Backpicks GOAT: #30 Bob Pettit

Key Stats and Trends

  • Never played on a dominant team
  • Despite strong box stats, limited evidence for elite peak

Scouting Report

There’s almost no video of Bob Pettit – the closest thing we have to a continuous reel of game tape is probably the 1962 All-Star game – so this will be the briefest scouting report in this series. It’s clear from the limited evidence that Pettit was a fluid athlete who had a good first step and an effective outside jumper. (He hit two shots near 3-point range in the first half of that ’62 ASG.) He could drive and finish around the hoop, was an active offensive rebounder and seemed to constantly probe for better position off the ball. Pettit himself felt his offensive rebounding was his best attribute, discussed below in this wonderful video on his career:

In the limited archives, there aren’t many instances of Pettit finding a great pass. However, there are some clips of decent assists or outright creation, setting up teammates after drawing defensive attention. Combined with his typical assist per game figures (often in the 3s) it’s likely that Pettit was a moderate creator for his time.

On film, his defense looks like a mixed bag. He occasionally reached when guarding the ball, but otherwise constantly swiveled his head to check his positioning. His recovery and shot-blocking don’t pop in any available footage, and he wasn’t known for verticality. However, it appears he was a strong defensive rebounder, but not quite elite in that realm.

Using estimates of rebounding, it’s likely that he was around 17 percent in total rebounding rate during his best seasons, comparable to modern bigs like Anthony Davis or Pau Gasol. In the first five seasons rebounding percentage were officially tallied — when defensive rebounding rates were chronologically closest to the ’60s — Pettit’s numbers would have ranked about 10th in a given season, or around the 80th percentile among big men.

As his career unfolded, Pettit’s physical condition changed dramatically. According to his account, he was a slender 210 pounds when he entered the league. After taking punishment in the paint, including 140 career stitches in his face and a broken hand that forced him to wear a cast at times in 1957 and ’58, he added 35 pounds with weight training, bulking to 245 pounds (at 6-feet-8 in socks). Pettit retired at 32, tearing a ligament in his knee in his final season in 1965.

Impact Evaluation

The shot-clock was to the NBA what the Cambrian explosion was to biology. Before Danny Biasone’s timekeeping innovation, the league was in a dull place, contracting a team in 1953 (Indianapolis) before another disbanded in 1954 (Baltimore). In 1951, there was even a 19-18 game. With the clock’s implementation in the 1955 season, the league entered a period of exponential growth in which racial barriers eroded, rules evolved and money poured in, all of which attracted a larger talent pool. The game grew so fast (pun intended) that there were conversations about banning tall players.

One measurement of this growth is the prominence of new players, and as you can see below, an influx of rookies played larger roles at the outset of this period:

In the last 65 years, there have been only five seasons where rookies topped 13 percent of the 1500-minute players, and all five were between 1955 and 1963. The league was immature then, and the teams tightly packed; the hardest period in history to create any separation was in the late ’50s and early ’60s. So while parity prevented a dominant team until the Celtics empire, some of those 50-win teams were quite impressive.

Pettit entered the league in ’55 and immediately assumed a leading role, nearly doubling his second-best teammate in scoring. Despite frequent roster turnover and coaching turmoil during his first few years, the Hawks gradually improved, climbing from an also-ran to a .500 team, adding notables like Slater Martin and Ed Macauley. And a .500 team was good enough to win back then, as St. Louis took the ’58 title with a quotidian SRS of 0.8.

Pettit was the first great scorer of the shot-clock era and claimed two scoring titles in the ’50s. Thanks to his outside touch (visible on film), his efficiency was bested by only a handful of players during the post shot-clock explosion. Here’s how he stacked up in the first 15 years of the clock:

The Hawks peaked in 1959, playing at a 50-win pace (prorated to an 82-game schedule). Macauley moved to coaching and All-Star center Clyde Lovellette joined the team. More importantly, Pettit, free of his cast, spiked in scoring and efficiency while his assists ticked back up. Commensurate with Pettit’s individual improvement, the St. Louis attack finished first in the league in relative offensive rating in ’59. After two average seasons of offense, they posted +2.9 rORtg in ’59, a near identical number to their 1960 mark of +3.0. So while the defense remained steady, the offense turned them into potential challengers to Boston in those years.1

With rookie and future Hall-of-Famer Lenny Wilkens aboard in 1961, the Hawks produced another 50-win pace season. But the ’62 team fell apart, despite Pettit and Hagan logging big minutes. The defense betrayed St. Louis, dropping from well above average to well below it, losing 7.4 points in relative efficiency overnight.2 Lovellette was injured for half of the season, but the team wasn’t so hot with him either. Wilkens also missed most of the year for military service, and in the 20 games he played, St. Louis looked average (+0.6 SRS). Another key factor, along with any regression from aging, was St. Louis’s coaching carousel; the Hawks trotted out three coaches that season, including Pettit himself for the final six games! (He was the eighth Hawks coach in six seasons.)

After that, St. Louis strung together a few more runs behind Pettit (the player), Zelmo Beaty and Wilkens, playing at a 45 to 49 win pace for Pettit’s final three seasons while returning to defensive performances that were comparable to their pre-’62 numbers.

Unfortunately, we have limited data from those years to gauge Pettit’s impact. If we examine his missed time, his WOWY score in 35 missed games during his prime is unimpressive (+0.9), although some scaling of those numbers is required given how tightly compacted the league was then. Using a more robust method like WOWYR demonstrates decent positive impact, but his numbers are closer to Sam Jones than the giants of the era. Given his injuries, It’s likely these studies understate his peak play, although I do think they accurately reflect a lack of dominance compared to that period’s transcendent stars.

I could easily see Pettit a slot or two lower on this list. However, it’s harder for me to see him much higher. This is largely due to a lack of information and rapid change during the era; Pettit is really the earliest star of the shot-clock period, and because of that, some curving is required to account for the influx of talent that would hit the league in the ’60s. Still, I give him nine All-NBA type seasons with a peak that barely touched MVP status, good enough for the 30th most valuable career since 1955.

 

The Backpicks GOAT: The 40 Best Careers in NBA History

Welcome to the Backpicks GOAT, a list seven years in the making! You may have seen ESPN, Slam, Elliot Kalb and Bill Simmons take a crack at the top basketball players ever. Maybe you have your own list of the NBA greats. Or maybe you just like reading lists. Either way, this particular one is a little different.

This is less about The List and more about the exercise of player evaluation. It’s intended to be an historical reference, organized by player, that (hopefully) adds to the understanding and appreciation of players, coaches and teams over the years. If you like videos, charts and graphs, you’ve come to the right list.

What This List Is Not

This list will not make traditional “arguments” for players. I won’t attempt to balance Kobe’s championships without Shaq, nor do I care about accolades like All-Star teams or the number of Hall of Fame teammates someone played with. I also don’t care how many rings a player won; the very thing I’m trying to tease out is who provided the most lift. Sometimes that lift is good enough to win, sometimes it’s not.

There are no time machines either — it’s not about how players would do today if transported into the past or future. It’s about the impact each had in his own time over the course of a career.

What This List Is

This list also goes far beyond the box score — indeed, the box score is merely a reference for quantifying tendencies — so if you’re used to citing points per game and Win Shares, this will be a bit different.

Instead, this is a career-value, or CORP list; it ranks the players who have provided the largest increase in the odds of a team winning championships over the course of their careers. This means that having great Finals moments or winning the hearts of fans with innovative passes is irrelevant. You can make a great list with those criteria, but that’s not what this is exercise is intended to be.

This list is really about evaluating players based on “goodness,” not merely situational value. (If David Robinson backed up the two best centers ever, he wouldn’t be very valuable, but he’d still be very good.) Players do not earn credit for potential — Michael Jordan helped no one in 1994.

All told, in the last seven years I’ve evaluated over 1,500 player-seasons to compile this list.

 

Thinking Basketball

As you read player profiles, you will notice little mention of playoff performances or game-winning shots. That’s because sample-sizes are incredibly small; instead, playoffs are included as part of an entire evaluation. I’ll only call out the playoffs if they reflect something larger about a player. If you’re struck by the lack of discussion around clutch play or why “losing” players are ranked highly, all of these topics and more are explained in detail in Thinking Basketball. The book also examines critical components of team building (portability) and individual scoring that are foundational to these rankings. (Buying the book also supports the blog and is greatly appreciated!)

List Criteria

The first step is to evaluate a player season. My practice starts with film study in order to understand context.  Perhaps the most beautiful thing about basketball is that there are so many ways to skin the proverbial cat; 20 points per game for one player is not the same as 20 for another. Of course, some skills are more valuable than others. Here’s a guide to the major ones:

On defense, quality of rotations, court coverage, rim protection and length are all countermeasures to the above offensive criteria. (Rebounding counts too, separately for offense and defense.) I tracked these, shot selection, and passing habits in over 100 hours of video study specifically for this series. (To avoid winning bias, I watched segments of games from random quarters.)

After establishing the skill set and tendencies of a player (“Scouting Report”), I then leverage data to quantify the effect of these tendencies (“Impact Evaluation”). All of this ultimately leads to a numerical valuation that allows me to compare the impact of different seasons. The high-level criteria for determining “best career of all-time:”

  1. Evaluate how much a player impacts different lineups (Global offense and defense)
  2. Calculate the probability change in championships based on his health
  3. Add all his seasons together to determine CORP
  4. Adjust for longevity based on era
  5. Compare who has the highest impact

While the first step is my assessment of a player’s seasons, the next four steps are an attempt at an objective measure of career value using those assessments. To do this, I rely on a championship odds calculator I’ve developed over the years so I don’t have to worry about arbitrarily balancing “longevity” and “peak.” I then make an adjustment for era-based longevity, and typically sort out any close calls by defaulting to the player with the better peak or stronger era.

To simplify things, each player-season can be slotted into different tiers:

  • GOAT Season (30 percent or more chance of a title on a random team, or about +7 points per game on an average team)
  • All-Time Season (23-30 percent or +6)
  • MVP Season (17-23 percent or +5)
  • Weak MVP Season (12-17 percent or +4)
  • All-NBA Season (8-12 percent or +2.5)
  • All-Star Season (5-8 percent or +1)
  • Strong Role Player (3-5 percent or 0)
  • Role Player (1-3 percent or -2 to -0.5)

This allows for easy comparisons between multiple seasons; we can see if two MVP-level Bill Walton seasons are more valuable than, say, five All-NBA seasons from John Stockton.

