Physical determinants of golf swing performance in competitive youth golfers

ABSTRACT

This study investigated measures of physical fitness and golf swing performance in competitive youth golfers. Sixty-four golfers, subdivided into three handicap groups (CAT0, <1 handicap, n = 21; CAT1, 1–5 handicap, n = 20; CAT2, >5 handicap, n = 23), were assessed for isometric strength, power, movement competency and core endurance. Clubhead speed (CHS) and carry distance for 6-iron and driver were also measured. When controlled for maturity offset, CAT0 golfers produced significantly greater peak power, isometric absolute and relative peak force, movement competency, and jump distance than higher handicap golfers (all p < 0.05). Maturity status was strongly associated with CHS and carry distance for both clubs (R² = 0.552–0.784). Linear regressions showed maturity offset explained a larger amount of variance in CHS (driver = 78.4%, 6-iron = 71.3%) and carry distance (driver = 55.2%, 6-iron = 57.4%) than handicap. Multiple linear regression analyses showed that peak power explained 79.4% and 82.4% of variation in 6-iron and driver CHS, respectively, while isometric absolute peak force explained 69.6% and 74.3% of the variation in 6-iron and driver carry distance, respectively. Subsequently, interventions targeting the development of peak force and power could aid golf swing performance in young golfers.

KEYWORDS:

• Athletic development
• resistance training
• adolescent
• junior golf
• talent identification

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Correction

Introduction

In addition to the importance of shot accuracy, hitting the golf ball longer distances can contribute to lower scoring on the golf course. Studies have found that golfers who drive the ball further achieve lower scores, especially on par four and five holes (Hellström, 2009), likely due to a longer tee shot subsequently reducing the distance to the hole and enabling a golfer to a hit shorter, more accurate approach shot. Furthermore, research has highlighted that skilled golfers are able to produce a faster club head speed (CHS) and greater carry distances compared to less skilled golfers (Fradkin et al., 2004; Pelz et al., 2008). Given the importance of the interaction between the club head and golf ball at the point of impact, commercial launch monitors are often used to collect instantaneous data on golf swing variables, such as CHS and carry distance, which are subsequently used as surrogate measures of golfing performance.

The golf swing is influenced by multiple factors (i.e., attack angle, club path, launch angle, etc.); however, CHS is one of the most modifiable variables to enhance carry distance. Relevant training interventions have been shown to increase CHS, with players and coaches pursuing different forms of training (e.g., traditional strength training, plyometrics, or a combination of both strength and plyometric training) to enhance CHS to improve shot distance by enhancing measures of strength and power (Ehlert, 2020; Uthoff et al., 2021). The premise of strength and conditioning is to manipulate training prescription to target the physical determinants of a given sport and to meet the needs of the individual. Therefore, to best prepare golfers for the demands of their sport, it is imperative to identify the physical determinants of CHS. Research has shown that field-based measures of strength and power, such as medicine ball throw distances, countermovement and squat jump peak power, were moderately related to CHS (Parchmann & McBride, 2011; Read et al., 2013). Similarly, Parchmann and McBride (Parchmann & McBride, 2011) reported that one-repetition maximum (1RM) back squat strength was significantly correlated with clubhead velocity. More recently, a systematic review of data from 14 studies indicated that measures of upper and lower body strength and power were the strongest correlates of CHS (Ehlert, 2021). Cumulatively, it appears that various measures of strength and power are key physical determinants of CHS, and as such, should be tested and trained to enhance golf performance (Shaw et al., 2021).

While the existing correlational data indicate significant associations between indices of strength and power and CHS in adult golfers, the data remains scarce in youth golfers with only two papers to the author knowledge that have investigated this in youth golfers. Of which, both papers failed to consider maturation within their analyses. Current international consensus highlights an important role for strength and conditioning in the long-term development of young athletes to develop physical fitness and reduce the risk of injury in youth athletes of differing stages of maturation (Bergeron et al., 2015; Lloyd et al., 2016). Recent research has identified that golf coaches value the benefits strength and conditioning offers for young golfers (Shaw et al., 2022). However, the interaction between growth, maturation and training influences physical fitness in young athletes (Lloyd & Oliver, 2012) and, therefore, the data from adult population studies should not be generalised to paediatric cohorts. Biological maturation is non-linear in nature, and large inter-individual differences in maturity status will be present in a cohort of youths of the same chronological age group (Lloyd et al., 2014). Maturity status has been shown to influence physical performance (Dobbs et al., 2020b) and injury risk (Johnson et al., 2022) and is considered an influential factor in long-term athletic development (Lloyd et al., 2016). There is also interest from national governing bodies and professional sporting associations in developing their talent pool to maximise performance at all levels of competition (Coughlan & Ward, 2017). Therefore, establishing the physical determinants of golf swing performance in young golfers of varying maturity status and competitive levels will provide novel data.

