Those who saw the Timberwolves finish off the Suns in the first round of the playoffs and witnessed Ant-man’s 40 point explosion and this moment in particular:
We all had the same question pop into their minds: Where was Michael Jordan, nine months before Anthony Edwards was born? I have yet to confirm Jordan’s presence in Atlanta in December of 2000, but if the Internet is to believed, it will be confirmed soon.
While I can’t yet demonstrate the DNA link between the two players, I can confirm that Ant’s playing style is the most similar I’ve seen to MJ since Kobe. Is there a way to see this in the statistics? Where does Ant-Man 2023 rank on the “MJ Similarity” metric (the combined absolute value of the difference between last year’s stats and MJ’s career averages)?
Surprisingly, Ant is Only the Third Most Jordan-Like Player, Statistically
Looking more closely at Ant-Man vs. Jordan’s statistical DNA, you may notice that at least in terms of the big three stats (points, rebounds, and assists), Anthony is very similar to Michael. He’s 90% Jordan. Next year, if he improves by 11% across the board and gets 29 ppg, 6 rebounds, and 6 assists, the stat line will be very Jordan-like indeed. However, it’s in the defensive stats where he is the least GOAT-like. In terms of steals and blocks, he’s only 58% Jordan. Dad’s still got a few things to teach him about defense, but on offense, Ant-Man is indeed a superstar.
While we’re on the subject of defensive stats, let’s look at the league leaders from last season:
2023-2024 Season Top Players Ranked by Blocks + Steals
Looking at only two stats is an overly simplified way of looking at Defensive Player of the Year, but I’ll bet they’re a good indication that the DPOY will indeed be our favorite Alien. Wemby dominated defensively this season, but how good were his stats from a historical perspective? If you look at the best Blocks + Steals seasons over the last 50 years, it turns out he’s still got some room to improve before he’s at the Olajuwon / Robinson level. He’s only tied for the 25th best season with some defensive slacker named Michael Jordan. However, he did notch the best numbers on this list in almost 20 years!
Top Blocks + Steals Seasons in the last 50 Years
Because of his combination of offensive and defensive statistics, I’ll still argue that Wemby is the future of the NBA over Ant-Man. Once again, here are the top 20 most productive players (MPPs) last season:
Most Productive NBA Players (2023-2024 Season)
Notice that Ant Edwards and Wemby are in a virtual tie as the 11th and 12th most productive players this season.
However, as Wemby played six fewer games, Ant-Man drops further down the list when you compare their per-game productivity:
Most Productive NBA Players per Game (2023-2024 Season)
The biggest difference between the two is when you look at the per-minute productivity. Wemby skyrockets to 5th most productive in the league and Ant Edwards falls out of the top 20 with his 1.09 FPPM stat.
Most Productive NBA Players per Minute (2023-2024 Season)
Time will tell which player improves more in the next few years, but it’s safe to say that at 20 and 23 years old, Wemby and Ant-Man have years ahead of them to reach their full potential. It’s a great time to be an NBA fan.
Victor Wembanyama, affectionately dubbed “Wemby,” entered the NBA last October amid sky-high expectations, even higher than his towering 7’4″ frame. Before even stepping onto the court, predictions about his career varied widely — from potential future GOAT, evoking comparisons to the likes of Kareem Abdul-Jabbar, to the cautionary tales of Shawn Bradley and Ralph Sampson, whose careers, despite their impressive heights and skills, were cut short by injuries and ended with unmet expectations. Wemby’s unique blend of size, agility, and skill along with his rail-thin build set the stage for heated debates. LeBron James can be given credit for creating an otherworldly nickname when he spoke about Wemby’s abilities at a press conference: “Everybody’s been a unicorn over the last few years, but he’s more like an alien.”
Wemby was drafted #1 by the Spurs, which meant he would play for legendary coach Gregg Popovich who has more wins under his belt than any other coach in NBA history. The fact that the two prior #1 picks in Spurs history were David Robinson and Tim Duncan only added to the pressure. If Wemby performed at any level below hall-of-famer, fans would be disappointed. So how did he do?
When analyzing basketball data, I like to keep it simple: in a nod to fantasy sports, I just add up the stats of all the major categories. A player’s production on a per-minute basis (FPPM or “fantasy points per minute”) and on a per-game basis (FPPG) are informative statistics, as well as the total for the season (FPPG x # of games = “total production”). If you recall my blog entry last year, I defended “total production” as a solid way to rank players because it was simple and objective and correlated well with popular lists of all-time greats such as the one produced by ESPN.
So, how did Wemby’s year stack up against the most productive rookie seasons in the last 50 years?
Most Productive Rookie Seasons (Last 50 Years)
By this metric, Alien had a great rookie season, ranking #18 and above some clowns named Julius Erving, Magic Johnson, and LeBron James. However, since he only played in 69 out of the 82 games, it gets even better when you look at his per game productivity.
Most Productive Rookie Seasons Per Game (Last 50 Years)
On a per-game basis, he ranks 5th behind David Robinson, Michael Jordan, Shaq, and George McGinnis(?). Hold on, who’s this guy again? I looked him up. It turns out that the reason he’s a “rookie” in my data at the age of 25 is because he came from the ABA fresh after winning the ABA MVP award. Okay, so technically, he was an NBA rookie, but this was not his first year playing professionally. So, to reiterate: Wemby’s productivity on a per-game basis in his first year was higher than anyone in the last 50 years other than the ABA MVP in his first NBA season, Shaq, the GOAT, and David Robinson, who came into the league as a 24 year old. Did I mention that Wemby is only 20 years old?
But wait, there’s more! Wemby played much of the season on “minutes restriction” to protect his ankle, after stepping on a ballboy’s foot. Notice that he’s the only player on any of these lists having played less than 30 minutes per game. He was so frustrated by his lack of minutes, he even checked himself into a game without the coach’s consent once. (Coach Pop just took him back out again.) So you know this one’s going to be good: what are the most productive rookie seasons per minute? (Minimum 10 games and 10 minutes per game)
Most Productive Rookie Seasons per Minute (Last 50 Years)
You’re reading that right. On a per-minute basis, Wemby was the most productive rookie over the last 50 years. Let this sink in: his overall total productivity was higher than LeBron James’s rookie year despite playing 10 fewer games and an average of 10 minutes less per game. Bottom line: Wemby is more on the Kareem path than the Bradley one.
