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The New York Yankees will win a whopping 110 games this season, more than any other major league team, according to a mathematician who applies math to real-life situations.
The projection comes from a model that Bruce Bukiet of the New Jersey Institute of Technology developed and has used and updated for the past six years to predict how many games each team will win during the 162-game season.
So far, Bukiet is on track. The Yankees won their season opener against the Tampa Bay Devil Rays on Monday.
Bukiet’s model also predicts the outcome of individual games, a resource for those who like to place wagers.
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Bukiet's Forecast Major League Baseball is divided into the National League and the American League. Each league comprises three divisions. The winners of each division (the team with the best record) and one wildcard team play in each league’s playoffs to determine who will face off in the World Series. Here are Bukiet’s predictions for this year’s division winners: AL East: New York Yankees AL Central: Cleveland Indians AL West: Los Angeles Angels AL wildcard: either the Boston Red Sox, the Toronto Blue Jays or the Minnesota Twins NL East: New York Mets NL Central: close race between the Houston Astros and the St. Louis Cardinals NL West: San Diego Padres NL wildcard: Philadelphia Phillies
Bukiet's Forecast
Major League Baseball is divided into the National League and the American League. Each league comprises three divisions. The winners of each division (the team with the best record) and one wildcard team play in each league’s playoffs to determine who will face off in the World Series. Here are Bukiet’s predictions for this year’s division winners: AL East: New York Yankees AL Central: Cleveland Indians AL West: Los Angeles Angels AL wildcard: either the Boston Red Sox, the Toronto Blue Jays or the Minnesota Twins NL East: New York Mets NL Central: close race between the Houston Astros and the St. Louis Cardinals NL West: San Diego Padres NL wildcard: Philadelphia Phillies
The projected results of games are determined by how each hitter should perform against each pitcher.
Bukiet, a Mets fan who started the project to show kids how math can be used in fun ways, uses the model only to predict outcomes in baseball and cricket. The reason it works well for these sports, Bukiet told LiveScience, is the games are “a whole sequence of one-on-one events” that are easier to compare than team sports where multiple players affect the outcome of an event.
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Bukiet starts each season with the most likely starting line-up for each team and uses data from the past three years for each player to compute how many runs each team will score in a given game.
Of course, he cannot account early on for unknowns such as trades, injuries or the unpredictable performance of rookies.
Best batting order
When Bukiet first developed the model, he used it to determine the best batting order out of 360,000 possibilities. One of his surprising findings was that pitchers, who typically are last in the order when there is no designated hitter, should actually bat second-to-last to optimize runs. And the team’s top slugger should bat third rather than cleanup (or fourth in the order).
Bukiet says his model can also determine how many more or fewer games a team would win if they traded a certain player. Managers also could use the model to set salaries based on players' hit predictions, he figures.
Were the model to be commercialized, it could be updated on a play-by-play basis, which fans could monitor to see how every play changes the outcome of a game. “I think some fans would think that’s cool,” Bukiet said.
While Bukiet is the first to admit he’s not a baseball expert, in five out of the past six years, he says that his model has produced more correct than incorrect predictions.
“I thought it was neat that you can do just as well as the so-called experts,” he said.
Andrea Thompson is an associate editor at Scientific American, where she covers sustainability, energy and the environment. Prior to that, she was a senior writer covering climate science at Climate Central and a reporter and editor at Live Science, where she primarily covered Earth science and the environment. She holds a graduate degree in science health and environmental reporting from New York University, as well as a bachelor of science and and masters of science in atmospheric chemistry from the Georgia Institute of Technology.
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