Chasing Perfection: A Behind-the-Scenes Look at the High-Stakes Game of Creating an NBA Champion (2 page)

In the course of reporting and writing this book, I conducted over 125 interviews, watched nineteen of the NBA’s thirty teams play live in eight different NBA arenas, worked as a color analyst on college games, watched elite high school events in multiple states, spent about a million hours watching NBA League Pass, repeatedly got lost in
NBA.com
’s statistics sections, and dove headfirst into NBA Twitter in order to keep abreast of the latest smart writing going on.

Even with all of that preparation, the topic was a challenging one, both to report and to present. Basketball analysis and related technology keep evolving at a frantic pace, and as the NBA only has thirty franchises, with a very limited talent pool to draw from, any competitive advantages a franchise can establish are guarded ferociously. Many staffers are prohibited from talking about their team’s
personnel or anything to do with their internal analyses, and even if a person was allowed to broach the topic of analytics, most teams are so secretive and work in such silos that no one could really speak much about anyone else other than their own team. Third-party vendors are also very careful about identifying their clients, such is the level of secretiveness involved.

As such, the best way to attack the topic was to frame it within the 2014–15 NBA season and use detailed vignettes and case studies to attempt to explain what was happening. So, you will read about Gregg Popovich and the San Antonio Spurs—even though they (politely) refused to participate and wouldn’t even guarantee me practice access in San Antonio. You will also read about the analytics-crazy Rockets and their general manager, Daryl Morey—even though he (through a team spokeswoman) declined to be interviewed. You will get much more inside perspective from other players, coaches, team management, service providers, and media that will piece together a comprehensive view of how analytics are shaping the basketball we watch, and how those who are behind in the technology race are already feeling the competitive hit.

It was impossible to get everyone, though, and perhaps the most perfect summation of the high level of protectiveness came from Dallas Mavericks owner Mark Cuban. Cuban typically is willing to converse with mostly anyone via e-mail or his Cyber Dust app, and he is an investor in at least two of the major analytics technologies now widely used in the NBA. Under Cuban, Dallas is widely considered to be a leading franchise in terms of both analytical focus and monetary investment, and Cuban himself has been quoted as saying the Mavericks have a huge number of
data-related employees. It’s not a secret that the Mavs are doing this, although what exactly they’re doing is more of one.

His reply to an interview request: “Have to pass but I’ll read it.” Anything to get an edge.

CHAPTER 1

A Brief History of Modern Basketball Analytics

            
The similar revolution in baseball took a couple of decades, at least, and this took about half the time, in part because [baseball] helped pave the way.

—Kevin Pelton, NBA writer for ESPN Insider

L
ong before culling data from gigantic sets of inputs became a highly valued NBA front-office skill, and fans increasingly accepted various types of quantitative analysis as a growing necessity to better understand how the sport is played, an unknown economist may have spearheaded the first effort to use computers and statistics to project professional basketball performance.

Louis Guth was a senior vice president in the New York offices of National Economic Research Associates (NERA), an economics consulting firm, in the early 1980s. At the time, Guth was providing advisory services to the North American Soccer League in its antitrust lawsuit against the National Football League, one that alleged that the NFL’s prohibition on cross-ownership (owning franchises in multiple sports leagues) was damaging the soccer league by denying it access to potential sports capital investment and operational expertise.

As part of his work for the lawsuit, Guth had to conduct economic analyses of the value of sports franchises. As detailed in a July 1980
New York Times
article that Guth authored, he determined that sports franchises, especially those in major metropolitan areas with high levels of per capita income, such as New York City, were inherently undervalued based on how they were priced when they were bought and
sold in that era.

Guth’s analysis focused on the intrinsic, long-term values of franchises, which were based more heavily on factors like national TV revenues and the possible value of the home market at large, instead of ticket revenues and/or the current state of the franchise in terms of personnel. Those latter factors, Guth claimed (and was right about), were easily correctable with the hiring of better management and players, and had very little to do with the value of the franchise as an asset. Guth also smartly realized the unique position that sports franchises in that era had in terms of unpaid promotion through the major media entities in their respective cities.

“I said, ‘Look, you guys are getting two to three pages of newspaper [every day].’ Other businesses would kill for that,” Guth recalled via phone from Florida, where he is now retired.

In the
Times
article, Guth used that era’s New York Mets, who had just been purchased for what was an all-sports record price of $25 million by Nelson Doubleday and others, as a prime example. The Mets were terrible when they were purchased, but the club quickly developed a core of good players, traded for more, and eventually won the 1986 World Series. While that was happening, ticket sales for the team exploded, and the promise of the asset was realized.

