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If you were to look at a mainstream NBA box score today and compare it to one from 30 years ago, you'd be hard pressed to find many noteworthy differences in what is tracked. With the relatively recent addition of plus/minus excluded, most readers have been looking at the same exact postgame statistics since the day they were born.


As teams utilize more and more new methods to gather data from games, ranging from Synergy Sports Technology's video charting to STATS LLC's in-game camera tracking, savvy fans are left wondering when all the data will reach their fingertips, specifically in the instant post-game format they've grown so accustomed to consuming.

The "Box Score Rebooted" panel at MIT's Sloan Sports Analytics Conference this Saturday had a heavy baseball tilt, with ESPN's Dean Oliver being basketball's lone representative on the four person panel. Baseball has long been ahead of basketball in its statistical tracking, though much of that is due to the very different nature of the two games.

In contrast to baseball, where the static and isolated nature of the game produces seemingly endless amounts of useful raw data on relevant events, basketball is a dynamic and fluid game with 11 constantly moving parts – 10 players and the ball. This often makes it difficult to quantify certain basic events on the court, and even harder to properly assign responsibility among the players.

How does one distinguish the weighting of value between the passer and scorer on assisted field goals? When Jason Kidd makes an unchallenged pass to Dirk Nowitzki shooting a contested, fade-away, mid-range jumper, but Steve Nash gets in the lane, draws a double team, and kicks it out to a wide open Jared Dudley in the corner, are both of those passes providing equal value? How about if only Dirk's goes in - was Nash's worth nothing?

Indeed, Oliver pointed to proper division of credit and blame being among the largest challenges facing the advancement of statistics in basketball, and said he's actually spent some free time reading legal theory books trying to get a better handle on the issue.

While basketball will always face an uphill battle in untangling variables to apportion credit on the court, there is more potential for advancement in the area of collecting more reliable raw data in the near future, something that has plagued basketball's box score for some time.

With the exception of the notoriously subjective "assist," the conventional box score otherwise consists of largely objective facts, but there is a limit to how much more objective data can be extracted from the game beyond the basics, which is a large reason the box score remained stagnant for so long. Websites such as 82games.com, Hoopdata.com, and basketball-reference.com, among others, have made some modest advancements in pushing the limits of the objective data that can be gathered from the game in recent years, but there's only so much more that can be done in that fashion.

The real advancements to be made in the evolution of the box score and advanced basketball statistics will likely have to come from greater technology, such as the in-game camera tracking that's now installed in one third of NBA arenas. While this new technology provides a treasure trove of data that may be the key to better quantifying the box score's much-neglected defensive end of the floor, it once again goes back to the question of how to assign responsibility for events among players, to say nothing of the incredible difficulty of working with the immense amount of data coming from six cameras each snapping 25 frames per second.

For these reasons and others, most of the advancements in better quantifying the NBA are likely happening behind the walls of the most forward-thinking front offices in the league. Some teams already have employees hand-charting every single game in the NBA, recording various statistics not publicly tracked to try and get an edge on the competition. Others are likely going to work on the data coming from cameras, trying to measure how good players are at boxing out what players are the best at staying in front of their man on the defensive end.

Teams could already be creating their own defensive points per possession numbers for players based on their in-game charting, parsing up credit and blame for every play in the game based on who's involved and to what extent. They could also be solving our earlier conundrum about Nash and Kidd, crediting "assists" based on the expected value of the shot the pass creates rather than the inconsistent end result that's beyond the control of the passer.

Because teams have more of an incentive to utilize data to get an edge on their competition, a larger budget to develop solutions, and more confidence in using their own methodology to determine the division of credit and blame among players, the public is at a large disadvantage in seeing the most useful statistical advancements reach them. Still, the field of sports analytics is growing exponentially, and many of the advancements seen from teams will eventually make their way to the public in some form. In the mean time, there is still some that can be done with what is publicly available, and fans will just have to wait and see where the next innovation comes from.

This year’s Sloan Sports Analytics Conference featured a bevy of fascinating papers, panels and discussions, a good grip of them basketball-centric. Taken together, they paint a picture of an NBA stats landscape that had even Bill James, Godfather of Sabermetrics, suggesting the sport might soon rival baseball in its employment of advanced analytics.


But amidst the Matrix-like reams of data flowing forth from the analytics ether, an elephant in the room remains: If you can’t correlate the data to actual success, then what’s the point?

The question loomed large over a number of otherwise thought-provoking presentations; the silent specter of a science simultaneously shedding light upon -- and wilting in -- the shadow of its own singularity.

In From 5 to 13: Redefining the Positions in Basketball, the winning "Evolution of Sport" talk at the conference, Stanford senior Muthu Alagappan employs topological data analysis (It’s OK, neither do I) by way of 20 seasons of NBA statistics. What he winds up with are 13 distinct groupings of players, each encompassing its own specific skill sets. Alagappan contends that such categorization paints a much clearer picture for how teams should approach roster construction, rotation implementations and draft priorities.

