The times they are a changin’ … and so are the stats. The old process of judging baseball players and teams relied on simple counting stats (runs batted in, hits, wins) and basic rate stats (batting average, fielding percentage). The new era of baseball is defined by hyper-focused metrics based on real-time tracking data provided by Statcast’s array of radar and high-speed cameras installed in every MLB ballpark. Instead of recording only the outcome of a pitch or batted ball, we can now analyze what caused that final outcome.
New things aren’t good by default. Nor are they bad because they’re unfamiliar. Just because we have new data to manipulate, query and analyze, that doesn’t mean the new information is perfect (it’s not), or that it will reveal the secret truths behind the game. Baseball’s objective truth isn’t waiting to be unlocked by one algorithm or projection system. So instead, we use the tools at our disposal. To understand these new metrics, we have to analyze how they are created, what they attempt to evaluate, if they succeed at that intended goal, and how they are presented to the public. Only then can we truly understand them, and learn how to use them responsibly.
In an effort to better educate the masses on these new analytics that have increasingly made their way into mainstream baseball commentary, this is the first in a series of pieces examining Statcast metrics. We begin with Hit Probability, laying out the goals, limitations, misconceptions and calculations that come with one of Statcast’s most derided and misunderstood stats. If you have questions or feedback after reading, please let me know in the comments, where we can continue the discussion.
