In August, 2015, FX Networks president John Landgraf told a room full of television writers and critics that the massive increase in productions had led the industry to the point of “peak TV.”
A play on the chatter around “peak oil,” Landgraf’s argument was that there simply wasn’t enough audience to sustain the growth in the number of shows being delivered through an increasing number of channels and services. Anyone with a PVR filled to the brim and watchlists that never get watched has certainly lived through this concept.
I was reminded of this discussion several weeks ago, at the outset of spring training, as I looked for information on a pitcher. Scrolling about endlessly and flipping between four browser tabs, it occurred to me that baseball may be approaching “peak stats.”
This is by no means an anti-stat rant, beckoning back to the good old days when we had all the numbers we needed on the back of a baseball card. It would be hard to argue that our understanding and appreciation of the game of baseball hasn’t been enhanced over the past decade by the depth of the data that we have at our disposal.
But the amount of data and the complexity of statistical analysis being done with it makes it increasingly difficult for all but the most dedicated of professional or amateur analysts to keep current. The task of looking at a pitcher’s ERA, FIP, xFIP, ERA-, FIP- and xFIP-, not to mention conflicting numbers on the percentage of different pitch types thrown, four different calculations of wins above replacement, and even newer considerations such as spin rate or the percentage of balls “barrelled” by opponents. There are so many answers, you could forget the question that sparked your interest in the first place.
It’s not that there shouldn’t be any math involved in baseball fandom. But you’d probably hope to be able to do most of it in your head. There have been many moments over the past decade where I’ve considered my baseball acumen stunted by the fact that I have such inadequate skills with spreadsheet software, much less a grasp of what are probably undergrad-level statistical principles.
Thankfully, there are people smarter than I who have applied their intellects and knowledge to the further study and understanding of baseball. Until recently, these pieces read like a better, more informed version of sportswriting, focused on conveying new concepts to a broader audience. Lately, though, some of this writing feels more academic, wherein the research is being conveyed to a much narrower audience to further the science of baseball.
Furthering the understanding of the game, advancing the science and exploring new data are all laudable goals. Moreover, the idea that teams’ own analytics are always several steps ahead of even the most insightful experts outside of MLB organizations creates a compelling case for continuing to push forward with analysis of all new figures that become available.
But as this work is done, it is important that the discourse around baseball not become so arcane as to exclude the broader base of fans. An important aspect of science is to ensure that research is done in a manner that the outcomes have some ability to be conveyed to those who might have a practical application for the knowledge. This is known as “knowledge translation,” and within academia, it can be at times be neglected or looked down upon, given that most the greatest rewards within this sphere is the recognition of others within the research community.
But given that we’re talking about baseball, there’s almost no compelling reason not to include the broader collection of everyday fans into these discussions.
Understandably, this can be challenging, especially with new information being generated, because with only a few years – or even a few months – of available data to work from, it’s hard to have any sort of longitudinal study that definitively provides us new meaning. Spin rates, exit velocity and launch angles are incredibly compelling numbers, but it could take several more years until we have enough data to understand what these figures mean. Or how they could possibly create a rationale for a two-year deal of Justin Smoak.
Maybe the most positive development in this area was the recent announcement by MLB Advanced Media of new applications for their Statcast data, including Catch Probability and Hit Probability. These new metrics are informative and intuitive, and while not perfect, they will hopefully create a greater understanding amongst fans of what they’ve just witnessed on their TV or in the ballpark.
If we are approaching “peak stats,” and there isn’t much more that we can internalize or understand from the ongoing analysis, then the future of baseball stats won’t be in taking new data sets and creating a more complex analysis of the game. It will be in taking better data, and more instantaneously and intuitively contributing to a better understanding of baseball for a greater number of fans.