Ranges, Not Absolutes

This is still only one person’s opinion. A “better” list would come from a group of diverse and highly knowledgable evaluators, like realgm’s top 100 list. I see my value here as a video and data curator and as an analyst of that data; obviously, mileage may vary on the rankings, especially depending on criteria.

With that said, I will try and highlight where there’s wiggle room and the ranges that I believe players fall into, but the final order is based on the most likely answers to me (i.e. gun to my head, how good I think a career was).

Stats Glossary

Throughout this list, I’ll use the following metrics regularly:

  • Efficiency (for individual players) – This is measured in true shooting percentage (TS), or occasionally points per scoring attempt (PPA). In the simplest terms, PPA estimates how many “attempts” were actually two-shot fouls, and takes the total number of points scored from 3-pointers, 2-pointers and free throws divided by attempts. True shooting divides PPA by two. In order to compare efficacy across years, this is almost always cited as relative to the league average (rTS). NB: Postseason rTS values are relative to the league (not the opponent) unless otherwise specified.
  • Efficiency (for teams)
    • Offense  This is an estimate of points scored per 100 possessions, or the team’s offensive efficiency. It is often cited as relative to the league average or “relative offensive rating” (rORtg). For the playoffs, rORtg is the difference between the team’s raw offensive rating and the opponent’s regular season defensive rating.
    • Defense – This is an estimate of points allowed per 100 possessions, or the team’s defensive efficiency. It is often cited as relative to the league average or “relative defensive rating” (rDRtg). For the playoffs, rDRtg is the difference between the team’s raw defensive rating and the opponent’s regular season offensive rating.
  • Creation – This is an estimate of how many shots a player created for his teammates per 100 possessions played. It’s also sometimes referred to as a percentage.
  • SRS – The “Simple Rating System,” it is a measurement of point differential for teams, adjusted for schedule strength. SRS is highly predictive of regular season wins and more predictive of games and playoff series than win percentage alone. For this series, a teams “win-pace” is based on its SRS.
  • The Big 3 / Big 4 – These are the three primary offensive dimensions of the advanced box score: Scoring rate (points per 75 possessions), efficiency (rTS) and creation. A fourth dimension — “The Big 4” — includes turnovers (modified for the presence of creation). “Scaled” graphics (sometimes titled “Normalized”) shrink each dimension on an axis of the same length for an equal comparison between them.
  • WOWY / APM – These are the non-box score, scoreboard-based family of plus-minus metrics and some of the most important measuring tools we have in basketball. Most of the references to these are summarized in the historical WOWYR series and this post on the historical compilation of plus-minus metrics.

Who Am I?

The Backpicks Top 40

The list will snake around a bit until the final eight players are revealed in order. The series is intended to be read in the order the profiles are released, which is noted next to each player. Players 31-40 are profiled in small blurbs, most players from 21-30 have limited video-based scouting reports, and all profiles in the top-20 feature full video-based scouting reports.

*Limited video-based scouting report 

  1. March 19
  2. March 19
  3. March 19
  4. March 19
  5. March 19
  6. March 19
  7. March 19
  8. March 19
  9. March 19
  10. March 19
  11. December 14*
  12. January 22
  13. December 28*
  14. March 15*
  15. January 25*
  16. January 29
  17. January 8*
  18. February 5*
  19. February 22*
  20. February 26*
  21. February 8
  22. March 1
  23. March 5
  24. December 18
  25. January 1
  26. March 12
  27. February 12
  28. February 19
  29. December 21
  30. January 11
  31. January 15
  32. Wilt Chamberlain (1)
  33. March 22
  34. March 26
  35. April 2
  36. April 5
  37. April 12
  38. April 16
  39. April 19
  40. April 23

Post-Mortem: April 30

Backpicks GOAT: #9 Wilt Chamberlain

Note: This is the first profile in a historical series on the most valuable NBA careers of all-time. 

Key stats and trends

  • Overrated offensively (scoring blindness) – didn’t create and score at same time
  • Underrated defensively – anchored multiple top-tier defenses
  • Inconsistent, changed game multiple times (overly focused on stat du jour)

Scouting Report

We have limited film of Wilt, so piecing together his game is a matter of pairing the possessions we have with numerous journalistic accounts. He loved the left block, but didn’t work feverishly for deep post position like we might see from someone like Shaq at his apex. When he did establish deep position, Wilt was explosive and difficult to stop, either dunking or quickly wheeling for a finger roll. He also liked the fadeaway, demonstrating that he wasn’t merely a brute.

However, Wilt wasn’t always a fluid athlete, especially as he added muscle during his career. His footwork is the first thing that stands out on film; it was sometimes awkward and led to a number of travels or off-balance plays.

Once he started passing more, he became black-and-white with his attack – when he received the ball in the post with his back to the hoop, he would often start in a “pass mode.” Pass-mode Wilt waited for an open cutter, and if his receivers were covered, only then would he start a deliberate scoring move. Below, he surveys briefly before setting up his fadeaway:

This inability to simultaneously threaten the defense with scoring or hitting open players held him back as an offensive force in my estimation. In other words, he wasn’t a good playmaker. In 1966, Sports Illustrated alluded to this zero sum, baseball-like approach like this:

“But the tactical demands of using [Wilt] to his best advantage severely diminish his own team’s versatility and generally create morale problems among those who want the ball as much as he does.”

Wilt struggled to combine his own scoring with creation, as the best offensive players do. Additionally, his tendency to park himself on the block and remain there for the entire possession clogged driving lanes for his guards.1

As he grew older and was exposed to Alex Hannum, Wilt was a very willing passer. However the film demonstrates how teams responded to this “passing mode” differently. In 1964 (and again in 1967) Wilt was often double-teamed, and thus his passes to open cutters created a 4-on-3 power play, if properly spaced. In other words, defenses reacted to Wilt and he could create.

However, on the back nine of his career, teams didn’t seem to double this action. They just let Chamberlain stand there and hold the ball.2 Wilt was then truly making a “Rondo Pass,” where he would simply wait for the other four players to materialize an opening instead of helping them create the opening. This shrank his playmaking and minimized his overall impact.

Passes like this have some value, especially when surrounded by quality teammates, but they are more like jabs, whereas creating an open shot is a power hook. Wilt also might have been turnover prone. On my most recent film-study, I tracked 47 of his post possessions and seven were turnovers (a whopping 15 percent of the time).

That’s a super small sample, no doubt, but consistent with reports like this from Sports Illustrated during the 1973 Finals:

“Against Reed, who is taller, stronger, heavier and quicker than Lucas, Chamberlain’s attempts to back under the basket for his finger rolls and dunks yielded almost as many traveling calls, three-second violations and offensive fouls as they did goals.”

Because of this, I wouldn’t call Wilt a “high-IQ player,” although he did have a great feel for certain game dynamics, particularly when he could survey the court. (He had a nifty behind-the-back wrap-around pass that in one highlight led to a dunk and in two others clanked off a leg or sailed out of bounds.) As his career evolved, he looked to score less and less — although he still had power and spin moves in the post — and in his final seasons, he wasn’t a focal point on offense at all. Here (in 1972), he’s in position to attack, but thinks nothing of it:

Defensively, Wilt was a monster. Here he is in his later years defending Kareem brilliantly, first with active hands and then sitting on his sky hook to prevent Jabbar from comfortably wheeling to his left:

His defensive weakness was block-chasing. He tallied goaltending violations constantly in the limited film we have on him and occasionally fell out of position by chasing blocks. In the stunning clip below we can see his otherworldly athleticism combined with a propensity to rack up goaltends:

Otherwise, he generally stayed near the hoop and was an absolute terror protecting it. There’s plenty of this on film:

This led to dominant defensive rebounding and some of the most incredible blocked shots you’ll ever see. He ate up space with his 7-foot-8 wingspan and altered a number of shots from guards as they entered his domain.

Impact Evaluation

In Thinking Basketball, Wilt is the case study for Global Offense. He produced unrivaled individual scoring numbers, but they did’t move the needle much for his team. It’s only when his game shifted away from volume-scoring that his team’s offenses flourished. He’s perhaps the ultimate illustration that individual offense does not automatically equate to successful team offense.

The simplest way to see this is to look at the correlations between his offensive outputs (the x-axis) and his team’s offensive efficiencies (the y-axis):

There’s a massive negative correlation (-0.76) between Wilt’s scoring attempts and his team’s offensive rating. So, the less Wilt shot, the better and better his team’s offenses performed. I won’t rehash what’s outlined in detail in the book, but needless to say, Wilt’s skill set described in the scouting report contributed to this phenomenon; not creating for teammates is extremely limiting.

Most volume scorers will taper down on good offenses, but Wilt is unique in that he completely shifts his style of play away from scoring on all of his successful offensive clubs. In some ways, Wilt was the original “Black Hole” – when the ball went in to him, it wasn’t coming out.3

To put this into perspective, we can look at his ratio of true shot attempts (TSA) to assists.4 Historically, Jordan’s ’87 scoring spree comes in at 7.2:1 and Kobe’s ’06 barrage at 7.0:1. Those are the two highest scoring seasons per possession in NBA history. Wilt’s ’61 and ’62 seasons had ratios just under 20:1, good for sixth and seventh all-time, behind such legendary offensive forces as Howard Porter (1974) and Charlie Villenueva (2015). Even 1982 Moses Malone was around 15:1, and his favorite pass was off the backboard to himself. Here are Wilt’s outlier seasons visually:


So we know that early Chamberlain shot the ball a lot, didn’t create much, and (predictably) his team’s offenses weren’t very good. Can we infer how much he was actually moving the needle for those teams?

When Wilt joined the Warriors in 1960, the offense improved by about a single point per 100 possessions.5 That offense was still 2.4 points below league average (relative offensive rating, or rORtg), the first major signal that Wilt’s volume scoring didn’t automatically equate to great offense.

This was inline with his lack of creation; Chamberlain scored at 21.5 points per 75 possessions that year on efficiency 3.0 percent better than league average (relative True Shooting, or rTS). For comparison, 2017 Kevin Love was 22.7 at +2.0 percent. It would counter every trend in NBA history for this kind of isolation scoring or finishing (from offensive rebounds or off-ball scoring) to automatically generate quality team offense. If we plug in turnovers for Wilt — from low percentage to high percentage — his averages during those volume scoring years were 24 points per 75, +5.0 percent rTS and about a 3 percent creation rate (3 shots created per 100), closest historically to 1981 Robert Parish, 2007 Carlos Boozer, 1981 Moses Malone and 1996 Alonzo Mourning.

