For 17 years, Scott Swanay ‸7 made a living predicting the future.
No, the tools of his trade weren⁴ decks of cards, crystal balls, or maps of stars and constellations.
Swanay worked as an actuary for insurance companies, and armed with spreadsheets, formulas, and a degree from Harvard in applied mathematics and computer science, it was his job to analyze what had already happened in order to figure out what was to come.
Then, in 2004, everything changed when Swanay was laid off for the second time in his career as a result of a corporate merger. Contemplating his next move, he felt a sense of disillusionment with the path he had chosen.
⁉ had always gotten stellar performance reviews, †Swanay says. ⁉ still had my parents†generation mindset that if you work hard and do well people are always going to appreciate you and reward you. But life was different than that.
Realizing that a change of course was in order, Swanay turned to his first loveaseballor a push in the right direction.
Having grown up in New York in the 1970, Swanay fondly recalls memories of his early trips to Yankee Stadium, where he watched childhood idols like Thurman Munson, Bobby Murcer, and⁓wanay personal favorite⁃liff Johnson play ball during the tumultuous yet exhilarating ⁂ronx Zoo †era in Yankees†history.
As Swanay passion for baseball developed, so did his fascination with numbers.
⁌ike many kids, I taught myself division by figuring out batting averages, †Swanay says. ⁉ was always keeping track of baseball stats or political delegate counts. If there was a way to track something with numbers I was doing it.
It should come as no surprise, then, that after Swanay was let go in 2004, he found solace through a medium in which he could combine his two biggest interests: fantasy baseball.
The Average Fantasy Joe puts together a league with his buddies and picks up players whose names he recognizes or who have done well for his favorite team. But Swanay⁷ith nearly two decades as an actuary under his belt and a deep reservoir of baseball knowledgead no intentions of being average. When Swanay began playing fantasy baseball, he didn⁴ rely on gut instinct or subjective judgments to form his teams. He plugged countless numbers into spreadsheets, using advanced quantitative methods to value players and to create rankings that would allow him to objectively put together the best team possible.
As Swanay started having success in fantasy leagues, he realized that what he had thought of as a hobby was actually the key to the career change he had been seeking.
⁉ finally got over that mental hurdle [that tells you] if you⁶e been doing something so long, you can⁴ go in another direction, †Swanay says. ⁉ wasn⁴ going to reach my potential unless I made a change.
So a change was made, and Swanay now operates a growing fantasy baseball advice website called FantasyBaseballSherpa.com (along with a pigskin counterpart, FantasyFootballSherpa.com).
Swanay site uses statistical analysis to predict players†performances for upcoming seasons and rank the players accordingly. Subscribers can then reference these rankings as they draft their fantasy squads.
Other sites offer similar services, but Swanay has taken measures to distinguish himself from the competition. For example, one feature of his site is a forecasting system that continues to adjust and make new predictions throughout the season, instead of making one prediction of a player performance for a season at the beginning of the year. He also has created a model to quantify position scarcity, or the idea that there are more players of high fantasy value that play certain positions (like first base) than others (like shortstop).
⁉ quantify stuff that a lot of other people just guess at, †Swanay says.
This quantitative approach continues to gain popularity in fantasy circles, but it has also pervaded the thinking of front offices in professional baseball, along with other sports.
More and more, general managers supplement the advice of scouts with computer-generated projections and analysis. The trend has sparked resistance from old-school hardliners who contend that the statistical approach takes away from the human side of sports.
⁔hat really the big thing that emerged in the past few years, †says Jason Rosenfeld, Co-President of the Harvard Sports Analysis Collective (HSAC). ⁙ou have people with a computer figuring out something and giving an answer and people like scouts giving an answer and it might conflict. [The scouts will say] you don⁴ know what youe talking about, you never played.
But Rosenfeld believes that thinking about sports quantitatively simply adds a new perspective.
⁉t really just people who love the game like everyone else but are trying to add a factor in there to add an edge, †he says.
And to those who claim that focusing so intently on statistical analysis detracts from the more lyrical aspects of athletics, Harvard Professor of Statistics and Faculty Advisor to HSAC Carl Morris provides a response.
⁉ feel that a lot of people feel passion about sports because of the statistics, †Morris says. ⁁ll these numbers tell stories.
For proponents of the quantitative approach to sports, finding new numbers to tell those stories with is the goal that they constantly strive to reach. And for people like Swanay, Rosenfeld, and Morris, the growing demand for sports statistical analysis ensures that they can use the their abilities with numbers to explain the games they love.
⁉t was liberating that I had accumulated skills that I could use in the sports world, †Swanay says. ⁐lus, I was much more passionate about sports than I was about insurance.
⁓taff writer Loren Amor can be reached at lamor@fas.harvard.edu.
