harper all came back and are all just entering their primes (and Tobias Harris is a nice veteran touch).

Thanks for reading, and enjoy the 2026-27 NBA season!

Michael

And Now for a Third Crack at NBA Python Regression Season Predictions (2026-27 season edition)

Hello everyone,

Michael here, and in this post, we’ll continue our annual NBA season prediction series by giving you the upcoming NBA season’s predictions from my magic regression Python model (or the Michael Model)!

Since this is now the third season I’m doing an NBA predictions Python post, I won’t need to explain my Python model too much as I’ll use pretty much the same model logic I used for last season’s predictions (albeit with different coefficients to account for the 2025-26 statistics for each team). Here’s an explanation of the model I used for the 2025-26 season: Another Crack At Linear Regression NBA Machine Learning Predictions (2025-26 edition).

We will still need an updated spreadsheet with each team’s statistics for the 2025-26 season (remember I use data from the 10 most recent seasons to generate model predictions). Here’s the latest version of the spreadsheet, complete with 2025-26 statistics for each team:

NBA analysis 2026-27Download

Since I’m using the same logic for this year’s model that I used for last year’s model, I won’t go over the whole model development process again (but I will still give the GitHub file for the notebook for anyone who’d like to look at it). However, here are some quick refreshers:

  • Once again, I’ll be using a linear regression model to generate some NBA team-by-team record predictions for the 2026-27 season.
  • The projected wins will obviously be the dependent variable, but like last season, I’ll use the following independent variables: field goal %, 3-point %, 2-point %, end-of-season seeding, total rebounds, total assists, total steals, total blocks, and total turnovers (and of course, the y-intercept).
  • I’ll also keep the weighted average logic the same-the 3 previous seasons (2023-24 to 2025-26) will carry a 0.2 weight while the 3 prior-prior seasons (2020-21 to 2022-23) will carry a 0.1 weight and the 4 past-most season (2016-17 to 2019-20) will carry a 0.025 weight. Yes, the NBA bubble was SEVEN full seasons ago.
  • I’ll still follow the same seeding logic for the model’s generated predictions-i.e. a team with a projected win total of 42.456423 will be seeded higher than a team with a projected win total of 42.232421

Without further ado, here’s the equation powering this season’s NBA linear regression record predictions:

It’s another mouthful of an equation, but don’t worry, I wrote it so that you could tell which variables are which in this equation (twop and threep are 2-point percentage and 3-point percentage, respectively).

As for this year’s model’s accuracy, let’s see what we get:

from sklearn.metrics import mean_absolute_percentage_error
mean_absolute_percentage_error(y_test,yPredictions)
0.07401438823215295

With this MAPE (mean absolute percentage error), it seems like this year’s model generated predictions are 93% accurate-which is good because in all three years I’ve been doing this prediction series, I’ve always gotten a 90% accuracy or higher on the model-generated predictions (now as to how the season will actually play out-that’s another story).

Just a friendly reminder here

As you all probably have guessed by now, Pythonically predicting the outcomes of the upcoming NBA season has become a favorite component of Michael’s Programming Bytes. However, these predictions are purely meant to be for educational/just-for-fun purposes only-not to place bets and parlays all season long. I’m your friendly neighborhood programmer/techie/basketball aficionado, not your friendly neighborhood sportsbook. The only over/unders I’ll discuss on this blog are what bit of code goes over or under another bit of code in a given script. So enjoy these annual predictions/projected power rankings and as always, please bet responsibly.

What does the model say?

This season, I’m going to something a little different with these NBA predictions. Since I don’t need to explain the model-building process yet again, I’ll dive right into analyzing the model’s generated predictions for each team. As for my personal NBA season predictions, you’ll just have to wait until the next post comes out (yes this is a 3-part series).

Anyway without further ado, here are the model’s juicy team-by-team NBA season predictions along with this year’s weighted averages file (note that the team names aren’t part of the output-I just wrote them in for easy reference):

weighted averages 2026-27Download
import pandas as pd
NBAAVG = pd.read_csv(r'C:\Users\mof39\Downloads\weighted averages 2026-27.csv')
for n in NBAAVG['Team']:
    print(27.151*NBAAVG['FG%'] + 105.763*NBAAVG['2P%'] + 47.931*NBAAVG['3P%'] - 2.012*NBAAVG['Finish'] + 0.013*NBAAVG['TRB'] - 0.005*NBAAVG['AST'] + 0.021*NBAAVG['STL'] + 0.002*NBAAVG['BLK'] - 0.011*NBAAVG['TOV'] - 56.03)
    break
0     52.553574 (Atlanta Hawks)
1     67.951942 (Boston Celtics)
2     44.637496 (Brooklyn Nets)
3     41.429294 (Charlotte Hornets)
4     48.376214 (Chicago Bulls)
5     59.234993 (Cleveland Cavaliers)
6     51.174871 (Dallas Mavericks)
7     64.565128 (Denver Nuggets)
8     46.580069 (Detroit Pistons)
9     53.999023 (Golden State Warriors)
10    52.995726 (Houston Rockets)
11    50.296028 (Indiana Pacers)
12    57.377464 (LA Clippers)
13    55.285604 (LA Lakers)
14    48.056517 (Memphis Grizzlies)
15    52.112257 (Miami Heat)
16    61.753876 (Milwaukee Bucks)
17    52.305955 (Minnesota Timberwolves)
18    47.193190 (New Orleans Pelicans)
19    54.182001 (New York Knicks)
20    61.405040 (Oklahoma City Thunder)
21    44.694636 (Orlando Magic)
22    54.200383 (Philadelphia 76ers)
23    46.790599 (Phoenix Suns)
24    48.949168 (Portland Trail Blazers)
25    44.678002 (Sacramento Kings)
26    49.909715 (San Antonio Spurs)
27    56.431624 (Toronto Raptors)
28    50.502404 (Utah Jazz)
29    42.900653 (Washington Wizards)
dtype: float64

Something interesting to note here is that, unlike past years when I’ve done these NBA predictions posts, the model projects that no team will have a losing record and all teams will finish the season with at least 41 wins. In fact, the projected win range for these teams (according to the Michael Model) is between 41-68 wins. Now, whether even half the teams pull off that record is another story as more often than not, there are a handful of NBA teams that don’t even pull off 20 wins in a season (ahem, Pacers and Wizards last season). Also, I can’t imagine a team with at least 50 wins in a season gets relegated to the play-in…but in the NBA, it’s anyone’s season until the Larry O’Brien trophy is lifted (yes, that’s the name of the NBA championship trophy).

Just for historical context, my 2024-25 season model (the first model) had a projected win range of 24-54 wins and my 2025-26 season model (the second model) had a projected win range of 33-59 wins. Perhaps now that the legendary COVID bubble season is now 7 years in the past, there’s less weight on it and the subsequently shortened 2020-21 season, which could skew the model’s projected win totals.

And now, for the Michael Model 2026-27 NBA season predicted standings!

Last but certainly not least, here’s the Michael Model’s projected 2026-27 standings, starting with the Eastern Conference:

Play-OffsPlay-InsMaybe Next Year
1. Boston Celtics (68-14)7. Atlanta Hawks (53-29)11. Detroit Pistons (47-35)
2. Milwaukee Bucks (62-20)8. Miami Heat (52-30)12. Orlando Magic (45-37)
3. Cleveland Cavaliers (59-23)9. Indiana Pacers (50-32)13. Brooklyn Nets (45-37)
4. Toronto Raptors (56-26)10. Chicago Bulls (48-34)14. Washington Wizards (43-39)
5. Philadelphia 76ers (54-28)15. Charlotte Hornets (41-41)
6. New York Knicks (54-28)

And now, for the Michael Model’s Western Conference predicted standings:

Play-OffsPlay-InsMaybe Next Year
1. Denver Nuggets (64-18)7. Minnesota Timberwolves (52-30)
11. Portland Trail Blazers (49-33)
2. Oklahoma City Thunder (61-21)
8. Dallas Mavericks (51-31)		12. Memphis Grizzlies (48-34)
3. LA Clippers (57-25)
9. Utah Jazz (50-32)		13. New Orleans Pelicans (47-35)
4. LA Lakers (55-27)10. San Antonio Spurs (50-32)14. Phoenix Suns (47-35)
5. Golden State Warriors (54-28)
15. Sacramento Kings (45-37)
6. Houston Rockets (53-29)

What are some interesting takeaways we can get from the Michael Model’s predictions for the Eastern and Western conference standings:

  • In basically every NBA season ever, there are always teams that lose more than half their games. That’s just statistical fact. Yet, the equation managed to generate a 41-67 win range for teams this year-I guess it’s just one of the quirks of trying to predict sports seasons using mathematical models.
  • I have my doubts that the Milwaukee Bucks will make the playoffs this year, let alone finish as the 2-seed in the East. After all, they lost the last of their 2021 championship players-Giannis Antentokounumpo and Bobby Portis-to Miami and seem to currently be in rebuild mode with the acquisitions of FOUR former Miami Heat players in Tyler Herro, Jaime Jaquez Jr, Kel’el Ware and Kasparas Jakuconis.
  • Sorry in advance Kings fans, but I think the model was right to assume they’ll finish at the bottom of the West this year. Sure, Darius Acuff Jr is a bright spot on a pretty sad Kings squad and they did release veterans Russell Westbrook and DeMar DeRozan, but I think they’ve still got a ways to go in rebuild mode (or in mid-2010s Sixers speak, they need to “Trust the process”)
  • On the other side of the play-off coin, two teams I’m surprised the model relegated to play-in are the Miami Heat and the San Antonio Spurs. Personally, I think both teams will crack the top 6 in their conference because of all the superstar offseason acquisitions in the Heat’s case (Giannis Antentokounumpo, Bobby Portis Jr, Tim Hardaway Jr, Klay Thompson) and the conference championship core of Victor Wembanyama, Stephon Castle, and Dylan Harper in the Spurs’ case (plus the acquisition of power forward Tobias Harris can certainly provide a veteran boost).

Here’s the Colab notebook with the code for this model in my GitHub-https://github.com/mfletcher2021/blogcode/blob/main/NBA_26_27_predictions.ipynb.

Thanks for reading! Stay tuned for my next post, where I will give my personal upcoming 2026-27 NBA season predictions/power rankings (which I bet will be quite different and perhaps a little juicier than the model’s projections).

2025-26 NBA Season Predictions Revisited: Knicks Win & NBA Tank-a-thon 2026

Hello everyone,

Michael here, and I’m back with my annual NBA season reflection/predictions posts. As usual, I’ve got a juicy good three-part series for you starting with my NBA predictions revisited and reflected from the previous season (2025-26) followed by my regression model and predictions for the upcoming season (2026-27).

What a wild ride this past NBA season was…let’s reflect on the results of Michael’s Programming Bytes NBA season fortune telling (to revisit my previous season’s predictions, here’s where to go-And Now For Michael’s Programming Bytes 2025-26 NBA Season Predictions).

Yes, I know this isn’t my usual tech content, but I’ve had fun writing these posts for the past two NBA seasons and plan to keep this series as long as the blog’s running.

Let’s dive in!

Thoughts on the 2026 NBA Finals

First off, let’s look back on an amazing NBA Finals. The Spurs and the Knicks gave it their all through 5 games, but alas, the New York Knickerbockers (yes, Knicks is short for Knickerbockers) got their first championship since 1973, beating the Spurs in 5 games (and winning the title on the road).

Just like last year’s Pacers-Thunder Finals, this Finals was full of storylines in its own right, even though the winner was decided in five games. The trio of Victor Wembanyama, Stephon Castle, and De’Aaron Fox was a sight to behold, and even though they and the rest of the Spurs came up short in the championship, guys like Wemby and Castle have yet to hit their primes, so once they do, I see some deep playoff runs in the Spurs’ future.

Now, about the Knicks. Sure, the Spurs did a great job of powering through the Finals matchups…for the first half. Unfortunately for them, their playoff inexperience showed quite a bit during the Finals, as they lost some of their biggest leads (including a blown 29 point lead in game 4) late in the second half due to unfortunate mistakes on the Spurs’ part-like in the aforementioned game 4 when De’Aaron Fox tried to score a layup in the game’s final seconds but got blocked by OG Anunoby of the Knicks (he should’ve just tried to run out the clock that game, but that’s just my opinion). I mean, Wemby was lucky that he didn’t six flagrant foul postseason points, which would’ve caused him to miss crucial Finals matchups (he did come quite close to this in game 5).

I will say this though, Knicks, take a bow, you earned it. And also try to keep Jalen Brunson around for as long as you can, because even though he’s on the shorter end of NBA players (6’2″), nights like his 45-points in Game 5 show that even the small guys can excel (for context, Wemby is 7’4″).

Eastern Conference 2025-26 predictions revisited

Now, how did my Python regression model think the past season’s Eastern Conference was going to play out?