The 1960 Warriors also had improved roster continuity, and as a result two of their better players logged more time (Guy Rodgers and the NBA’s first “Mr. Everything” Tom Gola). All-Star Paul Arizin was a year older at 31 and coming off an All-NBA season. Otherwise, they returned the same core from 1959.

However, on defense, the Warriors showed massive improvement, jumping nearly 3.5 points relative to league average. This is a trend that would repeat itself throughout Wilt’s career. Here’s his entire timeline with the Warriors:

In 1962, with Frank Maguire taking over as coach and a second-year Al Attles in the rotation, Wilt averaged 50 points a night and the Warriors jumped to a 55-win pace. However, (again) the team offense budged only slightly, sitting 1.7 points above league average, the highest of any of his first seven seasons.

In 1963, yet another coach entered the picture and the Warriors lost Arizin to retirement. Wilt still had a monster scoring year, boasting an rTS of +5.8 percent for the second straight season, but the offense sunk to below-average. Sports Illustrated described the year like this: “The whole dull show was Wilt Chamberlain, who averaged 44.8 points a game while the rest of his team forgot to score.”

In 1964, one of the great coaches in NBA history, Alex Hannum, entered the picture (along with rookie and future Hall of Famer Nate Thurmond). SI wrote this at the end of the preseason:

“Hannum’s teams move constantly, and everybody works for shots. Could Chamberlain, who sometimes seems an immovable object, fit into the new style? The answer appears to be yes. The new Wilt is moving. He is passing, playing alert defense, running and rebounding, but not scoring nearly as much. He is getting some help from rookie Nate Thurmond (6 feet 11), who will be Wilt’s first relief man in his four seasons as a pro. Thurmond, who could start at center for many NBA teams, is also working as a forward, where he will back up Tom Meschery and Wayne Hightower, both of whom look much better this year…Wilt is the Warriors. They cannot win without him. Hannum feels they might win with him if he is really changing his technique.”

They returned to a 53-win pace in ’64…but it was with a devastatingly good defense (5.9 points better than average). Wilt still scored at volume and the offense waned. Again.

1965 was one of the stranger results in NBA history. The Warriors played at a 28-win pace with Chamberlain. His scoring went back up, his assists declined, and San Francisco finished with the worst offense in the league (-5.9 rORtg). Wilt was traded midway through the year for 40 cents on the dollar (for a 27 and 17 minute-per-game player) and the Warriors were only slightly worse without him. Meanwhile, Philadelphia picked up Chamberlain and improved from a 40-win pace to a 48-win pace.

1966 was Wilt’s final year volume-scoring, although he began to reincorporate passing more. And in 1967, when Hannum reunited with Chamberlain, he successfully sold him on a more global approach. SI wrote this before the year:

“[Jack Ramsay and Alex Hannum] are two of the finest brains, unprotected or otherwise, in basketball. It is doubtful that any franchise ever improved its top management so spectacularly as the 76ers did this year. The team was already excellent…Philly gets Larry Costello back, and the 76ers are younger than Boston and have a full-time coach. Besides, Hannum handled Wilt Chamberlain, at San Francisco, better than any man ever did. Who else but Hannum could say that he plans to use Luke Jackson in the pivot for up to 10 minutes a game and add, ‘Wilt will be agreeable if it’s right for the team.’ This is not psychological skirmishing, either; Wilt and Alex respect each other. Chamberlain did not enhance the relationship by reporting late, but Ramsay promptly fined him $1,050, and all the special considerations that Wilt had been given last year—private suites, travel arrangements—seemed far away indeed.”

The results spoke for themselves, as the 76ers started the season 37-4 and never looked back, posting the highest offensive rating in history at the time. Wilt’s assists spiked to nearly 8 per game en route to the title.

In 1968 Philadelphia’s offense regressed slightly. At the same time, Chamberlain became fixated on leading the league in assists. (He did.) However, based on film and reports, it seemed he was letting defenses off the hook by looking to pass too much – this took pressure off the opponent and essentially turned more of his passes into low-leverage “Rondo Assists,” as illustrated above in the scouting report. Based on the footage, I think a reasonable interpretation for the team’s offensive dip is that opponents stopped doubling Wilt as much as he looked to pass more and more.6

There’s also evidence that the late 1960s 76ers were absolutely loaded. Chet Walker had a smooth offensive game, good outside shot and the ability to create his own scoring (he made seven All-Star teams). Hal Greer made seven straight all-league teams. Billy Cunningham would rise to MVP prominence when given the reigns in the following seasons. Without Wilt, and before Luke Jackson’s season-ending injury in 1969, the 76ers were playing like a 60-win team.

Meanwhile, in 1968, the Lakers were working on their own Super Team. Coach Butch Van Breda Kolff implemented a system based on the Princeton offense and his collection of guards flourished. With Jerry West, they played at a 62-win pace, with an offense to challenge the record-setting 76ers from the year before. However, without West they were pedestrian, and the result went largely unnoticed in NBA history.

Despite success in Philadelphia, Wilt wanted to move to the glamour of Hollywood. SI wrote this before the ’68 season:

“Now that Wilt Chamberlain has decided not to acquire the Los Angeles franchise in the ABA or become a split end for the Jets or the heavyweight champion of the world but instead to play basketball for a salary approaching $250,000, the 76ers must be favored to win again.”

So at the end of the year, long before the Heatles, Wilt forced a trade to LA and joined superstars Elgin Baylor and West. However, Wilt’s prodding offensive game didn’t exactly fit into Van Breda Kolff’s Princeton schemes that emphasized space and open lanes, and the Lakers regressed with the addition of Chamberlain.

They were still quite good when healthy – a 57-win pace.7 Still, they were better the year before Wilt arrived. The Laker offense, spearheaded by West, still finished a quality 3.0 points above league average, but it’s clear that Wilt’s low and mid-post game didn’t enhance what LA had previously synthesized. Van Breda Koff was infamously ousted at the end of the year.

In 1970 Wilt missed most of the season with injury and returned for the playoffs. There are only small-sampled lineups to compare (shown above), but they are similar with and without Chamberlain. His final three years were likely his least effective offensively, as his free throw rates dropped severely and his scoring rates were close to Tyson Chandler levels.

It’s not a problem, per se, to combine the packages of Chandler and Rondo; such passing can still be additive when surrounded by offensive weapons like West and Gail Goodrich. Additionally, Wilt’s offensive rebounding helped too. But he became fixated on setting the field goal percentage record and at the end of the 1973 season would pass up easy shots to preserve his shooting numbers.

“March 28, 1973, Chamberlain didn’t attempt a shot or take a single free throw while playing 46 minutes in an 85-84 loss to Milwaukee. Coach Bill Sharman, when asked why Wilt didn’t shoot, said, ‘I don’t know why. You will have to ask him. That really hurt, him not shooting’ -St. Petersburg Times, March 29, 1973

“Wilt Chamberlain, who entered the game with 24 successful field goal attempts in a row, kept the streak alive in an unconventional fashion. He took no shots at all” – The Milwaukee Journal, March 28, 1973”

By all accounts, his last few years were some of his best defensively. He was built like a tank at that point – he claimed over 300 pounds – and anchored the second and third-best defense in the league in his final two seasons.

When we regress lineup data from that period (WOWYR) Wilt still shows strong impact. This is because of all the excellent teams that he was a major figurehead on – ’62, ’64, ’67, ’68, ’72 and ’73. All told, Wilt’s four best teams, by far, come from his non volume-scoring years, and the last two come from his “Tyson Chandler” vintage. This arc makes sense if you remember the scouting report – he wasn’t creating easy shots for his teammates, and his propensity to park in the lane helped muck up spacing that was already mucked. (After all, he was described by SI as “an immovable object.”)

Meanwhile, his willingness to pass (even those Rondo Passes) helped skilled teams, as did his occasional post move and presence as an offensive rebounder. But the major contributions came on the defensive end. There, he’s one of the greatest defenders ever, only overshadowed in his time by the greatest defender ever, Bill Russell. From the film of these seasons and from the data, we see Wilt’s tremendous impact and ability to block and alter shots while inhaling defensive boards.

Finally, there’s this tidbit to drive home these trends: Most relative defenses in the postseason are slightly worse. But Wilt’s improved by 1.9 points, far more than any other all-timer. On the other hand, most relative offenses improve in the playoffs, but Wilt’s teams declined by a point…more than any other all-timer. So while a “scoring blindness” drastically overstates his offensive impact, it also masks his tremendous defensive results.

He’s great, just not in the ways that the original box score predicts.

 

III. Historical Impact: WOWYR, 60 Years of Plus-Minus

In the previous post in this series we introduced the idea of regressing WOWY data — game-by-game plus-minus data — to gauge player impact. In other words, if you looked at all of the activity of players moving in and out lineups over the years, whose team changed the most based on a given player’s presence?

Our previous regression was done using a standard Ordinary Least Squares method, which does a decent job estimating value, but not quite as good as “Ridge Regression” for this data. I don’t want to give you a math-ache, but we can improve this method in the same way that RAPM improved upon APM for regressed play-by-play data.

