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A Small Glossary of Baseball Stats

advanced baseball stats glossary

It’s every true baseball fan’s favorite time of year- those February doldrums where all the name-brand free agents are off the board and pitchers and catchers haven’t yet reported to spring training. Yep, best time of year for baseball fans.

So, instead of sitting around trying to get excited about a team signing a 37-year-old backup catcher, how about brushing up on some baseball stats instead? Yeah, that sounds much more exciting.

Batting average and RBI have been rendered obsolete by the new wave of stats, and more advanced statistics like WAR and OPS+ are showing up everywhere. For anyone who might feel a little lost in the discussion of “WARs” and “ERA+s” and “Statcast,” here’s a quick little glossary of some of the more common terms floating around. This is nothing crazy, just stuff anyone could find on any Sunday Night Baseball broadcast.

Wins Above Replacement

WAR definition glossary

Image courtesy of Berry Gordy, Jr. and Motown Records

WAR is kind of the holy grail of baseball statistical analysis. It’s a distillation of everything a baseball player does (defense, offense, you name it) into a single number. WAR is, in short and without hyperbole, the single most convenient thing ever. Basically, WAR calculates how many more wins a certain player is worth to their team over a random AAA callup. There’s a few different ways to calculate it, but the two most common are found on Fangraphs (fWAR) and Baseball-Reference (rWAR or bWAR). Anyone interested can read about some of the differences between the formulas at this link.

A league-average starter will be worth around two WAR per year. An All-Star will usually be worth around five, and an MVP between eight and ten. For reference, 2018 Chris Davis was worth -2.8 bWAR. Meanwhile, 2018 Mike Trout was worth 10.2 bWAR. The highest bWAR since the sport’s integration was Dwight Gooden‘s gobsmacking 13.3 figure in 1985. The highest since the turn of the millennium was Barry Bonds‘s au naturale 11.9 in 2001.

On the other side of the coin, the lowest single-season bWAR belongs to Jerry Royster‘s 1977 campaign, in which he put up an awe-inspiring -4.0 in 140 games. Right behind him in the race to the bottom are Jim Levey (-3.9 bWAR in 1939) and George Wright (-3.7 in 1985).

Park/Era-Adjusted Stats

Coors Field park-adjusted stats
Image courtesy of Kit Leong and Shutterstock.

If a stat has a little plus sign after it, that means it’s been adjusted for league, park and era. It penalizes hitters who put up crazy numbers in hitter’s parks, for example, while giving bonuses to hitters who put up crazy numbers in pitcher’s parks. It’s a great way to compare players across eras and scoring environments since it puts everyone on the same playing field. 100 is always league-average. For example, an OPS+ of 139 is 39% above league-average, while an OPS+ of 83 is 17% below league-average.

For a real-life example, recently-inducted HOFer Larry Walker put up a brain-melting 1.172 OPS in 1997, his MVP year. However, his OPS+ was “only” 178 since Walker played in the ultra-low-gravity moon base that was steroid-era Coors Field. Since the scoring environment where Walker played was so crazy, his OPS+ might seem a little underwhelming compared to his raw numbers. (1.172 OPS!!!!!1!!11!)

Compare the case of Larry Walker to the case of Carl Yastrzemski, who had an OPS+ of 171 in 1968 despite his less immediately impressive .922 OPS. 1968 was the infamous “Year of the Pitcher,” where offensive output was at historically low levels across Major League Baseball. The average American League OPS in 1968 was .637, which is cartoonishly low. Even though Yaz’s OPS in 1968 was a full 250 points lower than Walker’s in 1997, their relative production ended up being almost identical due to the differences in scoring environments.

There’s a similar stat for pitchers called (wait for it) ERA+. All the same rules apply- pitchers who throw in hitter’s parks get bonuses from their park factors, while pitchers who throw in pitcher’s parks get penalized. For example, Pedro Astacio had an ERA of 5.27 for the Rockies in 2000. However, since 2000 Coors Field was “the deepest pit of pitching despair in baseball history,” Astacio’s 2000 ERA+ was an above-average 110.

Compare Pedro Astacio to Mike Kekich, who was the fifth starter for the Dodgers in 1968. He put up an ERA of 3.91 that year. That looks pretty good nowadays, but the league-average ERA in 1968 was 2.98. Kekich’s 3.91 was only good for an ERA+ of 71, 29% below league-average. So before you go and draft a rotation full of Mike Kekich-es for your fantasy team, remember to check those optimized stats. They just might save your life someday.


