Michael Jordan put up a legendary performance while battling the flu against the Utah Jazz in Game 5 of the 1997 NBA Finals. If you’ve never seen the “Flu Game,” or how teammate Scottie Pippen helped carry Jordan off the court after it ended, you can still get an idea from looking at his numbers: 38 points, 7 rebounds, 5 assists, 3 steals and 1 block.
Statistics help quantify greatness. The data can even be a fun way for fans to debate seemingly untouchable records in sports. For instance, many people think that legendary NBA player Wilt Chamberlain’s record-setting 100-point game in 1962 will never be duplicated or broken. Perhaps there’s a better choice for an unbreakable NBA record that, remarkably, comes from the same player in the same season. Chamberlain averaged an incredible 48.52 minutes per game in 1962, despite regulation NBA games lasting only 48 minutes.
Fans counting stats and averages represent the surface level of analytics in sports. Sports analytics now encompass highly complex metrics, statistical models and other mathematical constructs that help analyze and predict performance.
The Emergence of Sports Analytics
The earliest sports analytics in American sports came in 1876 when English journalist and statistician Henry Chadwick was inspired by a cricket scorecard to develop the modern baseball box score. Within a few years, Chadwick started tracking hits, home runs and total bases. He developed the concept of earned and unearned runs. Those statistics led to some early analytical formulas like batting averages, slugging percentages and earned run averages.
A major breakthrough came in 1971 with the founding of the Society for American Baseball Research (SABR). Members like Bill James, John Thorn, and Pete Palmer pioneered advanced baseball statistics, now known as “sabermetrics” from the society’s acronym. One innovation is witnessed in the runs created stat, which was devised by James to better quantify a player’s offensive contributions in comparison with batting average and counting stats. In “The Bill James Historical Baseball Abstract,” James said:
With regard to an offensive player, the first key question is how many runs have resulted from what he has done with the bat and on the basepaths. Willie McCovey hit .270 in his career, with 353 doubles, 46 triples, 521 home runs and 1,345 walks – but his job was not to hit doubles, nor to hit singles, nor to hit triples, nor to draw walks or even hit home runs, but rather to put runs on the scoreboard. How many runs resulted from all of these things?
Sabermetrics took off in the MLB. From 1969 to 1971, the Baltimore Orioles won three straight American League pennants after manager Earl Weaver became the first to use lefty-righty splits — how well pitchers or batters perform against the opposition, based on right- or left-handedness — as well as batter versus pitcher stats. In 1983, the Chicago White Sox shorted distances to the fences in Comiskey Park after a computer tracking system identified an interesting tendency.
According to Alan Schwarz in “The Numbers Game,” the system found that in the previous year, the White Sox hit more balls to the warning track that resulted in outs. Those hits were nearly home runs, so the team brought the fence closer. The next year, they led the league in scoring.
The most famous example of sabermetrics in the league was depicted in Michael Lewis’ book “Moneyball: The Art of Winning an Unfair Game.” It covers how Oakland Athletics general manager Billy Beane implemented analytics in the late 1990s to compete with teams that had much higher payrolls. Instead of focusing on home runs and batting averages, Beane concentrated on on-base percentages (how often batters reach base by any means, including walks and getting hit by a pitch). As a result, Beane avoided overpaying for the power hitters that larger-market teams coveted. Instead, he located undervalued players that contributed to the Athletics’ success.
A popular movie starring Brad Pitt was based on the book, which further propelled sports analytics into the spotlight.
Analytics in sports spread beyond baseball. Thorn and Palmer of SABR joined with historian and sportswriter Bob Carroll in 1998 to write “The Hidden Game of Football.” The book was the first to bring sabermetrics to the NFL, where it analyzed situations like when to “go for it” on fourth down and metrics like QB rating. The NBA also followed the MLB’s lead. According to big data expert Jean Paul Isson’s book “Unstructured Data Analytics,” more than 50% of NBA teams in 1990 used advanced sports analytics to analyze game stats and detect patterns. Coaches also used that information to change strategies and substitutions.
A New Wave of Analytics in Sports
The current generation of data analytics in sports has altered nearly every aspect of playing and watching sports. Due to the rate of innovation, technology’s impact on sports analytics is difficult to summarize.
One trend in the past decade is player-tracking technology. In the NBA, a system called SportVU uses computer-vision cameras mounted in arenas to track the coordinates of players, referees and the ball 25 times per second. Cameras can recognize each player individually by jersey color and number. As a result, there’s a significant amount of data that’s collected and then turned into advanced stats. NBA fans can look up their favorite player’s speed on the court or stats for catch and shoot shots (shots taken after possessing the ball at least 10 feet outside of the basket for two seconds or less without dribbling). The NFL integrated its version of player tracking fully in 2018, according to ESPN. NFL’s Next Gen Stats similarly cover speed and situational metrics, and they offer teams insight into predictive analytics and trends like double-team percentages. Data is tracked through devices in each player’s shoulder pads.
Wearable technology could be the future for sports analytics. It’s such an important frontier for professional teams that little is known about how teams use that information to make roster decisions, player development and more. One wearables company, Kinexon, offered some insight into how the Philadelphia 76ers used data to help manage the team. An article explained that tools allowed the team to use player profiles to manage stress and strain through the season.
As a result, the front office and coaches could monitor how many minutes star players were on the court in an effort to reduce the risk of injury. That concept is known as “load management,” and it helps to keep star players fresh and healthy. It’s one of the hottest topics in the NBA following how the Toronto Raptors managed Kawhi Leonard’s playing time in pursuit of their 2019 championship.
Sports analytics encompasses basic stats, predictive models, player tracking data and biometric/situational insight from wearable technology. There’s even more once you take into account how sports organizations approach data from a business standpoint. Given the data-driven nature of industries like sports, finance, marketing and healthcare, there’s strong demand for professionals who can analyze and visualize that data.
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