A little over a week ago, Moshe posted a piece asking if sabermetrics have gone too far. Since that time, after reading the article itself and various responses to it throughout the Web, I’ve been thinking of ways that could help bring sabermetrics into the mainstream.
Perhaps I’m being too ambitious here; after all, I’m just a blogger who’s not deeply embedded in the metric community, nor am I someone who’s involved in developing new stats and metrics. Regardless, I regularly use advanced metrics in my posts here, and I attempt to use them in conversation with other baseball fans (so far, I’ve only had one conversation of memory in which the other party was also versed in said metrics), so I’d like to see them become a little more popular.
Briefly touching on something Moshe discussed at length, I’ll say this: there are a ton of stats out there. The “market”, so to speak, may not be completely flooded, but the water level is rising. To some, like myself, there is an appeal to this. The more stats there are, the deeper I can delve into the game. The more stats there are, the more I can find out about the players I enjoy watching. To some, these advanced stats can be quite intellectually stimulating. To put a personal spin on things: I was awful at math all through school, elementary through undergrad. However, for some reason, I can grasp the theories and (sometimes) the procedures behind these stats. There is, though, a flip side to that coin. Some people are understandably put off by the amount and style of metrics out there. A sort of sensory overload occurs and the causal fan could be come very disinterested.
In my interactions with friends, family, and other fans on the internet, through reading various print and internet articles, there seems to have been a misinterpretation as to the object of metrics. It’s not to alienate older fans. It’s not to make it so that games are played on a spreadsheet from our mothers’ basements. The point of metrics is to inform and to entertain. We who use them do so to, as I said before, attempt to gain a better understanding of how the game works. For us, the metrics enhance our enjoyment of the game. The beauty of baseball is that each of the 162 games in the season is a different story. It’s like an every day soap opera with better performances and better lighting. Using metrics does not make us lose sight of that. The numbers we use just represent a different way to tell the same story. At the end of the day, that’s what all stats are: numerical representations of the games we watch and listen to.
So what can be done to make these numbers more accepted and widespread in mainstream baseball analysis and debate? First, the concept, or concepts, of each stat needs to be introduced. Starting with the broad aims and beliefs of the metrics makes understanding them much easier. So, instead of throwing FIP at you, I’d explain to you that it tries to measure the pitcher outside of his defense. While this is a bit abstract, it’s still easier to understand than it would be if I threw the formula for FIP at you. This helps to create a bit of a context for a hard-to-recognize number.
Creating a context for these stats is just a small part of making them more accessible. The biggest thing, though, that could be done to make these metrics more accessible is to do something simple (that’s already being done, really): make the scales familiar. Some stats already do this. For example, wOBA is on the same scale as OBP; EQA is on the same scale as BA; FIP is on a runs per nine innings scale, just like ERA. Though those stats are more complete–and slightly more complicated–than the typical stats, their scales make them easy to recognize and easy to comprehend. Hopefully, more metrics follow this model in the future.
Normalizing stats, that is, adding the ever awesome “+” at the end, is another technique that could help the casual fan become more engaged in advanced metrics. With some of these stats, especially ones like tRA, it’s hard to know what’s good and what’s bad. Luckily, there’s a tRA+–just like ERA+ and OPS+–to help us compare that number to the league average. Again, this helps to give the number context. Another new(er) stat that does this is Weighted Runs Created+ (wRC+). Any time we can quickly compare a stat to the league average, it’s incredibly helpful.
My last bit of “advice” is aimed more towards the causal fan: don’t worry about how exactly the stat is calculated. Just because a stat cannot be quickly calculated, or it takes more than a few steps, does not mean it is illegitimate. If a stat as convoluted as Quarterback Rating can be popular among NFL analysts and fans a like, there’s no reason a much less complicated stat, like wOBA, cannot become popular among mainstream baseball analysts and fans. The result of the calculation–the stat itself–is what matters most. If you can understand that answer and the concept behind it all, you’re in very good shape.
Obviously, these changes aren’t going to occur overnight. It will take a long time for these stats to be accepted into the mainstream. When they are, I’m sure there will be a whole new batch of metrics that will be knocking on the proverbial door to be let in. Regardless, this world of stats is ever changing and ever evolving. As writers, observers, and fans, we should be changing with it. So, I suggest to you–the ones who don’t get or don’t like these new metrics–give them a try. You never know, you just might like it.