Bergische Universität Wuppertal
Fachbereich Mathematik und Naturwissenschaften
Angewandte Mathematik - Numerische Analysis (AMNA)

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Matthias Ehrhardt

Zur Vorhersage von Fussballergebnissen

Mathematische Modellierung im Sport

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Die Zielgruppe sind Schüler ab der 11. Klasse.

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Beschreibung

Aus Anlass der Fussball-Weltmeisterschaft in Russland 2018 wollen wir den Fussball aus mathematischer Sicht betrachten. Wir wollen in diesem Vortrag untersuchen, wie man das Fussballspiel modellieren kann und inwieweit man das Ergebnis vorhersagen kann.

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Referenzen für den Vortrag

  1. J. Albert, R.H. Koning (eds.), Statistical Thinking in Sports, CRC PRESS, 2007.
  2. C. Anderson, D. Sally, The Numbers Game: Why Everything You Know About Football is Wrong , Penguin, 2014.
  3. P. Andersson, P. Ayton, C. Schmidt, Myths and Facts about Football: The Economics and Psychology of the World's Greatest Sport, Cambridge Scholars Publishing, 2008.
  4. M. Ausloos, A. Gadomski, N.K. Vitanov, Primacy and ranking of UEFA soccer teams from biasing organization rules, Physica Scripta 89(10), (2014).
  5. B.I.L. Benevides, S.M. dos Santos, The Football Economics: An empirical analysis, LAP LAMBERT Academic Publishing, 2016.
  6. E. Ben-Naim, N.W. Hengartner, Efficiency of competitions, Phys. Rev. E 76 (2007), 026106.
  7. E. Ben-Naim, S. Redner, F. Vazquez, Scaling in tournaments, Europhys. Lett. 77 (2007), 30005.
  8. Y. Berker, Tie-breaking in round-robin soccer tournaments and its influence on the autonomy of relative rankings: UEFA vs. FIFA regulations, European Sport Management Quarterly 14(2) (2014), 194-210.
  9. E. Bittner, A. Nussbaumer, W. Janke, M. Weigel, Self-affirmation model for football goal distributions, Europhys. Lett. 78 (2007), 58002.
  10. E. Bittner, A. Nussbaumer, W. Janke, M. Weigel, Football fever: goal distributions and non-Gaussian statistics, Eur. Phys. J. B 67 (2009) 459.
  11. C. Carling, M.A. Williams, T. Reilly, Handbook of soccermatch analysis, London: Routledge, 2005.
  12. A.C. Constantinou, N.E. Fenton, L. Jackson, H. Pollock, Bayesian networks for unbiased assessment of referee bias in Association Football, Psychology of Sport and Exercise 15(5) (2014), 538-547.
  13. C. Cotta, A.M. Mora, J.J. Merelo, C. Merelo-Molina, A network analysis of the 2010 FIFA world cup champion team play, Journal of Systems Science and Complexity 26(1) (2013), 21-42.
  14. M. Dixon, S. Coles, Modelling association football scores and inefficiencies in a football betting market, Appl. Statist. 46 (1997), 265-280.
  15. M. Dixon, M. Robinson, A birth process model for association football matches, The Statistician 47, 523 (1998).
  16. S. Dobson, J. Goddard, Persistence in sequences of football match results: a Monte Carlo analysis, European Journal of Operational Research 148 (2003), 247-256.
  17. S. Dobson, J. Goddard, The Economics of Football, Cambridge University Press, 2nd edition, 2011.
  18. J. Dowie, Why Spain should win the world cup, New Scientist 94(10) (1982), 693-695.
  19. J. Duch, J.S. Waitzman, L.A.N. Amaral, Quantifying the performance of individual players in a team activity, PLoS ONE 5(6) (2010), e10937.
  20. A. Gabel, S. Redner, Random walk picture of basketball scoring, arXiv: 11092825 (2012).
  21. D. Gembris, J. Taylor, D. Suter, Sports statistics-trends and random fluctuations in athletics, Nature 417 (2002), 506.
  22. A. Giannakos, V. Armatas, Evaluation of the goal scoring patterns in European Championship in Portugal 2004, International Journal of Performance Analysis in Sport, 6(1) (2006), 178-188.
  23. J. Greenhough, P. Birch, S. Chapman, G. Rowlands, Football goal distributions and extremal statistics, Physica A 316, 615 (2002).
  24. A. Heuer, O. Rubner, Fitness, chance and myths: an objective view on soccer results, Eur. Phys. J. B 67 (2009), 445.
