Predicting one’s future performance in sports is nearly impossible. There’s a multitude of factors that can affect any given outcome. In baseball, we tend to look at a variety of analytics in order to predict future performance levels. One of the most important analytics in baseball is BABIP, or batting average on balls in play.
This analytic measures how often a batted ball in play results in a hit. BABIP can be used to judge a player’s current performance and predict their future.
There are three main factors that can affect BABIP.
Defense – A fielder’s skill level and positioning have the greatest effect on the outcome of a ball batted in play, whereas a batter and pitcher have nearly no impact. If a pitcher is surrounded by great fielders, their BABIP will generally be lower than if they were surrounded by mediocre fielders.
Luck – A batter can receive a hit on a slow roller to third, or bloop to the outfield, which is extremely unlucky for a pitcher, as they executed, but did not get the desired result. This will cause their BABIP to rise even though they technically did their job. A batter can hit a line drive right up the middle, although if a shift is on, there may be a player there to make a play. This is unlucky for the hitter, as they executed, but did not receive the desired result either.
Talent – The harder a ball is hit, the more difficult it is to field. So, players with a higher exit velocity and harder contact rates generally have higher BABIPs. Players with above average speed also have an advantage when it comes to BABIP, as they have a better chance of beating out an infield hits.
For batters, BABIP can be a tell of the current quality of a player. With a proper sample size of three seasons or more, a player with a BABIP of .345 or above can be considered an above average hitter, as they are reaching base on over 1/3 of balls batted in play.
BABIP can also be used to predict a batter’s future value, as if a player with a career BABIP of .345 finds himself with a .400 BABIP after the first two months of a season, he may be getting lucky. With players like this, it is fair to say they will see some regression in their batting average. Some notable players with abnormally high BABIPs compared to their career rates are Ryan Zimmerman (.404), Avisail Garcia (.382) and Zack Cozart (.395).
Another specific player to look out for this season is Lorenzo Cain. His current BABIP is .313, which shows that he is receiving hits on slightly under 1/3 of the balls hit in play.
Cain has been in the major leagues for seven seasons, and sports a career BABIP of .342. It is safe to assume that his batting average will rise in the near future, as his current and career BABIPs are about 30 points apart.
This analytic is a great tool that can be used to assess whether a hitter is in a true slump, or is just getting unlucky. The only caveat with comparing a player’s current BABIP to their career BABIP is that a change in approach can reinvent a player. Therefore, a player with a new approach may find a severe change in their BABIP compared to their former self.
For pitchers, they have no control over whether a ball batted in play results in a hit or not. With this in mind, it is fair to say that if a pitcher has a low or high BABIP, it is bound to stabilize to the league average, which is approximately .300.
It is hard to use BABIP to predict a pitcher’s future performance, as they have nearly no control over their BABIPs, although we can look at the BABIP of a pitcher as a trend. If a pitcher’s BABIP is well above .300, it is safe to assume that it will begin to trend downwards, and vice versa.
In conclusion, it is safe to expect a batters BABIP to move closer to their career BABIP, whereas with pitchers, it is safe to expect their BABIP to stabilize to the league average.
For fantasy baseball purposes, BABIP is an integral tool that can used to assess a batter or pitcher’s current performance and which direction they will trend in moving forward. BABIP can be tricky to understand, although this should clear things up.
The majority of information was found at fangraphs.com.
(Featured image by Fansigner.com)
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