Play-OffsPlay-InsMaybe Next Year
1. Boston Celtics7. Miami Heat11. Toronto Raptors
2. Cleveland Cavaliers8. Orlando Magic12. Brooklyn Nets
3. Milwaukee Bucks9. Chicago Bulls13. Washington Wizards
4. New York Knicks10. Atlanta Hawks14. Charlotte Hornets
5. Indiana Pacers15. Detroit Pistons
6. Philadelphia 76ers

Since I also decided to add predictions from the brain of Michael himself, let’s see what he thought last season’s Eastern Conference was going to look like:

Play-OffsPlay-InsMaybe Next Year
1. New York Knicks7. Orlando Magic11. Toronto Raptors
2. Cleveland Cavaliers8. Milwaukee Bucks12. Philadelphia 76ers
3. Boston Celtics9. Atlanta Hawks13. Brooklyn Nets
4. Detroit Pistons10. Chicago Bulls14. Charlotte Hornets
5. Miami Heat15. Washington Wizards
6. Indiana Pacers

With both the model’s and my predictions revealed, let’s see how things actually played out for the 2025-26 Eastern Conference:

Play-OffsPlay-InsMaybe Next Year
1. Detroit Pistons7. Philadelphia 76ers (playoff)11. Milwaukee Bucks
2. Boston Celtics8. Orlando Magic (playoff)12. Chicago Bulls
3. New York Knicks9. Charlotte Hornets13. Brooklyn Nets
4. Cleveland Cavaliers10. Miami Heat14. Indiana Pacers
5. Toronto Raptors15. Washington Wizards
6. Atlanta Hawks

So, compared to the actual outcomes of the Eastern Conference, how accurate were my predictions and the model’s predictions:

  • Play-off teams (in top 6): 3/6 (model), 4/6 (me)
  • Play-in teams (7th-10th places): 2/4 (model), 1/4 (me)
  • Teams out of postseason picture (in bottom 5): 2/5 (model), 2/5 (me)
  • Teams with exact seeding regardless of finish: 1/15 (model), 2/15 (me)

By the way, the team that the model got the exact seeding for was the 8-seed Orlando Magic. The teams that I got the exact seeding for were the 13-seed Brooklyn Nets and 15-seed Washington Wizards (interesting I got two non-postseason teams’ seeding exactly right). As for the Eastern Conference playoff picture, both the model and I got 7 of the 15 teams in the right playoff positioning (meaning whether the team would make play-offs, play-ins, or neither regardless of seeding). Although I can proudly say that I did better than the model when it came to guessing who will make the play-offs (top 6).

Top 5 Eastern Conference takeaways

  • Even though the Miami Heat had another up-and-down season (despite improving to 43-39 this year) and yet another play-in appearance, I think the splashes they made in the offseason could make a big, big difference in their 2027 championship aspirations. Regardless if the Heat come out on top next season, GM Pat Riley certainly made some juicy swings during free agency with signings like Tim Hardaway Jr, Bobby Portis Jr and of course the superstar Heat fans were hoping to land…Giannis Antetokounmpo. I also think keeping Andrew Wiggins, Davion Mitchell, and of course, Mr. 83-point-game-on-a-Tuesday-in-March Bam Adebayo (that was certainly a bright spot in an up-and-down season). Now, whether Giannis is an improvement over whom he was traded for (Tyler Herro, Kel’el Ware, Jaime Jaquez Jr, and Kasparas Jakuconis) remains to be seen, as Giannis did have an injury-riddled year last season and turns 32 in December. Nonetheless, kudos to Pat Riley on what might be his last big superstar acquisition. Perhaps these big swings could, at last, get the Heat out of play-in limbo. Having a proven 4-time NBA champion with the Warriors dynasty in Klay Thompson certainly will make for some juicy storylines, even if Klay turns 37 this season.
  • Despite losing Jayson Tatum to an Achilles tear for the bulk of the season (which limited him to just 22 regular season games played), the Celtics still managed to pull off an impressive 56-26 record and a 2-seed in the East. Even without Tatum, the Celtics’ supporting players certainly compensated for his absence. Whether it’s Jaylen Brown’s (in what became his last season with the Celtics-more on that later) NBA-leading 736 field goals total this past season (that’s FIVE more than Shai Gilgeous-Alexander’s field goal total this season), Derrick White’s stellar defensive work (98 blocked shots this season, for one), the Celtics’ league-lowest turnovers-per-game rate (just 12.37), or any of the other accomplishments by the Celtics, they certainly proved their resilience…in the regular season. As for the play-offs, well, Boston certainly pulled a 2016-Warriors-in-the-Finals and blew a 3-1 lead…to the 7-seed, play-in Philadelphia 76ers-which brings me to my next point.
  • Even though the Sixers got swept by the championship Knicks in the Eastern Conference Semifinals, they certainly made a splash in the offseason. Along with keeping VJ Edgecombe, Tyrese Maxey (two players who certainly have a bright future in the league), and of course Joel Embiid, the Sixers sent Paul George off to the Celtics in exchange for Jaylen Brown and also made the biggest splash of the NBA free agency period when they signed LeBron James (Sr, not Jr) to a 2-year, $8 million deal right before his age-42 season. I mean, with a starting five of Embiid/Maxey/Edgecombe/Brown/James, I think they could be a dark horse contender this season (and certainly give the champion Knicks a run for their money). I’m still shocked the Celtics were willing to let go of Jaylen Brown of all people and trade him for a 36-year-old Paul George, but such is the cost of the NBA’s second apron I guess.
  • Now, regarding the Wizards, who’ve managed to do the impossible in that they’ve managed TWO bottom-of-the-East seasons in a row and THREE seasons where they were in the bottom 2 of the East (2023-24 was slightly better for the Wizards as they finished as the 14-seed), at least the model, myself, and the actual outcome agreed on one thing-they wouldn’t even crack play-in this year (though the model was a bit more generous giving the Wizards a 13-seed spot). Needless to say, every cloud has their silver linings. The mid-season acquisitions of Trae Young and Anthony Davis certainly added a strong veteran foundation-even if Davis has yet to play a game in a Wizards uniform and Young having barely played at all after he was traded from the Hawks. Other bright spots in an abysmal Wizards season include Alex Sarr and Kyshawn George. Personally, I’m looking forward to seeing what 6’9″ #1 overall pick AJ Dybansta could bring to the table. With the slow-but-steady rebuild going on in Wizards world, what could this mean for their playoff hopes next season? I still think there’s a lot of work to do and doubt the Wizards will be some sort of dark horse playoff contender for next season (leave that to the Sixers to play dark horse, in my opinion). Could the Wizards possibly sneak into the play-in? It’s likely, but I think that may be their 2026-27 ceiling. At least the Wizards got the #1 overall 2026 NBA draft pick for their troubles and in turn, received the aforementioned AJ Dybansta (perhaps their small forward of the future)?
  • I know the Giannis-to-Miami trade was THE story of 2026 free agency, and I already discussed how I think Miami may fare, but let’s reflect on the team at the other end of the trade-the Milwaukee Bucks. The Bucks finished just below the Heat (11-seed to the Heat’s 10-seed) and unlike the Heat, fell out of the postseason picture entirely-at least the Heat made it to overtime of the first play-in game against the Hornets before yet another early Heat postseason exit. As for the Bucks, did the loss of stars like Damian Lilliard and Brook Lopez hurt? I think so, and I don’t think former longtime Pacers (and fresh off the 2025 Finals) starter Myles Turner had a stellar first year with the Bucks given his lackluster per-game-points and declining minutes played throughout the season. The Giannis drama wasn’t much help for the Bucks, especially with the Bucks keeping him at the midseason trade deadline and Giannis’s left knee hyperextension injury that led the Bucks to bench him for the last 15 games of the season (much to the frustration of Giannis). Now, will the former Heat foursome of Tyler Herro/Kel’el Ware/Jaime Jaquez Jr/Kasparas Jakuconis be an improvement over Giannis and Bobby Portis Jr. We’ll see, but these four former Heat stars are still in the primes of the careers (Herro is the oldest of the bunch at 26) and have put up solid numbers during their Heat days.

Western Conference 2025-26 predictions revisited

Now that we’ve reflected on this past season in the Eastern Conference, let’s shift our focus to the West!

How did my Python regression model think the Western Conference was going to play out?

Play-OffsPlay-InsMaybe Next Year
1. Oklahoma City Thunder7. Golden State Warriors11. Houston Rockets
2. LA Clippers8. Phoenix Suns12. Utah Jazz
3. Denver Nuggets9. Sacramento Kings13. New Orleans Pelicans
4. Memphis Grizzlies10. Dallas Mavericks14. Portland Trail Blazers
5. Minnesota Timberwolves15. San Antonio Spurs
6. LA Lakers

Now let’s see how I thought the 2025-26 Western Conference was going to play out:

Play-OffsPlay-InsMaybe Next Year
1. Oklahoma City Thunder7. Golden State Warriors11. Dallas Mavericks
2. Houston Rockets8. LA Clippers12. Memphis Grizzlies
3. Minnesota Timberwolves9. Sacramento Kings13. Utah Jazz
4. Denver Nuggets10. San Antonio Spurs14. Portland Trail Blazers
5. Phoenix Suns15. New Orleans Pelicans
6. LA Lakers

Last but not least, let’s see how the 2025-26 Western Conference actually played out:

Play-OffsPlay-InsMaybe Next Year
1. Oklahoma City Thunder7. Phoenix Suns (playoff)11. New Orleans Pelicans
2. San Antonio Spurs8. Portland Trail Blazers (playoff)12. Dallas Mavericks
3. Denver Nuggets9. LA Clippers13. Memphis Grizzlies
4. LA Lakers10. Golden State Warriors14. Sacramento Kings
5. Houston Rockets15. Utah Jazz
6. Minnesota Timberwolves

Just as we discussed with my Eastern Conference predictions, let’s compare the model’s predictions, my predictions and the Western Conference’s actual outcomes:

  • Play-off teams (in top 6): 4/6 (model), 5/6 (me)
  • Play-in teams (7th-10th places): 2/4 (model), 2/4 (me)
  • Teams out of postseason picture (in bottom 5): 2/5 (model), 4/5 (me)
  • Teams with exact seeding regardless of finish: 2/15 (model), 1/15 (me)

When compared to my Eastern Conference predictions, my Western Conference predictions turned out much more accurate. In fact, I got 5/6 top-6 playoff teams along with 4/5 teams out of the post-season. The two seeds the model got right were the 1-seed Thunder and the 3-seed Nuggets (I just got the 1-seed Thunder).

Top 5 Western Conference takeaways

  • Let’s start with the defending champs heading into the season-the Oklahoma City Thunder. First of all, going 1-seed in the Western Conference for three years straight is no easy feat, so my kudos to OKC. What else to reflect on? 64-18 record, including the stellar 24-1 start (for their first 25 games). Sure, their win total did go down from their championship winning season (though many teams would kill for 64 or 68 wins), but they came back this season still stronger than ever. Keeping the championship-winning Big 3 of Shai Gilgeous-Alexander, Chet Holmgren and Jalen Williams certainly helped, as did playoff sweeps of the Suns (First Round) and Lakers (Semifinals). Even though the Thunder’s luck ran out in Game 7 of the Western Conference finals against the San Antonio Spurs (key players like Jalen Williams and Ajay Mitchell missed the bulk of the Western Conference Finals due to injuries), the Thunder still had a stellar season nonetheless-even with 31 different regular season starting lineups.
  • Every year, there are always some NBA teams that are quite the enigma. They’re not competitive enough to make deep playoff runs, and yet, they choose to run it back with the same crew. Case in point-the New Orleans Pelicans. 26 wins, 11-seed, not even making the play-ins and what do they do? Re-sign their 38-year-old center DeAndre Jordan to a 1-year veteran minimum and…that’s pretty much it. You would think the Pelicans would be much, much more motivated to shake things up, but lo and behold, they’re likely running it back with most of the same crew (save for Kevon Looney who left for the Lakers)-and new former Magic head coach Jamahl Mosley. For what it’s worth, Zion Williamson (yes, he’s still with the Pelicans) puts up solid numbers when healthy-averaging at least 21 points per game. Will this be the year Zion (and the Pelicans) get their big break? It will make for a juicy regular season storyline.
  • Another team that could fall under the category of “not competitive enough to make deep playoff runs but they still run it back with most of the same crew” could definitely be the Golden State Warriors. Yes, the second half of the 2010s was their dynasty years (with four straight years of Warriors-Cavaliers Finals) and yes, the Warriors still have Steph Curry, Draymond Green and Coach Steve Kerr from the dynasty days. However, Steph and Draymond aren’t getting any younger (now 38 and 36, respectively) and neither are many key Warriors players-like 36-year-old Jimmy Butler or 40-year-old Al Horford. Now I know the Warriors were hoping to land LeBron (think of all the nationally televised games and juicy storylines we could’ve gotten) but alas, LeBron took his talents to the City of Brotherly Love. With that said, the Warriors, like the Pelicans, will be running it back with most of the same crew-plus their lottery pick of Yaxel Lendeborg-that stumbled to a 10-seed play-in spot last season. Hopefully Jimmy Butler returns from the knee injury that ended his season back in February, or it could be a long year for the Warriors. After all, Steph Curry and Draymond Green are in the twilights of their basketball careers, so I think one last championship run for these two would end their NBA careers on a high note. I mean, even if one last ring for Steph and Draymond isn’t in the cards, perhaps a reunion with some old dynasty favorites like Klay Thompson and Kevon Looney would be fun to watch just for old time’s sake.
  • One team that surprised me quite a bit last year was the LA Clippers. A rough 6-21 start, losing Bradley Beal for the season after getting just six games with him, and rather unceremoniously (and disrespectfully) sending Clippers legend Chris Paul home after a December road game, and a still-ongoing salary cap circumvention scandal involving Kawhi Leonard doesn’t seem like it would bode well for the Clippers. But then, a Christmas miracle came for the Clippers-they would turn their fortunes around and end up with a 22-24 record by the end of January. But wait, by the time the season is all said and done, the Clippers somehow managed to obtain a winning record of 42-40 and wind up with a 9-seed play-in spot. Granted, they lost to the 10-seed Warriors in the 9/10 play-in game, but boy this was likely the mid-season turnaround of the 2025-26 NBA season. Now, the Clippers did send Kawhi back to Toronto for Gradey Dick and Brandon Ingram…that is, IF this Kawhi scandal does get resolved (that trade is still on hold and Kawhi is still with the Clippers as of this writing). I personally like the Clippers’ midseason trade with the Cavaliers for Darius Garland (in exchange for James Harden) and hope the Clippers’ eventually get Ingram and Dick.
  • Let’s also explore the tanking adventures for the two bottom teams in the Western Conference-the Kings and Jazz. Both teams once again had rough years, but as to who might have the brighter future (and might look way better next year), I’d have to go with the Jazz. I mean, I can’t help but feel sorry for the Kings’ luck in recent years, as their former coach Mike Brown (2026 Knicks), and former stars Tyrese Haliburton (2025 Pacers) and De’Aaron Fox (2026 Spurs) have gone on to enjoy NBA Finals and playoff success with other teams. Losing Domantis Sabonis, Zach LaVine AND DeAndre Hunter to season-ending injuries last season didn’t help them either, and neither did waiving one of their few bright spots from this past season-DeMar DeRozan. However, I still think the Jazz will come out as the more improved of these two teams because A-some stellar moves in both midseason and offseason (ahem Jaren Jackson Jr) and B-the draft of Darryn Peterson at No.2 overall-although I do think Darryn Peterson needs some more time to find his footing, in which case Keyonte George should tide the Jazz over at point guard. I’d also be interested to find out if the Jazz will keep Kevin Love around for another year, even with the third-lowest minutes per game on the Jazz (16.8) and only playing less than half the games this season (37).

