TrueHoop: stat geekery

Basketball enters the space age

March, 3, 2012
3/03/12
4:32
PM ET
By Jared Dubin/Hardwood Paroxysm
ESPN.com
Archive
BOSTON -- Where does the future of basketball analytics lie? After spending some time at the MIT Sloan Sports Analytics Conference in Boston, it has become increasingly clear that "where" is the operative word.

More and more, researchers are trying to identify, quantify and analyze the location of where NBA players operate on the court. The two research paper finalists at Sloan, "Deconstructing the Rebound with Optical Tracking Data" and "Court Vision: New Visual and Spatial Analytics for the NBA," addressed this new, exciting area of basketball analytics.

Deconstructing the Rebound with Optical Tracking Data “leverages STATS’ SportVu Optical Tracking data to deconstruct previously hidden aspects of rebounding.” Using SportVu’s technology, researchers Rajiv Maheswaran, Yu-Han Chang, Aaron Henehan and Samantha Danesis attempted to find the relationships between rebound location, shot location and offensive rebounding rates.

The chart and graphics below reveal some interesting data related to the probability of whether a rebound will be offensive or defensive depending upon how far from the basket that rebound is collected.

According to their research, rebounds collected within two feet of the basket have a 40 percent chance of being an offensive rebound. The percentage chance that the rebound will be offensive drops down to 22 percent between 2 and 10 feet from the basket. Once the ball moves farther outside that range, however, the chance that the rebound will be offensive starts to rise back up, passing the 40 percent plateau again when the ball gets 22 to 26 feet from the basket upon being officially rebounded. As detailed by the researchers, this generally aligns with the expectation that most offensive rebounds are grabbed very close to the hoop (such as tip-ins) or are long rebounds.


Interestingly, there was also a split in the data depending on which side of the court the ball landed on. As you can see in the graphic on the above, the location where a rebound has the highest percentage of being an offensive rebound is between 22 and 24 feet on the offense’s right side of the court. Rebounds in this range on this side of the court have a 49.2 percent chance of being an offensive rebound. In the same distance range on the opposite side of the court, rebounds only have a 31.0 percent chance of being grabbed by the offense.

However, this doesn't mean that players should just stand between 22 and 24 feet away from the hoop on the offense’s right side and just hope a rebound falls into their hands. The research discovered that there was another, possibly more important factor in determining how and where a rebound would land: the location of the actual field goal attempt. The chart below is similar to the one above; only this time it charts percentage of offensive rebounds by location of the shot attempt.

Predictably, shots extremely close to the basket, those from two feet and closer, stood the highest percentage chance of being turned into an offensive rebound. That percentage mostly declined the farther away from the basket the shot attempt came from, but it took a jump back up once the shot location passed the 3-point line. According to the research paper:

“We note that there is a “U”-like affect [sic] when looking at offensive rebound rates as a function of shot distance. This is very similar to the effective field goal percentage as a function of shot distance. This result implies that mid-range shots are even worse than previously characterized due to their effects on offensive rebound rates. Strategically, teams have even more reason to eschew mid-range shots for shots closer to the basket or three-pointers.”

This research paper’s contention that shot location was an extremely important factor in the probability of grabbing an offensive rebound dovetails nicely with the crux of the Court Vision: New Visual and Spatial Analytics for the NBA research paper. Researcher Kirk Goldsberry is at the forefront of a movement to identify which NBA players are the most effective shooters from specific places on the floor, as well as those who are the most effective shooters from the highest quantity of locations on the floor. While some Web sites and metrics track shot location by simple distance from the basket, Goldsberry’s study tracks the exact location of the attempt by using the spatially identified {x, y} coordinates from which the attempt came.

Goldsberry contends that field goal percentage (FG%) is not the best measure of who the best shooters in the NBA are. This is not really all that surprising a contention. Tyson Chandler leads the NBA in FG%, but you probably couldn’t find anyone who thinks he’s the best shooter in the NBA. Big men, and centers in particular, take a much higher percentage of their shots from closer to the basket, so their FG% is likely to be higher than a guard or wing player who shoots from all over the court. Close shots are easy to make. By mapping over 700,000 field goal attempts for every NBA game played between 2006 and 2011, Goldsberry was able to quantify shooting range in a different way.

By dividing the most common shot locations into 1,284 “cells,” Goldsberry created a metric called "Spread%." Spread% is a measure of how many of those 1,284 cells a player has attempted at least one shot from. This, of course, helps explain why no one would consider Chandler the best shooter in the league. His Spread% is much lower than someone like Ray Allen, who takes shots from many more locations on the floor. Allen’s shots typically come from locations where the shots are considerably harder to make, and, if he’s behind the 3-point line, worth an extra point. To illustrate this disparity, Goldsberry did a graphical comparison of the Spread% for Allen and Al Jefferson.