Speaking of Kareem, you may recall from last year’s blog that his 1975 season was the most productive single season in my dataset (last 50 years):
Most Productive Seasons (Last 50 Years)
Notice that his productivity per minute (FPPM of 1.33) was actually lower than Wemby’s this year (1.38)! It’s probably too much to imagine that Wemby could keep up his frenetic productivity for 41 minutes per game and 81 games in a season like Kareem did. Or is it? Considering the fact that the 20-year-old Alien may be the worst version of Alien that we will ever see, he actually has decent a shot at topping this list someday, especially if he can avoiding stepping on any more ballboys.
So it appears that Wemby will indeed be the future of the NBA, but who is the present? Well, there’s a new entry on the “Most Productive Seasons (per Game)” list:
Most Productive Seasons (per Game, Last 50 Years)
And it’s a new #1! On a per game basis, Luka Doncic just had the most productive season in the last 50 years. Joel Embiid is the new #4, having gone statistically wild during his injury-shortened season. And Giannis barely gets a mention for putting in the 13th most productive season in the last 50 years.
On a per minute basis, Embiid almost took the top spot in modern NBA history, where he played Wemby-like minutes and still put up 30 points and 14 rebounds per game.
Most Productive Seasons (per Minute, Last 50 Years)
I extended that list to the top 25 seasons, for no particular reason.
Anyway, back to the question of who’s the player of the present of the NBA. My winner of the MPP (“Most Productive Player”) for 2023 is…
MPP (Most Productive Player) Award Winners
Luka Doncic for the second year in a row! The guy is a beast and at 25 years old, is at the peak of his game. He’ll undoubtedly be considered among the all-time greats when he’s done.
Speaking of which, here’s the current list of the players with the highest average career productivity in the modern NBA…
Highest Average Productivity (Last 50 Years)
Luka just passed up LeBron James! Of course, we’re not comparing apples to apples here, because eventually, even Luka will get older (and slower?) and drop down the list by the time he retires. But who knows, if he continues at his current rate for a few years, he could even spend some time above MJ! At that point, maybe he should consider retiring young and claim statistical GOAThood. Jokic is also no joke, sneaking into the #5 spot. I also see some 20-year-old who’s new to the list at #10 somehow, despite not playing that much. The potential for that guy is off the charts.
For completeness, here’s how I rank the top 20 MPP candidates this year:
Jokic and the Greek Freak performed similarly to Luka Magic, but his incredible productivity per game put him out of reach. Also take note: LeBron is still in the top 10 at 39 years of age! His durability and consistency is truly incredible. I understand the “LeBron is GOAT” arguments. I don’t agree, but I understand. It’s becoming more clear that old LeBron > old Jordan. We’ll know for sure next year when we see if LeBron can top 40-year-old Jordan’s 82 game season with 20 PPG, 6.1 RPG, 3.8 APG, and 1.5 SPG.
When comparing MJ against LeBron, There’s definitely a pre-baseball Jordan and a post-baseball Jordan to consider…
Michael Jordan statistics by age compared to LeBron at same age
Before taking time off to play baseball, Jordan’s total productivity, per-game productivity, and per-minute productivity topped LeBron’s every single year (except when MJ had a broken foot in 1985). These are the types of statistics that Jordan supporters (like me) point to. Young Jordan > young LeBron, even if you ignore important considerations like Championships, MVPs, and his Defensive Player of the Year title.
However, look at the years after baseball. Suddenly LeBron is more productive per minute and per game and is only sometimes less productive overall because he’s not in the habit of playing 82 games per season.
What’s interesting is that Jordan came back out of retirement again at age 39 and gave us a couple more seasons to compare against LeBron. The comparison doesn’t look too good for the 39-year-old version of MJ. LeBron, the oldest player in the NBA, is still killing it. He’ll outperform “old Jordan” again next year if he can stay healthy and average over 20 points per game at age 40. There’s something to be said about LeBron’s unnatural ability to play at an extremely high level for such a long time.
But there isn’t a human alive who could dominate offensively and defensively like pre-baseball Jordan. But an Alien? Time will tell…
Despite the fact that LeBron James has now scored more total points than anyone in the history of the NBA, it appears that a consensus has been reached that his longevity and consistent greatness has never quite reached the Michael Jordan level. Recently, Jordan’s rookie card has skyrocketed in value, the MVP trophy has been redesigned in his likeness, and the majority of players asked have given him the nod, usually mentioning his playoff dominance (including two three-peats as champion and six Finals MVPs) or the intimidation factor his opponents experienced, playing against someone who was simultaneously the league’s best offensive player as well as the best defensive player.
However, as a data scientist, I can’t help but wonder how you’d rank players in a completely objective way. What if you just measured players based on their average performance during the regular season over their careers? They say there’s no “I” in team, but certainly the best player have collected all of the points, assists, rebounds, steals, and blocks that they could. Maybe calling it a ranking of the “best” players is too much, but you could certainly argue that it would be an interesting list of the most “productive” players. Would ranking players this way create a list similar to ESPN’s recent ranking of the 74 best players of all time or would it be completely different?
Unfortunately, I could only find complete data back to 1973 (NBA.com only had data back to 1996(?!)), so I missed Kareem Abdul-Jabbar’s first four seasons. However, I do have all of the stats necessary to compare LeBron and MJ and rank them among all of the players who had careers in the last 50 years. I know they say you can’t compare players across eras, but we’re going to do it anyway…
And yes, I know that more players averaged over 30 points per game this season than any season since the 1960s. When measuring productivity, it seems that with the modern higher-paced game, statistics would be easier to come by. I considered controlling for that, but I’m not so sure that it’s fair. What if modern-day players are generally just better than players were 20 years ago? My approach is: when in doubt, just keep it simple. It’s always okay to add asterisks later if something looks weird.
One last thing: in addition to adding up all of the points, assists, rebounds, blocks, and steals for every season played (“Total Production”), I also divided that by the number of games played in each season (for lack of a better name for the stat, let’s call it “Fantasy Points per Game”) and also by the number of minutes played (“Fantasy Points per Minute”). This will give us a few different ways to compare players. Total Production would favor durable players, FPPG would take durability out of it and measure what players do when they’re healthy, while FPPM would benefit players who may not play a lot, but definitely filled the stats sheets while they were on the floor.