By just about every conventional account, Tyson Chandler is a center. According to Alagappan’s research, however, he’s a “Rim Protecting Big.” Think Mike Conley is a point guard? Try “Ball-handling Defender.”

On its face, this all makes perfect sense: If data suggests that such categories more accurately reflect a player’s abilities, skills and statistics, clearly the time is nigh for teams to begin constructing their rosters accordingly. Because, presumably, doing so will translate into more wins.

Here’s the problem: Alagappan stops well short of drawing that conclusion. His one example only hinted at such a correlation: Team A (last year’s Dallas Mavericks) boasted an especially dynamic roster with no less than 10 of the 13 positions covered, while Team B (last year’s 17-win Timberwolves) was duplicative in numerous positions.

But that’s one season. To the extent that teams whose players are very good statistically are more likely to achieve postseason success, then of course your findings -- which draw from those same statistics -- will suggest their own efficacy. But do such tautologies really get closer to defining how NBA teams win?

Why not look at the last 30 or 40 seasons, and see if those same truths hold? If teams that boast dynamic rosters -- say where 10 or 11 of Alagappan’s 13 positions are covered -- do, in fact, win, then you’re on to something. As it was, Alagappan’s research shows no such data.

Alagappan talks of “objectively defining a player’s true position” (italics mine), without suggesting how such definitions might translate into team success. Unless you can show that changing the categories -- and filling as many of them as possible -- better sets your team up for success, what does it matter?

Or, to put it another way, what useful purpose would such a positional reconfiguration achieve? Rob Mahoney, writing at the New York Times’ Off the Dribble blog, tackles this point beautifully:

At its core, position is merely a reflection of a player’s role within the concept of a team. In that way, position is not necessarily indicative of how well a player plays … but how a player plays. To put it another way: a 3-point shooter is a 3-point shooter by way of attempts (which reflect utilization and role), not makes. James Jones and DeShawn Stevenson play similar roles, and in terms of positionality, the fact that one shoots 42 percent from beyond the arc and the other 26 percent is absolutely inconsequential.

Perhaps a GM will pick up on Alagappan’s modeling and actively look to construct a roster broad and dynamic in its skill sets. Perhaps that team will win multiple titles. Then again, the notion that last year’s Dallas Mavericks were really, really well-constructed is one which even stats agnostics seem quick to endorse.

Maybe certain teams are well-constructed because they win, and not vice versa. Maybe it’s much easier to pinpoint success in hindsight than it is to try and duplicate that hindsight into an actual strategy for team construction. After all, if every team employed Alagappan’s methods, there would inevitably remain teams like the 2010-11 Timberwolves that boast far too many players in specific positional groupings. In this scenario, there would still be just as many losses as wins, just as many sweet-shooting teams as brick-laying ones, and just as many dynamic rosters as those mired in redundancy.

That doesn’t mean certain statistics aren’t more useful than others; the days of per-game metrics guiding front office decisions are clearly waning. As such, the ideas and perspectives offered up by Alagappan and others are nothing short of ground-breaking in terms of the sheer data they impart.

But there’s a difference between changing what we know about the game, and what -- and how -- we think about it. Using advanced analytics can show what we know, but it’s in how they’re used -- contextually, strategically, often in the heat of a split second -- that can make the difference between winning and losing, between trophies and lotteries.

For as much as modern analytics gives us in the form of fascinating raw data, we’re still very much scratching the surface of how that data translates into wins. Which, after all, is what it’s all about, isn’t it? Perhaps one day we really will find ourselves fully immersed in a brave new sports world of medical, mathematical and scientific analytics, where the human body itself functions more as cog than cognition.

In the meantime, what we’re left with is the image of a splitting atom, without much of an idea of how we get that image to power our homes. Through research presented in forums like Sloan, we’re flush with information -- lots of it -- but information without a real vehicle, much less a GPS-guided road map to wins and championship. And that’s OK. Because it’s in that lag time -- the gap between information and actionable results -- that the art, the music, the poetry, indeed the chaos of sports is allowed to breath.

Instead of seeing them as the paint which coaches, front offices and franchises will use to compose the future of sports, we should instead see stats as the strengthening canvas -- the increasingly sturdy base without which you wind up with nothing but a mess on the floor -- where the game is the paint, and the players are, and remain always, the artists.

Winning cures all.

It sounds simple in theory, but you can’t realistically win all of the time. Not every franchise is the Lakers or Yankees. Most teams go through lulls while rebuilding on the fly.


There’s no clear-cut way to retool, though. If you don’t luck into a franchise-altering player, chances are you’ll be stuck in no man’s land for a while. In the meantime, smart organizations exercise proper damage control and keen fiscal management while trying to maintain the interest of the customer -- the fan.

But maybe there’s a way to withdraw the pressure to constantly succeed in the win column. Maybe there’s a way everyone has overlooked. Maybe this solution comes from a comedian, actor and game show host who doubles as a minority owner of the MLS’ Seattle Sounders.

