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# 2015 BABIP overperformers and underperformers

Which players overperformed and underperformed their expected BABIP the most in 2015?

In some ways, batting average on balls in play (BABIP) is a helpful way to quantify how "lucky" a batter is over a period of time. Typically, we can look at a batter's BABIP and have a good idea of whether or not his performance is sustainable over a longer period of time.

With that being said, there can some subtle differences in hitter BABIPs that can be sustained for a long period of time. For example, batters with good speed tend to have higher BABIPs because of their ability to reach base on infield hits. In addition, batters who make more hard contact also tend to have a higher BABIP, since hard-hit balls are less likely to be turned into outs by the defense.

Because of factors like these, analysts have attempted to create an expected BABIP (or xBABIP) statistic, which puts all of these factors together to determine what we would expect a hitter's BABIP to be. Earlier this year, Alex Chamberlain of RotoGraphs created an updated xBABIP formula which included FanGraphs' latest batted ball data from Baseball Info Solutions. The formula is:

xBABIP = .1975 — .4383*(True IFFB%) — .0914*(True FB%) + .2594*LD% + .1822*Hard% + .1198*Oppo% + .0042*Spd

This formula takes into account several relevant factors, including batted ball type, quality of contact, batted ball location, and speed. While it may be possible to find ways to improve this formula, I figured this it would be sufficient for the purposes of this article, especially since it incorporates some of the latest and most reliable batted ball data.

Using this formula, I calculated the xBABIP for all qualified batters in 2015 and compared this number to their actual BABIP. What follows is a summary of the five biggest BABIP overperformers and underperformers.

Overperformers

1. Kris Bryant (.378 BABIP, .316 xBABIP)

Last night, Bryant was crowned NL Rookie of the Year Award, and while he certainly lived up to the hype surrounding him coming into the season, part of his outstanding rookie season was the result of an inflated BABIP. While Bryant is faster than many people give him credit for (5.4 Spd), his batted ball distribution is weighted heavily towards fly balls (45.2%), which are almost always converted into outs when they stay in the ballpark. The good news for Bryant is that 15.8% of his fly balls went for home runs, which is well above league average. It is possible that the xBABIP formula is selling Bryant short by assuming that more of his home runs should be converted into outs.

2. Odubel Herrera (.387 BABIP, .332 xBABIP)

Herrera quietly put up an excellent rookie season, posting a 110 wRC+ and 3.9 fWAR as the Phillies' primary center fielder. While Herrera can be expected to maintain an above average BABIP due to his speed (5.3) and high ground ball rate (47.2%), it is unlikely that he will be able to come close to repeating his insanely high .387 BABIP.

3. Dee Gordon (.383 BABIP, .334 xBABIP)

Gordon thrived in 2015 due to a combination of speed (7.3 Spd) and ground balls (59.8%) that was even more extreme than that of Herrera. Gordon has posted high BABIPs throughout his career, and he will likely continue to do so, but his .383 mark is certainly an outlier, as his career BABIP coming into this season was only .326.

4. Bryce Harper (.369 BABIP, .324 xBABIP).

Harper had a historically excellent season in 2015, and as is the case with most historic seasons, there appeared to be some luck involved. Like the hitters above, Harper can be expected to hit for an above average BABIP, especially if he continues to own one of the highest hard contact rates in baseball, but it would be unrealistic to expect him to match his .369 BABIP from this past season. Even so, Harper will likely still be an elite hitter and MVP candidate for years to come.

5. Addison Russell (.324 BABIP, .280 xBABIP)

Russell is the first player on this list with a below average xBABIP, but he still outperformed it by 44 points. Unlike the players listed above, Russell does not appear to have an unusual skill (hard contact, ground ball/speed combination, etc.) that would allow him to maintain an above average BABIP going forward. (Side note: Russell is also the third rookie on this list. I have no idea if this is a coincidence or if there is some underlying reason as to why this is the case.)

Underperformers

1. Albert Pujols (.217 BABIP, .280 xBABIP)

Pujols had an unusual season, in that he was still an above average offensive player despite posting a dangerously low BABIP. He is clearly a below average runner, and he continues to pull the ball nearly half the time he puts it in play, which could be leading to an increase in defensive shifts against him. Still, given Pujols' quality of contact, it seems reasonable to expect a bounceback in his BABIP next season.

2. Ryan Howard (.272 BABIP, .333 xBABIP)

Typically, we think of Ryan Howard as the prototypical low BABIP hitter. He does not have great speed, and he is one of the most shifted hitters in all of baseball. However, in 2015, Howard posted a career best 27.7 percent line drive rate, which may explain his unusually high xBABIP. Given how often Howard pulls the ball, it seems likely that a lot of these line drives are being hit right into a defensive shift, which could explain the gap between his actual and expected BABIP.

3. Jay Bruce (.251 BABIP, .307 xBABIP)

Bruce's 2015 peripherals suggest that he should have carried a BABIP pretty close to league average. Indeed, his .251 BABIP this year was significantly lower than his .287 career BABIP. Like Pujols and Howard, Bruce sees a fair amount of infield shifts, especially since only 22.7 percent of his batted balls were hit to the opposite field. This is the second straight year that Bruce has posted a below average BABIP, so it is quite possible that the increase in shifts throughout baseball is having a significant impact on his production.

4. Logan Morrison (.238 BABIP, .292 xBABIP)

Morrison represents yet another pull-heavy home run type hitter, although he does not have the name recognition or career success of some of the other players on this list. His xBABIP is slightly lower than some of the other players on this list, likely due to his below average line drive rate and above average infield fly ball rate. It may be unrealistic to expect Morrison to post a league average BABIP going forward, but he should improve from his .238 mark.

5. David Ortiz (.264 BABIP, .314 xBABIP)

It only seems fitting that Ortiz would round out the top five, given the other names on this list. Ortiz is perhaps the most extreme example of a pull-heavy slugger with no speed who will see an infield shift nearly every time he steps up to the plate. Unlike the other hitters on this list, though, Ortiz has consistently posted an excellent hard contact rate, which is one of the major reasons that his xBABIP is well above league average. Like Bruce, Ortiz has posted BABIPs well below average for two straight seasons, so it is possible that the shift is finally catching up to him.

Given the names on this list, it appears as though the xBABIP formula tends to overrate pull-heavy sluggers. Initially, one may expect the inclusion of Oppo% to adjust for the effect of infield shifts (i.e. hitters with a low Oppo% will be shifted more often, resulting in a lower BABIP). However, upon reexamination, it appears as though the xBABIP formula was created using batted ball data going all the way back to 2002, when shifts were rarely used.

The good news is that this exercise may have  discovered an interesting flaw in the xBABIP equation, though the bad news is it may negate much of the analysis provided above. Perhaps it is time to develop a new and improved xBABIP formula. In any case, I hope this exploration provided some insight into the factors associated with differences in BABIP and the hitters who might be due for some regression in their BABIP going forward.

Nick Lampe is a featured writer at Beyond the Box Score and Viva el Birdos. You can follow him on Twitter at @NickLampe1.