People disagree on how data analytics have changed baseball. Some say it’s for the good, helping teams who never had a shot win big. Others say it’s destroyed the game, made it boring, and caused it to bleed eyeballs and ticket buyers.
Who is right? Who can say? It depends on what you want from baseball.
But the world of baseball is changed, and that change largely stems from big data analytics. Let’s take a look into some of the ways data has changed the sport of baseball. But first, let’s answer the question: what exactly are baseball analytics?
What Are Baseball Data Analytics?
Baseball data analytics are measurements of a baseball player’s abilities on the baseball field: hitting, pitching, base running, and fielding. Traditional baseball statistics like pitcher wins and batting average attempted to capture particular measures of player value. But baseball data analytics aim for a more comprehensive and deeper understanding of the value of a baseball player.
The history of baseball data analytics probably goes back to the GM of the Brooklyn Dodgers, Branch Rickey, hiring statistician Allan Roth. Rickey hired Roth to create advanced baseball analytics to help in the team’s decision-making. But it wasn’t until Bill James came along and founded SABR – the the Society for American Baseball Research – that the development of baseball analytics really got going.
When Billy Beane and his “Moneyball” brand of baseball came along, baseball data analytics really became mainstream. Learn more about what baseball data analytics are and their history here.
Billy Beane and the “Moneyball” Era
Data analytics started changing baseball when Billy Beane introduced his Moneyball conception to the Oakland Athletics in 2002. In 2001, Oakland A’s General Manager Billy Bean and his assistant Paul DePodesta were asked to slash the team’s budget. Despite going 102-60 in 2001, this had the potential to decimate the team’s chances of playing well the following year.
In order to compete against financial behemoths like the Yankees and the Sox, Beane and DePodesta used baseball analytics to find a competitive advantage. The Moneyball approach, documented in Michael Lewis’s famous book, was to look for undervalued players on the cheap . These were players who performed well in areas of the game that were overlooked.
Between 2000 – 2006, Oakland averaged 94.9 wins and reached the playoffs five times. They ranked 21st or worse in opening day payroll each year except once.
Moneyball started a baseball craze. This led more MLB teams to start pouring their money into data analytics departments. The results have most certainly changed baseball. Whether it’s for the better is up for debate.
Do Baseball Analytics Help You Win Championships?
The Billy Beane Moneyball approach in Oakland was both a major triumph and a terrible failure. It made the Oakland Athletics a highly competitive team with an itty bitty payroll. But, they were unable to do the one thing that matters most in baseball during this span. That’s winning a World Series Championship.
Does that mean baseball data analytics can’t help you win a World Series title? Not at all. But what it does mean is that big data can’t do it alone. You need that one other substance that allows you to gain a major competitive advantage in baseball: money.
Teams like the Astros, the Cubs, and the Red Sox have all leveraged baseball data analytics to help win championships. And they’ve also done it with huge payrolls.
In 2022, the Yankees had the third-highest payroll at 264.9 million, and the Red Sox had the 6th highest payroll at 225.7 million (but were actually terrible despite this). But, since the Red Sox have tired Tim Tippett, have won four championships. Before 2004, they had not won since 2018. Meanwhile, the Yankees haven’t won a championship since 2009.
Similarly, data-driven GM Theo Epstein and manager Joe Maddon helped drive the Chicago Cubs to a World Series championship in 2016. This was after experiencing the Billy Goat Curse Drought since 1908.
How Have Baseball Data Analytics Changed Baseball On The Field?
Less Manufacturing of Runs
Another way baseball data analytics have changed baseball is by disfavoring the idea of “manufacturing runs”. Once upon a time, the idea of manufacturing runs was all the rage. Manufacturing runs is when you scratch out runs using small ball.
For example, you might start by having a player bunt for a hit or legging out an infield single. Then, the next batter bunts them over for an out or moves them on a long sacrifice fly ball. Finally, the third batter drives the run home from second base with a line drive to left field.
This concept of “manufacturing runs” is now out, thanks to baseball data analytics. Data has shown that manufacturing runs is actually a pretty bad baseball strategy. For example, giving up outs on sacrifices is bad according to data analytics.
That’s because nothing is more precious than getting on base for scoring runs. So if there’s a player on first base, you don’t want to sacrifice an out by bunting them over. Instead, you want to earn a walk to move them over. Or, simply drive them home with a sweet two-run bomb.
Big data has also changed baseball on the field in other ways. For example, data analytics have revealed that stealing bases is often counterproductive. That is, unless you are successful at it enough of the time. For example, maybe in a given season, if you are able to steal safely 70% of the time, it’s worth trying. Otherwise, it’s not. You’ll give up more outs and lose more runs than you would by simply staying put.
Data has also led to infield shifts. There is more data on where a batter is likely to hit the ball. So, it makes sense to try and stick more fielders where that ball is likely to go. Of course, this led players to adjust but doing things like trying to improve their launch angles.
How Analytics Have Changed The Roster
Baseball data analytics have also changed a lot of things about roster construction. First, if a General Manager wants to use advanced analytics, they need to get buy-in from their managers, coaches, and players. Otherwise, it simply won’t work. But some baseball personnel may be more interested in the idea than others.
Future Versus Past Performance
One way data analytics has changed roster construction is by the understanding that what matters in baseball is future performance, not past performance. It’s wonderful if a player hit 500 home runs in their past seasons. But, how many more are they going to hit going forward is the important question.
Portfolios of Baseball Skills
Another important point is that you should use big data analytics to build a portfolio of players with different but complementary skill sets. For example, maybe you have one player who is a great slugger but a terrible fielder. You can compensate for this by acquiring a player who is better with defense and speed, but perhaps hits far fewer home runs.
Age and Player Development
Baseball data analytics have also changed baseball by leading to a focus on younger players and strong player development. Baseball players tend to perform well in their 20s, peak at around 27 to 30 years old, and then decline. In the past, you might try to take your time pushing a player through your farm system. But, holding them back is now seen as a strong disadvantage on the field. Once a young player is ready to go in the Majors, just let them fly.
Finally, data analytics has led to revolutions in things like pitching. We now know that pitchers are far less effective the third time through a lineup. And that throwing fewer pitches leads to longer pitching careers. This has led to innovations such as increased bullpen usage. In addition, we now see far fewer innings from starting pitchers. And we’ve even seen the introduction of baseball “openers” by teams like the Tampa Rays.
Is Data Good Or Bad For Baseball?
Whether or not baseball data analytics have changed baseball for the good or bad depends on why you love the game. If you love to watch small ball, or a really short, fast-paced game, you might hate the changes. Or, if you love home runs, relief pitching excellence, and don’t mind a longer, slower pace, the new baseball era might be right for you.
Either way, there is no doubt that baseball data analytics have changed the game of baseball significantly over time. And will likely continue to do so for as long as Major League Baseball remains one of our beloved national pastimes.