The Baseball Economist: The Real Game Exposed (21 page)

From Paul’s point of view, that was the great thing about college players: they had meaningful stats. They played a lot more games, against stiffer competition, than high school players. The sample size of their relevant statistics was larger, and therefore a more accurate reflection of some underlying reality. You could project college players with greater certainty than you could project high school players.
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Sophisticated statistical analysis of baseball data is a new technology, and a technology that is superior to the old technology. Yes, it is possible to build a successful ball club solely with scouts instead of spreadsheets, but why would any GM choose to do this? It requires a much larger operation to compensate for the greater errors in prediction. It just so happens that the performance methods are a cheap operation to run if you do it right. Rather than deploying an army of scouts across the globe to report back on thousands of personal observations, from which personal impressions can be quantified, why not target those who exhibit qualities of successful ballplayers? And the more you can afford to limit the talent pool you draw from, the more efficient you can be at evaluating it.

The personal observations from the past are not useful in the same way that they once were. Yes, this type of scouting can be used to put a winning team on the field. But just as a train can still get you from New York to Los Angeles, why would you want to take Amtrak when you can fly much cheaper in a fraction of the time? Statistical analysis is a tool that was no different a discovery than the radar gun that many scouts can’t live without. It reveals new information that was once hidden behind the millions of factors that influence the game.

Am I predicting an end to traditional scouting? I sincerely doubt that any baseball team, including any team run by Billy Beane, will ever abandon personal scouting. No matter how far the knowledge of predicting talent progresses, there will always be things that the stats will miss. Teams can learn from statistics what information they really need from scouts, and statistics can make the scouts more effective. For example, teams might notice that pitchers with high strikeout rates from Mid-State Junior College sometimes flame out while others are dominant in the pros. On a spreadsheet, these guys might have similar stats in every way. With a scout on hand to chart pitches, the team may learn that the pitching coach teaches some of his pitchers a dinky knuckle-curve that kills the junior college hitters just as badly as the guys with real talent on the team. When all of the guys are drafted the guys with natural movement, speed, and deception succeed; the knuckle-curve guys learn how to hit or move on. Statistical analysis does not eliminate the need for on-site scouting, but the role of the scout may be reduced and modified to focus on different things.

Also, other organizations that have been successful in evaluating talent through traditional methods have made their own innovations for evaluating talent that have caused them to be successful on a small budget, too.
Moneyball
speaks to the success of the implementation of one methodology, a method that happened to come to baseball from the outside, which makes it more interesting. It’s tempting to say the method the A’s employ—in which sabermetrics play a large role—is superior to all other methods and that’s why the A’s have been winning as of late. This upsets a lot of people. After all, haven’t teams been successful without employing sabermetric methods? There is no arguing that Beane used a sabermetric mind-set to stay ahead of his competitors. I’m going to resist the temptation to call the A’s’ method superior. We don’t know that other teams don’t employ similar methods, and other organizations have been just as successful as the A’s, if not more so, in winning with cheap talent.

In looking at the performance of baseball teams during Oakland’s recent run of success, the A’s clearly aren’t the only team that’s been winning, even in terms of their limited budget. This is a point that Clark Medal–winning economist Steven Levitt brought to light on his
Freakonomics
weblog. Table 22 ranks teams on total budget as a percent above or below the league-average payroll, and the number of playoff appearances by team from 2000 to 2005.

While the big-budget teams at the bottom of the table dominate the playoff appearances, the A’s are not the only members of the winning paupers club. The most noted comparable to Oakland is the Twins, with three postseason appearances over this span. Also, the Marlins and the White Sox have posted some success on smaller-than-average budgets, each with a World Series trophy. None of these teams are known to be performance scouting clubs—or at least they are not declaring
themselves to be using similar methods. We think of these franchises as successes of traditional scouting and good overall management, which allow them to win on a tight budget. And just as traditional scouting organizations would be wise to adopt innovations from sabermetric organizations, so too can stat-savvy clubs learn from the innovations in traditional scouting. Innovations are innovations. And clubs that wish to win will shift their resources to take advantage of these new methods.

Again, this is just the process of creative destruction at work. Old ways and old scouting methods may disappear, but the end result is a good one for the fan: better and cheaper baseball. While we might bemoan the loss of scouting jobs, we must remember that the old-school scouting ways are just as much a part of the process. The guys who took the train to watch games lost out to guys that had cars, the full-time day scouts lost out when part-time scouts could scout night games, and the Negro Leagues scouts replaced some of the guys with an expertise in the low-level white-only minor leagues. Someday a new technology will come along to sweep away the current wave of scouts. Maybe it’s a new statistical method or a computer that simultaneously records every baseball play in the world through computer chips placed in the ball. In any event, I’m glad for this change, because it brings us better baseball.

If statistical methods are just a new technology that contributes to the production of a winning ballclub, why does the creeping use of statistical analysis upset so many people inside the game? The fierce resistance to technological change is much older than baseball. Just as certain frightened seventeenth-century English textile workers known as Luddites vandalized machines that could do their jobs, those within the baseball establishment are scared of essentially the same thing.

