Statistics that demonstrate high correlations from year to year are useful in evaluating player skills since players generally tend to retain their core skills from year to year. 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. These statistics, especially K%-BB% and kwERA, focus on pitcher skills that don’t tend to change drastically year to year and thus have much higher correlations than ERA/RA9 which are influenced by fielder ability and positioning. The old k-NN only model didn’t vary by speed, but we see a clear increase in wOBAcon for players with higher speeds.

We must understand the advantages and shortcomings of xwOBA at the player level to recognize when and how to use xwOBA instead of other metrics.We first focused on determining the degree to which players maintain similar xwOBA and xwOBAcon from year to year. Aspects that we don’t control for include the fielding team, the park effects, and the weather.

Physics works!And temperature drives another trend: error increases in the most extreme months April and July.We also have data for wind speed and direction. We are interested in three outcomes for pitchers in the second time period: RA9, ERA, and wOBA. Something that I alluded to in my Plate Discipline piece earlier this week was a small change to the underlying xwOBA model. On the other hand, a hitter could have a brilliant game with a couple of bloop hits into the outfield or misplayed balls.

xwOBA is more indicative of a player’s skill than regular wOBA, as xwOBA removes defense from the equation.

However, we can see somewhat of a trend: faster winds blowing in from the OF decrease wOBAcon and faster winds blowing out to the OF increase wOBAcon.Moving on to fielding shifts we see some slight differences in RMSE coming where we might expect. Unlike xwOBA, Our Model ignores the well over 90% of batted balls that are not barrels. While xwOBA might not be more predictive than FIP, it can help explain how a pitcher has arrived at his runs-allowed total, ... DRA uses mixed models and incorporates much more granular pitch data — with location and type — to flesh out a pitcher’s skill and separate the role a defense might play in terms of potential outcomes. Not all barrels are created equal.

The real value in xwOBA is xwOBAcon since BB and SO are not altered by our model. So in order to do this, I added an additional fixed effect to my random-effects model for Home Team.The idea was to see if certain pitchers were being hurt in the model due to their own skills or if their home park was influencing them as well. 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. We very carefully chose variables to include based on their relation to player skill. Several months back I created a new era estimator that I called After looking over the model, I realized that while I was factoring in the pitcher and hitter for each of the different pitchers and hitters I was missing a major influence on wOBA, the field. To perform this test (and all subsequent tests) we utilized a weighted SWe can deduce a couple things from this table immediately. We built the first version of xwOBA with k-nearest neighbors (k-NN) regression to handle the obvious non-linearity of wOBA as seen in the plot above. What if it is hit to right field at Yankee Stadium? 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. 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.

Our new model that incorporates speed generally follows the linear trend of wOBAcon by speed for these more weakly hit balls.Let’s take a look at spray angle. Amazingly, 1st to 2nd half predictability for xwOBA is almost as good as season to season correlations and has half the sample size!

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. Quite simply, it is used to establish the value a player brings per plate appearance, accounting for unintentional walks, hit by pitches, and all base hits. Overall the change is a very small one, but it did cause small changes to the individual pitcher’s wOBA Influence. For batters, Barrel% is almost just as reliable a skill as xwOBAcon.

Our Model also ignores the specifics of how each barrel is hit, unlike xwOBA. If we look at only away games, xwOBA performs a little better relative to wOBA, but may still remain in the approximately ~0.033 margin of error we estimate for both correlations. We see that xwOBAcon is the most stable statistic by far for batters which means that batters generally don’t lose their ability to hit the ball hard (or soft) and in the air (or on the ground). These stats together will give us a solid sense of how well the pitcher performed. Thankfully, sabermetrician Tom Tango answered that question for us in his creation of wOBA. The Baseball Data Machine Learning team is focused on using data to tell the story of baseball and help serve baseball fans and clubs. We apply our existing k-NN model to the remaining liners and fly balls (where the batter’s speed has far less impact on the outcome), only using EV and LA to estimate wOBA.

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