Kevin Garnett, who announced his retirement from the NBA in late September after 21 seasons in the league, was a revolutionary player who shaped basketball history in important ways. In 1995, he ushered in the preps-to-pros era by declaring for the draft straight out of high school; in 1997, he signed with the Minnesota Timberwolves for what was, at the time, the richest contract in professional sports history (six years, $126 million). He even created the archetype of the modern big man — dominating with athleticism, length, skill and versatility rather than plodding bulk. In my mind, KG stands out as the first superstar of basketball’s Analytics Era. He was a great player whose legacy was further enhanced because we had better tools with which to measure his greatness.

Sabermetrics didn’t really hit the NBA in earnest until the late 1990s and early 2000s, when Dean Oliver, John Hollinger and Kevin Pelton began writing online features with a numerical slant and teams started tracking the data required to compute more complex statistics than could be found in the box score. Around that time, a semi-novel idea arose from the primordial state of the analytics community: A player’s value could be inferred by looking at how much his team outscored opponents when he was on the court, compared with the team’s scoring margin when he was on the bench. In essence, it was borrowing plus-minus from hockey, with the twist of judging it against the baseline of team performance in a player’s absence.

Economist Dan Rosenbaum took the plus-minus concept even further. He built upon a player-rating method that had been created behind closed doors for Mark Cuban’s Dallas Mavericks and carried all the advantages of basic plus-minus while controlling for the quality of a player’s teammates and opponents. Rosenbaum tweaked the Mavericks’ algorithm until a new uber-metric was born: Adjusted Plus/Minus. And in Rosenbaum’s APM model, which he debuted after the 2003-04 season, Garnett was the undisputed king of the NBA.

COMPONENT RATINGS PLAYER BOX SCORE LINEUP ADJUSTED PLUS/MINUS 1 Kevin Garnett +15.3 +19.3 +16.2 2 Tracy McGrady +15.8 +8.6 +12.6 3 Andrei Kirilenko +13.2 +11.1 +12.3 4 Tim Duncan +12.3 +10.3 +12.1 5 Shaquille O’Neal +12.4 +9.9 +11.8 6 Kobe Bryant +12.6 +4.3 +10.0 7 Dirk Nowitzki +9.5 +10.6 +9.9 8 Ray Allen +8.0 +9.0 +8.5 9 Baron Davis +7.7 +7.6 +7.6 10 Vince Carter +5.6 +11.1 +7.6 The original kings of Adjusted Plus/Minus The original APM, which debuted in 2004, combined lineup- and box-score-based data from the 2002-03 and 2003-04 seasons. Source: 82games.com

Garnett’s talent wasn’t exactly a well-kept secret, observable only via analytics. He was selected fifth overall in the 1995 draft and played well enough in a conventional sense to command that record-breaking contract after only two years in the league. And at the time that APM arrived on the scene, Garnett had just put the finishing touches on an MVP season. But what stuck out in the analytics was how little room for debate there was about KG’s supremacy. Tracy McGrady, who with Garnett led the league in Hollinger’s player efficiency rating (a simpler, box-score-based sabermetric stat) for the 2002-03 and 2003-04 seasons, was a very distant second in APM; the difference between No. 1 Garnett and No. 2 McGrady was roughly the same as the difference between T-Mac and No. 8 Ray Allen. The previous year’s MVP, Tim Duncan, was in fourth place, ahead of Shaquille O’Neal and Kobe Bryant. (Our old friend Andrei Kirilenko was third.) By the numbers, Garnett’s claim to the top slot was practically unquestionable.

The reasons for KG’s plus-minus dominance were varied, and each worked in concert with the other.

First, at his peak he had the game’s most versatile stat line — combining 21.3 points, 12.3 rebounds and 5.0 assists per 36 minutes — and Rosenbaum’s research showed that a player’s versatility tracks surprisingly well with his on-court influence.

Second, Garnett dominated the defensive side of the ball, with Minnesota allowing a staggering 7.3 fewer points per 100 possessions when KG was on the floor than when he was not. (In 2016 terms, that’s as good as Tim Duncan and Andre Iguodala put together.) Defense is half the game, but it wasn’t always treated as such in the past, when the only statistical traces it left were steals, blocks and defensive rebounds.

Finally, Garnett’s face-up game and mid-range shooting skills helped spread opposing defenses, giving his teammates more room to operate (not that they always used it well). Rosenbaum found that players who stretch the floor tend to have better APMs, even after controlling for their scoring volume and efficiency.

All of these distinctions came in areas that weren’t fully understood without advanced metrics and presaged today’s era of do-everything stretch bigs. Even though the modern era downplays the importance of midrange shooting, Garnett was always one of the few players — along with guys like Chris Paul and Dirk Nowitzki — who could actually do it well enough to make it worthwhile. Special players like that were and still often are the cornerstones of championship-caliber offenses.

Garnett also exemplified the way advanced metrics were reshaping our definition of player “value.” Unlike previous statistical indicators (such as PER) that purported to measure value but involved subjective tinkering around with weights assigned to various box-score numbers, APM was rooted in a more essential definition of the word. Because it explicitly measured how much a player helped his team win on the scoreboard while he was on the floor, it spoke directly to old arguments about how much blame a great player deserves when his team isn’t great. Garnett himself had to endure such debates when the Timberwolves missed the playoffs in 2005, despite his ongoing statistical dominance.

Because KG produced such brilliant numbers and was surrounded by such middling talent in Minnesota for most of his prime, he inadvertently helped expand the NBA’s criteria for greatness. Analytics needed to go beyond ring-counting and team records as proxies for individual performance. Garnett’s prime provided one of the earliest testing grounds for a more nuanced way of assessing players and their legacies.

Garnett entered the league before analytics had much of a footprint in the game and leaves it in an entirely different state, one inundated with numbers. However indirectly, it was a transformation Garnett helped play a role in.