Over the years, the growth of statistical analysis for baseball has been exponential. Crunching numbers has always been a part of the sport, but once analysts like Bill James and Tom Tango put their work front-and-center and sabermetrics went mainstream, Major League clubs have taken things to an entirely new level. That analysis has spilled over into the fantasy baseball world and thanks to web sites such as FanGraphs and Baseball Prospectus, the numbers are much more readily available to the public.

The problem, however, is that Joe Everyman isn’t a mathematician who understand complex equations, algorithms or things like coefficient multipliers and there seems to be an intimidation factor with regard to using the analytics. They’ll read an article that says, “Christian Yelich has a .359 wOBA against right-handed pitching,” and if they’re not completely turned off by the statement alone, they seek out a definition of wOBA and find this:

What the heck is that???

Some people may look at that and feel comfortable with the definition, but the majority of casual fantasy players turn and run. “Weights change slightly with the run environment? Um. No thank you.”

But it doesn’t have to be that way. You don’t have to be Blaise Pascal or Albert Einstein to use the vast number of metrics presented to you for your fantasy analysis. If you know your metric benchmarks, you can use all of them quite easily.

Let’s start off with something easy like Batting Average. Simply put, it is calculated by taking the number of hits a player has and dividing them by the number of at-bats. Easy right? Sort of. You just have to remember that walks, hit-by-pitch (HBP) and sacrifice flies don’t count as at-bats, so the actual formula is H/PA-(BB+HBP+SF). The abbreviation PA is plate appearances, the number of times a hitter actually steps to the plate.

But do you need all of that? If you look at FanGraphs, all of those individual numbers are there, but so is the each player’s calculated batting average. If you know that a .300 batting average is what you strive for, then you know what type of a player you are dealing with based on how much higher or how much lower than the benchmark their actual batting average sits. Last year, Jose Altuve had a .346 batting average. Yum! Todd Frazier had a .213 mark. Eww! That’s not to say Frazier doesn’t have his own merits, but you at least know if you’re looking for help in the batting average category, he’s probably not your first target.

Having that very simple understanding can take you a ridiculously long way as you research players and set up your Draft Day targets. Again, you don’t need to know how to calculate these metrics, you just need to know a general definition and the baseline number to know what’s good and what isn’t. Below is a list of some of the most-popular metrics used in player analysis with simple explanations and the baseline numbers you need to know.


OBP (on-base percentage) – measures how often a player gets on-base which includes home runs. It includes walks, sacrifice flies and HBP, things not accounted for by batting average. Many leagues are changing from using batting average to OBP because it is considered a better measure of the hitter’s overall talent. League average is roughly .320, so you’re obviously looking for players with a higher mark.

SLG (slugging percentage) – measures how many total bases a player achieves per at-bat. It reads just like batting average and OBP but simply weights each hit appropriately. A double is better than a single. A triple is better than a double, and so on. League average tends to hover in the .415 range.

OPS (on-base plus slugging) – all this does is combine on-base percentage with slugging, so if the league average for OBP is .320 and SLG is .415, then your average OPS is right around .735.

BABIP (batting average on balls in play) – it measures how often a ball-in-play falls for a hit. By definition, according to FanGraphs, “A ball is “in play” when the plate appearance ends in something other than a strikeout, walk, hit batter, catcher’s interference, sacrifice bunt, or home run.” This is where some people start to get a little lost and frustrated. The talk of reasons behind the number, variances, luck factors, etc. tend to turn people off. So look at it this way: If league average is .300, then you are looking for guys with career marks above it. Now if a player has a career mark of .320 and he’s off to a hot start with a .362 BABIP, you should probably expect him to cool off as his current BABIP should finish a lot closer to his career mark. You’ve heard of regression, right? Regress to the mean? That’s what they’re talking about. A .362 mark will likely regress to a number closer to the career average. Quick side note: regression doesn’t mean something bad. It is commonly misused that way. If a guy has a .320 career BABIP and during his slow start, has a .265 BABIP, that number will also regress to the mean. Regress doesn’t mean reduce. It means move closer to the average.

