wOBA vs xwOBA: Hitters With Large Differentials
Two of the newer sabermetrics used in the fantasy baseball community are wOBA and xwOBA. Both metrics used in unison can help us understand whether a player performed at the appropriate level. The majority of fantasy baseball hobbyists use statistics like batting average, on-base percentage, and slugging percentage. These three tell a good story about a player but leave out the plot of how he got there. That is when wOBA and xwOBA step into the spotlight. I want to help define these two metrics, help understand why they should be used, and highlight some players that experienced different fortunes or fate at the hands of them.
wOBA is a version of an on-base percentage that accounts for how a player reached base -- instead of simply considering whether a player reached base. The value for each method of reaching base is determined by how much that event is worth in relation to projected runs scored (example: a double is worth more than a single).
Expected Weighted On-base Average (xwOBA) is formulated using exit velocity, launch angle, and on certain types of batted balls, Sprint Speed.
In the same way that each batted ball is assigned an expected batting average, every batted ball is given a single, double, triple and home run probability based on the results of comparable batted balls since Statcast was implemented Major League-wide in 2015. For the majority of batted balls, this is achieved using only exit velocity and launch angle. As of 2019, "topped" or "weakly hit" balls also incorporate a batter's seasonal Sprint Speed.
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.
wOBA vs. xwOBA
Let's try to better understand what type of luck a batter encountered using these metrics. wOBA will show how a batter reached based while weighing the outcomes. Meanwhile, xwOBA will look at the weight of the outcomes and if the player should have reached base while using defense, park factors, and sprint speed. Basically, xwOBA is the percentage of how a batter would have reached base without defense. This is calculated using exit velocity and launch angles along with wOBA. A player with a higher xwOBA than wOBA should be considered unlucky and a player with a higher wOBA than xwOBA should be considered lucky.
The Lucky: wOBA > xwOBA
I highlighted a few names of lucky players (wOBA > xwOBA). Keep in mind that just because a player was lucky, it doesn't mean they performed poorly. For example, the MLB average xwOBA was .320, and .319 was the MLB average for wOBA. Alex Bregman would be considered lucky but had the #21 overall xwOBA(.378), not bad at all. These are the players that were the most fortunate.
Fernando Tatis Jr - Only had 227 balls put in play on 372 plate appearances, which are far below the rest on this list. The higher wOBA would suggest significant regression coming, and his expected batting average(xBA) backs this up as well. Tatis finished with a .312 batting average and a .259 xBA. Do not expect the same fortune in 2020.
Nolan Arenado - Arenado had the 14th best wOBA in the MLB but also the 83rd best xwOBA. Big time gap! That xwOBA was worse than Kyle Seager, Nick Markakis, and Brandon Belt. His .315 batting average should have been .272 with a slugging percent nearly 100 points lower.
Yuli Gurriel - Nothing about Yuli's season seems in line with his career. He only had 19 barrels yet hit 31 home runs. His .364 wOBA was above league average(.320), but the xwOBA was well below league average(.301). Expect significant regression in power with a higher BA.
Trevor Story - Couple things jump out with Story to me. He only had an 8.6% barrel rate that wasn't much better than the league average. There is a good chance his batting average comes down next season and potentially leaves him being a .260 hitter with sub-30 HRs.
Tim Anderson - I wouldn't be placing bets on Tim Anderson repeating the batting title. His xBA is .294, which is still good but not silver slugger worthy. There was a bit of fortune on Anderson's side in 2019 that most likely will not repeat. Expect some regression in many counting stat categories.
Kris Bryant - Bryant falls in line with Tim Anderson a bit. He outperformed all his underlying metrics and could slip even farther down. He doesn't hit the ball as hard as he used to, and he was very fortunate with his xwOBA being higher than his wOBA.
The Unlucky: xwOBA > wOBA
Here is the list of players that had a higher wxOBA than wOBA, thus making them the unlucky ones. These names could be overlooked or potentially still have more left on the table. Let your league mates overpay for using stats like batting average, on-base percentage, and slugging percentage. These names below could be league winners if their fate swung the other way.
Marcell Ozuna - 2019 was a rather unfortunate season for Marcell Ozuna. His batting average should have been nearly 50 points higher, and his slugging around 76 points higher. Expect positive regression in a better lineup for a high return.
Justin Smoak - His batting average looks real bad at .208, but it should have been .250. He suffered from terrible misfortune all season and if it corrects the other way in Miller Park, LOOKOUT!
C.J. Cron - He is going to get full-time work in Detroit now. The power is there; it just didn't show in the numbers. This year could be the breakout season everyone has been waiting on. His ADP makes him a super buy low.
Dansby Swanson - He was raking balls all over the field and into the bleachers until an injury set him back. The underlying numbers are showing he should have a higher BA and slugging percentage. Sneaky potential for a breakout here.
Mookie Betts - Last year, he finished with a .408 xwOBA. That is 8th overall in the MLB; his wOBA put him at 31. Maybe if he had better luck, we would have seen 2018 MVP, Mookie Betts. Trade to LA could be just what brings Mookie back to being considered as the #1 pick in fantasy.
Bryce Harper - I keep seeing Bryce Harper falling in drafts, so I wanted to put him in here. The potential to win you a league is still here. He batted .260 with a .279 xBA. There is plenty of room for him to outperform his stellar numbers. The xwOBA differential being -.021 made him the unluckiest slugger in the top 30 hitters.
Spend more time looking at the new fantasy baseball data presented to you. None of this is entirely predictive but it should help you locate players that didn't perform like that should have. Use wOBA and xwOBA as a piece of the puzzle to gain an advantage.
If you have any further questions on statcast metrics, please comment or DM me on Twitter.
Thanks for reading, and I hope you enjoyed it!
Twitter - @davithius
*Statistical credits: Fangraphs, BaseballSavant