  25. A. Heuer, C. Müller, O. Rubner, Soccer: is scoring goals a predictable Poissonian process?, Europhys. Lett. 89 (2010), 38007.
  26. A. Heuer, C. Müller, O. Rubner, N. Hagemann, B. Strauss, Usefulness of dismissing and changing the coach in professional soccer, PLoS ONE 6/3: e17664 (2011).
  27. A. Heuer, Der perfekte Tipp - Statistik des Fußballspiels, VCH Wiley, 2012.
  28. A. Heuer, O. Rubner, How Does the Past of a Soccer Match Influence Its Future? Concepts and Statistical Analysis, PLoS ONE 7(11): e47678 (2012).
  29. A. Heuer, O. Rubner, Towards the perfect prediction of soccer matches, arXiv:1207.4561, 2012.
  30. A. Heuer, O. Rubner, Optimizing the Prediction Process: From Statistical Concepts to the Case Study of Soccer, PLoS ONE 9(9): e104647 (2014).
  31. M. Hughes, I. Franks, Analysis of passing sequences, shots and goals in soccer, Journal of Sports Sciences 23 (2005), 509-514.
  32. W. Janke, E. Bittner, A. Nubaumer, M. Weigel, Football fever: self-affirmation model for goal distributions, Condensed Matter Physics 12(4) (2009), 739-752.
  33. A. Jarynowski, Anomalous interactions in network of Polish Football League - Life time of correlation, Wroclaw: WN, 2010 - academia.edu.
  34. G. Jurman, Seasonal Linear Predictivity in National Football Championships, arXiv:1511.06262 (2015).
  35. A. Khan, B. Lazzerini, G. Calabrese, L. Serafini, Soccer Event Detection, European Semantic Web Conference, 2018.
  36. M. Konefal, P. Chmura, E. Kowalczuk, M. Andrzejewski, J. Chmura, The impact of players' motor skills on match performance in top German Bundesliga teams, Trends in Sport Sciences 4(22) (2015) 185-190.
  37. R. Koning, Balance in competition in Dutch soccer, The Statistician 49, 419 (2000).
  38. S. Kuper, Soccernomics, HarperSport, 2014.
  39. M. Lames, Chance involvement in goal scoring in football - an empirical approach, German Journal of Exercise and Sport Research 48 (2018), 278286.
  40. A. Lee, Modelling scores in the premier league: is Manchester United really the best?, Chance 10 (1997), 15-19.
  41. M. Maher, Modelling association football scores, Statist Neerland 36 (1982) 109.
  42. J.M. Martin-Gonzalez, Y. de Saa Guerra, J.M. Garcia-Manso, E. Arriaza, T. Valverde-Estevez, The Poisson model limits in NBA basketball: Complexity in team sports, Physica A: Statistical Mechanics and its Applications 464 (2016), 182-190.
  43. G. Massiot, Quelques Problèmes de Statistique autour des processus de Poisson, Thèse Statistiques, École normale supérieure de Rennes, 2017.
  44. R. Mendes, L. Malacarne, C. Anteneodo, Statistics of football dynamics, Eur. Phys. J. B 57 (2007), 357-363.
  45. S. Mukherjee, Identifying the greatest team and captain - A complex network approach to cricket matches, Physica A: Statistical Mechanics and its Applications 391(23) (2012), 6066-6076.
  46. S. Mukherjee, Compley network Analysis in Cricket: Community Structure, Player's Role and Performance Index, Advs. Complex Syst. 16 (2013), 1350031.
  47. S. Mukherjee, Quantifying individual performance in Cricket - A network analysis of batsmen and bowlers, Physica A: Statistical Mechanics and its Applications 393 (2014), 624-637.
  48. S. Nadarajah, S. Chan, Discrete distributions based on inter arrival times with application to football data, Communications in Statistics - Theory and Methods 47(1) (2018), 147-165.
  49. T. Narizuka, K. Yamamoto, Y.Yamazaki, Statistical properties of position-dependent ball-passing networks in football games, Physica A: Statistical Mechanics and its Applications 412 (2014), 157-168.
  50. T. Narizuka, Y.Yamazaki, Statistical properties for directional alignment and chasing of players in football games, Europhysics Letters 116(6) (2017).
  51. R.N. Onody, P.A. De Castro, Complex network study of Brazilian soccer players, Physical Review E 70(3) (2004), 037103.
  52. L. Pappalardo, P. Cintia, Quantifying the relation between performance and success in soccer, Advs. Complex Syst. (2017).
