1932

Abstract

In recent years, analytics has started to revolutionize the game of basketball: Quantitative analyses of the game inform team strategy; management of player health and fitness; and how teams draft, sign, and trade players. In this review, we focus on methods for quantifying and characterizing basketball gameplay. At the team level, we discuss methods for characterizing team strategy and performance, while at the player level, we take a deep look into a myriad of tools for player evaluation. This includes metrics for overall player value, defensive ability, and shot modeling, and methods for understanding performance over multiple seasons via player production curves. We conclude with a discussion on the future of basketball analytics and, in particular, highlight the need for causal inference in sports.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-statistics-040720-015536
2021-03-07
2024-03-29
Loading full text...

Full text loading...

/deliver/fulltext/statistics/8/1/annurev-statistics-040720-015536.html?itemId=/content/journals/10.1146/annurev-statistics-040720-015536&mimeType=html&fmt=ahah

Literature Cited

  1. Alferink LA, Critchfield TS, Hitt JL, Higgins WJ 2009. Generality of the matching law as a descriptor of shot selection in basketball. J. Appl. Behav. Anal. 42:595–608
    [Google Scholar]
  2. Arel B, Tomas MJ III 2012. The NBA draft: a put option analogy. J. Sports Econ. 13:223–49
    [Google Scholar]
  3. Bar-Eli M, Avugos S, Raab M 2006. Twenty years of hot hand research: review and critique. Psychol. Sport Exerc. 7:525–53
    [Google Scholar]
  4. Basketball Reference 2020. Calculating individual offensive and defensive ratings. Basketball Reference https://www.basketball-reference.com/about/ratings.html
    [Google Scholar]
  5. Berri DJ. 1999. Who is most valuable? Measuring the player's production of wins in the National Basketball Association. Manag. Decis. Econ. 20:411–27
    [Google Scholar]
  6. Berri DJ, Brook SL, Fenn AJ 2011. From college to the pros: predicting the NBA amateur player draft. J. Product. Anal. 35:25–35
    [Google Scholar]
  7. Berry SM, Reese CS, Larkey PD 1999. Bridging different eras in sports. J. Am. Stat. Assoc. 94:661–76
    [Google Scholar]
  8. Blei DM, Ng AY, Jordan MI 2003. Latent Dirichlet allocation. J. Mach. Learn. Res. 3:993–1022
    [Google Scholar]
  9. Bornn L, Cervone D, Franks A, Miller A 2017. Studying basketball through the lens of player tracking data. Handbook of Statistical Methods and Analyses in Sports J Albert, M Glickman, TB Swartz, RH Koning 261–86 Boca Raton, FL: Chapman and Hall/CRC
    [Google Scholar]
  10. Bornn L, Daly-Grafstein D. 2019. Using in-game shot trajectories to better understand defensive impact in the NBA. arXiv:1905.00822 [stat.AP]
  11. Brown M, Kvam P, Nemhauser G, Sokol J 2012. Insights from the LRMC method for NCAA tournament predictions Paper presented at MIT Sloan Sports Analytics Conference, March 2–3 Boston, MA:
  12. Brown WO, Sauer RD. 1993. Fundamentals or noise? Evidence from the professional basketball betting market. J. Finance 48:1193–209
    [Google Scholar]
  13. Cervone D, Bornn L, Goldsberry K 2016a. NBA court realty Paper presented at MIT Sloan Sports Analytics Conference, March 11–12 Boston, MA:
  14. Cervone D, D'Amour A, Bornn L, Goldsberry K 2014. Pointwise: predicting points and valuing decisions in real time with NBA optical tracking data Paper presented at MIT Sloan Sports Analytics Conference, Feb. 28–March 1 Boston, MA:
  15. Cervone D, D'Amour A, Bornn L, Goldsberry K 2016b. A multiresolution stochastic process model for predicting basketball possession outcomes. J. Am. Stat. Assoc. 111:585–99
    [Google Scholar]
