I can't believe I'm going to make an argument for this, but with Luka on the team now, LeBron might actually barely reach 50k points.

This season, LeBron James is averaging 25 ppg and has only missed 5 games out of a possible 59. However, his scoring has shown a clear upwards trajectory over the past month, so assuming he will average 27 points, playing 20 out of the Lakers' possible 23 games, LeBron James will end this season with 42377 points.

We know LeBron will play in 2025-26. Let's assume he stays at 26 ppg for that year, playing around 73 games. This is around the same as last yr, and with a primary playmaker, we can assume a similar volume. At the end of the 2025-26 season, LeBron will have 44275 points.

Now we enter speculation zone. However, I think it's probable that LeBron would sign at least 1 more two year contract, especially if the Lakers are contending, which is seeming likely.

In 2026-27, LeBron would become 42, which means we should expect some dropoff.... but should we? I personally believe LeBron will lose some step by this time, but not so much that he would be scoring less than 24 ppg. Assuming he misses around 9 games again, this brings LeBron James to 46027 points.

At this point, we see the vision. If LeBron James decides to chase the 50k mark, he can play 3 more seasons playing only 60 games a season, and average 23 for that to get there.

If he's still averaging 25 for 70 games in 2028, he'd be at 47777 at the end of his contract, which means he'd only need to average 16 a game for two more seasons playing 70 games. If he only plays 60 games, for those last years, he'd only need to average 19.

It is possible, given his current trajectory, that LeBron James will retire with 50k career points to his name. The fuckery will, in fact, continue.

Inspired by Hardwood Paroxysm's tweet, I decided to do a slightly more in-depth analysis on the most efficient 30 PPG seasons in NBA history.

Methodology

I decided to calculate all of the efficiency myself based off of raw total stats, as basketball-reference rounds all of their per-game statistics to 1 decimal place, affecting precision. So the process was: fetch the total stats for the season in question -> calculate each player's averages by stat/GP -> filter out non-qualified players -> calculate shooting efficiency.

To qualify, a player must:

1. Play in at least 58 games for the season.
2. Have an average of at least 20 MPG for the season.
3. Have an average of at least 29.5 PPG (rounded to one decimal place) for the season.

To calculate eFG% the formula is ((PTS - FT) / 2) / FGA

To calculate TS% the formula is (PTS) / (2 * (FGA + (0.44 * FTA)))

To calculate their relative versions (reFG, rTS), it is the player's stat itself minus the league's average of the same stat. Meaning a rTS% of 5 is 5 percentual points above league average TS% for the season.

To calculate their adjusted versions (eFG+, TS+), it is the player's stat itself divided by the league's average of the same stat. Meaning a TS+% of 110 is 110% of the league average TS% for the season.

Per Game data goes back to the 1951-52 season. Per 75 Possessions data goes back to the 1973-74 season.

All data belongs to Sports Reference and was fetched and used in compliance with their Terms of Use.

Results

Per Game

Ranked by eFG%

Ranked by eFG+%

Ranked by reFG%

Ranked by TS%

Ranked by TS+%

Ranked by rTS%

Per 75 Possessions

Ranked by eFG%

Ranked by eFG+%

Ranked by reFG%

Ranked by TS%

Ranked by TS+%

Ranked by rTS%

Aggregations

Considering the average rank for each metric used, these are the most and least statistically efficient seasons ever:

Per Game

Per 75 Possessions

Artefacts

All of the used data and the source code used to generate the tables are available at: https://github.com/gtkacz/nba_efficiency#

A complete sheet of all qualified seasons can be found at: https://docs.google.com/spreadsheets/d/1DOhIu3i5gV1NQwAZc6rbBl7qU6NhloBr/edit?usp=sharing&ouid=114071196241084372453&rtpof=true&sd=true

Back in 2020, Basketball Reference completely reworked their BPM model, where they explicitly stated that Westbrook was the driving reason for the change — the short of it being that Westbrook's rebounding numbers as a guard 'broke the interaction' between rebounds and assists in their regression

Currently, Basketball Reference currently has Nikola Jokic as the 3rd best defender alltime by defensive BPM —my understanding as to why, is based on their description of their model's tendency:

Assists are interesting. For guards, the BPM and OBPM coefficients are similar. For bigs, though, the offensive value of assists is less than the total value. Assists are a significant indicator of defensive skill for bigs.

i.e, The model 'thinks' that assists have less offensive value for bigs, so the rest of Jokic's impact must come from the defensive end

This seems like a classic case of overfitting, in the same way they were overfitting for Westbrook's huge rebounding numbers — and while Jokic is a unicorn, the trend of bigs being an offensive hub includes other players like Sabonis, Wemby, Sengun, Bam, and others.

Jokic is probably a better defender than he gets credit for, but I think we can all agree he's not the 3rd most impactful defender of all time. Since it's so similar to the Westbrook update, do you think they need to adjust for him u/Basketball_Reference ?

Wanted to do an experiment looking at the superstars that won titles with the weakest supporting casts. There are 3 teams that have always come to mind for me, but I was curious how some advanced analytics might view things differently. The three I always come up with:

1. 1994 Houston Rockets. There are some good role players on this team with Otis Thorpe, Vernon Maxwell, Robert Horry, and Kenny Smith, but I think this is probably a 30-35 win team without Hakeem.
2. 2022 Golden State Warriors. One of the more amazing Finals run. With no Kevin Durant, this solidified Steph Curry as one of the top players of all-time.
3. 2011 Dallas Mavericks. Tbf, this team had an A+ collection of role players, but it lacked a 2nd star and no one thought they'd compete for a title before the playoffs.

So we'll see how my picks do versus the advanced stats.

For the record, this is far from "scientific". I simply summed VORP and W/S-48 stats from Basketball Reference for 25 different title teams dating back to the 1981 Celtics. I took the 2nd - 8th highest rated players on each team. So remove the superstar and take the next 7 best players (however, I did test both Shaq and Kobe for 2001; and Kawhi and Duncan for 2014). Then I normalized these two sums in Python and added them together.

So this is nothing super-technical. Just trying to come up with a baseline that might be reasonable.

I did this manually in Excel, so I did not get every title team. If anyone knows of any good APIs to do this in Python, please share! I haven't done a lot of basketball analytics, so still not sure what's out there, but I'd love to do this in a more programmatic way that can combine different advanced stats.