Ichiro BABIP stat glossary
Image courtesy of Kevin French and Getty Images.

Short for “batting average on balls in play.” Essentially, BABIP is a hitters batting average minus their home runs and strikeouts. This stat is a great indicator of whether or not a hitter is soon due for regression.

For example, it’s April 24th and there’s something badly wrong with your team. Jock Thunderbat, your favorite player and perennial All-Star and face of the franchise, is having a dreadful start to his year. He’s only hitting like .150 with three homers. You’re wondering if you should try and pawn your Thunderbat jersey while it still has any value. Meanwhile Scrubbo von Benchwarmer, the team’s fifth outfielder, is looking like the next Rod Carew. He’s hitting .430 even though he doesn’t have any home runs and he’s always been a pretty sucky hitter. Should you go out and buy a von Benchwarmer jersey before prices inevitably go up?

No you shouldn’t, because what you don’t know is that Jock Thunderbat’s BABIP is a miserable .218, even though he’s hitting the ball as hard as ever. And Scrubbo von Benchwarmer’s BABIP is an absurd .473, even though he’s hitting the ball as softly as ever (and also “Thunderbat” looks soooooo much cooler on the back of a jersey than “von Benchwarmer”). The difference between Thunderbat and von Benchwarmer right now is that von Benchwarmer’s happened to bounce some extra grounders through the holes in the infield, while Thunderbat’s been crushing liners right to the opposing fielders. Assuming von Benchwarmer doesn’t have early-2000s-Ichiro levels of speed and bat control, that .430 batting average will go down very quickly as more of those bleeders through the infield start to find gloves. Meanwhile, more of Thunderbat’s line drives will start to find gaps and his average will shoot up to normal levels.

Simply having a high BABIP doesn’t mean a batter’s getting lucky. Mike Trout is always going to have a high BABIP because he’s a Greek demigod in disguise and he’s always going to hit the absolute snot out of the ball. Someone like Alcides Escobar running a high BABIP is cause for concern, however, since Escobar’s never been much of a hitter. BABIP isn’t the be-all end-all marker of good luck, but it can definitely be a useful indicator of a batter whose production might be a little on the lucky side.

Fielding Independent Pitching

Cliff Lee FIP glossary
Image courtesy of Ronald Martinez and Getty Images.

Fielding Independent Pitching (FIP for short) is a useful way of estimating what a pitcher’s production should be. Back in the early 2000s, the wonderfully-named sabermetrician Voros McCracken came up with a revolutionary theory. Basically, McCracken figured out that the only outcomes of an at-bat that a pitcher can control are the number of guys he strikes out, the number of guys he walks and the number of home runs he gives up. Results on balls put into play are completely based on luck. This theory explains how Cliff Lee could have a 2.87 ERA in 2013 and a 3.65 ERA in 2014 even though his strikeout, walk and homer rates stayed roughly the same between those two years- he gave up way more hits per nine innings in 2014 than he did in 2013.

To use another fictitious example, it’s the All-Star break and your team’s fifth starter (34-year-old Jimmy Journeyman) has an ERA of 2.94 (waaaaay below his career ERA of 4.83). That’s kind of weird since he’s walking a lot of guys and not striking anyone out, but whatever. It looks like Jimmy Journeyman’s been reborn as Alan Ace and people are buying first-class tickets on the Jimmy Journeyman Cy Young train. But in his starts after the All-Star break, Jimmy pukes up an ERA of 7.03. This happened even though his strikeout, walk and home run rates are the same as they were before the break (i.e., below-average). It turns out that he has an FIP of around 5.75 and all those bloopers and line drives that his defenders were snagging before the break are finding gaps. Jimmy Journeyman finishes with a 4.97 ERA and gets DFA’d after the season.

Like BABIP, FIP isn’t a perfect tell-all indicator of a pitcher’s true performance. However, it can be a nice little barometer of how well a pitcher is actually doing, and whether or not regression (be it positive or negative) is on the way. For anyone interested, here’s a cool article about a pitcher who could never outperform his FIP.


And that’s all for this quick little dive into some of the newer mainstream stats. Linked below are a glossary of Fangraphs statistics and some information about Statcast (for those who want to get into some of the cuh-razier information out there). Hopefully this glossary is an informative scraping at the surface of baseball statistical analysis.

“The Glossary” (Fangraphs)

MLB’s Statcast glossary


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