Over the past couple of decades, baseball has undergone a substantial transformation. That transformation is predominantly data-driven. More than just an evolution, the sport has seen a revolution in analytics, also known as “Moneyball,” after the eponymous 2003 book by Michael Lewis. Today, we delve into the fascinating world of Major League Baseball (MLB) and its analytics revolution.
The advent of the Moneyball concept lies in the innovative approach of the Oakland Athletics general manager, Billy Beane, who had a unique vision. Facing financial constraints, he leveraged statistical analyses to identify undervalued players. With this fresh perspective, the Athletics began outperforming wealthier teams, sparking a new trend in baseball; the revolution had begun. Its success even led to a movie named “Moneyball” starring Brad Pitt and Jonah Hill to be created based on the A’s success.
Bill James, a baseball writer and statistician, is recognized as the father of sabermetrics – the empirical analysis of baseball. Sabermetrics applies statistical analysis to assess and predict player performance, providing teams with a competitive edge.
Traditional measures, such as batting average and pitcher wins, were supplemented, and sometimes replaced, by more comprehensive statistics like on-base percentage (OBP) and earned run average (ERA). This comprehensive approach offered a more precise picture of a player’s value and influenced the dynamics of MLB betting odds.
Data is at the core of this MLB analytics revolution. The MLB has even embraced sensor and video technology, amassing a vast amount of data, known as “Statcast data.” This powerful tool quantifies every player’s actions, offering a treasure trove of information. From pitching velocity and exit velocity off the bat to sprint speed and launch angle, this data collection provides deeper insight into the game.
The analytics revolution has significantly influenced player evaluation. Teams now have access to in-depth information about player performance, which often leads to unconventional but effective strategies. For example, the defensive shift, wherein players are rearranged based on the batter’s hitting tendencies, is one strategy from analytics. The shift, however, was banned starting this past year.
Player recruitment has also evolved. Potential draftees and trade targets are assessed based on their observed performance and predicted future performance. The focus has shifted from raw talent to potential value, turning the tables in favor of those with a strong understanding of analytics.
The radical shift towards data-centric approaches wasn’t welcomed by everyone. Many baseball purists were initially resistant, seeing it as an encroachment on the traditional ethos of the game. However, with teams like the Boston Red Sox and the Chicago Cubs achieving World Series success using these methods, more teams embraced analytics. Today, all MLB teams have an analytics department, signifying the widespread acceptance of this new approach.
The analytics revolution shows no signs of slowing. As technology advances, so will the data’s depth and breadth. With machine learning and artificial intelligence increasingly applied to this data, it’s conceivable that predictive analytics will play a more significant role in the future, possibly offering real-time strategic advice during games.
Moreover, player health and wellness could become a key area of focus. Predicting injury risk and optimizing player performance through load management are just a couple of potential applications of analytics in this domain.
The impact of the analytics revolution on MLB cannot be overstated. It has fundamentally changed how the game is played, analyzed, and understood. Although it faced initial resistance, this data-driven approach has proved its worth and is now an integral part of the sport.
The marriage of baseball and analytics has been a game-changer, and we can only anticipate what fascinating developments the future holds. The playing field is no longer just grass and dirt – it’s numbers, data, and predictive models. Baseball, as we know it, has changed forever.
While I am not a “baseball purist” and I do like analytics, truth be told, baseball has been de-revolutionized by all the analytics out there. Not only am I a baseball coach and trainer for over 30 years, with a Psychology degree (a lot of research, data analysis and stats) and multiple certifications (one in biomechanics), I see the problem with the analytics. They do not take into consideration the individual and their specific nuances. Teams and the training of their players are being “cookie cut” solely based on analytics – trying to make a clone out of everyone. While on the surface the analytics are good, most are being used incorrectly. Today’s Over Emphasis and Misinterpretation of Analytics Is Killing Baseball and Injuring Players.
Just because something can be measured and a stat for it can be generated doesn’t mean it is useful. And if it seems useful, how does it affect the player and the game in general. Let’s see…
From the pitching side, if the analytics are so good for players and teams, why have the number of pitchers on the DL increased? Why have the number of Tommy John surgeries increased? Why are we using 2 times more pitchers per game vs. just 10-15 years ago? If pitchers are so much better because of the “new” analytics, why have home runs soared? Humans are variable. What might be good for one pitcher to do, because of the analytics, it’s not for another because their biomechanics are different. You can’t take the human out of the equation and that’s what baseball is doing. Analytics are suppose to help not hurt players. They are suppose to make the the player and game better.
From the hitting side, you can spout off all the metrics and stats you want but the “Moneyball” analytics showed us one important thing. The number one concern for baseball teams, offensively, needs to be scoring runs in order to win games. While there are countless stats for hitting, at the end of the day it comes down to this: “Is a team getting men on base and are they scoring runs?” I ask, does today’s hitting training – which is almost exclusively based off countless analytics (mainly for home runs) – really do that? The facts below say a resounding “NO”.
Keeping in mind that the analytics revolution really didn’t start until around 2015 when Statcast was available in all MLB ball parks.
–In 2019, the most home runs hit in one season, there were 1,083 MORE Home Runs hit vs. the 2000 season, yet there were 1,504
MORE RUNS SCORED in 2000 – before the analytics revolution and during the Steroid Era.
–In the 2023 season, there were only 175 more Home Runs hit vs. the 2000 season, yet there were a whopping 2,539 MORE RUNS
SCORED in 2000.
Over this same time frame, 2000-2023, the number of runs scored by a home run has decreased, batting averages have decreased, OBP and OPS have also diminished. The most staggering stat? Strikeouts have skyrocketed by over 10,000 or more per season versus 2000! In the 2000 season there were 31,356 total strikeouts. In 2023, there were 41,826. That’s a 30% increase.
And what about walks. In 2000, the league wide total was 18,237. In 2019 there were 15,895 and in 2023 there were 15.819. So even walks have decreased by around 2,500. That’s a 30% increase.
In roughly the past 10 years analytics have replaced common sense. What’s coached, practiced and trained nowadays is just swinging as hard as you can. Which makes batters one dimensional – and that’s the main reason why pitchers seem so much better. They know batters are one dimensional.
From the hitting side, players are judged by the “analytic revolution” stats of “launch angle”, “exit velocity,” and “batted ball spin”. Therefore, batters are attempting to or being trained to artificially manipulate their mechanics to increase these three metrics because all three metrics are geared toward home-run production. Which we have factually seen is hurting baseball and players NOT helping them.
It’s not the metrics themselves, it’s how players and teams are attempting to achieve them. By training to artificially manipulate their mechanics. The thing very few realize these metrics are dependent on the location of the pitch, which pitch a batter choses to swing at and the timing of them.
Again, I love analytics, but the MLB is not utilizing them correctly at all. By evidence of just a few major things mentioned above. Unfortunately, I feel it will continue to get worse until the MLB takes the analytics with a grain of salt and apply them with common sense. Which is not happening.
In you conclusion, you should change “data-driven approach” to “data-supported approach”. If you work on an assembly line “data driven” would work, but baseball has too many variables to make “data driven” successful over the long haul.
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