Everything you know about basketball is wrong

Since 2010, the Sloan Sports Analytics Conference (SSAC) has exploded in size, having grown from 1,000 attendees to somewhere in the neighborhood of 12 million (preliminary estimate based on absolutely no statistical analysis). Why? People want to hear the best, most convincing evidence for things often chalked up to "eye tests" and "gut feelings." Sometimes, however, you come across evidence that is somewhat counter-intuitive. It's in these contrarian moments when our love of sports -- and the NBA in particular -- is put to the test. Does conventional wisdom match up with the evidence experts are gathering? Are experts beginning to develop their own dogmas? This post -- updated regularly throughout the conference -- will be your quick guide to some of the new ideas we're hearing that could very well impact the future of fandom. And so, a few things you thought you knew:

  • Why you're wrong about postseason experience: Everybody knows that it's good to bring in veterans when you're ready to make a postseason run, right? That way, you'll have a salty old P.J. Brown to keep his head when all around are losing theirs. Or...maybe not. In Experience and Winning in the NBA, University of Oregon graduate James Tarlow argues that, while teams with more player postseason experience are more likely to make the Playoffs, it doesn’t necessarily translate into actual postseason wins. Winning in the postseason, Tarlow argues, has much more to do with team chemistry – specifically in terms of how long certain core players have been together – than simple experience does. In short, bring in all the "battle-tested" vets you want, but you'll probably lose to the young team with continuity.

  • TrueHoop at MIT Sloan Sports Analytics Conference

  • Why you're wrong about positions: Robert Ayers’ paper “Big 2s and Big 3s: Analyzing How a Team’s Best Players Complement Each Other” was a fascinating look at team construction based around an extensive re-categorizing of player positions. Ayers categorized players by statistical profile, sorting them into groups like “high-scoring, high-assist point guards” and “versatile, dynamic power forwards” in order to see what types of players fit the best together. While some of his encyclopedic research failed to rock the boat--hardly anybody is surprised to find that elite centers are valuable, or that sweet-shooting wing players are helpful--his methodology is another blow to the notion of traditional positons.

  • Why you're wrong about coaching (via @BeckleyMason): Another brain-twisting tidbit from Ayer’s fantastic paper: When it comes to coaching, most don't have that significant an impact on wins.... Unless your name is Gregg Popovich. In fact, you could make the argument -- as Ayer does -- that having a coach Pop patrolling the sidelines for your team is just as important as having a LeBron James-caliber player. From Ayer's paper: "As a brief aside, the coefficients measuring the impact of the coaches are instructive, and in general, align with perceptions. For instance, Gregg Popovich was found to be the most effective coach (+23.19), followed mostly by very respected coaches. Most of the coaches with negative coefficients aren’t likely to surprise most fans as well. Two cases which might challenge conventional wisdom are Mike Brown and Avery Johnson, who do very well in this analysis and are perhaps slightly underrated."

  • Why you’re wrong about home-court advantage: It’s long been assumed that being at home helps players in high-pressure situations, right? How many analysts have said “I wouldn’t want that guy on the line in a Game 7 away?” Our Jared Dubin (@JADubin) digs in:"One of the more interesting research papers of the first half of the first day was Effort vs. Concentration: The Asymmetric Impact of Pressure on NBA performance. Researchers Matthew Goldman and Justin Rao discovered that, contrary to what may be the popular belief, players at home tend to struggle more in clutch moments on "concentration tasks" like free throw shooting, while visiting players are rarely affected by such moments. Goldman and Rao hypothesized that because of the increased "self-focus" involved in shooting free throws in close and late situations and the lack of distractions like crowd noise, NBA players playing at home concentrate too much on making their free throws and fail more often than they normally would. However, they also found that home players tend to increase their offensive rebounding rate in clutch moments, something Goldman and Rao attributed to offensive rebounding being an "effort task." Effort tasks don't involve the kind of "self-focus" that concentration tasks do, and home players can often be spurred on to give greater effort by a rowdy crowd." So, in short, it seems like pressure and being at home don’t have as clean a correlation as we thought, and in some tasks, being on the road is actually preferable. Do the advantages offset? That is—would you rather hit the clutch free throws or grab the clutch rebounds? Hard to say. Which is a nice little segue into…