Within the golfing literature, only two studies have investigated the correlates of golf swing performance in junior golfers, identifying several significant correlations between indices of strength and power with driver and 6-iron CHS and carry distance (Coughlan et al., 2020; Sanders et al., 2020). Notwithstanding the importance of these findings, the influence of maturation on CHS and/or carry distance has yet to be considered in the literature. Given the influence maturation has on strength and power (Lloyd & Oliver, 2012) along with the concept of “synergistic adaptation” for maximising training outcomes (Lloyd et al., 2016), it is important to understand the influence of maturity status on golf swing performance.

It should also be noted that the study by Coughlan, Taylor, Jackson, et al. (Coughlan et al., 2020) relied primarily on performance variables of strength and power using field-based testing methods such as medicine ball throw distances and jump heights and did not include any assessments of maximum strength (e.g., 1RM or isometric strength testing). Examining the associations between various kinetic data in maximal strength and power assessments and CHS will provide a more granular level of understanding about the physical determinants of golf swing performance in youth golfers. Finally, Coughlan, Taylor, Jackson, et al. (Coughlan et al., 2020) reported CHS for the driver, and considering the differences in swing mechanics for both driver (upward attack angle) and irons (downward attack angle), the associations between physical determinants and golf swing performance are consistent across different swing types in young golfers remains unknown.

Considering the current evidence examining physical predictors of CHS and the existing limitations within the paediatric golf literature not accounting for maturation within their analyses, the current study aimed to (i) examine the differences in physical fitness and golf swing variables (i.e., CHS and carry distance) in youth golfers of varying competitive levels; (ii) determine the predictive ability of maturity status and handicap on CHS and carry distance and (iii) identify the proportion of CHS and carry distance that can be accounted for by a range of physical fitness attributes. Our hypotheses were that: (i) players of higher competitive level would have greater CHS, carry distance, and heightened levels of physical fitness; (ii) maturity would be a key predictor of CHS; and (iii) that the variance in CHS and carry distance would be accounted for by physical fitness, with an increased contribution from strength and power variables.

Methods & materials

Study overview

This study used a cross-sectional design to (i) determine differences in golf swing performance and measures of physical fitness between youth golfers of different competitive levels (CAT0; <1 handicap, CAT1; 1–5, handicap, CAT2; >5, handicap) and (ii) establish the relationships and their magnitude between physical fitness and golf swing performance of golfers. Measures of CHS and carry distance were obtained from a commercially available golf launch monitor, while strength and power kinetics were collected from an isometric mid-thigh pull (IMTP) and countermovement jump (CMJ). Participants also completed standing long jump (SLJ), isometric plank hold, and the golf movement screen (GMS) (Gould et al., 2017).

Participants

Sixty-four male golfers playing at county, national or international levels, were grouped by handicap (CAT0; n = 21, CAT1; n = 20, CAT2; n = 23). These groupings were adopted to ensure there was appropriate divergence between groups and even distribution of the sample. Descriptive statistics for each group are shown in Table 1. Anthropometric data including standing height (cm), seated height (cm) and body mass (kg) were collected, from which somatic maturity was estimated as years from PHV (peak height velocity) using a sex-specific regression equation (Mirwald et al., 2002). Participants were all regularly participating in golf but did not engage in any physical training, golf practice or competitions on the day of the testing. Prior to the study commencing, parental consent and participant assent were obtained. No injuries were reported during the time of testing. Ethical approval for the study was granted by the institutional Research Ethics Committee.

Table 1. Descriptive statistics for all anthropometric variables (mean ± SD).

