WLAB: Baseball’s Newest Statistic Provides Fast Results For America’s Slowest GamE.
In modern baseball, very few advanced statistics tell a viewer precisely what happened on a given play, as most complex statistics report rates or averages over a large sample size. However, in today’s game, immediate statistics are a growing field that has the utmost importance. They create insights for a manager to evaluate a play and allow fans to understand what they’re watching in real-time. That’s why I've created the WLAB (an acronym for Win-Loss-At bat pronounced “Win Lab”) to fill the void and connect casual fans to the ever-growing analytical side of baseball. In addition, the statistic seamlessly integrates into the growing world of live sports betting in baseball. WLAB takes the mean exit velocity from the pitcher and the hitter for that season and averages the numbers together for a final “matchup average.” Then, based on a batted ball's exit velocity on any given play, the pitcher or the hitter wins. If the ball's exit velocity is above the matchup average of the two players, the hitter gets the win, regardless of the outcome of the play. If it's below, the pitcher gets the win, even if the ball went over the fence. Other potential outcomes of a play are relatively intuitive. For example, a walk would be a win for the hitter, and a strikeout would go to the pitcher.
The statistic can be split into two different metrics, WLAB and WLAB%. WLAB% is the long-term rate statistic you’ll find on the bottom of a ticker, like batting average and on-base percentage. On the other side, WLAB is the counting statistic that can tell you in real-time who won the at-bat you just watched. These can be thought of as hits and batting averages in which WLAB% takes into account WLAB for a season-long metric, while WLAB is merely a per-at-bat statistic to tell you who won any given matchup. Any at-bat will result in either a win or loss (excluding a few exceptions like catcher interference). This new statistic is different from current metrics like OPS and xwOBA because they aren’t predictive of what will occur, but rather evaluations of what happened in the past. WLAB is much more informative while watching a game and can coincide with live betting, which is becoming a massive deal in baseball, as evidenced by this Marcus Semien interview (https://www.youtube.com/watch?v=WyfdcaAmY7o).
I've taken six hitters and three pitchers to demonstrate this concept and matched them against traditional analytical tools like xwOBA and xFIP during the 2021 season. The six hitters are Fernando Tatis Jr., Bryce Harper, Mitch Haniger, David Fletcher, Mark Canha, and Tommy Pham. The three pitchers are Corbin Burnes, Joe Musgrove, and Dallas Kuechal.
Below is the way our statistic matches up (hitters):
xwOBA:
Bryce Harper – .430
Fernando Tatis Jr. – .407
Mitch Haniger – .353
Tommy Pham – .350
Mark Canha – .332
David Fletcher – .280
WLAB%:
Bryce Harper – 51.62%
Tommy Pham – 49.42%
Mark Canha – 48.39%
Mitch Haniger – 45.89%
Fernando Tatis Jr. – 45.62%
David Fletcher – 43.70%
Overall, these metrics are similar, other than the massive fall in rankings from Tatis Jr. However, the reasoning is pretty simple. The statistic works in a way in which the better the pitcher, the lower the threshold the hitter needs to meet because the “matchup average” is decreased. On the flip side, when the pitching is worse, the hitter has to hit the ball harder to achieve a win. This makes sense in Tatis Jr.'s case because he hits the ball exceptionally hard against backend rotation pitchers but tends to struggle against elite pitching compared to someone like Harper. Harper tends to clear the matchup average threshold more frequently versus the Jacob deGroms of the world. Additionally, a lot of the difference in the comparison between Harper and Tatis Jr. is due to Tatis Jr.'s inability to put together so-called “professional at-bats” against good pitching. On the other side, Harper has constantly put up battles and eventually draws a lot of walks against even the best pitchers. It’s important to note that the amount by which a hitter clears the matchup average threshold is unimportant, as the statistic only considers the frequency at which this occurs. In other words, all wins are equal. This is another significant reason why Tatis Jr. will be extremely high on the rankings of other statistics but fares worse with WLAB.