ISO (isolated power) – this is used to measure a player’s raw power and how often he hits for extra bases (doubles, triples and home runs). There is a formula used that weights each hit appropriately, but you can also find a player’s isolated power mark by subtracting their batting average from their slugging percentage. League average sits around .140 and the higher you go, the more power you’re getting. The most-coveted marks are over the .200 threshold.

wOBA (weighted on-base average) – The DFS community has really fallen in love with this statistic and you’ll hear it thrown around a ton when industry folk are doing their analysis and suggesting players. It combines all the elements of batting average, OBP, SLG and OPS and is considered the best measure of a hitter’s talents, reading just like batting average and the rest of them. The formula is complex complete with varying coefficient multipliers. Yuck! What you need to know is that league average is .320 and you’re definitely striving for numbers higher than that. In DFS for example, when Jason Vargas was throwing against the White Sox, you probably wanted Jose Abreu and Avisail Garcia in your lineups as they had respective wOBA marks of .428 and .434 against lefties.

wRC+ (weighted runs created plus) – Here’s another one the DFS community really helped bring into the foreground. Simply put, wRC attempts to quantify a player’s total offensive value and measure it in runs. The ‘plus’ compares it to league average after factoring in ballpark effects. League average is 100 and you’re obviously striving for players who hover significantly over that mark.


FIP (fielding independent pitching) – It reads just like ERA and the expanded acronym says it all. There are things a pitcher can control and there are things he cannot. Poor defense, lucky bounces, you name it, can all affect a pitcher’s ERA. What they’ve done here is measure a pitcher’s performance as if every pitcher had league-average defense behind him. It’s a good way to measure the level of the pitcher’s actual performance, but shouldn’t be the be-all, end-all, especially considering FIP isn’t a fantasy category anywhere, at least not a mainstream one. You can use it when deciding between pitchers to draft, or, what I like to do is to see which pitchers have the biggest disparity between their ERA and FIP to determine if they’re just having bad luck or if maybe a change at defense could help them. For example, Pitcher A has an ERA of 5.50 and a FIP of 3.50. When I look at the 2.00 differential, it tells me that maybe the pitcher has been unlucky with the defense behind him or getting some bad bounces. Chances are, we’ll see an improvement moving forward provided the defense steps up and some of those bad bounces start going his way. Conversely, if Pitcher A has a 3.50 ERA and a 5.50 FIP, what does that tell you? Trade him! League average sits around 4.20 and just like ERA, you are striving for a muc lower number.

xFIP (expected fielding independent pitching) – As if going from ERA to FIP wasn’t enough, the nerds had to try and take it one step further. FIP removes defense from the equation. What xFIP does is attempt to take away some of the other “randomness” from a pitcher’s performance. Apparently, the way to do that is to not use their actual home run allowed total but to go with how many home runs they would normally allow given their fly-ball rate. They do this by using the league-average HR/FB rate in the equation and, supposedly, it takes away some of the fluctuation you see in FIP due to long balls allowed. I’m not totally sold on this for a variety of reasons. Ballpark effects aren’t accounted for and let’s face it, there are some pitchers who get hit harder and give up more dingers as a result. I’m not saying to ignore it, but it’s definitely easier to read than to implement. Again, it reads just like ERA and league-average is 3.80. As with ERA and FIP, you’re looking for lower numbers when scouting players.

SIERA (skill-interactive ERA) – Yep. They did it to us again. From ERA to FIP to xFIP and now here to SIERA. The problem with ERA estimators is that no two pitchers are alike and the way they throw, the contact they give up, the choice of pitches, the ballparks, etc. are all variables that need to be taken into account. SIERA supposedly is a better indicator than the others as it puts more emphasis on strikeouts, walks and batted ball data (ground balls vs fly balls). They say it’s the best ERA estimator out there, but if you’re evaluating pitchers, it’s definitely best to look at FIP and xFIP as well. SIERA also reads just like ERA and the league-average (which tends to fluctuate year-to-year but hovers in a reasonably similar area) is 3.90 with the lower the number, the better.

BABIP (batting average on balls in play) – Yep, they use this for pitchers as well. The definition is exactly the same as above, but you’re obviously looking at it almost in reverse for studying pitchers. League average is still .300 but you obviously want a lower mark for your pitchers. They don’t often stray too far from the league average, but there is variance nonetheless.

Now one thing that should be stressed here is that we aren’t telling you to ignore how and why the actual formulas are calculated. If you have an interest, then, by all means, study away. This is more for the casual fantasy baseballer who wants to be able to analyze the players based on all the statistics they hear and read about, but don’t have a strong understanding of some of the complex equations or reasoning behind things like coefficient multipliers. If you do have an interest, then take it one at a time. Study the equations of ERA first. Then go through and understand FIP. Then xFIP. Then SIERA. Don’t try to do it all at once because if you don’t have a firm grasp of FIP, then understanding SIERA is going to be a mess.

One last thing….a nice helpful tool. Below is a chart that gives you all the metrics you’ve just read about with their league averages and the numbers you strive for. It should make for an easy and quick reference point for you.

MetricLeague AvgStrive For
MetricLeague AvgStrive For


*big thanks to our friends at FanGraphs for all the work they do.