Schedule

Please consult Smith’s academic calendar.

Baseball analytics

1 . Mon, Sep 8 ⚾

NoteHomework

2 . Wed, Sep 10 ⚾

NoteHomework

  • Watch Moneyball
    TONIGHT, 7:30 pm - 9:30 pm
    Hillyer Graham
    Practice Active Viewing (see page 12 of this)
  • HW 1 due Friday by 5 pm

3 . Mon, Sep 15 ⚾

NoteHomework

4 . Wed, Sep 17 ⚾

NoteHomework
  • HW 2 due Friday by 5 pm

5 . Mon, Sep 22 ⚾

NoteHomework
  • Read McCracken (2001)

6 . Wed, Sep 24 ⚾

NoteHomework

Analytics in other sports

7 . Mon, Sep 29 ⚾

NoteInvited Guest
  • Emma Strawbridge
    UMass doctoral student
    Bio: Emma Strawbridge (they/them) is a first year PhD student in Statistics at UMass Amherst. They graduated from Amherst college in 2025 with a Statistics and Math double major, and wrote an honors thesis in the statistics department titled “Capturing Baseball Pitch Patterns with Hidden Markov Models”. They have worked for the Chicago Blackhawks as a data science intern during the summer of 2024 and hope to work in sports analytics in the future. Emma is a fan of ice hockey, baseball, football, and occasionally cycling and tennis when the season is right. Go birds.
NoteHomework
  • Read Albert (2015)

8 . Wed, Oct 1

NoteHomework
  • HW 4 due Friday by 5 pm
  • Read Kubatko et al. (2007)

9 . Mon, Oct 6 🏀

NoteHomework
  • Read Cervone et al. (2016) This paper is tough going – just try your best!
    Try to understand the main ideas first – don’t worry about the details.

10 . Wed, Oct 8 🏀

NoteHomework
  • HW 5 due Friday by 5 pm
  • Read Lopez (2020)

11 . Mon, Oct 13

  • FALL BREAK – NO CLASS

12 . Wed, Oct 15 🏈

NoteHomework
  • Read Glickman (2013)

13 . Mon, Oct 20

NoteHomework
  • Review previous material
  • Come prepared with questions!

14 . Wed, Oct 22

NoteHomework
  • MIDTERM EXAM over WEEKEND (Oct 24-26)

Sports analytics research

15 . Mon, Oct 27

NoteHomework
  • Read Lopez, Matthews, and Baumer (2018)

16 . Wed, Oct 29

  • Playoff simulations
NoteInvited Guest:
  • Amanda Glazer
    Assistant Professor
    Statistics and Data Sciences
    UT Austin
    Bio: Prior to joining UT, I earned my PhD in Statistics from UC Berkeley. My research focuses on developing causal inference and nonparametric methods, and associated software and tools, that address real scientific problems. I am particularly drawn to problems that affect society such as issues of discrimination and social justice. Reproducibility, replicability and evaluating the appropriateness of statistical methods are especially important to me. I also conduct research in sports analytics and developed and taught a new Sports Analytics course at UT Austin in Spring 2025. Previously, I worked as a baseball operations associate analyst for the San Francisco Giants for four years.
NoteHomework
  • Read Baumer, Jensen, and Matthews (2015)

17 . Mon, Nov 3

NoteHomework
  • Read van Bommel et al. (2021)

18 . Wed, Nov 5

NoteHomework
  • Read Jensen, Shirley, and Wyner (2009)

19 . Mon, Nov 10

  • Gabriela: Jensen, Shirley, and Wyner (2009)
NoteHomework
  • Read Kovalchik (2016)

20 . Wed, Nov 12

  • Anna: Kovalchik (2016)
NoteHomework

21 . Mon, Nov 17

  • Tyler: Jensen, McShane, and Wyner (2009)
NoteHomework
  • Read Maymin (2021)

22 . Wed, Nov 19

  • Lorelei: Maymin (2021)
NoteHomework

23 . Mon, Nov 24

  • Eliana: Lopez and Matthews (2015)
NoteHomework
  • Read Deshpande and Jensen (2016)

24 . Wed, Nov 26

  • THANKSGIVING BREAK – NO CLASS

25 . Mon, Dec 1

  • Abby & Selam: Deshpande and Jensen (2016)
NoteHomework
  • Read Elmore and Matthews (2022)

26 . Wed, Dec 3

  • Abi: Elmore and Matthews (2022)
NoteHomework

27 . Mon, Dec 8

  • Projects

28 . Wed, Dec 10

  • Lightning talks (5 minutes, 3 slides)
  • Course feedback questionnaire
NoteHomework
Important

All work is due by 11:59 pm on December 17th!!

