# DFS Baseball 101: Learning to Properly Utilize BABIP

If there's one number in the often unpredictable world of baseball that we thought we could rely on, it's been batting average. Likely one of the first measures of success, or lack thereof, that you became familiarized with as a baseball fan, batting average purports to provide an accurate reading of how often a hitter's trips to the batter's box culminate in success.

However, as with many of conventional baseball statistics that were established and bandied about before the full dawn of modern-day sabermetrics, batting average lacks nuance. As we discussed in our previous installment on wOBA (Weighted On-Base Average), one of the inherent weaknesses of conventional batting average is its lack of differentiation between the different types of hitting "events" in a game (i.e. an infield single is treated and weighed the same as a grand slam).

BABIP Fundamentals and Formula

Batting Average of Balls in Play (BABIP) is another offshoot of batting average that aims to provide a measure of how often a hitter is successful on non-home-run contact, and conversely, how often a pitcher manages to secure an out each time the opposing hitter makes contact without the ball leaving the yard. While it's admittedly guilty of some of the same lack of sophistication as its ancestor, BABIP is a metric that can provide us with more of a glimpse, over time, of a hitter's or pitcher's true skill level.

First, some fundamentals and details about what goes into BABIP. Any at-bat that produces a single, double, triple or out is included in the formula. It does not factor in strikeouts, walks, a hit-by-pitch, catcher's interference, sacrifice bunt or a home run. Thus, the official formula for BABIP is as follows:

BABIP = (H – HR)/(AB – K – HR + SF)

Sample Size and Other Underlying Factors

One of the points we've emphasized in past articles is sample size, as the more extensive the time frame covered by the statistic, the more reliable it tends to be. This is particularly true for BABIP, where factors such as team defense can play such a pivotal role. The bigger the sample size, the better chance that the effects of stellar or sloppy fielding, as well as plain ol' good or bad fortune, tend to be nullified.

Awareness and understanding of those two components are actually paramount when evaluating BABIP as part of your daily fantasy baseball research. To begin with, both a hitter and pitcher's respective BABIP figures can be significantly affected by the quality of the defense they're either up against or have behind them. A batter who has the good fortune of facing a string of poor defensive teams can certainly see a corresponding temporary boost in his BABIP, while a pitcher's figure can easily be sabotaged to an appreciable degree if they're backed up by a penetrable infield that is slow of foot and questionable with the glove.

That being said, Lady Luck's potential influence on BABIP can't be understated either. The reality is that there are often stretches of time within any given season when balls hit off the fists fall in more frequently, or conversely, when a larger-than-average number of grooved pitches somehow end up rocketing right into a well-positioned fielder's glove. A temporary increase in these types of occurrences will naturally send a hitter's or pitcher's BABIP in either direction, which is why taking the long view is typically the safest way to gauge the metric.

A widely accepted parameter for when BABIP can be considered stable is after 800 balls-in-play for hitters and beyond 2,000-balls-in-play for pitchers. In other words, we're talking multiple seasons before we can ascertain what's considered an accurate BABIP figure that can then be measured against an in-season sample.

Evaluating BABIP from a DFS Perspective

As with virtually every other metric, BABIP certainly has its place as a component of your daily fantasy baseball research. Attempting to use it in the proper perspective can actually present somewhat of a conundrum, however, as the immediate nature of DFS does seem to diminish, to some degree, the importance of taking the long-term view we were just espousing. We'll attempt to steer you through the best way to combine the two approaches when creating your daily fantasy baseball lineup, while pointing out some corollary metrics that can often help explain BABIP figures that appear out of whack with a hitter's pitcher's overall body of work.

BABIP as an Evaluative DFS Tool for Hitters

Since we're primarily concerned about today when creating a daily fantasy baseball lineup, a hitter's current BABIP can carry great weight even if it is significantly higher or lower than his career figure. Normally, the major league average for hitters in terms of BABIP hovers around .300. Elite-level hitters, such as Bryce Harper and Mike Trout, can often push that number close to the .350 level over the course of a full season. However, every year we have outliers who are significantly outperforming or underperforming their usual BABIP standard by a significant amount at any given part of the season.