Here are the top 5 results:

1. 2023 Denver Nuggets, Jokic (Score: 0.00). According to this analysis, Jokic's championship run was even more impressive than it might've seemed. This was rated by far the weakest supporting cast, with a cumulative VORP of 6.7 and W/S48 of .701, giving it a combo normalized score of 0. No other team since 1981 that I surveyed even came close to have as weak of a supporting cast as '23 Denver.
2. 2021 Milwaukee Bucks, Giannis (Score: 0.39). Giannis scores 2nd on this with teammate cumulative VORP of 8.6 and WS48 of 0.818. While some of Giannis' teammates such as Holiday and Middleton score reasonably well, an overall lack of depth kept the score low.
3. 2001 LA Lakers, Shaq (Score: 0.46). This might seem surprising given that Shaq played with Kobe, but the advanced metrics viewed this Lakers roster as very thin overall. The Shaq-Kobe combo was spectacular, but without those 2 guys, this team may have only won 20 or 25 games.
4. 1994 Houston Rockets, Hakeem (Score: 0.48). The supporting cast for the '94 Rockets was a bit more balanced than the '01 Lakers, but unlike the Lakers who had 2 stars, Hakeem was the only true star on this squad. While it didn't come in at #1, it was pretty close, and I mention a mitigating factor below that probably supports the idea that this should be either #1 or #2 in reality.
5. 2003 San Antonio Spurs, Tim Duncan (Score: 0.64). Interestingly, my analysis suggested that most of Duncan's Spurs title teams were loaded, with this 1 exception. While this team was technically the only one to include David Robinson, Manu Ginobli, and Tony Parker, these "big names" make this a bit misleading. David Robinson was 37 and well past his prime. He was more of a role player on this team and he only averaged 8.5 ppg on this team. This was also Manu Ginobli's 1st season in the NBA and he only averaged 7.6 ppg; he hadn't yet become the phenomenal NBA player that he would in a few years. And Tony Parker was only 20 years old. So while it has the "big names", it was far from "loaded". The 2005 and 2007 teams scored much higher on "supporting cast" scores. In fact, they were some of the highest ones in the series.

There are several flaws in this methodology and I'm doing this more for fun and to create discussion.

One important trend I noticed:

Supporting casts have gotten worse over time. I suspect this is the result of salary cap changes. The teams from the 80s, 90s, and even 00s, tended to have much higher "supporting cast" scores. Less salary cap restrictions likely meant that the top teams were able to hoard more talent. So it may not be completely fair to compare, for instance, the 1986 Boston Celtics versus the 2022 Golden State Warriors on this metric. Bird's '86 supporting cast was better than Curry's 2022 supporting cast according to this analysis, but it's also likely that Bird's opponents in the playoffs had better supporting casts than Curry's opponents. So if I did a deeper dive on this with a Python API, I think I'd also look at the "supporting casts" of the other top 5'ish teams in the league that year to get a good baseline.

While my Houston 1994 pick didn't end up #1 in my analysis, I suspect it would move further up the list once you account for more roster parity over time. I still think the data largely supports my view on 1994 Houston, albeit now I'm considering 2023 Denver right up there with them.

Other stuff:

Jordan's 1st 3-peat more impressive than the 2nd. I only surveyed '91 and '96, but '91 had one of the lower supporting cast scores and '96 was the highest in the entire series, beating out 24 other teams. So you could say Jordan pulled much more weight in '91-93 than '96-98.

2008 Celtics. 2nd highest "supporting cast" score in the series behind the 1996 Bulls.

2022 Golden State and 2011 Dallas. While they were in the bottom half of scores, this particular model thought their supporting casts were better than I had given them credit for. Also, now that I've seen how "supporting casts" have gotten weaker over time, that might make the '22 Warriors seem less unusual, particularly given that the '21 Bucks and '23 Nuggets led the list. It's very difficult to teams to stockpile talent in today's game.

That's all I got. Hope you enjoyed!

Although 47 points doesn’t seem like a crazy amount with today’s pace of play, given both teams scored under 100, I think it may be an outlier. On the all-time single game scoring record list, there’s only one game where the total team score was under 100, and that was George Mikan when the game was just incomparably different. That list only goes down to 60 though… I can imagine someone chucked up a 50 piece in a losing effort on a terrible team, but found it difficult to track down.

Anyone else able to track down the single game scoring record in a sub-100 game? Where does Brunsons effort rank?

I

Upon Big Honey and the Nuggets winning the chip this year, I decided to take my own stab at the never-ending GOAT debate. For my approach, I decided to only use numbers/statistics (even if I don't think this approach is always most effective by itself), to see how accurate such a model could be. Here I will break down the formula I used and the top 30 that it produced (I'm not comfortable going beyond 30 since I didn't run the model on every great player of the past and don't want to risk missing folks, although I have run a little over 100 so far).

I will preface this with a few disclaimers:

• I believe that every number/stat I used is fallible in some way, so this is definitely not perfect! However, I tried to use the metrics I found most reliable for what I was trying to measure.
• I counted BAA and ABA accomplishments as equal to NBA, since I found no reason to arbitrarily weigh them less. I did not count NBL accomplishments, however, since bbref doesn't possess as in-depth of stats and franchises don't recognize their own NBL championships as official titles.
• Despite my desire for accuracy in the model, my own biases undoubtedly affect at least some (if not all) of its components.

​

Key components

Win shares

I'll be the first to admit that win shares is far from a perfect catch-all statistic. However, I settled on it due to other (perhaps better) catch-all metrics not covering pre-1970s.

The philosophy behind using this is simple: get at how much a player contributed to their team winning. In the formula, I used both WS and WS/48, reason being to get at both longevity (WS) and consistency (WS/48) and since both are too imperfect on their own. Additionally, I delineated between regular season WS and WS/48, and playoff WS and WS/48.

​

MVPs (kind of)

To tap into peak, I compiled each player's MVP award share, which is defined as the ratio of points awarded in a player's MVP voting. For example, a unanimous MVP for a season would get 1 MVP share, but most end up with .7-.995 since even MVP winners have people vote for them other than 1st. I figured this would be more accurate than simply using MVPs since it is more nuanced.

For players that played before the MVP was awarded, I retroactively assigned the MVP based on regular season WS. This is very much imperfect, but better than leaving those players without that part of the formula to consider (in my opinion).

​

All-NBAs (kind of)

Similar to MVP award shares, I also factored in All-NBA voting shares instead of pure All-NBA selections, for the same reason. The goal of including these was to reward consistent and recognized excellence, rather than just peak or longevity.

Notably, I did not include All-defensive voting shares, due to a) the recognition not existing until the late 60s, and b) wanting to recognize offense and defense equally, rather than valuing defense more (but mostly point a). Similar for DPOY - and I didn't feel comfortable trying to retroactively assign this award. Also, I did not count All-Star appearances, since a) those do not count full seasons like All-NBA does, and b) they have historically been a bit more dependent on popularity than All-NBA (but mostly point a). All-NBA recognition already taps into overall excellence, so I saw no need to overlap on that.

For players that won All-NBA during years where there was no voting, I simply assigned 1 full share to an All-NBA first team selection, and 0.5 shares for an All-NBA second.

​

Finals MVPs

Pretty self-explanatory: the goal here is to tap into excellence on the most important stage of the game. "Shares" were not used here due to the award being given without a voting process.

For players that played before the Finals MVP was awarded, I retroactively assigned the Finals MVP based on playoff WS (similar to MVP).

​

Finals appearances

I decided to include this since it is a feat in itself to reach the finals. However, it did not factor into the formula nearly as much as...

​

Championships

...of course. "Did you win or did you lose?" Number of rings is essential to the formula as it affects every other number. The philosophy behind this is that since winning the championship is the ultimate goal of playing professional basketball, it should wholly impact a player's legacy.