For the 50’s, 60’s and 70’s, the results are often similar to the OLS version, only this new model performs much better on held-out test data (when we leave a random chunk of data out of the regression and ask the model to predict it). Here are the improved 1954-1983 results using ridge:

PlayerWOWYRGP
Bird7.1355
Robertson..Oscar.6.5986
Moore..Otto.6.1350
Davis..Walter.6.0426
West..Jerry.5.7984
Kenon5.5426
Moore..Johnny.5.4175
Cunningham5.2665
Cartwright5.2326
McMillian..Jim.5.1519
Hill..Armond.4.9259
Schayes..Dolph.4.7551
Lanier4.6751
Thurmond4.6562
Russell..Bill.4.51097
Arizin4.4635
Abdul.Jabbar4.21041
Cheeks4.2535
Dawkins4.2343
Walton..Bill.4.2178
Frazier4.0629
Walk3.9361
Reid..Robert.3.9333
McMahon3.8330
Chamberlain3.71186
Free3.7608
Williams..Gus.3.6461
Jones..KC.3.6428
Toney3.6267
Johnson..Marques.3.6475
Silas..Paul.3.5790
Mix3.4424
Taylor..Brian.3.4238
Gilmore3.4560
Beaty3.3545
npArchibald3.3181
npHayes..Elvin.3.2240
Ray..Cliff.3.2682
McGinnis3.1504
May..Scott.3.1184
Neal..Lloyd.3.1217
Porter..Kevin.3.0489
Smith..Greg.3.0345
Erving2.9775
Robisch2.9196
Owens..Tom.2.9259
DeBusschere2.81026
Weiss2.8341
Barry..Rick.2.8754
Perry..Curtis.2.8416
Dukes2.7463
Hayes..Elvin.2.7934
Johnson..Dennis.2.6528
Counts2.6220
Cousy2.5852
Jones..Bobby.2.5701
Cleamons2.4407
Gola2.4602
Abdul.Aziz2.3256
Hawkins..Connie.2.3378
Marin2.3617
Corzine2.3258
Sloan2.2687
McGlocklin2.2595
Hazzard2.2492
Davis..Johnny.2.1345
Pettit2.1835
Barnett..Dick.2.1933
Jones..Sam.2.1744
Vandeweghe2.0227
Petrie2.0436
Hollins..Lionel.2.0550
Washington..Kermit.2.0334
Nichols2.0193
Yardley2.0452
Skoog2.0179
Baylor2.0934
Murphy..Calvin.1.9685
Carr..Kenny.1.9216
Howell1.9847
Unseld1.9945
Knight..Billy.1.9448
Chaney1.9529
Heinsohn1.8818
Shumate1.8200
Gale1.8331
Paultz1.8394
Havlicek1.71264
Boozer..Bob.1.7624
Brewer..Jim.1.7179
Ford..Chris.1.6681
Twyman1.6770
Parish1.6379
Williams..Ray.1.6402
Cowens1.5568
Thompson..Mychal.1.5254
Smith..Phil.1.5497
Jackson..Luke.1.5299
Sharman1.5585
Bing1.4821
Adams..Alvan.1.4742
Kojis1.4419
Bradley..Bill.1.4624
Attles1.4492
Moncrief1.4250
Smith..Elmore.1.4447
Kerr..Red.1.4863
Bellamy1.4951
Gross1.4440
Caldwell..Joe.1.4345
Issel1.4515
Green..Si.1.4268
Dantley1.3304
Buckner1.3384
Dunn1.3175
McCarthy1.3221
Roberson..Rick.1.3187
Dandridge1.3668
Van.Breda.Kolff1.2236
Johnson..Gus.1.2641
Monroe..Earl.1.2710
Johnson..John.1.2626
Paxson..Jim.1.2247
Mikkelsen1.2400
Sears1.2472
Edwards..James.1.2356
Martin..Slater.1.1461
Barnes..Jim.1.1211
Haywood..Spencer.1.1529
Drew..John.1.1533
Wilkes1.1675
Newlin1.1653
Brown..Fred.1.1670
Collins..Doug.1.1492
Mullins1.1506
Carr..Austin.1.1497
Jones..Dwight.1.0263
Archibald1.0232
Johnston1.0364
Jordon1.0250
Beard1.0249
Hawkins..Tom.1.0218
Chones1.0590
Sikma1.0428
Bridges0.9910
Siegfried0.9349
Benson0.9262
Boerwinkle0.9324
Gilliam..Herm.0.9292
Stokes..Maurice.0.9203
Embry0.9590
Washington..Jim.0.9571
Hagan0.9467
Roundfield0.8461
Robinson..Flynn.0.8268
White..Jo.Jo.0.8673
Smith..Bingo.0.8570
Walker..Chet.0.8938
Bianchi0.8228
Short0.8208
Carroll..Joe.Barry.0.7237
Bridgeman0.7478
Imhoff0.7378
Van.Arsdale..Dick.0.7837
Winters0.6450
Rule0.6305
Meriweather0.6259
Smith..Randy.0.6881
Riordan0.6328
Johnson..Eddie.0.6387
Hairston0.6655
Snyder..Dick.0.6641
Thompson..David.0.6413
Brooks..Michael.0.6246
Van.Lier0.6635
Davis..Brad.0.6217
Nelson..Don.0.5497
Loughery0.5672
Harrison..Bob.0.5228
Greer0.5893
Hetzel0.5193
Adams..Don.0.5330
King..George.0.5224
npCowens0.4192
Braun0.4489
Gray..Leonard.0.4175
Sobers0.4477
Heard0.4495
King..Bernard.0.4349
Guokas0.4268
Ramsey0.3460
Russell..Cazzie.0.3499
Scott..Ray.0.3687
Love..Bob.0.2584
Van.Arsdale..Tom.0.2742
Buse0.2431
Erickson0.2604
Westphal0.2493
Graboski0.2435
Rollins0.1344
Lovellette0.1555
Tyler0.1383
Chenier0.0558
Clark..Archie.0.0579
Grevey0.0274
Steele0.0226
Nixon0.0369
Trevsant-0.1251
Price..Jim.-0.1217
Hudson..Lou.-0.2745
Allen..Lucious.-0.2430
Reed..Willis.-0.3762
Gervin-0.3613
Meschery-0.3637
Scott..Charlie.-0.3418
Maravich-0.3591
Shue-0.4617
Mitchell..Mike.-0.4282
Robinson..Truck.-0.4724
Lacey-0.4215
Costello-0.4643
Richardson..Michael.-0.4278
Wilkens-0.41002
Lee..Clyde.-0.4400
Foust-0.4394
Henderson..Tom.-0.4402
Coleman..Jack.-0.4267
Webster..Marvin.-0.5226
Kelley..Rich.-0.5216
Bantom-0.5465
Malone..Moses.-0.5458
McGuire..Dick.-0.6287
Silas..James.-0.6255
Jones..Wali.-0.6480
Garmaker-0.6379
Natt-0.6316
Stallworth-0.7188
Guerin-0.7812
English-0.8353
Parker..Sonny.-0.8241
Dierking-0.8293
Block-0.9265
Lucas..John.-0.9376
Holland-0.9246
Charles..Ken.-0.9193
Komives-0.9535
Dischinger-0.9351
Smith..Adrian.-0.9318
Williamson..John.-1.0278
Maxwell..Cedric.-1.0453
Poquette-1.0238
Gallatin-1.0299
Goodrich-1.0768
Johnson..Mickey.-1.1608
Lantz-1.1317
Share-1.1405
Leonard..Slick.-1.2395
Lucas..Mo.-1.2594
McAdoo-1.2499
Wilkerson-1.2373
Davis..Jim.-1.2178
Rowe-1.2566
Ellis..Leroy.-1.2898
George..Jack.-1.3384
Greenwood-1.3331
Robinson..Cliff.-1.3178
Hawes..Steve.-1.3304
Shelton-1.3527
Russell..Campy.-1.4515
Theus-1.4334
Brewer..Ron.-1.4330
npGreer-1.4242
Naulls-1.4495
Sauldsberry-1.5279
Davis..Dwight.-1.6234
Nater-1.7278
Leavell-1.7227
Bristow-1.8322
Macauley-1.9379
Barnett..Jim.-1.9465
Adelman-1.9235
Lucas..Jerry.-1.9772
Money-1.9234
Smith..Larry.-2.0203
Kunnert-2.0314
Gianelli-2.1272
Coleman..EC.-2.1268
Neumann-2.2213
Strawder-2.2268
Green..Johnny.-2.2497
Miles..Eddie.-2.2521
Laimbeer-2.2190
Olberding-2.3328
DiGregorio-2.3220
Garrett..Dick.-2.5228
Lloyd..Earl.-2.6376
Carr..ML.-2.6217
Ohl-2.7771
Sanders..Tom.-2.7831
Bryant..Joe.-2.7318
Bibby..Henry.-2.7330
LaRusso-2.7721
Johnson..George.-2.7497
Tomjanovich-2.8687
Gambee-3.0306
Rodgers..Guy.-3.0784
Long..John.-3.4242
Haskins-3.7324
Fox..Jim.-3.8368
Watts-3.9179
Walker..Jimmy.-4.5380
Carter..Fred.-4.6453
Wicks-4.8683
Ballard-4.9316
Huston-5.0214
Jones..Caldwell.-5.6615
Chappell-5.9200
Bockhorn-6.0400
Kauffman-6.8231

Full WOWYR results

After 1984, basketball-reference provides minutes played for each player in every game. Instead of constructing “core lineups” based on season-long minutes per game averages, now we can create the lineups by using the actual minutes played in each game. If we combine all the seasons together and run our regression (with a few conditions discussed below) we can finally make a historical comparison across the last 60-plus seasons.

Below are the full WOWYR results of two models, one which carves out prime years for a player and another that does not. The prime model outperforms the career model, but it’s an interesting comparison nonetheless. (Note that this is not a true measurement of a player’s “prime” compared to his career.) The table is filtered for players with at least 400 qualifying games played.