Though known more for his on-screen antics than his contributions to sports, Drew Carey stole the show during the “Franchises in Transition” panel at MIT Sloan Sports Analytics Conference with his brutal honesty, unconventional insight and sensitivity to the fans’ interests.

For Carey, the key to being a successful franchise is appealing to fans and keeping them engaged. They’re the paying customers and need to be satisfied.

The relationship between fans and teams is arguably the most intimate in all of business. It's eerily similar to marriage. Each party invests time and money, and expects the other side to put in as much effort to make the partnership dually enjoyable.

There’s an unstated agreement between fans and their teams’ owners. Fans buy tickets, concessions and apparel, while owners do everything within their power to put together a winning, entertaining and enjoyable team.

But owners don’t always keep their side of the agreement.

“Some owners don’t care if they win or not. They’re just in it to make money,” said Carey.

In 2008, Carey’s Sounders implemented a process in which the team’s fan-based association -- made up of season-ticket holders and non season-ticket holders who pay a $125 annual fee -- votes every four years on whether or not to retain the current general manager.

If they so choose, they can have the current GM fired, and then Carey and the rest of “official” ownership will conduct a search and hire a new GM (basically fans can fire but not hire). The first referendum is set to take place in November 2012.

It's unheard of to place a general manager’s fate into the hands of fans. It's an undeniable risk that borders on insanity. The rest of the panel disagreed with the sentiment of giving that much power to the fans, but Carey affirmed his analysis.

The question is, is this a solution or a gimmick? Could an NBA franchise actually implement this system and not have near-catastrophic results? Do fans have enough credibility to influence decisions that directly involve millions of dollars?

Well, there are certainly a handful of franchises with questionable leadership in their front offices.

Maybe this process -- putting a GM's fate in the hands of the consumer -- would prevent complacent executives from playing it safe to keep their jobs. Maybe, with added pressure and scrutiny, we’d see maximum results. And maybe, if these GMs continue to underperform, teams could finally rid themselves of part of what’s holding their teams back from achieving success.

To simply blame the GM for every problem is nonsensical, though. In fact, the general manager isn't even always the person directly affecting the team’s performance (coaches, players and owners have just as much, if not more of an affect on the team).

Assigning blame to the general manager is always the easiest out. But giving fans the power to decide doesn’t guarantee anything. Want proof? Check out the results of the fan voting for recent All-Star Games (Allen Iverson, Vince Carter and Tracy McGrady have been voted in despite not producing anywhere near All-Star level).

Fans may vote to fire -- or vote to not fire -- a GM, possibly costing the franchise another four years of potential growth and development. Of course, the owner can offset this by simply firing the GM, but as history shows, many GMs end up overstaying their welcome for any number of reasons.

Translating professional soccer in North America to basketball is problematic, particularly on the balance sheet. Most MLS teams don’t have television deals and deal with a much smaller budget. It’s easier for them to take risks to try and mix things up, draw in fan interest and increase revenue.

In the NBA -- as well as NFL, MLB and, to a great extent the NHL -- teams don’t need to take unnecessary risks. They’ll make their profits through revenue streams that are traditionally reliable. No matter what, hardcore NBA fans will consume the product. Some fans may waver, but for the most part, they are loyal and forgiving.

Carey’s charismatic nature and out-of-the-box thinking opened up a topic for discussion that is begging to be addressed. Whether teams acknowledge it or not, there’s a social contract between the sides. They don’t have to honor it, but it’s the right thing to do.

What’s tough -- even impossible -- is the enforcement of that contract. Fans who aren’t pleased with their favorite team’s performance, or the owner’s decisions, can just walk away and stop watching.

But they never do. And owners know this.

Before Brandon Roy's knees degenerated and Greg Oden underwent his third microfracture surgery, the Portland Trail Blazers were the darlings of the NBA. With Roy, Oden, and LaMarcus Aldridge as its young core, Portland was a team built for a long and prosperous future. Portland ranked No. 1 in ESPN Insider's Future Power Rankings around the start of the 2009-2010 season.

How quickly could the Trail Blazers start winning big series deep into the postseason? Some argued in 2009 that they were too young and too inexperienced to win in the playoffs. And with Roy, Oden, and Aldridge in their early to mid-20s at that point in time, that claim seemed to conform to conventional wisdom. As the saying goes, you must fail before you can succeed.

But is that really true? Do teams with inexperience have to take their lumps before winning in the postseason?

According to James Tarlow of the University of Oregon, author of a study titled "Experience and Winning in the National Basketball Association," which he presented at the MIT Sloan Sports Analytics Conference, the answer is no.

Using a data set which consisted of 804 NBA seasons played by 30 teams between the 1979-1980 and 2008-2009 seasons, Tarlow concluded that two elements affect a team's ability to win playoff games: head coach postseason experience and team chemistry.

Coach postseason experience is defined as the number of postseason games coached as a head coach ... Chemistry then is defined as the number of years the five players playing the most minutes during the regular season have been on their current team with one another.