Before we get to the “most productive players” list, there were quite a few interesting Top 10 lists that you may have never seen…
First thing to note is that Kareem of 1975 was a beast: 28 points, 17 rebounds, 5 assists, and 4 blocks per game, and played 81 out of the 82 possible games. And #2 all-time goes to Russell Westbrook’s massive triple double season in 2016. Then there’s a lot of Michael Jordan. Notice that he’s the only player who played all 82 games on this list and he did it three times. Except for his broken foot season, the guy just wouldn’t take a day off (except for baseball, sigh).
Hey, while we’re here, let’s take a little trip through history and see who was the most productive player (MPP) each season for the last 50 years…
In the last 50 years, these would be the top recipients of the MPP trophy:
Michael Jordan: 6
Kareem Abdul-Jabbar: 6
LeBron James: 5
James Harden(!): 4
Kobe Bryant: 3
Shaquille O’Neal: 3
Kevin Durant: 3
Larry Bird: 3
David Robinson: 3
Not a bad list. It’s interesting to see that Jordan’s statistical reign of terror was when he 24-29 (at which point the big men took over the productivity title) and when he was 28, his championship reign of terror began and he won the NBA Finals in his next six complete seasons (stretching over 8 years because of the baseball hiatus). This might be the strongest argument for his GOAT status: For a stretch of a dozen straight years, MJ was either the statistically most productive NBA player or the leader of the championship team (or off playing baseball, sigh).
Another interesting “most productive of all-time” list is to compare by age, from the most productive 18-year-old (Kobe) to the most productive 44-year-old (Kevin Willis, who played 5 games and averaged a whopping 2.4 points per game, this game is not kind to “old” players!)…
Look at that TotalProduction column. It ramps up to a peak at 28-29 years old and then it’s “over the hill” in terms of nba productivity! No 30 year old in history has surpassed 4000 total stats (although LeBron fought father time and got close at age 33). Also surprising is to see Karl Malone own the ages 36-39, where I would think LeBron would take over the list. The key is the Games column: Karl pretty much played ALL the games and was just as durable and age-resistant as LBJ.
The top seasons of the last 50 years in terms of FPPG…
Bob McAdoo, there’s a name from the past! He only played half the season, but I wonder how he feels being sandwiched between Michael Jordan and Michael Jordan? The guy was a badass: scored 34 points and collected 14 rebounds per game that season! Also, we see a couple more recent seasons in the list, which might help explain how so many players can be averaging 30 points per game these days: “Load Management”. If it’s true that teams are resting their stars more than they used to (and it appears that way), then it looks like the idea of ranking players based on total production during a season instead of per game statistics won’t give the modern player the boost we’d expect based on the higher scoring averages.
For completeness, here are the TOP FPPM seasons in the last 50 years…
So THIS is what the game has evolved towards. No seasons are on this list before 2016. It looks like the modern approach is: play 85% of the games and play 70% of the minutes so you can just go full throttle. Each of the Greek Freak’s last 5 seasons is on the list of the 10 most productive seasons per minute since 1973. We should probably just rename the stat “GF” in his honor.
And I’m assuming this would be the “MJ” stat…Pretty much.
Wow, if it weren’t for the fact that it’s based on one season, that’s a pretty good looking GOAT list right there. These are the players with the highest output per minute, who also played every single game in a season. I suppose if you want an exclusive MJ list, you could just do this…
Anyway, without further ado, here’s the list of the top 50 players, ranked by their career average productivity per season. Note: there are some partial careers here (like Kareem) and players currently active (like LeBron and Luka) who can rise or fall in this list in the future.
The Top Average Productivity Ranking (Last 50 Years)
Rank
Player Name
Avg Season
1
Michael Jordan
3,201
2
Karl Malone
3,179
3
LeBron James
3,149
4
L. Doncic
3,013
5
Kareem Abdul-Jabbar*
2,968
6
N. Jokic
2,929
7
Russell Westbrook
2,915
8
Larry Bird
2,856
9
Giannis Antetokounmpo
2,829
10
James Harden
2,816
11
Trae Young
2,793
12
Magic Johnson
2,776
13
Hakeem Olajuwon
2,762
14
David Robinson
2,719
15
Charles Barkley
2,694
16
Kevin Durant
2,685
17
Tim Duncan
2,625
18
Damian Lillard
2,616
19
Dominique Wilkins
2,568
20
Allen Iverson
2,538
21
Shaquille O’Neal
2,536
22
Isiah Thomas
2,534
23
Clyde Drexler
2,529
24
J. Tatum
2,527
25
K. Towns
2,525
26
D. Issel
2,519
27
Patrick Ewing
2,511
28
J. Embiid
2,500
29
Kobe Bryant
2,478
30
Julius Erving**
2,445
31
J. Randle
2,440
32
Alex English
2,439
33
A. Edwards
2,429
34
Anthony Davis
2,426
35
Donovan Mitchell
2,399
36
Kevin Garnett
2,386
37
Stephen Curry
2,373
38
Dirk Nowitzki
2,344
39
Rick Barry
2,331
40
B. Daugherty
2,308
41
Chris Paul
2,304
42
P. Banchero
2,297
43
Gary Payton
2,283
44
John Stockton
2,273
45
Antoine Walker
2,271
46
Dwyane Wade
2,269
47
D. DeRozan
2,264
48
D. Booker
2,239
49
J. Morant
2,228
50
N. Vucevic
2,198
*Kareem’s first four years were better than his average years, so his true productivity average based on known statistics would be a little over 3075. However, steals and blocks weren’t tracked over 50 years ago, so you could argue that he missed #1 here based on a technicality!
**I also just peeked at Dr. J’s lifetime statistics and his average total productivity, including his ABA years, actually puts him above Michael Jordan! However, there’s big asterisk: in three of his four ABA years, he played 84 games, which isn’t possible in the NBA. If you prorate his stats down for those years to 82 games, he drops into the #2 slot below Michael. Then again, they didn’t count steals and blocks in 1971… it never ends!
I looked at this list a month ago and LeBron was #2, so his injury-shortened season this year dropped him down a slot. If he can stay healthy, I expect him to regain that #2 slot next year. And look at Luka Magic and Jokic! Jokic is closing in on that “over the hill” age and can be expected to start drifting down the list in a few years, but Luka is only 24 years old! Considering the season he just had, he may be starting his own reign of terror in the league right now.