PlayerPrime WOWYRPrime GPCareer WOWYRCareer GPPrime BeginPrime End
Magic.Johnson10.185710.088919801991
John.Stockton9.89216.5154819881997
David.Robinson9.19266.6107319902001
Michael.Jordan9.011028.2123919851998
Steve.Nash8.89446.0120420012011
Sidney.Moncrief8.65034.167519811986
Dikembe.Mutombo8.58977.1104319922002
LeBron.James8.511057.4118420052016
Robertson..Oscar.8.49868.598619611972
Yao.Ming7.54938.049320032011
West..Jerry.7.49847.398419611973
Paul.Pierce7.211815.7141320002013
LaMarcus.Aldridge7.16956.573620082016
Dirk.Nowitzki7.112565.1143720002014
Reggie.Lewis7.14134.041819891993
Don.Buse7.04736.043119841985
Anfernee.Hardaway6.84784.171519942000
Gary.Payton6.89464.0142419932003
Shaquille.O.Neal6.711245.2136919932006
Marin6.56176.061700
DeAndre.Jordan6.54791.651820112016
Kobe.Bryant6.513584.9148919982013
Dan.Majerle6.58525.796819902000
Russell..Bill.6.410976.2109719581969
Murphy..Calvin.6.36856.968500
Clyde.Drexler6.310525.9115219851997
Bruce.Bowen6.27243.682720012008
Russell.Westbrook6.24966.366120112016
Kareem.Abdul.Jabbar6.212384.1158819701985
Serge.Ibaka6.25508.955020102016
Kevin.Garnett6.213604.4149219972013
Greg.Ballard6.25664.856619841987
Julius.Erving6.110332.9109819721986
Darryl.Dawkins6.14955.549519841987
Barry..Rick.6.17545.275419661978
Bill.Laimbeer6.19386.0107819821991
Doc.Rivers6.06465.475519851994
Chamberlain6.011866.1118619601973
Rasheed.Wallace6.010575.2117119972009
Chris.Ford5.96815.168100
Amir.Johnson5.94285.642820072016
Jim.Paxson5.95323.160519811987
Schayes..Dolph.5.95516.155119541961
DeBusschere5.910265.5102600
McGinnis5.95046.850419761980
Chris.Paul5.88417.184120062016
McMillian..Jim.5.85195.251900
Vlade.Divac5.810615.4111119912004
Hakeem.Olajuwon5.711055.5132819851997
Chauncey.Billups5.79834.2106819992012
Tim.Duncan5.713634.7159519982013
Arizin5.66355.463500
Kevin.Durant5.66501.072820092016
Rasho.Nesterovic5.65236.553020002009
Deron.Williams5.65512.883020072013
Winters5.54504.645000
Larry.Johnson5.57505.075019922001
Alvan.Adams5.59694.5100919761987
Bob.Lanier5.48335.875119841984
Otis.Thorpe5.39143.3115119871997
T.R..Dunn5.35415.254119841991
B.J..Armstrong5.35063.556719911997
Dennis.Rodman5.38913.994519881998
Peja.Stojakovic5.37084.282620012010
npJohn.Stockton5.26270.0000
Jeff.Hornacek5.28974.0113719891998
Jones..KC.5.24284.842800
Larry.Nance5.19415.696919831993
Kenon5.14265.242600
Paul.Pressey5.14966.057919851991
Terry.Cummings5.17483.395619831992
Metta.World.Peace5.15901.896320022010
Patrick.Ewing5.010795.6123719861999
Thurmond5.05625.156219651974
Gus.Williams5.07064.770619841987
Charles.Barkley4.910624.1116719861999
Mark.Price4.95472.668919881995
Hersey.Hawkins4.99223.297519891999
Allen.Leavell4.94323.843219841989
Rashard.Lewis4.98505.396220012011
Marc.Gasol4.86124.161220092016
Ray..Cliff.4.76823.268200
Charles.Oakley4.612003.4129919872001
Tony.Parker4.611693.1125120032016
Frazier4.66294.362919681976
Isiah.Thomas4.69730.8102619831993
Eddie.Jones4.58924.396719952006
Cunningham4.56654.566519661975
McGlocklin4.55955.159500
Elden.Campbell4.56192.675419942002
Bryon.Russell4.55323.362619972003
Anthony.Parker4.44364.243620002012
Tracy.McGrady4.46912.484520002009
Theo.Ratliff4.44984.859619982006
Cousy4.48523.985200
Al.Horford4.46314.263120082016
Toni.Kukoc4.46463.474219942003
Chris.Bosh4.48144.196620062016
Chris.Webber4.47354.288319952006
Beaty4.35454.254500
Alonzo.Mourning4.36583.675719932002
Robert.Parish4.312632.6138919791993
Dennis.Johnson4.310883.1115519791989
Adrian.Dantley4.37923.383319771989
Scottie.Pippen4.311294.0131719892001
Zaza.Pachulia4.25153.451520042016
Bernard.King4.27854.479719781991
Raymond.Felton4.26682.374520062014
Antonio.Daniels4.26033.260319982009
Dwight.Howard4.27441.595320062014
Tony.Allen4.24994.549920052016
Artis.Gilmore4.28525.186319721987
Shane.Battier4.27794.596520022011
Jermaine.O.Neal4.16773.178320012010
Derrick.McKey4.17623.489619891998
Larry.Bird4.110455.8104519801992
Clifford.Robinson4.111363.2131219912004
Josh.Howard4.14414.949420052012
Lindsey.Hunter4.06013.264919942004
Nicolas.Batum4.05024.050220092016
Detlef.Schrempf4.08495.4102619891999
Steve.Smith4.07641.588319932002
Mitch.Richmond4.09243.293019892001
Kerry.Kittles3.95374.554419972004
Danny.Ainge3.97933.590619851993
J.R..Smith3.96963.177320072016
Andrew.Toney3.94704.447019841988
Porter..Kevin.3.94893.648900
Klay.Thompson3.94254.642520122016
Latrell.Sprewell3.98173.696519942004
Karl.Malone3.914263.1165319872002
Pettit3.88353.783500
Monta.Ellis3.87363.475320072016
Havlicek3.812643.2126419631976
Bill.Cartwright3.88493.492919801992
Petrie3.84364.043600
Roy.Hibbert3.84763.154020092015
Matt.Barnes3.86181.765720072016
Smith..Phil.3.84972.749700
Alton.Lister3.84704.847019841997
Charles.Smith3.75260.957059775992
Calvin.Natt3.75843.058419841990
Nate.McMillan3.76662.569619871996
Zydrunas.Ilgauskas3.77052.676319982009
Derek.Fisher3.710374.1116019982011
Trevor.Ariza3.76442.969820072016
Andrew.Lang3.74223.542219892000
Frank.Brickowski3.74343.843419851997
Mike.Gminski3.75952.160719811991
Jerome.Kersey3.75792.085019871993
Horace.Grant3.69884.0124919892000
Ben.Wallace3.67224.4101320012008
Billy.Knight3.65072.944819841985
James.Worthy3.67992.691619831992
Kevin.McHale3.67922.690219821991
Walt.Williams3.65474.154719932003
James.Posey3.66992.577520002009
Howell3.68473.784700
Dukes3.64633.446300
Mark.Aguirre3.57862.088019821991
Vince.Carter3.58452.7122220002010
Michael.Adams3.55222.857919881994
Ron.Harper3.55463.499619871994
Baron.Davis3.56962.879720012010
Jack.Sikma3.59933.8104919791990
Andre.Iguodala3.48924.197720062016
David.West3.47723.082820062015
Henderson..Tom.3.44023.640200
Greer3.48932.789300
Bobby.Jones3.48824.689439913994
Fat.Lever3.45070.965819851990
Jason.Kidd3.410472.9152319962008
Danny.Manning3.44492.272019891995
Steve.Francis3.45581.056420002007
Sloan3.46873.768700
Newlin3.46533.265300
Jones..Sam.3.47443.274400
Ray.Williams3.45483.954819841987
Sharman3.45853.158500
Rolando.Blackman3.49074.597419831992
Reggie.Miller3.411902.9148319892002
Jon.Koncak3.34544.145419861996
Ricky.Pierce3.36362.079019861994
Chaney3.35293.552900
Shawn.Kemp3.38005.190119912000
Lamar.Odom3.29113.698520002011
Stacey.Augmon3.26003.961319922004
Elvin.Hayes3.29342.893419691982
Tim.Hardaway3.27451.599040054017
Baylor3.29343.093400
Dale.Davis3.28652.699219932003
Ervin.Johnson3.24852.748519942005
Free3.26082.860800
Kojis3.24193.241900
George.Hill3.25154.454420102016
Kevin.Johnson3.16712.277419891997
Cliff.Levingston3.14494.444919841995
Glen.Rice3.17941.8101640054014
Chris.Dudley3.14263.242619882002
Beno.Udrih3.14162.641620052016
Hedo.Turkoglu3.17723.681220022012
Perry..Curtis.3.14162.641600
Rodney.Rogers3.16432.668119952005
Washington..Jim.3.15713.157100
Udonis.Haslem3.15583.167920052012
Erick.Dampier3.17243.275819982010
Sam.Cassell3.17683.596719962006
James.Donaldson3.17191.972619821992
Smith..Elmore.3.14473.044700
Charlie.Ward3.04512.945119962005
Tyson.Chandler3.07391.788820052015
Mike.Conley3.06602.866020082016
Joe.Johnson3.09802.7119820042015
Moses.Malone3.010162.2116519781990
Jameer.Nelson3.05882.469820062014
Embry3.05902.759000
Gola3.06022.960200
Reggie.Evans3.04402.944020032015
Brent.Barry3.06693.370419962007
Armen.Gilliam3.07241.276019881998
Manu.Ginobili3.05452.595420052011
Rik.Smits2.98063.580619892000
Dan.Roundfield2.96893.768919841987
Sears2.94722.747200
Joakim.Noah2.95172.453620082015
Jared.Dudley2.95042.350420082016
Bellamy2.99512.795100
Mehmet.Okur2.95273.856820042012
Mario.Elie2.96013.164419912000
P.J..Brown2.99474.3105419952006
Shawn.Marion2.911342.6120820012014
Danny.Granger2.94410.954020072012
Chenier2.95582.155800
Carmelo.Anthony2.97932.795820062016
Mike.Bibby2.98633.2104119992009
Marcin.Gortat2.94691.346920082016
Cuttino.Mobley2.97132.476020002009
Haywood..Spencer.2.95292.752900
Jeff.Teague2.84462.944620102016
Graboski2.84352.843500
Luol.Deng2.86444.786520072015
Anthony.Mason2.88441.891219922002
Terrell.Brandon2.84783.159419952002
Johnson..Gus.2.86412.664100
Hollins..Lionel.2.85502.155000
J.J..Redick2.85054.050520072016
Jason.Terry2.810050.4124020012012
Tree.Rollins2.76812.968119841995
Barnett..Dick.2.79332.793300
Mark.Jackson2.711322.4123819882001
Greg.Ostertag2.74122.041219962006
Anderson.Varejao2.74802.348120052015
Kerr..Red.2.78632.486300
Reggie.Williams2.74722.055339984010
Alex.English2.78844.0100519791989
Hazzard2.74922.549200
npGary.Payton2.64780.0000
Kendrick.Perkins2.65803.262120062014
Grant.Hill2.65541.8100819952005
Paul.Millsap2.66383.772920092016
Kenyon.Martin2.67032.778020012011
Mikkelsen2.64002.440000
Twyman2.67702.377000
Boozer..Bob.2.66242.462400
Alvin.Robertson2.65541.972719861992
Gilbert.Arenas2.54862.751920032011
Dennis.Scott2.54832.853619911998
Jalen.Rose2.56030.681319992006
Collins..Doug.2.54922.449200
Michael.Redd2.54872.355720032009
J.R..Reid2.54321.943219902000
Van.Arsdale..Dick.2.58372.283700
Terry.Porter2.57042.4109919871994
Bradley..Bill.2.56242.562400
Aaron.McKie2.55681.859619952004
Brendan.Haywood2.55661.956620022013
Hairston2.56552.365500
Wayne.Cooper2.54343.243419841991
White..Jo.Jo.2.56731.967300
Antonio.Davis2.48282.685119942005
Darren.Collison2.44490.744920102016
Kevin.Willis2.49351.7111919861999
Larry.Drew2.45050.251039994006
Pau.Gasol2.411551.9115520022016
Silas..Paul.2.47900.879000
Meschery2.46371.963700
Jamal.Mashburn2.46511.165119942004
Al.Harrington2.37542.183420022012
Darrell.Walker2.34911.852319851992
Elton.Brand2.36231.296120002008
Rudy.Gay2.36431.570220082016
Truck.Robinson2.37992.872419841985
James.Edwards2.37822.383619781991
Dan.Issel2.35852.164819711984
Yardley2.34522.345200
Kyle.Korver2.38081.982020052016
Lee..Clyde.2.34002.040000
A.C..Green2.39891.5115619871998
Andrew.Bogut2.35972.559720062016
Quentin.Richardson2.35622.661020012010
Brandon.Jennings2.24222.242220102016
Mark.West2.25252.053419851997
Marvin.Williams2.26802.274420072016
Carlos.Boozer2.27770.989520042014
Fred.Brown2.26941.367019841984
Dwyane.Wade2.28481.8100820052015
Love..Bob.2.25842.358400
Devin.Harris2.26052.562620062016
Robert.Horry2.28081.795419932003
Johnny.Davis2.24951.949519841986
Morris.Peterson2.15171.555620012008
Danny.Ferry2.14662.046619912003
npRon.Harper2.14500.0000
npAntonio.McDyess2.15320.0000
Larry.Smith2.16951.869519841993
Raja.Bell2.15761.559120032012
Kevin.Duckworth2.15443.456419881995
George.Gervin2.17573.381819741985
Snyder..Dick.2.06412.064100
Andrei.Kirilenko2.07211.474420022013
Tom.Chambers2.09043.5105119821992
Tony.Battie2.05002.050019982011
Van.Lier2.06351.963500
John.Long2.06192.061919841997
Nene.Hilario2.07192.974520032015
Sherman.Douglas2.05780.860719901999
Jeff.Foster2.04542.445420002011
Andre.Miller2.09242.2118620012011
Xavier.McDaniel2.06552.279319861993
Nick.Anderson1.96881.474019912000
Kyle.Lowry1.94860.360420102016
Boris.Diaw1.95013.089420062011
Mix1.94241.542400
Mullins1.95061.750600
Bo.Outlaw1.95481.155819942004
Walker..Chet.1.99382.093800
Mike.James1.94242.242420032013
Zach.Randolph1.99071.394620042016
Ty.Lawson1.94231.342320102016
