Working and Reading Group (WAR)

The SASL Working and Reading (WAR) group is taking the Fall 2021 semester off in order to focus on other SASL activities.  When it does meet, the group reads papers and discusses concepts from relevant areas of statistics with applications to sports analytics and sports science.  We expect to resume in Spring 2022, so check back then for updated information. In the mean time, to join our mailing list please email us!

 

Archive: Spring 2021 WAR reading schedule (Focus: player tracking data)

 

April 26, 2021

Daly-Grafstein, Daniel & Bornn, Luke. (2018). Using in-game shot trajectories to better understand defensive impact in the NBA, J. of Sports Analytics

April 19, 2021

Seshadri, D.R., et al, Wearable Technology and Analytics as a Complementary Toolkit to Optimize Workload and to Reduce Injury Burden, Front. Sports Act. Living, 21 January 2021 | https://doi.org/10.3389/fspor.2020.630576

March 22, 2021

Nielsen RO, Bertelsen ML, Ramskov D, et al. Time-to-event analysis for sports injury research part 1: time-varying exposures. British Journal of Sports Medicine 2019;53:61-68. (https://bjsm.bmj.com/content/bjsports/53/1/61.full.pdf)

 

Nielsen RO, Bertelsen ML, Ramskov D, et al. Time-to-event analysis for sports injury research part 2: time-varying outcomes. British Journal of Sports Medicine 2019;53:70-78. (https://bjsm.bmj.com/content/bjsports/53/1/70.full.pdf)

 

March 15, 2021

  • Fernández, Javier & Bornn, Luke. (2018). Wide Open Spaces: A statistical technique for measuring space creation in professional soccer.

Feb 22, 2021

  • Lohse KR, Sainani KL, Taylor JA, Butson ML, Knight EJ, et al. (2020) Systematic review of the use of “magnitude-based inference” in sports science and medicine. PLOS ONE 15(6): e0235318.https://doi.org/10.1371/journal.pone.0235318
  • Sainani, Kristin L (2018). The problem with "Magnitude-based Inference", Medicine & Science in Sports & Exercise: Oct 2018, Volume 50, Issue 10, p2166-2176. doi 10.1249/MSS0000000000001645

Feb 15, 2021

  • Santos-Fernández, E., Wu, P., Mengersen, K. (2019). Bayesian statistics meets sports: A comprehensive review.. Journal of Quantitative Analysis in Sports. 15. 10.1515/jqas-2018-0106. 

Feb 8, 2021

  • J. M. Hausdorff, C. K. Peng, Z. Ladin, J. Y. Wei, and A. L. Goldberger. Is walking a random walk? Evidence for long-range correlations in stride interval of human gait. Journal of Applied Physiology 1995 78:1, 349-358
  • Dot T, Quijoux F, Oudre L, Vienne-Jumeau A, Moreau A, Vidal P-P, Ricard D.  Non-Linear Template-Based Approach for the Study of Locomotion.  Sensors. 2020; 20(7):1939.

Feb 1, 2021

  • Chu, D., Reyers, M., Thomson, J., & Wu, L. Y. (2020). Route identification in the National Football League, Journal of Quantitative Analysis in Sports, 16(2), 121-132. 
  • Kinney, M. (2020). Template matching route classification, Journal of Quantitative Analysis in Sports, 16(2), 133-142.

Jan 25, 2021 

  • Ruddy, J. D. et. al. (2019). Modeling the Risk of Team Sport Injuries: A Narrative Review of Different Statistical Approaches. Frontiers in physiology.
  • Lopez, M. J. (2020). Bigger data, better questions, and a return to fourth down behavior: an introduction to a special issue on tracking datain the National football League. Journal of Quantitative Analysis in Sports.
     

Spring 2021 WAR Potential Reading List

 

Jiawei Bai, Jeff Goldsmith, Brian Caffo, Thomas A. Glass, and Ciprian M.Crainiceanu. Movelets: A dictionary of movement. Electron. J. Statist., 6:559–578, 2012.

