I am a data scientist. Data science is an emerging field commonly described as “the practice of deriving valuable insights from data,” and this thread runs through all of my work. My scholarly contributions have come in five main areas:
- sports analytics:
- Learning about how sports (particularly baseball) work through the analysis of data
- statistics and data science education:
- What, how, and why are we teaching? What, how, and why should we be teaching?
- data science:
- Building tools to make data-based research easier and more reproducible
- network science, and analysis of algorithms:
- Theoretical work about properties of networks and graph algorithms
- statistics and data science consulting:
- Aiding your research through statistical modeling and data visualization
Subfields of interest to me include network science, applied statistics, sabermetrics, sports analytics, statistical modeling, analysis of algorithms, combinatorial optimization, data visualization, graph theory, and combinatorics. My Erdös number is 3, as I have co-authored a paper with Amotz Bar-Noy, who has co-authored a paper with Noga Alon, who has co-authored a paper with Paul Erdös.
My background is academically diverse, in that my undergraduate degree is in economics (my first declared major was English), my doctorate is in mathematics, my thesis advisor is in computer science, and my professional experience is in statistics. As such, my research tends to be interdisciplinary, with an emphasis on applying available techniques from any discpline to address the question of interest.
In 2012, I completed my Ph.D. in Mathematics at the Graduate Center of the City University of New York, where my advisor was Amotz Bar-Noy, also of Brooklyn College. Previously, I earned an M.A. in Applied Mathematics from the University of California, San Diego, and a B.A. in Economics from Wesleyan University.
In 2019, I won the Significant Contributor Award from the Section on Statistics in Sports of the American Statistical Association.
C.V. for complete details on my work.
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Analyzing Baseball Data with R, 2nd Edition introduces R to sabermetricians, baseball enthusiasts, and students interested in exploring the richness of baseball data. It equips you with the necessary skills and software tools to perform all the analysis steps, from importing the data to transforming them into an appropriate format to visualizing the data via graphs to performing a statistical analysis.
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Contemporary data science uses both statistical modeling and computer programming to extract meaning from data. It requires a tight integration of knowledge from statistics, computer science, mathematics, and a domain of application. This book, which is intended for readers with some background in statistics and modest prior experience with coding, helps them develop and practice the appropriate skills to tackle complex data science projects. Most of the examples are done in R, but SQL, Python, and other cutting-edge tools are discussed as well.
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The Sabermetric Revolution cover
The Sabermetric Revolution
Since leaving the Mets, I’ve written a book, entitled The Sabermetric Revolution: Assessing the Growth of Analytics in Baseball, with leading sports economist Andrew Zimbalist. We examine the evolution of sabermetrics in baseball and other sports since the publication of Moneyball, summarize the current state of sabermetric thinking, and address the question of whether there is any evidence that sabermetrics has actually worked. The book will be published by the University of Pennsylvania Press and is scheduled for a December 2013 release.
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ETL packages for R
etl is an R package to facilitate Extract - Transform - Load (ETL) operations for medium data. The end result is generally a populated SQL database, but the user interaction takes place solely within R.
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C. Legacy, A. Zieffler, B. S. Baumer, V. Barr, and N. J. Horton, “Facilitating team-based data science: Lessons learned from the DSC-WAV project,” Foundations of Data Science
, 2021 [Online]. Available: https://arxiv.org/abs/2106.11209
N. J. Horton, B. S. Baumer, A. Zieffler, and V. Barr, “The Data Science Corps Wrangle-Analyze-Visualize program: Building data acumen for undergraduate students,” Harvard Data Science Review
, vol. 3, no. 1, pp. 1–8, Feb. 2021 [Online]. Available: https://hdsr.mitpress.mit.edu/pub/nvflcexe
A. A. McNamara, N. J. Horton, and B. S. Baumer, “Greater data science at baccalaureate institutions,” Journal of Computational and Graphical Statistics
, vol. 26, no. 4, pp. 781–783, 2017 [Online]. Available: https://doi.org/10.1080/10618600.2017.1386568
B. S. Baumer, A. Y. Kim, K. M. Kinnaird, M. Q. Ott, and R. L. Garcia, “Integrating data science ethics into an undergraduate major,” Journal of Statistics and Data Science Education
, 2020 [Online]. Available: http://arxiv.org/abs/2001.07649
D. J. Kelley, B. S. Baumer, C. G. Brush, M. Cole, M. Dean, M. Madavi, M. Majbouri, P. Greene, and R. Heavlow, “Global entrepreneurship monitor 2016/2017 women’s entrepreneurship report,” Global Entrepreneurship Monitor; Global Entrepreneurship Research Association, Jul. 2017.