Using that Spread% data, Goldsberry went even deeper and created another metric called "Range%." Range% is the percentage of locations on those 1,284 cells where a player averages more than one point per attempt (PPA). The leaders in this metric were an unsurprising mix of guards, wings and a forward. Steve Nash led the way with a 31.6 Range%, followed closely by Ray Allen at 30.1%, Kobe Bryant at 29.8% and Dirk Nowitzki at 29.0%.

By quantifying how many locations on the court a player is an effective shooter from and just how effective he is from those locations, these measures of shooting prowess can give us a better idea of who the best shooters in the NBA really are.

While these metrics are new, exciting and on the cutting edge of basketball analytics, they are barely scratching the surface of what we may eventually be able to quantify and evaluate through spatial analysis. How much ground does Dwight Howard cover when he defends a pick-and-roll? By how much does LeBron James’ FG% drop (or rise) when he shoots a fade-away rather than going straight up? Which NBA teams have the best and most efficient floor spacing on offense? These are all things that researchers, analysts, writers, general managers, coaches and fans are going to be able to track sometime in the near future.

Jared Dubin is a writer for Hardwood Paroxysm, part of the TrueHoop Network. Follow him on Twitter (@JADubin5).

Language and concepts

March, 3, 2012
3/03/12
3:44
PM ET
By Ian Levy/The Two Man Game
ESPN.com
BOSTON -- The MIT Sloan Sports Analytics Conference means different things to different people. For some this is an incredible opportunity to learn more about sports, the events and actions that our eyes miss, and those our brains misunderstand. For others it draws images of torturous high school calculus classes and of unwanted information, slowly leaching the fun from sports.

Shrinking the space between the analytics community and the "average fan" has been a thread through this entire event. Multiple panels and presentations have found themselves circling the idea of why that gap is closing, the rate at which it’s closing, how to speed up the process and whether it’s possible to close the gap completely.

In the "Box Score Rebooted Panel," Bill James coalesced the mission of the analytics community as reducing a mountain of data to the simplest possible concepts. The exact degree of that simplicity varies with the concept, but it’s a beautiful idea. Simplicity of concept in statistical analysis would seem to create the most useful assessments and allow for the most effective communication of those results.

For the most part the themes with a basketball slant presented at Sloan -- "fit," "space," "physical performance," "pressure," "chemistry" -- are foundational enough for even the most casual fan to interact with. However, the vehicles for discussion used here at Sloan are largely inaccessible and decidedly unpalatable for large swaths of sports fans.

My day job is teaching first grade, and this year we’ve been working with an incredible math consultant, re-thinking everything about our instruction. One of the ideas that drives this effort is "language containers." The premise is that human language has evolved into a means of capturing ideas by providing containers in which to place a concept. To fully and completely master a concept you must understand all the language that defines and informs that idea.

For example, to be entirely fluent and comfortable with the concept of subtraction your language container must include: "take-away," "less than," "smaller than," "fewer," "deficit," "debit," etc.

To understand the concept of usage rate, you have to build a language container that includes the definition of a possession. The concept of a possession, in turn, requires its own container, built on the understanding of the ways a possession can be used. This endless stacking of knowledge upon knowledge can’t be fully completed until each previous layer is filled in entirely. Language containers have their place with every concept, and basketball analytics is no exception.

I work with statistics quite a bit in my basketball writing. To me, they help bring order to an otherwise chaotic jumble of action. For all my self-taught statistical knowledge, there has been plenty in the research presentations I’ve viewed this weekend that went over my head -- in some cases way, way above my head.

I’m comfortable with many of the conclusions, but not being comfortable with the language of Voronoi Tessellations or the Z-Plane means to some degree I’m simply taking the presenter at his word. This is the same experience someone who hasn’t finished building a language container for possessions has when reading an article that uses Offensive or Defensive Rating.

The past two days, every corner of the Hynes Convention Center has been filled with conversations on sports. Those conversations bridge the same concepts as conversations held in bars, living rooms and backyards -- How does my team get better? Which players should my team pursue in free agency? Why can’t Player X stay healthy? But the conversations here use different words, terms and visual representations; a lexicon that has evolved separately, distinctly and quickly.

I refuse to believe that any sports fan doesn’t want to know more about the games he or she loves. The challenge then in disseminating data and analytic methods, and in obtaining acceptance for the results, is not a conceptual challenge, but a problem of language.