A certain degree of flexibility in how you evaluate BABIP is required, as it's prudent to look at it through more of a daily fantasy baseball prism. For example, if you were trying to determine whether a player with a considerably elevated BABIP was a good potential trade candidate or waiver-wire pickup in a season-long league, you'd likely be best served reviewing his career BABIP numbers and comparing them to his current figure. For daily fantasy baseball, however, you can afford to be a bit more impulsive, with only today's roster to be concerned with. Even if a player seems to be living right this season to the tune of a BABIP that is, say, 40 points higher than his career figure, you may do well to ride that wave and tab him for your daily fantasy baseball lineup.

That being said, you shouldn't utilize BABIP in a vacuum. Even when trying to capitalize on a hitter's current hot streak, you're still going to consider what quality of pitcher he's facing that night, what quality of pitchers he may have been amassing his explosive numbers against, and even what quality of defensive teams he's been facing.

However, simply dismissing an obviously inflated BABIP as an anomaly that's bound to come to a halt at any moment—and that you should therefore steer clear of—may not necessarily be the smartest move in daily fantasy baseball. Likewise, immediately ruling out a hitter due to a poor in-season BABIP can also cause you to potentially miss out on what seem like an unlikely opportunity.

BABIP as an Evaluative DFS Tool for Pitchers

When using BABIP to evaluate pitchers in your daily fantasy baseball research, you have some particular factors to keep in mind as well. A principle reason why pitchers' BABIP metrics take a lot longer to stabilize than hitters is that they have considerably less control over the number they generate. Once a hitter makes contact and the ball remains in the park, it's up to the defense to try and catch up with it for a put-out. Therefore, dramatic temporary fluctuations in a pitcher's BABIP can often be the result of a lower or higher quality of defense behind him than in previous seasons, a fact that can be largely corroborated by examining any personnel changes that could have impacted this.

If the defense hasn't been dramatically altered—or isn't currently playing at a level significantly below their standard—then some bad fortune, ballpark factors, a stretch of recent starts against very talented offensive teams, or even a combination of all three could be the culprits for a temporarily bloated BABIP. When evaluating a pitcher for daily fantasy baseball, it's important to rein in the impulsiveness a bit before making a decision.

The Curious Case of Raisel Iglesias

For instance, say you're evaluating the Reds' Raisel Iglesias for your daily fantasy baseball lineup the next time he's due to take the hill. A glance at his current BABIP reveals an unsightly .361, a figure well above the .286 he surrendered in his rookie 2015 season. To begin with, we're reminded that the BABIP for a pitcher with an abbreviated major league tenure like Iglesias is not yet fully stabilized. Additionally, when we check his ERA, we're likely to be surprised to find that it's a very respectable 3.49. A look at his strand rate—the percentage of runners that he leaves on base—is a solid 84.8 percent, ranking him within the top 20 for qualifying pitchers.

Iglesias' ERA would appear to indicate that he's certainly not a pitcher that's been constantly touched up, so what gives with that inflated .361 BABIP? To begin with, he's been victimized by the all-important defense factor. The Reds are tied for the lowest fielding percentage in the majors (.977) and have the second-most errors (40). Throw in some bloop singles here and there, and you can see where Iglesias' BABIP could rise quickly. However, because he's been significantly better than average at limiting the damage caused by the runners that have reached base against him—partly by striking out an impressive 9.21 batters per nine innings– he continues to boast a solid ERA and decent record.

Therefore, immediately disqualifying Iglesias as a candidate for your daily fantasy baseball lineup based solely on his discouraging BABIP would have certainly been premature, as his other numbers illustrate that he's still a talented pitcher who could be due for a bounce-back effort.