Evan.Turner1.94342.343420112016
Carr..Austin.1.94972.049700
Buck.Williams1.911912.6130919821995
Bridges1.99102.191000
Dave.Corzine1.86361.263619841990
Cedric.Maxwell1.87451.874519841988
Bing1.88211.382100
Chones1.85901.659000
Mike.Miller1.85840.981720022010
Craig.Ehlo1.86382.565419871996
Tyreke.Evans1.84171.541720102016
Martin..Slater.1.84611.546100
Unseld1.89451.894500
Eddie.Johnson1.812650.5146839673979
Dale.Ellis1.88250.5102919871997
Heinsohn1.88181.881800
Glenn.Robinson1.86971.871140104020
Jason.Collins1.74612.046120022014
Smith..Bingo.1.75700.357000
Amar.e.Stoudemire1.75970.479420042012
Allen.Iverson1.79510.897819972009
Brevin.Knight1.75130.951319982009
Mychal.Thompson1.77961.681919791990
David.Thompson1.74130.341319761981
Luis.Scola1.75451.554520082016
Ricky.Sobers1.7623-0.462319841986
Kenny.Anderson1.76412.174319932002
Guerin1.78121.481200
Eric.Snow1.76572.769719992007
Mark.Olberding1.75631.356319841987
Stephen.Jackson1.66821.381120032011
Sam.Mitchell1.65901.260719902000
Smith..Randy.1.68811.688100
Luke.Ridnour1.65691.062420052013
Nick.Collison1.65090.551320052015
Loughery1.66721.367200
Stephon.Marbury1.67390.483919982007
Cowens1.65681.756800
Shaun.Livingston1.64050.140520052016
Paul.Westphal1.64933.249319761981
Byron.Scott1.67801.8100319851993
Keith.Van.Horn1.65531.655319982006
Tom.Gugliotta1.65290.764319932000
Anthony.Johnson1.54111.641119982010
Jeff.McInnis1.54301.243019992008
Chris.Mullin1.55380.995619881995
Damon.Stoudamire1.57451.883519962005
Rajon.Rondo1.56501.769720082016
Russell..Cazzie.1.54991.449900
Cleamons1.54071.740700
Brad.Daugherty1.55790.557919871994
Van.Arsdale..Tom.1.57421.474200
John.Starks1.57402.478819922001
Chris.Mills1.45030.550319942003
John.Salley1.45292.954519871995
Rowe1.45661.056600
Wayman.Tisdale1.46761.068919861996
Hagan1.44671.346700
Dana.Barros1.44941.349419902002
Heard1.44951.649500
Tyrone.Hill1.46201.869619932003
Sean.Elliott1.45341.876419911997
Nelson..Don.1.44971.249700
Goran.Dragic1.34081.041720102016
Charles.Jones1.3436-0.145659695986
David.Lee1.35761.363720072014
Joe.Dumars1.39783.4107719871998
Russell..Campy.1.35150.651500
Taj.Gibson1.34171.241720102016
Jarrett.Jack1.3654-0.669620072016
Rick.Mahorn1.37001.374219821991
Komives1.35351.153500
Brad.Davis1.36160.661619841992
Chris.Duhon1.34341.443420052013
Grant.Long1.36951.282719891999
Voshon.Lenard1.34892.048919962006
Tayshaun.Prince1.38651.5103620042013
Johnson..John.1.36260.462600
Gerald.Wallace1.36462.166520052014
Marco.Belinelli1.34001.140020082016
Cliff.Robinson1.35141.651419841989
Chris.Morris1.35441.054419891999
Reed..Willis.1.37620.976200
LaSalle.Thompson1.25081.056019841991
Billy.Owens1.25140.951419922001
Mark.Eaton1.27353.076319841992
Corliss.Williamson1.25230.156819972005
Dominique.Wilkins1.2868-0.2100719841994
Christian.Laettner1.25621.275019932000
John.Drew1.26061.153319841985
Johnny.Newman1.26621.185819891998
Channing.Frye1.24970.849720062016
Vladimir.Radmanovic1.24651.846520022013
Brandon.Bass1.24981.549820062016
Wesley.Person1.25501.859419952003
Richard.Jefferson1.27201.3100820032011
Chris.Childs1.14690.246919952003
Jerry.Stackhouse1.15861.689019962003
Chris.Kaman1.14671.556220052013
Monroe..Earl.1.17101.071000
Clark..Archie.1.15791.157900
Thabo.Sefolosha1.15130.351320072016
Antawn.Jamison1.19641.1104420002012
Robert.Reid1.17460.674619841991
Gerald.Wilkins1.16470.882819871994
Eric.Williams1.14810.648119962006
Mookie.Blaylock1.18430.789319912001
Winston.Garland1.14001.240019881995
Dell.Curry1.16411.171119881999
Sam.Perkins1.19941.2122219851996
George.McCloud1.0469-0.246919902002
Ray.Allen1.011800.9143819982012
Blake.Griffin1.04543.645420112016
Maurice.Cheeks1.011031.4120819801990
Marques.Johnson1.07162.071619841987
Bobby.Jackson1.05011.250119982009
Joe.Smith1.06340.480019962005
Ramsey1.04600.946000
Derek.Anderson1.05140.352019982007
Dandridge1.06681.066800
npChris.Mullin1.04180.0000
Jeff.Malone1.08210.389419851995
Kenny.Thomas1.04931.349320002010
Danny.Schayes1.04640.746419841999
Kirk.Hinrich0.97650.882820042014
Matt.Harpring0.95261.252819992008
Earl.Watson0.95321.053220022013
Purvis.Short0.96180.661819841990
Shandon.Anderson0.94460.144619972006
Rod.Strickland0.97621.094719902000
Shue0.96170.861700
Kiki.Vandeweghe0.97290.572919841993
Brian.Grant0.96490.367319952004
Rod.Higgins0.94351.643519841995
Mo.Williams0.97170.674920052015
npMike.Dunleavy0.98140.0000
George.Lynch0.95140.451419942005
Tim.Thomas0.9593-0.370020002008
Jason.Richardson0.96120.886620042011
Derrick.Coleman0.86500.773719912002
Hot.Rod.Williams0.86691.882519871995
Rickey.Green0.8582-0.258219841992
Scott..Charlie.0.84181.341800
Marcus.Camby0.88070.190419972010
Michael.Finley0.87841.6110519962005
Allen..Lucious.0.74301.543000
Jamaal.Wilkes0.77773.377719841986
Costello0.76430.464300
Raef.LaFrentz0.74591.745919992008
Calbert.Cheaney0.7607-0.363119942004
Vinny.Del.Negro0.74701.155019891998
Steve.Blake0.75050.160820062014
Attles0.74920.949200
Terry.Tyler0.76311.263119841989
Gross0.74401.844000
Lovellette0.75550.655500
George.Johnson0.6583-1.258339693970
Braun0.64890.648900
Chucky.Atkins0.6504-0.050420002010
Leandro.Barbosa0.64721.052320042012
Corey.Brewer0.65050.150520082016
Cedric.Ceballos0.64230.942319912001
Jay.Vincent0.64680.246819841990
James.Harden0.65181.657520112016
Arron.Afflalo0.64951.853620102016
Thaddeus.Young0.65981.664720092016
Antoine.Carr0.65161.252019851998
Richard.Hamilton0.58190.995620012010
Rodney.McCray0.56390.471219851992
John.Wall0.5439-0.243920112016
Kevin.Martin0.55351.062520072015
Kurt.Thomas0.56431.980219992008
Brad.Miller0.56771.172120012010
Josh.Smith0.5724-0.487120072015
Bantom0.54650.346500
Dave.Greenwood0.56910.369119841991
Kevin.Gamble0.54110.841119891997
npTiny.Archibald0.44130.341300
Corey.Maggette0.45970.166320022012
Tyrone.Corbin0.48190.884419871999
Bonzi.Wells0.44461.044620002008
Doug.Christie0.4675-1.377619962004
Erickson0.4604-0.260400
Jason.Williams0.4691-0.573319992008
Roy.Hinson0.4463-0.546319841991
Wally.Szczerbiak0.44981.360220002007
Hudson..Lou.0.4745-0.174500
Caron.Butler0.46710.484520032012
Sleepy.Floyd0.46620.570419841992
Naulls0.44950.049500
Maravich0.45910.359100
Kendall.Gill0.4667-0.487019922001
npManu.Ginobili0.44090.0000
Darrell.Griffith0.45370.853719841991
Darrell.Armstrong0.3548-0.358019972005
Ben.Gordon0.36010.761420052013
Trenton.Hassell0.34900.849020022010
LaRusso0.3721-0.472100
Michael.Cooper0.37670.576719801990
Lucious.Harris0.34180.641819942005
Ruben.Patterson0.2487-0.648719992008
Maurice.Lucas0.27412.886519761985
Derek.Harper0.2817-0.4119119871996
Howard.Eisley0.24380.343819952006
Greg.Anthony0.1422-0.442219922002
Rafer.Alston0.1548-0.456420032010
Bob.McAdoo0.1616-0.261619841986
DeMar.DeRozan0.14730.452120112016
Scott..Ray.0.1687-0.168700
Nick.Van.Exel0.1797-0.685519942004
Kenny.Smith0.16900.370519881996
Jrue.Holiday0.14240.442420102016
Randy.Foye0.1535-0.453520072016
Steve.Kerr0.14710.547119902003
Mike.Woodson0.15520.555219841991
Sedale.Threatt0.0561-0.364419861995
Wilkens0.01002-0.5100200
Jason.Thompson-0.0439-0.443920092016
Vin.Baker-0.04480.170619952000
Lucas..Jerry.-0.0772-0.677200
Vernon.Maxwell-0.0646-0.974119891997
Orlando.Woolridge-0.1643-1.764319831994
Vern.Fleming-0.1622-1.970319851993
Ellis..Leroy.-0.18980.289800
Shawn.Bradley-0.1528-0.354819942003
Isaiah.Rider-0.1509-1.650919942002
npJason.Kidd-0.14760.0000
Lou.Williams-0.1554-1.656320082016
Joe.Barry.Carroll-0.26550.365519841990
Courtney.Lee-0.25260.452620092016
Shelton-0.25270.852700
Junior.Bridgeman-0.26700.467019841987
Avery.Johnson-0.27400.978719922002
Dee.Brown-0.35170.452739984010
Rodney.Stuckey-0.3529-0.252920082016
Juwan.Howard-0.3732-0.198519952004
Lamond.Murray-0.3508-0.550819952006
Anthony.Peeler-0.36010.164819932003
Blue.Edwards-0.35840.358819901998
Ed.Pinckney-0.3412-0.641219861997
Jamal.Crawford-0.3952-0.7104620042016
Rony.Seikaly-0.4621-0.362219891998
Goodrich-0.4768-0.776800
Thurl.Bailey-0.4667-0.778519851992
Mario.Chalmers-0.4578-0.557820092016
Donyell.Marshall-0.4673-0.771019952006
Vinnie.Johnson-0.58230.882319831992
LaPhonso.Ellis-0.5517-0.853419932002
Spud.Webb-0.5537-1.053819861996
Drew.Gooden-0.55960.763420032012
Travis.Best-0.5442-1.244219962005
Jones..Wali.-0.5480-0.748000
David.Wesley-0.57670.486619972006
Sam.Bowie-0.5435-0.043519851995
Wilson.Chandler-0.5417-0.841720082015
Norm.Nixon-0.5593-1.963019781986
Spencer.Hawes-0.5423-1.042320082016
Barnett..Jim.-0.5465-0.846500
Al.Jefferson-0.66320.671820072015
Andrea.Bargnani-0.6451-0.845120072016
Allan.Houston-0.6771-1.781719952004
Samuel.Dalembert-0.6645-1.064620042015
Larry.Hughes-0.6599-1.866220002009
Eric.Gordon-0.6404-1.640420092016
Wesley.Matthews-0.6446-1.251720112016
Brook.Lopez-0.7479-0.947920092016
Brian.Shaw-0.7555-0.665319912001
Lorenzen.Wright-0.75060.052519972006
Willie.Anderson-0.74620.646919891996
Chuck.Person-0.85620.785419871993
Greg.Monroe-0.8439-0.443920112016
Ohl-0.8771-1.477100
Jim.Jackson-0.8791-0.682619942005
Mickey.Johnson-0.9763-0.876319841986
Loy.Vaught-0.9448-1.244819912001
Bryant.Stith-0.9483-0.448319932002
npGrant.Hill-0.94540.0000
Antoine.Walker-1.0815-1.389919972006
Quinn.Buckner-1.04191.238419841986
Bob.Sura-1.0439-1.443919962005
Johnny.Dawkins-1.0418-1.241819871995
Michael.Cage-1.0742-0.288219871996
Kenny.Carr-1.1434-0.743419841987
Shareef.Abdur.Rahim-1.16530.478119972005
Rex.Chapman-1.1565-1.158319891999
Kevin.Edwards-1.1440-0.844019892001
Olden.Polynice-1.1538-1.668119911999
Mike.Mitchell-1.2598-1.559819841988
Share-1.2405-1.340500
John.Bagley-1.2488-1.048819841992
Muggsy.Bogues-1.2667-0.974019881998
Desmond.Mason-1.2555-1.759020022009
Harvey.Grant-1.2459-0.655719901996
Jeff.Green-1.3555-2.862220092016
Rory.Sparrow-1.3673-2.767319841992
Walter.Davis-1.4908-2.892319781991
Rick.Fox-1.46570.774819942003
Hubert.Davis-1.4424-1.542419932003
Carter..Fred.-1.4453-2.345300
Pooh.Richardson-1.4472-1.853019901996
Reggie.Theus-1.4929-0.992919791991
DeShawn.Stevenson-1.4474-0.951820042013
Troy.Murphy-1.4499-1.656220032010
Herb.Williams-1.57720.380919831992
Ramon.Sessions-1.5452-2.045220082016
Caldwell.Jones-1.5885-0.288519841990
Bimbo.Coles-1.6549-1.657619922003
Miles..Eddie.-1.6521-1.552100
Jose.Calderon-1.6580-2.066020082016
Clarence.Weatherspoon-1.6733-1.576319932003
John.Salmons-1.7612-1.468920062015
Ken.Norman-1.7515-1.055719891996
Rasual.Butler-1.7447-2.444720032016
John.Paxson-1.7555-2.056619851993
Emeka.Okafor-1.8570-1.857020052013
Terry.Mills-1.8444-2.344419912000
Benoit.Benjamin-1.9628-1.563319861996
Rodgers..Guy.-2.0784-2.478400
Bill.Hanzlik-2.2406-2.040619841990
Ron.Anderson-2.34410.244119851994
Jay.Humphries-2.3713-2.771319851994
Green..Johnny.-2.5497-2.749700
Sanders..Tom.-2.5831-2.383100
Doug.West-2.5448-2.944819902001
Scott.Skiles-2.6424-3.142419871996
Kelly.Tripucka-2.7558-2.355819841991
Wicks-2.8683-3.268300
O.J..Mayo-3.0521-2.652120092016
Ricky.Davis-3.1575-3.659420022010
Martell.Webster-3.1410-2.041020062015
Mahmoud.Abdul.Rauf-3.5448-2.944819912001
Tomjanovich-4.5687-4.768700