​

Formula

Here is the formula I settled on:

[(((rsMVP/1.6) + 1) * rsWS * rsWS/48 * (1+(AllNBA/4))) + ((fMVP + 1) * 10*pWS * pWS/48 * (1+(.015*FinalsApps)))] * (1+(.15*Championships))

​

Reasons for the arbitrary weights:

• rsMVP (MVP award shares) was divided by 1.6 since 16/10 = (total non-first-place points possible)/(total first-place points possible). This also serves to balance the weight of the metric sp that it is not impacting the overall score too heavily compared to the others.
• All-NBA shares was divided by 4 because 4/5 = (total non-first-place points possible)/(total first-place points possible), and there are 5 first-place spots available. (4/5)*5 = 4
• pWS was multiplied by 10 since playoffs are much more important than regular season. This number is completely arbitrary otherwise (although I did experiment with other weights here).
• A player's total score is boosted by 15% for every one championship they've won. Finals appearances account for a 1.5% boost of only their playoff score.
  • A notable flaw here is that some (including myself) would argue that not every player has contributed equally to their team's championships, so they should not be equally rewarded. I currently don't have an answer for how to accurately account for this mathematically. I considered using usage rate as the multiplier, but it not considering aspects of offense besides scoring and not considering defense at all made me wary (also, pre-1970s players can't afford the luxury of using such a statistic, even if it were better for these purposes). So, 15% is what I settled on as I tested many variations and found that this one balanced the top-end talent enough with not overrating lower-end greats that happened to win a lot with better players.
• Lastly and also noteworthy: The formula does not attempt to weigh some championships/seasons more than others, and does not attempt to quantify strength of era.

Overall summary of the formula: Winning contributions and accolades in the regular season are coupled with the same in the playoffs, and the sum is affected by their total championships.

​

The Top 30

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Here is a link to the data. I will likely be updating this as I find time here and there to add players. Please also note that like all rankings, placement variance increases the further down you go, so perceived accuracy will naturally wane.

​

Discussion

Let's address the elephant in the room first. LeBron > Jordan is very debatable, and common opinion still holds that Jordan is better. LeBron's longevity rewards him in this formula, as despite Jordan's score being boosted 30% more than LeBron's by championships, the latter's win shares in both the regular season and playoffs simply outmatch Jordan's too much. LeBron also holds slightly more MVP shares (8.815 > 8.115) and significantly more All-NBA shares (15.496 > 10.679). It would be silly of me to try to make a definitive conclusion for the #1 spot from this model, but I think both can be argued, so I'll leave it at that.

Maybe this is controversial, but I do believe the GOAT argument comes down to those two (especially with LeBron now being the all-time points leader). However, Kareem is a shoe-in 3rd all-time, in my opinion. Following him is Russell, whose score is boosted by a whopping 165% due to his 11 rings. Russell is also rewarded by 6 retroactive Finals MVPs (some believe he would have deserved even more). And rounding out the top 5 is Tim Duncan! He edges out Magic and the rest due to better longevity with still an equal or greater amount of rings.

The rest of the top 10 doesn't seem too controversial to me. Magic has to be at the high end, and Shaq vs Wilt simply comes down to how much rings are valued, so that is honestly a coin flip. Kobe and Bird follow. That seems like a pretty safe top 10 to me in terms of players present.

The first name outside of the top 10 may not be so safe, though! George Mikan, who many forget or find too difficult to rank, nearly cracks into the top 10 due to being the best player on 5 championship runs. He is also boosted by being awarded 3 retroactive MVPs (some think he would deserve more than that, even). Dr. J as high as #12 is also not common on most lists I see, but with this formula counting his ABA accomplishments as equal to his NBA ones, he comes away with a very impressive resume. K. Malone, KD, and Olajuwon round out the top 15. I find Olajuwon to be treated quite unkindly by this model.

Scores start to get uber close to one another from #15 onwards, so I won't touch on everyone else from here, except for some notables. Steph vs Big O is similar to Shaq vs Wilt in that it really comes down to how rings are valued. Also, Bob Pettit sneaking into the top 20 (ahead of other notable PFs) was unexpected, but his decade's worth of All-NBA first team selections speak for themselves. James Harden at #22 is... liberal, especially since he still doesn't have a ring, but his impressive peak (3.656 MVP shares) favors him greatly. Chris Paul's story is similar except with slightly less impressive peak but greater longevity and consistency. Havlicek's 8 titles (and 2 retroactive Finals MVPs) nearly propel him into the top 25, and Moses Malone perhaps still remains underrated to some. Lastly, Kawhi, Giannis, and Joker sneak into the top 30 with their recent Finals MVPs, which is very exciting for the modern NBA fan as all three still have ample time to rise in the rankings (although top 10 may be a tough ask). However, as these active players' WS/48 decreases after their primes, their scores may actually be negatively affected in spite of their growing WS.

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Conclusion

I hope I've provided something worthwhile here while also maintaining awareness of this model's shortcomings. While I am biased in favor of the general backbone, I'm bound to disagree with at least some rankings personally. But, let me know your thoughts and if you see ways I could improve it!

The NBA world has now caught on to the fact that Nikola Jokic is one of, if not the best, player in the NBA right now. Jokic’s offensive skillset has been the focus of the world’s attention – and rightly so. As per SecondSpectrum, a Jokic post-up is the most efficient half-court play in the past 10 years. Jokic’s season last year was the highest Player Efficiency Rating (PER) season of all time. Higher than even Wilt Chamberlains 50 points per game across a season or MJ’s legendary 1987-99 season. This year was no different, with his season’s PER ranked 11th all time.

However, it's Jokic’s defense that differentiates him from any other defender in the NBA at the moment: Jokic kicks the ball. Actually, Jokic kicks the ball a lot. It is weird to think of it, but Jokic’s unbelievable season this year was historic not just from his scoring and passing, but his kicked balls.
Which Players Led the League in Kicked Balls?

1. Nikola Jokic (DEN) Kicked Ball Violations: 47; Kicked Ball Per Game: 0.68

2. Nikola Vucevic (CHI) Kicked Ball Violations: 19; Kicked Balls Per Game: 0.23

3. Nic Claxton (BKN) Kicked Ball Violations: 18 Kicked Balls Per Game: 0.24

4. Jakob Poeltl (TOR) Kicked Ball Violations: 18 Kicked Balls Per Game: 0.25

5. Jaden McDaniels (MIN) Kicked Ball Violations: 16 Kicked Balls Per Game: 0.20

6. Domantas Sabonis (SAC) Kicked Ball Violations: 15 Kicked Balls Per Game: 0.19

7. Jusuf Nurkic (POR) Kicked Ball Violations: 13 Kicked Balls Per Game: 0.25

8. Luka Doncic (DAL) Kicked Ball Violations: 11 Kicked Balls Per Game: 0.17

9. Jonas Valanciunas (NOP) Kicked Ball Violations: 11 Kicked Balls Per Game: 0.14

10. Pascal Siakam (TOR) Kicked Ball Violations: 10 Kicked Balls Per Game: 0.14

Jokic kicked the ball 47 times this season. That is more than double Nikola Vucevic’s 19 kicked balls. This is the most kicked ball violations by a player since tracking data was introduced.

Not only was it a large absolute value, but Jokic’s kicked ball per game average was an unbelievable .68 (aka over 1 kicked ball for every 2 games played).

Across the NBA this season, there was only 333 total kicked balls, meaning Jokic had 14% of all kicked balls. Jokic also had more kicked balls than 27 other teams!

