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Professor Bruce Bukiet has developed a mathematical
model for analyzing the probability of a team winning a baseball game.
Math Professor Goes To Bat Against Experts
Baseball fans looking for the lowdown on which teams will reach the World Series could turn on the television and hear Tim McCarver's analysis, they could pick up a copy of Sports Illustrated or The Star-Ledger, or they could visit Cullimore Hall and listen to Bruce Bukiet. That's right -- associate professor of mathematics Bruce Bukiet.
Using a mathematical model designed to determine optimal batting orders, computer run distribution and predict total victories, Bukiet correctly identified six of the eight teams to make this year's Major League Baseball playoffs. In comparison, Street & Smith magazine, The Sporting News, and Baseball Weekly each picked five in their annual preseason editions.
Bukiet correctly chose the Atlanta Braves to win the National League East Division; the St. Louis Cardinals to prevail in the NL Central; the Cleveland Indians to easily win the American League Central; and the Oakland Athletics to finish first in the AL West. Bukiet also was on the money, predicting the Houston Astros as the NL wild card team. Bukiet predicted the defending world champion New York Yankees would finish in second place in the AL East, but make the playoffs as a wild-card entry.
"We have a copyrighted mathematical model for analyzing the probability a given batting order -- with a particular pitcher on the mound -- has of beating another lineup and its pitcher," said Bukiet, who has been using the model to predict games the past six seasons. "This probability implies what the odds of winning for that lineup should be on a given day."
Bukiet's "Markov Chain" method relies on a finite number of possible situations in a baseball game. A Markov process is a type of random analysis in which only the present state of the process is useful for predicting the future. Past history and the manner in which the process has evolved to the current state are both irrelevant.
"The big difference between a model and a simulation is that if you run a simulation more than once, depending on the random numbers, you get a different result," said Bukiet, who holds a master's degree and a doctorate in applied mathematics from the Courant Institute and a bachelor's degree from Brown University. "A model will give you the same result every time. So, by all measures, the Markov Chain approach outperforms the experts."
The model, which included projected starting lineups and five reserves from each team, as well as five starting pitchers and six relievers, is based on statistics covering the last three years. Again this year, Bukiet factored into the model reserves and a margin for limited ability bound on how poorly a player might perform for the first time.
Bukiet started developing the model for baseball in 1987 as a hobby. His findings have been published in a 10-page paper, "A Markov Chain Approach to Baseball," in the February 1997 issue of the journal Operations Research.
"As they say on Wall Street, past performance is no guarantee of future results," said Bukiet. "This is a lot of fun, but I expect to incorporate further refinements over the years."
Bukiet's original study found that the optimal spot for a pitcher in a batting order is seventh or eighth, putting him as far away as possible from the team's best hitter, traditionally used in the clean-up spot. The best hitter, by the way, should bat second, according to the computer model.
For the playoffs and World Series, Bukiet will update his predictions on his Web site based on prior results, injuries, and pitching match-ups.
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