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 Slugging Percentage (xSLG).
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First, we should clarify what ordinary slugging percentage is. Slugging Percentage is simply a batting average-like metric that gives added weight to extra-base hits. A double is worth twice as much as a single, a triple three times as much, and a home run four times as much. A home run and a single look the same in terms of batting average, but not slugging percentage (one-for-one with a single would be a 1.00 slugging percentage, one-for-one with a homer would be a 4.00 slugging percentage). The simplest calculation for this is Total Bases divided by At-Bats.
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. If you are looking at a batted ball that was hit at 103 miles per hour at a 40-degree angle, you could look to the past data and see that batted balls of that type have gone for home runs a high percentage of the time, meaning a home run would be the highest probability of being the true outcome. You would then categorize that batted ball as an expected home run, which would make the expected slugging percentage for that batted ball 4.00. If you are looking at a batted ball that has gone for an out 70% of the time in the past, you would categorize that as an out, or a zero slugging percentage.
Expected Slugging Percentage does this for every batted ball for every player and calculates slugging percentage using the expected singles, doubles, triples, home runs, and outs instead of the actual outcomes. Strikeouts are factored into the equation (since a strikeout still counts negatively against slugging percentage despite a ball not being put in play), and then you end up with a final xSLG metric.
The main application of all of this is to get an idea of hitters that have experienced good or bad luck. If a hitter hits 10 line drives at 90+ miles per hour, but they all go directly at fielders, that player’s expected slugging percentage will be much higher than his real-life slugging percentage, given the fact that he is doing exactly what he needs to do in order to get extra-base hits. Conversely, if a player sees a bunch of weakly hit bloopers drop in, his expected slugging percentage will be lower than his real-life slugging percentage, 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 xSLG that is higher than his SLG could be a potential buy low, and a player with a higher SLG than his xSLG 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.
