ME MECH CHANI NICA CAL DE DETER ERMI MINA NANT NTS S OF OF - - PowerPoint PPT Presentation

ME MECH CHANI NICA CAL DE DETER ERMI MINA NANT NTS S OF OF S SPR PRINT NTING NG ACC CCEL ELER ERATION N IN N RUG UGBY BY ATHL HLET ETES ES

Matt R. Cross, MSpEx Scott R. Brown, PhD Jean-Benoît Morin, PhD Matt Brughelli, PhD Robindronath Dé, MSc Pierre Samozino, PhD

What determines acceleration performance? Some preliminary evidence:

• General theory of force dominant profile being

advantageous over ‘short distances’

• Some information of velocity dominance for longer

distances (40-100 m) (Morin et al. 2012, 2016; Rabita et al. 2015)

• Limited information over short distances and

acceleration dominant (e.g. rugby) athletes

?

5 m 10 m 20 m (+)

Fh

Time

𝑄 = 𝐺 ∙ 𝑤

vs.

Velocity Force ‘Long’ ‘Short’

Sprint distance

5 m sprint 30 sprint +

Power

What determines ‘common’ split times?

TRANSFER TRANSFER

30 – 45 m maximal sprints 1 – 3 trials (~5 mins rest) Field conditions (grass / turf)

Approach

League

N=19

Sevens

N=46

Union

N=173

Scott R. Brown @scottiehype

238 athletes 5 seasons All levels (club → elite)

Force Power Velocity

• 1. Clarify horizontal force expression underlining

sprint performance at various distances;

• 2. Determine normative values and variables

correlated to Rugby player level.

Project aims

tbd

What mechanical capacities determine sprint performance [in rugby athletes]?

Mechanical data

• Macro. approach (Samozino et al. 2016)
• Fv & Pv = linear & polynomial fits
• F0 v0 Pmax & SFv calculated

Split times

• 5, 10, 20, 30 m

Statistics

• ≥2 trials averaged ▼ error

(Hopkins et al. 2010)

• Stepwise regression models:

1) Pmax + SFv → split times 2) F0 v0 → split times

Analysis + statistics

F0 v0 Pmax

Force Power Velocity

Max Force

‘F @ low v’

Max Velocity

‘v @ low F’

Max Power

‘Opt. FV output’

Results

Split 5 m 10 m 20 m 30 m F0

(1)

.895 .789 .353 .226 v0

(2)

.094 .199 .635 .761 Full model .989(1,2) .988(1,2) .988(2,1) .986(2,1) STD coeff.

• .87(1); -.32(2)
• .78(1); -.46(2)
• .65(2); -.61(1)
• .75(2); -.49(1)

Split 5 m 10 m 20 m 30 m Pmax

(1)

.947 .980 .950 .881 SFv

(2)

.037 .002 .031 .098 Full model .983(1,2) .982(1,2) .981(1,2) .980(1,2) STD coeff.

• .89(1); .21(2)
• .97(1); .04(2)
• 1.05(1); -.19(2)
• 1.07(1); -.34(2)

All correlations P<0.001. Step order of entry into full model denoted by superscript parenthesis.

Stepwise regression results for mechanical variables on split times

Full models explain >98% of variance in performance Pmax explains very large level of variance (>88%) Primary predictors ‘switch’ between 10 and 20 m

Model 1: Pmax and SFv Model 2: F0 and v0

Summary points

• Pmax strongly determines performance, but the way it is developed

still matters (degree requires more research and investigation)

• Cross-over of F/v demands at between 10 and 20 m
• All factors significantly** improved models (even max. velocity @ 5)

0.2 0.4 0.6 0.8 1 5 10 15 20 25 30

Correlation (R2) Sprint distance (m)

F0 + v0

0.2 0.4 0.6 0.8 1 5 10 15 20 25 30

Correlation (R2) Sprint distance (m)

Pmax + SFv

Practical applications

Proposed ‘hierarchy’ of training attention

• Lower level athletes benefit from generalised approach (‘tooth-paste analogy’)
• Focus on training at Pmax may provide biggest ‘return’ on investment
• Higher level athletes might benefit from targeted development of SFv

Do these results transfer to rugby?

• Horizontal F/v profiles in team sports may not match

what ‘optimizes’ sprinting performance

• Positional demands may dictate unique horizontal

force signatures from athletes (e.g. de Lacy et al. 2017)

F v

Actual Optimal

vs.

Force Velocity

What is optimal?

…more research needed

Acceleration conditions ‘modelled’ using spectrum of loading @ max (+error)

Horizontal power Velocity

Pmax

Horizontal force

v0 F0

• Horizontal resistance +

Early Late Sprinting acceleration

Target training:

• Use high resistance for

short distances (<10 m)

• Use low resistance for ‘long

distance’ sprinting (>20 m) Individual specific…

• Adaptations may depend on

pre-training profile

• ‘Force’ stimulus may improve

velocity in deficient athletes (& vice-versa)

𝐺

F v

𝑤

v F

Application to training

50% vDec

~80% BM sled

~10-20 m

Matthew.Cross@univ-savoie.fr @MattCrossNZ cross.matt.r Matt_Cross2

Thank you

Acknowledgements

Coaches + Athletes New Zealand-France Friendship Fund Collaborators Scott R. Brown Jean-Benoît Morin Matt Brughelli Robindronath Dé Pierre Samozino