  16. Chang YH, Maheswaran R, Su J, Kwok S, Levy T et al. 2014. Quantifying shot quality in the NBA Paper presented at MIT Sloan Sports Analytics Conference, Feb. 28–March 1 Boston, MA:
  17. D'Amour A, Cervone D, Bornn L, Goldsberry K 2015. Move or die: how ball movement creates open shots in the NBA Paper presented at MIT Sloan Sports Analytics Conference, Feb. 27–28 Boston, MA:
  18. Daly-Grafstein D, Bornn L. 2019. Rao-Blackwellizing field goal percentage. J. Quant. Anal. Sports 15:85–95
    [Google Scholar]
  19. Deshpande SK, Jensen ST. 2016. Estimating an NBA players impact on his team's chances of winning. J. Quant. Anal. Sports 12:51–72
    [Google Scholar]
  20. DiFiori JP, Güllich A, Brenner JS, Côté J, Hainline B et al. 2018. The NBA and youth basketball: recommendations for promoting a healthy and positive experience. Sports Med 48:2053–65
    [Google Scholar]
  21. Drakos MC, Domb B, Starkey C, Callahan L, Allen AA 2010. Injury in the National Basketball Association: a 17-year overview. Sports Health 2:284–90
    [Google Scholar]
  22. Drazan JF, Loya AK, Horne BD, Eglash R 2017. From sports to science: using basketball analytics to broaden the appeal of math and science among youth Paper presented at MIT Sloan Sports Analytics Conference, March 3–4 Boston, MA:
  23. Dutta S, Jacobson SH, Sauppe JJ 2017. Identifying NCAA tournament upsets using balance optimization subset selection. J. Quant. Anal. Sports 13:79–93
    [Google Scholar]
  24. Efron B, Morris C. 1975. Data analysis using Stein's estimator and its generalizations. J. Am. Stat. Assoc. 70:311–19
    [Google Scholar]
  25. Engelmann J. 2017. Possession-based player performance analysis in basketball (adjusted +/– and related concepts). Handbook of Statistical Methods and Analyses in Sports J Albert, M Glickman, TB Swartz, RH Koning 231–44 Boca Raton, FL: Chapman and Hall/CRC
    [Google Scholar]
  26. ESPN 2020. 5-on-5: Can LeBron catch Giannis for MVP. ? ESPN Feb. 27. https://www.espn.com/nba/story/_/id/28787687/5-5-lebron-catch-giannis-mvp
    [Google Scholar]
  27. Fearnhead P, Taylor BM. 2011. On estimating the ability of NBA players. J. Quant. Anal. Sports 7(3):11
    [Google Scholar]
  28. Fewell JH, Armbruster D, Ingraham J, Petersen A, Waters JS 2012. Basketball teams as strategic networks. PLOS ONE 7:e47445
    [Google Scholar]
  29. Franks A, Miller A, Bornn L, Goldsberry K 2015a. Characterizing the spatial structure of defensive skill in professional basketball. Ann. Appl. Stat. 9:94–121
    [Google Scholar]
  30. Franks A, Miller A, Bornn L, Goldsberry K 2015b. Counterpoints: advanced defensive metrics for NBA basketball Paper presented at MIT Sloan Sports Analytics Conference, Feb. 27–28 Boston, MA:
  31. Franks AM, D'Amour A, Cervone D, Bornn L 2016. Meta-analytics: tools for understanding the statistical properties of sports metrics. J. Quant. Anal. Sports 12:151–65
    [Google Scholar]
  32. Gandar JM, Dare WH, Brown CR, Zuber RA 1998. Informed traders and price variations in the betting market for professional basketball games. J. Finance 53:385–401
    [Google Scholar]
  33. Gauriot R, Page L. 2019. Fooled by performance randomness: overrewarding luck. Rev. Econ. Stat. 101:658–66
    [Google Scholar]
  34. Gilovich T, Vallone R, Tversky A 1985. The hot hand in basketball: on the misperception of random sequences. Cogn. Psychol. 17:295–314
    [Google Scholar]
  35. Goldman M, Rao JM. 2011. Allocative and dynamic efficiency in NBA decision making Paper presented at MIT Sloan Sports Analytics Conference, March 4–5 Boston, MA:
  36. Goldman M, Rao JM. 2013. Live by the three, die by the three? The price of risk in the NBA Paper presented at MIT Sloan Sports Analytics Conference, March 1–2 Boston, MA:
  37. Goldsberry K. 2012. Courtvision: New visual and spatial analytics for the NBA Paper presented at MIT Sloan Sports Analytics Conference, March 2–3 Boston, MA:
  38. Goldsberry K. 2019. Sprawlball: A Visual Tour of the New Era of the NBA Boston: Houghton Mifflin Harcourt
  39. Goldsberry K, Weiss E. 2013. The Dwight effect: a new ensemble of interior defense analytics for the NBA Paper presented at MIT Sloan Sports Analytics Conference, March 1–2 Boston, MA:
  40. Gray KL, Schwertman NC. 2012. Comparing team selection and seeding for the 2011 NCAA men's basketball tournament. J. Quant. Anal. Sports 8:1 https://doi.org/10.1515/1559-0410.1369
    [Crossref] [Google Scholar]
  41. Groothuis PA, Hill JR, Perri TJ 2007. Early entry in the NBA draft: the influence of unraveling, human capital, and option value. J. Sports Econ. 8:223–43
    [Google Scholar]
  42. Harmon M, Lucey P, Klabjan D 2017. Predicting shot making in basketball using convolutional neural networks learnt from adversarial multiagent trajectories. arXiv:1609.04849 [stat.ML]
  43. Hofler RA, Payne JE. 2006. Efficiency in the national basketball association: a stochastic frontier approach with panel data. Manag. Decis. Econ. 27:279–85
    [Google Scholar]
  44. Hollinger J. 2005a. PER: player efficiency rating explained. ESPN Aug. 17. https://www.espn.com/NBA/insider/columns/story?columnist=hollinger_john&id=2136379
    [Google Scholar]
  45. Hollinger J. 2005b. Pro Basketball Forecast, 2005–06 Washington, DC: Potomac
  46. Hughes G. 2017. How Europe changed the NBA game forever. Bleacher Report Sept. 6. https://bleacherreport.com/articles/1764154-how-europe-changed-the-nba-game-forever
    [Google Scholar]
  47. Jacobs J. 2017. Deep dive on regularized adjusted plus-minus I: introductory example. Squared Statistics: Understanding Basketball Analytics Blog Sept. 18. https://squared2020.com/2017/09/18/deep-dive-on-regularized-adjusted-plus-minus-i-introductory-example/
    [Google Scholar]
  48. James B. 1984. The Bill James Baseball Abstract, 1984 New York: Ballantine
  49. James B. 1987. The Bill James Baseball Abstract 1987 New York: Ballantine
  50. James B. 2010. The New Bill James Historical Baseball Abstract New York: Simon and Schuster
  51. Keshri S, Oh M-H, Zhang S, Iyengar G 2019. Automatic event detection in basketball using HMM with energy based defensive assignment. J. Quant. Anal. Sports 15:141–53
    [Google Scholar]
  52. Kubatko J, Oliver D, Pelton K, Rosenbaum DT 2007. A starting point for analyzing basketball statistics. J. Quant. Anal. Sports 3:31
    [Google Scholar]
  53. Lackritz J. 2017. Probability models for streak shooting. Handbook of Statistical Methods and Analyses in Sports J Albert, M Glickman, TB Swartz, RH Koning 215–30 Boca Raton, FL: Chapman and Hall/CRC
    [Google Scholar]
  54. Lamas L, Santana F, Heiner M, Ugrinowitsch C, Fellingham G 2015. Modeling the offensive-defensive interaction and resulting outcomes in basketball. PLOS ONE 10:e0144435
    [Google Scholar]
  55. Le HM, Carr P, Yue Y, Lucey P 2017. Data-driven ghosting using deep imitation learning Paper presented at MIT Sloan Sports Analytics Conference, March 3–4 Boston, MA:
  56. Lewis M. 2004. Moneyball: The Art of Winning an Unfair Game New York: WW Norton
  57. Linou K. 2016. NBA player movements. Software for Visualization of NBA Games https://github.com/linouk23/NBA-Player-Movements
    [Google Scholar]
  58. Lopez MJ. 2016. Persuaded under pressure: evidence from the national football league. Econ. Inq. 54:1763–73
    [Google Scholar]
  59. Lopez MJ, Matthews GJ. 2015. Building an NCAA men's basketball predictive model and quantifying its success. J. Quant. Anal. Sports 11:5–12