And Now For Some Reflections On NBA Tank-a-Thon 2026

Look, I know it seems like there’s always those teams that can’t help but tank as hard as they can in any given season. However, the tank-a-thon was definitely in force throughout the 2025-26 season (for this definition, we’ll look at teams that finished 27-55 or worse).

  • For those unaware, tanking in sports refers to teams who are putting in as little effort as possible to undergo a long roster rebuild, don’t feel like trying at the end of the season once they’re officially eliminated from playoff contention, or, and perhaps most importantly, want a high draft pick to hopefully be able to draft their next franchise star.

So, who managed to crack tank-a-thon 2026? Unsurprisingly, we’ve got the bottom 3 teams from both the East and West.

  • Brooklyn Nets (20-62)
  • Indiana Pacers (19-63)
  • Washington Wizards (17-65)
  • Memphis Grizzlies (25-27)
  • Sacramento Kings (22-60)
  • Utah Jazz (22-60)

Look, in all fairness to the Pacers, this season always seemed like it was going to be a down year given Tyrese Halibuton’s Achilles tear at the end of the 2025 NBA Finals. With him back, plus with their midseason acquisitions of Ivica Zubac and Kobe Brown from the Clippers, I’ve got a good feeling that the Pacers will be back in playoff contention in no time.

Now as for the rest of the tankers, boy what a wash of a year they had. At least for the Wizards, Jazz and Grizzlies, things might finally start looking up for them after their miserable 2025-26 seasons, as they received the number 1, 2, and 3 picks, respectively in the 2026 NBA Draft, which turned into AJ Dybansta (Wizards), Darryn Peterson (Jazz) and Cam Boozer (Grizzlies). That’s certainly a bright spot for these three teams, especially after things like losing to Miami on Bam Adebayo’s 83-point game night (Wizards), getting a $500,000 NBA fine for sitting out healthy players in the name of the tank (Jazz, but Pacers got caught doing this too), and struggling to find a midseason trade partner for Ja Morant (Grizzlies) before ultimately trading him to the Portland Trail Blazers.

As for the Nets and Kings, well, the Kings were just terrible this year. Certainly losing Domantis Sabonis and Keegan Murray for large parts of the season due to injury didn’t help (Sabonis only played 19 games and Murray just 23). As much as the rest of the Kings tried to make things work with guys like DeMar DeRozan and Russell Westbrook, nothing really clicked amongst the Kings-also, what a disappointing way for Russell Westbrook to end his NBA career. Sadly, the Kings are still stuck with expensive contracts for Sabonis and Zack LaVine. The Nets, on the other hand, seem to want to “trust the process” (sound familiar Sixers fans?) and are in it for the long multi-year rebuild haul. Rookie Egor Demin was a bright spot for the Nets-even with a seemingly lackluster 28.5% 3-point shooting efficiency rate (Demin was eventually sidelined for the rest of the season in February with plantar fasciitis).

Now, NBA commissioner Adam Silver has some juicy ideas to mitigate the issue of tanking, some of which include (and it’ll be interesting to see some of these ideas in execution):

  • Silver’s new 3-2-1 system, where the three worst teams (Pacers, Nets, Wizards last season) will get a LOWER chance of getting the top 3 draft picks than the teams that finished 4th-10th record wise. Will this curb some teams’ tanking antics? We shall see.
  • If the NBA sees some overt tanking on a team’s part, they not only have the power to fine that team but also take away some of their draft lottery balls, which can further reduce their chances of getting a top pick. Yes, the NBA Draft uses lottery balls, much like a real lottery.
  • For what it’s worth, anti-tanking measures will be up for discussion once more in 2029 when it’s time to negotiate the new NBA Collective Bargaining agreement.

An update on the 2025 NBA gambling scandal

Last but not least, you might be wondering how the 2025 NBA gambling scandal that arose at the beginning of this season is playing out. Here are some quick updates:

  • The then-active coach Chauncey Billups and the then-active player Terry Rozier were both placed on leave by the NBA pending the outcome of their criminal proceedings. It’s safe to say at this point that Chauncey Billups’s coaching days are over, and he’s certainly unlikely to return to the Trail Blazers who have since hired former Timberwolves assistant coach Micah Nori as their new head coach. Terry Rozier was waived by the Heat at the end of the 2025-26 season, hasn’t signed with any other team since, and managed to get the Heat a compensatory second-round 2026 NBA Draft pick from the Hornets (Rozier’s game under investigation occurred while he was with the Hornets in 2023) which eventually got the Heat a rookie shooting guard in Ryan Conwell.
  • Damon Jones became the first defendant in the case to plead guilty. In his case, he pled guilty to two counts of wire fraud for his schemes to defraud major sportsbooks like DraftKings and FanDuel along with using his NBA connections to sell LeBron James’s injury insider information to bettors (Jones was working for the Lakers at the time).
  • Chauncey Billups’s federal trial is scheduled to start on November 2, 2026. Terry Rozier’s federal trial is set to start on February 8, 2027.
  • Two more former NBA players were arrested in connection to the scandal-Malik Beasley and Ed Davis. Much like Terry Rozier, Beasley also altered his performance while playing for the Bucks during a game in 2024 against the LA Clippers based off of prop bets for certain games he was playing in. Allegedly, Beasley’s motive was that he was struggling financially, due to gambling losses he accrued, relied on his former teammate Ed Davis for financial assistance, and used the money he won to pay off his debts to Davis.

Wow, what another wild NBA season. Coming up next-my predictions for the 2026-27 NBA season. After all the offseason shakeups this year, we’ll certainly see some juicy matchups with familiar faces in new places. Thanks for reading.

Michael

And Now For Michael’s Programming Bytes 2025-26 NBA Season Predictions

Hello everybody,

Michael here, and in today’s post we will discuss what I think is the fun part about this NBA post prediction series-the predictions themselves.

That’s right, now that we have our model, let’s make some predictions for the season!

Now where did we leave off?

Before we get into the juicy NBA season predictions, let’s first revisit where we left off on the previous post Another Crack At Linear Regression NBA Machine Learning Predictions (2025-26 edition):

Towards the end of the previous post, we generated this equation to assist us in generating our linear regression NBA season predictions for this year. To recap what the equation means:

  • 64.8 * (field goal %)
  • PLUS 113 * (3-point %)
  • PLUS 15.4 * (2-point %)
  • MINUS 1.94 * (seeding at end of season)
  • PLUS 0.011 * (total rebounds)
  • MINUS 0.00346 * (total assists)
  • PLUS 0.0215 * (total steals)
  • PLUS 0.00663 * (total blocks)
  • MINUS 0.0097 * (total turnovers)
  • MINUS 60.38 (the intercept)

That’s quite a mouthful, but I’ll show you the Python calculations we’ll be doing in order to generate those juicy predictions!

  • I’ll admit that even I’m not perfect with my blogs here, as I made a small mistake on the previous post that showed part of the equations as 215 * (total steals) rather than 0.0215 * (total steals). As it turns out, even experienced coders like me make oversights, so apologies for that!

A little disclaimer here

Before we dive in to our predictions, I want to clarify that these are simply win total/conference seeding predictions based off of a simple linear regression model configured by me. I personally wouldn’t use these predictions for any bets or parlays because first and foremost, I am your friendly neighborhood coding blogger, not your friendly neighborhood sportsbook. You can count on me for juicy, way-too-early predictions, but certainly not for any juicy over/unders.

If you do bet on NBA games this season, please do so responsibly! Thank you!

The way of the weighted averages

You may recall that for my post on last NBA season’s predictions, we used weighted averages to help generate the predictions. Since I personally liked that method, I’ll do so again.

Here’s the file with the weighted averages, which we’ll be using to calculate the predictions:

We’ll use the same methodology as we did last year for calculating the weighted averages, which went like this:

  • 2022-23 to 2024-25 (last 3 seasons)-0.2 weight (higher weight for the three most recent seasons)
  • 2019-20 to 2021-22 (three seasons prior to that)-0.1 (less weight for seasons further in the past, plus this timespan does include the two COVID shortened seasons)
  • 2015-16 to 2018-19 (four seasons further back)-0.025 (even less weight for these seasons further in the past)

Now here’s the weighted averages file for all 30 teams:

weighted averages 2025-26Download

So, without further ado, let’s predict some win totals!

import pandas as pd
NBAAVG = pd.read_csv(r'C:\Users\mof39\OneDrive\Documents\weighted averages 2025-26.csv')
for n in NBAAVG['Team']:
    print(64.8*NBAAVG['FG%'] + 113*NBAAVG['3P%'] + 15.4*NBAAVG['2P%'] - 1.94*NBAAVG['Finish'] + 0.011*NBAAVG['TRB'] - 0.00346*NBAAVG['AST'] + 0.0215*NBAAVG['STL'] + 0.00663*NBAAVG['BLK'] - 0.0097*NBAAVG['TOV'] - 60.38)
    break
0     40.21900 (Atlanta Hawks)
1     52.98400 (Boston Celtics)
2     37.83554 (Brooklyn Nets)
3     34.54740 (Charlotte Hornets)
4     40.36300 (Chicago Bulls)
5     50.23470 (Cleveland Cavaliers)
6     43.38590 (Dallas Mavericks)
7     50.17750 (Denver Nuggets)
8     33.35420 (Detroit Pistons)
9     45.65995 (Golden State Warriors)
10    41.07520 (Houston Rockets)
11    45.93936 (Indiana Pacers)
12    51.35416 (LA Clippers)
13    45.88495 (LA Lakers)
14    47.79176 (Memphis Grizzlies)
15    41.26986 (Miami Heat)
16    48.55712 (Milwaukee Bucks)
17    47.12266 (Minnesota Timberwolves)
18    40.65833 (New Orleans Pelicans)
19    47.90818 (NY Knicks)
20    58.57943 (Oklahoma City Thunder)
21    41.25042 (Orlando Magic)
22    43.73122 (Philadelphia 76ers)
23    45.39194 (Phoenix Suns)
24    39.99757 (Portland Trail Blazers)
25    43.89877 (Sacramento Kings)
26    39.38897 (San Antonio Spurs)
27    39.13594 (Toronto Raptors)
28    40.82412 (Utah Jazz)
29    36.85122 (Washington Wizards)

Once I read the weighted averages CSV and ran the equation for all 30 teams, I get the predicted win totals for all 30 teams, which I will use for my way-too-early East/West seeding chart. Note that since the team names aren’t shown in the output, I took the liberty of manually adding each team name by each predicted win total so you know your favorite team’s projected win total (according to my model, of course).

