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A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
A Vision for Performance Analysis
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A Vision for Performance Analysis

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Keynote Address at IACSS09

Keynote Address at IACSS09

Published in: Sports, Technology
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  • 1. A Vision for Performance Analysis in Elite Sports Prof. Dr. Martin Lames Institute for Sports Science, University of Augsburg IACSS09, Canberra, Australia September, 23 rd – 26 th 2009
  • 2. <ul><li>Framework </li></ul><ul><li>Vision I: Better theories / models </li></ul><ul><li>Vision II: More support for practice </li></ul><ul><li>Vision III: Improved technologies </li></ul><ul><li>Final Remarks </li></ul>Program
  • 3. Framework Performance Analysis
  • 4. Tasks of performance analysis <ul><li>Theoretical performance analysis </li></ul><ul><ul><li>Explains structure of sports performances by general laws, e.g. </li></ul></ul><ul><ul><ul><li>Relation between performance and performance indicators (parts of performance, skills & abilities) </li></ul></ul></ul><ul><ul><ul><li>Search for new models to explain internal functioning of sports performances </li></ul></ul></ul><ul><li>Practical performance analysis </li></ul><ul><ul><li>Direct support for training and coaching, e.g. </li></ul></ul><ul><ul><ul><li>Performance analysis in training and competition </li></ul></ul></ul><ul><ul><ul><li>Implementation of new technologies in practice </li></ul></ul></ul>Framework
  • 5. Why distinguish? <ul><li>Theoretical and practical performance analysis are carefully to be distinguished </li></ul><ul><ul><li>Require different research strategies, samples, designs, methods, approaches </li></ul></ul><ul><ul><li>Are both necessary to provide full scientific support </li></ul></ul><ul><ul><li>Depend on each other: </li></ul></ul><ul><ul><ul><li>Practical PA is the most valuable source for hypotheses </li></ul></ul></ul><ul><ul><ul><li>Theoretical PA improves the impact of scientific support </li></ul></ul></ul>Framework
  • 6. Role of Computer Science <ul><li>Theoretical performance analysis </li></ul><ul><ul><li>Model-building and simulation </li></ul></ul><ul><ul><li>Knowledge generation by AI-Methods </li></ul></ul><ul><li>Practical performance analysis </li></ul><ul><ul><li>Technological support for performance analysis </li></ul></ul><ul><ul><li>Knowledge how to implement new tools in practice </li></ul></ul>Framework
  • 7. Life cycle of technological innovations <ul><ul><li>Demonstrate potential for sport </li></ul></ul>Framework <ul><ul><li>Invention </li></ul></ul><ul><ul><li>Routine procedure </li></ul></ul><ul><ul><li>Pilot implementation in practice </li></ul></ul>Computer Science <ul><ul><li>Sport </li></ul></ul>
  • 8. Vision I: Better Theories Problems
  • 9. Complexity and dynamics I Better theories Abilties and skills Perfor- mance Training
  • 10. Game Sports Better theories
  • 11. Complexity and dynamics II Better theories Cognition Motor Response Perception
  • 12. Problems we face <ul><li>Nature of our subject: </li></ul><ul><ul><li>Complex & dynamic </li></ul></ul><ul><ul><li>Non-linear phenomena </li></ul></ul><ul><ul><li>Games: Interaction </li></ul></ul><ul><li>Need for adequate theories! </li></ul>Better theories
  • 13. Vision I: Better Theories Example: Relative Phase
  • 14. Relative phase <ul><li>Players in net/wall games perform “cycles” from stroke to stroke </li></ul><ul><li>Relative phase describes the relation between those cycles, e.g. in-phase or anti-phase, it is thus a model of (positional) interaction during a rally </li></ul><ul><li>Hypotheses: Stable phases of rally = stable relative phase Instable phases of rally = instable relative phase </li></ul><ul><ul><li>Stable: Normal game, no pressure </li></ul></ul><ul><ul><li>Instable: Create pressure, go for a winner, force an error, perturbation </li></ul></ul>Better Theories
  • 15. Relative phase Better Theories
  • 16. Relative phase Better Theories
  • 17. Relative phase <ul><li>In invasion games relative phase measures the degree of coupling of </li></ul><ul><ul><li>teams </li></ul></ul><ul><ul><li>sub-units of a team (e.g. dyads) </li></ul></ul><ul><ul><li>player and opponent </li></ul></ul><ul><li>It characterizes an important aspect of tactical behavior </li></ul>Better Theories
