A ‘Texas Leaguer’, also known as a ‘blooper’ or ‘flare’, refers to a softly hit ball that falls between the infielder and outfielder. The term’s origins trace back to 1901 when rookie Ollie Pickering was called up from the Texas League and got four hits of the sort. It is regarded as a lucky, undeserving hit. Brewers fans know the Texas Leaguer all too well as it was Ryan Zimmerman’s broken bat single that dropped in front of Lorenzo Cain allowing the Nationals to rally, winning the Wild Card game and ultimately the World Series.
It seems unfair for such a poorly struck ball to become a hit, but in the eyes of the xwOBA statistic, it is one of the most worthy in the game. Weighted On Base Average (wOBA) takes the run value of each event and divides it by the number of plate appearances. It’s a solid way of evaluating a batter’s production with a single, unadjusted number. Expected Weighted On Base Average (xwOBA) estimates what the wOBA should have been based on a batted ball’s launch angle and exit velocity.
For example, Zimmerman’s blooper against Hader left his bat at 69.2 miles per hour with a launch angle of 33 degrees. Most balls hit that speed at that angle fall for singles, thus the xwOBA is .643. To give that context, Nelson Cruz had the highest average xwOBA on balls in play last year at .539. It may not be as glamorous as occasionally hitting a ball 450+ feet, but if consistently repeated, a Texas Leaguer is one of the most desirable batted balls for a hitter.
That being said, nobody consistently produces them. Some batters may be more line drive oriented than others, but no careers are built on chipping the ball just over the infielder’s heads. With the introduction of Statcast, there was a rush to create metrics utilizing launch angles and exit velocities to evaluate players. The assumption xwOBA makes is that these two variables are fully controlled by the players. It struggles differentiating which batted balls take skill to generate. This makes xwOBA more reflective than predictive as a statistic – it is useful in determining how someone should have done as opposed to what they will do in the future.
If a ball is hit with a launch angle around 17 degrees, xwOBA suggests it is better to hit the ball 80 as opposed to 103 miles per hour. Clearly, based on where the balls tend to land, the softer hit balls will produce better results. However, both in terms of truly evaluating how ‘lucky’ a batter was and predicting their future performance, this is misleading. Of course it always takes skill to put the ball in play against MLB pitching, but the harder hit ball is a far better exhibition and indication of skill.
There are other metrics that utilize exit velocities and launch angles to provide insight on how hitters produce favorable results. Sweet Spot percentage looks at launch angles conducive to high batting averages. Hard-hit Rate looks at batted balls at or above 95 miles per hour – the speed where balls start going over outfielder’s heads. Barrel percentage looks at batted balls prone to turning into extra base hits.
These metrics can combine to paint a picture of a hitter, but there is still plenty of work to be done. In addition to refining predictive metrics, quantifying a batter’s ability to control launch direction and the spin of batted balls is on the horizon. As more years pass and available data continues to mount, walks and strikeouts could be adjusted as well. Expected Weighted On Base Average currently estimates wOBA on balls put in play while leaving walks, strikeouts, and hit by pitches untouched. This naturally hurts batters who got rung up on bad calls and benefits those receiving favorable treatment.
Expected Weighted On Base Average is not perfect – it loves Texas Leaguers, does not appreciate certain hard hit balls, and leaves non-batted balls unadjusted – but neither is any other metric. Barrel percentage, Hard-hit rate, and Sweet Spot percentage all make flawed assumptions and groupings, but are insightful nonetheless. This wave of Statcast metrics is a solid first attempt at extracting value from heaps of data and ultimately serves as a building block moving forward.