The main goal of the advanced statistics that have come out of the Statcast revolution in baseball is to take luck out of the equation as much as possible. The way to do this with hitters is to drill every event down to only the things they have control over. A hitter has full control over making contact with the ball, but after the ball is put in play, the outcome of the at-bat is influenced by other factors (ballpark factors and the defense). Since Statcast tracking was instituted in all Major League Ballparks in 2015, every pitched and batted ball has been tracked closely, which has resulted in a mountain of data to study. This has led to statistics like Expected Batting Average (xBA) to being born.
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Since we have several years of data about every batted ball, we can now take an individual batted ball and compare it to thousands of other batted balls that were hit with similar velocities and at similar angles. You can each occurrence and see how often in the past that type of batted ball has gone for a hit. For example, a ball hit at 95 miles per hour at a 30-degree angle will go for a hit something like 80% of the time. Therefore, when a batter hits the ball like that, he would “expect” to get a hit.
Expected Batting Average does this for every batted ball. It takes each player and looks at each batted ball, compares it with the full history of batted balls, and decides whether or not that ball should be expected to result in a hit or not based on the probabilities received from the past data. The player’s strikeout rate is figured into the equation (players with higher strikeout rates will naturally have lower batting averages, of course), as well as his sprint speed (fast players will turn weakly hit ground balls into hits more often than slow), and then an expected batting average figure comes out of all of that.
The main application of all of this is to get an idea of what hitters have experienced good or bad luck. If a hitter hits ten line drives at 90+ miles per hour, but they all go directly at a fielder, that player’s expected batting average will be much higher than his real-life batting average, despite the fact that he is doing exactly what he needs to do in order to get hits. Conversely, if a player sees a bunch of weakly hit bloopers drop-in, his expected batting average will be lower than his real-life batting average, suggesting that he probably should not have as many hits as he actually does.
As a fantasy player, you can use this metric to quickly get an idea of buy-low or sell-high candidates. This is especially useful early in the season when the data samples are small and variability is high. A player with an xBA that is higher than his BA could be a potential buy low, and a player with a higher BA than his xBA could be a potential sell high.
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Jon Anderson is a featured writer at FantasyPros. For more from Jon, check out his archive and follow him@JonPgh.