  • 18. Team’s centers in x-direction Relative Phase Football Better Theories
  • 19. Team’s centers in x-direction, time window Relative Phase Football Better Theories
  • 20. Summary Relative Phase <ul><li>Research: </li></ul><ul><ul><li>Squash: McGarry et al. (1999); McGarry & Walter (2007) </li></ul></ul><ul><ul><li>Tennis: Palut & Zanone (2005); Lames & Walter (2006) </li></ul></ul><ul><ul><li>Basketball: McGarry (subm.) </li></ul></ul><ul><ul><li>Football: Lames, Ertmer & Walter (2008); Lemmink & Frenken (subm.) </li></ul></ul><ul><li>Result: </li></ul><ul><li>Relative Phase models </li></ul><ul><ul><li>Positional interaction of players in net/wall games </li></ul></ul><ul><ul><li>Coupling of teams, parts of teams and dyads </li></ul></ul><ul><li>Perspectives: </li></ul><ul><ul><li>Perturbation analysis </li></ul></ul><ul><ul><li>New approach for performance analysis </li></ul></ul><ul><ul><li>System dynamic modelling of sports </li></ul></ul>Better Theories
  • 21. Vision I: Better Theories Vision
  • 22. Vision <ul><li>Greater awareness of problem </li></ul><ul><ul><li>Complexity, dynamics, interaction are widely perceived as phenomena to be delt with </li></ul></ul><ul><ul><li>Insight in deficiency of linear models </li></ul></ul><ul><li>New approaches </li></ul><ul><ul><li>Dynamical systems theory </li></ul></ul><ul><ul><li>Game Sports: Field theory </li></ul></ul><ul><li>Challenges </li></ul><ul><ul><li>Not just fancy applications of new tools, but creation of substantial knowledge </li></ul></ul><ul><ul><li>Not just „fa çon de parler”, but substantial underpinnings </li></ul></ul>Better Theories
  • 23. Recurrence analysis in Football Better Theories 00:00 15:00 30:00 45:00 00:00 15:00 30:00 45:00
  • 24. Vision II: More support for practice Qualitative Game Analysis
  • 25. Coupling of training and competition Description Training Analysis Transfer to training Competition More Support
  • 26. Steps of Qualitative Game Analysis More Support Transfer in training Video training Peer-debriefing Qualitative main analysis (Thick Description) Peer-briefing Competition Recording/ Realtime diagnostics
  • 27. Concept of social intervention Social intervention More Support Theory Practice Coach & athletes Scientific intervention Akt 1 Akt 2 Akt 3 Akt 4 Akt 5
  • 28. Practical experiences <ul><li>Beach volleyball: </li></ul><ul><ul><li>Sydney 2000 </li></ul></ul><ul><li>Handball: </li></ul><ul><ul><li>U19 WC 2007 </li></ul></ul><ul><ul><li>U17 EC 2008, WC 2009 </li></ul></ul><ul><ul><li>German 1st league & 3rd league </li></ul></ul><ul><li>Football: </li></ul><ul><ul><li>German 1st league & 2nd league </li></ul></ul><ul><ul><li>Women: German Champion </li></ul></ul><ul><ul><li>Personal Tactics Advising </li></ul></ul>More Support
  • 29. Vision II: More support for practice Tactical video training
  • 30. Data base <ul><li>German national handball youth team (16-18) </li></ul><ul><li>Intervention time 2006-2009 </li></ul><ul><li>Competitions and training camps covered: 27 in last three years </li></ul><ul><li>Highlights: </li></ul><ul><ul><li>European Championships 2006  9th place </li></ul></ul><ul><ul><li>World Championships 2007  2nd place </li></ul></ul><ul><ul><li>European Championships 2008  Champion </li></ul></ul><ul><ul><li>World Championships 2009  5th place </li></ul></ul>More Support
  • 31. Theoretical framework More Support Model of mass communication (Merten, 1994) M 1 M 2 M 3 … Messages, game philosophy, feedback Recipient Sender M x I 1 I 2 I 3 … Internal context Pre-knowledge, motivation I x E 1 E 2 E 3 … External context Home club game situation E x Information triple
  • 32. Variants of video training <ul><li>Social configuration </li></ul><ul><ul><li>team, small groups, single players </li></ul></ul><ul><li>Teaching concept </li></ul><ul><ul><li>cognitive-instructive, self-organised, mixture </li></ul></ul><ul><li>Presentation concepts </li></ul><ul><ul><li>e.g. selection of positive vs. negative scenes, slow-motion </li></ul></ul><ul><li>Sequential study with succession of variants, qualitative assessment of effectiveness </li></ul>More Support
  • 33. Effectiveness of video training More Support <ul><li>Assessment of effectiveness </li></ul><ul><ul><li>communicative validation with stakeholders </li></ul></ul><ul><ul><li>strategy-tactics-comparison with external experts </li></ul></ul><ul><ul><li>qualitative interviews with players and coaches </li></ul></ul><ul><ul><li>assessment of recall and recognition (video-testing) </li></ul></ul>
