×
Baumer, Benjamin S.; Matthews, Gregory J.; Nguyen, Quang

Big ideas in sports analytics and statistical tools for their investigation. (English) Zbl 07910658

Wiley Interdiscip. Rev., WIREs Comput. Stat. 15, No. 6, Article ID e1612, 24 p. (2023).

MSC:

62-08 Computational methods for problems pertaining to statistics

Keywords:

datasets; pairwise comparisons; R packages; sports analytics; sports data

Software:

teamcolors; ggplot2; nflfastR; nflscrapR; nflWAR; R
Cite Review PDF
Full Text: DOI arXiv

References:

[1] Agresti, A. (2018). An introduction to categorical data analysis (3rd ed.). John Wiley & Sons. · Zbl 1407.62001
[2] Alamar, B. (2010). Measuring risk in NFL playcalling. Journal of Quantitative Analysis in Sports, 6(2), 11. https://doi.org/10.2202/1559-0410.1235 · doi:10.2202/1559-0410.1235
[3] Albert, J. (2015). Player evaluation using win probabilities in sports competitions. WIREs Computational Statistics, 7(5), 316-325. https://doi.org/10.1002/wics.1358 · Zbl 07912777 · doi:10.1002/wics.1358
[4] Albert, J., & Bennett, J. (2001). Curve ball: Baseball, statistics, and the role of chance in the game. Springer. · Zbl 0980.62110
[5] Albert, J., Glickman, M. E., Swartz, T. B., & Koning, R. H. (2016). Handbook of statistical methods and analyses in sports (p. 520). Chapman & Hall/CRC Press. https://doi.org/10.1201/9781315166070 · doi:10.1201/9781315166070
[6] Baumer, B., & Zimbalist, A. (2014). The sabermetric revolution: Assessing the growth of analytics in baseball (p. 187). University of Pennsylvania Press.
[7] 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 · doi:10.1515/jqas-2014-0098
[8] Baumer, B. S., Kaplan, D. T., & Horton, N. J. (2021). Modern data science with R (2nd ed., pp. 1-673). Chapman & Hall/CRC Presshttps://mdsr-book.github.io/mdsr2e/ · Zbl 1460.62004
[9] Baumer, B. S., & Matthews, G. J. (2020). Teamcolors: Color palettes for pro sports teams. http://github.com/beanumber/teamcolors
[10] Baumer, B. S., Nguyen, Q., & Matthews, G. J. (2022). CRAN task view: Sports analytics. https://CRAN.R-project.org/view=SportsAnalytics
[11] 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 · doi:10.1198/tast.2010.09147
[12] Bergman, D., & Imbrogno, J. (2017). Surviving a national football league survivor pool. Operations Research, 65, 1343-1354. https://doi.org/10.1287/opre.2017.1633 · Zbl 1383.90020 · doi:10.1287/opre.2017.1633
[13] Bornn, L., Cervone, D., Franks, A., & Miller, A. (2017). Studying basketball through the lens of player tracking data. In Handbook of statistical methods and analyses in sports (pp. 261-286). CRC Press.
[14] Boulier, B. L., & Stekler, H. O. (2003). Predicting the outcomes of national football league games. International Journal of Forecasting, 19(2), 257-270. https://doi.org/10.1016/s0169-2070(01)00144-3 · doi:10.1016/s0169-2070(01)00144-3
[15] Boulier, B. L., Stekler, H. O., & Amundson, S. (2006). Testing the efficiency of the national football league betting market. Applied Economics, 38(3), 279-284. https://doi.org/10.1080/00036840500368904 · doi:10.1080/00036840500368904
[16] 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 · doi:10.1515/jqas-2020-0027
[17] 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 · Zbl 0047.12903 · doi:10.2307/2334029
[18] Breiter, D. J., & Carlin, B. P. (1997). How to play office pools if you must. Chance, 10(1), 5-11. https://doi.org/10.1080/09332480.1997.10554789 · doi:10.1080/09332480.1997.10554789
[19] Brenzel, P., Shock, W., & Yang, H. (2019). An analysis of curling using a three‐dimensional Markov model. Journal of Sports Analytics, 5(2), 101-119. https://doi.org/10.3233/jsa-180279 · doi:10.3233/jsa-180279
[20] Buttrey, S. E. (2016). Beating the market betting on NHL hockey games. Journal of Quantitative Analysis in Sports, 12(2), 87-98. https://doi.org/10.1515/jqas-2015-0003 · doi:10.1515/jqas-2015-0003
[21] Carl, S. (2022). nflplotR: NFL logo plots in ggplot2. https://CRAN.R-project.org/package=nflplotR
