This is where xwOBA (pronounced “ex-woh-ba”), along with xwOBA is the most notable of our three “expected” Statcast metrics as it corresponds to the all-encompassing hitting metric, where xwOBAcon is the estimate for xwOBA on contact produced by the Statcast-based model and w[We base the currently public version of xwOBAcon on three variables: exit velocity (EV), launch angle (LA), and sprint speed.
When just looking at contact we might want to just look at Barrel% to predict forward.If xwOBA was designed to be predictive at the player level, there should be different weights assigned to SO and BB to regress these factors going forward. In the context of our xwOBAcon model, the model is trying to choose how many similar hit balls to average together to produce an xwOBAcon estimate as close to the actual wOBAcon as possible. For batters, Barrel% is almost just as reliable a skill as xwOBAcon.
These tend to be stadiums with short porches or unique aspects that make it easier or harder to hit HRs.Below are some of the best and worst performing stadiums. The number gives u…
Our Model also ignores the specifics of how each barrel is hit, unlike xwOBA. Given the outfield fence alignment, it is easy to see why these plots make sense.While model performance is important, many metrics are created to assess player performance. Elliott Baas finds potential risers and fallers for 2018 fantasy baseball drafts. The exact attribution of error to exogenous variables is a work in progress, but below we have explored some of the error to which these factors contribute. Statcast analysis of the xwOBA statistic as a predictive tool for starting pitchers. Aspects that we don’t control for include the fielding team, the park effects, and the weather. A hard-hit ball going a foot or two farther could turn a 0.000 wOBA (out) into a 2.000 wOBA (HR)!You may also be wondering, why didn’t we include these exogenous variables? We created two metrics with barrels, both pretty self-explanatory.It’s amazing how much information we can maintain by throwing out all the other batted ball types.
Again, when we limit our test to only away games, xwOBA improves relative to FIP since it isn’t biased by home park factors.Our makeshift barrel metric stands out in the tables above.
Something that I alluded to in my Plate Discipline piece earlier this week was a small change to the underlying xwOBA model. Hitters, and likewise pitchers, are able to influence exit velocity and launch angle but have no control over what happens to a batted ball once it is put into play. Great. The question that needs to be answered is how much of a difference is there?
These are factors that a batter or pitcher have limited control over, and thus, sometimes we don’t want to account for these elements if we want to compare players’ xwOBA under average conditions.Certainly, the exogenous variables are important to understand why players are under or over-performing their xwOBA. Not all barrels are created equal. Our team is constantly learning from the broader data science, machine learning, and sabermetric communities and will share our own experiences through this blog.A player’s stat lines often don’t align with our perception of their skills, a threshold known as the “eye test.” A hitter can go 0-for-4 with four line-outs or warning track drives. We all know that it benefits players to pull hard-hit balls down the line just because of the simple fact that fences are usually closer to home plate down the line. The main analytic takeaway most will remember from Moneyball is “he gets on base”. Overall, the changes to the leaderboard are small as it led to some shuffling near the top and some small adjustments to final Stuff-ERA.
The patterns for other hit types are a function of fielder alignment or distance to 1B in the case of poorly-weak hit balls. Because some events (e.g., home runs) are more valuable than others (e.g., walks), it uses a weighted scale to determine the given player’s output.