# The Next No-Hitter: May? Mathematician Uses Statistics to Predict Rare Baseball Events

May 1, 2005 — A mathematician has developed an estimate for how many no-hitters there will be in major league baseball this year, based on a simple statistical tool he uses in teaching his students. Researchers use a simple statistical tool known as the Poisson distribution to predict no-hitters and also the number of players hitting for the cycle, in which a player gets a single, double, triple and home run in the same game. A Poisson distribution predicts the number of events that will occur in a fixed time interval, provided that the events occur at random, independently in time, and at a constant rate.

WEST POINT, N.Y.--Baseball fans know a no-hitter is a rare event. But now mathematicians are stepping up to the plate, using their students' interest in baseball to teach them about probability with a new prediction for this year's no-hitters.

Raymond Chu is a baseball fan on a mission: To be in the stands during a no-hitter. "To witness a no-hitter would be great because it's such a rarity in sports," he says.

Imagine being able to predict such a big-league event! Now, mathematicians at the United States Military Academy at West Point have a way to predict no-hitters that might help fans like Chu finally fulfill their fantasy.

"We're looking forward to see if the prediction actually occurs," says West Point mathematician Lt. Col. Mike Huber.

The prediction doesn't say exactly which player will throw a no-hitter ... But a no-hitter could happen on or about the 730 game of this season. Lt. Col. Huber says that would be somewhere around the end of May or the first week of June we should see a no-hitter.

Researchers found that a simple statistical tool called a Poisson curve works for no-hit predictions and also may predict another baseball rarity called hitting for the cycle. That's when one player hits a single, double, triple and a home run -- all in one game.

Lt. Col. Huber says, "I don't think we can predict the actual player or team that's going to have a no-hitter or hit for the cycle. That's the great part about baseball, is that fans can speculate."

He predicts four people will hit for the cycle this season -- two in the American League and two in the National League.

Superstitious baseball fans may think the idea of predicting a no-hitter will jinx the chances of it happening ... But just to show validity of the prediction tool, the Washington Nationals' Brad Wilkerson has already hit for the cycle in the second game of this season.

By the way ... The pitcher who holds the record for the most no-hitters is Nolan Ryan, who threw seven in his career and is regarded as the undisputed king of no-hitters.

Rare events are, well, very rare, and this can make it difficult to know how likely they are to occur in any given time frame. Statisticians must look at a large enough body of data to make any useful prediction about it. But how do you find a large enough sample of events that donýt happen very often?

Fortunately, mathematicians have a useful tool to do this: the Poisson distribution, also known as the "law of large numbers."

The Poisson distribution describes the probability that a random, rare event will occur in a given interval of time, such as the number of no-hitters occurring over and entire 162-game baseball season. While the probability of the event occurring is very small, the number of opportunities for it to happen is so large that the event actually occurs a few times. The longer the time interval, the more the data begins provide a pattern from which predictions can be made.

The Poisson distribution is particularly useful in the study of how diseases spread through populations, called epidemiology. For instance, letýs say that we know that in any given population of 10,000 people exposed to a certain disease, 10 will actually develop it. If 14 people actually contracted the disease, this would not be ýstatistically significant,ý because it falls within the error rate. But if 200 people got the disease, this would be far above the so-called standard deviation.

The Poisson distribution can be used to determine birth defect probabilities; the number of sample defects on a car; the number of typographical errors on a printed page; or the number of insect parts likely to be found in a chocolate bar.

The American Mathematical Society contributed to the information found in the TV portion of this report.

Note: This story and accompanying video were originally produced for the American Institute of Physics series Discoveries and Breakthroughs in Science by Ivanhoe Broadcast News and are protected by copyright law. All rights reserved.

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