    [Google Scholar]
  60. Lucey P, Bialkowski A, Carr P, Yue Y, Matthews I 2014. How to get an open shot: analyzing team movement in basketball using tracking data Paper presented at MIT Sloan Sports Analytics Conference, Feb. 28–March 1 Boston, MA:
  61. Malarranha J, Figueira B, Leite N, Sampaio J 2013. Dynamic modeling of performance in basketball. Int. J. Perform. Anal. Sport 13:377–87
    [Google Scholar]
  62. Marty R. 2018. High-resolution shot capture reveals systematic biases and an improved method for shooter evaluation Paper presented at MIT Sloan Sports Analytics Conference, Feb. 23–24 Boston, MA:
  63. Marty R, Lucey S. 2017. A data-driven method for understanding and increasing 3-point shooting percentage Paper presented at MIT Sloan Sports Analytics Conference, March 3–4 Boston, MA:
  64. Masheswaran R, Chang Y, Su J, Kwok S, Levy T et al. 2014. The three dimensions of rebounding Paper presented at MIT Sloan Sports Analytics Conference, Feb. 28–March 1 Boston, MA:
  65. McCann MA. 2004. Illegal defense: the irrational economics of banning high school players from the NBA draft. SSRN https://papers.ssrn.com/sol3/papers.cfm?abstract_id=567745
    [Google Scholar]
  66. McCarthy MM, Voos JE, Nguyen JT, Callahan L, Hannafin JA 2013. Injury profile in elite female basketball athletes at the Women's National Basketball Association combine. Am. J. Sports Med. 41:645–51
    [Google Scholar]
  67. Melnick MJ. 2001. Relationship between team assists and win-loss record in the National Basketball Association. Percept. Motor Skills 92:595–602
    [Google Scholar]
  68. Metulini R, Manisera M, Zuccolotto P 2018. Modelling the dynamic pattern of surface area in basketball and its effects on team performance. J. Quant. Anal. Sports 14:117–30
    [Google Scholar]
  69. Miller A, Bornn L, Adams R, Goldsberry K 2013. Factorized point process intensities: A spatial analysis of professional basketball. Proceedings of the 31st International Conference on Machine Learning EP Xing, T Jebara 235–43 New York: ACM
    [Google Scholar]
  70. Miller AC, Bornn L. 2017. Possession sketches: mapping NBA strategies. Paper presented at MIT Sloan Sports Analytics Conference, March 3–4, Boston, MA
  71. Miller J, Sanjurjo A. 2018a. Momentum isn't magic—vindicating the hot hand with the mathematics of streaks. Scientific American March 28. https://www.scientificamerican.com/article/momentum-isnt-magic-vindicating-the-hot-hand-with-the-mathematics-of-streaks/
    [Google Scholar]
  72. Miller JB, Sanjurjo A. 2018b. A bridge from Monty Hall to the hot hand: restricted choice, selection bias, and empirical practice. OSF Preprints https://dx.doi.org/10.31219/osf.io/dmgtp
    [Crossref] [Google Scholar]
  73. Miller JB, Sanjurjo A. 2018c. Surprised by the hot hand fallacy? A truth in the law of small numbers. Econometrica 86:2019–47
    [Google Scholar]
  74. Myers D. 2020. About box plus/minus (BPM). Basketball Reference https://www.basketball-reference.com/about/bpm2.html
    [Google Scholar]
  75. NBA (Natl. Basketb. Assoc.). 2020. NBA Hackathon. NBA Media Ventures https://hackathon.nba.com/
    [Google Scholar]
  76. Neiman T, Loewenstein Y. 2011. Reinforcement learning in professional basketball players. Nat. Commun. 2:1–8
    [Google Scholar]
  77. Neudorfer A, Rosset S. 2018. Predicting the NCAA basketball tournament using isotonic least squares pairwise comparison model. J. Quant. Anal. Sports 14:173–83
    [Google Scholar]
  78. NFL (Natl. Footb. Leag.) 2020. Big Data Bowl. National Football League Operations https://operations.nfl.com/the-game/big-data-bowl/
    [Google Scholar]
  79. Oliver D. 2004. Basketball on Paper: Rules and Tools for Performance Analysis Washington, DC: Potomac