Using Batted Ball Profiles to Evaluate BABIP

To round out our overview of BABIP, it's helpful to know how a hitter's or pitcher's batted-ball profile can influence their BABIP numbers. In short, certain types of batted balls tend to fall more often for hits, while others are much more likely to result in outs. For instance, fly balls will fall in safely a lot less often than grounders. Subsequently, ground balls have a lower success rate than line drives, which grade out as the type of contact that will most likely get a runner on base.

When trying to determine the veracity of a hitter's or pitcher's current BABIP, a look at how they're making contact (in the case of hitters) or what type of contact is being made against them (in the case of pitchers) can provide the story behind the number. This information can then be compared against the profile of the pitcher or hitters the player is due to face that night, giving you a better perspective on whether they might be a smart choice for a daily fantasy baseball lineup.

Hitters Case Studies - Nick Castellanos, Martin Prado and Chris Coghlan

To better illustrate these factors from a hitter's perspective, we'll start with a quick look at the 2016 success stories of Nick Castellanos of the Tigers and 11-year veteran Martin Prado of the Marlins, before delving into the opposite side of the spectrum with Oakland's Chris Coghlan.

Castellanos led the majors with a jaw-dropping .444 BABIP over his first 93 plate appearances, which easily left the respective .326 and .322 figures of his previous two seasons in the dust. Has his skill level shot up that much after almost 1,200 major league plate appearances prior to this season? It's essentially impossible, given everything we've discussed about BABIP so far.

However, what changed for Castellanos was that his frequency of line drives went up to a career-high 29.9 percent. And while his fly ball rate went up to 44.8 percent, it didn't affected his BABIP, as he was knocking a greater percentage of them out of the park than in his previous two seasons. Castellanos' percentage of home runs on the fly balls he hit was at 13.3 percent, over four points higher than his previous high of 9.2 percent, established last season. In short, his elevated fly ball rate didn't hurt his BABIP because significantly fewer of those resulted in outs as compared to previous seasons.

Prado was right behind Castellanos with a .440 BABIP that qualified as the best in the National League. The veteran had plenty of sample size to compare it against, and a glance at some past numbers revealed that Prado's BABIP had fallen between .266-.335 over his career, with five of those seasons over the .300 mark. In other words, while he's certainly been a solid performer at the plate, it hasn't been to the Ty Cobb/Wade Boggs-level he performed at earlier this season. When looking further for an explanation, we unsurprisingly find that both Prado's line drive rate (24 percent) and ground ball rate (54.7 percent)—the two types of contact most likely to positively influence BABIP—were career-highs. His fly balls, meanwhile, were at a career-low 21.3 percent. Once again, these numbers bear out that certain types of contact can make a notable temporary difference in BABIP.

To show you how the other half is living, we turn to the somewhat hard-luck case of Coghlan. His first season in an A's uniform had been forgettable, and he had tallied a major league-low .153 BABIP, accompanied by a .151 batting average. That followed back-to-back seasons with the Cubs in which Coghlan posted respective .337 and .284 BABIP figures. As you might have guessed, his batted ball profile told a good part of his regression tale, as his line drive rate was at a career-high 25.6 percent in 2014 when he managed the former number, and a still solid (but correspondingly lower) 20.1 percent when he registered the latter. Earlier this season, however, Coghlan's line drive percentage languished at 16.1 percent, while his fly ball rate was a career-high 41.1 percent.

How could we use this information when trying decide on including either hitter in a daily fantasy baseball lineup? While the BABIP figures were not going to remain at those levels for either Castellanos or Prado over the course of the season, both were clearly in a zone. Particularly in Castellanos' case, we could be looking at another step forward offensively in a still-emerging player, and perhaps even some adjustments in hitting style. While some defense and good fortune have almost certainly played a part in his numbers, if he's drawing a matchup against a pitcher who tends to surrender hard contact and/or ground balls at an elevated rate, he could be an excellent addition to a daily fantasy baseball lineup.

