Advanced statistics in the National Basketball Association serve a special purpose, especially for players and fans. For players, their individual and even team statistics allow them to identify their strengths and weaknesses and for fans, it allows them to do the same and gives them ammunition for any playful or not-so-playful arguments about their favorite players with friends. What is important about advanced statistics in professional basketball is that there is anyone that can give information on which player is better than any other and impacts the basketball game from more than just one area. The NBA has a considerable quantity of statistics that are attributed to each player, such as assists per game, rebounds per game and assists per game. There is no clear answer on which statistic matters the most and to attempt to mathematically measure things like the heart, tenacity, grit and athletic ability of a player serves difficult. However, statistics are crucial within the NBA as it allows for players, fans, commentators, analysts and the like to better understand how people match up against each other.
The field of statistics has undergone change throughout its beginning, having to content with different types and sizes of data as well as the rapid advancement of technology. The quantitative methodology of statistics is a fundamental building block to social science, yet it is not commonly thought to be as instrumental to sports as it is to other social sciences. However, for the National Basketball Association, data, statistics and analytics have become increasingly important, following in the footsteps of Major League Baseball who has had its own “statistical revolution.” There is more information available in the world of sports, especially baseball, than ever before. Just as the NBA has performance statistics for assists and rebounds in the game, these same measures are present, yet tailored to baseball specifically: spin rates, exit velocity, WAR, BABIP and FIP. The goal of these statistics is not to eschew or dismiss traditional ways of thinking but as technology and people evolve, each sector of live evolves with it. The statistical revolution in baseball began in the early 1980s, when the “Bill James Baseball Abstract” was published widely for the first time. The reference type book was written by Bill James, American baseball historian and writer, and reached massive audiences and was often imitated to appeal to the masses, but never duplicated. Technology and data certainly have an impact when it comes to sports where performance is measured not only in accolades, but arithmetic. Milestones and number in baseball plays a large role in where a player gets his place in history. The greats, like Babe Ruth, Jackie Robinson and Lou Gehrig, are some of the most widely known and influential baseball players in history in their own respective rights and much of it has to do with numbers. For baseball, statistics were comprehensive and were difficult for the public to reach in the years before technology, something that would be unheard of today. Then, publications like Total Baseball and the Baseball Encyclopedia made use of more sophisticated technology to be standard baseball references. In the midst of this revolution, core statistics such as batting average, home runs, wins and strikeouts are instrumental for players, fans, team staff, managers and scouts to understand player value on an individual and group level. The argument could be made for baseball statistics is that it stands out among the rest of professional sports because they are a better measure of individual strengths and weaknesses because their performances and place in history is better determined on an individual level than that of a group level.
Basketball’s own statistical revolution came to a head in the mid 2000s, namely in 2013 when the NBA had finally unveiled a “redesigned, fully sortable engine” of a database that made nearly every statistic in NBA history available to the public. The data set dates back to the league’s inception by James Naismith in 1946. In the same year, the NBA installed player-tracking systems in all 29 of its arenas, a welcome complement to its online statistical database. Now, every player can be tracked, quantified and archived for future reference. The statistical revolution and evolution is laid out for viewers graphically, showing shooting tendencies, comparisons of lineup combinations, etc. At present, NBA fans have the mathematical history of each year of the NBA at their fingertips, being able sort through advanced metrics that had been primarily available on other sites besides the NBA’s official one (Beck 2013). Statistics can be analyzed by season, game, month, home and road games, wins and losses or any period of time. For the first time and in a monumental move, the NBA provides statistics that drives fan debate from its very own organization, no longer having to compete with or catch up to independent sites that are analyst- and fan-run. The NBA has now given the fans the tools to analyze the game straight for the source, making them verifiable, official and credible. The NBA database has the raw data that is updated no less than 15 minutes after the final buzzer of every game. The NBA’s openness and giving of information in statistics allows fans to digest the information the way they want to, opening up the organization to the people that support it and enable it to run. The days of simple box scores with point, assist and rebound totals are gone; there is an overwhelming pool of data that gives people unfettered access to what was previously not public fodder.
