Difference between Sports, Business Seen in Pay

Article excerpt

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 salaries.

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 home runs.

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 arbitration. …