“Technique, style and performance in sport: biomechanical variations on a theme?” By Carlton Cooke, Chris Low, Nassos Bissas, Giorgos Paradisis & Barney Wainwright (Carnegie Research Centre for Sport Performance) The presentation Defining skill, technique, style and constraints (High Jump) Analysing technique – 3 main steps Biomechanical Models – understanding variations in technique and style in performance (Kayak paddling) Variations in response to training (Sprint running) Dynamical systems theory (Gymnastics) The Uncontrolled Manifold (Football) Conclusion Definitions: Biomechanical classifications of movement • General Movement Patterns (e.g. Jumping) • Skill (e.g. High Jump) • Technique (e.g. Fosbury Flop) • Style (Individual variation in the performance of Technique) • Primary Mechanical Purpose (height of clearance, Objective/Outcome/Performance) (Kreighbaum & Barthels, 1996) Mechanics of the Fosbury flop Approach velocity is a predictor of height jumped Hip height at take off is a predictor of height jumped Why do some international high jumpers “buckle” ? (i.e. not even leave the ground) Not all Fosbury flops are the same (variation) Dapena (1980a and b) Medicine and Science in Sports and Exercise Factors effecting “Style” in Fosbury flop Factors effecting “Style” i.e. constraints • Leg strength and power • Flexibility • Height • Weight • Body composition • Individual constraints are variable between jumpers • What about variations within a jumper between attempts? Dapena (1980a and b) Medicine and Science in Sports and Exercise Analysis of technique 3 main steps: observation - several aids developed evaluation - fault diagnosis intervention - poorly addressed Observation Phase Analysis - descriptive process to divide movements into constituent parts Temporal Analysis - builds on phase analysis by specifying the timing of a movement Critical Features - components of movement that are essential to the performance of a skill Evaluation Coaching Manuals - descriptive templates based on expert performance Diagnosis of faults determined by deviations from the template Aware of variations in performance level and individual differences Criticisms of this approach based on premise that success and high technical skill have a reciprocal relationship (Hay & Reid, 1982; Bartlett, 2007) Hierarchical or deterministic models The model must be based upon fundamental mechanics that govern the movement, and each factor must be completely determined by those factors that appear in the level directly below it. (Glazier et al., 2007; Hay & Reid, 1982) Novel Sprint Running Training (uphill-downhill ramp 3 degree slope) () DCM () () Bissas and Paradisis (PhDs) () Hierarchical Model of Sprint Running Running Speed Step Length DCM TO Step Rate Step Time DCM TD Flight Distance Physique Posture knee angle () hip angle () shank angle () trunk angle () Contact Time Eccentric Concentric Acceleration (g) Height TO Air Resistance Speed TO thigh angle () Velocity TD Velocity change Force Exerted Paradisis and Cooke (2001) Journal of Sports Sciences Flight Time Time Forces Act -1 Running Velocity (m.s ) Group changes in max running velocity (MRV) Bissas PhD 0.6 0.5 0.4 0.3 0.2 0.1 0 -0.1 -0.2 -0.3 ** **P<0.01 Training Control Group changes in stride rate Stride Rate (Hz) 0.3 0.2 0.1 0 -0.1 ** -0.2 **P<0.01 Training Bissas PhD Control Individual variation in response to training 25.0% Improvement Post-Pre Training Changes (%) 20.0% 15.0% 10.0% 5.0% 0.0% -5.0% Decline -10.0% N=10 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 MRV 4.0% 2.4% -1.4% 6.4% 2.4% 3.4% 5.5% 9.1% 11.1% 1.9% 2.5% SR 3.6% 6.3% 0.0% 3.2% 2.7% 3.5% 3.0% 6.6% 10.2% 0.0% 0.0% SL 0.5% -3.8% -1.4% 3.2% -0.5% 0.0% 2.3% 2.2% 0.7% 1.9% 2.5% CT 4.7% 6.7% 6.7% 5.9% 5.6% 6.3% 6.3% 0.0% 0.0% 0.0% 5.9% FT 2.3% 5.6% -5.9% 0.0% 0.0% 0.0% 0.0% 11.1% 18.8% 0.0% -7.1% Participants Bissas PhD Dynamical Systems Theory Motor control theory that looks at how multiple degrees of freedom are controlled (Utley & Astill, 2008) The athlete is considered as a complex, biological system (Davids et al., 2008) Consider the system as a whole, where the parts of the system interact and affect each other. Dynamical Systems Theory Functional role of variability in analysis of movement DST contrasts with information processing view that variability is noise in the sensorimotor system that needs to be removed In DST concept of representative trial does not exist Dynamical Systems Theory (Newell 1986 model) Coherent framework for understanding how co-ordination patterns emerge during goal directed behaviour Environmental Perception Functional co-ordination pattern selected under Action constraint Organismic Task (Davids et al., 2008) Participant and performance A former member of