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Talent Development, Performance Potential and Computer Support in Sports

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A paper presented at IACSS09

A paper presented at IACSS09

Published in: Sports, Business
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  • 1. Mathematics and Sports: an Optimal Line Up Prof.dr. Gerard Sierksma University of Groningen ORTEC-TeamSupportSystems August, 2009
  • 2.
          • Intro Filmpje
  • 3. Comparing Athlete’s Performances the influence of technology
  • 4. Athletic performance + technological progress
  • 5. Tight fit clothing Indoor rinks Klapskate Artificial ice rinks Progress of the World Records
  • 6. From absolute times to time differences - Times decrease -Differences become smaller
  • 7.
    • The Competition Crisis
    • The mutual differences between athlete`s performances
    • become smaller and smaller .
    • Sometimes the differences are not measurable anymore:
    • they are within the error margins.
    • Some examples:
      • Was the fifth gold medal of Michael Phelps in China indeed gold?
      • If Simon Kuipers and Shani Davis would have skated in different pairs, then nobody would have ‘seen’ that Kuipers was the actual winner;
      • What happens exactly when skating times are permanently adjusted after the race??
      • Will the Olympic 1000m get the right gold medal winner???
  • 8. Davis Kuipers
  • 9.
    • Talent Tracking
    • Performance Potential Development
    • PerPot Tracking
    • and
    • Line up Formation
    • Computer Support
  • 10.
    • Two types of qualities/competencies:
    • Objectively measurable ( tangible ), e.g.,
      • VO2 max
      • Body-mass index
      • Fat percentage
      • Et cetera
    • Subjectively measurable ( intangible ), e.g.,
    • - Stress resistency
    • - Leadership
    • - Et cetera
  • 11.
    • For each attribute
    • target scores
    • per discipline need to be determined.
      • The estimation of the ‘target scores’ is carried out with real data and regression analysis .
  • 12. Fit score of an athlete w.r.t. an attribute is a number that expresses the relationship between that attribute and the expected/target.
  • 13. COACH & SCOUT ASSISTANT Collecting and analyzing data concerning development of skills and competences of individual players with respect to the team. PerPot graphs Line up
  • 14. DEMO CSA Pro
  • 15.
    • Subjectivity of the scores
    • Differences of Opinion:
    • Relevant or Irrelevant?
  • 16. KNSB Olympic Selection System
  • 17.
    • Which speed skaters (women/men) will represent the Netherlands at the Olympic Winter Games in 2010?
  • 18. Objective
    • Maximize the number of (gold?) medals.
  • 19. Model S1 S2 S3 Si 500m (4) 1000m (4) 1500m (4) 5000m (3) 10000m (3) Probability of winning … Skaters Disciplines
  • 20. Model data
    • Parameters
    • C ij = prob. of winning … of skater i on distance j.
    • Decision variables:
    • X ij = 1 skater i starts on distance j;
    • 0 else.
    • Z i = 1 skater i is selected;
    • 0 else.
  • 21. Integer Linear Programming Model
  • 22.
    • Effectivity in Action
    • measuring during the contest
    • on-line computer support
  • 23. Effectivity in Action
    • How can we determine the effectiveness of a player and the team?
    • What do we mean by effectiveness?
    • How to measure?
    • What is the purpose and the objective?
  • 24. Effectivity in Action
    • The effectiveness of an action of a player
    • is the rate at which
    • - the player enables a teammate to make a scoring- oriented next action, and
    • - an opponent player is prevented from making a scoring- oriented action.
  • 25. Soccer
    • The objective is scoring and preventing the opponent from scoring.
    • Action on the ball are crucial!
    • What about off-the-ball actions?
    • They are less important compared e.g. with Amer. Football.
  • 26.
    • The system
    • Effectivity in Action
    • collects and valuates all ‘ around-the-ball ’ actions.
    • The result is a graph that depicts real time the course of the effectiveness.
  • 27.  
  • 28.  
  • 29.  
  • 30.  
  • 31.  
  • 32.
    • Effectivity in Action
    • The Robben wissel
  • 33. Netherlands – England August 12, 2009
  • 34.  
  • 35.  
  • 36.  
  • 37.  
  • 38. Duitsland – Nederland 2-1 1974
  • 39. 1 2 3 4 5 6
  • 40.  
  • 41. Intuition is the engine of creativity It puts the ‘nose in the right direction’ for motivating the team players; knowing how far a team player can improve on his/her qualities; finding the right new team player.
  • 42.
    • University of Groningen, The Netherlands
    • Ruud Koning, prof. of sport economics
    • Elmer Sterker, prof. of economics, performance analysis
    • Gerard Kuper, assoc. prof. econometrics , return on investment
    • Koen Lemmink, assoc. prof., movement studies
    • Gerard Sierksma, prof., quantitative logistics and sports
    • Special programs for top sport students.
  • 43.
    • Be Quick filmpje
  • 44. What is the objective? - short - long - cheap - fast
  • 45. Laserstraal boort elke 15 sec. 3036 gaatjes in computerprintplaatjes. Probleem: Wat is de snelste route? Optimale oplossing onbekend!