Tarlow also discovered that postseason player experience increase a team's ability to reach the playoffs but doesn't increase its ability to win playoff games.

First, the most common criticism is of the experience of younger teams and this study does not support this conclusion, regardless of whether their NBA experience or playoff experience is the top of discussion. Second, the number of years of experience a coach has in the NBA is an irrelevant figure. It is a coach's playoff experience, not the length of their NBA coaching career, which is relevant to winning in the postseason. Finally, it suggests that what should be assigned more attention is the value associated with keeping teammates together.

In the case of the Trail Blazers, with Aldridge, Roy, Travis Outlaw, Steve Blake, and Rudy Fernandez logging the most minutes during the regular season and playing in their first year together, while being led by a coach in Nate McMillan with some postseason experience, they lost in the first round of the 2009 NBA Playoffs against the Houston Rockets, a team coached by Rick Adelman -- someone who had an expansive playoff resume with the Trail Blazers and Sacramento Kings -- with Yao Ming, Luis Scola, Ron Artest, Shane Battier, and Aaron Brooks leading the way in minutes played and also playing in their first year together. In a series that was relatively close, could Adelman have been the difference based on the conclusions reached in Tarlow's paper?

Perhaps.

Over the next two seasons, Portland lost to the Phoenix Suns and Dallas Mavericks respectively in the first round of the playoffs. Based on Tarlow’s criteria, team chemistry probably worked in the Suns’ favor in 2010 while team chemistry and head coach postseason experience likely aided the Mavericks in 2011 as they began their quest for an NBA title they eventually won.

Certainly there were other reasons why the Trail Blazers lost three consecutive first-round series, like injuries and matchups. But, as Tarlow has suggested, inexperience likely wasn't one of them.

Dallas proved during their championship run last season that head coach postseason experience and team chemistry does matter.

Just ask the Miami Heat.

Putting it into practice
How do the contenders this season stack up using Tarlow’s criteria?

In this case, the Heat, Chicago Bulls, and Oklahoma City Thunder will be examined. Based on minutes played this season, five players are outlined for each team in that order. Listed in parentheses is the number of seasons those players have played with one another. The number of games stated in parentheses for each head coach is the amount they’ve coached in the postseason for their careers.

Chicago Bulls: Deng-Noah-Boozer-Rose-Brewer (2nd season), Thibodeau (16 games)

This is the Bulls’ second go-round with this group. Richard Hamilton, brought in during the offseason to replace Keith Bogans in the starting lineup at shooting guard, has been hobbled with injuries this season. For the sake of continuity, Chicago may be better off relying on Ronnie Brewer more.

Miami Heat: James-Bosh-Chalmers-Haslem-Wade (2nd season), Spoelstra (33 games)

Like the Bulls, this five-man unit is enjoying their second season together. The difference is that Udonis Haslem has been healthy during the regular season this year. Will improved synergy and Erik Spoelstra’s growing playoff coaching resume be enough for Miami to win a title?

Oklahoma City Thunder: Durant-Westbrook-Harden-Ibaka-Perkins (2nd season), Brooks (23 games)

After acquiring Kendrick Perkins at the trade deadline last season, the Thunder’s first full season with this quintuplet together has been a resounding success so far. With coaches like Gregg Popovich, George Karl, and Rick Carlisle in the Western Conference casting a shadow on Scott Brooks, Oklahoma City can only hope chemistry will trump all.

Looking ahead
Assuming both teams stay healthy heading into the playoffs (which is asking a lot given the truncated season), it appears that the Heat have a slight leg up against Chicago with Spoelstra at the helm since there’s no discernible difference in the chemistry makeup of both teams.

As for the Thunder, what may derail their hopes is the fact that teams like the San Antonio Spurs, Denver Nuggets and Dallas Mavericks are led by coaches oozing with postseason experience.

Taking Tarlow’s findings into account, consider the next few months an exercise in examination.

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On Jan. 7, 2011, the Pittsburgh Penguins announced that for the first time in his already legendary career, Sidney Crosby would miss an NHL game due to a concussion. Crosby had actually sustained two concussions: first on Jan. 1, then just four days later when he was driven headfirst into the boards during an 8-1 rout.

Hockey is a collision sport, but basketball can be plenty dangerous as well. Most recently Kobe Bryant suffered a broken nose and a mild concussion after being thwacked by Dwyane Wade in the All-Star Game. Just three days later, Bryant was cleared to play against the Minnesota Timberwolves.

According to author and researcher Chris Nowinski, who gave an Evolution of Sport presentation regarding concussions at the Sloan Sports Analytics Conference, Kobe’s quick comeback may have come too soon.

“I was very disappointed to see Kobe playing, even though he passed all the protocol put in place by the NBA,” says Nowinski, who is the Co-Director of Boston University's Center for the Study of Traumatic Encephalopathy, the world's foremost center for traumatic brain injury research.