Bransen, L., Van Haaren, J., & van de Velden, M. (2019). Measuring soccer players' contributions to chance creation by valuing their passes. Journal of Quantitative Analysis in Sports, 15, 97-116.

Burris, K., & Coleman, J. (2018). Out of gas: quantifying fatigue in MLB relievers. Journal of Quantitative Analysis in Sports, 14, 57-64.

Casals M, Finch CF. Sports Biostatistician: a critical member of all sports science and medicine teams for injury prevention. British Journal of Sports Medicine 2018;52:1457-1461.

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, 585-599. doi:10.1080/01621459.2016.1141685

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, 121-132.

Cook C. Predicting future physical injury in sports: it's a complicated dynamic system. British Journal of Sports Medicine 2016;50:1356-1357.

Daly-Grafstein, D., & Bornn, L. (2019). Rao-Blackwellizing field goal percentage. Journal of Quantitative Analysis in Sports, 15, 85-95.

Deshpande, S. K., & Evans, K. (2020). Expected hypothetical completion probability. Journal of Quantitative Analysis in Sports, 16, 85-94.

Deshpande, S. K., & Wyner, A. (2017). A hierarchical Bayesian model of pitch framing. Journal of Quantitative Analysis in Sports, 13, 95-112.

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, 143-161.

Fagan, F., Haugh, M., & Cooper, H. (2019). The advantage of lefties in one-on-one sports. Journal of Quantitative Analysis in Sports, 15, 1-25.

Edgar Santos-Fernandez, Francesco Denti, Kerrie Mengersen, and Antoni-etta Mira. The role of intrinsic dimension in high-resolution player trackingdata – insights in basketball, 2020.

Floyd, C. M., Hoffman, M., & Fokoue, E. (2020). Shot-by-shot stochastic modeling of individual tennis points. Journal of Quantitative Analysis in Sports, 16, 57-71.

Franks, A., Miller, A., Bornn, L., & Goldsberry, K. (2015, 3). Characterizing the spatial structure of defensive skill in professional basketball. Ann. Appl. Stat., 9, 94–121. doi:10.1214/14-AOAS799

Glickman, M. E., & Hennessy, J. (2015). A stochastic rank ordered logit model for rating multi-competitor games and sports. Journal of Quantitative Analysis in Sports, 11, 131-144.

Graham, J., & Mayberry, J. (2014). Measures of tactical efficiency in water polo. Journal of Quantitative Analysis in Sports, 10, 67-79.

Gramacy, R. B., Jensen, S. T., & Taddy, M. (2012). Estimating player contribution in hockey with regularized logistic regression. Journal of Quantitative Analysis in Sports, 9, 97-111.

Beniamino Hadj-Amar, Barbel Finkenstadt Rand, Mark Fiecas, FrancisLevi, and Robert Huckstepp. Bayesian model search for nonstationary periodic time series. Journal of the American Statistical Association,115(531):1320–1335, 2020.

Healey, G. (2019). A Bayesian method for computing intrinsic pitch values using kernel density and nonparametric regression estimates. Journal of Quantitative Analysis in Sports, 15, 59-74.

Hizan, H., Whipp, P. R., & Reid, M. (2010). Validation of Match Notation (A Coding System) in Tennis. Journal of Quantitative Analysis in Sports, 6, 1-13.

Hunter, D. J. (2018). New metrics for evaluating home plate umpire consistency and accuracy. Journal of Quantitative Analysis in Sports, 14, 159-172.

Keshri, S., Oh, M.-h., Zhang, S., & Iyengar, G. (2019). Automatic event detection in basketball using HMM with energy based defensive assignment. Journal of Quantitative Analysis in Sports, 15, 141-153.

Kovalchik, S. A., & Albert, J. (2017). A multilevel Bayesian approach for modeling the time-to-serve in professional tennis. Journal of Quantitative Analysis in Sports, 13, 49-62.