Ian Levy writes for The Two Man Game. Follow him on Twitter (@HickoryHigh).

Building the modern athlete

March, 3, 2012
3/03/12
2:46
PM ET
By Devin Kharpertian/Nets Are Scorching Tom Sunnergren/Philadunkia
ESPN.com
BOSTON -- "Performance Analytics," an MIT Sloan Sports Analytics Conference panel of John Brenkus, Mike McCann, Kevin Pritchard, Angela Ruggiero, Mark Verstegen, and moderator Peter Keating, says that while our understanding of the athletic body is fast-progressing, there are, and will continue to be, hurdles.

The issues, surprisingly, are more scientific than moral.

The central conflict comes between a player’s right to privacy and the team’s imperative to get as much actionable information about them as possible. Genetic testing that identifies predispositions to, say, ACL tears or inflammatory conditions, could help the team better identify risks and help the player avoid them. But it would also leave players at a disadvantage in contract negotiations and draft positioning.

Genetic testing also can create some red herrings. Brenkus pointed out that predisposition to a condition and actually having a condition are two different categories: Someone with a predisposition to ACL issues might have trained and strengthened their ligament to the point that the predisposition was mitigated or completely wiped out. In this case a monomaniacal focus on genetic markers can lead a team astray.

A large part of athletic performance of course is, and will continue to be, performance-enhancing drugs. McCann said the regulation of such compounds -- steroids, HGH, etc. -- is wrongheaded: There isn’t a relationship, or at least isn’t one that’s nearly strong enough, between the risks of specific PEDs and whether they’re allowed in certain sports leagues. He posed a hypothetical: If we were to develop a steroid that carried no health risks, but just made people stronger, would we embrace it?

The line of the segment? When Pritchard, the Indiana Pacers' director of player personnel, was asked tongue-in-cheek to break the news of rampant steroid use in the NBA, he replied: “I like my job too much for that.”

While some performance-enhancing drugs get a certain ethereal status as "magic pills" in the sports world, all the panelists agreed on one in particular: sleep. Collectively, they emphasized the need for a full night's sleep to maximize recovery; your body builds its muscle in the times between your workout regimen, and the best way to let your body rest is, quite simply, to rest.

To stress the importance of sleep, Brenkus cited one study in which one set of mice was denied food, and another set was denied sleep. Each group of mice died almost simultaneously.

The most exciting frontier of athletic performance enhancement though, the panel agreed, is the brain. Verstegen spoke to what he calls “training above the neck.” Verstegen said that in order to excel in any sport you need a lot of reps—specifically about 10,000 hours worth—but the body breaks down under the weight of this training load: it can’t support what the mind requires. The solution to this problem comes from an uncoupling of physical and cognitive training. Verstegen said brain-training programs are becoming increasingly common in professional athletics.

Devin Kharpertian is a writer for Nets Are Scorching, part of the TrueHoop Network. You can follow him on Twitter (@uuords). Tom Sunnergren is a writer for Philadunkia, part of the TrueHoop Network. You can follow them on Twitter (@Philadunkia).

Analytically Inclined Coach, Oxymoron?

March, 2, 2012
3/02/12
6:19
PM ET
By Dan Feldman/Piston Powered
ESPN.com
Archive
“I’m not smart enough to do the math.”

- Jeff Van Gundy

From an analytic expert's perspective, the ideal coach understands all the numbers. He gives minutes to the team’s most productive players in the ideal lineup combinations. He plays the odds, regardless of potential public backlash, every time.

From a player’s perspective, the ideal coach relates. He pushes the right buttons to inspire and motivate. He understands the unique challenges facing professional athletes.

The ideal coach, to both groups, also wins. In a perfect world, there are coaches who would satisfy both camps. But I’m not sure how many people have the technical prowess to use analytics properly and the charisma to get their players behind him.


Jeff Van Gundy has been the star of Day 1 of the Sloan Sports Analytics Conference. During his two panels, he had the crowd smiling and laughing. Daryl Morey, a co-chair of the event, joked that he needed to get Van Gundy on every panel. It’s easy to see why players would like him. I can’t imagine players happily spending hours and hours during the season answering to some of the presenters of the conference’s more in-depth papers.

This is not to lump people exclusively into the nerd-or-macho paradigm. All the paper presenters I saw spoke eloquently. And many who’ve spent time playing in the NBA -- often a perquisite for coaching -- are very smart. But spending the hours necessary to excel in statistical analysis or playing basketball rarely leaves enough time to thrive in the other field. Leaving this merged task up to a single person is asking coaches to fail.

