The slides to the presentation I held at the World Scientific Congress of Golf 2016

1. TheVariability in Swing
Kinematics and Carry in a
Longitudinal Study of Elite
Golfers
James Parker, John Hellström ,
Andreas Ivarsson, Urban Johnsson, M
Charlotte Olsson.

2. Purpose
• Hit further and with high accuracy is important in the
long game
• Between shot variance is evident for elite golfers.
• Consistency of shot outcome is determined by
– biomechanics of the golfer
– club head speed (CHS) and position
– initial ball launch conditions
– environmental factors.
• Thorax rotation speed (Joyce et al., 2013; Myers et al.,
2008) and lead arm speed (LAS)(Tinmark et al., 2007)
have been associated with variance in CHS, .

3. • Furthermore, pilot data from our laboratory
indicated moderate non-significant relationship
between CHS and carry in elite male golfers
when studied over time.
– Including club head speed as a fixed effect
covariate on level 1 generated no improved model
fit, clubhead speed (β=.70, p=.304).

4. Aim:
To explore, when the objective is shot
consistency, the relationship between peak speed
of the pelvis, thorax, lead upper arm and carry
over time, investigating both within and between
session variability in elite male golfers.

5. Methods
• Six elite male golfers (handicap range -3 to
+0.5) (age range 21-23 years).
• Studied on four separate occasions over a
year:(1) October; (2) February; (3) May; and (4)
October.
• Each test occasion at indoor range hitting out.

6. Data Collection
• Swing kinematics
– collected using a four sensor electromagnetic
motion capture system at 120Hz (Polhemus Inc.
USA).
– Nine landmarks were digitized to define segment
lengths, orientations and joint axes.
• Ball Trajectory

7. Data reduction
• The swing events
– Resultant and angular velocities of theThorax and
pelvis rotation were calculated using the joint
coordinate system method (Grood et al., 1983) and
lead arm was calculated using the humerus joint
coordinate system (first option) (Wu et al., 2005) in
conjunction with advanced motion measurement
software (AMM 3D, USA).

8. Statistical Analysis
• Hierarchical multilevel modelling (MLM) was used to
analyse the data.
• Carry was used as outcome variable and kinematics as
predictor variables
– First a baseline model without predictors was run.
– Then MLM was repeated using:
– Level 1(within session level)= Carry and kinematic data
from each shot.
– Level 2 (between sessions)=Carry were nested within
sessions.
– Level 3(between subjects)= Carry was nested within
subjects.

9. Results: The base line model
32.5%
38%
28.5% Between
Subjects
Between
Session
Within
Session
Within
Session
(or within
subject)
Within
Session
Within
Session
Fig. 1 A baseline model without predictors explaining the variance in carry at each level: Level 1(within session
level); Level 2 (between sessions) and ; Level 3(between subjects).
• The results showed a statistical significant intercept
(Estimate=226.24m, p<.001)

10. Within session carry.
Improved model fit (-2 LL & BIC)
Peak LAS
(β=.17, p=.001)
Peak speed pelvis
(β=.02, p=.67)
Peak speed thorax
(β=-.04, p=.36)
Peak CHS
(β=.70, p=.30)

11. Discussion
• The present study found
– 32.5% of variation in shot consistency can be
explained at the within session level
– Influenced by for example variance in centeredness
of impact
– 38% of variation in shot consistency can be
explained at the between session level
– Influenced by for example environmental factors.

12. • Our results indicated peak LAS speed as a
predictor of within session variance in carry
• Partly supported by previous research who
found golfers with higher arm speed had higher
ball velocity than golfers with lower arm speed
(Healy et al., 2011).

13. However,
• Results from our pilot study differ from previous
research (Joyce et al., 2013; Myers et al., 2008)
which reports a relationship between peak thorax
speed and driver performance.
• Could be due
– Longitudinal data at intra-individual level, whereas
previous studies have used a cross-sectional study
design reported at an inter-individual level.
– Had a larger sample size
– Different analysis methods

14. In conclusion,
• Our preliminary data show that within session
lead arm speed is a predictor of carry distance
when the objective is shot consistency.

15. References
• Grood, E. S., & Suntay,W. J. (1983).A joint coordinate system for the clinical description of
three-dimensional motions: application to the knee. Journal of biomechanical engineering,
105(2), 136-144.
• Healy,A., Moran, K., Dickson, J., Hurley, C., Smeaton,A., O'Connor, N. , . . . Chockalingam, N.
(2011).Analysis of the 5 iron golf swing when hitting for maximum distance Journal of Sports
Sciences, 29(10), 1079-1088.
• Joyce C, Burnett A, Cochrane J, Ball K. (2013).Three-dimensional trunk kinematics in golf:
between-club differences and relationships to clubhead speed. Sports Biomechanics.12(2):108-
120.
• Myers J, Lephart S,TsaiYS, Sell T, Smoliga J, Jolly J. (2008).The role of upper torso and pelvis
rotation in driving performance during the golf swing. Journal of Sports Sciences. 26(2):181-
188.
• Tinmark, F., Halvorsen, J.,& Thorstensson,A. (2009).Analysis of elite golfers’ kinematic sequence
in full and partial shots ISBS - Conference Proceedings Archive, 27 International Conference on
Biomechanics in Sports (2009).
• Wu, G., van der Helm, F. C.T.,Veeger, H. E. J., Makhsous, M.,Van Roy, P.,Anglin, C., . . . Buchholz, B.
(2005). ISB recommendation on definitions of joint coordinate systems of various joints for the
reporting of human joint motion—Part II: shoulder, elbow, wrist and hand.. Journal of
Biomechanics 38(5), 981-992