Group	N	Handicap	Age (years)	Height (cm)	Mass (kg)	Maturity Offset (yrs)
Whole sample	64	5.1 ± 6.5	15.0 ± 1.8	172.2 ± 1.7	60.1 ± 15.6	1.2 ± 1.7
CAT0	21	−0.46 ± 1.2^ab	16.7 ± 1.0	179.7 ± 5.2	67.4 ± 6.1	2.7 ± 0.7^ab
CAT1	20	3.7 ± 1.3^c	14.8 ± 1.2	175.0 ± 8.5	62.6 ± 12.3	1.18 ± 1.29^c
CAT2	23	11.5 ± 6.6	13.7 ± 1.6	162.8 ± 10.9	55.8 ± 15.6	−0.2 ± 1.5

^a = significant difference between CAT0 and CAT1; ^b = significant difference between CAT0 and CAT2; ^c = significant difference between CAT1 and CAT2.

Procedures

All testing was completed on the same day, albeit with golf swing performance testing occurring at a separate location (i.e., outdoor driving range) to the strength and power testing (i.e., lab-based). For the strength and power testing, participants took part in a standardised 10-min warm-up consisting of dynamic stretching and relevant activation exercises. Familiarisation of each protocol took place at the beginning of the testing session, which involved a demonstration and provision of standardised coaching cues. Participants then practiced the protocol until the principal investigator was satisfied with their technical competency. For the golf swing performance testing, participants completed the same 10-min dynamic warm-up before performing self-selected practice swings as part of their standard pre-shot routine.

Golf swing performance

Participants hit three full shots using Titleist ProV1 golf balls (Acushnet Company, MA, USA) with their own custom fitted 6-iron and driver to ensure shot outcomes were maximised for the individual golfer. If a shot went wider than 20 yards of the intended target line, the trial was discounted, and the participant performed another trial until three acceptable trials were completed. For each trial, the TrackMan® launch monitor (TrackMan® 4 A/S, Denmark) was positioned as per manufacturer guidelines, specifically 2.7 m behind the participant at the same height as the area the golfer is hitting from and in line with their intended target in the distance. For golf swing performance, CHS (mph) and carry distance (yards) were determined from the mean of three trials. Acceptable reliability for 6-iron and driver CHS and carry distance has previously been established in youth golfers (coefficient of variation = 0.74%−2.65%) (Shaw et al., In Press).

Isometric mid-thigh pull

Isometric mid-thigh pull data were collected within a laboratory setting using a dual wireless force plate system and a portable IMTP specific rig sampling at 1000 Hz with an automatic low-pass filter with a 50 Hz cut off frequency, via proprietary software (Hawkin Dynamics Inc., Maine, USA). Three maximal trials were collected with 2-min rest between trials. The participants’ second pull position for the power clean was identified for bar height placement to promote optimal force production and rate of force development (Stone, 1993). Once bar height was set, lifting straps were used to secure the participant to the bar to limit the effects of grip strength on performance outcomes (Haff et al., 2015). All participants then received standardised instructions used in previous literature (Moeskops et al., 2018) and were afforded one practice trial. After a three-second countdown, participants performed the test maximally for a 5-sec period of data collection, as per previous studies (Dobbs et al., 2020a; Moeskops et al., 2018, 2022). Trials were discounted if a visible countermovement was observed or if a participant lost grip. In addition to, their usage within the golf and paediatric literature (Coughlan et al., 2020; Dobbs et al., 2020a; Moeskops et al., 2022; Shaw et al., 2021; Wells et al., 2018), our own reliability analyses showed that absolute (IMTP_AbsPF) and relative (IMTP_RelPF) peak force and peak force at 50 ms (PF₅₀) and 100 ms (PF₁₀₀) had acceptable random variation (CV% = 5.0–10.0%) and good-to-excellent relative reliability (ICC = 0.83–0.97) and were therefore selected for subsequent analyses.

Countermovement jump and standing long jump

Countermovement jump data were collected using a dual force plate system sampling at 1000 Hz (Pasco, 2 Axis force platforms, Roseville, CA). Force-time data were filtered (MATLAB, R2018a, Mathworks, MA, USA) using a low-pass fourth-order recursive Butterworth filter and analysed using a custom-built automated Excel (Microsoft Excel for Mac version 16.62) spreadsheet (Chavda et al., 2018). Participants completed three maximal jumps interspersed with ~30-s rest and were instructed to “jump as fast and as high as possible” using a self-selected countermovement depth whilst keeping their hands on their hips. Based on previous literature (Coughlan et al., 2020; Dobbs et al., 2020a; Moeskops et al., 2022; Shaw et al., 2021; Wells et al., 2018) and our own reliability analysis, jump height (CMJ_JH), absolute and relative peak force (CMJ_AbsPF and CMJ_RelPF) and peak power (CMJ_AbsPP and CMJ_RelPP), eccentric (CMJ_EccImp) and concentric (CMJ_ConImp) impulse and concentric power (CMJ_ConPower) achieved acceptable random variation and good-to-excellent relative reliability (CV% = 7.1–9.1% and ICC = 0.86–0.95) were selected for further analysis.