If we were looking at the complete set of historical NBA stats, Wilt Chamberlain would almost certainly be the regular season productivity GOAT. I have a friend who’s constantly pushing the “Wilt is GOAT” narrative online. Rather than argue with him about championships and stuff, I usually respond with “I have eyes”. If you’ll open your mind to subjective arguments for a second, pretend you’re an NBA scout and that Wilt’s highlight reel actually came from a college prospect you were thinking about drafting:
Be honest. How skilled does he look and how skilled do his opponents look?
The game has clearly changed, and modern players are much more skilled than the average player 50+ years ago. Evidently the “economics of pro basketball exploded” in the 1970’s, so it’s hardly surprising that the quality and skill of the average athlete would explode as well. You can argue Wilt was the most “dominant” player of all time. But not GOAT.
Anyway, I digress.
For the “LeBron is GOAT” people out there, here’s the list you want to see: top players by TOTAL productivity.
The Iron Men of the NBA (last 50 years)…
Rank
PlayerName
Lifetime Productivity
1
LeBron James
62,974
2
Karl Malone
60,408
3
Kevin Garnett
50,114
4
Tim Duncan
49,871
5
Hakeem Olajuwon
49,719
6
Kobe Bryant
49,566
7
Dirk Nowitzki
49,216
8
Shaquille O’Neal
48,186
9
Michael Jordan
48,012
10
Kareem Abdul-Jabbar*
47,491
11
Russell Westbrook
43,730
12
John Stockton
43,184
13
Charles Barkley
43,101
14
Patrick Ewing
42,679
15
Jason Kidd
41,503
16
Chris Paul
41,478
17
Carmelo Anthony
41,411
18
Paul Pierce
41,143
19
Kevin Durant
40,281
20
Robert Parish
39,863
21
Vince Carter
39,445
22
James Harden
39,426
23
D. Howard
39,089
24
Gary Payton
38,805
25
Pau Gasol
38,690
26
Dominique Wilkins
38,526
27
David Robinson
38,063
28
Clyde Drexler
37,937
29
Moses Malone
37,392
30
Larry Bird
37,126
31
Alex English
36,590
32
Dwyane Wade
36,306
33
Magic Johnson
36,089
34
Scottie Pippen
35,841
35
Ray Allen
35,824
36
Allen Iverson
35,535
37
Reggie Miller
35,391
38
L. Aldridge
33,315
39
Buck Williams
33,107
40
S. Marion
32,978
41
Isiah Thomas
32,939
42
Steve Nash
32,393
43
Kevin Willis
32,190
44
O. Thorpe
32,069
45
Z. Randolph
31,966
46
T. Cummings
31,905
47
Joe Johnson
31,807
48
Clifford Robinson
31,778
49
D. DeRozan
31,690
50
A. Jamison
31,391
*Actually, when adding Kareem’s missing first four years, even his known statistics would put him at the top here with 65,854. So LeBron needs another year or two before he can safely say that there is no asterisk and that he’s definitely the all-time total productivity leader of the NBA.
Maybe this is the way you rank the players, but I don’t think people generally agree with that. Actually, we can quantify how close these lists approximate what people think of as the GOAT by looking at the correlation between these lists and the ESPN list (the gaps in the ESPN list below are players who didn’t play in the last 50 years).
(1) The Average Productivity list correlates better with the ESPN list (0.56 correlation > 0.43 correlation), which makes sense. People usually think of basketball as a sprint, not a marathon.
(2) Karl Malone is #2 on both my lists and #17 on the ESPN list. Is he underrated? Statistically, I suppose so, but I also know why ESPN has him lower than you’d expect. I remember the experience of watching him play. Let’s just say that the hardest all-time record LeBron to break may be Malone’s free throw record. Basically, he found a stats-hack and rode that puppy for years. Not entertaining, but effective, I’ll give him that. It’s also possible that ESPN penalized him for his trademark hand behind the head dunk.
(3) My ranking is not kind to players who have a lot of injury-shortened seasons. I’m actually a little surprised Jordan comes out on top in spite of two very short seasons: broken foot and returning from baseball. Those two seasons brought his average down a lot and don’t forget that he even played for the Wizards at age 40! Other players with a handful of injury seasons like Moses Malone get thrown far down the list.
(4) Kawhi Leonard! ESPN has him at #25 and I’ve got him at #167. What’s going on there? This is clearly a case where winning rings is highly respected in the sports community, but they’re not reflected in the regular season statistics my number-crunching is focused on. In the case of Steve Nash, MVP trophies carry a lot of weight in the ESPN rankings as well.
(5) As I look down the list for underrated players, it’s mostly players who are currently playing and may regress towards the ends of their careers. However, there are a few exceptions worth noting: Dominique Wilkins (my rank = 19, ESPN rank = 46), Clyde Drexler (my rank = 23, ESPN rank = 57), and Alex English (my rank = 32, ESPN rank = 67). These guys seem to have filled up the stat sheet much more than ESPN recognizes. Here’s a comparison between Dominique Wilkins (ESPN rank 46) and Kawhi Leonard (ESPN rank 25)…
It’ll probably take a second to make sense of what I did here. I joined Dominique’s career stats with Kawhi’s career stats by age. For example, how did 23-year-old ‘Nique compare with Kawhi at 23? If you look at the PlayersAge column and find 23, you’ll see that Dominique had 17.5 ppg, 5.8 rpg, 1.6 apg, while Kawhi had 12.8 ppg, 6.2 rpg, and 2 apg. In terms of my summary statistics, they were dead even in terms of stats per minute. However, Dominique played 4 minutes more per game on average, so his stats per game was higher. And he played 16 more games (the full 82), so his total productivity is much higher (+622).
It turns out that this same pattern pretty much holds for every year in common between the two players. The only year in which Kawhi had higher total productivity was when they were both 32, Dominique played only 42 games and put 168 fewer points on the stat sheet than Kawhi. Over their careers on average, the players are even per minute, but Dominque is +912 in total annual productivity. Basically, Dominque = Kawhi + minutes + games. My list doesn’t consider championships, so it’s like “where’s the love for ‘Nique and Drexler?”