  80. Omidiran D. 2011. A new look at adjusted plus/minus for basketball analysis Paper presented at MIT Sloan Sports Analytics Conference, March 4–5 Boston, MA:
  81. Page GL, Barney BJ, McGuire AT 2013. Effect of position, usage rate, and per game minutes played on NBA player production curves. J. Quant. Anal. Sports 9:337–45
    [Google Scholar]
  82. Page GL, Fellingham GW, Reese CS 2007. Using box-scores to determine a position's contribution to winning basketball games. J. Quant. Anal. Sports 3:41
    [Google Scholar]
  83. Patton A. 2019. How can we visualize a player's shooting gravity. ? Fansided/Nylon Calculus Blog July 22. https://fansided.com/2019/07/22/nylon-calculus-visualizing-NBA-shooting-gravity/
    [Google Scholar]
  84. Pelton K. 2019. The WARP rating system explained. SBNation http://sonicscentral.com/warp.html
    [Google Scholar]
  85. Pesca M. 2009. The man who made baseball's box score a hit. NPR July 30. https://www.npr.org/templates/story/story.php?storyId=106891539
    [Google Scholar]
  86. Piette J, Pham L, Anand S 2011. Evaluating basketball player performance via statistical network modeling. Paper presented at MIT Sloan Sports Analytics Conference, March 4–5 Boston, MA:
  87. Poling A, Edwards TL, Weeden M, Foster TM 2011. The matching law. Psychol. Record 61:313–22
    [Google Scholar]
  88. Prentice RL, Kalbfleisch JD. 1979. Hazard rate models with covariates. Biometrics 35:25–39
    [Google Scholar]
  89. Price J, Wolfers J. 2010. Racial discrimination among NBA referees. Q. J. Econ. 125:1859–87
    [Google Scholar]
  90. Reich BJ, Hodges JS, Carlin BP, Reich AM 2006. A spatial analysis of basketball shot chart data. Am. Stat. 60:3–12
    [Google Scholar]
  91. Rosenbaum DT. 2004. Measuring how NBA players help their teams win. 82games Apr. 30. http://www.82games.com/comm30.htm
    [Google Scholar]
  92. Ruiz FJ, Perez-Cruz F. 2015. A generative model for predicting outcomes in college basketball. J. Quant. Anal. Sports 11:39–52
    [Google Scholar]
  93. Sampaio J, Drinkwater EJ, Leite NM 2010. Effects of season period, team quality, and playing time on basketball players' game-related statistics. Eur. J. Sport Sci. 10:141–49
    [Google Scholar]
  94. Sandholtz N, Bornn L. 2020. Markov decision processes with dynamic transition probabilities: an analysis of shooting strategies in basketball. Ann. Appl. Stat. 14(3):1122–45
    [Google Scholar]
  95. Sandholtz N, Mortensen J, Bornn L 2020. Measuring spatial allocative efficiency in basketball. J. Quant. Anal. Sports 16(4):271–89
    [Google Scholar]
  96. Schwartz J. 2013. The NBA loves stats. That's a problem. Slate Feb. 28. https://slate.com/culture/2013/02/nba-stats-gurus-cant-work-together-anymore-thats-a-problem.html
    [Google Scholar]
  97. Shah R, Romijnders R. 2016. Applying deep learning to basketball trajectories. arXiv:1608.03793 [cs.NE]
  98. Shortridge A, Goldsberry K, Adams M 2014. Creating space to shoot: quantifying spatial relative field goal efficiency in basketball. J. Quant. Anal. Sports 10:303–13
    [Google Scholar]
  99. Sicilia A, Pelechrinis K, Goldsberry K 2019. Deephoops: evaluating micro-actions in basketball using deep feature representations of spatio-temporal data. arXiv:1902.08081 [stat.AP]
  100. Sill J. 2010. Improved NBA adjusted plus-minus using regularization and out-of-sample testing. Paper presented at MIT Sloan Sports Analytics Conference, March 9, Boston, MA
  101. Silver N. 2012. The Signal and the Noise: Why Most Predictions Fail—But Some Don't London: Penguin
  102. Silver N. 2015. We're predicting the career of every NBA player. Here's how. FiveThirtyEight Blog Oct. 9. https://fivethirtyeight.com/features/how-were-predicting-NBA-player-career/