Kovalchik, S. A., & Ingram, M. (2018). Estimating the duration of professional tennis matches for varying formats. Journal of Quantitative Analysis in Sports, 14, 13-23.

Natalie Kupperman, Jay Hertel; Global Positioning System–Derived Workload Metrics and Injury Risk in Team-Based Field Sports: A Systematic Review. J Athl Train 1 September 2020; 55 (9): 931–943. doi: https://doi.org/10.4085/1062-6050-473-19

Lopez, M. J. (2020). Bigger data, better questions, and a return to fourth down behavior: an introduction to a special issue on tracking datain the National football League. Journal of Quantitative Analysis in Sports, 16, 73-79.

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, 95-120.

Maymin, A. Z., Maymin, P. Z., & Shen, E. (2012). The Individual Factors of Successful Free Throw Shooting. Journal of Quantitative Analysis in Sports, 8, 1-17.

Metulini, R., Manisera, M., & Zuccolotto, P. (2018). Modelling the dynamic pattern of surface area in basketball and its effects on team performance. Journal of Quantitative Analysis in Sports, 14, 117-130.

Nadimpalli, V. K., & Hasenbein, J. J. (2013). When to challenge a call in tennis: A Markov decision process approach. Journal of Quantitative Analysis in Sports, 9, 229-238.

Pasteur, R. D., & Janning, M. (2011). Monte Carlo Simulation for High School Football Playoff Seed Projection. Journal of Quantitative Analysis in Sports, 7, 1330-1330.

Piette, J., & Jensen, S. T. (2012). Estimating Fielding Ability in Baseball Players Over Time. Journal of Quantitative Analysis in Sports, 8, 1-36.

Alen Rajsp and Iztok Fister. A systematic literature review of intelligent data analysis methods for smart sport training. Applied Sciences,10(9):3013, 2020.

Ruddy, J. D., Cormack, S. J., Whiteley, R., Williams, M. D., Timmins, R. G., & Opar, D. A. (2019). Modeling the Risk of Team Sport Injuries: A Narrative Review of Different Statistical Approaches. Frontiers in physiology, 10, 829. https://doi.org/10.3389/fphys.2019.00829

Sandholtz, N., & Bornn, L. (2020, 9). Markov decision processes with dynamic transition probabilities: An analysis of shooting strategies in basketball. Ann. Appl. Stat., 14, 1122–1145. doi:10.1214/20-AOAS1348

Santos-Fernandez, E., Wu, P., & Mengersen, K. L. (2019). Bayesian statistics meets sports: a comprehensive review. Journal of Quantitative Analysis in Sports, 15, 289-312. R

Sarkar, S. (2018). Paradox of crosses in association football (soccer) – a game-theoretic explanation. Journal of Quantitative Analysis in Sports, 14, 25-36.

Shortridge, A., Goldsberry, K., & Adams, M. (2014). Creating space to shoot: quantifying spatial relative field goal efficiency in basketball. Journal of Quantitative Analysis in Sports, 10, 303-313.

Changjia Tian, Varuna De Silva, Michael Caine, and Steve Swanson. Use of machine learning to automate the identification of basketball strategies using whole team player tracking data. Applied Sciences, 10(1):24, 2020.

Emmanuel Vallance, Nicolas Sutton-Charani, Abdelhak Imoussaten, Jacky Montmain, and Stephane Perrey.Combining internal-and external-training-loads to predict non-contact injuries in soccer. Applied Sciences,10(15):5261, 2020.

Yousefi, K., & Swartz, T. B. (2013). Advanced putting metrics in golf. Journal of Quantitative Analysis in Sports, 9, 239-248.

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, 163-182.

Yurko, R., Ventura, S., & Horowitz, M. (2019). nflWAR: a reproducible method for offensive player evaluation in football. Journal of Quantitative Analysis in Sports, 15, 163-183.