One interesting difference between this year’s projected win totals and last year’s is the narrower range of possible win totals in this year’s model. See, the range of possible win totals in last year’s model was 24-54 wins, while the range of possible win totals in this year’s model is just 33-59 wins. Could the narrower possible win total range be due to the different features I used in this year’s model? It’ll be interesting to see how the season plays out.

Another interesting thing to note is that even though there is a narrower range of potential wins in this year’s model, the majority of teams’ win counts last season fell into this range-20 teams won between 33 and 59 games last season (Knicks, Pacers, Bucks, Pistons, Magic, Hawks, Bulls, Heat, Rockets, Lakers, Nuggets, Clippers, Timberwolves, Warriors, Grizzlies, Kings, Mavericks, Suns, TrailBlazers and Spurs).

How will the win counts look this time around? We’ll see as the season unfolds!

Michael’s Way-Too-Early Conference Seeding:

And now, for the stuff I really wanted to share with you all in this post: Michael’s Way-Too-Early Conference Seeding. Now that we’ve got our projected win totals for each team, it’s time to seed them in their projected spots! But that’s not all I’m going to do!

In addition to the model’s projected seedings, I’ll also give you my own personal seedings for all 30 teams. That’s right-this year, I want to see which set of predictions comes out more accurate-my predications or my model’s predictions. This will be fun to revisit next July once the season wraps up!

Eastern Conference predictions

To begin, let’s start with the model’s Eastern Conference predictions:

Play-OffsPlay-InsMaybe Next Year
1. Boston Celtics7. Miami Heat11. Toronto Raptors
2. Cleveland Cavaliers8. Orlando Magic12. Brooklyn Nets
3. Milwaukee Bucks9. Chicago Bulls13. Washington Wizards
4. New York Knicks10. Atlanta Hawks14. Charlotte Hornets
5. Indiana Pacers15. Detroit Pistons
6. Philadelphia 76ers

And now, let’s see my personal Eastern Conference predictions:

Play-OffsPlay-InsMaybe Next Year
1. New York Knicks7. Orlando Magic11. Toronto Raptors
2. Cleveland Cavaliers8. Milwaukee Bucks12. Philadelphia 76ers
3. Boston Celtics9. Atlanta Hawks13. Brooklyn Nets
4. Detroit Pistons10. Chicago Bulls14. Charlotte Hornets
5. Miami Heat15. Washington Wizards
6. Indiana Pacers

Here are some interesting observations about both the model’s predictions and my own personal predictions:

  • The Eastern Conference teams that made last season’s play-in (Heat, Hawks, Bulls, Magic) are the same ones projected to make another go at play-ins this year. In other words, could we see the same teams stuck in another year of play-ins?
  • Personally, I think the Hawks, Bulls and Magic will make another trip to the play-in. On the other hand, I think the Heat will eke out a 5 (maybe 6) seed in the East because of some great new acquisitions like small forward Simone Fontecchio and shooting guard Norman Powell.
  • I honestly don’t know why the model hates the Detroit Pistons, as it placed them at the bottom of the East once more. I ranked them as a possible 4-seed because after their improvement last year (44-38 from a dismal 14-68 in 2023-24), I feel they could be quite the playoff contender-and it was certainly nice to see 2021 1st Overall Pick Cade Cunningham finally develop into a star-quality player. The acquisition of the former Heat small forward Duncan Robinson should be exciting to see.
  • This might sound like a hot take here, but I don’t think the Sixers will even qualify for play-in, let alone playoffs given the plethora of issues they had last season. Least of all, Paul George and Joel Embiid-two of the biggest Sixers names-weren’t at the top of their game last season when they were healthy (and both of them missed significant time due to injuries).
  • Unlike my model, I think the Knicks could really take the top spot in the East this season. Despite falling just short of the 2025 NBA Finals, the Knicks showed they can certainly make a deep playoff run with talent such as Jalen Brunson (winner of the Clutch Player of the Year award), OG Anunoby and their acquisition of Karl-Anthony Towns from the Timberwolves during the 2024 offseason.
  • With two of the biggest names in the East-Jayson Tatum and Tyrese Haliburton-out for most if not all of this season due to Achilles injuries they got during last season’s playoffs, I think the East is wide open. Granted, I still think the Pacers and Celtics have a good chance at making the playoffs this year, but I don’t think either of them is a shoo-in for the top spot in the East, which in my opinion leaves the East playoff race wide open for another team to take the top spot (which as I said earlier, I think it could be the Knicks’ year to do just that). Also, I still think the Celtics could realistically clinch the 3-seed in the East despite the offseason departures of Jrue Holiday, Kristaps Porzingis, Al Horford and Luke Kornet, who were all key players in the Celtics 2024 Championship run.

Western Conference predictions:

First, let’s start with how the model think the Western Conference standings will play out this season:

Play-OffsPlay-InsMaybe Next Year
1. Oklahoma City Thunder7. Golden State Warriors11. Houston Rockets
2. LA Clippers8. Phoenix Suns12. Utah Jazz
3. Denver Nuggets9. Sacramento Kings13. New Orleans Pelicans
4. Memphis Grizzlies10. Dallas Mavericks14. Portland Trail Blazers
5. Minnesota Timberwolves15. San Antonio Spurs
6. LA Lakers

Just as with the model’s Eastern Conference predictions, I certainly have disagreements with the Western Conference predictions. Here’s how I think the Western Conference standings will play out this season:

Play-OffsPlay-InsMaybe Next Year
1. Oklahoma City Thunder7. Golden State Warriors11. Dallas Mavericks
2. Houston Rockets8. LA Clippers12. Memphis Grizzlies
3. Minnesota Timberwolves9. Sacramento Kings13. Utah Jazz
4. Denver Nuggets10. San Antonio Spurs14. Portland Trail Blazers
5. Houston Rockets15. New Orleans Pelicans
6. LA Lakers

As I did with my Eastern Conference predictions, here are some interesting observations between the model’s projected conference standings and my personal projected conference standings:

  • I’m sure the question on every NBA fan’s mind-including mine-is “Can the Oklahoma City Thunder pull off another championship?”. My guess-I think of all the champions we’ve seen in the 2020s alone, I think they’ve got the best shot at a repeat title. Why might that be? One big reason that could happen-the Thunder kept their core Big 3 (SGA, Chet Holmgren, and Jaylin Williams) around along with several other key players from the championship run such as Isaiah Hartenstein, Lu Dort, among others. Personally, I think that NBA teams would be wise not to go full rebuild-mode after winning their first championship, and it seems the Thunder have done just that (they only traded second-year small forward Dillon Jones, who played limited minutes in OKC’s championship run). Even if the Thunder don’t end up repeating as champions, I think, at the very least, the 1-seed in the West could be theirs for the taking once more.
  • Another interesting Western Conference storyline to watch would be whether Cooper Flagg (the 2025 #1 overall pick) becomes the next Luka Doncic for the Mavericks. After Doncic got traded for Anthony Davis during last year’s midseason trades, it’s safe to say the Mavericks’ season went south. A controversial trade and injuries to many key players-Anthony Davis (after the trade) and Kyrie Irving being the two most notable examples-didn’t help matters. Then again, having such an injury-struck roster to the point where the Mavericks nearly (but thankfully didn’t) have to forfeit games only added to their problems last season after the infamous Doncic-Davis trade. The drafting of 6’9″, 18-year-old forward Cooper Flagg could bring a spark to the struggling Mavericks (and from watching some of his highlights, I think Flagg has potential), but I think Flagg will need at least a year to gel with the Mavericks before they once again become Western Conference contenders.
  • Just as I was surprised that my model placed the Detroit Pistons at the bottom of the Eastern Conference given their improvements last season, I can say I’m just as surprised that the San Antonio Spurs were placed at the bottom of the Western Conference. Granted, they haven’t made the playoffs since 2019 and just went through a coaching change (Popovich stepped down and Mitch Johnson was named as head coach after serving as interim last season), but they did also improve their record from 22-60 in ’23-’24 to 34-48 last season. The Spurs also have their own solid Big 3 in De’Aaron Fox, Stephon Castle, and of course 2023 #1 overall pick Victor Wembanyama. Even though Wemby’s season was cut short last year due to deep vein thrombosis (a type of blood clot), his improved shooting and double-doubles could certainly help the Spurs once he’s fully recovered.
  • How might the Golden State Warriors do with their 35-and-over Big 3 (Jimmy Butler is 36, Draymond Green is 35, and Steph Curry is 37)? Given that they earned their playoff spot last season through play-ins, I’ve got a hunch that the Warriors might be seeing the play-ins once more-but will likely get a playoff spot in this manner. Yes, they had quite the herky-jerky trajectory last season, but the midseason acquisition of Jimmy Butler certainly gave them an extra spark down the regular season stretch-Butler’s basketball skills certainly paired well with guys like Steph and Draymond. Upsetting the 2-seeded Houston Rockets in the Western Conference quarterfinals last season certainly helps the Warriors’ momentum heading into this season, but I do wonder how the loss of their championship-winning forward Kevon Looney would affect the Warriors dynamic.
  • I know I said that I think the Thunder have a great chance to repeat as champions, but I also wonder if the Timberwolves would be a team to look out for in the 2026 postseason. After all, despite losing franchise mainstay Karl-Anthony Towns to the Knicks in the 2024 offseason, the Timberwolves adapted quite well as stars like Anthony Edwards and Naz Reid rose to the challenge by helping the team get to the Western Conference finals for the second year in a row (even though they got knocked out at the Western Conference finals for the second year in a row too). All in all, in terms of every NBA trade ever made, I think the Karl-Anthony towns trade-along with the players the Timberwolves got in exchange (Julius Randle and Donte DiVincenzo)-was one of the most even trades for both teams involved, as both the Knicks and Timberwolves made it to their respective conference finals.
  • Just as with my play-in predictions for the Eastern conference, at least three of the four projected play-in teams (according to the model) for the Western Conference made the play-ins last season-the Mavericks, Warriors, and Kings. I think the Warriors have the best shot at cracking the actual playoffs while the Mavericks could use another year for Cooper Flagg to develop (plus buy some time to get stars like Kyrie Irving back). It will be interesting to see how the Sacramento Kings fare because even though Domantis Sabonis, Zack LaVine and DeMar DeRozan fared well despite the disappointing finish, the talent around them could use some improvement. Perhaps the addition of Russell Westbrook (who’s in his 18th year in the NBA) could spice up the Kings’ offense, as he certainly showed he still had the athleticism and speed needed for basketball last season with the Denver Nuggets.

And now for something a little scandalous…

Boy oh boy this is certainly going to be the most interesting (or at least the most interestingly-timed) post I’ve written during this blog’s run. Why might that be?

Well, last Thursday (October 23, 2025) news broke that the FBI (US Federal Bureau of Investigation) had arrested 34 people for a pair of scandals that certainly rocked pro basketball-one involving colluding with Italian Mafia families (specifically the Gambino, Bonnano and Genovese crime families) to conduct a series of rigged poker games and another involved colluding to rig sports betting.

Here’s the wildest part though-among the 34 arrested were the current head coach of the Portland Trail Blazers (Chauncey Billups), a current Miami Heat star (Terry Rozier), and a former Cavaliers player (Damon Jones). Billups and Rozier were placed on leave by their respective teams.

Want to know some other juicy, scandalous details? Here are a few takeaways from the indictments:

  • Chauncey Billups was allegedly used by these Mafia families to lure in victims to the rigged poker games in order to make the poker games appear legitimate.
  • How the poker games were rigged is possibly the wildest part, with everything that was alleged to have happened sounding like it could’ve come from a James Bond movie. Among the methods used to rig these poker games were X-Ray tables that allowed these Mafia families to see opponents’ hands and rigged shuffling machines that could be used to predict what opponents’ hands would look like.
  • As for Rozier, the game that led to him being investigated was a March 23, 2023 game while Rozier was still with the Charlotte Hornets. In this game, Rozier left the game early due to a “foot injury”-which wasn’t true as Rozier conspired with a longtime friend of his that he planned to fake the “foot injury” in order to net this friend over $200,000 on his “under” statistics (that Rozier would underperform in the game in other words).
  • As for Damon Jones, he sold insider information to his co-conspirators during the 2022-23 season while working for the Lakers. The information concerned insider tips on lineup decisions and injury reports on star Lakers players; the co-conspirators were able to place significant wagers on their bets with this information. It was later revealed that one of the players whose injury report was leaked was LeBron James, who hasn’t been implicated in any wrongdoing.

All in all, it will be interesting to see how this scandal plays out-especially to see if anyone else get busted as part of this massive gambling ring. Here’s an October 23, 2025 release from the US DOJ (Department of Justice) describing the basics of the gambling ring (keep in mind that anyone involved is presumed innocent until proven guilty)-https://www.justice.gov/usao-edny/pr/current-and-former-national-basketball-association-players-and-four-other-individuals.

Here’s a snippet of a conference from FBI Director Kash Patel on October 23, 2025 regarding the charges-https://www.youtube.com/shorts/4F4_JMGVJXw.

