Fantasy Baseball: Advanced Stats Primer

ISO helps tag future power threats like Seth Smith. Photo Credit: Jon Konrath

Intimidated by advanced stats? No worries. We’re here to take you by the hand and help you become a better Fantasy Baseball owner by explaining some basic (and incredibly useful) advanced statistics.

If you are still using batting average, home runs, and ERA to gauge the value of baseball players in Fantasy, you are putting yourself at a tremendous disadvantage. Sure advanced stats can seem intimidating when you are checking them out for the first time, but once you get used to them, you’ll wonder how you lived without them. Why, before “Moneyball” came out, many people thought OBP was some sort of venereal disease. Now it is a widely accepted part of our baseball lexicon, and hopefully you’ll add a couple more wacky acronyms to your vocabulary by the time I’m done with you.

Some of you may think that you don’t need advanced stats to win Fantasy baseball leagues, and that may be true to an extent. Beating your frat brothers Jed and Hambone may require little more than a casual interest in baseball statistics, but if you plan on stepping up your game, advanced stats are a tremendous way to boost your analytical ability.

Below are some basic yet essential advanced stats that will give you a leg up on your competitors still stuck on OPS. We’ll start off with a couple of basic stats that you have probably seen before:

Statistic: Batting Average on Balls In Play
Abbreviation: BABIP
How It Is Calculated: It measures the percentage of plate appearances ending with a batted ball in play (excluding home runs) for which a batter is credited with a hit.
Use It Instead Of: Batting Average
Why It’s Valuable: While BABIP is a better statistical barometer for pitchers than hitters, it works for both sides of the ball. It basically determines how much “luck” (if any) a player had with balls that were put in play. In other words, if a hitter who bats .260 most of his career comes up with a .295 BA one year and his BABIP is much higher that season than his career mark, you can infer that said hitter probably had a lot of luck on his side. This good fortune can be in the form of hitting grounders that fielders, for whatever reason, had a hard time getting to or can be affected by an inordinate amount of errors being counted as hits. The same goes for pitchers. Ricky Nolasco, for example, has had high BABIPs the last two years and that has negatively affected his ERA. The Marlins have been a notoriously bad defense during that span, so when you have a bunch of players with poor defensive range playing behind a pitcher, the BA of balls put in play will be higher than that of a pitcher who is playing in front of a strong defense. BABIP is one of the more basic advanced stats out there, but it’s a good starting point when evaluating a player to see whether they are dealing with a significant amount of luck (be it good or bad).

Statistic: Line Drive Percentage
Abbreviation: LD%
How It Is Calculated: It calculates the number of batted balls in play that are line drives.
Use It With: BABIP
Why It’s Valuable: LD% is a great companion stat to use with BABIP. If you see a player has an abnormally high or low BABIP, you want to see their LD% next. When you see a pitcher posts a very low BABIP, it may be explained by the fact that they have a low LD%. Thus, rather than determining the pitcher is “lucky,” we can infer that the pitcher was inducing weaker contact, which leads to more field-able plays, which leads to a lower BABIP. Similarly, if a hitter registers a high BABIP and also sees a significant uptick in his LD%, we can rule out luck to an extent as the batter is making harder contact and putting more balls into play that are simply harder to field. On the flip side, if you see a pitcher has a high BABIP but their LD% has stayed neutral or has even lowered, you can determine that they are dealing with bad luck. You can also use Ground Ball Percentage (GB%) with BABIP as grounders are generally easier to field and do less damage against opposing pitchers. Thus, a low BABIP from a pitcher can have nothing to do with luck if they are getting a lot more grounders than normal as those plays are easier for the defense to field.

Statistic: Isolated Power
Abbreviation: ISO
How It Is Calculated: It’s slugging percentage minus batting average.
Use It Instead Of: Slugging Percentage
Why It’s Valuable: SLG is just too unreliable a metric to determine a player’s power production. For example, if you go 2-for-4 with two singles in a game, you have a .500 SLG, which is considered a pretty high mark. Of course, two singles isn’t much power at all, and that’s where ISO comes into play. By taking SLG and subtracting BA, you are removing all the singles calculated into SLG and are coming up with a number that takes into account only extra base hits. As far as determining what is a good ISO, anything around .200 is good for at least 20 HRs (usually around 25) for a full season. An ISO of .250 and above is entering elite territory. Anything that is between .150 and .180 is good for double-digit totals, but can vary between 13 and 22 HRs depending on park factors. An ISO below .140 is pretty underwhelming and ranges from the low teens to single digits. Whenever I am judging a player’s power potential, I look strictly at ISO and ignore SLG as Isolated Power tends to be very accurate in measuring a player’s power. For example, Seth Smith has an ISO over .215 the last two years in limited playing time so if he gets an opportunity to start, it’s very possible that he slugs close to 30 HRs. Funny thing is, his SLG was worse in 2010 (.469) than it was in 2009 (.510), yet his ISO actually was up from ’09. The reason his SLG was worse is because he posted a much lower batting average, so all those singles that he hit in 2009 were gone and gave him a lower SLG in 2010, when in reality he was actually pounding more extra base hits last year than ever before.