The NBA’s policy is that players who have suffered a concussion must be asymptomatic for a full 24 hours before returning to the lineup. In order to get back on the court, Bryant had to first pass a number of physical and neurological tests. He did so and was thus cleared to play.

But to Nowinski, even strict policies like the NBA’s do not address the inescapable realities of brain trauma. Simply put: we know too little about how concussions affect the brain to employ a one-size-fits-all policy. To him, the term "concussion" is as unspecific as calling someone’s torn ACL a leg injury.

“People have to remember that the science, the technology is not that strong for determining when somebody’s actually safe to return,” explains Nowinski. “So even if you pass all the tests, there’s still a risk -- especially in that first week after the concussion -- a huge risk that you go back out there and get another one or just another hit to the head and it’s career ending.”

Though we know that multiple concussions can lead to physical and psychological issues later in life, it’s exceptionally difficult to pinpoint or predict an exact moment at which someone is absolutely safe to return from a concussive injury.

In his presentation, Nowinski argued that while it’s impossible to exactly predict when the brain’s metabolic function will return to normal following a concussion -- this is different than not showing the symptoms of a concussion -- it typically takes about 10 days.

Thankfully, Kobe Bryant has played symptom- and injury-free in the week since returning to the hardwood.

But what if he’d taken another serious knock to the head or absorbed another elbow under rim? Would playing a few games at this point in the season -- or any point -- be worth an increased risk of long-term damage?

We don't know that Kobe was at any increased of more serious brain injury because he came back so soon, but our lack of understanding of traumatic brain injuries argues for conservatism.

Certainly the league’s current concussion policy is an improvement over the recent past. As recently as last season, when Heat forward Mike Miller suffered concussions in three consecutive games, there was no league-wide policy regarding concussion treatment.

The new policy has been stringent enough to keep New Orleans Hornets forward Jason Smith out for more than a month after Smith sustained two blows to the head in a Feb. 4 contest with the Detroit Pistons. Smith returned to the game after his first injury, a decision Hornets coach Monty Williams now regrets.

“I'm kicking myself on putting him back in the second time," said Williams. “I'm not going to let that happen again. I don't care how many games we lose."

Though in-game testing would be ideal, it’s rare that a player will ask to come out if he can shake off the cobwebs enough to continue playing. After sustaining his injury Bryant continued to compete in the All-Star Game even though it was just an exhibition. Nowinski says the current gold standard of sideline tests take 20 minutes and even this method is “not yet validated.” It’s hard to imagine Bryant, Smith, or any other player self-reporting concussion symptoms in a game that matters.

Unlike hockey and football, we don’t consider basketball to be a game that demands an aggressive concussion policy. The ethic of “playing through it” and the warrior mentality of many professional athletes often triumph over long-term medical concerns. But each of us gets only one brain, and it takes only one concussion to change it for life.

Last November, right around the time when the NBA was revamping its concussion policy, Sidney Crosby saw NHL ice for the first time in more than 10 months. In his first game back, Crosby inspired by netted two goals and two assists. Less than three weeks later, the Penguins removed him from the lineup as his symptoms returned. He has yet to play another game this season.

It’s tragic to think that perhaps the greatest player in hockey may have seen his last professional competition at just 24 years old.

After a concussion, athletes are often desperate to return to competition. Few players, fans or coaches worry about them being too brave. But when it comes to concussions, the NBA, its teams and players can’t be too cautious.

Dwyane Wade knew he would be removed from the game before the computers did.

Before the three-millimeter silicon sticker that sat unobtrusively on the inside of his bottom lip gleaned from his saliva that his 1.020 hydration level was flirting with the bottom register of acceptability, that his glucose had dipped, that his basal catecholamine excretions had decreased, and that their aggregate effect suggested nine, 12, and five percent expected dips in his straight line speed, vertical leap, and visual reaction time, while raising CRP, fibrinogen, and white blood cell count to levels that increased his probability of sustaining a tendon strain by 0.04; before all this information was recorded, transmitted to the screens Rick from quality control stared at each night, and routed down to Coach Battier -- its arrival announced by a ‘bing’ of the iPad he wore around his wrist -- Wade sensed it.

So when he was taken out, he was neither surprised nor angry. It just happened.

The 40-year old sat on the bench, gulped the 12.4 fluid ounce solution prepared for him, and waited for it to work. As he’d been taught to do, he quieted his mind -- let it slack, dropping each piece of emotion and anxiety accumulated over his last eight minutes of floor time -- before re-engaging with the game. He closed his eyes and walked through scenarios. Then, with four minutes remaining, Battier tapped him on the shoulder. He was back in.

Despite having entered the game at something of a peak -- their collective win expectancy was 5 percent higher than their season mean, a fact that owed largely to a propitious number of players who registered optimal sleep scores over the previous week -- the Heat were losing to the Supersonics by six points. And despite Wade's unusual workload -- Kyle Lowry’s elevated injury markers forced the veteran into an expanded role, and demanded the absorption of 19 percent more force by his almost four-decade-old knees than his pregame target prescribed -- he had performed capably. 24 points on 14 shots. Nine rebounds against an expected total of 5.84. A wins produced differential of +0.073.