Standing long jump distance (cm) was collected with the furthest distance jumped horizontally and laterally left (SLJ_L) and laterally right (SLJ_R) (Leidersdorf et al., 2022) being used for further analysis. Participants stood behind a marked line and were instructed to maximally jump as far forward or sideways (lateral to medial) as possible with arm swing and to stick the landing. Jump distance (cm) was measured from the marked line to the rearmost portion of the foot, if a participant’s foot shifted after landing, then the trial was discounted. For both the horizontal and lateral SLJ, acceptable random variation and excellent relative reliability have been previously reported for both the horizontal and lateral SLJ (CV% = 3.1% and 4.4%; ICC = 0.97 and 0.94 respectively) (Meylan et al., 2009).

Golf movement screen and isometric plank hold

The Golf Movement Screen (GMS) utilises golf-specific movements to assess the movement competency of golfers and has shown good inter- and intra-rater reliability (ICC = 0.94) when screening youth golfers (Gould et al., 2017). Participants were given standardised instructions for six exercises (trunk inclination, seated hamstring, seated thoracic rotation, rotation over fixed foot, lunge, and overhead squat) and then proceeded to conduct each movement to a set number of repetitions (four to nine repetitions dependent on exercise) where the researcher assessed the movement based on the original authors’ defined criteria. Each individual score was collated for a total score (maximum score 66) and used for further analysis.

Isometric plank hold was measured for time (s) until failure without compromising form (body parallel to the floor with the forearms supporting bodyweight and elbows at a 90-degree angle). Timing started when participants reached the top of the plank and stopped when form was compromised (visually determined by the researcher), or when the participant voluntarily stopped. The isometric plank hold has shown moderate-to-good inter- and intra-rater reliability (ICC = 0.62 and 0.83 respectively) in youth (Boyer et al., 2013).

Statistical analyses

Descriptive statistics (means ± SDs) were calculated for all variables, and the assumption of normality was assessed via the Shapiro–Wilk test. Sphericity was assessed through Mauchley’s test to ensure non-violation of the respective assumption, where violated, a Greenhouse–Geisser adjustment was implemented. Coefficients of variation (CV%) were used to determine absolute reliability (Hopkins, 2000) and intraclass correlation coefficients (ICC) were used to establish relative reliability for within-session reliability of all variables. Those variables that achieved a CV < 10% and ICC > 0.5 were deemed acceptable to use for further analysis. Magnitudes of ICC being classified according to thresholds previously reported by Koo and Li (Koo & Li, 2016) (>0.9 excellent, 0.75–0.9 good, 0.5–0.75 moderate, <0.5 poor) were included within this study. Analysis of variance (ANOVA) was used to detect differences in maturity offset and handicap, whilst between-group differences were analysed using an analysis of covariance (ANCOVA) to assess differences in golf swing performance and handicap with maturity offset as a covariate. Regression analysis was conducted to assess the correlations between golf swing performance and handicap and maturity offset. Multiple stepwise regression analysis was also conducted to investigate the predictive ability of key variables from each physical fitness assessment on golf swing performance, with maturity offset used as a covariate. Cohen’s d effect sizes were reported to interpret the magnitude of between-group effects, with the following effect size thresholds used: <0.20 (trivial), 0.20–0.59 (small), 0.60–1.19 (moderate), 1.20–1.69 (large), and >1.70 (very large) (Sullivan & Feinn, 2012). Statistical significance was determined as p < 0.05. All statistical analyses were performed in SPSS v28 (SPSS Inc, Chicago, IL). ICC and CV% were calculated using an online spreadsheet processed through Microsoft Excel for Mac version 16.62 (Hopkins, 2017).

Results

Between-group differences

Between-group analysis showed that CAT0 golfers were significantly more mature than those in CAT1 and CAT2 (p < 0.001) (Table 1), while CAT1 golfers were significantly more mature than CAT2 golfers (p < 0.05). CAT0 golfers produced significantly longer driver and 6-iron carry distances than CAT1 and CAT2 golfers (p < 0.05; d = 1.59–2.39 respectively), but there were no significant between-group differences in CHS (Table 2).

