A lot of ink has been spilled over the old-school versus new school debate in baseball analysis and while I’m decidedly on the new school side of things, I firmly believe that the reasons we have a difficult time winning converts is because we’re often too quick to act like our views are obviously the right ones. This isn’t a matter of sabermetricians getting the wrong answers, but we don’t often do enough to make our findings clear to the public. Sometimes we get caught up talking to each other and not talking to everyone.
Don’t get me wrong, I love Fangraphs and other sabermetric heavy sites, but we don’t always do the best job of making the basic principles clear. When someone writes a great post at Fangraphs, they don’t explain why they use wOBA instead of OPS or batting average, they take it as a given and expect the reader to know why or to look it up. Which makes less informed baseball fans weary. It’s not that they’re stupid, I don’t think that at all, it’s that they haven’t been given a proper explanation for why we think what we think on this side of the debate.
The sabermetric community offers a lot of resources that explain statistics, but we leave the curious fan with little guidance. It’s not hard to tell why some people here us talking about Wins Above Replacement and start thinking we’re nuts. It’s out job to explain what we’re doing and it’s our job to sell the message correctly. We’ve done so much groundwork in baseball research that we often forget that a new person is learning about the value of walks everyday, and that’s something we just take as a given.
Which is why it’s important for baseball analytics to have a public relations aspect of it too. Brian Kenny from MLB Network and NBC Sports Radio is a great voice for that part of the task. He’s done excellent work bringing sabermetrics into the mainstream of sports coverage. Plenty of others do excellent work on the matter, but he’s made it a mission.
At New English D, we’d like to be a part of that, and often publish basic explanations of sabermetric stats and principles while also pointing out some flaws in the basic stats. Today, I’d like to do something different. Today, I’d like to explain why you should give sabermetrics a try, period. I don’t care how skeptical you are, give me the next 5 minutes.
Here are 5 reasons:
1) The basic statistics were crafted during another era.
Batting average, runs, RBI, SB, wins, ERA, and the other statistics you’re familiar with quite readily were invented in the 1920s to keep track of what happened on the field. They are scoring statistics to record exactly how the game progressed. They’re descriptive and that is great. You can look at a box score and see exactly who was on base and who was at the plate when each run scored, but you can’t always tell which players were most responsible for the win or loss. These stats don’t tell you that much about value. It’s not because these stats are stupid, it’s because they didn’t have calculators and computers to do calculations when these numbers were invented. When you’re using a slide rule or pen and paper to track stats, things have to be simple. They don’t have to be simple anymore because we have the power to compute more information. It doesn’t mean getting a hit with a runner on second isn’t important, it means RBI is a crude way to measure that skill.
2) Progress is good.
Sabermetricians have introduced many new statistics into the world in the last couple decades, and while that might seem unseemly and cluttered, it’s actually no different than anything else. We didn’t use to fly on airplanes or drive cars, we didn’t used to be able to watch any baseball game on the internet. Heck, we didn’t even have the internet until the 1990s. No one is running around telling everyone to write more letters and put them in mailboxes, we have all pretty much embraced e-mail, texting, and instant messaging. Communication got better and more efficient. We’re better off. Baseball analysis is the same way. These new stats tell us more about baseball than we used to know. Players who walk a lot used to be really undervalued until someone with a computer looked at a lot of baseball games and realized that getting on base is really good, whether you get on via a hit or a walk. Things get better when we develop new technologies. You wouldn’t disable your internet connection, don’t immediately shut out new stats.
3) We’re asking the same questions.
Sabermetricians and traditional analysts both care about what leads to wins. Traditional analysts tend to just focus on who wins and loses and reverse engineer the explanations, but sabermetrics is just breaking it down a different way. Let’s go through a little thought experiment:
- How do you win? You score more runs than the other team.
- How do you score more runs than the other team? You score runs and you prevent runs.
- How do you score runs? You get on base.
- How do you get on base? You get a hit or you walk.
- How do you prevent runs? You don’t let the other team get on base.
- How do you keep them off the bases? You don’t allow hits or walks.
- How do you prevent hits? Don’t let them put the ball in play or hit homeruns, so strikeouts are good. You can also induce groundballs and use your defense if they are good.