References

Albert, J. 2015. “Player Evaluation Using Win Probabilities in Sports Competitions.” Wiley Interdisciplinary Reviews: Computational Statistics 7 (5): 316–25. https://doi.org/10.1002/wics.1358.
Baumer, Benjamin S, Shane T Jensen, and Gregory J Matthews. 2015. “openWAR: An Open Source System for Evaluating Overall Player Performance in Major League Baseball.” Journal of Quantitative Analysis in Sports 11 (2): 69–84. https://doi.org/10.1515/jqas-2014-0098.
Cervone, Daniel, Alex D’Amour, Luke Bornn, and Kirk Goldsberry. 2016. “A Multiresolution Stochastic Process Model for Predicting Basketball Possession Outcomes.” Journal of the American Statistical Association 111 (514): 585–99. https://doi.org/10.1080/01621459.2016.1141685.
Deshpande, Sameer K., and Shane T. Jensen. 2016. “Estimating an NBA Player’s Impact on His Team’s Chances of Winning.” Journal of Quantitative Analysis in Sports 12 (2). https://doi.org/10.1515/jqas-2015-0027.
Elmore, Ryan, and Gregory J Matthews. 2022. “Bang the Can Slowly: An Investigation into the 2017 Houston Astros.” The American Statistician 76 (2): 110–16. https://doi.org/10.1080/00031305.2021.1902391.
Glickman, Mark E. 2013. “Introductory Note to 1928 (= 1929).” In Ernst Zermelo - Collected Works/Gesammelte Werke II: Volume II/Band II - Calculus of Variations, Applied Mathematics, and Physics/Variationsrechnung, Angewandte Mathematik Und Physik, edited by Heinz-Dieter Ebbinghaus and Akihiro Kanamori, 616–71. Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-70856-8_13.
Jensen, Shane T, Blakeley B McShane, and Abraham J Wyner. 2009. “Hierarchical Bayesian Modeling of Hitting Performance in Baseball.” Bayesian Analysis 4 (4): 631–52. https://doi.org/10.1214/09-BA424.
Jensen, Shane T, Kenneth E Shirley, and Abraham J Wyner. 2009. “Bayesball: A Bayesian Hierarchical Model for Evaluating Fielding in Major League Baseball.” The Annals of Applied Statistics, 491–520. https://doi.org/10.1214/08-AOAS228.
Kovalchik, Stephanie A. 2016. “Searching for the GOAT of Tennis Win Prediction.” Journal of Quantitative Analysis in Sports 12 (3): 127–38. https://doi.org/10.1515/jqas-2015-0059.
Kubatko, Justin, Dean Oliver, Kevin Pelton, and Dan T Rosenbaum. 2007. “A Starting Point for Analyzing Basketball Statistics.” Journal of Quantitative Analysis in Sports 3 (3). https://doi.org/10.2202/1559-0410.1070.
Lopez, Michael J. 2020. “Bigger Data, Better Questions, and a Return to Fourth down Behavior: An Introduction to a Special Issue on Tracking Data in the National Football League.” Journal of Quantitative Analysis in Sports 16 (2): 73–79. https://doi.org/10.1515/jqas-2020-0057.
Lopez, Michael J, and Gregory J Matthews. 2015. “Building an NCAA Men’s Basketball Predictive Model and Quantifying Its Success.” Journal of Quantitative Analysis in Sports 11 (1): 5–12. https://doi.org/10.1515/jqas-2014-0058.
Lopez, Michael J, Gregory J Matthews, and Benjamin S Baumer. 2018. “How Often Does the Best Team Win? A Unified Approach to Understanding Randomness in North American Sport.” The Annals of Applied Statistics 12 (4): 2483–2516. https://doi.org/10.1214/18-aoas1165.
Maymin, Philip Z. 2021. “Smart Kills and Worthless Deaths: eSports Analytics for League of Legends.” Journal of Quantitative Analysis in Sports 17 (1): 11–27. https://doi.org/10.1515/jqas-2019-0096.
McCracken, Voros. 2001. “Pitching and Defense: How Much Control Do Hurlers Have?” Baseball Prospecus. https://www.baseballprospectus.com/news/article/878/pitching-and-defense-how-much-control-do-hurlers-have/.
van Bommel, Matthew, Luke Bornn, Peter Chow-White, and Chuancong Gao. 2021. “Home Sweet Home: Quantifying Home Court Advantages for NCAA Basketball Statistics.” Journal of Sports Analytics 7 (1): 25–36. https://doi.org/10.3233/JSA-200450.