Last Updated on November 27, 2022 by Lil Ginge
What are baseball data analytics and why should they matter to baseball fans? Baseball data analytics – also called “sabermetrics” – are an attempt to analyze the true impact of a player’s performance on the baseball field in all aspects of the game – hitting, pitching, base running, and defense. In essence, it is the empirical or scientific analysis of baseball events.
The idea behind data analytics is this: traditional baseball statistics such as batting average and pitcher win-loss records are highly flawed ways of evaluating a player. They tell you very little about the actual quality of a player’s performance.
In this article, we’ll take a brief look at the history of baseball data analytics, some of the key components of sabermetric stats, and a look at some of the most important statistics themselves. We’ll also examine why some of the older stats are so flawed.
History of Baseball Data Analytics
Baseball analysts have always tried to statistically capture what is happening on the field in baseball. And to be able to measure an individual player’s performance. This led to the creation of things like the box score in the 1800s. As well as traditional baseball stats – like batting average and pitcher wins.
However, some savvy baseball minds in the mid-twentieth century began to realize that the traditional baseball stats did a poor job of evaluating an individual player’s contribution. So they began to seek out new ways to evaluate how a player performed through statistical analysis.
Data Analytics Introduced to MLB
In 1947, the Brooklyn Dodgers – under the guidance of owner Walter O’Malley and President and GM Branch Rickey – hired a statistician named Allan Roth. Roth developed advanced stats that helped to improve the team’s decision-making apparatus. This included how the team went about making trades. Roth would work year-round, both during the season and off-season, to statistically advance the team’s goals.
In the mid-twentieth century, Earnshaw Cook began doing advanced baseball analysis and wrote a book called Percentage Baseball, published in 1964. In the beginning, most teams and baseball pros dismissed the book as baseball quackery.
But along came Bill James and things began to change. In 1971, James founded SABR – the Society for American Baseball Research – which is where the term “sabermetrics” originates. James also started publishing annual Baseball Abstracts, and by the later 1970s, James’s baseball ideas began to spread (albeit slowly).
Data Analytics and Moneyball
The concept of baseball data analytics finally went mainstream and blew up with the publication of the now-classic book Moneyball: The Art of Winning an Unfair Game by Michael Lewis. The book tells the story of how the 2002 Oakland Athletics, under Billy Beane and Paul DePodesta, used the advanced stats and baseball concepts created by Bill James and his sabermetrics systems. Bean and DePodesta used these stats to find ways to replace costly superstars who left for free agency with inexpensive players highly undervalued by the market.
The result of Bean and DePodesta’s experiment was a 103-win season and a playoff berth all while having the 6th smallest payroll in baseball. Executives starting to look at sabermetrics seriously. And the book Moneyball even went on to become a highly successful and celebrated film starring Brad Pitt and Jonah Hill as Bean and DePodesta. You know you’ve made it big time when Brad Pitt plays you in a movie.
Data Analytics In The Modern Era
These days, every team in baseball now has a data analytics department. And the best teams tend to rely on them heavily. The mainstream public still looks at traditional stats like batting average, RBIs, pitcher wins, and errors. But sabermetric stats have also entered the popular lexicon for both sportswriters and fans. It is just as common to see fans debating Wins Above Replacement (WAR) and Fielding Independent Pitching (FIP) on the internet as batting average and earned run average.
Many people have now come to see traditional baseball statistics as highly flawed for a wide variety of reasons. One great example is that batting average counts all hits – including singles and home runs – as equally valuable. But that’s obviously untrue, and it excludes valuable events like walks and hit-by-pitches altogether.
Similarly, the performances of players surrounding an individual player largely drive RBIs. It has less to do with how well the individual player is hitting themselves. You’ll have a lot more RBI opportunities if you hit behind Aaron Judge than if you hit behind a far lesser player. The public and baseball professionals have both become far more aware of the limitations of such stats. And they have adjusted their baseball data analytics as professionals and fans accordingly.
Some Important Baseball Data Analytics
There are many different baseball analytics that you should know if you want a better understanding of the game. Some of the most important ones include the following:
- wOBA (weighted on-base average)
- wRC+ (weighted runs created plus)
- FIP (Fielding Independent Pitching)
- Exit Velocity
- WAR (Wins Above Replacement)
Let’s take a look at each of these important baseball statics:
wOBA (Weighted On-Base Average)
The benefit of weighted on-base average is that it gives more credit to extra-base hits than singles. And it gives slightly less credit to walks or hit-by-pitches. wOBA does a better job of capturing hitting prowess than looking at batting average, average, on-base percentage, and slugging percentage by themselves. A .320 wOBA is about average, whereas .400 is excellent and .290 is bad. The stat is scaled like traditional on-base percentage.
wRC+ (Weighted Runs Created Plus)
wRC+ is a park-adjusted (that’s what the + stands for) version of wOBA. It is scaled such that 100 equals a league-average performance. The higher the number, the better the offensive performance. The metric also adjusts for time era. So, you can compare players from one hundred years ago to players of today. It may be slightly less accurate than wOBA because park effects can only be estimated. But, wOBA may still give you a more accurate context-free analysis of how a player actually performs.
Exit velocity measures the speed with which a ball leaves after being hit by the bat. It essentially measures how hard the batter hits the baseball. Hitting the baseball hard is generally a good thing. The higher the exit velocity, the harder they hit the ball. Analysts consider an exit velocity of 95 miles per hour or above to be a hard-hit ball. Data analysis can also combine exit velocity with launch angle. Launch angle measures the extent to which a ball is a groundball, line drive, or fly ball.
FIP (Fielding Independent Pitching)
FIP is a new version of earned run average (ERA) that strips each event of the defense’s contributions. In other words, only the aspects of the game that a pitcher truly controls as an individual – strikeouts, walks, and home runs – do analysts include in the metric. The stat assumes that a pitcher will have average luck on balls put into play based on a team’s defense and such. The metric is scaled to ERA so a 4.20 FIP is league average and a 3.00 FIP is very good.
WAR (Wins Above Replacement)
WAR is an attempt to measure an individual’s entire contribution with just one stat. You can apply it to both position players and pitchers. But WAR is an estimation and only gives you a rough idea of a player’s total value. Analysts combine it with many other stats for a fuller picture of player performance.
Because WAR is meant to be an estimation, analysts dispute the best way to calculate it. Two versions of WAR have become industry standard: Fangraphs WAR (fWAR) and Baseball-Reference WAR (bWAR). They can be quite different, so an analyst can look at both together and in context to better understand a player’s total performance.
Final Thoughts on Baseball Data Analytics
Knowing advanced data analytics has become essential to better understand what’s actually happening on the baseball field. You can enjoy baseball without them, But, your assessments of what’s happening on the field and why a team is winning or losing may be pretty off.
Because the profession of baseball now acknowledges this, data analytics are going to remain increasingly important to front offices across the sport. And there will likely be an ever-growing stream of baseball analytics jobs created in the future.
Finally, it might seem like dry math, but having a deeper understanding of the events of a game of baseball true makes it more interesting and insightful. It’s fun to know what’s actually happening on the field in a way that goes beyond the old calling of balls and strikes.
Learn more about how baseball analytics have changed the game.
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