the men’s national gymnastics squad performed one trial of 12 continuous backward longswings on the Men’s Horizontal Bar at self-selected speeds in the following order: 3 normal, 3 fast, 3 slow, 3 fast He then completed a second trial performing a Kovacs. All trials were performed on a standard competition high bar. Data capture Qualisys Capture System Capture freq:150Hz Ave. Residual of cameras < 1mm S.D. Wand length 2mm Data Processing Motion data into Visual3D Butterworth filter with cut-off at 10Hz Calculated planar angles at shoulder and hip wrist shoulder hip knee Mean RMSD values between Kovacs Prep & Action and Longswings performed at different self-selected speeds θS (°) ωS (°s-1) θH (°) ωH (°s-1) Kovacs Prep Kovacs Action Kovacs Prep Kovacs Action Kovacs Prep Kovacs Action Kovacs Prep Kovacs Action Normal 5 6 46 61 7 22 70 183 Fast 1 5 6 47 56 7 19 54 156 Kovacs Prep = initial longswing Kovacs Action = longswing before Kovacs Slow 5 7 56 62 7 23 73 183 Fast 2 6 5 47 51 5 18 41 151 Kovacs and variations in longswings The lower RMSD values for the fast longswings indicates that varying the speed of the longswing can lead to greater similarities between the longswing action and the Kovacs skill. Functional variability of the longswing action may therefore be useful in the acquisition of the Kovacs, suggesting that longswing progressions should encourage the development of variable longswing movements. Interestingly, there were greater similarities in the hip joint motion observed in the fast longswings performed after a series of slower longswings, suggesting that sequence of speed variation may be important. Low and Cooke (2008) Conclusions on Kovacs & longswings Sequential variation in the speed of longswings induced movements that have a greater similarity to those movements associated with a high level skill. Functional variability in the longswing action may therefore be beneficial to gymnasts in terms of acquisition of high level skills, such as the Kovacs. Low and Cooke (2008) What is next? Chris will keep working on gymnastics New PhD student looking at intra subject variability in football kicking Both will be looking at the possibility of partitioning variability into functional and dysfunctional variation using a quantitative technique known as the “uncontrolled manifold” (UCM) (Latash et al, 2003). The Uncontrolled Manifold (UCM) The UCM establishes if trial-to-trial variability of elemental variables shows a stability in performance variables (Latash et al, 2007). The elemental variables describe degrees of freedom in the motor system for the task. The performance variable(s) describe what is essential in fulfilling the task variable (e.g. foot position and velocity when kicking the ball). The task variable depends on the outcome of a specific performance variable (e.g. the task variable of kicking accuracy is dependent on the performance variable of foot position relative to the ball at the time of the kick). The Uncontrolled Manifold (UCM) The UCM links the variance of elemental variables and variance of a performance variable, using the Jacobian matrix. The Jacobian matrix partitions the variance of the elemental variables into two: 1. that indicates flexible combinations of elemental variables across trials leading to the same value of the performance variable or, 2. changes in the performance variable. If 1 is greater than 2 the performance variable is stabilised by compensation among the elemental variables and a SYNERGY is said to exist . The higher 1 is, the greater the amount of compensated variability, which suggests a stronger synergy and more stability. Therefore, the UCM goes beyond analysing the variability within a technique by also indicating whether the variability is useful or not. Conclusion Variability can be positive and negative in sports-specific tasks Variation can assist in providing flexible movement solutions for successful performance Constraints can limit performance Understanding the different dimensions of inter and intra variability in technique, style and how they do or don’t explain performance in sport is key to not only biomechanists, but also performers, coaches, and teachers. References Bartlett R. (2007) Introduction to Sports Biomechanics (2nd Ed.). Routledge Bartlett, R. Wheat, J. & Robins, M. (2007). Is movement variability important for sports biomechanists? Sports Biomechanics, 6, 224-243. Black, D. P. Riley, M. A. & McCord, C. K. (2007). Synergies in Intra- and Interpersonal Interlimb Rhythmic Coordination. Motor Control, 11, 348-373. Button, C. Macleod, M. Sanders, R. & Coleman, S. (2003). Examining movement variability in the basketball free-throw action at different skill levels. 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