[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 |