  • Why everybody is wrong about crunch-time : There’s a nice little mini-debate going here at the conference about what constitutes crunch time, whether it actually exists, and how it affects players. During the basketball analytics panel, Jeff Van Gundy echoed many stats devotees when he said “The game’s as much a first quarter game as it is a fourth quarter game.” In essence, he’s channeling quant luminary and Sloan poster boy Daryl Morey, who notably said that good teams don’t win close games, they avoid them. But wait! As our Ian Levy (@hickoryhigh) notes, it may be more complex:“Analyst Mark Bashuk weighed the win probabilities at different times of NCAA games to determine whether any one part of the game was more valuable than the others. He found that win probabilities were more accurate predictors of the outcome when heavily weighted toward late-game scenarios—essentially, Bashuk argues that the end of a game matters more than the beginning. This is a fascinating argument, as it complicates recent statistically-driven arguments that “crunch time” doesn’t matter any more than the rest of games. Specifically, Bashuk found that the first 25 minutes should be weighed about 36% in predicting the outcome, while the last 15 minutes are worth 64%.”So on the one hand, analytically-minded folks think crunch time weighs no more than the rest of games; on the other hand, it seems twice as useful in predicting the outcome of games. When you add the fact that players’ response to pressure varies between home and road games, you have the rarest of all gems, the very reason science was invented: an issue about which nobody is definitely correct.

  • Why you're wrong about coaching priorities (via Ian Levy, @HickoryHigh): Most analytical systems and research in the past few years has shown coaches having something of a minimal impact. The Power of Belief in Sports' presentation, delivered by Peter Blanch, made a powerful, data-driven argument that belief in a positive outcome affects the likelihood of that outcome occurring. This would seem to emphasize the value of coaching -- but a particular kind of coaching, where decisions on offensive sets, rotations, and the like take a back seat to getting your team to believe in their individual ability to accomplish specific tasks and succeed as a group.

  • Why you're wrong about "intangibles" (via Mike Pina, @ShakyAnkles): It might've come from the Football Analytics panel, but former Head Coach Eric Mangini had a pretty interesting take on what is, at its core, a completely unquantifiable concept – at least right now. He calls it the “forced multiplier” effect. Basically, certain players can be expected to make others around them better or worse, based on purely on their high or low character. Mangini cites Ray Lewis and Tim Tebow as examples of players whose "core characteristics" brings out tangible changes and improvements in the play of others. Perhaps it’s not the most groundbreaking of ideas, but there’s clearly a value to be had in front offices exploring this concept, and even trying to quantify it. Who knows, one day maybe NBA teams will be less interested in signing a high talent, low character player if this "forced multiplier" effect can be measured, playing as they do in a league with small squads more conducive to interpersonal dynamics analysis.

  • Why you're wrong about WAR and baseball metrics' superiority (via Tom Sunnergren, @Philadunkia): Bill James, in a line that dropped at least one jaw, told Bill Simmons in yesterday’s BS Report that he believes basketball analytics are “better” than baseball’s. After the podcast, James explained that his contention isn’t that we are further along towards a complete understanding of the sport of basketball than we are of baseball—he emphasized that this wasn’t the case at all—but that given basketball’s incredible comparative complexity (it’s five on five versus one on one, etc.) the strides that the sport’s statistical thinkers have made in a very narrow time frame are actually more impressive than what baseball's quants have managed. Eat it, WAR.

Keep checking up as we continue to catalog the human capacity for folly.

Follow Magic Basketball's Dan Nowell (@DMNowell) and KnickerBlogger's Jim Cavan (@JPCavan) on Twitter.