12

How to Judge a Hitter or a Pitcher

—JOHN 7:24

THERE’S NOTHING SUBTLE about the Cincinnati Reds’ Adam Dunn. The six-feet six-inch, 275-pound left fielder was slated to play football at the University of Texas before he turned his attention to baseball. In 2004, Dunn became the owner of a dubious record: he struck out more times in a single season (195) than any other player in the history of baseball. While many were quick to chastise Dunn for his tendency to strike out, few noticed that Dunn was the eleventh best run producer in the National League that year. In 2003, the Atlanta Braves’ Russ Ortiz led the National League in wins with twenty-one, while being only the fiftieth best run preventer. By 2006, Ortiz’s deficiencies had become so obvious that the Arizona Diamondbacks cut him in mid-season, eating the remainder of his $22 million contract—the largest amount any team has ever paid to waive a player.
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Evaluating players is a contentious issue among fans. But rigorous answers to the arguments in bars and managerial offices can be established. That is, with a little help from sabermetrics and an economic perspective. I often hear fans bash or praise the same player for the right reasons. “He hit thirty home runs and had 120 RBI, how can you complain about that?” one fan says. Another responds, “He strikes out all the time and rarely gets on base when he doesn’t hit.” We ought to be concerned with viewing the complete player. The key to evaluating players is not to focus on the good and the bad independently; it’s to focus on all the things a player does and
weigh
the good and the bad. Sometimes the answers are surprising. Some .300 hitters aren’t as good as players who hit .240. Some pitchers with a lot of wins aren’t as good as those with very few wins.

One thing that we need to be careful of is to not let our evaluations be biased by our memories, particularly when it comes to grand events. In 1993, Joe Carter won the World Series for the Toronto Blue Jays with a walk-off home run, and few people will forget that. He had plenty of home runs and RBI in his career—he’s forty-fifth on the all-time home runs list with 396 and fifty-second in RBI with 1445—which is why we tend to think of him as a very good player. But actually, Carter was very average. When he wasn’t hitting home runs, he was making a lot of outs. Nearly 70 percent of his trips to the plate resulted in an out for his team, compared to the league average of 67 percent. As fans, we find it easy to remember the home runs and runs batted in. They are visible good events that we recall fondly, because they directly produce runs. In a game where an out is the most common outcome, outs do not have the same visible impact on run production as hits and they don’t stick in our memories. However, nineteenth-century French economist Frédéric Bastiat pointed out that just because something is less noticed doesn’t mean it’s less important than things that are obvious:

In the department of economy, an act, a habit, an institution, a law, gives birth not only to an effect, but to a series of effects. Of these effects, the first only is immediate; it manifests itself simultaneously with its cause—it is seen. The others unfold in succession—they are not seen: it is well for us, if they are foreseen. Between a good and a bad economist this constitutes the whole difference—the one takes account of the visible effect; the other takes account both of the effects which are seen, and also of those which it is necessary to foresee. Now this difference is enormous, for it almost always happens that when the immediate consequence is favorable, the ultimate consequences are fatal, and the converse.
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Hitters have long been recognized as especially complicated to evaluate. Unlike pitchers, who can be more closely evaluated based on their direct impact on run prevention (but do have their own complexities), batters do lots of things than indirectly impact run production for their teams. Ichiro Suzuki of the Seattle Mariners hits for a high average, but has little power. While Adam Dunn of the Cincinnati Reds hits for a low average, but he has a lot of power and walks quite a bit. It’s easy to see how fans can disagree about who is a more valuable contributor in terms of producing runs.

Most of these arguments are rarely resolved, despite the fact that we have the capability to do so. Using certain analytical techniques, the sabermetrics community has done quite a good job of weighing the values of the different skills that hitters have. In fact, this analysis can get quite complicated and detailed. In his book
Baseball’s All-TimeBest Sluggers,
Michael Schell (a biostatistician at the University of North Carolina) goes so far as to measure the specific impacts of different eras and parks for nearly every possible batting outcome in the entire history of baseball. More modestly it is possible to demonstrate simple rules of thumb that are useful for evaluating players based on a few readily available statistics. Certain things are definitely worth noting in hitting performance—for example, Coors Field inflates and Dodger Stadium deflates hitting success—but even without these corrections we can judge the general hitting contributions of players. There are more complex and accurate measures of hitters, but those few simple numbers flashed under a player’s name on the scoreboard before a batter steps to the plate contain quite a bit of information.

A batter steps to the plate and leaves it with an out or a nonout. He could strike out, put himself out, or give the fielder a choice: put out an existing base runner or put the batter out. For a batter, the best nonouts are hits: singles, doubles, triples, and home runs. The batter can reach base without making an out by a walk, being hit by a pitch, through a fielding error, and a few other trivial ways, including catcher interference and a strikeout combined with a wild pitch. Given these events, we judge batters by three main statistics: on-base percentage, batting average, and slugging percentage. In total, these stats measure players’ abilities to reach base in any manner, to hit safely, and to advance many bases per hit.

The on-base percentage (OBP), sometimes known as the on-base average (OBA), is the number of times a player reaches base safely without making an out. It’s sort of a fancy batting average.