Strategic changes to metrics and statistics in basketball is fairly rare. The methods employed by Red Auerbach and John Wooden used a half-century ago still reign supreme in talent and success evaluation. Thankfully, new approaches for analyzing games in the NBA have been enabled by the quality and quantity of information available on the internet. Out of thirty NBA teams, twenty-two of them have an analytics department in the front offices, a trend that appears to have positive growth. NBA teams have taken advantage of the technology available to all and put it to use, integrating advanced statistical analysis into scouting processes and team studying. Teams now have to choose which metric or value means the most to them in each potential player, like size, shooting, speed, rebounding or even a combination of those factors and many others. Of course, all teams make decisions based on the information they have but some metrics provide concrete data on several factors that can positively and negatively impact the team. Understanding of the game of basketball is available to any and all with an Internet connection and a computer or smartphone. Historically, basketball teams were evaluated on per game statistics, for measures such as possessions. Per possession statistics put teams on equal footing and allow for comparison of teach, no matter how fast or slow they might play. Before the advent of new media technology, newspapers and other publications were not keeping track of such scores in boxes, yet the information age has close the statistics gap. Now, there are online logs that one can use to determine how many possessions occurred in a game at any given time. The whole point of advanced statistics, found in formula for shot opportunities, field goal attempts, and free throw attempts, is to make up for where traditional statistics have lacked, allowing for a better understanding for individual success, and offense and defense at a team level. However, basketball is an interdependent sport, unlike baseball or football, which makes it difficult to isolate how impactful a player is on a team. Individual statistics that are measured are also contributed to how a team supports an individual. At times, how good a player is individually is also a measure of how well his team enables him to be as good as he is. Keeping score in basketball does not have the advantage of isolating individuals and their accomplishments from that of a team. This makes it unfair for successful enough players who seem to carry their teams on their backs and their individual accomplishments only serve to minimally make the team better.
The problem with player value metrics is that, even with the revolution of technology, there is little to validate them and no metric is at the forefront of advanced statistical analysis in basketball, positioning it as the best. New metrics for basketball team and player performance develop just as much as the technology that enables analysis, and sometimes what does develop only helps for scouting. Player valuation metrics before the turn of the century consisted of numbers that ranked from best to worst and were quite simple in nature. Now, several pieces of information have come to represent player success and skill such as creating better shots on the perimeter, doing so at the rim, who does not get back on defense and who makes effective drives to make a shot. A system of player metrics that does not put new statistics into perspective of winning and losing and individual success will lack in analytic usefulness just as much or more than older metrics that do not have those statistics. Data is a step toward information yet just because there is all this data that is available to team staff, analysts, scouts and the fans does not make player value obvious, but it certainly makes it easier to measure in individual and group contexts.
There are plenty of skeptics and critics that downplay the usefulness of statistics and metrics in basketball, including former 76ers coach Larry Brown. Brown has argued that advanced analytics do not work in basketball, citing the glaring differences between it and baseball. In his mind, he states, “it doesn’t work in basketball” and the science should come down to acquiring draft choices, picking the best and greatest players, having solid and fair contracts and having a coach that can develop young talent to become the greatest player possible. It can be argued that for Larry Brown, analytic based stats are not of importance because analysis and metrics done with traditional methods before technology worked just fine. Although he stops just short of calling them entirely pointless and worthless, Brown argued that basketball is not a sport and statistics, but instead about making teammates better and focusing on efforts to play great defense. Brown is not alone in his identification of the blind spots in basketball statistics, joined by the likes of former player and current commentator Charles Barkley, that have made other old-school disciples like him skeptical of how useful they are. While statistics do not measure critical aspects of the game such as chemistry, defense and teamwork, to call them pointless would be remiss. Without them, there would be no understanding of what makes players great on paper instead of just having likability and amazing athletic prowess for fans. While it is disheartening, it is not shocking; analytics professor Stephen Shea of Saint Anselm college has commented that it is not unusual that highly successful players and veterans want to stick to the status quo (Ross 2015). Without mathematical and statistic measures of how players perform, it would make it increasingly difficult for those who rely on numbers and concrete data for performance to do their jobs, like sports analysts and team scouts. Statistics require care and knowledge and more can be improved upon and learned from adjusting statistics, but the results of analysis shed a light on what kind of player, and who, makes a team win or lose.
With numbers, it will never be possible to tell the real value of a player. To attempt to do so with only numbers ignores defensive play, athletic ability, hustle, and mostly passion, all immeasurable factors. The NBA carries a considerable number of statistics that speaks to a player’s ability, but what it lacks in personality, it more than makes up for in concrete data that pulls no punches about what players can and cannot do off the court. Statistics is an invaluable tool for any part of life requiring mathematical analysis and for a sport like basketball and the NBA, the rise of big data is ushering in the new and eschewing the old, as it should happen. The former way of judging how well a player or team performs relied on gut instinct that came from watching a game unfold, but now is the time for concrete formulas and fingers. If statistics cannot accurately measure everything about a player, then it would not make sense for human and “gut” instincts to be an accurate measure of such either, which deals a blow to the argument that old-school and traditional methods of measuring player value and performance were satisfactory. It would be remiss and wrong to deny the impact of technology and the rise in such that has allowed for the NBA to create better players and identify strong and weak spots so that improvement is even possible. There is no arguing with numbers and formulas that can produce only one result. Regardless of who can appreciate statistics or not, the basketball league has undergone a major transformation in the past few years that sees it relying on data to measure likelihood of winning, losing and everything in between. Naysayers argue that analytics takes the fun out of basketball, yet the NBA has shown time and again that analytics not only works, but it is here to stay in the world of basketball. It was best present in the victory of the Golden State Warriors over the Cleveland Cavaliers, the game that became the most watched finals in NBA history since the golden age of Michael Jordan, considerably the best basketball player of all time. While detractors of analytics refute the evidence, there is undeniable proof that extends beyond the excitement of the Warriors-Cavaliers finals. The Houston Rockets is a team well known for its use of data. Backed by a peculiar ideology that long-range two-point shots is one of the worst strategies in basketball, coupled with its difficulty of being measured and little evidence in contributing to overall points, is analytics at hard work.