Why Does Jokic Kick the Ball So Much?
NBA analysts and casuals last season used Jokic’s high kicked balls as a criticism. ESPN’s Zach Lowe said “Jokic has 45 kicked ball violations this year. [Number two] has 17. It's his way of saying, ‘I just don't feel like playing defense.' It's smart, but we shouldn't allow guys to karate kick the ball.”
However, I personally think it is an exceptionally solid defensive tactic. Kicked balls most often occur when a play on the wing is trying to feed a pass into a player who is either cutting to the rim or who is posting up. In the event where a player is cutting for a layup, a kicked ball essentially resets the defense and protects an exposed rim. If Jokic is in a position to kick the ball, then it is likely that he is not in a good position to contest a backdoor cutter.
This begs the question, should more players adopt this defensive method? It seems on the face of it that it is a particularly effective way to disrupt an offense.

An alternative question might be should the league start looking to crack down on kicked balls to increase the pace and scoring of the game.

Now that a couple of months have passed (and in fact we're close to the start of next season) we can assess claims such as these coolly and dispassionately, without being prisoners of the moment.

How did this arise?

The Knicks beat their playoff opponents by an average of 14.9ppg, said to be the highest in history. Actually this isn't quite true. Mikan's 1956 Lakers lost a three-game first-round series two games to one, sandwiching two one-point losses around a 58-point win, for an average of +18.7 net. Since it doesn't mean a thing without the ring, henceforth we'll only look at champions, to prevent such outliers.

Is this anything?

Since the next four teams are the 1971 Bucks (+14.5), 2017 Warriors (+13.5), 2001 Lakers (+12.8) and 1991 Bulls (+11.7), I'd say this is a pretty good list to be on top of. Notably, the Knicks remain ahead even if you remove each team's biggest win, with their 51-point win over the Hawks cancelled out by the Bucks beating the Warriors by 50 and the Lakers beating the Spurs by 36. Adjusting for pace shuffles the order a bit, but the Knicks remain first (+15.5 net rating) followed by the Lakers, Warriors, Bucks, Bulls (+13.7, +13.5, +13.4 and +13.2 respectively).

Is this everything?

Absolutely not. In fact we don't have to scroll down too far to remember the 2024 Celtics beating the pants off some very mediocre opponents. Strength of schedule matters. Fortunately, we have a Simple Rating System (SRS) for that, and very helpfully, CraftedNBA have calculated a playoff-specific version. Here at last the Knicks (+19.17) cede the crown, by the barest of margins, to the those same Lakers (+19.18). Warriors (+16.9) stay steady in 3rd, and we have some new contenders: the 1996 Bulls (+16.6) edge their 1991 cousins (+15.7), and the Cavs (+14.9) overtake the Bucks (+14.5).

Can we go deeper?

Yes. Again citing the Celtics, the main problem with SRS is that it assumes every opponent is their regular season selves, without factoring in trades, injuries or shortened playoff rotations. This is a problem I've been working on for a number of years, using Box Plus-Minus (BPM) as a proxy. This model even predicted the Knicks would beat the Spurs. Much like when 538 picked Raptors over Warriors, I didn't quite believe what the numbers were telling me. But updating that chart from a couple of years ago gives the following top 6:

Does winning matter?

The odd one out in that group is not New York but OKC, who of course got pushed to seven games twice. While winning by big margins might be a better predictor of long-term success, and there's an element of luck in close games, ultimately we're judged on results, which is why I'd push the Lakers and Warriors higher than the raw numbers show (and there's even an outside case for the 1983 Sixers, although the numbers are murkier). Still, 16-3 is really damn good.

Does the regular season matter?

Yes and no. Obviously it shouldn't affect the impressiveness of the run itself, but people want to know if it's replicable, which is why they've been so quick to tear down the non-repeat champs of this parity era. The Knicks won the fewest games of any of the teams we've looked at, and only the Lakers are vaguely close, who obviously backed it up. To be continued...

Conclusion

Whichever way you slice the numbers, the Knicks are in the top tier of champions, ever. Although I wouldn't personally have them number one, I certainly don't blame their fans for doing so, especially when you take into account unquantifiable factors such as comebacks.

Basketball reference has preseason title odds for every champion dating back to 1985. Here are the biggest underdog title teams in that time span:

The Suns were +4000 to win a title in the preseason, and the Hawks were +10000 (same as the Wizards and Pacers). If either team won the championship this year, they be by far the biggest underdogs in the past 35 years to win a title, and if the Hawks were to do so, they'd likely be the biggest preseason underdogs in NBA history to win a ring.

LeBron James is ridiculous. I split his first 17 years of his career into three parts: 6 years, 5 years and 6 years. I should have done it 7/4/6 but I wanted to leave it more “equal” parts. If I done this then the first Cleveland era would have 2 MVP's and higher counting stats and more awards, but I wanted to keep the segments as more similar time periods

The First 6 years is the "weakest" but its still pretty damn incredible. I listed out his awards, games played, averages, playoff runs, titles, etc on here. I wanted to see his career on paper split into three parts. The ridiculous thing is he can play another 5 years at an ELITE level and add to this.

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Part 1: 2003/04 to 2008/09

Regular season:

Rookie of the Year

Rookie All first team

3x All NBA first Team

2x All NBA second team

1x All NBA defensive team

1x Scoring Title

1x NBA MVP

1x Second place MVP

5x NBA All Star

2x All Star MVP

Regular Season Games played : 472 games

Averages per game: 27.5 points/ 7 rebounds/ 6.7 assists

Totals: 12,986 pts / 3,311 reb/ 3,138 ast

Post season:

2xECSF

1x ECF

1x Finals

Playoff Games played Playoffs: 60 games

Averages per game: 29.9 pts/ 8.3 reb/ 7.2 ast

Totals: 1,763 pts/ 495 reb/ 436 ast

International:

1x Olympic Bronze

1x Olympic Gold

1x FIBA Americas Gold

1x FIBA World Bronze

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Part 2: 2009/10 to 2013/14

Regular season:

5x All NBA first team

4x All NBA defensive first team

1x All NBA defensive second team

5x All Star

3x NBA MVP

2x Second place MVP

Regular Season Games Played: 370 games

Averages per game: 27.5 pts/7.5 reb/ 7 ast

Totals: 10,169 pts/ 2,777 reb/ 2,631 ast

Post Season:

1x ECSF

4x Finals appearances

2x Championships

2x FMVP

Playoff Games Played: 90 games

Averages per game: 27.3 pts/ 8.6 reb/ 6.1 ast

Totals: 2,658 pts/ 836 reb/ 585 ast

International:

1x Olympic Gold

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Part 3: 2014/15 to 2019/20

Regular season:

5x All NBA first team

1x All NBA third team

6x NBA All Star

2x Second Place MVP

1x Second Place DPOY

1x All Star MVP

1x NBA Assist Leader

Regular Season Games Played: 423 games

Averages per game: 26.2 pts/ 7.8 reb/ 8.4 ast

Totals: 11,078 pts/ 3,307 reb/ 3,555 ast

Playoffs

5x Finals appearances

2x Championships

2x FMVP

Playoff Games Played: 109 games

Averages per game: 30.1 pts/ 9.96 reb/ 8.34 ast

Totals: 3,072 pts/ 1,016 reb/ 853 ast

What do you think? Are all three parts of his career easily hall of fame? Its hard to admit someone who only plays 5 years but the statistical resume, counting stats and awards/success would be hard to not make it in EASILY. Im sure I missed somethings as well.

https://www.espn.com/nba/player/stats/_/id/1966/type/nba/seasontype/2

According to FiveThirtyEight analyst Ryan Best, Disney/ABC is no longer supporting FiveThirtyEight's sports division, including all forecasts and presumably the RAPTOR model as well.

