# What Is Sabermetrics

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How does Tom Seaver stack up against Cy Young?

Is Mike Schmidt fit to carry Babe Ruth's glove?

Baseball fans have long wrangled over questions like these, and for almost as long they've thrown figures around to prove their point.

Batting averages (for example- the most common index of hitting prowess) have been around since 1876, when the National League began compiling them. Now, however, thanks to a small band of computer wizards, fans can find more kinds of numbers than ever before to back up their arguments. Managers, too, may soon be able to call up new types of statistics to help plot their strategy. The new analysts have named their science sabermetrics- defined as the mathematical and statistical study of baseball. The name comes from the abbreviation for Society for American Baseball Research (SABR), to which many of the sabermetricians belong.

To those who follow the national pastime from a distance, it may seem that baseball is awash in statistics already: won lost records, batting averages, runs-batted in, fielding percentages, earned run averages. But the traditional measurements of performance, say the proponents of sabermetrics, are not always the most revealing ones. Take batting averages, for example. A player's average is computed by dividing his total number of hits by his official times at bat. But not all hits are equal. One player may single with the bases empty and two out and then be left stranded at first. Another may double with two on in the ninth, driving home the tying and winning runs. Yet the two hits count equally in computing batting averages.

As every fan knows, bases on balls can also determine a game's outcome. A walk may advance a runner who later scores, and the player drawing the walk may eventually score himself. Yet walks are not reflected at all in batting averages, since a player who draws a base on balls is not credited with an official time at bat.

One analyst who's developed a new statistical measure based on runs produced rather than on hits is Pete Palmer. A computer programmer who also serves as a statistical consultant to the American League, Palmer is the creator of a method of rating hitters which he calls Linear Weights. (Baseball, Palmer explains, is a "linear" game. Unlike such sports as basketball or football, results depend on a sequence of actions performed by individuals.) Palmer begins by assigning a statistical weight to each kind of hit, as well as to walks, stolen bases, and other factors that put men on base and move them around to score. These go into a complex formula that is applied to each player's record; the result shows what contribution each individual made to the total number of runs his team scored. His ranking can then be compared with the showing of other players. Suppose, for example, the computation reveals that over the year a batter accounted for 30 runs more than the league average for all hitters. Since, according to Linear Weights, a team will win one extra game for every ten runs it scores above the league average for all teams, that batter's performance is good for three extra wins-enough to make a difference in a close pennant race.

The results of assessing players by using Linear Weights can be surprising at first glance. Mike Schmidt of the Philadelphia Phillies, whose .316 average earned him ninth place among major league batters in 1981, comes out as the top hitter of the year on Palmer's scale. Schmidt is credited with being responsible for 49 more runs than the average for his league. On the other hand, Bill Madlock of the Pittsburgh Pirates, who led both leagues in batting in 1981 with a .341 average, gets a Linear Weights rating of 28, good for only tenth place. The difference: Schmidt got more extra-base hits and walks. He led both leagues in 1981 in slugging percentage (the total number of bases a batter hits for, divided by his official times at bat), and he led the National League in walks. Overall, however, Linear Weights produces ratings that are pretty much in line with traditional estimates of baseball greatness it ranks Babe Ruth, Ted William s, Ty Cobb, and Hank Aaron as the game's finest hitters, in that order.

Most of the new sabermetricians are computer buffs who've turned their skills to the analysis of their favorite pastime.

Dick Cramer, a chemist who designs drugs by computer, formed a service called STATS (Sports Team Analysis and Tracking Systems), whose clients include the Oakland A's and the Chicago White Sox.

Bill Lames, probably the best known of the new analysts, began publishing his Baseball Abstract out of his Lawrence, Kan., home in 1977; in 1982, for the first time, the book achieved commercial publication and distribution.

James points out that a great deal of baseball information is already on hand, simply waiting to be analyzed; baseball is one of the best documented of sports.

Not every relevant factor has been recorded, however; no one knows, for example, how often a sing le advances a runner from first to third. Until now, the sheer volume of potential data has made it impossible to collect and preserve everything. That is no problem, however, for computers, and new data bases are now being assembled.

What computers can already do is impressive, and it's not limited to amassing figures. A computer can flash a diagram of a baseball field, for example, showing where a given batter has most often hit the ball in the past against a given pitcher or a certain type of pitch.

Not surprisingly, sabermetrics has met with a mixed reception from players, managers, sportswriters, and fans. Up to now, no club has made much use of the more novel kinds of data. The data keep accumulating, however, and the day may not be far off when the computer terminal makes its appearance in the dugoutgiving a manager instant information on a batter or pitcher's strengths and weaknesses in the situation at hand.

What does the advent of sabermetrics mean for the way the game is played?

Will the time-honored matching of one manager's strategy against another's give way to simply pitting computer against computer?

Not at all.

Managers, players, and fans have always known instinctively many of the things that computers can now quantify. They know that a long-ball hitter poses a greater scoring threat than a si ngles hitter, that a walk is sometimes as good as a hit, and that certain hitters don't do so well against certain pitchers. What sabermetrics offers is a means of recording and making available more knowledge, and organizing it in more and different ways. Judgment and skill will st ill be decisive, but they'll be more broadly and more precisely informed. Fans, too, will have more information at their fingertips when they square off, in season or out, to debate the questions that have always stirred baseball lovers.

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