How to Evaluate Pitching: An Incomplete Idea

I had an idea today, but I’m not sure if it would work, first of all, and even if it did work theoretically, it may be astoundingly impractical. But I’ll spell it out here, and you all can decide.

So a big question among sabermetricians – no, really just all baseball analysts/fans – is how to evaluate pitching. That is, what metrics/stats should we employ when trying to determine how well a pitcher has performed? Traditionally, Cy Young and sometimes MVP voters have used wins and ERA, but as countless smart people have pointed out, there are major flaws with these metrics.

Wins are very strongly influenced by offense, defense, the bullpen, and luck. Over a very large sample, win totals are indicative of how talented a pitcher is, but even then the variance is huge. ERA is better, but it is still influenced by defense and luck. If you have only Mike Trout’s in the outfield and only Ben Zobrist’s in the infield, A.J. Burnett becomes Roy Halladay (well not really, but you get the idea). Not to mention the fact that a large sample size is needed just to remove the random noise from ERA.

Sabermetricians have come up with various solutions. One of them is FIP, or Fielding Independent Pitching, which uses only strikeouts, walks, and home runs as the contributing factors to pitching performance. This solutions removes the defense and luck components out of balls in play, but it ignores any skill that may be, and often is, involved in turning balls in play into outs.

Others prefer to use methods based on runs allowed to measure pitching, thus including a pitcher’s ability to affect the outcome of balls in play. Of course, in doing so, they include luck and defense, which aren’t under the pitcher’s control.

My solution: wisdom of the crowds.

Good idea, right? Oh, you want more? Fine, I’ll explain.

What if there was a website in which baseball fans could, as they were watching a game, answer questions about every batted ball? The answers to these questions would determine the likelihood of that ball turning into an out, or a single or double or triple, given an average defense. With enough responses and data, we could determine what a pitcher’s true BABIP is, based on the types of balls that batters put into play against him.

The hard part is determining which questions to include, and how well fans would be able to accurately describe the plays they saw without being biased by the result. Here are some of the types of questions I’ve been thinking about:

1. How hard was the ball hit, on a scale from 1-10?
2. What type of batted ball was it (Choose one)?  Options: Weak grounder, Grounder, Weak Liner, Liner, Fliner, Flyball, Popup
3. Where was the ball hit (Choose one)?  Options: Infield (3rd base line, left side, middle, right side, 1st base line), Shallow outfield (left, center, right), Deep Outfield (left, center, right)
4. How much credit, or blame, do you think the pitcher deserves for the outcome of this batted ball, taking into account both defense and luck, from 0%-100%?

I’m certain that there are a multitude of issues with these questions. How well would fans really be able to determine how hard a ball was hit? The options in the second question overlap with the answer the first question – is this a problem, or would it lead to more accuracy? Does the location of the ball really matter, or only how hard it was hit? Obviously a shallow fly ball is different than a deep flyball, but can a pitcher really control whether a groundball is hit to short or down the middle? I don’t know. These are all important issues that I don’t know the answers to.

The fourth question would probably not be important once enough data had been collected, as the answers from the first three would be compared to the actual results of the batted balls in question. However, it may still matter, since two batted balls with identical answers to the first three questions could potentially be very different with regards to the fourth.

Maybe some examples would help my thinking and clarify what I’m suggesting. Below is the first video highlight that came up on mlb.com (I have a feeling the embed won’t work. If it doesn’t, just click on the picture and it’ll bring you to the vid).

 

1. How hard was the ball hit, on a scale from 1-10? 3. This is a pretty slowly hit ground ball, but it could be slower. It’s hard to place a number on it, but 3 sounds reasonable.
2. What type of batted ball was it?  Weak grounder. It might be a regular grounder, but the results would probably be mixed, and would show that it’s somewhere in between.
3. Where was the ball hit?  Infield – left side.
4. How much credit, or blame, do you think the pitcher deserves for the actual outcome of this batted ball, taking into account both defense and luck, from 0%-100%? 10%. Ok, I’m realizing now that this is a very confusing question. Maybe it would be better if there were only two choices, credit or no credit. I’m not sure.

Let’s try another:

1. How hard was the ball hit, on a scale from 1-10? 7. This is a fairly hard hit ball, but it’s not a screaming line drive and it’s not too deep. Maybe 6 or 8 would be better, but I’ll go with 7.
2. What type of batted ball was it ?  Fliner. This is exactly what I think of what I think of a fliner.
3. Where was the ball hit ? Shallow outfield – right. I’m thinking maybe I should add left-center and right-center, but that implies that a pitcher can control the location of a batted ball that precisely, which I’m not confident he can.
4. How much credit, or blame, do you think the pitcher deserves for the actual outcome of this batted ball, taking into account both defense and luck, from 0%-100%? 20%. The pitcher probably doesn’t deserve to get rewarded for an out here, but at least it wasn’t a deep, hard-it fly ball.

