With the benefit of greater life experience, not to mention the realization that accumulating the most toys is probably really tough, my point of view has evolved. No longer is the value of the time spent on this spinning rock inextricably linked to one’s tally of cash and cars. As much as it helps with the down payment, money alone will not ensure a happy or rewarding existence. Continuously learning and expanding you knowledge and understanding of topics that most interest you... that's the good shit right there!
In basketball, as in life, it’s important that we measure success not only by statistics accumulated in one’s time on the floor, but also in terms of how well this time is spent. Not all 30-point performances are created equal. Taken out of historical context, 1961-62 is arguably the greatest statistical season in NBA history. Bill Russell’s 18.9 points and 23.6 rebounds per game were good enough to earn him the league MVP, but not a spot on the All-NBA First Team. That’s the season in which Wilt Chamberlain famously put up 50.4 points (and 25.7 rebounds, which is sometimes glossed over) per game and Oscar Robertson averaged a ridiculous 30.8 points, 12.5 rebounds and 11.4 assists. So whacky were the numbers in 1961-62 that Walt Bellamy’s 31.6-point, 19-rebound rookie year effort, Jerry West’s 30.8-7.9-5.4 and Elgin Baylor’s 38.3-18.6 (in just 48 games; he was an Army Reservist, called into active duty during the season, and only granted a weekend pass, meaning he could only take part in games played on weekends. He’d join the Lakers wherever they happened to be each weekend, before returning to Washington State.) have been reduced to afterthoughts.
What these raw numbers ignore, however, is the fact that half a century later, 1961-62 represents the NBA’s highwater mark for uptempo play. Although they played a schedule that consisted of two fewer games than the league’s current 82 and shot a lesser percentage from the field (42.6%, v. 46.1%), compared with 2009-10, the average NBA team in 1961-62 attempted 28.6% more shots (8,619, v. 6,700), scored 18.3% more points (118.8 per game, v. 100.4) and grabbed a staggering 67% more rebounds (5,713, v. 3,421). Despite the appearance of an offensive boom, NBAers in 1961-62 only made more shots because they took A LOT more shot, and grabbed an obscene number of rebounds because they weren’t all that good at making the shots they were taking.
Does this mean that we should remove Wilt, Oscar, West and Baylor from the ranks of the all-time greats? Absolutely not. While the style of the era did lend itself to video game-esque statistical inflation, these guys still had to take the court and dominate. Drop any of these guys into any era in NBA history and they’d rank among the best.
That no one had averaged a triple-double before Oscar did so and no one’s duplicated the feat in 49 years since (Wilt, Michael Jordan, Jason Kidd and Magic Johnson three times are the only other players ever to average 10-8-8; Oscar did it five times) speaks to his transcendent greatness. Say what you will about Wilt’s physical dominance and the relative ease with which the game came to him, but don’t ignore the fact that only eight times in NBA history has a player come within 15 points of Wilt’s 1961-62 average- and four of them are by Wilt himself.
With all of that said, in order to better understand the performances of the all-time greats (all players, actually) in the context of not only the era in which they played, but all of NBA history, it’s imperative that we, as intelligent, intellectually curious observers of the game, embrace the idea that we cannot have too much knowledge. In order to effectively do this, we must continue to look beyond (no need to abandon them altogether) the simple statistics of the past and leverage the new tools and statistical metrics that explain the journey that led to those numbers. For decades we’ve had the “what.” In recent years, we have- and will continue to- become empowered with information to help us explain the “how.”
For instance, while it's certainly relevant that Kevin Love is averaging 15+ rebounds per game, but equally relevant is knowledge regarding the way in which he’s arriving at that total. By virtue of playing on a bad team that misses a ton of shots, is he simply a beneficiary of early-1960s-style “rebounding inflation?” Or is he simply the greatest rebounder pro basketball has seen since Dennis Rodman? The answer? Both.