Analytics has made basketball more open, honest and unfiltered. With the new tracking and data systems, players can no longer hide their weaknesses, deficiencies and blind spots on the court. Not only do the fans, coaches and teammates see their every move, but so do the cameras. There is knowledge and proof laid out there on the court, contributing to a shift in how players are evaluated. While the constant watching can make some liken it to Big Brother watching, without the conspiracy theory, the unrestricted access that there is to numbers for individual players makes for more knowledge about how they perform individually and with their teammates. The shift in technology and analytics is cultural as well. For example, Steph Curry is arguably the greatest shooter in NBA history, having the same level of acumen as his NBA veteran father Dell Curry. Yet his talent does not just come from genetics, but a cultural shift where measures like long-range shots, although eschewed by the Houston Rockets, became an integral part of what a player could do, especially what could amaze the fans and analysts during a game. It became a prized skill and not just a luxury, but also somewhat of a party trick that gets crowds’ energy going. Analytics have enabled the casual fan to become more engaged with players and the game of basketball overall. With access to information in every corner, fans can and do give attention to more areas of the sport, like defense, indicating another change in basketball, now for the average viewer. What had been intangible and inaccessible to fans by way of numbers is now a common factor when engaging and being a fan of the sport.
The evolution of the NBA and the game should be engaging and fun for all to watch, especially as people have thoroughly enjoyed and changed their lives with several advents of technology, such as online encyclopedias and social networks, which have changed the digital landscape. For this to be present in basketball is no different; a transformation of a league that has been around for seven decades should be welcomed. The era of smarter basketball is here as coaches and staff pore over numbers, formulas and figures to determine the most effective methods of coaching and most importantly, winning. New statistical methods are now entrenched in the nature of the NBA and just as with the rest of technology, it is not going anywhere anytime soon—if ever. Players are under a microscope and every move they make—or don’t make—is available for as many people to see it as possible. There is a new standard as to what matters in basketball, which allows decisions about personnel to be much easier (Oliver 2013). The new NBA is about efficiency and overall performance, which is directly linked to the impact of each action or inaction that a player takes. The eras of basketball that were characterized by slower pace and placing the individual and his performance over that of the team has come and gone. Like with all things in history, eras come in, make a splash and then exit in a cycle that has enabled evolution to occur for centuries. The NBA is no different; eras must come and go, ushering in new ways of thinking and doing. Analytics should not be looked upon as a magnet that takes the fun of magic and unpredictability out of a sport. It should be looked at as a useful instrument in a fluid sport that changes with time and people. Strategies that worked in 1946 eventually met their end in the decades that came after the development of basketball and the current data-heavy culture of professional basketball is not a far cry. For some, the league is “more aesthetically pleasing than ever before” (Ross 2015). No person or thing is impervious to advancement and change and the NBA is one unique organization that proves it.
Works Cited
- Beck, Howard. “Advanced Statistics Added to Revamped N.B.A. Site.” The New York Times, The New York Times, 14 Feb. 2013, www.nytimes.com/2013/02/15/sports/basketball/nbas-site-to-feature-updated-statistics-database.html.
- Kubatko, Justin. “KEEPING SCORE; Basketball’s Statistical Revolution.” The New York Times, The New York Times, 12 Nov. 2010, query.nytimes.com/gst/fullpage.html?res=9A0CE5D6143BF931A25752C1A9669D8B63.
- Oliver, Dean. “How numbers have changed the NBA.” ESPN, ESPN Internet Ventures, 15 Nov. 2013, www.espn.com/nba/story/_/id/9980160/nba-how-analytics-movement-evolved-nba.
- Patel, Nilkanth. “Sam Hinkie and the Analytics Revolution in Basketball.” The New Yorker, The New Yorker, 18 June 2017, www.newyorker.com/news/sporting-scene/sam-hinkie-and-the-analytics-revolution-in-basketball.
- Ross, Terrance F. “Welcome to Smarter Basketball.” The Atlantic, Atlantic Media Company, 25 June 2015, www.theatlantic.com/entertainment/archive/2015/06/nba-data-analytics/396776/.
Originally published 15.10.2019