When you think about the question like that, you realize we’re all asking the same thing. Sabermetricians break it down into how you score and prevent runs and they look for what leads to both of those outcomes. It’s nothing devious or nerdy. It’s 100% about scoring runs and preventing them. We’ve just looked at enough data to know which actions lead to both and which actions don’t. Sometimes there is luck involved and you can’t predict luck. We’re all about playing the odds. That’s no different from anything else, it just looks different because we’re using numbers instead of intuition.
4) More information is good.
Even if you like the old statistics, that doesn’t mean the new ones are wrong. If a player has a high batting average, that tells you something about their performance. But so does their on base percentage. So does their slugging percentage. So does Weighted On Base Average (wOBA). So does Wins Above Replacement (WAR). It’s all information about the players and teams. Sabermetricians like these new stats for a reason. The reason is that they tell us something the other statistics do not. Batting average is fine, but it doesn’t tell you if the player is getting on base via a walk. You might not think walks are as good as hits (we don’t either!), but walks are WAY BETTER than outs. Batting average pretends walks don’t exist and we think that’s silly. RBI tells you how many runs a batter has driven in, but it doesn’t tell you how many opportunities that batter has to drive someone in. It’s not fair to Joey Votto that he hits behind Zack Cozart and Prince Fielder gets to hit behind Miguel Cabrera. Those two players are in different contexts. Sabermetrics likes to provide context neutral information. Players can only control certain aspects of the game and we don’t think it’s right to judge a player on things outside of his control. This is especially true for pitchers, who can’t control how much run support they get, how well their defense plays, or which pitcher is on the mound for the other team. Sabermetrics looks at that and says, wins aren’t a great way to measure a pitcher’s performance because most of what leads to a win is out of their control. Let’s look at what is in their control and see how well they do at that.
5) The logic is exactly the same.
When you look at RBI or Wins or Batting Average to judge a player, you’re using statistical information to make an inference about how good that guy is. You’re taking information recorded in the past to make a claim about the present and future. It doesn’t matter if you’re using your eyes during an at bat or a spreadsheet in January, the logic is the same. Past behavior informs predictions about the future. For sabermetricians, we’re just using a lot more information because we have found that using more information and certain kinds of information tends to help make better inferences. For example, this is where the tired phrase “small sample size” comes into play. We’ve looked at a ton of data and see that a really good batting average over a ten day stretch doesn’t predict what the player will do on day 11 very well. For statistics to reflect true talent, you needs bigger samples. It’s simple logic and you use it every day. If you think a player is about average and then they have two great days, how much do you change your mind? Not much. If you think a player is average and they have six great months, how much? Probably a lot more. Sabermetrics isn’t any different than that, it’s merely crunching the numbers to give us a better estimate about when information starts to become meaningful.
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If you think about it like that, sabermetrics aren’t that foreign or nerdy. You might need to be a nerd to program a computer to spit out an answer to a question, but you don’t have to be anything but curious to understand what the answer is telling you. It’s isn’t that the old stats are terrible, it’s that they were developed when they had limited power to make sense of a complex game. You wouldn’t want a surgeon trained in the 1920s to operate on you, why let a statistic from 100 years ago inform you. Progress is good. Progress leads to more information and better understanding. You can absolutely disagree with a new stat, but you absolutely cannot disagree with a stat because it’s new. We’re asking the same questions and using the same logic, it’s just about being willing to expand the data you’re willing to use to evaluate those questions. You judge players by batting average, why wouldn’t you look at on base percentage too?
Ultimately, sabermetrics are a way to learn more about baseball and I can’t imagine not wanting to do that. I challenge you to learn more or to help others do the same. We have lots of information on this site under out “Stat of the Week” section and other sites offer much of the same. I’ll even make you a guarantee because I love baseball and learning that much. I will answer any question you have about baseball stats. Hit me on Twitter, in the comments, or on e-mail (See “About” above) and I will explain why I like one stat over another or what the best way is to measure something. Anything. That’s my offer. There’s no excuse not to give it a try, I’m pretty sure you’ll like it.
New English D 