As Van Gundy explained after the panel, he didn’t run numbers when he coached the Rockets. He just got them from Morey -- using them when they fit his message, and when they didn’t, lying to his players about the numbers. To Van Gundy, statistics served his means rather than informing his ends. Even though Van Gundy didn’t completely trust the relevance of all Morey’s numbers, their relationship might illustrate the framework of the ideal setup, where numbers, from their origin, are passed down and simplified and passed down and simplified until they reach the coach.

But even that scenario contains a number of potential challenges. The chain must contain trust at every link, and the longer the chain, the more opportunities for a breakdown. Even when a coach completely trusts the analyst, the data can be dangerous in the wrong hands.

Once, Van Gundy asked Morey for the analytical answer to how many minutes Yao Ming should play. Morey determined, statistically, Yao’s ideal minutes per game was 48. No matter how tired Yao got, he was better than the alternative. Mindful of other factors -- such as Yao’s long-term health -- the Rockets obviously didn’t act on that finding.

Detroit Lions defensive end Lawrence Jackson said he spends time on his own analyzing plays, looking for the big trends, the offensive lineman who put their foot back on 90 percent of pass plays from a given formation. But what about the lineman who puts his foot back 54 percent of the time? Areas like that are where a more-trained statistician could flesh out which stats are significant and, ideally, improve a team's chances of winning.

Someday, will that statistician be Jackson’s coach? Would that make a team more likely to win?

By the way, Jeff Van Gundy enrolled at Yale out of high school. Whether or not he was being slightly self-aggrandizing in that lead quote, basketball statistics have reached such a level of dizzying complexity that they sail above the heads of Ivy Leaguers.

Professional basketball requires leaders who can deliver the message in a way players respond to, but it also requires leaders who can use numbers to determine the right message. Even a coach could tell you finding someone who satisfies both traits isn’t statistically likely.

Dan Feldman writes for Piston Powered. Follow him on Twitter (@PistonPowered).

Making fun of geeks

March, 2, 2012
3/02/12
5:18
PM ET
Strauss By Ethan Sherwood Strauss
ESPN.com
Archive

You, me, Synergy, the Big 2 and the Big 3

March, 2, 2012
3/02/12
4:26
PM ET
By Ian Levy/Two Man Game
ESPN.com
Archive
NBA roster building is driven by a hybrid of financial concerns, talent evaluation and the almighty "fit." Each team weighs those factors according to its own equation when adding, subtracting and trading players. Looking around the league, you’ll see an incredible number of variations.

Although the combined talent of LeBron James and Dwyane Wade was celebrated last summer, there were plenty of questions about the apparent duplication of skill. On the other end of the spectrum, you’ll find the NBA draft lottery, littered with the remnants of teams that were handicapped by overpaying for less talented players who appeared to fit what was already in place.



Conventional wisdom has a powerful hold on the idea of player fit. You don’t want two players with the same focused skill set on the floor. Duplication is destruction, unless the talent is enough to overwhelm it. Powerful low-post scorers should always be surrounded by strong outside shooters. These ideas, repeated until they reach immutability, form some of the most basic foundations of team construction.

Today at the MIT Sloan Sports Analytics Conference, two different research papers took a swing at turning the idea of fit and complementary skills into measurable data.

Robert Ayers presented a research paper, Big 2’s and Big 3’s: Analyzing How A Team’s Best Players Complement Each Other. Using statistical profiles, he classified players into categories such as high-scoring, dynamic guards; high-scoring, high-rebounding centers; versatile, 3-point shooting wings, etc. He then looked at the effect different two- and three-player combinations of those categories had on a team’s wins.

Ayers found the two-player combination that had the greatest positive effect on wins was a versatile, 3-point shooting wing with a high-scoring, high-rebounding center. Throw in a high-scoring, high-usage point guard and you have the most effective three-player combination.

In all, four combinations of three different categories had statistically significant positive impacts on a team’s performance. Of those four combinations, three featured a versatile, 3-point shooting wing -- think Paul Pierce. Now take a moment and try to count how many players in the league fit that mold. For this group, we’d be talking about players clustered around a per-game stat line of roughly 16-4-4, shooting about 37 percent from the field. Only nine players in the league this season are approaching that stat line, and only four -- James Harden, LeBron James, Kevin Durant, Luol Deng -- are wings. That particular player type fits very well with many other different combinations of players, but finding one that’s suitably talented to help lead a team is exceedingly rare. Scarcity is often the limiting factor in achieving fit.