A. B. Elam, C. G. Brush, P. G. Greene, B. S. Baumer, M. Dean, and R. Heavlow, “Global entrepreneurship monitor 2018/2019 women’s entrepreneurship report,”
Global Entrepreneurship Monitor; Global Entrepreneurship Research Association, Nov. 2019 [Online]. Available: https://www.gemconsortium.org/file/open?fileId=50405
B. S. Baumer and A. S. Zimbalist, “The impact of college athletic success on donations and applicant quality,” International Journal of Financial Studies
, vol. 7, no. 2, p. 19, 2019 [Online]. Available: https://www.mdpi.com/2227-7072/7/2/19
M. Papaiakovou, N. Pilotte, B. S. Baumer, J. Grant, K. Asbjornsdottir, F. Schaer, Y. Hu, R. Aroian, J. Walson, and S. A. Williams, “A comparative analysis of preservation techniques for the optimal molecular detection of hookworm DNA in human fecal specimens,” PLOS Neglected Tropical Diseases
, vol. 12, no. 1, pp. 1–17, Jan. 2018 [Online]. Available: http://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0006130
M. S. Schwartz, J. Schnabl, M. P. H. Litz, B. S. Baumer, and M. Barresi, “-SCOPE: A new method to quantify 3D biological structures and identify differences in zebrafish forebrain development,” Developmental Biology
, vol. 460, no. 2, pp. 115–138, Apr. 2020 [Online]. Available: https://doi.org/10.1016/j.ydbio.2019.11.014
R. D. De Veaux, M. Agarwal, M. Averett, B. S. Baumer, A. Bray, T. C. Bressoud, L. Bryant, L. Z. Cheng, A. Francis, R. Gould, A. Y. Kim, M. Kretchmar, Q. Lu, A. Moskol, D. Nolan, R. Pelayo, S. Raleigh, R. J. Sethi, M. Sondjaja, N. Tiruviluamala, P. X. Uhlig, T. M. Washington, C. L. Wesley, D. White, and P. Ye, “Curriculum guidelines for undergraduate programs in data science,” Annual Review of Statistics and Its Application
, vol. 4, no. 1, pp. 1–16, 2017 [Online]. Available: http://www.annualreviews.org/doi/abs/10.1146/annurev-statistics-060116-053930
A. M. Bertin and B. S. Baumer, “Creating optimal conditions for reproducible data analysis in R with ‘fertile’,” Stat
, vol. 10, no. 1, p. e332, Dec. 2020 [Online]. Available: https://doi.org/10.1002/sta4.332
M. Lopez, G. J. Matthews, and B. S. Baumer, “How often does the best team win? A unified approach to understanding randomness in North American sport,” Annals of Applied Statistics
, vol. 12, no. 4, pp. 2483–2516, 2018 [Online]. Available: https://projecteuclid.org/euclid.aoas/1542078053
M. Çetinkaya-Rundel, J. S. Hardin, B. S. Baumer, A. A. McNamara, N. J. Horton, and C. W. Rundel, “An educator’s perspective of the tidyverse,” Technology Innovations in Statistics Education
, 2021 [Online]. Available: https://arxiv.org/abs/2108.03510
S. Stoudt, L. Santana, and B. Baumer, “In pursuit of perfection: An ensemble method for predicting march madness match-up probabilities,” in JSM proceedings, 2014.