So there you have it. Theoretically, if there were hundreds of responses for each play, we could estimate, with some degree of accuracy, how much credit a pitcher deserves for a given batted ball, and by combining all of the batted balls, determine the pitcher’s skill at turning batted balls into outs.

Now it’s your turn. Is my idea completely idiotic? Would it work theoretically? Would it work practically? How could I change the questions to improve accuracy and response frequency? Once I perfected it, how could I implement it? And what would I do with the results? I would love to hear any and all comments.

In Defense of Sabermetrics

One of the most remarkable and unique aspects of baseball that differentiates it from almost all other sports is its compatibility with statistics. Unlike most team sports, in which every player works together to achieve a common goal, baseball is individualistic; each play starts with two people, the pitcher and the hitter, and ends in an easy to measure outcome. For this reason, statistics have always been a central aspect of baseball. Even when the game began in the 1800s, records were kept of batting average and earned run average for every player. As the game evolved, so did the complexity and intricacy of how fans, analysts, and management measured all aspects of the game.

From this increasing usage of statistics came sabermetrics, a term derived from SABR, an acronym for the Society of American Baseball Research. Since Bill James coined it in the 1980s, the term has come to acquire many different connotations and uses, but all broadly depict it as the analysis of baseball through the use of empirical data and evidence. The recent film adaptation of Michael Lewis’ book Moneyball has brought sabermetrics into the public eye. It tells the story of Billy Beane, the general manager of the A’s who revolutionized baseball management by using sabermetrics to exploit market inefficiencies. By relying largely on advanced statistics rather than scouting and traditional assumptions about what makes a player valuable, Beane was able to build a high-performing team with very little money.

Despite Beane’s relative success and its objective, scientific nature, sabermetrics carries a very negative stigma among many baseball enthusiasts. The reasons for this, as I see it, are twofold. Firstly, sabermetrics questions many of the traditional assumptions about baseball that have existed since its conception: that batting average, home runs, and RBIs determine a hitter’s prowess; that ERA and wins determine a pitcher’s; that there is such a thing as clutch hitting, that sacrifice bunts are useful, and so on.  Sabermetrics uses statistical data, research, and analysis to determine what truly makes a player valuable, how to predict performance and the correct strategy for certain situations, but in the process it comes to conclusions that go against the status quo. This is why it took so long for managers and general managers to use more advanced statistics; on the one hand, they wanted to do whatever necessary to win, but on the other hand, they wanted to keep fans interested. Running a baseball team is a business, and the risks of throwing away deeply held intuitions about how to evaluate players could be detrimental to the fan base of the team.

The second reason that sabermetrics often carries a negative stigma comes from inaccurate representations of its purpose and methods. I have heard people say that “stats people” don’t watch the games, think all players are robots, claim to be able to predict every event perfectly, don’t take scouting or emotions into account, and so on. Though these depictions may describe the beliefs of a small number of people, they absolutely do not represent the purpose of sabermetrics as a whole. Bill James originally called sabermetrics “the search for objective knowledge about baseball,” without mention of statistics, intangibles, or scouting. James did believe that the use of more advanced statistics could more effectively discover “objective knowledge” about baseball, but that does not mean that other methods are not still useful or that his method was foolproof.

Part of the problem with Moneyball was that it gave the impression that Billy Beane disregarded the advice of scouts or that he completely ignored the mental and emotional aspects of the game. This was simply not true. Beane could not have created such a strong core of homegrown players without good scouting. What Beane did was realize that there were certain biases present in not only scouting evaluations, but in the entire free agent market.  By being willing to question assumptions and use all methods available to gain a better understanding of players, Beane was able to exploit inefficiencies in the market and gain an advantage over other teams.

As a philosophy major, I have realized that there are a lot of similarities between philosophy and sabermetrics. Both carry a negative stigma because of their willingness to question assumptions. Both try to search for objective knowledge about their relative domain of study (for sabermetrics, this is baseball; for philosophy, this is, well, everything). Because of these two things, both lead us to change the way we understand certain questions, issues, and the way we view the world and baseball. Nevertheless, neither philosophy nor sabermetrics is the be-all and end-all of how to understand their respective fields. We cannot learn everything about baseball through statistics and objective research, and more importantly, we must admit that there is more to baseball then the search for objective truth within it, just as we must understand that there is more to experiencing life than rational thought and Socratic dialogue.

There are many ways to be a fan of baseball. I use sabermetrics as a way to understand and appreciate the game, but many other prefer to hold on to the traditional beliefs that have been a part of baseball for ages. This is completely understandable, and appropriate in the sense that baseball is not a science, but a game in which tradition plays a central role. What people should understand, however, is that sabermetrics does not take away meaning from baseball, but adds it, bringing more complexity, analysis, and intricacy to the game. “Stats nerds” like myself don’t want to take the fun out of baseball, but have more fun following it by being able to gain a deeper understanding of the game.