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Math professor calls Detroit Tigers a favorite to win World Series

Date:
October 23, 2012
Source:
New Jersey Institute of Technology
Summary:
Since the Major League Baseball Division Series and League Championship Series have determined which teams will compete in the World Series, mathematics professor Bruce Bukiet has again analyzed the probability of each team taking the title.

Since the Major League Baseball Division Series and League Championship Series have determined which teams will compete in the World Series, NJIT Math Professor Bruce Bukiet has again analyzed the probability of each team taking the title.

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"The Detroit Tigers have a solid advantage over the San Francisco Giants. The Tigers, who surprisingly swept the New York Yankees in four straight games in the American League Championship Series to reach the World Series, have a 58 percent chance of beating the Giants in the best of seven series," he said.

At the season's start, Bukiet used his mathematical model to project the number of wins each team should earn (http://m.njit.edu/~bukiet/baseball/SeasonProjections2012.html). While Bukiet's model picked only six of the ten post-season teams back in March (including the Tigers but not the Giants), he said that his performance was still comparable with many experts. Bukiet updates the probabilities of each team taking their series throughout the Major League Baseball post-season at http://m.njit.edu/~bukiet/baseball/playoffs12.htm.

For the National League Championship Series, Bukiet's model had the St. Louis Cardinals as large favorites (68 percent chance of winning) over the San Francisco Giants and the Cards jumped out to a 3 games to 1 lead, but the Giants won the final three games to make it into the World Series. Bukiet's model had the Tigers as slight favorites (53 percent chance of winning) over the Yankees.

On Bukiet's website, he provides the likelihood of each team taking the series in a given number of games. Going into the series, the most likely outcome (20 percent) is for the Tigers to win in five games. The Giants most likely winning scenario is their 17% chance of winning the series in seven games.

This is Bukiet's 12th year using his model to determine whether it is worthwhile to wager on games each day during the baseball season. His picks (posted on www.egrandslam.com) have led to positive results for 8 of the 12 years (counting 2012's thus far positive performance). He has also used the model to predict how teams should perform during a baseball season and these results have compared favorably with experts over the years. In addition, the method enables one to rank player performance during the season in terms of the influence on the number of wins a given player would have added (or taken away) from an average team. This allows the computation of who really should win the Most Valuable Player and Cy Young Awards. Bukiet will publish those results for 2012 in the coming week.

Bukiet's model originally appeared in the journal Operations Research. More recent improvements were published by Bukiet and his research team in the International Journal of Performance Analysis in Sports. The model computes the probability of a team with given hitters, bench, starting pitcher, lineup, relievers and home field advantage winning a game against another team.


Story Source:

The above story is based on materials provided by New Jersey Institute of Technology. Note: Materials may be edited for content and length.


Cite This Page:

New Jersey Institute of Technology. "Math professor calls Detroit Tigers a favorite to win World Series." ScienceDaily. ScienceDaily, 23 October 2012. <www.sciencedaily.com/releases/2012/10/121023161250.htm>.
New Jersey Institute of Technology. (2012, October 23). Math professor calls Detroit Tigers a favorite to win World Series. ScienceDaily. Retrieved December 19, 2014 from www.sciencedaily.com/releases/2012/10/121023161250.htm
New Jersey Institute of Technology. "Math professor calls Detroit Tigers a favorite to win World Series." ScienceDaily. www.sciencedaily.com/releases/2012/10/121023161250.htm (accessed December 19, 2014).

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