Analyzing xBABIP: Not All BABIPs are Created Equal (Part 2)

So what is xBABIP and how can we use it to evaluated players? In part 2, we are covering players whose xBABIP outpaces their BABIP.

Statcast has given us a lot of data to play with and those smarter than I am are putting it to good use. Andrew Perpetua released his new batting average on balls in play results on Fangraphs, titled xBABIP. And as always, I want to dive into these to determine if we can syphon any underlying value in analyzing a player’s potential fantasy value. If you read part 1, you can skip to the section where I talk about the BABIP and xBABIP differences. And in part 2, we are covering players whose xBABIP outpaces their BABIP.

EXPLAINING XBABIP

BABIP alone is batting average on balls in play and the calculation is pretty simple; out of all balls put into the field of player (excluding strikeouts, excluding home runs essentially), what is that player’s batting average? League average BABIP is right around .300. Here is the formula below.

BABIP = (H – HR)/(AB – K – HR + SF)

So Perpetua is taking that same concept but using strictly Statcast data to create a BABIP based on a player’s batted ball abilities. He does a pretty good job at explaining his process and the kinks that haven’t quite been worked out in the article linked in the opener. But here are a few points I’ll highlight.

1. xBABIP is looking to pull out the luck aspect of BABIP from itself. Ever get into an argument with someone over whether or not X Player’s BABIP is luck or skill? Well this attempts to separate those so we can determine if an above average BABIP can be credited to a player for just being a damn good hitter.
2. Physics complicates things and Perpetua admits as much. The longer the ball hangs in the air means the longer time a player has the chance to make a play. He hasn’t quite worked out how to measure this with 100% accuracy.
3. His version of xBABIP is a pure batting stat and “only measures the player’s ability to bat the ball…conducive to reaching base safely.” This means two things:
   • He isn’t incorporating player speed, as a fast player will typically result in a higher BABIP even if his xBABIP is lacking.
   • This doesn’t take into account shift data as of yet. So player’s with lower BABIPs due to successful shifts against them might result in higher xBABIPs.

THE APPLICATION OF XBABIP IN EVALUATING PLAYERS

Now those two subpoints above are being worked on but I also wonder if he even should. A big contention in pitcher evaluation and the reason we have xFIP and FIP is a result of the home run ball and is it able to be controlled by the pitcher. Some evaluators pick a side on which is the better stat but the truth is you should use both; for some pitchers, it’s more appropriate to use FIP — like Jordan Zimmerman or Gio Gonzalez, who have produced a below average FIP for pretty much their entire careers. Other pitchers and other circumstances call for us to use xFIP to evaluate pitchers.

I see that if we can hone and tone xBABIP, we can use it similarly to how we use discretion when using xFIP and FIP. Where BABIP might be more appropriate for your Dee Gordon‘s and xBABIP might be more appropriate for your David Ortiz‘s. Or not. We really don’t know yet.

But what I love about this new metric being able to calculate BABIP using only batted ball data (Statcast specifically) is that I finally have data to point to when we make assumptions about BABIP.

For example, a lot of people have said Nick Castellanos is about to crash back to earth. To which I responded in a thread, “Regression is coming but people who point to his BABIP being unsustainably high…that’s what happens when you have a 31.1% Line Drive rate. You are going to have a way above average BABIP at that point, especially when you are only making Soft Contact 10% of the time.”

More from Fantasy Baseball

• 5 fantasy baseball waiver wire pivots to replace Triston McKenzie
• Fantasy baseball mock draft 2023, 12-team: Aaron Judge over Trea Turner?
• 3 fantasy baseball sleepers being drafted too late
• NBA DFS picks December 25: Merry Bucking Christmas
• Fantasy Baseball: Hot pitchers worthy of starting this weekend

Well in that instance, I was simply using the Fangraph provided Batted Ball data to make an assumption that a guy who hits Line Drives at that rate will have a great BABIP. But now with xBABIP, I actually have a BABIP to point to to say, “This is where his BABIP should be because he is hitting those line drives at that particular pace with the quality of contact he has shown.”

So in defense of Castellanos — who I selected as my #1 breakout third baseman earlier this year — I’m interested to see what his xBABIP is. And at the point of writing this sentence, I have no idea what his is yet; I said in that same comment, “He’ll fall somewhere between what he is doing now and what he did last season…” and let’s see if I’m right. But first, in looking at his spreadsheet, here were some things I’ve found.

PLAYERS WITH HIGHER XBABIP THAN BABIP

The chart below highlights players with at least 100 PAs that I felt were interesting enough to include. If you want to search particular names, Perpetua provides a download to the list in his article. Since my last article, some of the players’ xBABIP and BABIP differences have started to normalize but we still have some interesting names.

1. Slow-Footed SluggersIf you remember at the end of Part 1, I theorized that we were going to see three things: slow-footed sluggers, left-handed batters who are shifted on, and the players getting unlucky this season. Well, I think there are a healthy amount of all these players on this list.

There are some great example of how a lack of speed affects your BABIP. A lot of these players are older so guys like Matt Holliday, Jason Werth, and Matt Kemp just aren’t achieving their xBABIP now. I’m not saying that this is the majority of the reason why but it certainly plays a role.

Albert Pujols is another great example of how lack of speed can truly affect your BABIP. But not only that, but on the leaderboards, Pujols has been the most shifted on right-handed batter. Look, teams have been shifting for the past decade but I think this is truly the year where we are going to see just how effective it can be for both right-handed and left-handed batters.

2. Shifted Batters

When I started looking at the data, I thought it would be confined to left-handed batters but we are seeing right-handed batters also make this list. First let’s get guys like Prince Fielder, Ryan Howard, Carlos Santana, and Kendrys Morales out of the way.

