Fear Not the Abbrv: A Baseball Advanced Stats Primer

By: Dan Grant

The World Series begins Wednesday between two of baseballs oldest teams, the Boston Red Sox and the St Louis Cardinals. My man Chris Dagonas will have more on that tomorrow. The reason I bring it up now is because what I’m going to be discussing today is something that has inspired no end of controversy – from books to movies to endless blog posts, articles and even entire websites. Advanced statistics in baseball, more than perhaps any other sport, have come to define the new generation of fans. It has also divided fans in to two ‘schools’: those who believe in traditional evaluation – the eye test, ‘tools’, RBI’s and so forth – and those who have fully embraced these new statistics, known as ‘sabermetrics’.

Don't worry, there will not be a test at the end of this column.

Don’t worry, there will not be a test at the end of this column.

I’m here today because I want to tell you, the casual baseball fan, that these statistics are not scary! I feel like I’m in a unique position to do this because I’m someone who believes in the romance of baseball. I love that teams like the Red Sox and Cardinals are meeting 100 years into their history. I love looking up old names and matchups, I love going to the ballpark and just enjoying a good game. I’m not someone who’s constantly breaking down the sabermetrics, unless there’s a reason for it.

‘But Dan’, you lament, ‘baseball is boring!’

Well, if that’s how you feel, I’m probably not going to change your mind. But I would like to say this: Baseball might not have the fast pace of hockey or basketball, the slow burning tension of soccer or football, but more than any other sport, it’s one of strategy. There’s strategy to each game, each batter, each pitch. And these statistics can help you understand that strategy! That’s really the only reason for them – that and evaluating the success of past strategies. They’re tools, nothing more, nothing less. Are they the be all and end all? Of course not. Can you enjoy modern baseball without them? Of course you can! But if you find baseball ‘boring’, it’s likely because you don’t completely understand what’s going on – understanding these statistics can help remedy that.

Now what I’ve included below is a layman’s guide to advanced baseball stats. This is by no means a comprehensive list, and under the title for each stat, I’ve included a link to the excellent Fansgraphs site, which outlines a more complicated breakdown of each, including how exactly many of these metrics are calculated.

‘Metrics, Dan?!’, you say, ‘This is too complicated already!

It’s not that hard. I promise.



What it stands for: Batting Average on Balls in Play

What it sounds like: An exhausted parent trying to remind their child to be quiet. Think Dr. Evil.

‘But Mommy, I want to—‘

‘BABIP-BIP-BIP’, Mom said, holding a finger to her lips, ‘shhhhh’.

What it actually is: This is an excellent tool for evaluating whether a hitter is about to come out of a cold streak, or is actually swinging well but just getting unlucky. In baseball, a screaming line drive to the third baseman is the exact same ‘out’ as a dribbling grounder to the pitcher. One creates the same final effect as the other Essentially, what BABIP does is eliminate strikeouts from the calculation and tell us how many of a player’s actual batted balls are falling for hits. Players, almost without fail, will regress to their career average – meaning if their BABIP is very high, they’re getting lucky and are due to cool off and just the opposite if their BABIP is very low. BABIP can obviously be affected by certain variables, such as opposing defensive skill, but generally over a long sample, players will come back to the average.

I have this listed under the hitting category but it’s also a valuable tool for evaluating pitchers. The same good luck/bad luck can apply to a pitcher, so if you see a previously poor pitcher on a hot streak, or vice versa, checking out their BABIP to see if they’re getting lucky is always a worthwhile exercise.


What it stands for: Isolated Power

What it sounds like: A bad retirement savings plan that one might dip into for ill-advised purchases.

‘Well Jerry, I’ve got a few bucks squirrelled away in my iSO, so I should be able to buy that sand sculpture of Gore Vidal before too long!’

What it actually is: We’re all familiar with batting average. That is, you take a player’s at bats, and you take their hits, and you divide at bats by hits, and you wind up with JP Arencibia breaking records for futility. What teams have tried to do in recent years is create statistics that show exactly how good a player is at hitting for power – many of us will have heard of a stat called ‘slugging percentage’. This is the percentage of player’s hits that go for extra bases (anything beyond a single). What ISO does, is separate that into a more concentrated statistic by subtracting the batting average from the slugging percentage. It winds up showing exactly what percentage of at bats a player is hitting for extra bases in.


What it stands for: Weighted on-base Average

What it sounds like: A Star Wars villain.

‘Han, we need to drop this shipment or we’re going to be boarded!’

‘Sorry kid, if I do that, wOBA will fry my ass. Why am I still smuggling anyway? I’m married to a princess!’

What it actually is: Many casual baseball fans will have heard of a stat called OPS. Essentially this is the number we get when we add a player’s on-base percentage (hits plus walks) to their slugging percentage (extra-base hits only). OPS is useful, but is essentially flawed because it assumes that on-base and slugging are of equal value.