  • 46. Elke dag vliegen helicopters naar de 51 boorplatforms op de Noordzee om werknemers te vervangen. Probleem: Minimaliseer het aantal te vliegen zeemijlen. Optimale oplossing niet bekend!
  • 47. … .. how many tours are there? Locations Tours 3 1 4 3 5 12 6 60 7 360 8 2520 9 20160 10 181440 11 1814400 12 19958400 13 239500800 20 60822550204416000 50 30414093201713378043612608166064768844377641568960512000000000000 ….. a lot!
  • 48. Practical problems cannot wait for scientist to find the solution.
  • 49. When is a problem hard and when is it easy ? ‘ Paradox’: Very difficult decision problems are ‘ easy’ to solve!! Complexity Theory
  • 50.
    • What has this all to do with team composition?
  • 51. Coach & Scout Assistant
    • Coach & Scout Assistant selects an optimal team of players from a large selection.
    • The number of possibilities to choose a line-up of 11 persons from a group of, say, 26 players is 308,403,583,488,000 big number!
    • Each choice has a certain (team-)value.
    • The “eleven” with the highest team value is the optimal team.
    • ………………………………………… ..……… How to find?
  • 52. Gasunie N.V. Nederland
    • Multiple Team Problem :
    • Design a (given) number of teams, where the team
    • members are selected from one group of
    • persons (applicants), with team values
    • as close as possible to given target values.
    • Multi-objective optimization : minimize the
    • deviations from the team targets.
  • 53. Last minute changes If a player is not mentally fit, then the coach changes its score on ‘mental fitness’ to a lower number. Within seconds Coach & Scout Assistant presents the new line-up together with the loss in the team score.
  • 54.  
  • 55.  
  • 56.  
  • 57. More difficult problems
    • e.g. heptathlon and decathlon training schedules
  • 58.
    • Three main functionalities:
    • Talent Track System:development of each player on all positions;
    • Scouting: surplus/financial value, and best position of scouted players;
    • Line up: optimal team composition.
  • 59. Further applications : Volleyball Hockey Rowers-8 Amer. Football Speed skating Decathlon Management teams
  • 60. Coach & Scout Assistant - Screenshots
  • 61.  
  • 62.  
  • 63.  
  • 64. Gould’s Hypothesis 1986
    • Due to an increase of the maturity of the sport over time, athletes will perform ‘better’ and the variance between the performances will decrease.
  • 65.  
  • 66. Best Skaters All Times, man Name Country Score Best four years 1 Eric Heiden USA -0.25 1979 1978 1980 1977 2 Ard Schenk NED -0.24 1972 1973 1971 1967 3 Johann Olav Koss NOR -0.19 1994 1991 1993 1990 4 Oscar Mathisen NOR -0.15 1912 1914 1908 1913 5 Gianni Romme NED -0.14 1998 2000 2003 2002 6 Jaap Eden NED -0.11 1896 1895 1893 1894 7 Hjalmar Andersen NOR -0.11 1951 1952 1950 1954 8 Ivar Ballangrud NOR -0.08 1936 1926 1930 1929 9 Rintje Ritsma NED -0.05 1995 1998 1996 1993 10 Clas Thunberg FIN -0.04 1925 1924 1929 1931
  • 67. Best Skaters All Times, woman Name Country Score Best four years 1 Gunda Niemann GER -0.46 1995 1991 1996 1998 2 Karin Enke GDR -0.41 1986 1987 1984 1980 3 Andrea Mitscherlich GDR -0.24 1985 1984 1987 1983 4 Lidia Skoblikova URS -0.16 1963 1964 1960 1962 5 Claudia Pechstein GER -0.12 2000 1998 1994 2001 6 Anni Friesinger GER -0.10 2005 2004 2001 2002 7 Stien Kaiser NED -0.08 1967 1972 1971 1965 8 Inga Artamonova URS -0.08 1965 1958 1962 1957 9 Cindy Klassen CAN -0.06 2006 2005 2003 2001 10 Natalya Petrusyova URS -0.04 1981 1982 1980 1979
  • 68.  
  • 69. Tournametsooien
    • Prestaties op de volgende toernooien zijn momenteel bepalend:
    • Internationale toernooien;
    • Nationale toernooien;
    • Nationale selectie/skate-offwedstrijden.
  • 70. Bepalen van de winkans
    • Resultaten internationale toernooien.
    • Statistische analyse, waarmee de kans bepaald wordt dat een schaatser bij de beste 3 zit. (of bij de beste 4, 5, 6, …)
    • Deze analyse is gebaseerd op onderlinge verschillen in de gereden schaatstijden.
  • 71. TURIJN 2006 Olympische Spelen
    • Wedstrijden vanaf WChSD 2005
    • OKT ook meegenomen
  • 72. Olympische Schaatploeg Turijn 2006 KNSB Selectie /////////////////////////////////////////////////////////// Erben Wennemars Carl Verheijen Simon Kuipers Stephan Groothuis Simon Kuipers Sven Kramer Beorn Nijenhuis Bob de Jong Jan Bos 10k 5k 1500m 1000m 500m DSSselectie Beorn Nijenhuis ///////////////////////////////////////////////////////////// Erben Wennemars Carl Verheijen Mark Tuitert Stephan Groothuis Simon Kuipers Sven Kramer Gerard v Velde Bob de Jong Jan Bos 10k 5k 1500m 1000m 500m
  • 73.
    • Globally there are two types of attributes:
    • “ The more, the better”,
    • and “The less the worse”;
      • “ The more deviating from the target score, the worse”.
  • 74. PerPot graph of athlete i on the 500m supply demand PerPot scores
  • 75.
    • General problem:
      • What is the relationship between
      • the performance of an individual athlete
      • and
      • the performance of (groups of) athletes from the same discipline?
      • Team and time (rankings),
  • 76. Wiskundige aanpak
    • Maximaliseren van
    • de winkans op goud, of
    • de winkans op medailles, of
    • nog iets anders,
    • op basis van behaalde resultaten.
    • Wij hebben voorlopig gekozen voor ‘de winkans op medailles’.

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