All of these guys — and there are more on the list — fit the left-handed batter getting shifted on a ton. Even for the switch hitting Morales, He faces right-handed pitchers so when he is shifted on as a lefty, it absolutely kills his batted average.

But it was also interesting to see guys like Randal Grichuk on this list; right-handed batters are getting shifted on more than ever before. So Grichuk wasn’t immune to that as early in April he was experiencing the shift. What is great about Grichuk is that we’ve actually seen a commitment to going the opposite way, with a 26% Oppo% so far in 2016. Even though we are using statcast data, I can see just in the batted ball profile that a FB% of 40% and a LD% of just 12.5% will not lead to a high BABIP, regardless of your contact quality and distribution. But the adjustments of Grichuk can’t be ignored.

I actually was watching the game while writing this piece and Grichuk hit an opposite field home run yesterday against Jake Arrieta during this section. He also had an RBI single to right field against Arrieta to add on to his opposite field walk-off home run a couple of nights ago. Just a nice little note for you guys.

And even guys like Jason Heyward are proving that your speed doesn’t make you immune to an effective shift. His .339 xBABIP is fantastic but his .284 BABIP would be the worst of his career in a non-injury season. As you can see, he is already halfway towards the amount of times he was shifted on last season.

Going forward this year and next, I’m going to be monitoring shift data pretty heavily. It is suppressing batting average across the board. The 2015 batting average was .254 across the entire league. And while it is only at .251 so far in 2016, I think it is more likely to fall as the season progresses than rise.

It’s time for us to really consider shifted data when analyzing all players and our projections. And As you can see above in the image from Sports Illustrated, shifts are up to 29.5% of balls-in-play which is a full 10% higher than last season.

I truly hate to do this because he is a core member of my keeper league team, but Anthony Rizzo is becoming truly worrisome. Since the beginning of the season, he has struggled to keep his batting average at a Rizzo-esque line.

First, let me get it out of the way that it’s possible to be successful and have a great batting average even if you’re shifted on constantly. David Ortiz teaches us that and Rizzo and Big Papi are #2 and #1 most shifted on players in the MLB.

However, when you are a player like Rizzo or Ortiz, you BABIP is much more dependent on luck and fluctuates year by year. For example, the last four seasons Ortiz has posted pretty much identical Batted Ball metrics. However, his BABIP has varied from .321 to .256 to .264 to .345 due to the randomness of baseball.

According to his batted ball profile in 2015, he should have posted an above .300 BABIP because it’s pretty much identical to his 2013 season. But alas, that’s the effect of shifted hitters: they are less predictable in terms of average.

More from FanSided

• MLB Trade Grades: Dodgers reunite with familiar face in Red Sox deal
• NHL Rumors: Legendary Patrice Bergeron retires, which 3 players could be next to hang up their skates?
• MLB Rumors: Cardinals budging on Arenado, Orioles-Ohtani package, Yankees backup plan
• Patriots backup plan for DeAndre Hopkins is a shot in the dark
• Raiders: Saquon Barkley’s new contract may have screwed over Josh Jacobs

So when Anthony Rizzo comes out and says, “Balls really not falling for me at this moment…it’s a little frustrating for me.” Well, it’s been a little frustrating for us as well. First off, Rizzo is not striking the ball well with his 15.9% line drive rate and that’s a big reason for his dip in average. But even as that raises, thus raising his xBABIP, can we really have much certainty about his average as a player going forward?

I mean, in the above list, his xBABIP was second worst next to Ryan Howard. So I disagree Rizzo, it’s not just about balls not falling. You might need to change that approach a little and adjust to this new shift-heavy league.

3. Unlucky Players

So now the question most people want to know is…who are the buy lows?

Adam Jones is someone I would have to put in this category. A career .310 BABIP guy, we can truly mark this up to bad luck and a poor start due to injury. He isn’t being shifted on much more than normal and he is still a speedy runner even though the steals have fallen. Also, his xBABIP in 2015 was .306, which doesn’t indict his .299 BABIP this season too much.

Kyle Seager is another batter I would target and it actually goes beyond the xBABIP and BABIP differential. He has actually go to the opposite field more this season than he has in his entire career. Almost 30% of his batted balls are to the opposite field this season. He has also shown a better approach at the plate, raising his BB-rate to 10.1% and cutting his K-rate to 13.2%.

I’ve called Seager one of the most dependable fantasy options in the past. Since he is a player with a great line drive rate historically, it’s reasonable to suggest Seager might produce the best average, on-base percentage, and slugging of his entire career when this season concludes.

There may even be an opportunity to buy on guys that people think they are selling high on but actually aren’t. On the surface, Victor Martinez looks to be having a career year with a .346 BABIP being the best he has ever posted. But his xBABIP suggests that this is real even though the line drive rate should stabilize. He is almost a lock to bat .300 this year and the counting stats should be there in the Tigers offense.

CONCLUSION AND PART 3 PREVIEW

We have already started to see the effects of the shift in baseball and it effects the studs just as much as it does the duds. But what is encouraging is seeing those hitters who are now using the opposite field to counteract the shift. I think going forward, we are going to look at BABIP for players in a completely different way and each hitter should be looked at separately. Not all BABIPs are created equal.

Next: Pittsburg Pirates Promote Jameson Taillon

Part 3 will be focusing on batters that have BABIPs that are outpacing their xBABIPs. Prediction: we are going to see the speedsters of the league who leg out those infield hits routinely as well as those batters who are sell high options due to a fortuitous BABIP. Stay tuned.