Since James Naismith first nailed a peach basket to the wall of a gym in Springfield, Massachusetts during the winter of 1891, passing has been an integral part of basketball. Youth-league coaches nationwide have told players the ball moves faster than opposing players since time immemorial, and the best way to break down a good defense is to pass crisply and quickly.

Setting up other players with scoring opportunities is a critical part of a smooth offense, and some players like Rajon Rondo and John Stockton made a living on assisting. Tantalizing dime-droppers like Magic Johnson and Jason Williams even made passing cool.

Teams like the ‘86 Boston Celtics and the ‘14 San Antonio Spurs, both of whom hoisted the Larry O’Brien Trophy at season’s end, stand out as units that moved the ball so well that their offensive attack was elevated to an elite level. With the rock zipping around, all five players on the floor became threats to score in open space. The open shot, almost regardless of where you get it, is the best shot in the game, and crisp passing creates these uncontested attempts. That’s Basketball 101 (well, maybe 201, after you master terminology).

If ball movement means good offense, then it follows that passing to teammates is the best way to achieve that. And thus, more passing is better, right?

That’s an elementary science fair-level logical progression, but we do know watching the ball hum around the perimeter and find the open man is basketball in its most intoxicating form.  Saying ball movement is a critical component to elite offense is hardly revolutionary, especially in the space-and-pace era where passing has become a premium skill for players at all positions.

Rather, asking the right questions about ball movement is important: Do good teams pass the ball more? Or are they more productive with the passes they do make?

My hypothesis is good teams will have a higher percentage and number of passes “for profit,” but not necessarily a higher raw number of passes. Moving the ball a lot in a possession isn’t necessarily going to amount to a better attack, but the higher the assist or secondary assist rate, the greater the scoring potency.

Methodology

Although the assist has been tracked as far back at the 1946-47 season in the Basketball Association of America, the ability to track the number of passes (and stats like secondary assists, or passes that lead to assists) is a relatively new addition to the game, made possible by the installation of SportVu cameras in NBA arenas during the 2013-14 season. Since then, statisticians have acquired a deluge of data pertaining to player movement, including the number of passes thrown, with the intent of increasing the analytical understanding of the game.

Using the numbers provided by NBA.com (obtained with the aid of those SportVu cameras), I  compiled a database that contained stats from the 2013-14 to 2016-17 regular seasons (four years). It features some straightforward statistics like assists, offensive rating, wins, pace and more, and adds to that passes and secondary assists. To normalize the numbers (and to make them comparable between teams and seasons), I calculated against each team’s pace to determine assists, points, passes, assists and secondary assists per 100 possessions.

With that data, I created a metric called “Quality Passes” (QP), which are passes that lead to points, by adding assists and secondary assists per 100 possessions. The quality passes number was then laid against the number of passes each team threw to get a quotient number of “Quality Pass Percentage” (QP%)—how often passes made lead to a bucket.

To visualize the data in the most digestible way, the charts contained in the data section are quality passes per 100 possessions, quality pass percentage and passes per 100 graphed against wins and offensive rating. Wins are the ultimate measure of efficacy in sports and thus the best measuring stick for statistics. But the question is not “Does X make a team win?” so much as “Do winning teams do X?”, which is to say “Do winning teams make more quality passes/a higher percentage of quality passes/more passes in general?”

Data

Results

The control data (passes per 100 compared to both wins and offensive rating) seems to corroborate that good teams don’t necessarily pass more often (the r-squared score on the wins chart is effectively zero, showing virtually no correlation between the two). And the distribution is all over the place (a good thing for control groups), with the better teams even tending to cluster on the lower end of number of passes thrown compared to offensive rating.

In the test groups, however,  a strong correlation between winning teams (i.e. “good”) and profitable passing is present. One solitary fact gives credence to the validity of this data: The teams we generally regard as the best in the league, namely the San Antonio Spurs and Golden State Warriors, are in the top-right corner of nearly all the test charts, with the past three Warriors squads nearly creating their own set of outliers on the QP Per 100 v. Offensive Rating chart. While that might seem like more of an “eye test” hot take, those two teams won 503 of their combined 656 contests during the four-year span this study encompasses—a winning percentage of 0.767.

Both organizations, from head men Gregg Popovich and Steve Kerr on down, have practiced and preached the concept of team basketball even with star players on the rosters. And perhaps the largest revelation taken indirectly from this data comes from the idea that getting players to buy into a “team first” system will generate wins.

But it does bring up the earlier question of causality: Are the Spurs and Warriors successful because of their ability to move the basketball profitably? Or have they both crafted amalgamations of talented players who are simply better than the opposition, allowing the passing to come from an area of trust outside of scheme?  Unfortunately, math can’t answer every question, but we can see a positive correlation between ball movement and on-court success does exist, even if the cause of such success is still evasive.

In fact, the top end of each chart is laden with the best teams from each regular season. The 60-win ‘15 Atlanta Hawks, the Western Conference finalist ‘15 Houston Rockets and the ‘14 Los Angeles Clippers (the best Chris Paul-led Clippers squad) all showed well. These units were all thought as championship-caliber ones during their respective regular seasons and eventually lost out to one of the other teams in the same grouping (all four champions are in the upper-right quadrant of the test charts).

And the inverse is also true.

The historically putrid 10-win ‘16 Philadelphia 76ers, the ‘17 Brooklyn Nets and the “Kobe Bryant’s Farewell Tour” ‘16 Los Angeles Lakers are all featured prominently in the lower left of each graph. Each of these teams had two notable features: A) a dearth of talent and B) some prominent ball-stoppers (Ish Smith and Jahlil Okafor for Philly, Jeremy Lin and Brook Lopez for Brooklyn and the Kobe/D’Angelo Russell pairing in Los Angeles) who held up the flow of the game.

But having an offensive ball-stopper isn’t a surefire limitation to offensive effectiveness, and that is another area the data neglects. Last year’s Oklahoma City Thunder managed to scrape together 47 wins and a middle-of-the-pack offense on nearly all the test charts while featuring the all-time single-season leader in usage percentage, Russell Westbrook. Theoretically, a team with such a singular focus would rank very low in the passing statistics, but this isn’t the case. While showcasing a large portion of winning teams on the upper echelons, the data set isn’t a picture-perfect representation of how different offenses got the job done.

No groundbreaking data should be mined from all this. However, having baseline data to confirm what we think is important. Seeing actual numbers that shows a positive correlation between moving the ball profitably and winning games is an interesting place to start the argument for basketball driven by team concepts.

As the SportVu data becomes more prevalent and plentiful, studies will be conducted on areas of the game that have been neglected by traditional statistical measures. Whether you are an “eye test” hardliner (somehow I doubt it, since you’re reading an article on a website called NBA Math) or an ardent stat-head, the growth and importance of this data once viewed as extemporaneous is hard to ignore. The game is getting smarter, and teams are harnessing whatever analytics they can get their hands on to try getting a leg up.

Check out the numbers for yourself. Find your favorite team and see how they stack up over the last four seasons. And then take to Twitter to tell me why I’m wrong.     

Follow Alex on Twitter @AlexWestNBA.

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Unless otherwise indicated, all stats are from NBA Math, Basketball-Reference or NBA.com and are accurate heading into games on December 31.