[22] Carl, S., & Baldwin, B. (2022). nflfastR: Functions to efficiently access NFL play by play data. https://CRAN.R-project.org/package=nflfastR
[23] Caro, C. A., & Machtmes, R. (2013). Testing the utility of the Pythagorean expectation formula on division one college football: An examination and comparison to the Morey model. Journal of Business & Economics Research (JBER), 11(12), 537-542. https://doi.org/10.19030/jber.v11i12.8261 · doi:10.19030/jber.v11i12.8261
[24] Carroll, B. N., Palmer, P., Thorn, J., & Pietrusza, D. (1988). The hidden game of football (p. 415). Total Sports.
[25] Carter, V., & Machol, R. E. (1971). Technical note—Operations research on football. Operations Research, 19(2), 541-544. https://doi.org/10.1287/opre.19.2.541 · doi:10.1287/opre.19.2.541
[26] Casals, M., Fernández, J., Martínez, V., Lopez, M., Langohr, K., & Cortés, J. (2022). A systematic review of sport‐related packages within the R CRAN repository. International Journal of Sports Science & Coaching, 1, 621-629. https://doi.org/10.1177/17479541221136238 · doi:10.1177/17479541221136238
[27] Cervone, D., D’Amour, A., Bornn, L., & Goldsberry, K. (2014). Pointwise: Predicting points and valuing decisions in real time with NBA optical tracking data. http://www.lukebornn.com/papers/cervone_ssac_2014.pdf
[28] 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 · doi:10.1080/01621459.2016.1141685
[29] Che, J., & Glickman, M. (2022). Athlete rating in multi‐competitor games with scored outcomes via monotone transformations. arXiv Preprint arXiv:2205.10746. https://arxiv.org/pdf/2205.10746
[30] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., Li, M., Xie, J., Lin, M., Geng, Y., Li, Y., & Yuan, J. (2022). Xgboost: Extreme gradient boosting. https://github.com/dmlc/xgboost
[31] 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 · doi:10.1515/jqas-2019-0047
[32] Clair, B., & Letscher, D. (2007). Optimal strategies for sports betting pools. Operations Research, 55(6), 1163-1177. https://doi.org/10.1287/opre.1070.0448 · Zbl 1167.91329 · doi:10.1287/opre.1070.0448
[33] Clark, N., Macdonald, B., & Kloo, I. (2020). A Bayesian adjusted plus‐minus analysis for the esport Dota 2. Journal of Quantitative Analysis in Sports, 16(4), 325-341. https://doi.org/10.1515/jqas-2019-0103 · doi:10.1515/jqas-2019-0103
[34] Cochran, J. (ed.), Bennett, J. (ed.), & Albert, J. (ed.) (Eds.). (2017). The Oxford anthology of statistics in sports, volume 1: 2000-2004 (p. 544). Oxford University Presshttps://global.oup.com/academic/product/the-oxford-anthology-of-statistics-in-sports-9780198724926 · Zbl 1384.62010
[35] 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 · doi:10.1515/jqas-2019-0050
[36] 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), 51-72. https://doi.org/10.1515/jqas-2015-0027 · doi:10.1515/jqas-2015-0027
[37] 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 · doi:10.1515/jqas-2017-0027
[38] Dewan, J., & Zminda, D. (1993). STATS basketball scoreboard 1993-1994 (p. 288). HarperPerennial.
[39] Douglas, C., & Scriven, R. (2021). Retrosheet: Import professional baseball data from retrosheet. https://github.com/colindouglas/retrosheet
[40] Drucker, R. (2022). sportyR: Plot scaled ggplot representations of sports playing surfaces. https://github.com/sportsdataverse/sportyR
[41] Elo, A. E. (1978). The rating of chess players, past and present. Arco Publishing.
[42] Fernandez, J., Bornn, L., & Cervone, D. (2021). A framework for the fine‐grained evaluation of the instantaneous expected value of soccer possessions. Machine Learning, 110(6), 1389-1427. https://doi.org/10.1007/s10994-021-05989-6 · Zbl 07432839 · doi:10.1007/s10994-021-05989-6
[43] FIDE. (2022). Rating calculator. https://ratings.fide.com/calc.phtml
[44] Friendly, M., Dalzell, C., Monkman, M., & Murphy, D. (2022). Lahman: Sean Lahman baseball database. https://CRAN.R-project.org/package=Lahman