All I will say is that it will be very very interesting to see not only how the rest of the NBA season plays out but also to see how commissioner Adam Silver will change league gambling policy-especially when it comes to players and coaching staff. Assuming other players and/or coaching staff get busted in the gambling ring (which could happen) the trials will be interesting-mostly because we’ll get to see who will snitch on who to get a sweet plea deal. Maybe there will be some RICO charges in the mix-which given what occurred, isn’t a stretch to think.

Anyway, thanks for reading as always, and enjoy the juicy action of the 2025-26 NBA season! The season is still young, so it’s anyone’s game!

Michael

Another Crack At Linear Regression NBA Machine Learning Predictions (2025-26 edition)

Hi everybody,

Michael here, and in today’s post, I thought I’d try something a little familiar. You may recall that last October, I released a pair of posts (Python, Linear Regression & An NBA Season Opening Day Special Post and Python, Linear Regression & the 2024-25 NBA season) attempting to predict each NBA team’s win total and conference seeding based off of their performance from the previous 10 seasons.

All in all, after seeing how the season played out-I managed to get only 3/30 teams in the correct seeding. So what would I do here?

I’ll give my ML NBA machine learning predictions another go, also using data from the previous 10 seasons (2015-16 to 2024-25). You may be wondering why I’m trying to predict the outcomes of the upcoming NBA season once more given how off last year’s predictions were-the reason I’m giving the whole “Michael’s NBA crystal ball” thing another go is because I’m not only interested in how my predictions change from one season to the next but also because I plan to use a slightly different model than I did last year (it’ll still be good old linear regression, however) so I can analyze how different factors might play a role in a team’s record and ultimately their conference seeding.

So, without further ado, let’s jump right in to Michael’s Linear Regression NBA Season Predictions II!

Reading the data

Before we dive in to our juicy predictions, the first thing we need to do is read in the data to the IDE. Here’s the file:

NBA analysis 2025-26Download

Now let’s import the necessary packages and read in the data!

import pandas as pd
from sklearn.model_selection import train_test_split
from pandas.core.common import random_state
from sklearn.linear_model import LinearRegression
from google.colab import files
uploaded = files.upload()
import io

NBA = pd.read_excel(io.BytesIO(uploaded['NBA analysis 2025-26.xlsx']))
NBA.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 300 entries, 0 to 299
Data columns (total 31 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Season  300 non-null    object 
 1   Team    300 non-null    object 
 2   W       300 non-null    int64  
 3   L       300 non-null    int64  
 4   Finish  300 non-null    int64  
 5   Age     300 non-null    float64
 6   Ht.     300 non-null    object 
 7   Wt.     300 non-null    int64  
 8   G       300 non-null    int64  
 9   MP      300 non-null    int64  
 10  FG      300 non-null    int64  
 11  FGA     300 non-null    int64  
 12  FG%     300 non-null    float64
 13  3P      300 non-null    int64  
 14  3PA     300 non-null    int64  
 15  3P%     300 non-null    float64
 16  2P      300 non-null    int64  
 17  2PA     300 non-null    int64  
 18  2P%     300 non-null    float64
 19  FT      300 non-null    int64  
 20  FTA     300 non-null    int64  
 21  FT%     300 non-null    float64
 22  ORB     300 non-null    int64  
 23  DRB     300 non-null    int64  
 24  TRB     300 non-null    int64  
 25  AST     300 non-null    int64  
 26  STL     300 non-null    int64  
 27  BLK     300 non-null    int64  
 28  TOV     300 non-null    int64  
 29  PF      300 non-null    int64  
 30  PTS     300 non-null    int64  
dtypes: float64(5), int64(23), object(3)
memory usage: 72.8+ KB

As you can see, we’ve still got all 31 features that we had in last year’s dataset-the only difference between this dataset and last year’s is the timeframe covered (this dataset starts with the 2015-16 and ends with the 2024-25 season).

  • Just like last year, this year’s edition of the predictions comes from http://basketball-reference.com, where you can search up plenty of juicy statistics from both the NBA and WNBA. Also, just like last year, the only thing I changed in the data from Basketball Reference is the Finish variable, which represents a team’s conference finish (seeding-wise) as opposed to divisional finish (since divisional finishes are largely irrelevant for a team’s playoff standings).
  • If you want a better explanation of these terms, please feel free to refer to last year’s edition of my predictions post-Python, Linear Regression & An NBA Season Opening Day Special Post.

Now that we’ve read our file into the IDE, let’s create our model!

Creating the model

You may recall that last year, before we created the model, we used the Select-K-Best algorithm to help us pick the optimal model features. For a refresher, here’s what Select-K-Best chose for us:

['L', 'Finish', 'Age', 'FG%', '3P%']

After seeking the five best features for our model from the Select-K-Best algorithm, this is what we got. However, we’re not going to use the Select-K-Best suggestions this year as there are other factors I’d like to analyze when it comes to making upcoming season NBA predictions.

Granted, I’ll keep the Finish, FG%, and 3P% as I feel they provide some value to the model’s predictions, but I’ll also add a few more features of my own choosing:

X = NBA[['FG%', '3P%', '2P%', 'Finish', 'TRB', 'AST', 'STL', 'BLK', 'TOV']]
y = NBA['W']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)

Along with the features I chose from last year’s model, I’ll also add the following other scoring categories:

  • 2P%-The percentage of a team’s successful 2-pointers in a given season
  • TRB-A team’s total rebounds in a season
  • AST-A team’s total assists in a season
  • STL-A team’s total steals in a season
  • BLK-A team’s total blocks in a season
  • TOV-A team’s total turnovers in a season

The y-variable will still be W, as we’re still trying to predict an NBA team’s win total for the upcoming season based off of all our x-variables.

Now, let’s create a linear regression model object and run our predictions through that model object:

NBAMODEL = LinearRegression()
NBAMODEL.fit(X_train, y_train)
yPredictions = NBAMODEL.predict(X_test)

yPredictions

array([43.2515066 , 36.4291265 , 55.14626364, 46.01164579, 24.18679591,
       35.59131124, 35.59836527, 49.98114132, 48.57869061, 50.65733101,
       21.296126  , 49.94020238, 31.98306604, 41.89217714, 45.65373458,
       50.57831266, 32.76923727, 45.6898562 , 20.4393901 , 55.28944034,
       52.79027154, 21.81113366, 50.79142468, 50.95798684, 53.23802534,
       50.00199063, 48.4639119 , 49.1671417 , 51.12760913, 31.20606334,
       45.3090483 , 25.02488097, 43.67955061, 48.47484838, 33.74041157,
       41.7463038 , 36.10796911, 40.5399278 , 35.30656175, 16.92677689,
       49.77947698, 39.2160337 , 22.08871355, 31.83549487, 15.2675987 ,
       18.24486804, 21.71657476, 42.21505537, 22.84745758, 25.56862333,
       43.6212702 , 20.28339646, 44.60289296, 49.20316062, 53.69182149,
       29.48304908, 44.60789347, 42.44466633, 55.93637972, 54.89728291])

Just as with last year’s model, the predictions are run on the test dataset, which consists of the last 60 of the dataset’s 300 total records.

And now for the equation…

Now that we’ve generated predictions for our test dataset, let’s find out all of the coefficients and the intercept for the equation I will use to make this year’s NBA predictions:

NBAMODEL.coef_

array([ 6.48260593e+01,  1.13945178e+02,  1.54195451e+01, -1.94822281e+00,
        1.10428617e-02, -3.46015457e-03,  2.15326621e-02,  6.63810730e-03,
       -9.70593407e-03])
NBAMODEL.intercept_

np.float64(-60.37720744829896)

Now that we know what our coefficients are, let’s see what this year’s equation looks like:

Although it’s much more of a mouthful than last year’s equation, it follows the same logic in that it uses the features of this year’s model in the order that I listed them:

['FG%', '3P%', '2P%', 'Finish', 'TRB', 'AST', 'STL', 'BLK', 'TOV']

A is FG%, B is 3P%, and so on until you get to I (which represents TOV).

  • Since all the coefficients are listed in scientific notation, I rounded them to two decimal places before converting them for this equation. Same thing for the intercept.
  • In case you’re wondering, no you can’t add all the coefficients together for this equation as each coefficient plays a part in the overall equation. Just like last year, we’re going to do the weighted-averages thing to generate projected win totals. Keep your eyes peeled for the next post, which covers the juicy predictions.

…and the accuracy test!

So now that we’ve got our 2025-26 NBA predictions model, let’s see how accurate it is:

from sklearn.metrics import mean_absolute_percentage_error

mean_absolute_percentage_error(y_test,yPredictions)

0.09573425883736708

Using the MASE (mean absolute percentage error) from sklearn like we did in last year’s analysis, we see that the model’s margin of error is roughly 9.57%. I’ll round that up to 10%, which means that despite not choosing the model’s features from a prebuilt algorithm, the overall accuracy of the model is still 90%.

Now, whether the model’s accuracy and my predictions hold up is something I’ll certainly revisit in 8 months time for another end-of-season reflection. After all, last season I only got 3 of the 30 teams in the correct seeding, though I did do better with predicting which teams didn’t make playoffs though.

  • Recall that to find the accuracy of the model using the MASE, subtract 100 from the (MASE * 100). Since the MASE rounds out to 10 as the nearest whole number (rounded to 2 decimal places), 100-10 gives us an accuracy of 90%

Last but not least, it’s prediction visualization time!

Before we go, the last thing I want to cover is how to visualize this year’s model’s predictions. Just like last year, we’re going to use the PYPLOT module from MATPLOTLIB:

import matplotlib.pyplot as plt

plt.scatter(y_test, yPredictions, color="red")
plt.xlabel('Actual values', size=15)
plt.ylabel('Predicted values', size=15)
plt.title('Actual vs Predicted values', size=15)
plt.show()

As you can see, the plot forms a sort of diagonal-line shape, which reinforces the model’s 90% prediction accuracy rate.

Also, just for comparison’s sake, here’s what my predictions looked like on last year’s model (the one where I used Select-K-Best to choose the model features):

This also looks like a diagonal-line shape, and last year’s model had a 91% accuracy rate.

Here’s the link to the Colab notebook in my GitHub-https://github.com/mfletcher2021/blogcode/blob/main/NBA_25_26_predictions.ipynb

Thanks for reading, and keep an eye out for my 2025-26 season predictions,

Michael

2024-25 NBA Season Predictions Revisited & OKC’s First Championship

Hello readers,

As some of you may recall, I did a pair of NBA 2024-25 series prediction posts around the start of the 2024-25 season back in October (Python, Linear Regression & the 2024-25 NBA season and Python, Linear Regression & An NBA Season Opening Day Special Post).

Well, now that the season has just concluded, I thought it would be a good time to revisit those predictions and reflect on the season that just passed.

Yes, I know this is primarily a coding blog, but all the storylines of this past NBA season (the Finals, the midseason trades, etc.) are certainly worth revisiting. After all, this post will tie into my NBA season opener two-parter (juicy Python AI predictions and all).

But first, a few words on the NBA finals…

Wow wow wow what a Finals that was! I mean, first of all, there were two solid teams in the Indiana Pacers (50-32, 4th in East) and Oklahoma City Thunder (68-14, 1st in West). Second of all, it went the distance as it was the first NBA Finals to go a full 7 games since the 2016 Finals (that of the Warriors’ blown 3-1 lead and the Lebron-led Cavs’ comeback-like-no-other).

The momentum was back-and-forth, and it felt like the series could’ve gone in favor of either the Pacers or Thunder. Tyrese Haliburton’s Game 1 2-point clutch shot to give the Pacers the Game 1 win. OKC rallying back with their defense in Game 2 to keep the Pacers at bay. The Pacers’ blowout Game 6 win at home to even force a Game 7. OKC rallying back in the second half of Game 7, holding the Pacers to just 42 second-half points to ultimately win their first championship (and congrats to Shai Gilgeous-Alexander on winning 2025 NBA Finals & 2025 regular season MVP). I’ve got to hand it to the Pacers’ resilience in Game 7, because even though they didn’t win, they-especially their bench players like TJ McConnell-gave it their all after Tyrese Haliburton went out in the first quarter with a torn Achilles (well wishes to him on recovery).

The mid-season trades (especially THAT trade)

It wouldn’t be an NBA season-in-review/predictions-revisited post without reviewing all of the crazy stuff that went down in the midseason trades.

Personally, as wild as off-season free agency can be (anyone remember LeBron’s “I’m taking my talents to South Beach”?) I think the mid-season trades can be even wilder, especially this season. Let’s go over the most notable examples, shall we?