FiveThirtyEight's forecasts and RAPTOR were ubiquitous among online NBA conversations over the last few years, for better or for worse. Professional sports writers and awards voters relied on RAPTOR to analyze performance and make decisions that cost players tens of millions of dollars. Gamblers and sportsbooks, no doubt, also used the forecasts to regularly evaluate and adjust lines.

I think almost everyone had a love/hate relationship with RAPTOR. For as much complexity as it had in implementing all the nuances of an individual player's performance into an all-in-one stat, it also seemed to treat team basketball performance as an exercise in basic addition. Great role players like Derrick White were valued more highly than most marquee starters. The most predictable thing about the predictions was their fallibility.

What does everyone make of this momentous change in the popular analytics landscape? Will lesser-known advanced stats fill in the vacuum on message boards and Reddit threads? Will FiveThirtyEight's most committed analysts stay teamed up to develop something bigger and better? Or will we breathe a sigh of relief that casual observers will have to look harder to find stats to back up their opinions - and maybe learn a thing or two in the process?

I'll miss checking the forecasts and player stats throughout the season and hope a new source of capital materializes to carry forth RAPTOR's lineage. At the very least, I hope someone is able to take the models and migrate them elsewhere to carry the torch just a bit longer. RAPTOR was never the most accurate advanced stat, but it was always the most polarizing, which is a value in itself.

EPM and LEBRON will no doubt take some of the thunder, but being behind a paywall inherently limits their accessibility. Perhaps it's DARKO's time to shine. If anyone knows of any other stats poised to fill RAPTOR's place in the public consciousness, please do share!

In the NBA right now everything focus around the 3 ball. Even for Centers and Power Forwards. If you can’t shoot the 3. You’re basically dead with the exception of a few ELITE defensive talents like the Thompson Twins and Dyson Daniels.

Right now shots inside the paint hit at about 62%, around 69% directly at the rim 0-5 ft. This being worth 2 points is ok because players also draw the most fouls in this area. Interestingly I found that 62% of all fouls called in the NBA are at the restricted area and under the basket. This isn’t even including the rest of the paint.

Now a piece of information but it’s stated that on average teams foul in total around 18-22 times per game over the last 10 years. Finally the league average free throw rate sits at around 77-78%. Cool.

Next we go to the midrange where efficiency takes a drop. Particularly where if we just account for midrange shots not in the paint, league averages sit at around only 40-42%. Quite terrible when you look at the percentages above. On top of this the foul percentage that’s called in the midrange is only at around 23%.

Finally we go to the 3 point line where league averages sit sits at around 35-36% with a foul rate on average of only 15%.

Now let’s do some math:

Shooting in the paint only using the numbers above:

Formula: ((shot value x efficiency %) x 100) + ((20 FTA x percentage of FT’s in area) x league average FT rate)

For the paint:

= ((2 x 0.62) x 100) + ((20 x 0.62) x 0.78)

= 116 + 9.672 (round up to 10)

= 134points per 100 shot attempts at the rim including ft attempts.

For the midrange:

= ((2 x 0.41) x 100) + ((20 x 0.23) x 0.78)

= 82 + 3.588 (round up to 4)

= 86 points per 100 shot attempts from the midrange including ft attempts

For the 3 pointer:

= ((3 x 0.36) x 100) + ((20 x 0.1) x 0.78)

= 108 + 1.56 (round up to 2)

= 110 points per 100 shot attempts from the 3 pt line including ft attempts.

Before we continue I know someone is gonna comment about me using only 0.1 at the FT multiplier instead of 0.15 but the reason for that is because in the NBA the corner 3 is fouled almost at double the rate of a around the arc 3 pointer. Using this knowledge, let’s go to our next point.

From this analysis we can see that the by far the paint and even more so at the rim scoring is the most efficient shot even if you’re just a league average player. Next is the 3 pointer. Slightly inefficient compared to the paint but ft’s are inconsistent. You can’t control the refs but you can control your own shooting. That leaves the midrange in a dead zone that has no value on the court unless as a last second shot to avoid a shot clock violation.

So… how do we fix this. How do we make it so that the midrange finds a home on the NBA court once again?

Well it starts with addressing the elephant in the room, the corner 3. I didn’t bring it up because I wanted to focus on just the value of each point initially. Now let’s play a game of what ifs. Imagine team took 100 corner 3’s per game how many points would they generate? Well, let’s use that formula again.

Only corner 3’s:

= ((3 x0.4) x 100) + ((20 x 0.2) x 078)

= 120 + 3.12 (round down to 3)

= 123 points per 100 shot attempts at the corner 3 including ft attempts

Look at that number for a second… 123 points, the corner 3 is almost as efficient a shot as a shot from the paint.

No wonder the league has turned to what it is today, it’s a drive and lay, or drive and kickout. If not that you’re looking for a cutter to dunk or a lob to dunk. That’s it.

There is no variety to the game anymore. So again I ask the question how do you fix the NBA spacing issue. It’s simple really.

Step 1) remove the corner 3. By removing the corner 3 in theory you go ahead and create more of a interior game which some people worry will lead to scores going down and efficiency dropping leading to a worse NBA product but that’s why you can’t just remove the corner 3.

Step 2) move the 3 point line back to 24’6”. By doing this the arc naturally end at the side of the court and doesn’t create a very tiny sliver of corner 3 area.

Now I know someone is gonna say but won’t these measures drop the 3 points shot efficiency? Yes, most likely to around 30% league average.

Now that makes the 3 pointer almost as inefficient as the midrange 2 pointer but this is where step 3 comes in.

Step 3: non-paint mid range shots are 3 points and the arc becomes a 4 point shot.

Let’s do some math.

New midrange value:

= ((3 x 41) x 100) + ((20 x 0.23) x 0.78)

= 123 + 3.588 (round up to 4)

= 127 points per 100 shot attempts at the midrange including ft attempts

New 4 pointer:

= ((4 x 0.3) x 100) + ((20 x 0.15) x 0.78)

= 120 + 2.34 (round down to 2)

= 122 points per 100 shot attempts at the 4 point line including ft attempts

With these new changes right now paint scoring sits at 134, midrange 127, and beyond the arc 122. This makes the league much more balanced but more importantly it will make the game more dynamic.

For example, you can still play the drive and kick game. The corner 3 still exists, just in the midrange… CRAZY RIGHT.

That’s the big idea. You keep today’s playstyle in tact, because I understand… there are a lot of fans that love this style of basketball and they love watching it so I didn’t want to kill the corner 3, instead I wanted to reinvent it in a way that benefits the rest of the court.

Drive and kick is one game. Play pace is another. Odds on, with the extended 4 points arc, fast players that drive well might have an increased role at shooting at the rim.

This is more of a prediction but with a larger midrange and farther arc, I think the paint would be even more desire-able probably increasing the FT rate even more imo.

The return of the PF that maybe can’t shoot beyond the arc but has a good post up game just outside the paint or for example the midrange stop and shoot specialists could make a return to the league but most importantly, those big bombing 4 point specialists are going to be a unique weapon in how teams guard them and how teams defend them because the last thing you want to do is give up 4 FT attempts or an and one.