The game ebbed and flowed. Over the next three minutes, Wade’s readings stayed squarely in optimal range as Miami narrowed the lead to two. Then, as he still had a tendency to do, despite the humbling influence of old age, Wade became emboldened. With seven seconds left, he called his own number: a left-side screen at the top of the key. Wade, ball in hand, read the coverage, made a decision, and deked right before crashing left -- flush into a hedging Derrick Favors. He didn’t anticipate the seven-footer’s floor location, and so when he resorted to his only remaining option -- a fallaway 3-pointer with, he’d later learn, a .17 probability of success given his present bioindicators, adjusted career averages, and floor location -- the shot lacked the arc and lift it might have had a decade earlier. It clanked off the front of the rim and into the hands of Anthony Davis.

Game.

On the long flight home, while the other players gossiped and played cards, Wade sat alone and reflected on that final play. His performance had been, on balance, a good one, but the final sequence conveyed a lapse of discipline. His chance to complete a wonderful narrative, create an ethereal moment in which he was 25 again for just one night, ended with just another loss. He’d been selfish.

Wade slouched back in his seat. He slipped his biofeedback headphones over the electrode-equipped skullcap he favored on long flights, and listened to the playlist that had been tailored specifically to his typical postgame EEG readings. As the sounds and sensors gently calibrated his neuroelectricity, Wade pulled out a touch screen and reviewed his metrics. His emotional regulation, focus, and attention capacity, as measured by the skullcap, were fine. Better than that. While physically, he was on a downward trajectory, cognitively he was in his prime. As he often did, he'd made intelligent, snap decisions the entire game -- up until that final play.

A message popped onto his screen and alerted him that his program was ready.

Shortly after game's end, as he did at least 82 times a year, Rick converted the game tape to a VRML file, ran it through an algorithm that identified the most high-leverage mistakes each individual player had made, and then corrected them. In Wade’s simulation, now, rather than flying into Favors, he cuts left into the screen, then bounces back to the right as Rubio fights to slip it. With the defense spread, Wade has the virtual option of pulling up for an open 15-footer, a shot associated with a 0.53 probability of success, or eking out one final drive to the basket -- a more difficult success rate to peg, but somewhere in 0.49-0.63 range. Neither optimal, but each significant improvements.

The three-dimensional file, uploaded to the virtual reality immersion glasses Wade could only choose the frames for last season, trained both his accuracy and speed. The glasses come in handy on the road, as he’s unable to use the team’s live-action neurofeedback facility then. When Wade first encountered the program nine years earlier, he’d needed one to two seconds to recognize the play, assess his options, and choose the optimal course. Now, his decision-making had become almost wholly subconscious; it rarely took him more than a quarter of a second to play the odds.

Wade put on his glasses and began his training. He relived the scenarios he’d just faced over and over again. He saw himself succeeding, hitting the jumper, making the layup, and a confidence slowly pushed aside his sense of failure. As he ran through his 94th rep, a team medical assistant wordlessly placed a bottle of water and an oblong pill -- a compound of vitamins A, B, D, glutamine, niacin, DHEA, piracetam, and aspirin, specially titrated to optimize Wade’s unique blood chemistry -- on the tray beside him.

Wade hit pause, removed his glasses, and stared blankly at the back of the seat ahead of him. Wade’s troubled, at times, when he has room to think about things that aren’t basketball and their implications: the ways his life has changed, the way he’s abetted it, the steadily shrinking space that belongs just to him. When his celebrity first came, aspects of his privacy disappeared. When it deepened, his personal space became cramped by it. But now even the most basic facts of his existence -- his DNA, his blood, his urine -- are public. They’re collected, coded, and probed and prodded by teams of strangers who haven't as much as made eye contact with him but are still acquainted with the most intimate, private details of his life. What’s left that’s just his?

Wade placed the pill on his tongue, chased it with the glass of water, and slid the glasses back on. They were scheduled to touch down in Miami in three hours, and he still had plenty to do.

Thank you to Jason Sada of Axon, Isaiah Kacyvenski and Ben Schlatka of mc10 and Richard Dry of Edge10 for their insights.

To witness an NBA game in person is to be swaddled in an incomparable atmosphere. Over 15,000 people cram themselves inside a really large space, and almost by accident manage to magnify the game’s excitement, manufacturing an authentic and unequaled energy. As a side effect of all the clapping and shouting, your ears, hands, and throat are all throbbing by the fourth quarter. You simply can’t find that environment anywhere else, including a musical concert, because in basketball everyone’s reacting to the same thing at the same time. There’s nothing like it.

At least that’s the the belief of Dallas Mavericks owner Mark Cuban.

Because of a scheduling conflict, Cuban could not participate in the Sloan Sports Analytics Conference's Fanalytics panel on Saturday afternoon, but during a live BS Report just a few hours later, Cuban made a few points that were different from what established officials in other professional sports had just said.

