Colloquium presentation

In a colloquium, there is usually a different speaker each week. During the second half of the semester, our class will run as a quasi-colloquium, wherein you will take turns presenting a sports analytics paper to the rest of the class. This seminar is very common in graduate school, and is a great way to learn how to read a research paper at a deep level.

Your presentation should take about half of the class period (i.e., 35-40 minutes). Your goal is maximize each other student’s understanding of the paper by the time they leave the room.

Here is some advice and ground rules:

Expectations

You will choose a partner, and a paper, and a date during the second half of the semester, and send them to me for approval. If I approve, we will put you on the schedule and post the paper to the rest of the class. If I do not approve, try again. By Wednesday, October 8th we will have the colloquium schedule and topics set.

  • If you choose to work alone, you will present one paper
  • If you choose to work with a partner, you will present two papers

Suggested papers

You are free to present any paper on any topic in sports analytics, provided that:

  • it is published in a peer-reviewed journal
  • it’s not one of the other papers we are reading for this class
  • it is approved by me

Good places to look for papers:

The following is a non-exhaustive list of high-quality papers which I think would be appropriate.

Baseball

  1. Lindsey, G. R. (1963). An investigation of strategies in baseball. Operations Research, 11(4), 477–501. https://doi.org/10.1287/opre.11.4.477
  2. Albert, J. (2015). Player evaluation using win probabilities in sports competitions. Wiley Interdisciplinary Reviews: Computational Statistics, 7(5), 316–325. https://doi.org/10.1002/wics.1358
  3. Elmore, R., & Matthews, G. J. (2022). Bang the can slowly: An investigation into the 2017 houston astros. The American Statistician, 76(2), 110–116. https://doi.org/10.1080/00031305.2021.1902391
  4. Baumer, B. S., Jensen, S. T., & Matthews, G. J. (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
  5. Yan, S., Burgos, A., Jr, Kinson, C., & Eck, D. J. (2025). Comparing baseball players across eras via novel full house modeling. The Annals of Applied Statistics, 19(2), 1778–1799. https://doi.org/10.1214/24-AOAS1992
  6. Jensen, S. T., McShane, B. B., & Wyner, A. J. (2009). Hierarchical bayesian modeling of hitting performance in baseball. Bayesian Analysis, 4(4), 631–652. https://doi.org/10.1214/09-BA424
  7. Sidle, G., & Tran, H. (2018). Using multi-class classification methods to predict baseball pitch types. Journal of Sports Analytics, 4(1), 85–93. https://doi.org/10.3233/JSA-170171
  8. Bouzarth, E., Grannan, B., Harris, J., Hartley, A., Hutson, K., & Morton, E. (2021). Swing shift: A mathematical approach to defensive positioning in baseball. Journal of Quantitative Analysis in Sports, 17(1), 47–55. https://doi.org/10.1515/jqas-2020-0027
  9. Deshpande, S. K., & Wyner, A. (2017). A hierarchical bayesian model of pitch framing. Journal of Quantitative Analysis in Sports, 13(3), 95–112. https://doi.org/10.1515/jqas-2017-0027

Basketball

  1. Deshpande, S. K., & Jensen, S. T. (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
  2. Cervone, D., D’Amour, A., Bornn, L., & Goldsberry, K. (2016). A multiresolution stochastic process model for predicting basketball possession outcomes. Journal of the American Statistical Association, 111(514), 585–599. https://doi.org/10.1080/01621459.2016.1141685
  3. Franks, A., Miller, A., Bornn, L., & Goldsberry, K. (2015). Characterizing the spatial structure of defensive skill in professional basketball. The Annals of Applied Statistics, 9(1), 94--121. https://doi.org/10.1214/14-AOAS799
  4. Terner, Z., & Franks, A. (2021). Modeling player and team performance in basketball. Annual Review of Statistics and Its Application, 8(1), 1–23. https://doi.org/10.1146/annurev-statistics-040720-015536
  5. Lopez, M. J., & Matthews, G. J. (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