    [Google Scholar]
  103. Silver N. 2019. How our RAPTOR metric works. FiveThirtyEight Blog Oct. 10. https://fivethirtyeight.com/features/how-our-raptor-metric-works/
    [Google Scholar]
  104. Simmons B. 2001. Ewing theory 101. ESPN May 9. https://www.espn.com/espn/page2/story?page=simmons/010509a
    [Google Scholar]
  105. Skinner B. 2010. The price of anarchy in basketball. J. Quant. Anal. Sports 6:11–18
    [Google Scholar]
  106. Skinner B, Goldman M. 2015. Optimal strategy in basketball. arXiv:1512.05652 [physics.soc-ph]
  107. Skinner B, Guy SJ. 2015. A method for using player tracking data in basketball to learn player skills and predict team performance. PLOS ONE 10:e0136393
    [Google Scholar]
  108. Smith C. 2018. Kinexon's wearable sensor is changing the way NBA teams prep for success. Wareable Nov. 2. https://www.wareable.com/sport/kinexon-wearable-sensor-NBA-basketball-6679
    [Google Scholar]
  109. Stone DF. 2012. Measurement error and the hot hand. Am. Stat. 66:61–66
    [Google Scholar]
  110. Tu YK, Gunnell D, Gilthorpe MS 2008. Simpson's paradox, Lord's paradox, and suppression effects are the same phenomenon—the reversal paradox. Emerg. Themes Epidemiol. 5:2
    [Google Scholar]
  111. Vaci N, Cocić D, Gula B, Bilalić M 2019. Large data and Bayesian modeling—aging curves of NBA players. Behav. Res. Methods 51:1544–64
    [Google Scholar]
  112. van Bommel M, Bornn L 2017. Adjusting for scorekeeper bias in NBA box scores. Data Mining Knowl. Discov. 31:1622–42
    [Google Scholar]
  113. Vinué G, Epifanio I. 2019. Forecasting basketball players' performance using sparse functional data. Stat. Anal. Data Mining 12:534–47
    [Google Scholar]
  114. Vinué G, Epifanio I, Alemany S 2015. Archetypoids: a new approach to define representative archetypal data. Comput. Stat. Data Anal. 87:102–15
    [Google Scholar]
  115. Wakim A, Jin J. 2014. Functional data analysis of aging curves in sports. arXiv:1403.7548 [stat.AP]
  116. Xin L, Zhu M, Chipman H 2017. A continuous-time stochastic block model for basketball networks. Ann. Appl. Stat. 11:553–97
    [Google Scholar]
  117. Yaari G, Eisenmann S. 2011. The hot (invisible?) hand: Can time sequence patterns of success/failure in sports be modeled as repeated random independent trials. ? PLOS ONE 6:e24532
    [Google Scholar]
  118. Yam DR, Lopez MJ. 2019. What was lost? A causal estimate of fourth down behavior in the National Football League. J. Sports Anal. 5:153–67
    [Google Scholar]
  119. Yu SZ. 2010. Hidden semi-Markov models. Artif. Intel. 174:215–43
    [Google Scholar]
  120. Yuan LH, Liu A, Yeh A, Kaufman A, Reece A et al. 2015. A mixture-of-modelers approach to forecasting NCAA tournament outcomes. J. Quant. Anal. Sports 11:13–27
    [Google Scholar]
  121. Yue Y, Lucey P, Carr P, Bialkowski A, Matthews I 2014. Learning fine-grained spatial models for dynamic sports play prediction. Proceedings of the 2014 IEEE International Conference on Data Mining670–79 Washington, DC: IEEE
    [Google Scholar]
  122. Ziv G, Lidor R, Arnon M 2010. Predicting team rankings in basketball: the questionable use of on-court performance statistics. Int. J. Perform. Anal. Sport 10:103–14
    [Google Scholar]
  123. Zuccolotto P, Manisera M, Sandri M 2018. Big data analytics for modeling scoring probability in basketball: the effect of shooting under high-pressure conditions. Int. J. Sports Sci. Coach. 13:569–89
    [Google Scholar]
/content/journals/10.1146/annurev-statistics-040720-015536
Loading
/content/journals/10.1146/annurev-statistics-040720-015536
Loading

Data & Media loading...

Supplemental Material

Supplementary Data

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error