While baseball believes its beauty lies in the fans' ability to have a “social experience” during the “leisure time” of a game, and football seems to be working its way through a multi-million-dollar wifi dilemma, Cuban said he wants his paying customers at basketball games looking at the actual game in front of them.

Since buying the Mavericks in 2000, Cuban has upgraded American Airlines Center’s sound system and installed the league’s largest 1080 high-definition video replay system in an attempt to bring people in. Despite losing two fan favorites to free agency this past offseason in Tyson Chandler and J.J. Barea, Dallas currently ranks No. 2 in overall attendance.

Why does this matter? Using various new-age strategies, such as placing usable barcodes on tickets that extract information from downloaded iPhone applications and interacing with people through Twitter, Facebook, and YouTube, professional sport organizations are making more of an effort to know what’s ticking inside the brains of their collective fan bases. A consumer’s decision to purchase a ticket and attend a basketball game is becoming more and more of an “I’ll scratch your back, you scratch mine” type of relationship. Here are a few reasons why getting people to attend NBA games is important, from both points of view:

• First presented at the conference in a paper titled “Effort vs. Concentration: The Asymmetric Impact of Pressure on NBA Performance,” studies have shown that crowds are able to influence the mindset of certain players when the stakes are raised in a pressure situation. For example, home teams show a statistically significant improvement in offensive rebound rate; from this data an assumption could be made that supportive crowds subconsciously make players try harder. Obviously, if true, this is a big deal.
• You’re able to view the raw athleticism of players who are, for practical purposes, naked -- absent of bats, gloves, helmets, shoulder pads, sticks, or skates. It’s 10 large men battling it out on a large wooden rectangle, and from a fan's perspective this is where you're able to learn a great deal about players that you can't observe from a television.
• Despite a large difference in physical dimensions between the average player and the average observer, fans in attendance are capable of relating to the sweating competitors on stage before them. When a player gets tired or doesn’t exert the proper amount of effort, everyone sees it, and there’s no dugout or clubhouse to crawl inside and use as a safe haven. Conversely, when he's giving his all there's opportunity to give thanks.

But one common issue that all sports are currently struggling with is their ability to identify who, exactly, is attending their games. With recent advancements in the aforementioned bar code ticket technology, organizations have made strides to identify attendees, but where things currently stand it’s still an incredibly difficult task.

According to Tim Brosnan, president/CEO of Major League Baseball Enterprises, last year, in a certain section at Quicken Loans Arena -- also known as the house LeBron James built, then introduced to a wrecking ball -- the average ticket changed hands nine times, from its first purchase to game night. Identifying why this is is an ongoing mystery, and one that analytics are still being used to figure out.

As was previously stated, the NBA’s stance with fan interaction and technological involvement is impressive and growing. The league's use of the internet as a communicative tool has been amazing, adaptive and really fun. But as they head into the future a main issue they share with everyone else is discovering who exactly it is that loves their product. Once teams are able to do that, and price their tickets/concessions at a level that consistently makes sense, one might assume that fans will reciprocate the favor and come out to support their teams, which has a small yet cyclical effect on the way players perform.

It's been a widely accepted fact throughout time that the more games you win, the more people will come. But what if the relationship is a bit more complicated? What if things are the other way around?

Celtics coach Doc Rivers is credited with saying that “offense is spacing.” At the Sloan Sports Analytics Conference, Harvard researcher and Michigan State professor Kirk Goldsberry extended that logic to the whole game, explaining, “Basketball is a spatial sport.”

But when we compare shooters, it's important to note that field goal percentage is not a spatial statistic. It values shots from anywhere inside the 3-point line equally (as does the scoreboard). So while field goal percentage can tell us something about scoring, it doesn’t tell us much about shooting profiles. After all, who really thinks Tyson Chandler, the league leader by percentage, is really the game’s best shooter?

So who is?

Using spatial data mapping to track shooting, Goldsberry found that, in fact, Steve Nash is the game’s best shooter from the most places. To be more exact: Nash averages a point per shot (equal to 33 percent shooting on 3’s and 50 percent on 2’s) from more places than any other player.

As you can see in his CourtVision chart, Nash shoots just about every shot besides the baseline 2-pointer pretty well.

That might not shock people who read this 2010 story from John Hollinger that came to the same conclusion (Insider).

But if the information contained in the charts isn’t new, the presentation is. Indeed, what’s really great about these charts is that they address the constant theme of the entire conference: Data is great, but players and coaches need information they can easily understand and reliably use.

The enormous amount of data that goes into these charts can be instantly understood at a glance. It’s not hard to imagine a time in the near future when every coaching staff in the NBA will carry an iPad-like tablet on the sideline to instantly relate this kind of shooting tendency data to players.

Example: Each defender could get a map of where his man is most likely to make a shot in the last two minutes and a map of the other team’s collective shooting tendencies. With this information, the defenders could better force opposing offensive players to the places on the court from where they are the worst shooters.