Football

  1. Yurko, R., Ventura, S., & Horowitz, M. (2019). nflWAR: A reproducible method for offensive player evaluation in football. Journal of Quantitative Analysis in Sports, 15(3), 163–183. https://doi.org/10.1515/jqas-2018-0010
  2. Nguyen, Q., Yurko, R., & Matthews, G. J. (2024). Here comes the strain: Analyzing defensive pass rush in american football with player tracking data. The American Statistician, 78(2), 199–208. https://doi.org/10.1080/00031305.2023.2242442
  3. Brill, R. S., Yurko, R., & Wyner, A. J. (2025). Analytics, have some humility: A statistical view of fourth-down decision making. The American Statistician, 1–17. https://doi.org/10.1080/00031305.2025.2475801
  4. Dutta, R., Yurko, R., & Ventura, S. L. (2020). Unsupervised methods for identifying pass coverage among defensive backs with NFL player tracking data. Journal of Quantitative Analysis in Sports, 16(2), 143–161. https://doi.org/10.1515/jqas-2020-0017
  5. Mallepalle, S., Yurko, R., Pelechrinis, K., & Ventura, S. L. (2020). Extracting NFL tracking data from images to evaluate quarterbacks and pass defenses. Journal of Quantitative Analysis in Sports, 16(2), 95–120. https://doi.org/10.1515/jqas-2019-0052
  6. Yurko, R., Matano, F., Richardson, L. F., Granered, N., Pospisil, T., Pelechrinis, K., & Ventura, S. L. (2020). Going deep: Models for continuous-time within-play valuation of game outcomes in american football with tracking data. Journal of Quantitative Analysis in Sports, 16(2), 163–182. https://doi.org/10.1515/jqas-2019-0056
  7. Yam, D. R., & Lopez, M. J. (2019). What was lost? A causal estimate of fourth down behavior in the national football league. Journal of Sports Analytics, 5(3), 153–167. https://doi.org/10.3233/jsa-190294
  8. Martin, R., Timmons, L., & Powell, M. (2018). A Markov chain analysis of NFL overtime rules. Journal of Sports Analytics, 4(2), 95–105. https://doi.org/10.3233/JSA-170198
  9. Deshpande, S. K., & Evans, K. (2020). Expected hypothetical completion probability. Journal of Quantitative Analysis in Sports, 16(2), 85–94. https://doi.org/10.1515/jqas-2019-0050
  10. Urschel, J., & Zhuang, J. (2011). Are NFL coaches risk and loss averse? Evidence from their use of kickoff strategies. Journal of Quantitative Analysis in Sports, 7(3), 14. https://doi.org/10.2202/1559-0410.1311
  11. White, C., & Berry, S. (2002). Tiered polychotomous regression: Ranking NFL quarterbacks. The American Statistician, 56(1), 10–21. https://doi.org/10.1198/000313002753631312
  12. Glickman, M. E., & Stern, H. S. (2017). Estimating team strength in the NFL. In Handbook of statistical methods and analyses in sports (pp. 113–136). Boca Raton, FL: CRC Press. http://glicko.net/research/nfl-chapter.pdf
  13. Chu, D., Reyers, M., Thomson, J., & Wu, L. Y. (2020). Route identification in the National Football League: An application of model-based curve clustering using the EM algorithm. Journal of Quantitative Analysis in Sports, 16(2), 121–132. https://doi.org/10.1515/jqas-2019-0047
  14. Lock, D., & Nettleton, D. (2014). Using random forests to estimate win probability before each play of an NFL game. Journal of Quantitative Analysis in Sports, 10(2). https://doi.org/10.1515/jqas-2013-0100
  15. Reyers, M., & Swartz, T. B. (2021). Quarterback evaluation in the National Football League using tracking data. AStA Advances in Statistical Analysis. https://doi.org/10.1007/s10182-021-00406-8

Hockey

  1. Thomas, A., Ventura, S. L., Jensen, S. T., & Ma, S. (2013). Competing process hazard function models for player ratings in ice hockey. The Annals of Applied Statistics, 1497–1524. https://doi.org/10.1214/13-AOAS646
  2. Brian, M. (2011). A regression-based adjusted plus-minus statistic for NHL players. Journal of Quantitative Analysis in Sports, 7(3). https://doi.org/10.2202/1559-0410.1284
  3. Beaudoin, D., & Swartz, T. B. (2010). Strategies for pulling the goalie in hockey. The American Statistician, 64(3), 197–204. https://doi.org/10.1198/tast.2010.09147

Tennis

  1. Kovalchik, S. A., & Reid, M. (2019). A calibration method with dynamic updates for within-match forecasting of wins in tennis. International Journal of Forecasting, 35(2), 756–766. https://doi.org/10.1016/j.ijforecast.2017.11.008
  2. Kovalchik, S. A. (2016). Searching for the GOAT of tennis win prediction. Journal of Quantitative Analysis in Sports, 12(3), 127–138. https://doi.org/10.1515/jqas-2015-0059

Multisport

  1. Macdonald, B. (2020). Recreating the game: Using player tracking data to analyze dynamics in basketball and football. Harvard Data Science Review, 2(4). https://doi.org/10.1162/99608f92.6e25c7ee
  2. Franks, A. M., D’Amour, A., Cervone, D., & Bornn, L. (2016). Meta-analytics: Tools for understanding the statistical properties of sports metrics. Journal of Quantitative Analysis in Sports, 12(4), 151–165. https://doi.org/doi:10.1515/jqas-2016-0098
  3. Bradley, R. A., & Terry, M. E. (1952). Rank analysis of incomplete block designs: I. The method of paired comparisons. Biometrika, 39(3/4), 324–345. https://doi.org/10.2307/2334029
  4. Lopez, M. J., Matthews, G. J., & Baumer, B. S. (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

Other

  1. De Veaux, R., Plantinga, A., & Upton, E. (2025). Age and performance in masters swimming and running. Journal of Quantitative Analysis in Sports, 21(2), 137–152. https://doi.org/10.1515/jqas-2024-0018
  2. Hamilton, I., Tawn, N., & Firth, D. (2023). The many routes to the ubiquitous bradley-terry model. arXiv Preprint arXiv:2312.13619. https://doi.org/10.48550/arXiv.2312.13619
  3. Maymin, P. 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
  4. Elo, A. E. (1978). The rating of chessplayers, past and present. New York: Arco Publishing.