[45] Funt, D. (2022). At Sloan sports conference, criticism mounts over diversity, access. The Washington Post. https://www.washingtonpost.com/sports/2022/06/13/sloan-sports-conference-diversity/
[46] Gandar, J., Zuber, R., O’Brien, T., & Russo, B. (1988). Testing rationality in the point spread betting market. The Journal of Finance, 43(4), 995-1008. https://doi.org/10.1111/j.1540-6261.1988.tb02617.x · doi:10.1111/j.1540-6261.1988.tb02617.x
[47] Gilani, S. (2022). SportsDataverse. https://sportsdataverse.org
[48] Glickman, M. E., & Sonas, J. (2015). Introduction to the NCAA men’s basketball prediction methods issue. Journal of Quantitative Analysis in Sports, 11(1), 1-3. https://doi.org/10.1515/jqas-2015-0013 · doi:10.1515/jqas-2015-0013
[49] Glickman, M. E., & Stern, H. S. (1998). A state‐space model for national football league scores. Journal of the American Statistical Association, 93(441), 25-35. https://doi.org/10.1080/01621459.1998.10474084 · Zbl 0915.62078 · doi:10.1080/01621459.1998.10474084
[50] 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). CRC Presshttp://glicko.net/research/nfl-chapter.pdf
[51] Goldner, K. (2012). A Markov model of football: Using stochastic processes to model a football drive. Journal of Quantitative Analysis in Sports, 8(1), 1-16. https://doi.org/10.1515/1559-0410.1400 · doi:10.1515/1559-0410.1400
[52] Goldner, K. (2017). Situational success: Evaluating decision‐making in football. In Handbook of statistical methods and analyses in sports (pp. 199-214). CRC Press.
[53] Guan, T., Nguyen, R., Cao, J., & Swartz, T. (2022). In‐game win probabilities for the National Rugby League. The Annals of Applied Statistics, 16(1), 349-367. https://doi.org/10.1214/21-aoas1514 · Zbl 1498.62347 · doi:10.1214/21-aoas1514
[54] Hamilton, H. H. (2011). An extension of the Pythagorean expectation for association football. Journal of Quantitative Analysis in Sports, 7(2). https://doi.org/10.2202/1559-0410.1335 · doi:10.2202/1559-0410.1335
[55] Healey, G. (2017). The new Moneyball: How ballpark sensors are changing baseball. Proceedings of the IEEE, 105(11), 1999-2002. https://doi.org/10.1109/JPROC.2017.2756740 · doi:10.1109/JPROC.2017.2756740
[56] 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(1), 59-74. https://doi.org/10.1515/jqas-2017-0058 · doi:10.1515/jqas-2017-0058
[57] Horowitz, M., Yurko, R., Ventura, S., & Dutta, R. (2020). NflscrapR: Compiling the NFL play‐by‐play API for easy use in r. https://github.com/maksimhorowitz/nflscrapR
[58] Hvattum, L. M. (2019). A comprehensive review of plus‐minus ratings for evaluating individual players in team sports. International Journal of Computer Science in Sport, 18(1), 1-23. https://doi.org/10.2478/ijcss-2019-0001 · doi:10.2478/ijcss-2019-0001
[59] Imbrogno, J., & Bergman, D. (2022). Computing the number of winning NFL survivor pool entries. The College Mathematics Journal, 53(4), 282-291. https://doi.org/10.1080/07468342.2022.2099704 · Zbl 1505.97068 · doi:10.1080/07468342.2022.2099704
[60] James, B. (2003). The new Bill James historical baseball abstract. Free Press.
[61] Kaplan, E. H., & Garstka, S. J. (2001). March madness and the office pool. Management Science, 47(3), 369-382. https://doi.org/10.1287/mnsc.47.3.369.9769 · Zbl 1232.90284 · doi:10.1287/mnsc.47.3.369.9769
[62] Koning, R. H. (2017). Rating of team abilities in soccer. In Handbook of statistical methods and analyses in sports (pp. 371-388). CRC Press.
[63] Koopman, S. J., & Lit, R. (2015). A dynamic bivariate poisson model for analysing and forecasting match results in the English premier league. Journal of the Royal Statistical Society: Series A (Statistics in Society), 178(1), 167-186. https://doi.org/10.1111/rssa.12042 · doi:10.1111/rssa.12042
[64] 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 · doi:10.1515/jqas-2015-0059
[65] 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 · doi:10.1016/j.ijforecast.2017.11.008