Let’s be honest though, you’re not reading this blog to see the comparison of Dominque vs. Kawhi. It’s this one…
In terms of overall averages, there’s actually not a lot of statistical daylight between these two! However, LeBron made up huge chunks of his productivity deficit in two years: (1) when they were 23, MJ broke his foot and played limited minutes in 18 games and (2) when they were 32, MJ came back for the last 17 games of the season in “baseball shape.”
However, it’s fair to say that in terms of total productivity, Jordan gets the clear nod. In the 13 ages they played in common, MJ was more productive in 9 of them. The only other season LeBron was more productive than MJ was the year after the baseball return. So you could say that MJ went 10-3 against LeBron in a head-to-head productivity competition.
However, there is something for LeBron fans here. Notice the FPPM and FPPG statistics. There is a significant difference between BB (“before baseball”) and AB (“after baseball”). MJ beat LeBron in both of these stats in every single season BB (except for the broken foot season when there was a cap on MJ’s minutes, much to his frustration). However, LeBron has been beating MJ in both of these stats every single season AB. The only reason Jordan comes out ahead in overall productivity after baseball is because he played 82 games per season. After baseball, Michael transitioned a bit from Air Jordan to Chill Jordan, but still came to play every game. That said, kudos to LeBron for coming to work every single season.
It’ll be interesting to see if LeBron can keep it up and outperform MJ’s Washington Wizard years. In particular, Jordan’s final year at 40 years old was pretty remarkable: 82 games played with 20 ppg, 6.1 rpg, and 3.8 apg. In fact, it puts him on top of this list, which you’ve probably never seen:
These guys left the game with their heads held high. As a kid I remember being pretty upset about Dr. J retiring, thinking “this guy is still great, why is he leaving?” It’s rare for a star to be able to swallow their pride like Vince Carter did and try to push their rickety 40-year-old bodies to keep up with the young guns.
For symmetry, here are the most productive rookie seasons of the last 50 years…
David Robinson spoiled Jordan’s potential “best first year and best last year” flex. He came into the league as a 24-year-old after his active duty in the Navy was complete, ready to dominate. He even surpassed his idol Ralph Sampson’s first year stats (Sampson is the reason Robinson wore the #50). Also of note, Dr. J’s “first year” here is his NBA rookie year. He was in the ABA from 1971-1976.
I’ve probably worn you guys out with the endless stats here, but I’ve got a couple last good ones for you. I like the idea above of head-to-head comparisons “by age”, so a question came to mind. What if I compare ESPN’s top players to each other in a round-robin tournament in which all of them matched up against every other one and I calculated their “productive season win %” (recall that MJ had a 10/13 = 77% win rate against LeBron – it’s in the list below).
Match-up results…
PlayerName1
PlayerName2
Win Percentage
Kareem Abdul-Jabbar
Kobe Bryant
72.73
Kareem Abdul-Jabbar
Larry Bird
77.78
Kareem Abdul-Jabbar
LeBron James
66.67
Kareem Abdul-Jabbar
Magic Johnson
83.33
Kareem Abdul-Jabbar
Michael Jordan
50.00
Kareem Abdul-Jabbar
Shaquille O’Neal
84.62
Kareem Abdul-Jabbar
Tim Duncan
92.86
Kobe Bryant
Kareem Abdul-Jabbar
27.27
Kobe Bryant
Larry Bird
38.46
Kobe Bryant
LeBron James
31.58
Kobe Bryant
Magic Johnson
46.15
Kobe Bryant
Michael Jordan
15.38
Kobe Bryant
Shaquille O’Neal
41.18
Kobe Bryant
Tim Duncan
50.00
Larry Bird
Kareem Abdul-Jabbar
22.22
Larry Bird
Kobe Bryant
61.54
Larry Bird
LeBron James
38.46
Larry Bird
Magic Johnson
77.78
Larry Bird
Michael Jordan
25.00
Larry Bird
Shaquille O’Neal
76.92
Larry Bird
Tim Duncan
46.15
LeBron James
Kareem Abdul-Jabbar
33.33
LeBron James
Kobe Bryant
68.42
LeBron James
Larry Bird
61.54
LeBron James
Magic Johnson
69.23
LeBron James
Michael Jordan
23.08
LeBron James
Shaquille O’Neal
72.22
LeBron James
Tim Duncan
76.47
Magic Johnson
Kareem Abdul-Jabbar
16.67
Magic Johnson
Kobe Bryant
53.85
Magic Johnson
Larry Bird
22.22
Magic Johnson
LeBron James
30.77
Magic Johnson
Michael Jordan
11.11
Magic Johnson
Shaquille O’Neal
50.00
Magic Johnson
Tim Duncan
45.45
Michael Jordan
Kareem Abdul-Jabbar
50.00
Michael Jordan
Kobe Bryant
84.62
Michael Jordan
Larry Bird
75.00
Michael Jordan
LeBron James
76.92
Michael Jordan
Magic Johnson
88.89
Michael Jordan
Shaquille O’Neal
71.43
Michael Jordan
Tim Duncan
80.00
Shaquille O’Neal
Kareem Abdul-Jabbar
15.38
Shaquille O’Neal
Kobe Bryant
58.82
Shaquille O’Neal
Larry Bird
23.08
Shaquille O’Neal
LeBron James
27.78
Shaquille O’Neal
Magic Johnson
50.00
Shaquille O’Neal
Michael Jordan
28.57
Shaquille O’Neal
Tim Duncan
38.89
Tim Duncan
Kareem Abdul-Jabbar
7.14
Tim Duncan
Kobe Bryant
50.00
Tim Duncan
Larry Bird
53.85
Tim Duncan
LeBron James
23.53
Tim Duncan
Magic Johnson
54.55
Tim Duncan
Michael Jordan
20.00
Tim Duncan
Shaquille O’Neal
61.11
Which can be summarized neatly like this…
Player Name
Avg Win %
Kareem Abdul-Jabbar
75.43
Michael Jordan
75.27
LeBron James
57.76
Larry Bird
49.73
Tim Duncan
38.60
Kobe Bryant
35.72
Shaquille O’Neal
34.65
Magic Johnson
32.87
Kareem and Michael are in a virtual tie for first place in this “tournament.” Keep in mind that each of these players is matching up against other players only for the ages they both played, so this is a complicated statistic to calculate. In fact, for the SQL geeks out there, enjoy the gory details behind this query at the bottom of this article.
