If you’ve watched videos previous to this one, you likely already have a solid understanding between the advantages and shortcomings of statistics such as batting average, slugging percentage, and weighted on-base average.

These types of metrics are calculated by counting and weighting traditional baseball stats. One of the drawbacks to these traditional stats is that they’re unable to take into account batted ball metrics such as exit velocity or launch angle, which can be a great predictor of success. This is what leads us to the concept of “Expected Statistics.”

Expected stats are calculated metrics that provide a better context of outcomes by comparing factors such as batted ball data with historical information. Some of these calculations help us quantify new stats such as “expected” batting average, slugging, and weighted on-base average.

**Expected Stats**

Expected batting average, or xBA, presents the probability that a batted ball should land for a hit based on its exit velocity and launch angle by comparing it to similarly struck batted balls. As an example, suppose historically that line drives hit with a 100 mph exit velocity and a ten-degree launch angle fall for a hit 70% of the time. A batted ball with those qualifiers would then have an expected batting average of .700.

Expected slugging, or xSLG, and expected weighted on-base average, or xwOBA, are quantified using a similar methodology, but instead of simply assigning batted ball events as hits, or not hits, they are assigned the values that correspond to the same outcomes within the original formulas. The difference between xSLG and xwOBA, other than the values, is that it includes outcomes like walks and strikeouts, giving the clearest picture of what the hitter’s performance is expected to be.

Like many other Statcast metrics, xBA, xSLG, and xwOBA can all be used to evaluate pitchers too. You can use these stats to measure how pitchers and hitters perform within individual pitch types as well. For both hitters and pitchers alike, xwOBA is the most commonly used of the three because of its ability to correlate with greater offensive production.

**Summary**

Expected stats are useful because they can tell us what a hitter or pitcher’s production may have been based on how the ball was hit. Since hitters have less control over what happens to a ball once it’s put into play and factors like ballpark dimensions or defensive alignment can play a role, it is important to know what their metrics would be compared to a similarly batted baseball across all of MLB.

For example, if a hitter has a large gap between their expected and actual stats, we may see their actual stats start to regress closer to their expected figures over time. This is why understanding expected stats can be useful when looking at small sample sizes.

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