xwOBA and the Oakland A’s: Part 1

In a world of hypotheticals the Oakland Athletics are among the best. I am not talking about the what-if’s of trades gone by or the prospect who had all the tools he needed to be the best, but just couldn’t hit a curveball. I mean the strange new world of Statcast.

Perhaps Statcast isn’t new to you as we have often seen the state of the art data used on this site, including a fantastic look at Jonathan Lucroy’s rebound by Alex yesterday morning. In the comments section here at AN terms like exit velocity, launch angle, and xwOBA have been coming up more frequently this season. The metric on which I’d like to focus is xwOBA.

What is xwOBA

As described by Baseball Savant:

“In the same way that each batted ball is assigned a Hit Probability, every batted ball has been given a single, double, triple and home run probability based on the results of comparable batted balls -- in terms of exit velocity and launch angle -- since Statcast was implemented Major League wide in 2015.

All hit types are valued in the same fashion for xwOBA as they are in the formula for standard wOBA: (unintentional BB factor x unintentional BB + HBP factor x HBP + 1B factor x 1B + 2B factor x 2B + 3B factor x 3B + HR factor x HR)/(AB + unintentional BB + SF + HBP), where “factor” indicates the adjusted run expectancy of a batting event in the context of the season as a whole.

Knowing the expected outcomes of each individual batted ball from a particular player over the course of a season -- with a player’s real-world data used for factors such as walks, strikeouts and times hit by a pitch -- allows for the formation of said player’s xwOBA based on the quality of contact, instead of the actual outcomes. Likewise, this exercise can be done for pitchers to get their expected xwOBA against.”

I realize that many are skeptical of using information like this since it is based on things unseen, such as exit velocity and launch angle. If a player hits the ball hard into a glove, why does it matter what the expected outcome was if the actual outcome was an out? My reply would be why use FIP, xFIP, SIERA, or BABIP, or any other sabermetric data set you like? Most are based on what could be, not what is. Just like FIP aims to remove things a pitcher cannot control, such as defense, xwOBA attempts to remove things a batter cannot control.

Putting xwOBA to Use

xwOBA in particular helps us understand how good a given batter or pitcher should be because “every batted ball has been given a single, double, triple and home run probability based on the results of comparable batted balls.” And amazingly we can narrow findings down to a single play.

A perfect example came in the third inning of the game versus Houston last night. With the bases loaded Khris Davis hit a rocket out to right field that was guaranteed to score at least one run, but likely even more. Unfortunately George Springer made a great catch on the line drive above his head. The box score says “sac fly” but Statcast tells us a whole lot more about that play.

Last season balls hit at 107 MPH had a wOBA of .983 and balls batted at 18 degrees had a wOBA of 1.001. Most often those batted balls turned into doubles.

Baseball Savant

The xwOBA on this exact play was a strong 1.065. Just like Sean Manaea’s xFIP from last night’s game was 2 whole runs better than his ERA, Davis’ xwOBA on that play was entirely better than his actual wOBA, which was 0.000. Had Springer not made the catch at least one more run likely would have scored, there would have been one less out o=in the inning, and the game’s outcome might have been entirely different.

A single plate appearance doesn’t have much predictive value, even if that PA results in the hardest (or softest) hit ball of all time. However, a pattern of hard contact and/or a new sustained launch angle can point to a potential breakout (ex. Yonder Alonso) or rebound. That ball that Davis hit will forever be recorded as a sac fly - that doesn’t make the batted ball type any less desirable or mean Davis should avoid hitting the ball 107 MPH at 18 degrees. The opposite is true. Davis should aim for similar batted-balls each and every time he steps into the box.

I wanted to present this introduction to xwOBA to you as part one of a mini series on xwOBA and the A’s. The on-field results haven’t been ideal for A’s hitters lately. That doesn’t mean Oakland is not hitting the ball well.

Other Statcast Resources

If you haven’t already, take some time to peruse Baseball Savant. There are some really cool things you can do with the data it provides. If you’re looking for the quick version, check out the Statcast Leaderboard. Additionally, below are some tweets from a few of my favorite twitter follows as it pertains to Statcast.

I’d love to hear from you like or dislike about Statcast. Let’s hear it in the comments!