  • 34. Applications of video training <ul><li>Video training with teams </li></ul><ul><li>Video training with groups of players </li></ul><ul><li>Individual video training </li></ul><ul><li>Training interventions </li></ul><ul><li>Motivation enhancing videos before match </li></ul><ul><li>Half-time interventions </li></ul><ul><li>Team scouting </li></ul><ul><li>Player scouting </li></ul><ul><li>Personal Tactics Advising </li></ul><ul><li>Video tests for efficiency of tactical instruction </li></ul><ul><li>Intranet-platform </li></ul><ul><li>Instruction of coaches and referees </li></ul>More Support
  • 35. Vision II: More support for practice Vision
  • 36. Vision <ul><li>Improvement in practical support </li></ul><ul><ul><li>Better understanding of interventions </li></ul></ul><ul><ul><li>Insight into the qualitative nature of diagnostics </li></ul></ul><ul><ul><li>Methodological flexibility </li></ul></ul><ul><ul><li>Assessment and control of effectiveness of interventions and learning processes </li></ul></ul><ul><ul><li>Towards a Theory of coaching </li></ul></ul><ul><ul><li>Towards a new profession: Sports Analyst </li></ul></ul>More Support
  • 37. Vision III: Improved technologies <ul><ul><li>General development </li></ul></ul>
  • 38. General developments <ul><li>Technological development drives progress in sports </li></ul><ul><ul><li>Miniaturisation </li></ul></ul><ul><ul><li>Cost reduction </li></ul></ul><ul><ul><li>Power increase </li></ul></ul><ul><li>Things are now ubiquitious we didn‘t dare to dream of 15 years ago and … </li></ul><ul><li>this is going to continue!!! </li></ul>New Technologies
  • 39. Vision III: Improved technologies <ul><ul><li>Example: Image Recognition </li></ul></ul>
  • 40. Image recognition in tennis Image Recognition
  • 41. Image recognition in canoeing Image Recognition
  • 42. Image recognition in swimming Image Recognition
  • 43. Image recognition in football Image Recognition
  • 44. Automated model recognition positions actions situations tactics assessments <ul><li>Automated tracking of positions by image processing </li></ul><ul><li>Bottom-up inferences by methods of artificial intelligence Beetz et al. (2009). ASPOGAMO. IJCSS Vol. 8, Ed.1 </li></ul>Image Recognition
  • 45. Heatmaps Heatmap Materazzi, FIFA-Final 2006 Image Recognition
  • 46. Heatmaps Heatmap Gallas, FIFA-Final 2006 Image Recognition
  • 47. Relative Positions Relative Positions Buffon, FIFA-Final 2006 Image Recognition
  • 48. Relative Positions Relative Positions Pirlo, FIFA-Final 2006 Image Recognition
  • 49. Middle Right Left Direction Toni Totti Camoranesi Gattuso Zambrotta Cannavaro Materazzi Grosso Perrotta Pirlo Buffon
  • 50. Middle Right Left Direction Toni Totti Camoranesi Gattuso Zambrotta Cannavaro Materazzi Grosso Perrotta Pirlo Buffon Perrotta Pirlo Gattuso Camoranesi Totti Toni Grosso Materazzi Cannavaro Zambrotta Buffon
  • 51. Vision III: Improved technologies <ul><ul><li>Vision </li></ul></ul>
  • 52. Silhouette detection shot put Image Recognition
  • 53. Silhouette detection Gymnastics I Image Recognition
  • 54. Silhouette detection Gymnastics II Image Recognition
  • 55. Silhouette Detection <ul><li>Silhouette detection </li></ul><ul><ul><li>Based on video data (multiple perspectives) </li></ul></ul><ul><ul><li>Fits 3D-Model </li></ul></ul><ul><ul><li>53df stick figure is underlying </li></ul></ul><ul><ul><li>No markers required </li></ul></ul><ul><li>Perspectives </li></ul><ul><ul><li>Full kinematic description of movements available to video recording </li></ul></ul><ul><ul><li>Training and competition </li></ul></ul><ul><ul><li>Highly automated, fast processing </li></ul></ul>Image Recognition
  • 56. Final Remarks
  • 57. Good Perspectives <ul><li>Theoretical performance analysis </li></ul><ul><ul><li>Progress in theoretical modeling </li></ul></ul><ul><ul><li>Capability to develop formal models with practical relevance </li></ul></ul><ul><li>Practical performance analysis </li></ul><ul><ul><li>Computer-based coaching gives real-time support </li></ul></ul><ul><ul><li>Wide-spread in top-level sports </li></ul></ul><ul><ul><li>Professional branch: game analysists </li></ul></ul><ul><li>Technological developments </li></ul><ul><ul><li>Continuous stream of challenges </li></ul></ul><ul><ul><li>Unforeseeable increase of impact </li></ul></ul>Final Remarks
  • 58. Vielen Dank!

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