When Guth was done with his work for the trial, the valuation analyses he had done made him think more specifically about how that type of work, aided by early-era computer technology, could be applied to sports themselves—and more specifically, to the monetary
and performance value of players. While baseball already was in the early stages of its own statistical analysis revolution, driven by stats pioneer Bill James and the Society for American Baseball Research (SABR, which is the acronym that spawned the term
sabermetrics
for baseball analysis), Guth didn’t see any comparable presence in professional basketball. That was in large part due to the inherent differences between the two sports. Baseball was a far more popular spectator sport than the NBA was during that era—one in which the NBA Finals were still shown late-night on tape delay—but more important was the nature of the sport and the history involved in its record keeping.

Baseball is a game of discrete, one-on-one, well-defined interactions between a hitter and a pitcher, and while current-era data analysis has expanded our understanding well beyond what was happening in 1980 (especially on the defensive side of the sport), it’s still a much simpler sport to analyze than basketball, in which each play on the court involves ten players moving in dynamic, undefined, and unlimited patterns. It is quite easy to determine exactly how much offense a batter is able to produce or how effective a pitcher is in limiting opposing offenses. It is much more difficult to accurately assess the value of individuals in a sport of team-based actions.

That whole series of factors created a market opportunity that Guth was eager to step into.

“To my knowledge, there wasn’t a heck of a lot of statistical analysis on basketball, and I migrated from other things I was doing because it looked like a wide-open field at the time,” Guth said. “You had Bill James coming out with his baseball people and were looking at numbers a lot, and there was an early piece in the
American Economic Review
[about baseball]. But, to my knowledge, I’m not sure there were other things out there [about basketball].”

Guth set out to examine basketball through the lens of the economic principles that underscored his normal work. In his mind, a
lot of the work at the time being done on baseball dealt with estimating the value of what he called the individual players’ “marginal product,” which, in economics parlance, is the output that results from one additional unit of a factor of production. Essentially, once you determined what a batter’s capabilities were, it was reasonable to be able to project how he would do in a series of individual at-bats and to determine his composite product by adding up all of his estimated at-bats for a season. Additionally, while baseball games are constrained by outs, they are not constrained by a particular number of at-bats, or production “units,” so to speak.

Because of its team-based, dynamic nature, basketball isn’t nearly that linear and is much more complicated. The other four players on the court with a particular player directly impact his ability to produce, for better or worse. Also, because professional games are 48 minutes long, with five players on the court at any one time, you are constrained to 240 total minutes of production for a game. As a result, as Guth explains, “any time you add somebody, you can’t say he brings all the talent he has. He also replaces somebody,” which has to be accounted for in the analysis.

By 1982, Guth had created a database of all available NBA statistics from the league’s most recent few seasons, and built a proprietary program first called FAMS (Free-Agent Market Simulator) and then FAME (Free-Agent [and Trades] Market Emulator) that crudely allowed him to determine a value for adding a new player to an existing team. It was groundbreaking stuff. As Guth noted with a chuckle during our phone conversation, his biggest mistake may have been in how he marketed his output.

“I should have called the stat ‘wins against replacement,’” he said, paraphrasing a calculation that’s now commonly used in sports in similar replacement analyses. “WAR is a fundamental Economics 1 concept, adapted to the reality of sports, which over the course of the season is pretty well set.”

As detailed in an August 1982
Sports Illustrated
article written by now-famed basketball writer Alexander Wolff, Guth became most well known for his analysis concerning Moses Malone, the league’s reigning MVP and a future Hall of Fame center who at the time was a free agent after having completed his
contract with the Houston Rockets. Thanks in part to collusive efforts of the NBA owners at the time, Malone was not receiving offers from franchises other than Houston, and Guth believed that to be a huge mistake on those other teams’ parts.

Guth’s economics roots shaped a system that focused on which teams should pursue a player like Malone based on the projected financial gain that player would give a new team, driven by improved performance on the court in relative terms. But when he focused more singularly on the projected on-court performance of the new player, exclusive of the monetary aspects, the story changed a bit in terms of where a specific player like Malone would make the most impact.

As detailed in a NERA company newsletter in 1984, much of Guth’s system was based on proprietary formulas that tried to place values on teams’ outputs at both ends of the floor. Offensive rebounds factored strongly
into Guth’s offensive formula, and in that era, there was one top-level team that seemed to have many of the ingredients of a world champion, but was relatively weak on the offensive glass: the Philadelphia 76ers.