This is my vision and what I hope the NBA does because it fixes everything wrong with the NBA today while also retaining the core piece of the modern NBA that everyone loves. It’s a win-win.

For those who don’t know, Justin Tinsley, a frequent contributor to Around the Horn on ESPN, has a rule that he has coined “The 2-1-1 Theory.” The simplest explanation for this is that to win 4 games to advance in the playoffs, 2 of your wins needs to come from your best player playing great. 1 win needs to come from your second best player playing great. And the 4th win to advance needs to come from a role player having a surprising game to push your team over the edge.

An example of this would be the Nuggets-Suns series. Game One was won in major part by Jamal Murray’s play. Games Two and Five were won by Jokic. And Game Six, while Jokic was their best player, Caldwell-Pope’s 21 helped push the game to a blowout.

For the Lakers-Warriors, Game One was won by Anthony Davis. Games Three and Six were won by Lebron being Lebron. Game Four was a victory for the Lakers in major part because of Lonnie Walker IV’s play late in the game.

So, is there credence to this 2-1-1 theory? Or is this something that just sounds true but doesn’t hold up?

In sports, and specifically basketball, fans are obsessed with the idea of a “clutch gene.” They are quick to label players as clutch or chokers. However, in my opinion, in the vast majority of the time, star players don’t have some crazy mental switch, or mystical powers that make them better or worse in the clutch. It’s just fans collectively being horrible statistician, and extrapolating from a sample size that is way too small.

The NBA defines clutch shots, as any shot taken in the final five minutes of the fourth quarter or overtime when the score is within five points. However, to most fans, including myself, clutch plays refer most strongly to the final possession or two of an important game. Think about it, when you picture clutch plays you think of Rey Allen’s game tying three against the Spurs or Jordan’s mid range game winner against the Cavs. You don’t think of a De Aaron Fix fast break lay up with 4:20 on the clock.

The issue is, there just aren’t that many truly clutch shot attempts in the playoffs. Certainly not enough to judge a players “clutch gene.” For example, Curry has recently been labeled not clutch. Statistically, he is 0/14 on game tying or go ahead attempts in the last 50 seconds of play off games. However, that is an insanely small sample. Can you imagine if we judge players free throw, or three point shooting abilities off 14 shots over the course of their career, or played a 14 game regular season. We would come to some wacky, and frankly incorrect conclusions, because 14 shots is a ridiculously small sample size, especially in a sport as variable in basketball.

In my opinion only 2 players, Kobe and LeBron, have had a large enough sample size of “clutch moments” over the course of their career, that we can even begin to discuss if they are good in the clutch. LBJ has shot 17/50 (34%) on game winners on his career, slightly above the league average of 29.8%. If you restrict it to only play offs he is has shot a scorching hot 50% on game winning attempts. Kobe js a similar story shooting 14/56 (25%), bit below average for his entire career, but an excellent even 50% in the playoffs. Even these two show the issue with small sample sizes. Both shoot right around league average efficiency in the clutch, when given a large enough sample of shots, but when limited to a small sample size of just play offs they become outliers.

Do any of you have convincing arguments for the existence of the mythical clutch gene, other than a gut feeling?

I wanted to figure out what supermax numbers should look like. I couldn’t find anything so I decided to run the numbers myself. A supermax player makes 35% of the salary cap. Most contenders are at least in the first apron so I transposed 35% of the cap to the first apron number which works out to 27% of the salary cap.

I took the top 4 playoff teams in the four big statistical categories and found what 27% of the average points/rebounds/assists/turnovers would be. I’m sure there are other stats I could factor into to this an id love suggestions on what to add or any flaws in my math.

The supermax stat line is 30/12/6.8/2.97.

That seems awfully high and I’m guessing I’ve overlooked something somewhere. However if my numbers even remotely close Giannis, Jokic and Shai are the only guys in the league that are remotely close to a supermax Level of production.

These are professionals and even a 60% FT shooting night would be considered abysmal. The fact that they shot 50% on 36 total free throws is so improbable. I understand that players are used to loud arenas but is there the possibility that one high pitched scream standing out in a relatively quiet arena with the added nerves of an elimination game actually affected a significant amount of free throws? Even hitting 4 more for a 60% would’ve changed the outcome.

I hope this does not get marked as a meme, I am genuinely concerned on how this seemingly non-factor could have played a role in an elimination game. I believe that she definitely played a role in at least 2 free throws. If anyone has any insight on what it’s like to shoot in front of a large crowd and how one high pitched scream would effect a shooter that would be appreciated.

Michael Jordan - 1986 EC1 (43.7), lost 0-3
Rick Barry - 1967 Finals (40.8), lost 2-4
Elgin Baylor - 1962 Finals (40.6), lost 3-4
LeBron James - 2009 ECF (38.5), lost 2-4
Jerry West - 1969 Finals (37.9), lost 3-4
Hakeem Olajuwon - 1988 EC1 (37.5), lost 1-3
Russell Westbrook - 2017 WC1 (37.4), lost 1-4
Bob McAdoo - 1975 ECS (37.4), lost 3-4
Elgin Baylor - 1961 WDF (37.1), loss 3-4
Wilt Chamberlain - 1961 EDS (37.0), lost 0-3
Donovan Mitchell - 2020 WC1 (36.3), lost 3-4
LeBron James - 2015 Finals (35.8), lost 2-4
Luka Doncic - 2021 WC1 (35.7), lost 3-4
Michael Jordan - 1987 EC1 (35.7), lost 0-3
Allen Iverson - 2001 Finals (35.6), lost 1-4
Kevin Durant - 2021 ECS (35.4), lost 3-4

There have been 16 instances of a player averaging 35+ PPG in a playoff series on the losing team.

George Gervin averaged 30+ PPG in 5 series, losing each one. Tracy McGrady averaged 30+ PPG in 4 series, losing each one.

Kareem, James Harden, Damian Lillard, Giannis, and Gilbert Arenas also all had losing series where they barely missed the 35 PPG mark.

In the ABA, Spencer Haywood 37 PPG in the 1970 WDF, losing in 5 to the LA Stars.

Now that every team has played their 60th game, it's that time of year when everyone is talking about the Phil Jackson '40 before 20' rule - that is, to be a championship contender, you have to win your 40th game before you lose your 20th. According to this rule, the only three teams that can win it all this year are the Thunder, Cavs and Celtics. But exactly how useful is it?

The timeframe is arbitrary.

Everyone always adds the 'since 1980' caveat, which Phil never said. But why is that? Could it be that the 1979, 1978 and 1977 champs all failed to qualify? No, it has to be the addition of the 3pt line, despite the fact that the 1980 finalist Lakers and Sixers made one 3pter combined across the entire six-game series. The NBA of 1995 (the first 'exception') is far closer, both stylistically and chronologically, to the late seventies than it is to today - and Phil should know: those were his playing days. But it was also the golden age of parity, post the 1976 ABA merger (which makes far more sense if we're going to draw an arbitrary dividing line). With all the talk about the parity of today, why exclude those champion Blazers, Bullets and Sonics?

[This alone should be enough to discredit the rule, but I'll humour the Phil apologists (Philologists?) and only talk about the 3pt era from here]

Are early wins inherently more valuable?

