The new formula is based on work of Bill James, the baseball author and statistician who inspired sabermetrics, the study of how statistics relate to success on the baseball diamond. James developed a basic formula, which has been tweaked over the years, that uses the number of runs scored per game (RPG) and runs given up per game to estimate a team's winning percentage.
Whisnant took that formula a step further by considering run distributions. When you consider how much a team's run production varies? Does it help if a team consistently scores runs? Does it hurt if a team scores a lot of runs one day and very few the next? And is slugging percentage (SLG, total bases divided by at bats) a good measure of that consistency?
Whisnant's answer, based on a Markov chain analysis that simplifies and simulates an infinite number of baseball games while eliminating the random fluctuations found by analyzing actual data from a finite number of games:
W1/L1 = (RPG1/RPG2)^a (SLG1/SLG2)^b
a = 0.723 (RPG1 + RPG2)^.373 and b = 0.977 (RPG1 + RPG2)^( -.947)
"Bottom line: More consistent teams (narrower run distribution) tend to win more games for the same RPG (runs per game). Teams with higher SLG (slugging percentage) tend to have a narrower run distribution. Given two teams with the same RPG, a team with a SLG .080 higher will on average win one more game a season. If their pitching/defense has the same RPG allowed but a SLG allowed .080 lower, that would add another game, says" Whisnant.
"My study shows that runs alone don't tell the whole story," he said. "Consistency is another factor. You want to score runs, and you want to be consistent."
Across an entire 162-game season, Whisnant said more consistency could mean two additional wins. And that can be the difference between making the playoffs and calling it quits the first week in October.