One last thing. For those of you curious what such a “round-robin tournament” list would look like right now with some of today’s stars, you’re welcome…
Player Name
Avg Win %
LeBron James
81.80
Giannis Antetokounmpo
59.85
Kevin Durant
59.47
K. Towns
56.81
N. Jokic
55.79
L. Doncic
52.14
James Harden
51.99
Russell Westbrook
51.69
L. Ball
50.00
Damian Lillard
47.50
Trae Young
47.22
J. Morant
44.79
Anthony Davis
42.80
Stephen Curry
41.24
J. Embiid
36.62
K. Irving
23.05
Okay, enough already with the stats overload. Go take a nap! (And let me know what I overlooked and need to include in my next blog).
– J
“The SQL Query”…
–TotalProduction Summarized!…………………………………
WITH Players AS (
SELECT * FROM nba WHERE PlayerName IN (‘Kareem Abdul-Jabbar’, ‘Larry Bird’, ‘Kobe Bryant’, ‘Magic Johnson’, ‘Michael Jordan’, ‘Shaquille O’Neal’, ‘Tim Duncan’, ‘LeBron James’)
),
PlayerCombinations AS (
SELECT
P1.PlayerName AS PlayerName1,
P2.PlayerName AS PlayerName2,
P1.Age AS Age1,
P2.Age AS Age2,
P1.TotalProduction – P2.TotalProduction AS TotalProductionDiff
FROM
Players P1
CROSS JOIN Players P2
WHERE
P1.PlayerName <> P2.PlayerName
AND ROUND(P1.Age, 0) = ROUND(P2.Age, 0)
),
WinCount AS (
SELECT PlayerName1, PlayerName2, COUNT(*) AS Wins
FROM PlayerCombinations
WHERE TotalProductionDiff > 0
GROUP BY PlayerName1, PlayerName2
),
LossCount AS (
SELECT PlayerName1, PlayerName2, COUNT(*) AS Losses
FROM PlayerCombinations
WHERE TotalProductionDiff < 0
GROUP BY PlayerName1, PlayerName2
),
TotalCount AS (
SELECT
W.PlayerName1,
W.PlayerName2,
W.Wins,
L.Losses,
W.Wins + L.Losses AS Total
FROM
WinCount W
INNER JOIN LossCount L ON W.PlayerName1 = L.PlayerName1 AND W.PlayerName2 = L.PlayerName2
By now you’ve probably seen the viral video of Steph Curry draining 105 3-pointers in a row during practice. You’re probably thinking “that guy can shoot well!”, but it’s a lot more than that. Historically, he’s shot an amazing 65% during the NBA 3-point shootout competition, but let’s be generous and say that his 3-point shooting percentage during practice is 80%. To put that in perspective, there are only a few hundred NBA players in history who have a career free throw percentage that high. So, what are the chances, you ask, that someone who shoots with an accuracy of 80% would make 105 in a row? About 1 in 15 billion. Yeah, it was a lucky streak.
Now, there is another explanation which traditionally hasn’t been taken seriously by people in the data wonk profession: the Hot Hand. It’s even been called the Hot Hand Fallacy. There have been several studies concluding that there is no evidence that those of us who feel like we’re on a “hot streak” aren’t deluding ourselves and being fooled by randomness. They have a point that randomness is much streakier than we expect, but I’ve always thought that if we had a controlled environment, the evidence for the Hot Hand would become clear (and not just because I somehow made 32 free throws in a row once). After examining the NBA Three-Point Shootout data, Gary Smith and I showed in our latest book that, given the relatively few contests in history, the mere existence of Craig Hodges 1991 streak of 19 consecutive baskets provides strong evidence in favor of the hot hand hypothesis.
Now, cherry-picking extreme examples is normally not a good way to gather statistical evidence. However, it can be considered compelling if you appropriately take into account how large the number of events there were from which you were cherry-picking. In the Hodges case, this means looking at how many participants there have been in the history of the NBA Three-Point Shootout. There simply haven’t been enough shooters in the contest’s history to expect a shooting streak like that if there’s no such thing as a Hot Hand!
Some other NBA news today indirectly provided another reason to believe in the Hot Hand. Curry just broke the Warriors franchise record for most consecutive free throws made in a row: 61. The NBA all-time record is 97 straight. That’s an amazing number, but how can the all-time streak of consecutive makes from 15 feet be smaller than the number of consecutive makes on Curry’s highlight reel of 3-pointers, more than seven feet further away from the basket?
Once again, I argue that the Hot Hand is the answer. In the NBA, free throws are usually shot in pairs, so players don’t have a chance to settle in and establish a hot streak. Shooting free throws is more in line with the statisticians assumption that each shot is an independent event; whether or not you made your last free throw 10 minutes ago doesn’t affect the likelihood that you’ll make the free throw you’re shooting now.
In order to decide whether or not Curry’s videotaped shooting streak is evidence that the Hot Hand is real, we need to account for the universe of attempts from which his 3-point shooting streak was cherry-picked. Let’s say there are 500 trillion basketball players in the world and that they all shoot 50% from behind the 3-point line (the NBA average is 35%). Now, let’s assume that they’ve each made a videotaped attempt at Curry’s feat once per day for the 13.8 billion year history of the universe (never mind that 3-pointers were invented only 40 years ago). How many times would you expect to see Curry’s feat replicated? About 0.00006 times. Of course, that’s if you assume the Hot Hand is a fallacy.
A couple days ago, college basketball player J. J. Culver made the news by scoring an astounding 100 points in a game. Since it was against Southwestern Adventist University and not the New York Knicks, it’s not as impressive as Wilt Chamberlain’s 100-point game in the NBA. However, players at every level have progressed a lot since 1962. Or have they?
It’s incredibly difficult to compare players across different eras, since the defense evolves along with the offense. It certainly appears in video footage that NBA players in the 1960s are light-years behind modern players. Even the championship teams of that era subjectively look like Division I college teams at best. However there are two statistics that can be compared across decades: free throw percentage and the NBA Three-Point Contest shooting percentage. If players have gotten better over the years, there’s no reason to think that they only improved in some ways and not others, so their improvement should be statistically apparent across the board, including these two.