A second paper, NBA Chemistry: Positive and Negative Synergies in Basketball, by Philip Maymin, Allan Maymin and Eugene Shen, tackled the same issue with a slightly different statistical strategy. Instead of characterizing players and looking at past results to see how they fit, their method creates skill ratings and projects how players with those different skills would work together. This paper has been circulating for a while, and Kevin Arnovitz wrote about it a few months ago.

The knockout punch in their work is the idea that trading Chris Paul for Deron Williams at the end of 2009-10 would have made both the Hornets and Jazz better teams. Their data points to Paul’s ability to create more turnovers as a boon for the Jazz. In New Orleans, Williams would have had to share the ball less and would have been able to exploit his individual scoring skills. His tenure in New Jersey puts that last assertion in doubt, but the idea is intriguing nonetheless.

The predictive value of their system allowed them to identify some 220 trades around the league that would have been mutually beneficial, essentially identifying how inefficiently player skills are distributed among the 30 NBA teams.

Both statistical models give a soft shove, pushing "fit" toward a home for both objective and subjective analysis. The steady march of basketball analytics continues -- from which players are successful, to which combinations of players are successful, and now to how and why those combinations succeed.

Ian Levy writes for The Two Man Game. Follow him on Twitter (@HickoryHigh).

The science of sleep

March, 2, 2012
3/02/12
3:02
PM ET
By Tom Sunnergren/Philadunkia
ESPN.com
Archive
“For me, sleeping well could mean the difference between putting up 30 points and living with 15.”

-Steve Nash

Good sleep is crucial.

An NBA blogger on assignment in Boston who tossed and turned on his cousin’s air mattress the night before and so got barely a wink of it can tell you that. So can Stephan Fabregas.

“Scientifically speaking, a single night without sleep is the equivalent to being legally drunk, and if you’re getting four or five hours a night, every night”—Fabregas, who seems really well rested, demonstrates a sharp downward slope with his hand—“your performance goes like that.”

Fabregas is a research scientist with ZEO—a company that’s developed a headband device users wear in the comfort of their own bed that measures sleep architecture, analyzes it, and translates it to a simple sleep score. He and ZEO are of the mind that improved sleep quality can significantly improve physical and cognitive performance—to say nothing of general well being—and so has broad application in the wide world of sports.

The science has their back.

Sleep, or a lack of it, profoundly depresses mood, memory, strength, speed, muscle tissue repair, immune function, and a laundry list of other systems. And, professional athletes, he added, are often more affected than the general population by sleep disorders.

“They’re traveling from city to city, so they have no regular sleep schedule. They have no regular performance schedule either: sometimes they’re asked to perform at 2 o’clock in the afternoon, sometimes they’re asked to perform at night. On the other end of that, after a game stops they’re up partying all night and then they have to get up in the morning.”

In addition to the lifestyle factors that cause sleep issues in athletes, their superb conditioning can, counterintuitively, affect their sleep as well. Body mass index is, independent of body fat ratio, a contributor to sleep apnea. In other words, the more muscle an athlete carries—we’re looking at you here LeBron—the more likely they are to suffer sleep disordered breathing and the many complications it triggers.

This isn’t a fact the sporting world is ignorant to. But despite the reality that improving athlete’s sleep often requires just a few simple fixes (cutting caffeine, lowering bedroom temperature, etc.) diagnosing the problem is tricky: getting a good measure of sleep quality usually requires an overnight stay in a sleep lab for a polysomnography—and so often goes undone. Enter ZEO.

“Every time I have this conversation with a player or a coach, when I say, ‘Look, here’s how important sleep is to what you’re doing and here’s how we can help,’ they say, ‘I get it. I get it. How much does it cost?”

Jarrod Shoemaker, a US triathlete, measures sleep quality fanatically. He claims to have gotten his highest sleep score the night before he won the US National Championship.This comes as no surprise to Fabregas.

“Every time I watch a game and a player makes a mistake, everybody goes, ‘What was he thinking about?’” said the scientist. “I’m just wondering how much sleep he got last night.”

Tom Sunnergren writes for Philadunkia. Follow them on Twitter (@Philadunkia).

The NBA under pressure

March, 2, 2012
3/02/12
2:54
PM ET
By Nick Flynt/ClipperBlog
ESPN.com
Archive


Nick Flynt is a writer for ClipperBlog, part of the True Hoop Network. You can follow Nick on Twitter at @NickFlynt.

Everything you know about basketball is wrong

March, 2, 2012
3/02/12
12:31
PM ET
By Jim Cavan / KnickerBlogger & Dan Nowell / Magic Basketball
ESPN.com
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.

  • 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.













Jared Dubin is a writer for Hardwood Paroxysm, part of the True Hoop Network. You can follow Jared on Twitter at @JADubin5.

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