That the Timberwovles rank in the league’s bottom six in Offensive Efficiency (101.5 points per 100 possessions) and True Shooting Percentage (52.4%; this is FG% adjusted for free throws and 3-pointers) while playing at the fastest pace of any NBA team (99.7 possessions per 48 minutes) means that during an average game, Love has a larger pool of errant shots from which to accumulate rebounds. However, while he encounters an inordinate number of potential rebounds per game, Love’s rebound rates- the percentage of available rebounds he grabs in his time on the floor- across the board are spectacular. He leads the league (minimum 50 games played) in both Total Rebound Rate (23.7%) and Defensive Rebound Rate (34.1%), the latter tied for the third highest single-season mark in history, behind only Dennis Rodman in 1992-93 (36.8%) and Ben Wallace in 2002-03 (34.9%), and tied with 1991-92 Rodman. At the offensive end he's almost as great, as his 13.8% ORR trails only San Antonio’s DeJuan Blair and Memphis’ Zach Randolph, who are tied for the league lead, at 14.8%.
So sure, Love might average “only” 11 or 12 rebounds on better-shooting team that plays at a slower pace, but given the rate at which he maximizes his opportunities to crash the boards, we can comfortably claim that he’s a historically great rebounder. See what we did there? Wasn’t that fun? Now we know more!
Advanced metrics are hardly a perfect science but, as Rob Mahoney pointed out fantastically in a post on The New York Times’ Off The Dribble blog and again over the weekend on ESPN’s TrueHoop (in fact, check out all of TrueHoop’s coverage from the MIT Sloan Sports Analytics Conference- exceptional stuff!), it’s the collective response to this imperfection, along with the noble- but inevitably fruitless- search for an all-encompassing “superstat” that is continually expanding the understanding of (in my opinion) the most knowledgeable fan/executive base in professional sports. In the truest sense of the phrase, the journey, and not the final destination, is the greatest reward of pro basketball's analytics’ movement.
As tends to be the case with any new, game-changing movement, basketball analytics has its share of detractors. Interestingly, this resistance stems not from the league itself or its teams (reportedly, 20 of the NBA’s 30 teams were represented at the Sloan Conference), but from veteran members of the mainstream media (and Timberwolves’ GM David Kahn, a former media member and an exquisitely subpar executive), claiming that they’d rather believe what they see with their own eyes over the output from a computer model. Additionally, many of these defenders of the status quo cite the imperfection of advanced stats and the lack of (in Mahoney’s accurate words) a “’one-number metric’ - a single rating that can accurately describe a player’s impact on the floor or a team’s relative standing” as grounds for rejecting the new metrics.
For an experienced group of professionals- whose sole (pardon my redundancy) professional responsibility is to convey the sport’s latest developments to an eager fan base- to opt for willful ignorance is an embarrassment. Innovation in the worlds of finance, science, medicine and technology arrives via baby steps, not drastic, one-fell-swoop paradigmatic shifts. Imagine Walt Mossberg, the Wall Street Journal’s senior technology reporter, railing against the iPod in the product’s early days, not because of some technological deficiency, but because he longed for the bygone days of the phonograph. (Take a look here. Which company do you think was resistant to change?)
Where else does this happen? In what other industry do the traditional (but fortunately no longer only) gatekeepers of information sprint away from fresh knowledge?
And please, save me the “don’t tell me that my eyes are deceiving me” spiel. The suggestion that Allen Iverson was inefficient (PER of 25+ just once in his 14-year career) and a ballhog (he led the NBA in Usage Rate six out of seven years between 1998 and 2005, with a USG% of at least 32.9% all seven years and 35%+ four times) is neither groundbreaking nor heretical. It’s merely confirmation of something we’ve all known for a decade and a half. This doesn’t change the fact the Iverson was one of the most devastating scorers of his time and arguably the toughest player in NBA history. Nor does it denigrate the visceral experience of watching an in-his-prime Iverson in person, which will forever rank among my favorite NBA memories. It doesn't erase the memory of dragging Eric Snow, Aaron McKie and Jumaine Jones to the 2001 Finals, nor does it erase dropping 48 in Game One at Staples Center and single-handedly saddling the Lakers with their first loss (and only) of that postseason. We saw what we saw. We’re just now learning how to put what we saw into context.
And like I said, advanced metrics are imperfect. Hell, I hate that advanced stats are unfriendly to Derrick Rose and personally consider that a flaw in the stats more so than in Rose’s game, but I'm not going to sprint in the opposite direction just because a relatively small slice of an entire practice runs counter to my opinion.