A ball lands softly in the grass. Before the outfield can react, Steven Kwan is standing on second base.
But how, exactly, does he manage to do it? Kwan finished dead last among qualified hitters in hard contact percentage according to FanGraphs. A close examination of his rookie season — using both expected and actual numbers from public sources — indicates hidden power within the outfielder’s game.
I’m going to provide a few metrics about Steven Kwan’s 2022 season juxtaposed with the rest of the league. It is also useful to bring along expected statistics. These are available for both the league and individual players. They intend to tell us — based on data from every batted ball event since 2015 (citation fairly certain) — what kind of slugging percentage one would expect based on launch angle, exit velocity, and park factors.
Another way to say this is that expected statistics on batted ball events assume that a perfectly average, representative player is at the plate. Steven Kwan is an outlier.
Kwan, 2022 SLG: .400
MLB, 2022 SLG: .395
Kwan, 2022 xSLG: .341
MLB, 2022 xSLG: .405
Kwan, 2022 BABIP: .323
MLB, 2022 BABIP: .290
Consider that Steven Kwan slugged five points better than league average. Note that his xSLG is 59 points lower than his actual slugging. Meanwhile, the league as a whole slugged slightly worse than expected based on contact. Finally, Kwan posted a fine BABIP of .323. This is good, and also not out of the ordinary for certain types of players. The league earned a .290 BABIP as a group.
My question is this: How did Steven Kwan slug for league-average while annihilating his xSLG and posting the worst hard-hit ball percentage among qualifiers?
The answer is baserunning, but there is a bit of a gap in the literature. Ultimate base running (UBR) is widely-cited and assigns value to a player’s effort on the bases. It is one aspect of FanGraphs’ model of baserunning.
However, the instruction manual itself notes that an important piece cannot be seen by this formula: “2) A batter getting thrown out trying to advance an extra base on a hit (if he successfully does, we don’t know it, as he is simply awarded a double, for example, on a usual single where he advances an extra base).” (emphasis mine, yo)
Steven Kwan finished 15th in the league, creating fewer than three runs according to UBR. It is also true that Statcast only references sprint speed for expected metrics when a batted ball is classified as “topped”.
I propose that current sabermetric models have a gap that cannot be easily filled. How do we decide when a player starts to run? What do we do if a player’s swing is also his first step to first base? Just how valuable are coaches like Mike Sarbaugh and Sandy Alomar?
Let’s combine this with the eye test:
Steven Kwan hits left-handed, scampers out of the box, and quickly reaches a full-effort sprint. While there is no public metric that directly measures “agility” or “acceleration” for baseball players, we do have some related metrics. Outfielder jump is the best of these.
Kwan is in the 92nd percentile, firmly among the best in baseball. Making things even more interesting is that this all comes from what Statcast calls his “burst”. Phrasing concerns aside, this is defined as the amount of ground a player covers after his initial reaction to a batted ball event in the field.
Steven Kwan was sixth among all outfielders in burst in 2022. To rephrase, he covers more ground than almost any other player in baseball between 1.6 and 3 seconds after contact in the outfield. This time window perfectly aligns with a player and first base coaches decision to round first. They called it peeling the banana when I played pinto ball, and I cannot believe it might be that important. Is it really as simple as Kwan planning to round first hard on any ball into the outfield?
We are staring directly at the value of hauling ass on every single batted ball. It is right there. Buntotron isn’t real and so I need help with this one.