Betting on Bayes-ball [Commentary]

In the beginning, it wasn't about the money. But if the baseball gods deal you a 60 to 80 percent chance of winning, what would you do?

On opening day of last year's baseball season, a friend and I waited for our beloved Washington Nationals to take the field under a spring sky that hung above us like blue scrim. To pass the time, we talked shop. We are science geeks, obsessed with guiding public policy with empiricism, with the deluge of information that is "big data."


That afternoon, giddy over Bryce Harper's two home runs, I resolved to conduct an experiment. I would demonstrate the power of big data using nothing but my laptop, free software and an Internet connection. Today, a year later, the experiment has hit — if not a home run, then perhaps a triple. All thanks to Thomas Bayes, an English cleric who lived 260 years ago.

A Presbyterian minister, Bayes studied statistics to prove God's existence. His contemporaries were preoccupied with earthly concerns, such as games of cards and dice. To win, they had to guess a game's probabilities. Bayes conceived a method to solve the inverse issue: based on empirical observations, what is an event's probability?


I became convinced I could apply this method to baseball after analyzing the pitches of Washington's Stephen Strasburg, Jordan Zimmerman and Ross Detwiler. Plotting the velocity and location of their pitches across home plate, I was struck by two observations. First, each plot was distinct, as unique to each pitcher as his fingerprints. Second, these plots scarcely varied from year to year. The implication was clear. Because of biology and life-long habits, a ballplayer's game conforms to systematic patterns. I could use Bayes' method to predict these patterns.

Girding myself with Bayesian algorithms, I launched into a morass of baseball data. Pitch velocity. Hits per inning. Number of errors. I considered everything.

Finally in July, I devised a computational model that devours thousands of rows of data culled from the online cloud. I coded a program that zips through the Internet every day, navigating the web's infrastructure to harvest the data I feed my model.

July was an inconvenient time to start my experiment. I was vacationing in the Philippines during typhoon season. Access to electricity was intermittent. Given the 12-hour time difference, U.S. ball games played themselves out in the morning. Before my family woke, I would download data from the U.S. to make bets from the Philippines with an online sports gamer in Costa Rica.

In the third week, Typhoon Glenda swept through Manila with wind speeds of 115 mph. The storm left thousands homeless and plunged the city into darkness. Meanwhile, on a temperate evening in the U.S., winds of 9 mph wafted through San Diego, where the Padres hosted the New York Mets.

The Mets were underdogs, but my model predicted they would win. The model prompted me to wager $180, a huge sum to a neophyte sports gamer. Over breakfast the next morning, I learned the Mets edged out the Padres by one run.

Robust decisions don't require perfect predictions. My model cannot predict if the Mets will beat the Padres on a given night. It simulates a thousand games from which I estimate the likelihood of a team win. Based on the odds, particularly when they predict a win with 60 to 80 per cent probability, I make my wager.

I was preoccupied by these thoughts as I rode through the flooded streets of Manila, my hometown. The typhoon had ravaged the city, trashing it with the remains of battered homes, fallen trees and mangled fishing boats. We cannot tell whether global warming caused this particular typhoon. But we do know that science models, some built on Bayesian principles, predict that climate change is increasing the likelihood of violent storms.


Over baseball's regular season, I've won 155 out of 256 games in which I made a wager. Ignoring the results of a disastrous three weeks, after which I modified my model when I realized it failed to account for the end-of-July team trades, my success rate is 65 percent. I've grown a few hundred dollars into $1,000, a 233 percent return on my investment.

These results are trivial against the suffering of people unable to protect themselves against catastrophic, environmental harms. My baseball experiment convinces me we have the tools to better predict them, thereby hewing critical decisions to a solid, empirical foundation.

We need only muster the will to use these technologies for our common good.

Pasky Pascual is a scientist and lawyer who frequently writes about environmental policy. He used to lead a government task force on computational models. His email is


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