N. J. Horton, B. S. Baumer, and H. Wickham, “Setting the stage for data science: Integration of data management skills in introductory and second courses in statistics,” CHANCE
, vol. 28, no. 3, pp. 40–50, 2015 [Online]. Available: http://chance.amstat.org/2015/04/setting-the-stage/
B. S. Baumer and P. Badian-Pessot, “Evaluation of batters and base runners,”
in Handbook of statistical methods and analyses in sports
, J. Albert, M. E. Glickman, T. B. Swartz, and R. H. Koning, Eds. Chapman; Hall/CRC Press: Boca Raton, FL, 2016, pp. 1–37 [Online]. Available: https://www.crcpress.com/Handbook-of-Statistical-Methods-and-Analyses-in-Sports/Albert-Glickman-Swartz-Koning/p/book/9781498737364
B. Baumer, G. Rabanca, A. Bar-Noy, and P. Basu, “Star search: Effective subgroups in collaborative social networks.”
ACM; ACM, New York, NY, USA, pp. 729–736, 2015 [Online]. Available: http://dl.acm.org/citation.cfm?id=2810062
J. Hardin, R. Hoerl, N. J. Horton, D. Nolan, B. Baumer, O. Hall-Holt, P. Murrell, R. Peng, P. Roback, D. Temple Lang, and others, “Data science in statistics curricula: Preparing students to ‘think with data’,” The American Statistician
, vol. 69, no. 4, pp. 343–353, 2015 [Online]. Available: http://www.tandfonline.com/doi/abs/10.1080/00031305.2015.1077729
B. Baumer, M. Çetinkaya-Rundel, A. Bray, L. Loi, and N. J. Horton, “R Markdown: Integrating a reproducible analysis tool into introductory statistics,” Technology Innovations in Statistics Education
, vol. 8, no. 1, 2014 [Online]. Available: http://escholarship.org/uc/item/90b2f5xh
A. Bar-Noy, B. Baumer, and D. Rawitz, “Changing of the guards: Strip cover with duty cycling,” Theoretical Computer Science
, vol. 610, pp. 135–148, 2016 [Online]. Available: https://doi.org/10.1016/j.tcs.2014.09.002
B. S. Baumer, S. T. Jensen, and G. J. Matthews, “OpenWAR: An open source system for evaluating overall player performance in Major League Baseball,” Journal of Quantitative Analysis in Sports
, vol. 11, no. 2, pp. 69–84, 2015 [Online]. Available: https://doi.org/10.1515/jqas-2014-0098
A. Bar-Noy, B. Baumer, and D. Rawitz, “Brief announcement: Set it and forget it - approximating the set once strip cover problem.”
ACM, pp. 105–107, 2013 [Online]. Available: https://dl.acm.org/citation.cfm?id=2486162
B. S. Baumer, J. Piette, and B. Null, “Parsing the relationship between baserunning and batting abilities within lineups,” Journal of Quantitative Analysis in Sports
, vol. 8, no. 2, pp. 1–17, 2012 [Online]. Available: https://doi.org/10.1515/1559-0410.1429
B. S. Baumer and P. Terlecky, “Improved Estimates for the Impact of Baserunning in Baseball,” in JSM proceedings, 2010.
B. S. Baumer and D. Draghicescu, “Mapping Batter Ability in Baseball: A Study in Spatial Modeling,” in JSM proceedings, 2010.
B. S. Baumer, A. Galdi, and R. Sebastian, “A Survey of Methods for the Statistical Evaluation of Defensive Ability in Major League Baseball,” in JSM proceedings, 2009.
B. S. Baumer, “Using Simulation to Estimate the Impact of Baserunning Ability in Baseball,” Journal of Quantitative Analysis in Sports
, vol. 5, no. 2, pp. 1–16, 2009 [Online]. Available: https://doi.org/10.2202/1559-0410.1174
B. S. Baumer, “Why On-Base Percentage is a Better Indicator of Future Performance than Batting Average: An Algebraic Proof,” Journal of Quantitative Analysis in Sports
, vol. 4, no. 2, pp. 1–11, 2008 [Online]. Available: https://doi.org/10.2202/1559-0410.1101