Associations Between Physical Characteristics and Golf Clubhead Speed: A Systematic Review with Meta-Analysis

Abstract

Background: Historically, golf does not have a strong tradition of fitness testing and physical training. However, in recent years, both players and practitioners have started to recognise the value of a fitter and healthier body, owing to its potential positive impacts on performance, namely clubhead speed (CHS).

Objective: The aim of this meta-analysis was to examine the associations between CHS (as measured using a driver) and a variety of physical characteristics.

Methods: A systematic literature search with meta-analysis was conducted using Medline, SPORTDiscus, CINAHL and PubMed databases. Inclusion criteria required studies to have (1) determined the association between physical characteristics assessed in at least one physical test and CHS, (2) included golfers of any skill level but they had to be free from injury and (3) been peer-reviewed and published in the English language. Methodological quality was assessed using a modified version of the Downs and Black Quality Index tool and heterogeneity assessed via the Q statistic and I². To provide summary effects for each of the physical characteristics and their associations with CHS, a random effects model was used where z-transformed r values (i.e. z_r) were computed to enable effect size pooling within the meta-analysis.

Results: Of the 3039 studies initially identified, 20 were included in the final analysis. CHS was significantly associated with lower body strength (z_r = 0.47 [95% confidence intervals {CI} 0.24-0.69]), upper body strength (z_r = 0.48 [95% CI 0.28-0.68]), jump displacement (z_r = 0.53 [95% CI 0.28-0.78]), jump impulse (z_r = 0.82 [95% CI 0.63-1.02]), jumping peak power (z_r = 0.66 [95% CI 0.53-0.79]), upper body explosive strength (z_r = 0.67 [95% CI 0.53-0.80]), anthropometry (z_r = 0.43 [95% CI 0.29-0.58]) and muscle capacity (z_r = 0.17 [95% CI 0.04-0.31]), but not flexibility (z_r = - 0.04 [95% CI - 0.33 to 0.26]) or balance (z_r = - 0.06 [95% CI - 0.46 to 0.34]).

Conclusions: The findings from this meta-analysis highlight a range of physical characteristics are associated with CHS. Whilst significant associations ranged from trivial to large, noteworthy information is that jump impulse produced the strongest association, upper body explosive strength showed noticeably larger associations than upper body strength, and flexibility was not significant. These findings can be used to ensure practitioners prioritise appropriate fitness testing protocols for golfers.

Fig. 1

Flow diagram showing the identification and selection of studies. Included studies often reported more than one physical characteristic, which is why the bottom row of numbers (n = 50) is greater than the total number of studies (n = 20). Anthro. Anthropometry

Fig. 2

Forest plot showing the association (Fisher’s z_r) between lower body strength and clubhead speed. n = sample size. CI confidence interval, F females, ISOS isometric squat, M males, PF peak force, RE random effects, RM repetition maximum

Fig. 3

Forest plot showing the association (Fisher’s z_r) between upper body strength and clubhead speed. n = sample size. CI confidence interval, F females, M males, Pec Dec resistance machine primarily involving use of the pectoralis major muscles, RE random effects, RM repetition maximum

Fig. 4

Forest plot showing the association (Fisher’s z_r) between jump displacement and clubhead speed. n = sample size. CI confidence interval, CMJ countermovement jump, F females, M males, RE random effects, SJ squat jump

Fig. 5

Forest plot showing the association (Fisher’s z_r) between jump impulse and clubhead speed. n = sample size. CI confidence interval, CMJ countermovement jump, RE random effects

Fig. 6

Forest plot showing the association (Fisher’s z_r) between jumping peak power and clubhead speed. n = sample size. CI confidence interval, CMJ countermovement jump, F females, M males, RE random effects

Fig. 7

Forest plot showing the association (Fisher’s z_r) between upper body explosive strength and clubhead speed. n = sample size. CI confidence interval, F females, M males, RE random effects

Fig. 8

Forest plot showing the association (Fisher’s z_r) between anthropometric measures and clubhead speed. n = sample size; *Denotes that directional differences existed in individual correlations, resulting in negatively aligned data being multiplied by minus 1, to directionally align all data prior to pooling. CI confidence interval, F females, M males, RE random effects

Fig. 9

Forest plot showing the association (Fisher’s z_r) between flexibility measures and clubhead speed. n = sample size. CI confidence interval, F females, M males, RE random effects

Fig. 10

Forest plot showing the association (Fisher’s z_r) between balance and clubhead speed. n = sample size. BESS Balance Error Scoring System, CI confidence interval, F females, M males, RE random effects

Fig. 11

Forest plot showing the association (Fisher’s z_r) between measures of muscle capacity and clubhead speed. n = sample size. CI confidence interval, F females, M males, RE random effects

Fig. 12

Summary forest plot showing the association (Fisher’s z_r) between different physical characteristics and golf clubhead speed. CI confidence interval