Guth went and compared the 76ers to the two other premier teams in the league at that time: the Boston Celtics, who had won the 1981 NBA championship, and the Los Angeles Lakers, who had won the title over Philadelphia in both 1980 and 1982. Against both of those imposing foes, the 76ers had an offensive efficiency disadvantage, especially against the Lakers thanks to the dominant inside scoring of Hall of Fame center Kareem Abdul-Jabbar.

The easiest fix to that problem as Guth saw it, based on his formula, was for Philadelphia to improve its offensive rebounding.
During the 1981–82 season, the 76ers had only collected 1,031 offensive rebounds, which Guth calculated to be a 30 percent offensive rebound percentage. That was far below what the Celtics and Lakers were doing on that end, and something that could be fixed very quickly with the addition of a dominant offensive rebounder. It so happened that one was potentially available during the summer of 1982 in Moses Malone.

The free-agent rules at the time allowed a player’s previous team to have right of first refusal on releasing a free agent that signed an offer sheet with another team. In a move that would make modern-day front-office personnel tip their caps in appreciation, the 76ers attempted to load their offer sheet for Malone with financial incentive clauses that were designed for the Rockets not to match, so Philadelphia could sign Malone without providing any compensation to Houston.

The case ultimately ended up in arbitration, where the 76ers were determined to have violated multiple league rules in the structure of their offer sheet, and the Rockets eventually matched the modified version. That then allowed the Rockets to trade Malone to the 76ers in exchange for forward Caldwell Jones and the 1983 first-round pick of the Cleveland Cavaliers, who were expected to be terrible and in contention for the No. 1 overall pick. (As it turns out, the Rockets collapsed without Malone, winning just fourteen games in 1982–83 and winning the coin flip for No. 1 themselves. They selected Ralph Sampson with that pick, and also got Rodney McCray at No. 3 with the pick obtained from the Cavaliers through the 76ers.)

Meanwhile, the 76ers had just acquired the big-time rebounder and defender they needed to add to a terrific core of Julius Erving, Andrew Toney, Bobby Jones, and Maurice Cheeks. From the outset, Philadelphia’s new arrival, even though he was the reigning league MVP, seemed to understand his role.

“I know it’s Doc’s show,” Malone told the
Philadelphia Inquirer
after the trade, referencing Julius “Dr. J” Erving’s status as the team’s
main star, “and I’m happy to be part of Doc’s show. . . . Doc’ll still be the show, but maybe now
it’ll be a better show.”

Indeed, it was. The expected uptick in offensive rebounding thanks to Malone’s arrival helped close the projected offensive efficiency gap between the 76ers and
Lakers in Guth’s model, and bumped his regular-season forecast for the 76ers up to sixty-six wins. He was pretty much spot on. The 76ers ended up corralling 1,334 offensive rebounds (a top-50 total in NBA history), went 65–17, and rolled to their first world championship in sixteen seasons, going 12–1 in the playoffs and sweeping the Lakers in the Finals.

“The 76ers were not the one [Malone] would contribute the most to,” Guth recalled when asked about the analysis, “but when he came to them, it led to the prediction that they would advance.”

Had this happened maybe twenty years later, Guth would have received more recognition and interest in his work, but he said after the 76ers’ projection panned out, he didn’t hear from any NBA teams about his system. There just wasn’t much interest at the time in computer analysis.

“It was almost a one-shot deal,” Guth said. “We probably did it for a couple of years, then I got heavily involved in the baseball [antitrust] hearings, and also got involved with the PGA Tour.”

Still, Guth thinks back to the days of the Hewlett-Packard mainframe and its findings, many of which turned out to be very prescient, even with the relatively limited amount of information at the time. Seeing what the industry has become today, with billion-dollar franchise values and entire submarkets built around Big Data analysis, he wonders what might have happened had he stuck with it and trusted his innate sense that all things in sports were undervalued in that era.

“If I applied my own analysis,” Guth noted, “I should have built my own consulting firm.”

The principal keeping of basketball statistics, basically since the beginning of the game, has centered around “counting” stats—the numbers observers can compile just by watching the game and adding. It’s easy to track the number of points a team or player scores in a game, or their scoring averages, or how many shot attempts and makes there were. You can easily count rebounds for individual players, and sum them up for each team. You can track assists by whatever definition you create to identify one. Eventually, “steals” and “blocked shots” also became official categories. And all of those stats ended up being compiled into box scores that were printed in newspapers around the nation as a way to summarize what had happened in a game.