This is the first key plank of the argument - that banking wins earlier in the season allows teams to rest up and prepare for the playoffs later. In fairness, there's some evidence for this. But is winning two-thirds of your first 60 games really better than winning two-thirds of your games full stop? That works out to a 55-win pace. But none of the four famed 'exceptions' to the rule (1995 Rockets, 2004 Pistons, 2006 Heat, 2021 Bucks) reached that threshold either, so that doesn't really help us. We'll have to widen the net.

[Bucks had a shortened season but were on pace to miss. Henceforth I'm excluding both COVID and lockout years]

Everyone measures this the wrong way.

Any previous analyses I've seen along these lines have been only skin-deep: 'A high percentage of NBA champions meet this criterion; therefore it's a good one.' Wrong. I could just as easily create a u/teh_noob_ rule which says, 'You have to win 52+ games to be a champ.' That would cover all winners except the Rockets, but it would also massively increase the rate of false positives.

[Hell, lower it to 47 games if you want to hit 100 per cent]

Nobody ever looks at the other side of the coin - that is, 'How likely are Phil Jackson contenders to win?' You know why? It's more difficult, and people are lazy. But here you have it: 175 teams have met that threshold over the relevant timespan, a little over four per year. With 38 champs, that's a success rate of just over 20 per cent. Pretty good, right? Well, going back to our previous point, there have been 179 teams who won 55 games over the same period. The fractionally lower hit rate is statistically insignificant.

Can we fix it?

Now we've established that the 'early wins' part of it doesn't really matter, does 55 games strike the right balance between breadth and depth of contenders? Well, no team has won exactly 55 games and gone on to win the title, so we can safely bump it to 56, knocking off a bunch of pretenders without losing any real contenders and increasing your winning odds to about 25 per cent. But in fact only one team won at the 56-game mark, Phil's own 2001 Lakers - an all-time masterclass in taking the regular season off. It would be no great loss to write them off as another exception and raise the bar to 57 wins.

Where does it end? Obviously the more wins you have, the higher your title odds. At 63-64 wins you cross the line of 'more likely to win than not'. That's not mere contenders; those are title favourites. About three teams win 57 games per year. That's a contender for me. Your mileage may vary.

[Amusingly, you're only 50% likely to win the title with 70+ wins]

Case studies

I omitted to mention earlier that there are two teams who met 40-20 and failed to reach 55 wins yet still won the title, and they both happened quite recently: the 2022 Warriors and 2023 Nuggets. The Warriors are easily explained. They won 70% of their games with Steph in the lineup (and even higher with Dray). Only injuries determined which combination of 40/20, 55+ and champion they would meet. The Nuggets are a bit more in the spirit of the rule, coasting and resting down the stretch (which cost Jokic MVP). But as has been well publicised, they didn't face any 50-win teams in the playoffs, let alone 55+ or 40/20.

[But kudos to Phil for the out-of-sample predictions]

Conclusion

Fear not, fans of the Lakers/Knicks/Grizz. You may have narrowly missed Phil's seal of approval, but if you win 55-57 games, you're still in it with a chance.

[Hell, even Bucks and Rockets are mathematically possible]

Further research

The extended hypothesis would be whether speed of reaching 40 wins is a better predictor of playoff success than overall record amongst teams who both hit that mark, or to find out who did better out of non-champion teams that reached one of 40/20 or 55+ but not the other.

[With nearly 50 such teams, this was beyond my scope]

I was looking at 538 and Basketball reference and noticed that in the last two years especially, Jokic has lead the league in Defensive box +/- and last year was second in the league in defensive raptor behind Gobert and this year is third behind Brook Lopez and Anthony Davis.

This would suggest that Jokic is an incredible defensive, DPOY candidate level defender, however the narrative around him is that he is a poor defender and rim protector and that these defensive metrics hideously overrate him. Is this true, is this because his back-ups are poor, inflating his +/-? and if so, what is it that causes this anomaly in the numbers and how good a defender is Jokic actually?

I used machine learning models to predict players' positions from the 2022-23 season.

I trained two machine learning models on a dataset containing players (above 40GP and 24MPG) since the '1996-97 season. I used a total of 24 different stats to train the models, including their shooting efficiency and tendency by different distances (eg. 0-3ft FG%), shooting tendency by shot type (eg. Cnr3 FG%) and advanced offensive metrics (eg. OREB%, AST% etc).

These models gave me probabilities for each position for every 22-23 player (above 40GP and 24MPG). I wanted to find the most positionless player, so I created a metric that measures how equally distributed a player's predicted position is. I calculated the variance of a player's positional probabilities and adjusted it to a 0-100 scale to create my “positionless” metric, which I call POSL%.

THE TOP 15 POSITIONLESS PLAYERS:

1. Jayson Tatum (86.37%)
2. Markelle Fultz (85.74%)
3. Jordan Clarkson (85.42%)
4. De’Aaron Fox (85.42%)
5. Ben Simmons (82.54%)
6. Draymond Green (82.31%)
7. Kevin Porter Jr. (81.16%)
8. Jalen Suggs (80.46%)
9. Shai Gilgeous-Alexander (78.89%)
10. P.J. Tucker (78.36%)
11. Pascal Siakam (78.18%)
12. Kyle Anderson (78.15%)
13. Jaylen Brown (77.57%)
14. Josh Giddey (76.22%)
15. DeMar DeRozan (76.18%)

WHY WAS TATUM THE MOST POSITIONLESS PLAYER?

I got prediction explanations for how his stats impacted the prediction for each position.

• As a modern player, he takes a lot of shots at the rim (0-3ft) or in the paint (3-10ft) which increases the probability for PF/C and decreases SG
• Of course, he also takes a lot of shots from deep, increasing the probability for SF/SG and decreasing PF/C
• He dunks at an above-average rate, which increases the probability for SF/PF/C and decreases PG/SG
• Despite his size, he has an average offensive rebounding rate, increasing the probability for SF/SG and decreasing PG/C/PF
• He's developed into a secondary playmaker with an above-average assist rate, increasing the probability for PG/SG and decreasing SF/PF
• With a lot of defensive attention, he doesn’t get many corner 3-point attempts, decreasing probability for SF

Basically, he does a little bit of everything that each position does according to the models.

WHAT'S WITH THE GUARDS??

Players like Tatum, Ben Simmons, Draymond and Pascal are the players you think about when hear positionless. Jordan Clarkson, De’Aaron, KPJ and Shai are not - they’re obviously guards.

All the guards that were ranked highly seem to be unorthodox in some kind of way. It seems like they can be described as “guards with distinctly non-guard-like characteristics” such as:

• High offensive rebounding rate
• High efficiency at the rim
• High volume dunks
• Low volume/effeciency 3-pointers
• Below average assist rate (for a guard)

Each of the obvious guards above has some combination of the above characteristics and gave them above expected probabilities for SF/PF/C.

​

I wrote a full article where I went into depth into the method and some other insights, so if you're interested give it a read here.

He's averaging almost as many points per game, a higher FG%, more assists, more rebounds (offensive and defensive), more steals, more blocks, and an overall better shooting percentage of 53.8% vs 51.7%. ALL ON LESS MINUTES PLAYED PER GAME.

KD is averaging more points, more percentage from 3, fewer turnovers, and a significantly better free throw percentage.

Steph isn't Stephing like he normally Stephs at the moment, so is Giannis the best in the league?

