A multitude of “hidden” numbers tell a bigger story
It has always been a commonly held belief that, in sports, the team with the best players will win. In the past, decision-making — whether coaching within the game or outside the game at the front office — has commonly been based on traditional practices or “gut-feelings.” Decision-making roles in professional sports have predominantly been taken up by former athletes, as many assume that a former athlete can best identify team needs and properly evaluate player talents.
Since the start of the new millennium, however, a grand shift has taken over nearly every sport at almost every level. Instead of former athletes, teams have hired graduates from Harvard, Yale and other elite universities. “Ordinary” people with degrees in statistics or mathematics are now filling front office and coaching staff roles. The analytical age has come upon the sports world, and it keeps growing every year.
For the most part, strategy and player evaluation in sports has gone away from the “eye test.” It has now been identified that there are a multitude of “hidden” numbers that tell a bigger story. A team that lacks talent can still remain competitive, and a team that is bursting with talent can maximize its potential through utilizing data. That is where analytics comes into play.
The earliest and most popular use of analytics in the modern era can be traced back to the 2002 Oakland Athletics. Michael Lewis’ 2003 book, “Moneyball,” tells the story of how the A’s and general manager Billy Beane used “sabermetrics” to change the way the team acquired talent under tight financial constraints.
According to the Society for American Baseball research, sabermetrics is “the search for objective knowledge about baseball.” It was defined and made popular by American Baseball writer Bill James in 1980, but, since then, researchers have tried to advance the process of statistical analysis to challenge the traditional ways by which players are measured.
According to the best available data at the time, Beane and the A’s management made note of the fact that teams with a high on-base percentage scored more runs, thus translating into more victories. After a series of minor lineup tweaks and personnel changes, the A’s went from a mediocre ballclub to one that went on a then-American League record 20-game win streak and won its division. Although the team never won a World Series title during that era, the A’s were consistently in the postseason mix and competitive without having big names filling out the roster.
Baseball seems to be the sport most ahead of the pack when it comes to the numbers game, mainly because the game relies on statistics so heavily. But, a recent shift in the National Basketball Association (NBA) has also taken place, with the Houston Rockets leading the charge.
Despite having never played or coached at the NBA level, Daryl Morey was hired as the Rockets’ general manager in 2007. Since then, Morey has become the pioneer of analytics in basketball. He holds a computer science degree from Northwestern University and an MBA from M.I.T., but, in the sports world, that meant nothing — until he stepped in.
The main thrust of Morey’s philosophy is that since a three pointer is worth more than a regular two-point basket, it is more efficient to shoot threes instead of other jump shots. In essence, the more three pointers a team takes, the more points the team will be able to score, and the more games the team will win.
By simply looking at the Rockets’ statistics from this season, you can see that nearly half of the shots they take are from three. They have led the league in three pointers, leading each of the last four seasons by a wide margin. This is all part of Morey’s design: trying to tip the odds in Houston’s favor by playing the numbers game.
“We need a lot more randomness in the NBA so people can compete and not know before the start they have no chance,” Morey said at the M.I.T. Sloan Sports Analytics Conference in 2016. “There are very few teams who can win the championship in a given year.”
While his team is not the most talented in the league, Morey can find a way to close that talent gap through a moneyball-like approach. Like the Athletics, however, the Rockets have yet to reach the pinnacle that is winning a championship in their sport, but they have been making some deep playoff runs and were one win away from a Finals appearance during the 2017-2018 season.
The Rockets are the most well-known among the NBA franchises for following analytics, but data is being used more across all teams in a variety of different ways as they try to find a way to use the massive amount of information they now have at their disposal.
In comparison with the other major professional sports leagues, the National Football League (NFL) lags behind. The NFL has slowly become younger, according to a 2018 article published by The Ringer, and the availability of new data — like those of “player-tracking” — has changed decision making on and off the field in the way rosters are built and what plays are called in certain situations.
Decisions that were once made because of “gut-feelings,” like 4th down plays or even draft picks, are now seen in a different light among some front offices across the league. Football is rooted heavily in the notion of imposing your will onto the other team, so it isn’t all that surprising that the NFL has been slow to adapt, but more teams have begun the shift.
As with baseball, we are starting to see even younger faces take the head coaching reigns in the NFL. This new crop of coaches seem to have more confidence in the value of analytics, as opposed to older coaches who tend to stick to tradition. There are some teams more ahead of the curve than others, but, overall, there is still a long way to go in the analytics revolution.
Analytics has not only taken over professional front offices, but also the fans, who are now consuming more stats and numbers everyday. There are constant reports, analyses and even websites dedicated to giving fans an even deeper look into their team’s specific data. Websites like FiveThirtyEight.com, advancedsportsanalytics.com and many others are easily available to anyone who would like to get informed on more than just the traditional metrics.
The prevalence of analytics has been met with open arms in some cases but also heavy criticism in others: The responses differ depending on who is running a team. One example of such criticism is that of Hall of Famer and former New York Yankee pitcher Goose Gossage.
“The game is becoming a freaking joke because of the nerds who are running it,” Gossage told ESPN in 2016. “I’ll tell you what has happened, these guys played rotisserie baseball at Harvard or wherever the f— they went and they thought they figured the f—ing game out. They don’t know s—.”
Though his criticism may seem harsh, there are those who feel a similar way across all major sports.
For so long, player evaluation across all leagues was seen by some as unfair, as there is more to a player than just height, build, speed and strength. The constant advancement of analytics can be tied to improving the way players are evaluated. The problem with that, however, is that sports are an imperfect science. No matter what numbers, formulas or strategies are created, there are millions of unaccounted for or “intangible” variables in sports that make it nearly impossible to guarantee success.
The flaw in analytics is that it looks at a player as nothing more than a few numbers, instead of taking into account the wide-range of human factors that have a tremendous impact on the game. Those are all things that may be a part of the next analytics breakthrough, but as of now, everyone is trying to find a way to figure it out and reach the mountaintop. Still, sports have changed drastically and those once thought of “unbreakable” traditions are becoming easier to forget.
Written by: Omar Navarro — firstname.lastname@example.org