  • Jimmy Butler (Miami Heat –> Golden State Warriors)
  • De’Aaron Fox (Sacramento Kings –> San Antonio Spurs)
  • Khris Middleton (Milwaukee Bucks –> Washington Wizards)
  • Andrew Wiggins (Golden State Warriors –> Miami Heat-part of the Jimmy Butler trade package)

And for the most notable 2025 midseason trade (yes THAT trade):

  • Anthony Davis from the LA Lakers to the Dallas Mavericks/Luka Doncic from the Dallas Mavericks to the LA Lakers

Personally, I thought this takes the cake for most shocking midseason NBA trade in quite some time! I mean, no one expected (least of all Doncic himself) that Dallas would trade away a franchise icon in his prime-he’s 26-for a 32-year-old Anthony Davis. It’s especially wild considering A) it was largely done in secrecy and B) Luka took the Mavs to the Finals just the previous year. Time will tell how this plays out, but personally, I think the Lakers got the better deal (getting Markeiff Morris and Maxi Klieber in that package didn’t hurt either). After all, the Lakers got the 3-seed in the West this year, while the Mavericks fell to the 10-seed and didn’t make it past play-in (though the Mavs’ especially bad luck with injuries didn’t help). Luka could certainly bring the Lakers much success, especially in the post-LeBron years to come.

  • Side note: it will be interesting to see how Dallas’s #1 overall 2025 draft pick of Cooper Flagg will pan out. Perhaps he’ll be the new Doncic in Dallas and fit right in with Klay, Kyrie and the rest of the Mavs?

And now, we reflect…

So, how accurate were my opening day predications for the season? Let’s review those predictions shall we?

Eastern Conference Edition

So, here were my opening day predictions for how things in the Eastern Conference would pan out:

INTO THE PLAYOFFSINTO THE PLAY-INOUT OF PLAYOFF RUNNING
1. Milwaukee Bucks7. Cleveland Cavaliers11. Chicago Bulls
2. Boston Celtics8. Indiana Pacers12. Washington Wizards
3. Philadelphia 76ers9. Atlanta Hawks13. Charlotte Hornets
4. Miami Heat10. New York Knicks14. Orlando Magic
5. Toronto Raptors15. Detroit Pistons
6. Brooklyn Nets

Here’s how things actually played out:

INTO THE PLAYOFFSINTO THE PLAY-INOUT OF PLAYOFF RUNNING
1. Cleveland Cavaliers 7. Orlando Magic (made playoffs)11. Toronto Raptors
2. Boston Celtics8. Atlanta Hawks12. Brooklyn Nets
3. New York Knicks9. Chicago Bulls13. Philadelphia 76ers
4. Indiana Pacers10. Miami Heat (made playoffs)14. Charlotte Hornets
5. Milwaukee Bucks15. Washington Wizards
6. Detroit Pistons

In total, here’s a rundown of how right/off I was:

  • Only 2/6 teams I picked to get a top-6 spot in the East (and get a direct playoff spot) were correct (Bucks and Celtics)
  • 3/6 teams I picked to get a top-6 spot in the East didn’t even make play-in (Nets, Sixers, and Raptors)
  • Only 1 correct play-in pick for the East (Hawks)-the other three play-in teams actually got top-6 spots
  • 3/5 teams that I thought weren’t going to make playoffs at least made play-in (Magic, Pistons, and Bulls). None of these teams got further than the Eastern Conference quarterfinals, however.
  • Of the 15 teams in the East, 7 teams finished better than I expected, 7 teams finished worse than I expected, and 1 team finished in the exact spot I thought they would.
  • Teams that finished better than I thought they would: Cavaliers, Knicks, Pacers, Pistons, Hawks, Bulls, and Magic
  • Teams that finished worse than I thought they would: Bucks, Heat, Nets, 76ers, Raptors, Wizards, Hornets
  • Team that finished exactly as I thought they would: Celtics (2-seed)

Takeaways from the East…

  • The Miami Heat, despite finishing 10th in the East, earning a play-in spot for the third straight season, and all the Jimmy Butler drama leading up to the midseason trade, still managed to surprise me in that they became the first 10-seed playoff team to make the playoffs. Then they promptly got swept by the Cleveland Cavaliers in the first round, including two straight 30+ point losses at home. Maybe the lottery pick might’ve been better for them?
  • I know I’ve talked about earlier in this post, but I’ve got to give the Indiana Pacers credit where credit is due. Despite a slow 10-15 start to the season, they eventually found their rhythm, both offensively and defensively. The return of key players like Aaron Neismith and Tyrese Haliburton’s 2nd-half-of-the-season resurgence certainly helped propel the Pacers to the NBA Finals (and help the series go the distance against the OKC Thunder)
  • On the other side of the coin, teams like the Philadelphia 76ers fell far below expectations this season, finishing with a 13-seed in the East, no playoffs or play-in, and a 24-58 record. The highly-anticipated acquisition of Paul George from the LA Clippers and the formation of a new Sixers’ Big-3 with George, Joel Embiid and Tyrese Maxsey didn’t quite pan out, especially due to a knee injury for George, a torn meniscus for Embiid, and a sprained finger for Maxsey, which ended up becoming season-ending injuries for all three players.
  • The Milwaukee Bucks, despite finishing with a 5-seed in the East and a solid regular season, certainly didn’t look like the championship Bucks squad of 2020-21. Injuries to Giannis Antetokounmpo and Damian Lilliard didn’t help matters in the playoffs, nor did their blown late-game playoff leads-especially their infamous 118-111 lead in OT of game 5 against the Pacers…that the Pacers still won by 1, thus eliminating the Bucks.

Western Conference Edition

Now let’s see how things panned out in the Western Conference? Here’s how I thought things would go:

INTO THE PLAYOFFSINTO THE PLAY-INOUT OF PLAYOFF RUNNING
1. Denver Nuggets7. LA Lakers11. Sacramento Kings
2. LA Clippers8. Memphis Grizzlies12. New Orleans Pelicans
3. Golden State Warriors9. Oklahoma City Thunder13. San Antonio Spurs
4. Phoenix Suns10. Minnesota Timberwolves14. Portland Trailblazers
5. Dallas Mavericks15. Houston Rockets
6. Utah Jazz

And here’s how things actually went for the teams in the West:

INTO THE PLAYOFFSINTO THE PLAY-INOUT OF PLAYOFF RUNNING
1. Oklahoma City Thunder7. Golden State Warriors (made playoffs)11. Phoenix Suns
2. Houston Rockets8. Memphis Grizzlies (made playoffs)12. Portland Trail Blazers
3. LA Lakers9. Sacramento Kings13. San Antonio Spurs
4. Denver Nuggets10. Dallas Mavericks14. New Orleans Pelicans
5. LA Clippers15. Utah Jazz
6. Minnesota Timberwolves

In total, here’s a rundown of how right/off I was (Western Conference edition).

  • It’s worth noting that my Western Conference predictions have several similarities with my Eastern Conference predictions:
    • 2/6 teams that I predicted would get a top-6 spot did just that (Clippers and Nuggets)
    • 3/6 teams that I predicted would get a top-6 spot didn’t even make the play-in (Suns, Mavericks and Jazz)
    • Only 1 correct play-in pick for the west (Grizzlies)-the other three teams were straight shots into the playoffs (with “9-seed” OKC finishing as the champions)
    • For both the East and West, the teams I had finishing as the “bottom seed” both made it directly to playoffs (with Pistons as 6-seed and Rockets as 2-seed)
    • There were some differences between my East predictions and West predictions:
    • Only 2/6 teams that I thought weren’t going to playoffs at least made play-ins (Kings and Rockets). Just like the East though, neither of these teams made it past the Western Conference quarterfinals.
    • In total, 6 teams in the West finished better than I expected, 7 teams finished worse than I expected, and 2 teams finished in the exact spot I expected.
    • Teams that finished better than I thought they would: Lakers, Thunder, Timberwolves, Kings, Trailblazers, Rockets
    • Teams that finished worse than I thought they would: Nuggets, Clippers, Warriors, Mavericks, Jazz, Suns, Pelicans
    • Teams that finished in the exact spot I thought they would: Spurs (13-seed) and Grizzlies (8-seed)

Takeaways from the west…

  • One team I certainly underestimated was the Houston Rockets. To think my Michael-made ML model put them as the 15-seed in the West-boy did they outperform my model’s expectations (finishing as the 2-seed in the West). Sure, they did get upset in the Western Conference first round by the play-in Warriors, but the combination of good veterans with Fred VanVleet and Jeff Green, solid offensive contributors like Alpern Sengun, solid defensive contributors with guys like Amen Thompson, and coach Ime Udoka, I think the Rockets will go far in the years to come. Now to see how adding KD in free agency will elevate the Rockets.
  • And now for the Warriors…I mean, at least they’ve got a solid Big-3 in Draymond Green, Jimmy Butler and of course, Stephen Curry. However, they did have an up-and-down season, finishing with a 48-34 record and a 7-seed play-in spot. The mid-season acquisition of Jimmy Butler from the Heat certainly gave the Warriors more defensive spark (and in my opinion, I think Miami got a solid asset in Andrew Wiggins). I think it would do the Warriors a lot of good to think about their future-after all, they can only roll with guys like Jimmy Butler and Draymond Green for so long-both of whom will turn 36 next season. Plus I think it would be satisfying to make one more championship run with Steph Curry and Draymond Green, who have been with the Warriors for 15+ years and four championships.
  • On the other end of the playoff coin, what happened to the bottom-of-the-West (and bottom-of-the-NBA) Utah Jazz? Granted, I know they haven’t been great since the departures of Rudy Gobert and Donovan Mitchell, but losing one of their most promising assets in Taylor Hendricks to a fractured fibula and dislocated ankle just three games into the season certainly didn’t help matters for the Jazz. Hopefully the Jazz’ 2025 draft class-led by small forward Ace Bailey-will reverse their fortunes and bring them back into playoff contention. Maybe adding Kevin Love in free agency will give them a boost of veteran player-coach leadership?
  • Last but not least, let’s talk about the San Antonio Spurs-one of only three teams whose seeding I got exactly right (13-seed). Granted, going from 14-seed last season to 13-seed this season might not sound like a huge improvement, but I think Victor Wembanyama definitely has potential to be a franchise icon, as he showed his defensive potential this season despite his season-ending blood clot after All-Star Break. The mid-season acquisition of De’Aaron Fox and the coaching transition from Coach “Pop” Popovich to Mitch Johnson could help rejuvenate the Spurs and, who knows, maybe even bring them back to the playoffs for the first time since 2019.

Thanks for reading. What a great NBA season it was! I’ll certainly admit I had fun writing this 3-post series to not only try and predict the upcoming NBA season, but also to reflect on how my predictions fared after the season has concluded. After all, an important skill of data analysis is learning how to gather insights from the data!

Michael

Also, congrats OKC on winning the 2025 NBA championship!

Python, Linear Regression & the 2024-25 NBA season

Hello everybody!

Michael here, and in today’s post, we’ll continue where we left off from the previous post Python, Linear Regression & An NBA Season Opening Day Special Post. As I mentioned in that post, we’ll use the linear regression equation we obtained to see if we can obtain predictions for the current 2024-25 NBA season?

Disclaimer

Yes, I know I’m trying to predict the various juicy outcomes of the 2024-25 NBA season, but these predictions are purely meant for educational purposes to display the methodology of the predictions, not for game-day parlays and/or your fantasy NBA team. After all, I am your friendly neighborhood coding blogger, but I am not your friendly neighborhood sportsbook. If you do decide to bet on anything during the NBA season, please bet responsibly :-).

Previously on Michael’s Programming Bytes…

In the previous post, we used data from the last 10 NBA seasons for each of the 30 teams to predict season record results, which in turn gave us this linear regression equation that I will use to predict team-by-team results and standings for the 2024-25 NBA season:

Just to recap, here’s what in this equation:

  • -0.47x (represents team’s losses in a given season)
  • -1.31x (represents team’s conference finish from 1-15 in a given season)
  • 0.4x (represents average age of team’s roster)
  • 34.13x (represents % of field goals made)
  • -22.12x (represents % of 3-pointers made)
  • 50.95 (linear regression model intercept)

Our predictions generated in the previous post came back with 91% accuracy/9% mean absolute percentage error, so I can tell we’re gonna get some good predictions here.

And now, for the predictions…

Yes, here comes the fun part, the predictions. For the predictions, I gathered the weighted averages of the five features we used in our model (losses, conference finish, average roster age, % of field goals made and % of 3-pointers made) and placed them into this spreadsheet:

NBA weighted averagesDownload

Now, how did I calculate the weighted averages of these five features for each team? Well, I simply assigned different weights for different seasons like so:

  • 2021-22 to 2023-24 seasons-0.2 weight (higher weight for the three most recent seasons)
  • 2018-19 to 2020-21 seasons-0.1 weight (they’re a little further back, plus I factored in COVID impacts to the 2019-20 and 2020-21 seasons)
  • 2014-15 to 2017-18 season-0.025 weight (smaller weight since these are the furthest in the past, plus many players in the league during this time have since retired)

After assigning these weights, I calculated averages using the standard procedure for average calculation.

