The following essay appeared as an introduction to Baseball Prospectus 2000.
“Stat-drunk computer nerd.”
You can earn a lot of derision when you look at things in a new way, and the people who have applied statistical tools to evaluate baseball players and teams have heard the above epithets and more. The work of people such as Bill James, Craig Wright and Clay Davenport has often been dismissed as the mind-numbing analysis of people who need to put their slide rules away and get out and watch a game once in a while. Their efforts, which have been dubbed “statistical analysis,” have expanded and improved the body of objective baseball knowledge, and their work is even beginning to penetrate the insular world of baseball front offices.
But the term “statistical analysis,” as applied to baseball, isn’t descriptive enough. Actuaries analyze statistics, and while the work pays well, it is pretty dry stuff. Life-expectancy tables and risk/benefit workups aren’t going to get your average Red Sox fan excited, nor should they: baseball fans care about their teams, and the players on them, not a series of numbers.
But baseball statistics are not numbers generated for their own sake. Statistics are a record of performance of players and teams. Period. Benjamin Disraeli’s oft-quoted line–“There are three types of lies: lies, damned lies, and statistics”–just doesn’t apply.
Looking at statistics–looking at the record of player and team performance–helps analysts reach conclusions about players. So when Rany Jazayerli writes that
The distinction is critical in moving this type of baseball analysis from an outsider view to the mainstream, so that in the front office of a major-league team it can be as acceptable to look at a player’s on-base percentage as to look at a scout’s opinion of his foot speed. Organizations need to credit a pitcher for his consistently good Triple-A performance in the same way that they mark him down for his below-average fastball.
Reshaping the debate between traditional baseball people and the analyst community will give a significant push to what is currently a creeping movement. If you look at the success of the New York Yankees of the late 1990s or the 1999 Oakland A’s, it’s clear that some teams have embraced one of the fundamental tenets of baseball analysis: the importance of on-base percentage in scoring runs. In fact, the A’s have become the first organization to emphasize plate discipline in their player-development program.
We have seen the work of Wright and Rany Jazayerli on pitcher usage, particularly young-pitcher usage, start to make inroads within the game. Teams have become increasingly aware of the workloads they put on their best pitching prospects, recognizing relationships between workload and injury and workload and ineffectiveness. Given the significant investments that organizations make in their top talent, this is a prime area in which baseball analysis can make a financial impact as well as an on-field impact.
Performance analysis does not, and should not, exist in a vacuum. First of all, it is important to understand the context of statistics, and the Davenport Translations you see in this book are prima facie evidence of this. The line “.280/.350/.450” is about as informative as a George W. Bush campaign speech. At what level was this performance? How old is the player? In what park and what league does he play? What position?
And once you have all those answers, you still have only half the picture. Every player has a skill set, abilities that make him the player he is. Each player has certain strengths and weaknesses. Skills analysis–the province of scouts, managers and coaches–isn’t made obsolete by performance analysis. It’s enhanced by it.
Knowing that a 23-year-old right-hander has a live fastball, a middling curve and a change-up he can spot at will is essential. A pitcher’s repertoire, a hitter’s bat speed, a short-stop’s arm…if you’re going to develop a complete, accurate picture of any player, you must know these things. But you also want to know if the pitcher has an acceptable strikeout rate, because that’s the best predictor of career length. You want to know if the player can drive the ball, as measured by his slugging percentage and isolated power. And if that shortstop is among the league leaders in assists and double plays, it’s an excellent indication that he is great at using his arm to get outs. Isn’t that what fans and general managers really want to know?
Performance analysis has limitations. Amateur baseball players, with aluminum bats, shorter schedules, and widely variable levels of competition, are best analyzed by their skills. Performance analysis of players in short-season leagues is also unreliable, both because of limited sample sizes and the adjustments that the players, usually new to professional baseball, are making. Given a choice between a scouting report and a Davenport Translation on an 18-year-old with 200 plate appearances in the Gulf Coast League, the scouting report will be a better tool.
Performance analysis paired with skills analysis is how successful teams are going to be built in the 21st century. Good organizations will accept that there’s as much to be gained from looking seriously at a player’s track record as there is from looking at the scouting reports on him. Successful teams will be built on principles that have developed from performance analysis. Ideas that were radical just 10 years ago will become conventional wisdom, as people like Billy Beane have success, and as other organizations imitate what made the A’s successful.
Reshaping the debate continues a cycle that began with Branch Rickey’s conclusions about on-base percentage and continued through Bill James’s work in popularizing sabermetrics. It provides a means for the baseball mainstream to embrace the concepts of performance analysis while maintaining their established, valuable methods of skills analysis. Eventually there will be no debate, as both will be used routinely in evaluating talent and building baseball teams. A better brand of baseball for everyone will be the ultimate legacy of performance analysis.