Win-stay lose-shift strategy in formation changes in football

EPJ Data Science

Volumn 4, Issue 1, 2015, Pages 1-19

  Tamura, Kohei ^a,b   Masuda, Naoki ^c  

^a UNIVERSITY OF TOKYO   (Japan)

^b JAPAN SCIENCE AND TECHNOLOGY AGENCY   (Japan)

^c UNIVERSITY OF BRISTOL   (United Kingdom)

Author keywords

reinforcement learning; soccer; sports

Indexed keywords

DECISION MAKING; HUMAN RESOURCE MANAGEMENT; MANAGERS; REINFORCEMENT LEARNING;

MANAGERIAL DECISION MAKING; RULE OF THUMB; SHIFT STRATEGY; SOCCER; TEAM SPORTS; TEMPORAL PATTERN;

SPORTS;

EID: 84958778477     PISSN: None     EISSN: 21931127     Source Type: Journal    
DOI: 10.1140/epjds/s13688-015-0045-1     Document Type: Article

References (44)

1. Fudenberg D, Levine DK (1998) The theory of learning in games. MIT Press, Cambridge
2. Camerer C (2003) Behavioral game theory: experiments in strategic interaction. Princeton University Press, Princeton
3. Pearce JM (2013) Animal learning and cognition: an introduction. Psychology Press, Hove
4. Sutton RS, Barto AG (1998) Reinforcement learning: an introduction. MIT Press, Cambridge
5. Schultz W, Dayan P, Montague PR (1997) A neural substrate of prediction and reward. Science 275(5306):1593-1599
6. Glimcher PW, Camerer C, Fehr E, Poldrack RA (2009) Neuroeconomics: decision making and the brain. Academic Press, New York
7. Kraines D, Kraines V (1989) Pavlov and the prisoner’s dilemma. Theory Decis 26(1):47-79
8. Nowak M, Sigmund K (1993) A strategy of win-stay, lose-shift that outperforms tit-for-tat in the Prisoner’s Dilemma Game. Nature 364(6432):56-58
9. Wedekind C, Milinski M (1996) Human cooperation in the simultaneous and the alternating Prisoner’s Dilemma: Pavlov versus Generous Tit-for-Tat. Proc Natl Acad Sci USA 93(7):2686-2689
10. Milinski M, Wedekind C (1998) Working memory constrains human cooperation in the Prisoner’s Dilemma. Proc Natl Acad Sci 95(23):13755-13758
11. Hayden BY, Platt ML (2009) Gambling for Gatorade: risk-sensitive decision making for fluid rewards in humans. Anim Cogn 12(1):201-207
12. Scheibehenne B, Wilke A, Todd PM (2011) Expectations of clumpy resources influence predictions of sequential events. Evol Hum Behav 32(5):326-333
13. Mesoudi A, O’Brien MJ (2008) The cultural transmission of Great Basin projectile-point technology I: an experimental simulation. Am Antiq 73(1):3-28
14. Mesoudi A, O’Brien MJ (2008) The cultural transmission of Great Basin projectile-point technology II: an agent-based computer simulation. Am Antiq 73(4):627-644
15. Mesoudi A (2014) Experimental studies of modern human social and individual learning in an archaeological context: people behave adaptively, but within limits. In: Akazawa T, Ogihara N, Tanabe HC, Terashima H (eds) Dynamics of learning in Neanderthals and modern humans, vol 2. Springer, Heidelberg, pp 65-76
16. Vyse SA (2013) Believing in magic: the psychology of superstition, updated edn. Oxford University Press, Oxford
17. Panja T (2013) Top soccer leagues get 25% rise in TV rights sales, report says. In: Bloomberg Business. http://www.bloomberg.com/news/articles/2013-11-11/top-soccer-leagues-get-25-rise-in-tv-rights-sales-report-says. Accessed 28 Feb 2015
18. Dobson S, Goddard J (2001) The economics of football. Cambridge University Press, Cambridge
19. Dixon MJ, Coles SG (1997) Modelling association football scores and inefficiencies in the football betting market. J R Stat Soc, Ser C, Appl Stat 46(2):265-280
20. Wilson J (2013) Inverting the pyramid: the history of soccer tactics. Nation Books, New York
21. Audas R, Dobson S, Goddard J (1997) Team performance and managerial change in the English Football League. Econ Aff 17(6):30-36
22. Dawson P, Dobson S, Gerrard B (2000) Estimating coaching efficiency in professional team sports: evidence from English association football. Scott J Polit Econ 47(4):399-421
23. Audas R, Dobson S, Goddard J (2002) The impact of managerial change on team performance in professional sports. J Econ Bus 54(6):633-650
24. Bangsbo J, Peitersen B (2000) Soccer systems & strategies. Human Kinetics, Champaign
25. Hirotsu N, Wright MB (2006) Modeling tactical changes of formation in association football as a zero-sum game. J Quant Anal Sports 2(2):4
26. J-League Data site https://data.j-league.or.jp/SFTP01/. Accessed 4-6 June 2014
27. Kicker-online http://www.kicker.de/. Accessed 14-16 Aug 2014
28. Fussballdaten http://www.fussballdaten.de/. Accessed 28-31 July 2014
29. Goh KI, Barabási AL (2008) Burstiness and memory in complex systems. EPL 81:48002
30. R Core Team (2014) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. http://www.R-project.org/
31. Bates D, Maechler M, Bolker B, Walker S (2014) lme4: linear mixed-effects models using Eigen and S4. http://CRAN.R-project.org/package=lme4. R package version 1.1-7
32. Koning RH (2000) Balance in competition in Dutch soccer. J R Stat Soc, Ser D, Statist 49:419-431
33. Parent E, Rivot E (2012) Introduction to hierarchical Bayesian modeling for ecological data. CRC Press, Boca Raton
34. Henningsen A, Toomet O (2011) maxLik: a package for maximum likelihood estimation in R. Comput Stat 26(3):443-458
35. Albert J, Koning RH (2010) Statistical thinking in sports. CRC Press, Boca Raton
36. Taiji M, Ikegami T (1999) Dynamics of internal models in game players. Physica D 134(2):253-266
37. Macy MW, Flache A (2002) Learning dynamics in social dilemmas. Proc Natl Acad Sci USA 99(Suppl 3):7229-7236
38. Masuda N, Ohtsuki H (2009) A theoretical analysis of temporal difference learning in the iterated prisoner’s dilemma game. Bull Math Biol 71(8):1818-1850
39. Masuda N, Nakamura M (2011) Numerical analysis of a reinforcement learning model with the dynamic aspiration level in the iterated prisoner’s dilemma. J Theor Biol 278(1):55-62
40. Neiman T, Loewenstein Y (2011) Reinforcement learning in professional basketball players. Nat Commun 2:569
41. Bar-Eli M, Avugos S, Raab M (2006) Twenty years of “hot hand” research: review and critique. Psychol Sport Exerc 7(6):525-553
42. Bialkowski A, Lucey P, Carr P, Yue Y, Matthews I (2014) “Win at home and draw away”: automatic formation analysis highlighting the differences in home and away team behaviors. In: MIT Sloan sports analytics conference (SSAC)
43. Bialkowski A, Lucey P, Carr P, Yue Y, Sridharanand S et al. (2014) Large-scale analysis of soccer matches using spatiotemporal data. In: IEEE international conference on data mining (ICDM)
44. Stan Development Team (2014) Rstan: the R interface to Stan, version 2.5.0. http://mc-stan.org/rstan.html