Well, it turns out that I’ve compiled a nice dataset with all of the scores in history from the NBA 3-Point Contest from various sources (including watching videos) and historical NBA free throw stats are readily available. Unfortunately, there are various numbers of 2-point “money balls” in the shooting contest, so I don’t have the exact shooting percentage, just the percentage of possible points scored. However, it’s reasonable to use this as a very good approximation of shooting percentage, since it’s hard to imagine why a player would shoot significantly better or worse when he knows the ball is worth two points. So let’s do this!
Starting with the less interesting stats, there is a significant improvement in league-wide free throw percentage over the years…
It’s not a big difference, but it’s there. The trendline shows a historical 0.06% improvement per year with a p-value of less than 0.0001, which means that it’s extremely unlikely that there would be a trend like this if the year the stats were collected were unrelated to shooting percentage. However, it looks like there were a few really bad years at the beginning that could be making all the difference. So let’s look at it since 1950.
The slope has definitely decreased (it’s now at 0.04% per year), but it’s still statistically significant at the p=0.0001 level). Of course, it would’ve been easier to simply average the shooting percentage of the last five years in the dataset and compare it to the average for the first five years and show that it’s improved by 6.9% since then. However, doing a linear regression like this provides a more accurate estimate of the actual improvement (73*0.0006 = 4.4% improvement since the beginning) since it considers all of the data. It also tells you whether or not the relationship is statistically significant. So you can see why linear regressions are a statistician’s best friend; it’s easily interpretable and fun! (BTW, the R-Squared metric is a “goodness of fit” measure that ranges from 0 to 1 (perfect fit) and this is saying that the year explains 30% of the variance in free throw scores. The other 70% probably being the presence of Wilt Chamberlain or Shaquille O’Neal dragging the percentage down. Joking!)
Okay, now for the fun one: Are NBA 3-point shooters getting better as well? During the first years of the NBA 3-Point Shootout first took place, there were some incredible performances from shooters like Craig Hodges (made 19 straight shots in one round) and Larry Bird (won the first three contests). Maybe the “splash brothers” from Golden State are outliers and that the long-distance shooting accuracy has generally remained stable since the 1980s.
It doesn’t look like it! Due to the small number of shots in the contest each year, the data is much noisier than the free throw percentage, but the trend is still clear: recent players are better shooters. The slope is steeper than the free throw trend, with an improvement of 0.26% per year, but because of the volatility in the data, the p-value isn’t as small (p=0.0013). Another way to think of the level of statistical significance is to say that we have provided strong evidence against the hypothesis that shooters are equally skilled across the decades. In science, you can’t really prove a hypothesis is true, you can only provide evidence that’s consistent with it or falsify it.
We can’t talk about the history of the NBA three-point contest without addressing the question: who is the best shooter in the contest’s history? If you simply sort by shooting percentage, this is what you get:
So, the highest shooting percentage in the contest history is Joe Harris, with an astonishing 75% accuracy. However, this isn’t the whole story. Here is the same list, with important piece of additional data: the total number of shots taken:
There is a statistical tendency for the tops (and bottoms) of ranked lists to be monopolized by the smallest sets of data. This is because it’s easier to achieve extreme results with only a few tries. Intuitively, this makes sense. If someone says “I’m the best shooter in basketball; I’m shooting 100% this year”, you already know that they’ve only made a couple baskets at most. In this case, the effect is not as extreme as it normally is, because if a shooter was on fire in one shooting round, they probably advanced to another round (it’s truly remarkable that Danny Green didn’t make it to the finals in 2019 after shooting 68%!). However, you do see a lot of players at the top of the list who only shot a couple rounds. So, how do we adjust for this “small sample size effect” and compare shooters with varying numbers of shots?
I don’t think we can. How can you say who’s a better shooter, between Joe Harris, who made 75% of his 50 shots, or Steph Curry, who made 65% of 250 shots? The only thing I think we can do is to control for the number of shots and compare players who shot the same number of rounds. Starting with the most shots and working backwards, the winner of the “475 shots” category is…
Craig Hodges! Of course, it’s hard to call him the best shooter since there’s nobody with that many shots to compare him with, and his 56% shooting isn’t particularly noteworthy (the overall average was 52%). However, he did leave us with this unforgettable performance, so he deserves recognition for that.
Similarly, Dale Ellis, and Dirk Nowitzki were the only shooters with their number of shots and only shot about 50%. However, when we get down to players with 250 shots, it gets interesting…
These shooters are no clowns. Who could have imagined that Reggie Miller would rank last on any 3-Point Shooting ranking? So, we have our first candidate for best shooter in the Three-Point Contest history. Steph Curry, with 65% accuracy, ranks best in the first contested category.
Next up is the 200-shot category.
With a 60% shooting accuracy, Kyrie Irving edges out Curry’s coach Steve Kerr. Now, I realized that I lied earlier. We can compare across these categories, if the shooter with more shots also has a higher percentage! We can unquestionably state that Curry’s 65% with 250 shots is more impressive than Irving’s 60% performance with 200 shots. So, Curry can still be considered the top shooter.
Next up: 175 Shots.
Klay Thompson takes this title with 63% shooting. Now, it’s unclear whether his higher percentage would hold up if she shot another 25 times, so we can’t clearly rank him above Kyrie Irving. However, we can say that Curry’s higher percentage is still objectively the best.
150 Shots…
We finally have a true contender for Curry. Tim Legler has technically shot a higher percentage than Curry (66% to 65%), however if he took another 100 shots, it’s fairly likely that he would regress toward the mean a bit and fail to keep up his accuracy. Since we don’t truly know what his mean is, I don’t think there’s an objective way to judge who’s more impressive unless we got Tim out there to shoot a few more rounds.
125 shots…
In the 125-shot category, Devin Booker takes it with 62% shooting. Both Curry and Legler outperformed this with more shots, so this means we can leave Booker out of the overall “most impressive shooter” contention.
100 shots…
Marco Belinelli takes this category, with a Steph-matching 65% accuracy. However, it’s more impressive to shoot 65% over 250 shots than to do it with 100 shots, so Steph and Legler’s performances are still the best.
75 shots…
Jim Les wins this one with 56% shooting, but again falls behind Steph and Legler.
Next up is the highly-contested 50-shot category. These are all the remaining players who won at least one shootout round.
Joe Harris shot a remarkable 75%, so even though the sample size is so small, we can’t rule out that his performance gives him the title for most impressive 3-Point Contest shooter in history.