Prado is much less likely to see any dramatic long-term spike in performance considering his more advanced age and extended major league tenure, but he's proven his mettle as a big-league hitter. Therefore, while he'll eventually see a BABIP dip, he too could pay dividends in the right matchup.

Coghlan was due for positive movement in his BABIP as the season unfolded, as he's still a relatively young player (age 30) whose bat speed appears intact, as evidenced by a 29.8 percent hard contact rate that mirrors that of his aforementioned 2014 season. He also hit four homers over 88 plate appearances despite the struggles, and had a career-best 17.4 percent HR/FB ratio, meaning he still carries upside and the potential to improve over a larger sample size.

A Tale of Two Pitchers - Jake Arrieta and Drew Smyly

We can apply these same batted ball profiles to pitchers, and we'll will take a look at two who generated the stingiest BABIPs this season, but with markedly different results. We begin with Cubs stud Jake Arrieta, whose BABIP allowed was down to a minuscule .176 after winning his sixth game. Already a quality pitcher, Arrieta's career-low BABIP could be at least partly attributed to one of the factors we've pointed out: the line drive rate on batted balls was at a career-low 18.4 percent. The Cubs also play solid defense behind him, so while he was inducing ground balls on a career-high 58.3 percent of his batted balls, they were largely being converted to outs. Finally, since he was also stranding an astounding 95.9 percent of the hitters that reached base against him, Arrieta's microscopic 0.84 ERA matched the quality of his BABIP.

Smyly's case, meanwhile, is a textbook example of why we can't look at any number in a vacuum when doing daily fantasy baseball research. His BABIP (.173) was actually superior to Arrieta's and was largely explained by Smyly predictably giving up a career-low percentage of line drives (11.3) on his batted balls. His fly ball rate was also unsurprisingly the highest it's ever been in his young career (53.8 percent). However, while Arrieta's record was a sparkling 6-0, Smyly was currently saddled with an ugly 1-3 mark, despite a very solid 2.60 ERA. Since his BABIP figure clearly indicated that his teammates haven't sabotaged him with shoddy defense, we had to surmise that perhaps an important factor that isn't figured into BABIP was at least partially undermining him. As you'll recall, home runs aren't factored in when calculating BABIP, and sure enough, Smyly's 1.30 HR/9 (home runs surrendered per nine innings) rate betrayed the fact that he'd given up some untimely gopher balls over his first 34.2 innings of the season.

As a result, while we would be able to fully trust Arrieta's BABIP as a reflection of his overall stellar pitching, we'd have to be a bit more discerning when evaluating Smyly as a candidate for a daily fantasy baseball lineup. While he clearly pitched well in certain aspects this season, we'd have to think about steering clear if he was taking on a particularly long-ball proficient offense, especially in anything resembling a hitter's park. Furthermore, given the moribund state of the Rays' offense, we'd have to consider the likelihood that he could be provided minimal run support as well.

A Useful Tool When Kept in Perspective

As illustrated, the seemingly straightforward nature of how BABIP is calculated belies the various components that play a part in its variances. These factors are of particular importance to us when conducting daily fantasy baseball research, as they can give us key insight on whether a hitter or pitcher is in the right matchup that day to extend his string of success, or break out of a temporary slump. Conversely, it may help us pinpoint which matchups we may want to avoid, given the tendencies of both the player in question and their opponent.

The author(s) of this article may play in daily fantasy contests including – but not limited to – games that they have provided recommendations or advice on in this article. In the course of playing in these games using their personal accounts, it's possible that they will use players in their lineups or other strategies that differ from the recommendations they have provided above. The recommendations in this article do not necessarily reflect the views of RotoWire. Juan Carlos Blanco plays in daily fantasy contests using the following accounts: DraftKings: jcblanco22, FanDuel: jc_blanco22, DraftPot: jc_blanco22, FantasyDraft: jc_blanco22, OwnThePlay: jcblanco22.