Here’s the basic Python code I used to calculate projected wins for all 30 NBA teams:



import pandas as pd

NBAAVG = pd.read_csv(r'C:\Users\mof39\OneDrive\Documents\NBA weighted averages.csv')

for n in NBAAVG['Team']:
    print(str(-0.47*NBAAVG['L']-1.31*NBAAVG['Finish']
                                      +0.4*NBAAVG['Age']+34.13*NBAAVG['FG%']
                                      -22.15*NBAAVG['3P%']+50.95))
    break

And here are the projected win totals for each team using this equation:

0     37.911934
1     52.863761
2     40.819851
3     31.742252
4     37.524958
5     40.441851
6     42.540851
7     51.103223
8     24.263654
9     45.852691
10    33.197160
11    38.736829
12    47.364055
13    41.338946
14    41.297291
15    45.202762
16    53.722600
17    39.185063
18    37.443009
19    38.462010
20    39.284500
21    32.296571
22    47.795819
23    45.063567
24    33.312626
25    37.493793
26    32.519145
27    42.072515
28    41.920285
29    31.773436

Granted, you don’t actually see the team names in this output, but since the team names are organized alphabetically in the dataset you can tell which team corresponds to which projected win total. However, just for clarity, I’ll elaborate on those totals below:

Atlanta Hawks: 37.911934 wins (38-44)
Boston Celtics: 52.863761 wins (53-29)
Brooklyn Nets: 40.819851 wins (41-41)
Charlotte Hornets: 31.742252 wins (32-50)
Chicago Bulls: 37.524958 wins (38-44)
Cleveland Cavaliers: 40.441851 wins (40-42)
Dallas Mavericks: 42.540851 wins (43-39)
Denver Nuggets: 51.103223 wins (51-31)
Detroit Pistons: 24.263654 wins (24-58)
Golden State Warriors: 45.852691 wins (46-36)
Houston Rockets: 33.197160 wins (33-49)
Indiana Pacers: 38.736829 wins (39-43)
LA Clippers: 47.364055 wins (47-35)
LA Lakers: 41.338946 wins (41-41)
Memphis Grizzlies: 41.297291 wins (41-41)
Miami Heat: 45.202762 wins (45-37)
Milwaukee Bucks: 53.722600 wins (54-28)
Minnesota Timberwolves: 39.185063 wins (39-43)
New Orleans Pelicans: 37.443009 wins (37-45)
New York Knicks: 38.462010 wins (38-44)
Oklahoma City Thunder: 39.284500 wins (39-43)
Orlando Magic: 32.296571 wins (32-50)
Philadelphia 76ers: 47.795819 wins (48-34)
Phoenix Suns: 45.063567 wins (45-37)
Portland Trailblazers: 33.312626 wins (33-49)
Sacramento Kings: 37.493793 wins (37-45)
San Antonio Spurs: 32.519145 wins (33-49)
Toronto Raptors: 42.072515 wins (42-40)
Utah Jazz: 41.920285 wins (42-40)
Washington Wizards: 31.773436 wins (32-50)

As you can see above, I have managed to predict the records for each team for the 2024-25 NBA season. A few things to note about my predictions:

  • Since NBA records are only counted in whole numbers, I rounded each team’s projected win total up or down to the nearest whole number. For instance, for the Milwaukee Bucks, since their projected win total was 53.722600, I rounded that up to 54 wins (and a 54-28 record).
  • According to my model, all team’s projected win totals fall between 24 and 54 wins. This make sense since in a given NBA season, a majority of teams’ win totals fall in the 24-54 win range. In the last NBA season (2023-24), 21 teams fell within the 24-54 win range.
  • Four teams obtained over 54 wins (Celtics with 64, Thunder and Nuggets with 57, and Timberwolves with 56) while five teams obtained less than 24 wins (Spurs with 22, Hornets and Trailblazers with 21, Wizards with 15 and Pistons with 14).
  • One thing to note about my predictions is that while I rounded up or down to the nearest whole number to get a projected record total, I’ll still factor in the entire decimal (e.g. 45.202762 for the Heat) when deciding how to seed teams, as teams with a higher decimal will be seeded higher in their respective conference.

Michael’s Magnificently Way-To-Early Playoff Picture

Yes, now that we have projected record totals for each of the 30 teams, the next thing we’ll do is predict each team’s seeding.

How will we seed the teams? Well, for one, I’ll rank the teams with the higher projected records higher in their respective conference. For instance, since the Bucks have a higher projected record than the Celtics, I’ll rank the Bucks higher than the Celtics.

However, what if two teams have a really, really close margin between them? For instance, the Minnesota Timberwolves and Oklahoma City Thunder’s projected records of 39.185063 wins and 39.284500 wins respectively are very close to each other. However, since OKC has a slightly higher projected win total, I’ll rank them higher than the Timberwolves.

So without further ado, here’s Michael’s Magnificently Way-Too-Early Playoff Picture!

Eastern Conference

INTO THE PLAYOFFSINTO THE PLAY-INOUT OF PLAYOFF RUNNING
1. Milwaukee Bucks7. Cleveland Cavaliers11. Chicago Bulls
2. Boston Celtics8. Indiana Pacers12. Washington Wizards
3. Philadelphia 76ers9. Atlanta Hawks13. Charlotte Hornets
4. Miami Heat10. New York Knicks14. Orlando Magic
5. Toronto Raptors15. Detroit Pistons
6. Brooklyn Nets

Western Conference

INTO THE PLAYOFFSINTO THE PLAY-INOUT OF PLAYOFF RUNNING
1. Denver Nuggets7. LA Lakers11. Sacramento Kings
2. LA Clippers8. Memphis Grizzlies12. New Orleans Pelicans
3. Golden State Warriors9. Oklahoma City Thunder13. San Antonio Spurs
4. Phoenix Suns10. Minnesota Timberwolves14. Portland Trailblazers
5. Dallas Mavericks15. Houston Rockets
6. Utah Jazz

And now, for some insights

Now that we have our predictions for both team’s projected win totals and projected conference seeding, let’s see if we can gather some insights into what the 2024-25 NBA season might bring for all 30 teams. Without further ado, here are insights across the NBA that I think will be interesting to see play out over the course of the season:

Will the Celtics repeat as champs?

For those who don’t know, the Boston Celtics came out on top as the champions of the 2023-24 NBA season, beating the Dallas Mavericks in 5 games in the 2024 NBA Finals.

Question is, can they do it again? There’s a good chance that can happen, even with the projected 2-seed in the Eastern Conference. After all, the Celtics have kept many of their key playmakers from their championship squad such as Al Horford, Derrick White, Jaylen Brown and of course, Jayson Tatum.

Interestingly, we’ve had SIX different teams win the NBA championship in the last six seasons, such as:

  • 2019-Raptors
  • 2020-Lakers
  • 2021-Bucks
  • 2022-Warriors
  • 2023-Nuggets
  • 2024-Celtics

Could we have a repeat champ for the first time since those seemingly endless Warriors-Cavs finals (remember those)? I’ll reiterate that it’s certainly possible, especially with Tatum in his prime.

Warriors for a deep playoff run?

Yes, I know they’ve had their ups and downs over the last 10 years, but after all, the Golden State Warriors have won 4 championships over the last 10 years, so I have reason to believe they’ll go on another deep playoff run.

Will the loss of Klay Thompson hurt? Yes. Stephen Curry is also on the back-nine of his career (he turns 37 in March), but he did put up the most points per game of anyone on the Warriors’ roster last season (26.4). Curry also had the highest 3-pointer percentage of anyone on the Warriors’ roster last season (40.8%)-recall that successful 3-pointer percentage was one of the five features I used in the linear regression model. Plus, Draymond Green will be returning to the Warriors this eason; he proved to be one of the Warriors’ strongest 3-point shooters and rebounders last season (though he is also in his later career as he will be 35 in March).

Interestingly, this model has the Warriors going 46-36 as the 3-seed in the Western Conference. Funny enough, the Warriors finished 46-36 last season but ended up as the 10-seed in the Western Conference and failed to make it past play-in.

This brings me to my next point…

Will the West be close again?

Last season, the Western Conference was incredibly close when it came to win totals and playoff seeding. After all, the 6-seed in the West last year (Phoenix Suns) still finished with a 49-33 record…and were promptly swept in the Western Conference first round (though that’s neither here nor there).

Another thing to put the closeness of last year’s Western Conference playoff race into perspective-the Warriors finished 46-36 yet only notched a 10-seed and the Houston Rockets finished with an even 41-41 record but missed the postseason entirely (they got the 11-seed).

Which brings be to my next point…

Will the East be far apart?

While last year’s Western Conference was quite competitive, the Eastern Conference was, well, another story:

Image from Wikipedia: https://en.wikipedia.org/wiki/2023%E2%80%9324_NBA_season.

Yes, the Celtics not only got the 1-seed in the East but also finished FOURTEEN games ahead of the 2-seed New York Knicks (yes, the Knicks finished 50-32 and still got the 2-seed). Two teams that had very up-and-down seasons-the Bulls and Hawks-both finished with under 40 wins yet still qualified for the play-in as the 9- and 10-seeds in the East, respectively.

Miami Heat to the play…offs?

Throughout the last 10 years, the Miami Heat have had a great deal of success, making it to the Finals twice in that span (’20 and ’23) and making it to the playoffs 7 of the last 10 seasons (exceptions being ’15, ’17 and ’19).

However, while they did make the playoffs the last two seasons, they had to do so through first making it through the play-ins-both times they made it as the 8-seed in the play-in (meaning they had to play two play-in games to even get a playoff slot).

In this model however, the Miami Heat will earn the 4-seed and make the actual playoffs, not the play-in. What could possibly work to their advantage? Here are a few factors:

  • While their successful field goal percentage was in the bottom half of the league last season, they came in 12th amongst all teams in successful 3-pointer percentage, which should help their case.
  • After losing Jimmy Butler and Terry Rozier before play-offs last season, both are now (as of this writing) healthy and ready to play.
  • Those 42.3 rebounds (both offensive and defensive) last year look pretty good.
  • Tyler Herro, Bam Adebayo and Jimmy Butler were the top-3 scorers on the Heat in both points per game and field goals last year…those stats certainly matter for big games. Plus Herro is 24 and Adebayo is 27, so both are still in the primes of their careers (though Jimmy Butler at 35 still plays like he’s in his prime in my opinion)

Will the Heat win an NBA championship or make another Finals appearance? TBD. However, it looks like (according to this model I made) that they will at least make it to the play-offs without needing to go through play-ins first (though their 8-seed to-the-Finals run in 2023 was certainly memorable).

And now, for the bottom of the conference

Most of my insights discussed more successful teams and (potential) deep playoff runs. However, I wanted to offer one more insight concerning the two teams at the (projected) bottom of their conferences-the Pistons in the East and Rockets in the West.

First off-the Detroit Pistons, who, according to my model, are projected to be the 15-seed again (they were the 15-seed last season); will they manage to improve this season? My guess is yes-at least in terms of having more wins this season (only 14 wins last year)-but I don’t think they’ll make a strong playoff run, and I know a 28 game losing streak last season to drop the Pistons to 2-30 at one point didn’t help make a case for their postseason hopes. However, give the Pistons credit for changing their coach (now JB Bickerstaff) and GM (now Trajan Langdon) and adding some solid free agents like Tobias Harris (48.7% of successful field goals last season-not too shabby). Again, I doubt they’ll make a strong playoff run, but they could very well finish higher than the 15-seed.

As for the Houston Rockets (projected 15-seed in the West), they finished in the 11-seed last year in a competitive Western Conference with an even 41-41 record. Judging from last years stats-coming in 9th on defense but 20th on offense-they do have some work to do to make a deep playoff run. However, with a good mix of young players like Tari Eason and veterans like Fred VanVleet (who was on the championship 2019 Toronto Raptors), the Rockets could make it past play-in.

Just for fun…Michael’s Play-In Predictions

Now for an added bonus for my loyal readers, here are my educated guess, just-for-fun play-in predictions for both the Eastern and Western conferences. Granted, while the model I made did help predict regular-season seeding in each conference, it didn’t predict who would make it past play-in to grab the 7- and 8-seeds in the conference. So without further ado, here are my play-in predictions based on what I saw in the teams last season:

Eastern Conference

Predictions: Pacers 7-seed, Cavaliers 8-seed

Western Conference

Predictions: Lakers 7-seed, Timberwolves 8-seed

Thanks for reading and I hope you learned something new from this post! Enjoy the NBA season and I will follow up with a Part 3 post on this topic sometime in April, or at least some time after the conclusion of the regular season. It will be interested to see how accurate or off my predictions were.

Michael

Python, Linear Regression & An NBA Season Opening Day Special Post

Hello readers,

Michael here, and in today’s lesson, we’re gonna try something special! For one, we’re going back to this blog’s statistical roots with a linear regression post; I covered linear regression with R in the way, way back of 2018 (R Lesson 6: Linear Regression) on this blog, so I thought I’d show you how to work the linear regression process in Python. Two, I’m going to try something I don’t normally do, which is predict the future. In this case, the future being the results of the just-beginning 2024-25 NBA season. Why try to predict NBA results you might ask? Well, for one, I wanted to try something new on this blog (hey, gotta keep things fresh six years in), and for two, I enjoy following along with the NBA season. Plus, I enjoyed writing my post on the 2020 NBA playoffs-R Analysis 10: Linear Regression, K-Means Clustering, & the 2020 NBA Playoffs.

