Sports used to be simple. In baseball, players just stepped up to the plate and swung at the ball, while in football it was merely X’s and O’s on a chalkboard. Even in poker, a solo game confined by 52 cards, it was once about a man and his hand. The times have changed.
The rise of the computer brought about a new data-driven era in sports, and the world of information and analytics have forever changed the way games are played.
Below are just three examples of such changes.
Billy Bean and the Rise of Sabermetrics
If you’ve ever seen the 2011 movie Moneyball starring Brad Pitt, then you’ve been introduced to Billy Beane. According to IMDB, the film is about:
“Oakland A's general manager Billy Beane's successful attempt to assemble a baseball team on a lean budget by employing computer-generated analysis to acquire new players.”
It doesn’t sound too exciting – how do you make a feature-length motion picture about analytics? – but it was a hit with both fans and critics.
Beane is a former player and front office executive, and while he did fine in the former career, it was in the latter that he left his mark on the game by using statistical analysis. He used what is known as sabermetrics, defined as the empirical analysis of baseball, especially statistics that measure in-game activity.
This included analysing batting, pitching, and fielding measurements, which resulted in popular sabermetric statistics such as Value Over Replacement Player (VORP), Wins Above Replacement (WAR), and Batting Average On Balls In Play (BABIP).
There’s a lot of higher mathematics and calculus involved, so if that’s a little above your head, check out the aforementioned flick, or pick up the book of the same name, to see just how it all changed the face of baseball as we know it.
As Beane said, “Adapt or die.”
Poker Not Always a Solo Game
In 2008, the World Series of Poker (WSOP) introduced the concept of the “November Nine.” Essentially, it stopped play when the annual $10,000 buy-in Main Event reached the final table of nine, giving the finalists a nearly four-month break. This was done to allow for excitement and anticipation to build among fans and media, but it also opened the door for players to change their game.
With an $8 million first-place prize or more usually up for grabs, players quickly realised they needed to take any advantage they could. Oftentimes, this meant hiring a coach to study the game, fix their flaws, and what have you. With hundreds of poker books, online training sites, and tools like the Odds Calculator at their disposal, there was no time to rest on their laurels.
It also meant putting together a team to help during the final table. Despite being an individual game, poker changed in that players were able to solicit their friends for help. Because the November Nine was broadcast worldwide on a delay, usually 15 to 30 minutes, players could have their friends watch the action and report back on exactly what’s happening either during breaks or in between poker hands.
With so much information at their fingertips -- VPIP% (average % of time a player voluntarily puts chips in the pot), PFR% (how often a player raises before the flop), and WTSD% (how often a player is willing to go to showdown after seeing the flop) are just three examples of stats that have developed in the poker world. It only made sense for players to harness the power of data to help make decisions.
NFL Teams Begins Hiring Analytic Executives
In early 2016, the Cleveland Browns hired analytics expert Paul DePodesta as Chief Strategy Officer. If you’ve seen Moneyball, then you might be interested to know Jonah Hill’s character, Peter Brand, was based on DePodesta, a Harvard graduate.
He was brought on, as Browns owner Jimmy Haslam would say, to “add a critical dimension to [the] front office.” That’s because the team, a long-time loser in the NFL, was intent on embracing analytics
"My focus is to bring whatever experience and perspective I can to collaborate with the team, with the intent of helping us make more informed and successful decisions,'' said DePodesta at the time.
It was an untraditional move by a football franchise, but evidence that they hoped analytics could change the game much as it did for baseball, specifically by helping with player development and sports science.
Unfortunately for the Browns, there was no immediate change as they finished a league-worst 1-15 in 2016. Still, these things take time, and now they have the first overall pick in the 2017 NFL Draft. We’ll have to wait and see if DePodesta and company make it count, but rest assured they’ll use analytics in making their decision.