Given the current data, we can create baselines to put players into four categories using WLAB%. The first baseline is above 50%, putting the hitter in the “elite” category. The following baseline is the “good” category from 47-50%. The third baseline is from 43-47%, which would put the hitter in the “average” column. Finally, below 43%, puts the hitter in the “poor” column.
Now, on the pitcher's side, we see a different story.
xFIP:
Corbin Burnes – 2.30
Joe Musgrove – 3.65
Dallas Keuchel – 4.74
WLAB%:
Corbin Burnes – 64.60
Joe Musgrove – 58.48
Dallas Keuchel – 48.31
The margins between players on this side of the ball are significantly broader and follow a similar order to current advanced statistics. For example, Burnes and Musgrove were both elite pitchers in 2021 with exceptional ERAs and xFIP metrics, but when it comes to WLAB%, their percentages are multiple points off. Overall, there is a glaring difference in how WLAB% can be used with hitters and pitchers.
One primary use case of WLAB% for Major League teams would be during pinch-hitting or platoon situations. In these cases, a manager can match up hitters to their win percentages against the pitcher currently in the game. Knowing how often a player “beats” a particular pitcher is essential because other metrics for determining exit velocity use static metrics as a threshold (like HardHit%, which has a threshold of 95 mph). This is incomplete because the statistic doesn’t adjust to the pitcher a hitter faces. For our current purposes, we’ll assume Tatis Jr. and Harper were on the same team, and the pitcher in the game was Max Scherzer. As the manager, these situations are challenging to decide and often come down to a lefty or righty matchup and previous outcomes against the current pitcher. This is where WLAB% can come into use as it creates a better process to determine these situations as it accurately represents previous at-bats versus the pitcher in the game. In the 2021 season, Tatis had a win percentage of 45% win rate against Scherzer (5/11), while Harper had a 57% win rate (4/7). With this metric, a manager can confidently decide in tight situations. Additionally, this metric is a better way of looking at previous matchups because it determines the hitter's actual performance instead of the outcome of the play, which is the only proper way to determine how a hitter matches up against a pitcher in an objective way. This shines through in our example as one of Tatis' wins against Scherzer was a homerun which may cloud a manager's decision-making and cause wrong decisions. Additionally, the statistic could work in favor of pitchers as well. When deciding who should come out of the bullpen, you can look at their win rates against the hitters due up and make a confident decision to close out a game. However, there are still flaws with WLAB%. For example, the sample size of a hitter's appearances against a particular pitcher may be limited; at this point, it may be advisable to use a hitter’s overall WLAB% for the season or to get creative and group pitchers with similar profiles to create a larger usable sample size. Thus, the number could not accurately represent the hitter's ability, but it’s important to note that the current method of looking at previous performance doesn't solve this issue either.
Another use for the statistic, and good use of the WLAB counting statistic, is sports betting. Baseball has some of the most downtimes out of any significant sport leading to a loss in viewership every year. To prove this point, a random Thursday Night Football matchup had triple the viewership of a pivotal World Series game last year. However, live sports betting on every play may reverse the trend for many fans. As of now, baseball betting is based on whole game outcomes, meaning the viewer has to watch all nine innings (or, in other cases, a significant portion of the game) to determine the result of the bet. But with WLAB, a bet can be placed on every at-bat. The sports betting applications could calculate the mean exit velocity for both the hitter and the pitcher and average it out, displaying the figure on the screen. Then, the fans could place a bet that wagers on the outcome of the at-bat. Naturally, the bet can't give 1:1 odds because pitchers dominate the statistics (just like batting average), and there are different matchups. This is where the sports betting apps would create money lines, so it's a fair bet for the fan. This could revitalize the way baseball is viewed in sports betting as other sports have "prop bets'' where they can bet on various things. For example, in basketball, you can bet whether the first pass is a bounce pass. However, these are less common in baseball, so the WLAB can create a bet where fans can tune in and out of the game. Of course, this would only solve some of Major League Baseball's viewership issues. Still, it would undoubtedly draw quite a few more eyes to their local networks on a Thursday or Monday night when the MLB competes with the National Football League.