We often talk about the “smell test” in these kinds of metrics, and seeing a collection of MVPs cluster at the top smells like Thanksgiving. Pretty much any tweaking of variables yields the same names at the top (although of the big names, Larry Bird moves around a bit). If we include players with only a few seasons of data, there’s the occasional Otto Moore among the legends, but that’s expected in any regression model, especially when the sample starts to become small.

There’s a lot to talk about here, and in a later post, I’ll dissect these numbers in greater detail. I caution you to hold off on reaching conclusions that are too strong until I lay out the historical nuances in these numbers, but for now, feel free to peruse them using the search function.

The rest of this post will include a lot of math/APBR stuff. A special thanks to the legendary Evan Z, who answered all of my questions related to running penalized regressions. In the next post, I’ll dive into all of these historical numbers and what they mean.

WOWYR Methodology

Lineups

Before 1984: To qualify for a lineup, players needed to play over 25 minutes per game for a team. When only four players on a team averaged over 25 mpg for the season, additional players were included in the “core lineup.” In such cases, players who clustered near 23-24 mpg completed the lineup for a team. (Yes, this means that some teams have lineups with 5 players and some with more.)

Players with fewer than 83 qualifying games in total were converted to “replacement” players. This is different than an RAPM replacement player, who represents someone on the end of the bench. Here, it’s someone who could not make other starting/core units for more than a year’s worth of games. Test-sample accuracy (measured by MSE) improved by converting these players to replacements, which removes a significant amount of noise from the model.

Note: Some early lineups from the 50’s and 60’s were manually constructed and may be missing lineups that only played a few games here and there.

After 1984: After 1984, we have the actual minutes played by each player in each gameTo qualify for a lineup, a player had to play at least 20 minutes in a game. Values ranging from 20-30 minutes were explored, and 20 minutes yielded the best test-sample results.

Most core players will play for 20 minutes in a given night, barring some kind of injury or rare situation in which they have extreme foul trouble. Furthermore, since this is not a per-possession metric, it’s assumed that it’s rare to impact a game by playing in under 40% of its possessions. This also means that when someone plays only a few minutes in a game, he is counted as missing that game.

Because of the change in qualifying criteria, a separate replacement player was created for this era. Test-sample accuracy improved with replacement players set at 82 games. (Again, all postseason games were included.)

