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No matter if you read stuff at SP Streamer or NBC Sports Edge you will see underlying metrics mentioned in virtually every article. As writers, we sometimes forget that not everyone knows what we know, and not everyone knows how advanced stats are meant to be used.

This reminds me of when I first started. One of my friends introduced me to a website called FanGraphs. The first metric he ever introduced to me was BABIP and I remember constantly looking at a player’s BABIP to tell if they were lucky or unlucky. I was relating players’ BABIPs to the .300 mark to tell how they would perform in the future, which couldn’t have been more wrong.

Basically, I stepped into the baseball metric abyss learning basic metrics the wrong way. I was one of those victims in a scary movie where you come to a crossroads and the sign says live one way and cliff the other way. Yea, I drove off the cliff.

If you find yourself seeing these metrics thinking “what the heck is this gibberish?” don’t worry, I am here to guide you!

We are going back to the basics! Here are the most simplistic baseball metrics and how you can use them to step up your game.

BABIP

BABIP stands for Batting Average on Balls in Play. To quote from Fangraphs “BABIP is a statistic which measures how often non-home run batted balls fall for hits.” League average BABIP is always right around .300.

In essence, BABIP is used to spot if a player (both hitter and pitcher) was lucky or unlucky throughout the season. The key here is to use a player’s career average BABIP. There are a few reasons for that and one of them being speed. If a hitter has a lot of speed he can continuously hold a high BABIP because he can beat out throws and create infield singles.

For instance, this is why we use career average: Tim Anderson has a career .353 BABIP while Joey Gallo has a career .264 BABIP. A disparity like this can throw off someone who doesn’t know the basics of this metric. If you assume all players should be around the .300 mark you would think Tim Anderson was lucky last season while Joey Gallo was unlucky even though that is untrue.

To reiterate, BABIP is a good metric to use for luck but make sure you look at pitchers’ and hitters’ career averages before making an evaluation.

wRC+

To understand wRC+, we must first go over wOBA. Batting average awards points for all hits no matter if it is a home run or a single, but we know not all hits are equal. The same is true of On Base Percentage, a walk and home run both credit a batter with reaching base. Weighted on Base Average (wOBA) weights to each outcome according to what value it produces, creating a better picture of the batter overall than just AVG or OBP. The weights used in calculating wOBA differ slightly each year, but as you can imagine a home run is weighted the heaviest and so on.

wRC+ stands for Weighted Runs Created Plus. Based off wOBA, wRC+ also accounts for league average statistics and Park Factors. The values are easy to read, 100 is always league average, anything below 60 is terrible, and anything over 160 is excellent. For instance, last season’s wRC+ leader was Bryce Harper with 170 while the worst wRC+ last season belonged to Kevin Newman with 54.

wRC+ is just a way to take batting average and skill set a step further. wRC+ is a key metric everyone uses for hitters and you will continue to see it talked about in articles.

FIP

According to Fangraphs “Fielding Independent Pitching (FIP) is a statistic that estimates a pitcher’s run prevention independent of the performance of their defense.”

This means if the pitcher had league-average results on balls in play, FIP is what their ERA would look like. FIP is descriptive, not predictive though which means this shows if a pitcher was lucky or unlucky but it doesn’t mean the luck will correct itself moving forward.

FIP is good to use with other statistics, it’s essentially a building block. If a pitcher has a lower FIP than their ERA look for other indicators like K-BB% to see if in fact, they could perform better moving forward.

xFIP

xFIP is an underlying metric that can be used as a predictive ERA estimator. By definition of Fangraphs: xFIP is a regressed version of Fielding Independent Pitching (FIP), developed by Dave Studeman from The Hardball Times.

It’s calculated in the same way as FIP, except it replaces a pitcher’s home run total with an estimate of how many home runs they should have allowed given the number of fly balls they surrendered while assuming a league average home run to fly ball percentage (between 9 and 10% depending on the year).

In other words, it normalizes a pitcher’s home run rate while also using FIP to create an ERA estimate. There are some big issues with this metric and I see it being used wrong all of the time. Some pitchers are extremely home run prone while others aren’t and that is something xFIP will not see. This is why using SIERA and K-BB% to predict ERA is far better than xFIP.

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Michael Simione

Michael Simione

Michael Simione is the owner of SPStreamer.com. He started the blog based on a Twitter account he created back in 2018. He specializes in pitching as well as streaming pitchers. He most importantly is a die-hard Mets fan.

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