  53. L. Pappalardo, P. Cintia, P. Ferragina, E. Massucco, D. Pedreschi, F. Giannotti, PlayeRank: Multi-dimensional and role-aware rating of soccer player performance, arXiv:1802.04987 (2018).
  54. L. Posch, Wie man den Ausgang der österreichischen Fußballbundesliga berechnen kann, Diplomarbeit, Universität Wien. Fakultät für Mathematik, 2011.
  55. F. Radicchi, Who is the best player ever? A complex network analysis of the history of professional tennis, PloS ONE 6: e17249 (2011).
  56. N.T. Rauscher, A Statistical Analysis of Landmark Conference Women's Soccer, Action Research Paper, Goucher College Master of Education, 2014.
  57. H.V. Ribeiro, R.S. Mendes, L.C. Malacarne, S. Picoli Jr., P.A. Santoro, Dynamics of tournaments: the soccer case - A random walk approach modeling soccer leagues, The European Physical Journal B 75(3) (2010), 327-334.
  58. H.V. Ribeiro, S. Mukherjee, X.H.T. Zeng, The Advantage of Playing Home in NBA: Microscopic, Team-Specific and Evolving Features, PLoS ONE 11(3): e0152440 (2016).
  59. D. Riedl, A. Heuer, B. Strauss, Why the Three-Point Rule Failed to Sufficiently Reduce the Number of Draws in Soccer: An Application of Prospect Theory, Journal of Sport and Exercise Psychology 37(3) (2015).
  60. H. Rue, O. Salvesen, Prediction and retrospective analysis of soccer matches in a league, The Statistician 49 (2000), 399-418.
  61. J. Sannemo, S. Lindholm, Comparing the Predictive Power of Past Results Between Soccer Leagues, Bachelor Degree Project in Technology, KTH, School of Computer Science and Communication (CSC), Stockholm, 2016.
  62. A.L. Schaigorodsky, J.I. Perotti, O.V. Billoni, Memory and long-range correlations in chess games, Physica A: Statistical Mechanics and its Applications 394 (2014), 304-311.
  63. A.L. Schaigorodsky, J.I. Perotti, O.V. Billoni, A Study of Memory Effects in a Chess Database, PLoS ONE 11(12): e0168213 (2016).
  64. R. da Silva, M.H. Vainstein, L.C. Lamb, S.D.Prado, A simple non-Markovian computational model of the statistics of soccer leagues: Emergence and scaling effects, Computer Physics Communications 184(3) (2013), 661-670.
  65. R. da Silva, S.R. Dahmen, Universality in the distance between two teams in a football tournament, Physica A: Statistical Mechanics and its Applications 398 (2014), 56-64.
  66. C. Sire, S. Redner, Understanding baseball team standings and streaks, Eur. Phys. J. B 67 (2009), 473.
  67. K. Staufenbiel, D. Riedl, B. Strauss, Learning to be advantaged: The development of home advantage in high-level youth soccer, International Journal of Sport and Exercise Psychology 16(1) (2018), 36-50.
  68. S. Stigler, Statistics on the Table. The History of Statistical Concepts and Methods, Harvard University Press, 2002.
  69. B. Strauß, N. Hagemann, F. Loffing, Die Drei-Punkte-Regel in der deutschen 1. Fußballbundesliga und der Anteil unentschiedener Spiele, (Eine Replik auf den Beitrag von Dilger und Geyer 2007), Sportwissenschaft 39(1) (2009), 16-22.
  70. N. Tax, Y. Joustra, Predicting The Dutch Football Competition Using Public Data: A Machine Learning Approach, Transactions on Knowledge and Data Engineering.
  71. M. Tolan, So werden wir Weltmeister - Die Physik des Fussballspiels, Piper, 2010.
  72. K. Tuzilova, Pre-play interactive trading in tennis: probability to win a match in Grand Slam tournaments, Master Thesis, Universidade de Évora, 2017.
  73. J. Wesson, The Science of Soccer, Institute of Physics Publishing, 2002.
  74. F. Wunderlich, D. Memmert, Analysis of the predictive qualities of betting odds and FIFA World Ranking: evidence from the 2006, 2010 and 2014 Football World Cups, Journal of Sports Sciences 34(24) (2016), 2176-2184.
  75. Y. Yamamoto, K. Yokoyama, Common and unique network dynamics in football games, PLoS ONE 6(12) (2011), e29638.

Links zum Vortrag


University of Wuppertal
Faculty of Mathematics and Natural Sciences
Department of Mathematics
Applied Mathematics & Numerical Analysis Group

Last modified: 06/16/2005 16:16:24   Disclaimer   ehrhardt@math.uni-wuppertal.de