Prime vs. Non-Prime Seasons: To improve the performance of the model, players with clear jumps in their play were split into two different players — “prime” and “non-prime” versions of themselves. For example, it treats Kobe Bryant differently in 1996, 2006 and 2016. There will always be a certain amount of “smoothing” across a player’s “regular” years; this is an attempt to trim the extremes on the edges. Young players usually take a year or two to develop, while aging veterans still log minutes despite being completely different versions of themselves (Kevin Garnett says hello).

This division should not be thought of as an attempt to carve out a players “best years.” Instead, the line is often drawn when there are clear jumps in minutes per game.

Players with smooth descents, like Garnett, are slightly harder to differentiate, where there might be a two or three-year period that could qualify as “he’s not the same guy anymore.” Either way, aging is always a tricky thing to control for in multi-year studies, but this is far better than telling the model to treat 37 year-old Vince Carter as the same guy who made eight consecutive All-Star games playing nearly 15 mpg more per night.

Point Differentials

Every unique lineup for a given season has (1) a number of games played and (2) a point differential. The point differential is calculated by taking the season-long SRS of all opponents for that lineup and using that as a strength of schedule adjuster, along with three points from home-court advantage.

(This means the “true” opponent quality will not always be captured, in instances where the opponent is fielding a lineup that differs greatly in performance from it’s season-long average SRS. Future versions of WOWYR could pit lineups directly against lineups in the traditional adjusted plus-minus manner.)

The Regression

The regression uses the lineups (as described above), the point differential of the lineup and the number of games played to weight the lineup. (The OLS version was technically Weighted Least Squares.) Weighting uses the inverse of the variance of the number of games played by the lineup.

Variance (based on number of games played by a lineup) was calculated by taking seasons from different consistent core lineups (the same group of players) and then randomly sampling differing numbers of games to create a power function that represents the standard deviation for any number of games.

Standard Deviation = 14.402x-0.6567 where x is the number of games.

Regression used 10-fold cross-validation. Best test results were found using 90% data to train the model and 10% for testing, with a test MSE of 3.0 and RMSE ratio of 1:1 between train and test data.

Future Directions

There are a few key areas to explore in the future. It’s possible using under 20 minutes per game at a game-by-game level might be better. Additionally, the difference in data resolution between pre-1984 and post-1984 (Orwellian data!) means that there might be better ways to blend the models. Additionally, areas like prime seasons could be tweaked.

 

I. Historical Impact: WOWY Score Update

How valuable is a player? How many points per game is he worth? In sports, these are Holy Grail questions that play-by-play data has helped estimate. But how do we compare Magic and Bird when they played before play-by-play was available? How do we compare Russell and Chamberlain when they don’t even have a complete box score?

A few years ago, I circulated a method that takes a stab at these questions by using injuries, trades and free agent signings to compare teams with and without a given player. The result is an historical, (mostly) apples-to-apples comparison of value between players, called WOWY. (There’s a full primer on WOWY attached to the end of this post.) The lineup data — not from play-by-play, but from game-by-game — gives us the same insight into players for the last 60 years.

Take Bill Walton’s legendary rise and fall in Portland. All other things being equal, how did the team fare with and without him in the lineup? It turns out, Walton’s missed time from those years produces the best WOWY score in NBA history. (See the third tab, titled “Top WOWY Runs.”) In other words, Walton had the biggest observable impact of qualifying players (i.e. players who were injured or traded) on any team ever, his presence improving the Blazers by more than eight points per game.

In researching Thinking BasketballI examined hundreds of these WOWY runs. For those familiar with it, I also cleaned up the data, adding controls and incorporating postseason games for over 1,500 instances since the inception of the shot clock in 1954-55. And if we combine those instances for players — only focusing on what I’ve liberally called their “prime” — we can see who left a large impact when in and out of the lineup for an entire career.

Below are the top 10 prime WOWY scores of all-time, with a minimum sample of 20 games missed:

Top 10 WOWY Scores All Time

Indeed, the best combined numbers are from players often found in all-time top-10 or top-20 lists. You can see all the results in this spreadsheet of over 200 qualifying players.

The two outliers — Robertson and West — make most top-20 or top-15 lists. (ESPN had them at 11 and 13, respectively, in their recent top-100 rankings.) While Oscar is largely revered, most people don’t know that his impact was quantifiably enormous, dragging an otherwise inept team in Cincinnati to respectability, then later catapulting Kareem’s Bucks into the upper stratosphere.

Meanwhile, when West was healthy, many of his teams were elite, only overshadowed in history by the dynastic Celtics. Amazingly, West’s teams performed better with him in all 12 lineups that he missed time. Oscar did the same for 11 consecutive lineups. (Note that about one third of WOWY scores on that list are negative.)

538’s Benjamin Morris ran a limited version of this years ago to argue for the greatness of Dennis Rodman, although he only used a minimum of 15-game injury blocks. Rodman’s good, but he clocks in at 16th here. And yes, Kobe (26th) beats Jordan (32nd), but MJ’s number comes largely from 1986 when he broke his foot and missed most of the season. (His 22 missed games from 92, 93 and 95 respectively would elevate him to 25th on the list.)

While this is all valuable data, it’s still limited. It doesn’t help answer our original question for players who don’t miss much time, like Chamberlain and Russell (and even Jordan). We’ll address that issue in Part II of this series on historical impact. For now, I’ll leave you with a WOWY primer below…


What’s WOWY?

It stands for “With or Without You,” and compares the performance of a roster with a given player and without that given player over the course of an entire game. It is an attempt to isolate a player’s impact on that given roster.

I almost always control for players who played at least 25 minutes per game (noted in the control column of this spreadsheet). This typically yields five to seven-man rotations for most teams, depending on how they distribute the minutes. There are some instances where I’ll control for the entire starting 5, even if someone is below the 25-minute mark. Similarly, there are situations that call for including two players at around 23 to 24 minutes per game because there is no clear-cut fifth man on a team.

How is WOWY different from On/Off?

On/Off captures changes within a game. WOWY captures changes from game-to-game. One strength of WOWY is that multi-collinearity is not a problem; in other words, player values cannot be confounded by moving in and out of the game together. In that sense, it is an incredibly pure representation of a player’s value to a given roster, troubled by issues like sample size (major issue), team growth (minor issue), opponent unhealthy lineups (minor) and valuable bench cohorts (minor).

(Note that some lineups have synergistic effects where the whole is greater than the sum of the parts, and removing any player from that equation can disrupt the synergy.)

What’s a WOWY Score?

It’s an attempt to quantify how impressive a given WOWY run is. It takes into account sample size, the distribution of SRS scores in a given era and the quality of the player’s team.

What is “95% +/-?”

It is a confidence interval, based on the SRS-variance of a typical NBA team. For example, from 1977-1978 the Blazers were a -1.2 team in 26 controlled games without Bill Walton. A 95% +/- value of “3.5” means that 95% of the time, the actual full-season SRS of such a team will fall within 3.5 points of that value, or somewhere between -4.7 and +2.3 SRS. (Note: More consistent teams will be slightly penalized by this and more inconsistent teams with benefit from it.)

What is SIO?

It stands for “simple in/out,” and is a basic curving of impact based on the quality of a team. It means that taking a -10 team to -5 is given less value than taking a +5 team to +10.

When combining runs for a “prime score,” why is SIO different than WOWY score?

Uneven samples can provide extremely warped results due to some basic math illusions. Take Michael Jordan, who missed the majority of his games in 1986. His team’s “out” totals will then largely reflect the 1986 Bulls (who were below .500), but his “in” totals will be weighted heavily by the Bulls dynastic teams. So, even if his team performed the same with or without him, his out sample largely be from a -3 SRS team, while his in sample would be teams closer to 9 SRS.

WOWY score was designed to correct this problem — for multiple seasons, it takes the impact (SIO) from a given sample and weighs it accordingly. For instance, if a player makes a team 10 points better in a five-game sample, and then two points better in a 20-game sample, his weighted impact is 3.6 points (because 80% of the sample is from the two-point change).

The actual in and out values are included for posterity, but unless a player played on relatively consistent teams, the numbers won’t reflect the actual impact the player had on his lineups.

Why are there multiple entries for the sample player-season?

The controls are different. Players might miss games from one lineup and then, following a team trade, might play with and without a different lineup.

How 2016 NBA Teams Differentiated Themselves on Offense

Dean Oliver’s Four Factors uses box score data to determine how teams are successful in key elemental areas. Instead of looking at box stats like turnovers and rebounding, what if we used different types of plays to determine a team’s offensive strengths? Synergy tracks a number of play types, but not all have a large impact on the game. Based on the 2016 data on nba.com, the following were the most common play types this year:

  • 25% were pick-n-roll plays
  • 20% were spot-ups
  • 15% were in transition

Naturally, teams differentiate themselves from the pack based on the plays they run the most; The Lakers led the league in isolation plays, but their efficiency was below-average on those plays, so they lost lots of ground on the average offense. The five categories from Synergy with the largest degree of differentiation were:*

  1. Pick-n-Roll (PnR)
  2. Spot Up
  3. Transition
  4. Post Up
  5. Off Screen

Below is a visual of how every team in the NBA this year fared in these five factors.

2016 Differentiation by Play Type

The y-axis represents the per-game differentiation based on efficiency of a given play type (relative to league average). For instance, if a team ran 820 post ups (10 per game) and averaged 0.10 points per play more than league average, they would generate an extra point per game.

Not surprisingly, the most differentiating play type during the 2016 season was a Golden State spot-up shot. Of the 203 players with at least 100 spot-ups, Steph Curry was 2nd in efficiency at 1.49 points per play and splash brother Klay Thompson 15th at 1.18 points per play. (League average was 0.97 points per spot-up.) Let’s simplify the above visual and just focus on the final eight teams left in this year’s playoff field:

2016 Differentiation Final 8

Now it’s easier to see how the remaining teams stack up. The Warriors don’t really have a post-up game, but so what? They excel at everything else and created the most differentiation of any team in the league in three major categories (PnR, Spot Up and Off Screen.) On the other hand, the Spurs were dominant in the post and excellent in their own right at spot-up plays, but they don’t do damage in transition. (San Antonio also led the league in “put backs” by a large degree, generating over a point of separation alone in that category.) The East’s best team, Cleveland, was above-average at everything.

*Isolation plays would be the 6th major play type. However, no team in 2016 created a point of positive or negative differentiation from isolation plays, which accounted for 8% of all plays tracked during the season.