NEW YORK _ As professional sports becomes big business, it is
getting increasing attention from economists, rookies and otherwise.
Baseball is a fertile field for economic analysis because of
the comprehensive record keeping; the passionate interest of
owners, players and fans in the data; the intense bargaining
between players and their union and club owners and the hot
competition for talent.
Professor Lawrence R. Klein, a Nobel laureate in economic
science at the University of Pennsylvania, found that in his last
class in econometrics before his retirement this month, three of
his students wanted to write term papers on sports _ two on
baseball, one on basketball.
The best of three fine papers, in Klein's judgment, was the one
turned in by Joshua A. Engel, a junior from Omaha. Klein liked it
because "it shows what an industrious and bright undergraduate can
do these days _ armed with large data banks, powerful computers (PC
variety), and abundant software; it could never have been done, as
a term paper, in our days."
The Engel paper's aim is to explain and predict the
relationship between a ballplayer's performance and pay. It goes
beyond what is regarded as the classic study by Gerald Scully, "Pay
and Performance in Major League Baseball,"
published in the American Economic Review in 1974.
For batters, Scully used the lifetime slugging average (total
bases hit divided by times at bat); for pitchers he used the
lifetime strikeout-to-walk ratio to estimate how much marginal
(extra) revenue each player contributed to his team's earnings.
But the Scully model has not been too successful in predicting
players' salaries. To develop a better model, Engel studied all 157
players who filed for arbitration awards after the 1990 season.
Using performance criteria, the arbitrator chooses either the
team's or the player's proposal. But there are myriad measures of
performance. The issue is which are most significant in determining
After testing a host of variables, Engel found that the best
determinants of this year's salary for hitters was a combination of
last year's salary, last year's batting average, home runs hit, and
"runs created" _ the sum of runs scored plus runs batted in minus
Fielding averages and stolen bases did not correlate with pay.
For pitchers, he found, the best salary predictors were last year's
salary, last year's earned-run average, games won, games saved,
total innings pitched and total games played.
The Engel models explain about 94 percent of the salary one
year ahead for major league ballplayers who filed for salary