[66] Kumagai, B., Nahabedian, M., Châtel, T., & Stokes, T. (2021). Bayesian space‐time models for expected possession added value. Hockey‐Graphs. https://hockey-graphs.com/2021/07/06/bayesian-space-time-models-for-expected-possession-added-value-part-1-of-2/
[67] Lacey, N. J. (1990). An estimation of market efficiency in the NFL point spread betting market. Applied Economics, 22(1), 117-129. https://doi.org/10.1080/00036849000000056 · doi:10.1080/00036849000000056
[68] Lente, C. (2020). Chess: Read, write, create and explore chess games. https://github.com/curso-r/chess
[69] Lewis, M. (2004). Moneyball: The art of winning an unfair game (p. 336). WW Norton & Company.
[70] Lindsey, G. R. (1961). The progress of the score during a baseball game. Journal of the American Statistical Association, 56(295), 703-728. https://doi.org/10.1080/01621459.1961.10480656 · Zbl 0101.37103 · doi:10.1080/01621459.1961.10480656
[71] 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 · Zbl 0111.33406 · doi:10.1287/opre.11.4.477
[72] 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 · doi:10.1515/jqas-2013-0100
[73] Lopez, M. 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 · doi:10.1515/jqas-2020-0057
[74] Lopez, M. J. (2022). Personal communication.
[75] 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 · doi:10.1515/jqas-2014-0058
[76] 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 · Zbl 1412.62216 · doi:10.1214/18-aoas1165
[77] Macdonald, B. (2012). An expected goals model for evaluating NHL teams and players. Proceedings of the 2012 MIT Sloan Sports Analytics Conference. https://assets.pubpub.org/mku181yp/ee0d61ed-af35-4b1f-ba86-71c216935690.pdf
[78] Marchi, M., Albert, J., & Baumer, B. S. (2018). Analyzing baseball data with R (2nd ed., p. 360). Chapman & Hall/CRC Press. https://doi.org/10.1201/9781351107099 · doi:10.1201/9781351107099
[79] 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 · doi:10.3233/JSA-170198
[80] 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 · doi:10.1515/jqas-2019-0096
[81] McFarlane, P. (2019). Evaluating NBA end‐of‐game decision‐making. Journal of Sports Analytics, 5(1), 17-22. https://doi.org/10.3233/jsa-180231 · doi:10.3233/jsa-180231
[82] Metrick, A. (1996). March madness? Strategic behavior in NCAA basketball tournament betting pools. Journal of Economic Behavior & Organization, 30(2), 159-172. https://doi.org/10.1016/S0167-2681(96)00855-4 · doi:10.1016/S0167-2681(96)00855-4
[83] Miller, S. J. (2007). A derivation of the Pythagorean won-loss formula in baseball. Chance, 20(1), 40-48. https://doi.org/10.1080/09332480.2007.10722831 · doi:10.1080/09332480.2007.10722831
[84] Mills, E. G., & Mills, H. D. (1970). Player win averages: A complete guide to winning baseball players. The Harlan D. Mills Collection. https://trace.tennessee.edu/utk_harlan/6/
[85] Nichols, M. W. (2014). The impact of visiting team travel on game outcome and biases in NFL betting markets. Journal of Sports Economics, 15(1), 78-96. https://doi.org/10.1177/1527002512440580 · doi:10.1177/1527002512440580
[86] Niemi, J. B., Carlin, B. P., & Alexander, J. M. (2008). Contrarian strategies for NCAA tournament pools: A cure for March madness?Chance, 21(1), 35-42. https://doi.org/10.1080/09332480.2008.10722884 · doi:10.1080/09332480.2008.10722884
[87] Paul, R. J., & Weinbach, A. P. (2014). Market efficiency and behavioral biases in the WNBA betting market. International Journal of Financial Studies, 2, 193-202. https://doi.org/10.3390/ijfs2020193 · doi:10.3390/ijfs2020193
[88] Pelechrinis, K., Winston, W., Sagarin, J., & Cabot, V. (2019). Evaluating NFL plays: Expected points adjusted for schedule. International Workshop on Machine Learning and Data Mining for Sports Analytics, 11330, 106-117. https://doi.org/10.1007/978-3-030-17274-9_9 · doi:10.1007/978-3-030-17274-9_9
[89] Petti, B., & Gilani, S. (2022). Baseballr: Acquiring and analyzing baseball data. https://CRAN.R-project.org/package=baseballr