EDIT - Giannis is a top 3 defender in the league, and this lends massive strength to the argument that he's better than KD.

I mean, he shot 41% from three on five attempts per game in college. That’s pretty solid.

Obviously, NBA defenses are different and he was gonna see a drop in his percentage no matter what, but he went from a great college shooter to a terrible NBA shooter at least for his first couple years?

What was his jumpshot fundamentally broken or was it just yips?

We all know guys like Steph, Bird, and Reggie are all great shooters, but what about Kyle Korver's ability to knockdown a corner 3? Hakeem had legendary post moves, but Zach Randolph also had a bag full of tricks in the low post. Here are some guys I think had a specific HOF level skill, despite not having HOF talent other wise:

Peja Stojakovic- contested 3 pointers

Shawn Kemp- pick and roll finisher

Jason Richardson- finishing a contested dunk

Jason Williams- handles

Leandro Barbosa- speed while dribbling

Mark Jackson- court vision

Anderson Varejao- taking a charge

Mark Eaton- rim protector

Enes Kanter- boxing out

Matthew Dellavedova- diving after a loose ball

Bill Laimbeer- setting a physical screen

Nate Robinson- vertical leap

The 2026 draft is officially wrapped, which means front offices are about to start dumping contracts to clear cap space and minutes for the new guys.

I was curious who is actually on the chopping block, so I ran a model that tracks a player's actual on-court production against their current cap hit to see who is severely overpaid.

Based on the numbers, here are a couple of guys who are massive "Sells" right now:

• Patrick Williams (CHI): The Bulls just took Caleb Wilson at No. 4 overall. Meanwhile, the model shows Patrick Williams holding a brutal $-15.8M efficiency deficit. He’s only producing about $2.3M in actual value. He is officially dead weight blocking Wilson's path and they need to move him ASAP.
• Deandre Ayton (LAL): With the draft over and Austin Reaves just agreeing to a massive $185M max extension, the Lakers' cap situation is on fire. Ayton is a huge reason why. The math shows his actual on-court production is only worth about $15.2M, but his cap hit is $33.6M (giving him an $-18.5M efficiency deficit). If LA wants to flesh out the roster around Reaves' new deal next week, Ayton has to be the first one traded.

Who do you guys think is the most obvious trade casualty this week?

The National Basketball Association (NBA) is a forever-evolving league for a sport that changes each generation. For this reason, arguments like the Greatest of All Time (GOAT) are never-ending because each decade gets dominated by a different element in the game of basketball. This decade’s trends have shown to be high-scoring and fast-paced offenses paired with carefree and lackluster defense. I am by no means saying that watching a player go off for 40 or 50 points is bad for the sport, but when every night you have a player going off for 40 or 50 points, you have a problem. High-scoring performances like these are supposed to be a dime a dozen, but according to ESPN, as of Jan. 7, there has been a 40-point performance every night of NBA action dating back to Dec. 11 of last year. Which includes nine 50-point performances and a 71-point game by Donovan Mitchell, which has earned the record of the 8th highest-scoring performance ever. When high-scoring outputs occur this frequently it ruins the scarcity of the event and takes away from the accomplishment. Being able to score 40 points against the best players in the world should be celebrated, but there is no point to celebrate every night. According to Basketball Reference, a reputable site for all levels of basketball statistics, there have been 566 50-point games in NBA history since the anomaly of Wilt Chamberlain, who has accounted for a whopping 118 of those games, let us subtract him out of the equation and call it 448 games. This may sound like a lot, but with 82 games a season for the majority of the game’s existence, there have been roughly 128,386 games and counting according to StatMuse. This means roughly .3% of games have had a 50-point scorer in NBA history; however, with 14 50-point games occurring already this year the scoring milestone has become 5 times more likely this season than the historic average. With the inclination of offense and declination of defense, there must be a source to all of it.

Efficient offense = inefficient defense and vice versa The cause of this scoring eruption is one of two things: historical offensive talent currently playing in the league or a historical lack of defense. So far this season there are six players scoring 30 points a game, and two others averaging 29. Ten years ago, in the 2012-13 season, not a single player averaged more than Carmelo Anthony at 28.7 a night. Ten years before that, in the 2002-03 season, Tracy McGrady and Kobe Bryant were averaging over 30 points a game, but outside of the two all-time greats, the next best was Allen Iverson at 27.6. The real kicker, though, is the team scoring. This season, 27 of the 30 teams are pouring in at least 110 a game with the bottom three teams still scoring no less than 108. In the 2012-13 season, no team scored more than 106 points a game, with 11 teams over 100 points a game, and in 2002-03 just four teams scored more than 100 points on a nightly basis. One contributor to this scoring eruption is the percentage of shots being made. This season 27 teams are shooting 45% or better from the field, while in 2012-13 11 teams shot 45% or better and nine teams in 2002-03. However, this is more than just the shots starting to fall, there is a lack of defense and that is the reason for the scoring surge. In the 2002-03 season, there were 27 players averaging over 1.5 steals a game and 20 players averaging over 1.5 blocks a game. Ten years later, there were 20 players averaging over 1.5 steals and 17 players averaging 1.5 over 1.5 blocks. As of today, there are just 11 players averaging over 1.5 steals and only eight players averaging over 1.5 blocks. Players like Dennis Rodman, Ben Wallace and Gary Payton used to take pride in their defensive efforts and at times would be the only reason they were playing in the NBA, but now if you have defensive prowess you need a three-point shot to pair.

Evolution of the three-point shot The evolution of the three-point shot has changed the game. In the 2002-03 season just one team attempted more than 25 threes a game. The 2012-13 season was not much different with two teams attempting 25 or more. This season; however, has seen all 30 teams attempt at least 25 and 29 teams attempting more than 30. The three-point shot is hardly at fault for the scoring though, as each decade has its own identity. The 2000s were dominated by big men like Shaquille O’Neal, Tim Duncan, David Robinson, Yao Ming and Dwight Howard. The 2010s were dominated by the mid-range with Kobe Bryant, Carmelo Anthony, Kevin Garnett, Dirk Nowitzki and Kevin Durant. While starting in the late 2010s, this decade is highlighted by elite perimeter guards and wings like Steph Curry, Luka Doncic, Trae Young, Jayson Tatum, Donovan Mitchell and Devin Booker.

Officiating and rule changes Another answer to the offensive surge and defensive plunge may be that the rise of offense is the fall of defense and vice versa, but I believe there is a core source of officiating. Referees have become offensive friendly allowing for two steps and a gather when driving to the basket rather than a strict two, getting rid of defense committing an intentional foul in transition and although for the good, eliminating flopping from defenders has given the offense even more of an advantage. According to the DeseretNews, older rule changes have included removing hand checking in 2005 which allowed the defense to have a hand on the ball handler’s hip which enabled them to stay in front easier and instant replay modifications occurred every year from 2007-15. In 2017, timeouts were reduced from nine to seven per game and in 2018 reset to 14 seconds after an offensive rebound rather than 24, which forces a faster tempo.

Whether it is the changing of an era in the NBA or simply the way the game is officiated, all we can do at the end of the day is enjoy the scoring because this offensive output is not going to change for a long time.

Would appreciate it if you went to fisherstigertimes to give it a read but I had to take out the link. :) story by me, David Jacobs