Skip to main content
Kobe Haters Are Stuck In 2008

Kobe Bryant played his final game Wednesday and sent himself off in spectacular style by scoring 60 points (albeit on 50 shots from the floor). It was quintessential Kobe — grabbing the lead headline even on the night the Golden State Warriors set the all-time NBA record for single-season wins. Kobe could never fade quietly into retirement.

For a stathead such as myself, tracking Kobe’s career arc has been fascinating because it’s existed in near-perfect overlap with the lifespan of basketball analytics. When Bryant made his NBA debut, on Nov. 3, 1996, the field (if you could even call it that) was in an embryonic state. Dean Oliver and John Hollinger were proto-blogging in relative anonymity; the APBRmetrics forum — an early petri dish of smart basketball folks — wouldn’t even become a discussion group board for four-plus years; there was no, no Player Efficiency Rating, no Sloan Conference, no Nylon Calculus. Over the past 20 seasons, as Kobe’s career unfolded through its successes and growing pains, analytics did too, with the former serving as a touchstone — and lightning rod — for the latter.

The stats were not always kind to Kobe, least of all in his perpetual, mythic struggle against Michael Jordan. Perhaps that comparison would have been less harsh in an earlier era, thanks to a similar ring count and a passing statistical resemblance, but the advanced metrics have consistently debunked the parallel. (They’ve essentially taken on the role of the old noodge at the bar or barbershop, reminding “kids these days” of their historical betters.) Kobe wasn’t nearly as efficient as Jordan, they’d remind; he’d likely never be as valuable no matter what the rings said. Likewise, the numbers always seemed to find some other contemporary upon whom to bestow the “Next Jordan” mantle, be it LeBron James or Dwyane Wade or even Tracy McGrady. As if chasing Jordan’s shadow wasn’t hard enough, the shadow seemed to be armed with the cold, compassionless weaponry of data.

It didn’t help that hoops analytics went through its contrarian phase right around the time Kobe peaked. Every sabermetric movement has a period in which its sport’s sacred cows are officially on notice, and basketball’s came in the mid-2000s — known around these parts as the Hollinger Era — when Bryant embodied many bits of conventional basketball wisdom in need of rigorous auditing. Back then, it was fashionable to unearth the deep cuts, the guys like Carl Landry or Gerald Wallace or, uh, Landry Fields, who didn’t get as much play on SportsCenter but contributed efficiently within their roles. Obsessed with efficiency over context, many in the field downplayed the value of Kobe’s greatest skill — relentless, tireless scoring — and went so far as to suggest that an average player could have notched as many points if given the same number of opportunities. (Note: This is, and always was, insane.) Others raised more valid questions about Kobe’s reputation for clutch shooting and lock-down defense, and these cut more to the core of what fans wanted to know about him and players of his caliber. It was a crucial point for basketball stats; perhaps a fractious relationship between Kobe and stat-geekdom was simply the necessary collateral damage.

Listen to our sports podcast, Hot Takedown, discuss the Warriors’ record-setting season.


But the Question of Kobe has undeniably helped the analytics movement grow. Rather than pretending that basketball was baseball and settling on those initial narratives about supposedly inefficient star players, the second wave of basketball metrics tended to illuminate the first generation’s blind spots — namely, the dynamic aspects of the game, such as a player’s tangible on-court impact, how different skill sets complement one another and what value should be assigned to every bit of real estate on the floor. As a byproduct, the metrics came around again to the old-school realization that scoring workload matters — and few players in NBA history carried a bigger scoring burden than Bryant, particularly in his prime.

Of course, some of the new stats co-signed longstanding doubts about Bryant’s game. Despite receiving 11 all-defensive team nods from 2000-01 to 2013-14, for instance, he was only in the 41st percentile of defenders by Real Plus-Minus over that timeframe. But others — such as his No. 4 overall ranking by offensive RPM in the same data set — confirmed that the true benefits of Kobe’s game were being masked by box score metrics wearing true-shooting blinders. Had today’s most cutting-edge metrics — like SportVU’s ability to track a shot’s difficulty (not just its efficiency) — existed during Bryant’s prime, we’d be able to interrogate questions like whether Kobe is the “best difficult-shot-maker” ever.

In a lot of ways, we have Bryant to thank for the tools we have available to appreciate the full contribution of stars — like Russell Westbrook — who would have slipped through the cracks during that first wave of basketball analytics, because those tools were at least in part developed to make sense of Kobe.

As the ink dries on this final, morbid chapter of Kobe’s career, even the most stats-savvy of analysts have to acknowledge Bryant’s all-time greatness. According to Value Over Replacement Player, a measure of total contribution that tries to emulate RPM for historical seasons,1 Bryant ranks as the NBA’s 15th-best regular-season player since 1973-74 and its eighth-best in the playoffs, both of which track with the No. 12 all-time ranking he received in a recent ESPN poll of NBA experts.

Those rankings are still probably not as high as many observers would place the Black Mamba. But they do represent a kind of compromise between the traditionalist viewpoint and the first wave of sabermetric assessments that harshly criticized Bryant for his relative lack of efficiency. Bryant’s game had its flaws, and he was certainly no Jordan, but he was a player of undeniable historical importance. His résumé speaks enough to the on-court portion of his legacy, but for statheads, Kobe’s career helps us track the evolution of basketball analytics over time, both in its reaction to his performance and its ability to capture the meaningfulness of that performance in the first place.

Check out our latest NBA predictions.


  1. For technical sticklers: VORP uses as its input Box Plus/Minus, which is premised on estimating a player’s RPM-style on-court effects in seasons before play-by-play data.

Neil Paine is a senior sportswriter for FiveThirtyEight.