[90] R Core Team. (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computinghttps://www.R-project.org/
[91] Reyers, M., & Swartz, T. B. (2021). Quarterback evaluation in the National Football League using tracking data. AStA Advances in Statistical Analysis., 107, 327-342. https://doi.org/10.1007/s10182-021-00406-8 · Zbl 07706170 · doi:10.1007/s10182-021-00406-8
[92] Romer, D. (2006). Do firms maximize? Evidence from professional football. Journal of Political Economy, 114(2), 340-365. https://doi.org/10.1086/501171 · doi:10.1086/501171
[93] Sauer, R. D. (1998). The economics of wagering markets. Journal of Economic Literature, 36(4), 2021-2064https://www.jstor.org/stable/2565046
[94] Schuhmann, J. (2021). NBA’s 3‐point revolution: How 1 shot is changing the game. NBA.com. https://www.nba.com/news/3-point-era-nba-75
[95] Schwarz, A. (2004). The numbers game: Baseball’s lifelong fascination with statistics. Thomas Dunne Books/St. Martin’s Press.
[96] Sicilia, A., Pelechrinis, K., & Goldsberry, K. (2019, July). DeepHoops: Evaluating micro‐actions in basketball using deep feature representations of Spatio‐temporal data. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. https://doi.org/10.1145/3292500.3330719 · doi:10.1145/3292500.3330719
[97] 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 · doi:10.3233/JSA-170171
[98] Sievert, C. (2015). pitchRx: Tools for harnessing MLBAM ‘gameday’ data and visualizing pitchfx. http://cpsievert.github.com/pitchRx
[99] Skinner, B. (2011). Scoring strategies for the underdog: A general, quantitative method for determining optimal sports strategies. Journal of Quantitative Analysis in Sports, 7(4). https://doi.org/10.2202/1559-0410.1364 · doi:10.2202/1559-0410.1364
[100] Soebbing, B. P., & Humphreys, B. R. (2013). Do gamblers think that teams tank? Evidence from the NBA. Contemporary Economic Policy, 31(2), 301-313. https://doi.org/10.1111/j.1465-7287.2011.00298.x · doi:10.1111/j.1465-7287.2011.00298.x
[101] Spann, M., & Skiera, B. (2009). Sports forecasting: A comparison of the forecast accuracy of prediction markets, betting odds and tipsters. Journal of Forecasting, 28(1), 55-72. https://doi.org/10.1002/for.1091 · doi:10.1002/for.1091
[102] Stern, H. S. (1994). A Brownian motion model for the progress of sports scores. Journal of the American Statistical Association, 89(427), 1128-1134. https://doi.org/10.1080/01621459.1994.10476851 · doi:10.1080/01621459.1994.10476851
[103] Tango, T. M., Lichtman, M. G., & Dolphin, A. E. (2007). The book: Playing the percentages in baseball. Potomac Books.
[104] Turner, H., & Firth, D. (2020). BradleyTerry2: Bradley-Terry models. https://github.com/hturner/BradleyTerry2
[105] 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 · doi:10.2202/1559-0410.1311
[106] White, C., & Berry, S. (2002). Tiered polychotomous regression: Ranking NFL quarterbacks. The American Statistician, 56(1), 10-21. https://doi.org/10.1198/000313002753631312 · doi:10.1198/000313002753631312
[107] Wickham, H., Chang, W., Henry, L., Pedersen, T. L., Takahashi, K., Wilke, C., Woo, K., Yutani, H., & Dunnington, D. (2022). ggplot2: Create elegant data visualisations using the grammar of graphics. https://CRAN.R-project.org/package=ggplot2
[108] Winston, W. L., Nestler, S., & Pelechrinis, K. (2022). Mathletics: How gamblers, managers, and fans use mathematics in sports (2nd ed.). Princeton University Press. · Zbl 1485.00006
[109] 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 · doi:10.3233/jsa-190294
[110] 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 · doi:10.1515/jqas-2019-0056
[111] 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 · doi:10.1515/jqas-2018-0010
[112] Zivkovic, J. (2022). chessR: Functions to extract, clean and analyse online chess game data. https://github.com/JaseZiv/chessR
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.