One of the most complicated advanced metrics, wOBA is also one of the most effective. It essentially eliminates the shortcomings of batting average and on-base percentage (which both give equal value to all hits, so a home-run affects them in the same way a single does). By applying a linear formula that changes slightly each season to compensate for league averages, wOBA gives us the most accurate and comprehensive picture of what a player has achieved at the plate.


What it stands for: Weighted runs created

What it sounds like: Terrible packages a used car salesman might offer you.

‘I can see that you’re going to need insurance! Now, our wRC package is basic and will cover your needs, but might I recommend the wRC+? I’ll throw in a free set of snow tires!’

What it actually is: Moneyball was an excellent book and a solid film. One thing the film skimmed over was how all these advanced stats got started. Bill James is considered the godfather of sabermetrics and wRC is an enhanced version of one of his earliest metrics, so credit is due to him here.

Now based off wOBA (as opposed to James’ original method), wRC essentially calculates exactly how many runs a player was worth to his team in a given year. Meanwhile, wRC+ goes further, comparing this number to the league average, where the average is always set at 100, just to avoid going into negative numbers.

Potent visual metaphor.

Potent visual metaphor.


There are a lot of less complicated stats that have worked their way into pitching in the past few years that are really valuable when viewed as a whole. WHIP (walks/hits per innings pitched) is a very common one, and even the driest baseball announcer will now mention a pitchers K’s/9 – that is, how many strikeouts they average over a nine inning period. BB’s/9 (walks per nine innings) will generally be mentioned in the same breath, as strikeouts and walks are generally seen as the only two outcomes a pitcher controls totally, along with home runs allowed.

Helpful percentages such as fly ball rate and ground ball rate can also tell us how effective a pitcher is being and which pitchers might succeed in certain ballparks.


What they stand for: Fielding Independent Pitching/Expected Fielding Independent Pitching

What it sounds like: Some type of elite British Soldier.

‘Terrence served as a FIP in Grenada, but since he’s been forcibly retired, he spends all day drinking in xFIP bars and—‘ Wow, this one got dark. Let’s move on!

What it actually is: FIP calculates what a pitcher’s ERA should have been were their defense the exact league average. Sometimes poor pitchers can be helped out by factors such as the park they play in, or if their team is exceptional defensively. Great pitchers can be affected negatively by the same factors. FIP gives us a truer picture of how the pitcher is performing in and of themselves. This can be a helpful tool for determining trade value, as well as why a pitcher might pitch differently at home, as opposed to on the road.

xFIP is a further development of this in that it adjusts for park factors. It’s created in the same way as FIP but then it further adjusts for what a pitcher’s home run rate should have been. Obviously parks can affect this – a home run in one park is not a home run in the other. By taking the league average home run-fly ball rate and multiplying it by that particular pitchers personal fly ball rate, we can figure out what they would have allowed were the parks not a factor.



What it stands for: Ultimate Zone Rating

What it sounds like: In a post-apocalyptic world, only the strong survive. Increase your Rating by pillaging travellers and the innocent! Welcome to the Ultimate Zone!

What it actually is: Essentially an amalgam of a lot of different defensive statistics, UZR is a widely used defensive stat that attempts to assign run value to defense – essentially it shows how many runs saved or lost a player was worth at a specific position. It does this by comparing estimated runs allowed to that of a league average fielder at that position for the season.

One thing I’ve found useful for UZR is to compare players year to year. For example, even though many assume he was an elite outfielder again this year because of his spectacular rookie season, Mike Trout actually fell off a cliff in terms of UZR this year.

Flaws here include players that play multiple positions – it’s hard to definitively discuss their ability if the sample size is under 50 games. The creation of UZR/150 can combat this, but it’s still important to take sample size into account.

The basic premise for UZR came from a stat known as Defensive Runs Saved (DRS). This is also a complicated metric, but can be helpful when viewed in conjunction with UZR.



What they stand for: Wins Above Replacement/Value Over Replacement Player

What it sounds like: Some type of rude Eastern European insult.

‘Tomasz, if you don’t give me back that china hutch, I’m going to WAR all over your VORP!’

What it actually is: It’s a (probably) ultimately flawed attempt by the advanced statistical community to create a tell all statistic, including all relevant statistics for one player. Simplified, a league average player creates 1.0 wins for a team – that being the ‘replacement player’, or a middling player at that position. Any wins above this tell you the value of a player to their team.

WAR is one of the more controversial statistics in baseball right now, as many of the old guard resist a stat that seems so theoretical. All I can say is that when you look at the WAR leaders each season, they’re invariably the players who are most important to their team’s success. Similarly, VORP is essentially the same calculation by a different group of nerds!


See? Not so scary. Anyway, I hope this has been helpful. I’ll be diving into advanced statistics for the NBA, NHL and NFL in the coming months but I thought it best to start now with the godfather of all statistical sports.

Have a great World Series everyone.

Go Cardinals.

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