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