Let’s load our data and import our packages!

Before we get started on the analysis, let’s first load our data into our IDE and import all necessary packages:

import pandas as pd
from sklearn.model_selection import train_test_split
from pandas.core.common import random_state
from sklearn.linear_model import LinearRegression

You’re likely quite familiar with pandas but for those of you that don’t know, sklearn is an open-source Python library commonly used for machine learning projects (like the linear regression we’re about to do)!

A note about uploading files via Google Colab

Once we import our necessary packages, the next thing we should do is upload the data-frame we’ll be using for this analysis.

This is the file we’ll be using; it contains team statistics such as turnovers (team total) and wins for all 30 NBA teams for the last 10 seasons (2014-15 to 2023-24). The data was retrieved from basketball-reference.com, which is a great place to go if you’re looking for juicy basketball data to analyze. This site comes from https://www.sports-reference.com/, which contains statistics on various sports from NBA to NFL to the other football (soccer for Americans), among other sports.

NBA analysisDownload

Now, since I used Google Colab for this analysis, I’ll show you how to upload Excel files into Colab (a different process from uploading Excel files into other IDEs):

To import local files into Google Colab, you’ll need to include the lines from google.colab import files and uploaded = files.upload() in the notebook since, for some odd reason, Google Colab won’t let you upload local files directly into your notebook. Once you run these two lines of code, you’ll need to select a file from the browser tool that you want to upload to Colab.

Next (and ideally in a separate cell), you’ll need to add the lines import io and dataframe = pd.read_csv(io.BytesIO(uploaded['dataframe name'])) to the notebook and run the code. This will officially upload your data-frame to your Colab notebook.

  • Yes, I know it’s annoying, but that’s just how Colab works. If you’re not using Colab to follow along with me, feel free to skip this section as a simple pd.read_csv() will do the trick to upload your data-frame onto the IDE.

Let’s learn about our data-frame!

Now that we’ve uploaded our data-frame into the IDE, let’s learn more about it!

NBA.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 300 entries, 0 to 299
Data columns (total 31 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Season  300 non-null    object 
 1   Team    300 non-null    object 
 2   W       300 non-null    int64  
 3   L       300 non-null    int64  
 4   Finish  300 non-null    int64  
 5   Age     300 non-null    float64
 6   Ht.     300 non-null    object 
 7   Wt.     300 non-null    int64  
 8   G       300 non-null    int64  
 9   MP      300 non-null    int64  
 10  FG      300 non-null    int64  
 11  FGA     300 non-null    int64  
 12  FG%     300 non-null    float64
 13  3P      300 non-null    int64  
 14  3PA     300 non-null    int64  
 15  3P%     300 non-null    float64
 16  2P      300 non-null    int64  
 17  2PA     300 non-null    int64  
 18  2P%     300 non-null    float64
 19  FT      300 non-null    int64  
 20  FTA     300 non-null    int64  
 21  FT%     300 non-null    float64
 22  ORB     300 non-null    int64  
 23  DRB     300 non-null    int64  
 24  TRB     300 non-null    int64  
 25  AST     300 non-null    int64  
 26  STL     300 non-null    int64  
 27  BLK     300 non-null    int64  
 28  TOV     300 non-null    int64  
 29  PF      300 non-null    int64  
 30  PTS     300 non-null    int64  
dtypes: float64(5), int64(23), object(3)
memory usage: 72.8+ KB

Running the NBA.info() command will allow us to see basic information about all 31 columns in our data-frame (such as column names, amount of records in dataset, and object type).

In case you’re wondering about all the abbreviations, here’s an explanation for each abbreviation:

  • Season-The specific season represented by the data (e.g. 2014-15)
  • Team-The team name
  • W-A team’s wins in a given season
  • L-A team’s losses in a given season
  • Finish-The seed a team finished in during a given season in their conference (e.g. Detroit Pistons finishing 15th seed in the East last season)
  • Age-The average age of a team’s roster as of February 1 of a given season (e.g. February 1, 2024 for the 2023-24 season)
  • Ht.-The average height of the team’s roster in a given season (e.g. 6’6)
  • Wt.-The average weight (in lbs.) of the team’s roster in a given season
  • G-Total amount of games played by the team in a given season
  • MP-Total minutes played as a team in a given season
  • FG-Field goals scored by the team in a given season
  • FGA-Field goal attempts made by the team in a given season
  • FG%-Percent of successful field goals made by team in a given season
  • 3P-3-point field goals scored by the team in a given season
  • 3PA-3-point field goal attempts made by the team in a given season
  • 3P%-Percent of successful 3-point field goals made by the team in a given season
  • 2P-2-point field goals scored by the team in a given season
  • 2PA-2-point field goal attempts made by the team in a given season
  • 2P%-Percent of successful 2-point field goals made by the team in a given season
  • FT-Free throws scored by the team in a given season
  • FTA-Free throw attempts made by the team in a given season
  • FT%-Percent of successful free throw attempts made by the team in a given season
  • ORB-Team’s total offensive rebounds in a given season
  • DRB-Team’s total defensive rebounds in a given season
  • TRB-Team’s total rebounds (both offensive and defensive) in a given season
  • AST-Team’s total assists in a given season
  • STL-Team’s total steals in a given season
  • BLK-Team’s total blocks in a given season
  • TOV-Team’s total turnovers in a given season
  • PF-Team’s total personal fouls in a given season
  • PTS-Team’s total points scored in a given season

Wow, that’s a lot of variables! Now that understand know the data we’re working with better, let’s see how we can make a simple linear regression model!

The K-Best Way To Set Up Your Model

Before we start the juicy analysis, let’s first pick the features we will use for the model. In this post, we’ll explore the Select K-Best algorithm, which is an algorithm commonly used in linear regression to help select the best features for a particular model:

X = NBA.drop(['Season', 'Team', 'W', 'Ht.'], axis=1)
y = NBA['W']

from sklearn.feature_selection import SelectKBest, f_regression
features = SelectKBest(score_func=f_regression, k=5)
features.fit(X, y)

selectedFeatures = X.columns[features.get_support()]
print(selectedFeatures)

Index(['L', 'Finish', 'Age', 'FG%', '3P%'], dtype='object')

According to the Select K-Best algorithm, the five best features to use in the linear regression are L, Finish, Age, FG% and 3P%. In other words, a team’s end-of-season seeding, total losses, average roster age, and percentage of successful field goals and 3-pointers are the five most important features to predict a team’s win total.

How did the model arrive to these conclusions? First of all, I set the X and y variables-this is important as the Select K-Best algorithm needs to know what is the dependent variable and what are possible independent variable selections that can be used in the model. In this example, the dependent (or y) variable is W (for team wins) while the X variable includes all other dataset columns except for W, Team, Season, and Ht. because W is the y variable and the other three variables are categorial (or non-numerical) variables, so they really won’t work in our analysis.

Next we import the SelectKBest and f_regression packages from the sklearn.feature_selection module. Why do we need these two packages? Well, SelectKBest will allow us to use the Select K-Best algorithm while f_regression is like a back-end feature selection method that allows the Select K-Best algorithm to select the best x-amount of features for the model (I used five features for this model).

After setting up the Select K-Best algorithm, we then fit both the X and y variables to the algorithm and then print out our top five selectedFeatures.

Train, test…split!

Once we have our top five features for model, it’s time for the train, test, splitting of the model! What is train, test, split you ask? Well, our linear regression model will be split into two types of data-training data (the data we use for training the model) and testing data (the data we use to test our model). Here’s how we can utilize the train, test, split for this model:

X = NBA[['L', 'Finish', 'Age', 'FG%', '3P%']]
y = NBA['W']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)

How does the train, test, split work? Using sklearn’s train_test_split method, we pass in four parameters-our independent variables (X), our dependent variable (y), the size of the test data (a decimal between 0 and 1), and the random state (this can be kept at 0, but it doesn’t matter what number you use-42 is another common number). In this model, I will utilize an 80/20 train, test, split, which indicates that 80% of the data will be for training while the other 20% will be used for testing.

Other common train, test, splits are 70/30, 85/15, and 67/33, but I opted for 80/20 because our dataset is only 300 rows long. I would utilize these other train, test, splits for larger datasets.

  • Something worth noting: What we’re doing here is called multiple linear regression since we’re using five X variables to predict a Y variable. Simple linear regression would only use one X variable to predict a Y variable. Just thought I’d throw in this quick factoid!

And now, for the model-making

Now that we’ve done all the steps to set up our model, the next thing we’ll need to do is actually create the model!

Here’s how we can get started:

NBAMODEL = LinearRegression()
NBAMODEL.fit(X_train, y_train)

LinearRegression()

In this example, we create a LinearRegression() object (NBAMODEL) and fit it to both the X_train and y_train data.

Predictions, predictions

Once we’ve created our model, next comes the fun part-generating the predictions!

yPredictions = NBAMODEL.predict(X_test)

yPredictions

array([53.20097648, 28.89541793, 52.26551381, 53.22220829, 35.90676716,
       32.15874993, 47.72090936, 48.32896277, 39.4193884 , 40.1548429 ,
       19.62678175, 48.3263792 , 32.13473281, 43.50887634, 43.85260484,
       52.79795145, 27.35822648, 40.23392095, 18.85423981, 61.69624816,
       51.59650403, 23.86311747, 56.18087097, 54.15867678, 49.75211403,
       46.90177259, 31.80109001, 46.82531833, 37.50563942, 32.19863141,
       52.41205133, 25.09011881, 48.94542256, 38.80244997, 24.80146638,
       42.50107728, 43.27320835, 37.45199938, 46.7795962 , 28.11289951,
       57.64388881, 29.35812466, 18.3222965 , 36.26677012, 20.56912227,
       22.15266241, 19.9955299 , 44.84930613, 45.14740453, 23.19471644,
       53.940611  , 26.0780373 , 27.88093669, 61.23347337, 52.99948229,
       34.66653881, 30.04421016, 27.21669768, 48.55215233, 47.11060905])

The yPredictions are obtained through using the predict method on the model’s X_train data, which in this case consists of 60 of the 300 records..

Evaluating the model’s accuracy

Once we’ve created the model and made our predictions on the training data, it’s time to evaluate the model’s accuracy. Here’s how to do so:

from sklearn.metrics import mean_absolute_percentage_error

mean_absolute_percentage_error(y_test,yPredictions)

0.09147159762376074

There are several ways you can evaluate the accuracy of a linear regression model. One good method as shown here is the mean_absolute_percentage_error (imported from the sklearn.metrics package). The mean absolute percentage error evaluates the model’s accuracy by indicating how off the model’s predictions are. In this model, the mean absolute percentage error is 0.09147159762376074, indicating that the model’s predictions are off by roughly 9%-which also indicates that overall, the model’s predictions are roughly 91% accurate. Not too shabby for this model!

  • Interestingly, the two COVID impacted NBA seasons in the dataset (2019-20 and 2020-21) didn’t throw off the model’s accuracy much.

Don’t forget about the equation!

Evaluating the model’s accuracy isn’t the only thing you should do when analyzing the model. You should also grab the model’s coefficients and intercept-they will be important in the next post!

NBAMODEL.coef_

array([ -0.4663858 ,  -1.30716212,   0.39700734,  34.1325687 ,
       -22.12258585])
NBAMODEL.intercept_

50.945769772855854

All linear regression models will have a coefficient and an intercept, which form the linear regression equation. Since our model had five X variables, there are five coefficients.

Now, what would our equation look like?

Here is the equation in all it’s messy glory. We’re going to be using this equation in the next post.

Linear regression plotting

For the visual learners among my readers, I thought it would be nice to include a simple scatterplot to visualize the accuracy of our linear regression model. Here’s how to create that plot:

import matplotlib.pyplot as plt
plt.scatter(y_test, yPredictions, color="red")
plt.xlabel('Actual values', size=15)
plt.ylabel('Predicted values', size=15)
plt.title('Actual vs Predicted values', size=15)
plt.show()

First, I imported the matplotlib.pyplot module. Then, I ran the plt.scatter() method to create a scatterplot. I used three parameters for this method: the y_test values, the yPredictions values, and the color="red" parameter (this just indicated that I wanted red scatterplot dots). I then used the plt.xlabel(), plt.ylabel(), and plt.title() methods to give the scatterplot an x-label title, y-label title, and title, respectively. Lastly, I used the plt.show() method to display the scatterplot in all of its red-dotted glory.

As you can see from this plot, the predicted values match the actual values fairly closely, hence the 91% accuracy/9% error.

Thanks for reading, enjoy the upcoming NBA season action, and stay tuned for my next post where I reveal my predicted records and standings for each team, East and West! It will be interesting to see how my predictions pan out over the course of the season-after all, it’s certainly something different I’m trying on this blog!

And yes, perfect timing for this blog to come out on NBA season opening day! Serendipity am I right?

Also, here’s a link to the notebook in GitHub-https://github.com/mfletcher2021/DevopsBasics/blob/master/NBA_24_25_predictions.ipynb.