Ben Baumer grew up in Northampton before spending three years in California and nine years in New York City. He is thrilled to be back in Northampton and teaching at Smith.

Baumer is a data scientist—his research and teaching is focused on extracting meaning from data. This interest is informed by both his graduate work, which focused on discrete mathematics and theoretical computer science, and his professional experience, where he served as the Statistical Analyst for the New York Mets from 2004 to 2012. Having extensive experience and training in mathematics, computer science and statistics, Baumer enjoys the interplay between these three fields, particularly in data science and network science. He is also knowledgeable about sabermetrics, sports analytics, applied statistics, statistical modeling, analysis of algorithms, combinatorial optimization, data visualization, graph theory and combinatorics.

His work in sabermetrics (statistical analysis applied to baseball) has been published in the Journal of Quantitative Analysis in Sports (for which he serves as an associate editor), the Eastern Economic Journal, CHANCE Magazine, ESPN.com, and most notably in his first book (co-authored with Andrew Zimbalist), The Sabermetric Revolution: Assessing the Growth of Analytics in Baseball. Baumer and his co-authors shared the 2016 Contemporary Baseball Analysis Award from the Society for American Baseball Research for their paper on comprehensive measures of player evaluation.

Baumer's work in network science has been published in the journals Algorithmica and Theoretical Computer Science, as well as at the conferences ECML/PKDD, SPAA, SIROCCO, ALGOSENSORS and MSWiM. He has also published articles on statistics and data science education in The American Statistician, Technology Innovations in Statistics Education, and CHANCE Magazine. He is a co-organizer of the annual ASA Five College DataFest.

Baumer is an Accredited Professional Statistician™.

Selected Publications

Modern Data Science with R (2nd edition)

Analyzing Baseball Data with R (2nd edition)

How often does the best team win? A unified approach to understanding randomness in North American sport

Integrating data science ethics into an undergraduate major: A case study

The Sabermetric Revolution: Assessing the Growth of Analytics in Baseball

openWAR: An Open Source System for Overall Player Performance in MLB

A Data Science Course for Undergraduates: Thinking with Data

R Markdown: Integrating A Reproducible Analysis Tool into Introductory Statistics

Quanitfying Market Inefficiencies in the Baseball Players’ Market

Changing of the Guards: Strip Cover with Duty Cycling

The smallest non-autograph