A Vision for Performance Analysis


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Keynote Address at IACSS09

Published in: Technology, Sports

A Vision for Performance Analysis

  1. 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. 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. 3. Framework Performance Analysis
  4. 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. 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. 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. 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. 8. Vision I: Better Theories Problems
  9. 9. Complexity and dynamics I Better theories Abilties and skills Perfor- mance Training
  10. 10. Game Sports Better theories
  11. 11. Complexity and dynamics II Better theories Cognition Motor Response Perception
  12. 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. 13. Vision I: Better Theories Example: Relative Phase
  14. 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. 15. Relative phase Better Theories
  16. 16. Relative phase Better Theories
  17. 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. 18. Team’s centers in x-direction Relative Phase Football Better Theories
  19. 19. Team’s centers in x-direction, time window Relative Phase Football Better Theories
  20. 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. 21. Vision I: Better Theories Vision
  22. 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. 23. Recurrence analysis in Football Better Theories 00:00 15:00 30:00 45:00 00:00 15:00 30:00 45:00
  24. 24. Vision II: More support for practice Qualitative Game Analysis
  25. 25. Coupling of training and competition Description Training Analysis Transfer to training Competition More Support
  26. 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. 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. 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. 29. Vision II: More support for practice Tactical video training
  30. 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. 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. 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. 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. 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. 35. Vision II: More support for practice Vision
  36. 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. 37. Vision III: Improved technologies <ul><ul><li>General development </li></ul></ul>
  38. 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. 39. Vision III: Improved technologies <ul><ul><li>Example: Image Recognition </li></ul></ul>
  40. 40. Image recognition in tennis Image Recognition
  41. 41. Image recognition in canoeing Image Recognition
  42. 42. Image recognition in swimming Image Recognition
  43. 43. Image recognition in football Image Recognition
  44. 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. 45. Heatmaps Heatmap Materazzi, FIFA-Final 2006 Image Recognition
  46. 46. Heatmaps Heatmap Gallas, FIFA-Final 2006 Image Recognition
  47. 47. Relative Positions Relative Positions Buffon, FIFA-Final 2006 Image Recognition
  48. 48. Relative Positions Relative Positions Pirlo, FIFA-Final 2006 Image Recognition
  49. 49. Middle Right Left Direction Toni Totti Camoranesi Gattuso Zambrotta Cannavaro Materazzi Grosso Perrotta Pirlo Buffon
  50. 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. 51. Vision III: Improved technologies <ul><ul><li>Vision </li></ul></ul>
  52. 52. Silhouette detection shot put Image Recognition
  53. 53. Silhouette detection Gymnastics I Image Recognition
  54. 54. Silhouette detection Gymnastics II Image Recognition
  55. 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. 56. Final Remarks
  57. 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. 58. Vielen Dank!