Statistic: Weighted On Base Average
Abbreviation: wOBA
How It Is Calculated: It is a statistic based on linear weights that measures a player’s overall offensive contributions per plate appearance. It takes into account various offensive events.
Use It Instead Of: OPS
Why It’s Valuable: We already touched on the inherent flaw in SLG, and OPS is a bit rudimentary as well. For starters, OPS counts the ability to get on base (OBP) and SLG as exact equal values. wOBA, on the other hand, uses a complex formula to spit out a number that is scaled to look like OBP. wOBA is great because all you have to do to is know what a good OBP mark is to know whether a player has a good wOBA. .350 or above is ideal, with anything above .370 being great and anything beyond .400 being among the best in baseball (Albert Pujols has a .434 career wOBA). What’s really useful about wOBA in terms of player analysis is that it’s a quick reference on a player’s overall offensive contributions. For instance, while Jimmy Rollins’ overall numbers are not as great as they used to be, most owners still think he’s a fine offensive catalyst. However, one look at his wOBA the last two years (which has been below .320) and it’s clear that he is just not as good at the plate as he was in years past (for a comparison, he had a .378 wOBA during his career-year in 2007). There are a lot of underlying stats we can use to further prove Rollins’ demise as a Fantasy force, but wOBA is a quick way to see if an individual player is trending (negatively or positively) compared to seasons past.

Statistic: Home Run to Fly Ball Ratio
Abbreviation: HR/FB
How It Is Calculated: It’s the percentage of fly balls that go over the fence for home runs.
Use It Instead Of: Just looking at HRs
Why It’s Valuable: HR/FB can depend greatly on ballpark factors, but it’s great for evaluating individual players. If a batter spends four years on one team and their HR/FB ratio sits at around eight for the first three years and then jumps to 14 percent the fourth year, one of two things has happened. Either that hitter has developed more power which has led to more balls flying over the fence (use ISO to cross reference any real jump in power), or that hitter was lucky and perhaps even had some outside forces affect their performance (i.e. wind). If you see a player with a significant shift in HR/FB, you should be skeptical about their performance. Take Clay Buchholz for example. He plays in a hitter’s park in Fenway and had a HR/FB above 14.5 percent in 2008 and 2009, but posted a 5.6 percent mark in 2010. He’s due for a major regression in HR/FB in 2011 as more balls should fly out of the park and that will negatively affect his overall numbers. Remember, sometimes there is a good explanation for a sudden shift in HR/FB (see: Jose Bautista’s change in his swing to produce awesome power), but if you do notice a dramatic change, be sure to investigate.

Statistic: Fielding Independent Pitching and Expected Fielding Independent Pitching
Abbreviation: FIP and xFIP
How It Is Calculated: It’s a measurement that only uses stats that are within the pitcher’s control (HRs allowed, strikeouts, walks, etc.) to come up with a number that is scaled to look like ERA.
Use It Instead Of: ERA
Why It’s Valuable: Runs allowed by a pitcher can be the result of events beyond a pitcher’s control like fielders with poor range, wacky bounces, and relievers allowing inherited runners to score. FIP takes those factors, removes them, and calculates a number that looks just like ERA. This number is great in determining what a pitcher’s ERA should look like. If a pitcher has an ERA of 2.97 and an FIP of 4.11, then you know that said pitcher had a great deal of luck working in their favor and an ERA correction is on the way. Similarly, if a pitcher has a high ERA but an FIP in the mid-3.00s, then they are a great bounce back candidate. Like wOBA, FIP is a quick reference that can tell you a lot about a pitcher’s performance in one clean number. There is also xFIP (or Expected FIP) which is virtually the same as FIP. The only difference is that it uses a league average HR/FB for all pitchers in order to remove the effect of any specific ball parks (i.e.- pitchers tend to have higher HR/FBs in smaller stadiums like Fenway Park while hurlers pitching in San Diego tend to have much lower HR/FBs compared to the rest of the league). xFIP is a great tool to use for pitchers who switch teams, because they are leaving their home confines and regular FIP will be skewed by their home park effect.

Full disclosure: MDS holds a grudge against xFIP/FIP and has modified the statistic to his own liking.

Chris Carbonell is a smooth sooth-sayer who does his thing as a co-founder of Hit him on the hip at or follow the man of a thousand names at