“There is an epidemic failure within the game to understand what is really happening. And this leads people who run Major League Baseball teams to misjudge their players and mismanage their teams… Baseball thinking is medieval. They are asking all the wrong questions.”
This is a line from the 2011 movie Moneyball, adapted from the book Moneyball by Michael Lewis. It happens to be one of my favourite movies of all time – but that’s not why I’m writing this blog. Since its inception, baseball has been a game dominated by rich teams. Unlike other sports such as basketball and football, baseball has no salary cap, meaning that the big market teams such as the Yankees, Red Sox and Dodgers were consistently among the best teams in baseball because of their ability to gut the small market teams of their best players, just because they could afford to pay them mammoth salaries. Baseball was a very unfair game – until 2001. Billy Beane, the General Manager of the Oakland Athletics, did something in 2001 that would change the game forever by leveling the playing field for all 30 MLB teams. A few years ago, another innovation was made that has allowed these small market teams to be competitive. But let’s start by talking about the thing that came first.
Big Data in Baseball
Moneyball brought to light a phenomenon that occurred in 2001 in baseball called sabermetrics. It took years and years of data from every major league at bat, and boiled down success into a few metrics that people had been misusing. What Billy Beane and his team realized is that teams held an imperfect view of where wins come from. They had been signing hitters who have the most home runs and most hits, as opposed to the hitters with the highest on-base percentage. Without getting bogged down in the details, teams were looking at the wrong statistics when trying to see which players would be good fits for their teams. Then along came the 2001 Oakland A’s, who, despite having one of the lowest salaries in the league, almost made it to the World Series. Since then, a sabermetric approach to finding value in previously unwanted talent has become widely accepted and embraced by all teams to the point that it has become the new norm. This leaves teams like the A’s looking for the next big thing that could give them an edge. Enter PitchF/x.
PitchF/x is a pitch tracking system installed in every MLB stadium. This system tracks the velocity, movement, release point, spin, and pitch location for every pitch thrown in baseball, allowing pitches and pitchers to be analyzed and compared at a detailed level. Two mounted cameras in each stadium are used to track each pitch and establish each aforementioned aspect.
What sort of separation in movement does a certain pitcher get with his pitches? Did he change his release point at some point during the season? Which pitcher had the most break on their curveball? These are just a sample of the multiple questions you can answer using PITCHF/x data. The possibilities are nearly endless. PitchF/x has become the new way in which teams can gain an advantage. In the same way that small budget teams harnessed sabermetrics in order to bridge the salary gap, teams are employing the data that is produced by PitchF/x to level the playing field.
Technology has permeated sports in all kinds of ways, mostly in terms of officiating. We all watch Hawkeye tell us if a ball lands out or in during Wimbledon every year. In the same way, hawkeye is incredibly influential round the world in the game of cricket. Video replay exists in almost every sports these days, now including baseball.
The effect of sabermetrics and PitchF/x was almost immediate. Whereas before this data revolution, we rarely saw the small market teams finish anywhere but last. Now every year it feels like it is almost anyone’s game. It will be interesting to see how this advanced thinking will affect the game in the future.