The cliché is that a picture is worth 1,000 words. In a 20-second timeout, when valuable talking time is scarce, how much could coaches say with a picture worth 10,000 data points?

In terms of on-court success, the 1996-97 season was a disaster for the San Antonio Spurs. But ironically, it may have been the greatest thing to ever happen to the franchise.

Since acquiring David Robinson with the first pick in the 1987 NBA draft, San Antonio had developed into a top-tier team. They made the Western Conference finals in 1995 and had grown accustomed to making deep playoff runs. But with the Admiral playing only six games in 1996-97 due to a back injury and, later, a broken foot, the team went into the tank.

San Antonio, whose leading scorer was a 37-year-old Dominique Wilkins, finished as the second-worst team in the West, ahead of only a lowly Vancouver Grizzlies franchise that won just 14 games in its second season in existence. Then the Spurs won the draft lottery, selected Tim Duncan and won four championships over the next decade.

With success comes imitators, and as any fan who has paid money to attend a late-season NBA game involving a hopeless team can attest, this "tanking" strategy is now commonplace. Even before Duncan, it was no secret that high draft picks led to titles and that playing bad led to good picks, but it has increasingly seemed like many poor teams aren't putting their best effort -- and in some cases, their best players -- into meaningless late-season games. At this point, "lose now, win later" may as well be the official motto of rebuilding in the NBA just as "Suck for Luck" was last year in the NFL.

At the 2012 MIT Sloan Sports Analytics Conference on Saturday in Boston, Adam Gold suggested what he considers a better way: winning to win. "We should never have to consider that a loss can be more helpful than a win," said Gold, a Ph.D. candidate at the University of Missouri. While fans and hoops bloggers all criticize teams for tanking, Gold also brought proof: in the seven years following the 2004-05 season, teams that missed the playoffs won just 32 percent of their games after they were mathematically eliminated from the postseason compared to 37.5 percent beforehand.

For Gold, this became a logical time to gauge when teams would start to tank -- and the mark when the system should help ensure they don't. His solution:

Give the first pick in the draft to the team that wins the most games after being officially eliminated from playoff contention. Then the team with the second highest number of wins gets the second pick. And so on.

The theory is that the worst team in the league will be the one that is mathematically eliminated first. Thus, it will get the most chances to pile up wins. If it takes advantage of those opportunities, it will be rewarded with the No. 1 pick.

If we use last season as a guide, the Sacramento Kings played 20 games (going 9-11) after being eliminated whereas the Milwaukee Bucks played only four (going 3-1). Those nine wins would have given the Kings the No. 1 pick under Gold's scheme. Under the current draft lottery system, however, they picked seventh: not exactly a reward for a team that managed a winning percentage of .450 for the final quarter of the season after playing .319 ball up to that point.

Presumably, it would have also led to the Minnesota Timberwolves not opting to keep Kevin Love on the bench with an achy leg for his team's final six games, which the Timberwolves lost by a combined 80 points. Such futility helped them end up with the second pick. I'm sure Timberwolves fans are happy about that fact now, but I doubt the ones who bought tickets to watch their favorite team lose by 19 to the Rockets on the final night of the season enjoyed it at the time.

"I believe that maintaining fan interest in their favorite teams and holding franchises accountable for a justifiable level of success is more important than trying to list teams from worst to first," said Gold. The current worst to first means of handing out picks clearly incentivizes losing. And it is hard to see how this framework wouldn't improve that.

But what about teams that are just horrible? What about the Bobcats?

No team is more in need of the No. 1 pick in this summer's draft than Charlotte. But under Gold's proposed plan, there is little hope they would get it even though they should have plenty of chances to win games after they are soon mathematically eliminated from the playoffs. Because at this point, the question is less "How many more wins will they get?" and more "Will they get another win?" They're just that bad.

When asked about this seeming flaw, Gold said that all teams should be expected to maintain some level of quality. There should be some standard of winning. That is fine in theory, and it holds management accountable for never allowing a team to become as bad as Charlotte has under Michael Jordan's stewardship, but I think his logical leanings may be skewed by the fact that he focuses on hockey.

I'm no NHL expert, but I think it is a lot easier for an overmatched roster that plays a low-scoring sport that requires multiple lines of players to win a few games here and there than it is for an NBA team with a similar dearth of talent. During the Bobcats' current nine-game losing streak, for example, they have only once lost by fewer than six points. And that was against the Washington Wizards. In Charlotte. I don't think anyone is questioning their effort so much as their players' ability to play basketball.

Ultimately, there is little question that the current draft lottery system, which is based on losing as many games as you can and then crossing your fingers, needs reform. And Gold's idea would likely be an improvement. But I don't think it is the best solution. Perhaps someone will offer up a better one next year at MIT. Until then, the Bobcats' future will continue to be decided by how the ping pong balls fall and how Corey Maggette's shot doesn't.

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).

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).