So we’ve boiled it down to three contenders. Who do you consider most impressive? Your guess is as good as mine…
Steph Curry: 65% of 250 shots
Tim Legler: 66% of 150 shots
Joe Harris: 75% of 50 shots
UPDATE: Since publishing this blog article, my co-author Gary Smith (he knows a lot of things) pointed out that there is a statistical way to compare Joe Harris to Steph Curry here. He said “there is a difference-in-proportions test for the null hypothesis that two probabilities are equal” which is another way of saying that you can attempt to falsify the hypothesis that Curry’s and Harris’s shooting percentages are the same. Here’s what I get when I plug in Curry’s and Harris’s shooting percentages…
So, this is saying that the shot data we have is not sufficient to argue that Harris’s shooting performance was significantly better than Curry’s. However, it is suggestive that he may be better; in fact, if he had made two more shots out of the 50, this test would have supported the idea that Harris’s performance was significantly better than Curry’s. Time will tell!
(Below is the data I used… enjoy! Oh yeah, and buy my book, thanks!)
You may have heard that, contrary to conventional wisdom, most research discredits the idea that NBA players can get “hot”(see here and here). Researchers say that sequences of made baskets occur just as often as you’d expect if they each shot was completely independent. I have always been skeptical of this conclusion, but other than nit-picking the way the studies analyzed the data (for example, not accounting for varying levels of defense or time elapsed between shots), I couldn’t think of a way to statistically demonstrate it. I felt that best way to measure hot streaks would probably be to analyze the 3-point-shooting contest, which takes most of the other variables out of the equation, but how could there possibly be a large enough sample size to demonstrate that a player’s shooting percentage is a less accurate predictor of a shot’s success rate than the result of the prior few shots?
Then, I read Economist Gary Smith‘s upcoming book, Lucky – The Surprising Role of Luck in Our Everyday Lives (keep an eye open for it this year – it’s great), and saw an argument that considered the length of the longest streaks of made baskets in relation to the total shots taken. In my mind, this was the missing tool I’d been waiting for. We have a record of all NBA participants in the 3 point contest (on sites like this) and we have a list of the longest streaks of made shots (see this one on Wikipedia). So we’re set!
Before we get to the data crunching, here’s a little background: once a year, the best 3-point shooters in the NBA are invited to compete against each other for the title of 3-point contest champion. The way the contest works is that the players take turns trying to make as many shots as they can out of 25 (from 5 different spots behind the 3-point line) in one minute. There have been slight variations in the rules over the years regarding “money balls” that are worth 2 points each, but for the purpose of this analysis, we’ll consider only the makes vs. the misses.
The first thing we need to know is the general shooting percentage for the competitors. Historical data shows that the competitors average about 51% of the best possible score, so that’s what I’m considering the baseline percentage. Since there’s not a huge discrepancy between the shooting percentages of the top players, I simplified the problem by treating them all as if they are the same player, with a shooting percentage that’s indistinguishable from a coin-flip.
Here’s how we can determine whether or not hot streaks exist: suppose they don’t exist. This means that the series of makes and misses from these contests should be indistinguishable from a series of people flipping coins against each other. That’s because coin flips are known to be independent events and have streaks that are only due to luck, and not a “hot hand.” If it turns out that I can tell you how to look at a string of 1s and 0s and determine whether it represents baskets or coin flips, then the baskets must not be independent events.
Well, there have been almost 350 competitors over the years, so the first question I had was: if 350 people were flipping sets of 25 coins, how often would we see a streak of heads like this. Yes, that happened; Craig Hodges drained 19 in a row in 1991. The math gets a little hairy with the principal of inclusion / exclusion considerations, so I was more comfortable writing a small computer program to simulate 10,000,000 sets of coin flips (Monte Carlo method) and keep track of the longest streaks. Here’s what I got…
Streaks of at least 19 heads occurred 68 times out of 10 million. So that means that my friend Mr. Carlo would put the probability that we’d only see a Craig Hodges of coin-flippers 0.00068% of the time, even if we had 350 cracks at it. This was pretty strong evidence for the existence of a hot streak (at least for one man), but I wasn’t totally comfortable with the conclusion yet. The second longest streak was only 13 makes in a row (Stephen Curry this year), and based on the simulation, a streak of that length wouldn’t be an outlandish result after all these years, happening almost a third of the time.
Here were the average number of occurrences of each streak length per 350 sets (25 flips for each set)…
19 heads – 0.00
18 heads – 0.00
17 heads – 0.01
16 heads – 0.02
15 heads – 0.03
14 heads – 0.08
13 heads – 0.16
12 heads – 0.33
11 heads – 0.75
10 heads – 1.53
It didn’t sit well with me that the evidence for hot streaks seemingly rested on one supernatural accomplishment, so I looked for another way to spot the “hot hand” in the data. Instead of just looking at the longest streak, what if we considered the total number of very long streaks? Based on the data above, we’d expect 2.91 players on average to have streaks of at least 10 by now. Well, it turns out that we have seven…
This seems pretty strong; we’ve seen more than twice as many streaks of 10 or more than we’d expect from flipping coins. But how unlikely is it?
I had my friend Mr. Carlo run 100 alternate histories of 350 sets in which NBA players flipped coins instead of shooting baskets and here’s what I got…
Well, I think this is enough to call it. If hot streaks are imaginary, seeing 7 players out of 350 hit at least 10 in a row would only occur about once in 100 times. So, the next time you hear someone complaining about how stupid people are to think that streaks exist, like this guy, ask him the last time he saw someone flip 19 heads in a row. Or 77 in a row.
Some guy named Michael Jordan had 4 of the top 10 seasons of all time.
The first 20 seasons in my dataset
And the last 30. Congrats Luka on your first MPP this year!
A couple recent seasons sneak into the list
Just kidding! The statistical pitfall of small sample size in all of its glory – Mr. Mitrou-Long played 30 seconds and got three points. Best season ever! 
Greek Freak! And all recent seasons (half of the list is in the last two seasons)!
Pretty much.
But this is data-mining (don’t tell Gary Smith on me)
Side-by-Side Comparison with ESPN’s list (in ESPN order)
Too small to read, click here!
Who’s more productive, MJ or LeBron?
This could be called the “why did they retire again?” list
A lot of familiar names here