Table 2. Adjusted mean ± SD between-group differences in golf swing measures.

Variable	Group
	CAT0 (n = 21)	CAT1 (n = 20)	CAT2 (n = 23)
Driver CHS (mph)	105.9 ± 4.7	97.2 ± 9.4	89.3 ± 9.4
Driver Carry (yards)	232.0 ± 24.9^ab	194.4 ± 22.4	171.3 ± 25.9
6-Iron CHS (mph)	88.6 ± 4.2	81.5 ± 6.3	75.6 ± 6.5
6-Iron Carry (yards)	163.9 ± 15.9^b	144.4 ± 14.8	124.5 ± 25.3

^a = significant difference between CAT0 and CAT1; ^b = significant difference between CAT0 and CAT2; ^c = significant difference between CAT1 and CAT2.

When controlled for maturity, CAT0 golfers produced significantly higher CMJ_RelPP (p < 0.05; d = 1.11 and 1.42 respectively) (Table 3) and higher GMS score than CAT1 and CAT2 golfers (p < 0.05; d = 0.55 and 1.2 respectively). CAT0 golfers produced significantly greater IMTP_AbsPF and IMTP_RelPF than CAT2 golfers (p < 0.05; d = 2.26 and 0.19 respectively) (Table 4). Both CAT0 and CAT1 golfers produced higher GMS score and jumped significantly further in the SLJ, SLJ_R and SLJ_L than CAT2 golfers (p < 0.05; d = 0.55–1.98). No significant differences were seen between-groups for plank hold time (p > 0.05).

Table 3. Adjusted mean ± SD between-group differences in isometric mid-thigh pull measures.

Variable	Group
	CAT0 (n = 21)	CAT1 (n = 20)	CAT2 (n = 23)
IMTPAbsPF (N)	2226.6 ± 404.6^b	1769.9 ± 389.3	1392.6 ± 332.2
IMTPRelPF (N.kg^−1)	32.9 ± 56.1^b	28.3 ± 31.5	24.9 ± 21.3
PF50 (N)	788.4 ± 124.4	755.0 ± 159.3	657.5 ± 225.9
PF100 (N)	884.5 ± 211.5	873.8 ± 202.9	735.8 ± 235.7
PF150 (N)	1007.0 ± 320.9	1041.6 ± 242.9	843.9 ± 286.8
PF200 (N)	1131.3 ± 362.0	1192.7 ± 280.7	941.8 ± 318.0
PF250 (N)	1252.8 ± 388.0	1304.7 ± 305.8	1015.9 ± 342.7
Time to PF (s)	3.87 ± 1.80	3.32 ± 1.80	2.87 ± 1.54

^a = significant difference between CAT0 and CAT1; ^b = significant difference between CAT0 and CAT2; ^c = significant difference between CAT1 and CAT2.

Table 4. Adjusted mean ± SD between-group differences in jump-based measures.

Variable	Group
	CAT0 (n = 21)	CAT1 (n = 20)	CAT2 (n = 23)
CMJJH (cm)	29.3 ± 4.8	27.4 ± 8.3	23.1 ± 4.7
CMJAbsPF (N)	907.9 ± 206.5	793.8 ± 274.2	671.8 ± 247.8
CMJRelPF (N.kg^−1)	13.3 ± 3.0	13.0 ± 3.2	12.7 ± 3.3
CMJAbsPP (W)	3241.6 ± 453.7	2531.2 ± 558.4	2113.1 ± 660.9
CMJRelPP (W.kg^−1)	47.6 ± 4.9^ab	41.9 ± 5.4	39.7 ± 6.1
mRSI	0.33 ± 0.12	0.30 ± 0.09	0.27 ± 0.08
CMJEccImp (Ns)	70.4 ± 13.6	57.7 ± 18.2	49.3 ± 15.8
CMJConImp (Ns)	167.2 ± 17.6	136.2 ± 29.5	113.4 ± 33.3
CMJConPower (W)	1744.6 ± 252.4	1419.7 ± 327.5	1184.1 ± 368.3
CMJEccPower (W)	−300.5 ± 84.0	−248.8 ± 86.2	−218.0 ± 96.0
SLJ (cm)	202 ± 16^b	182 ± 21^c	160 ± 25
SLJR (cm)	179.15 ± 13^b	168.5 ± 17^c	144 ± 22
SLJL (cm)	180 ± 19^b	169 ± 14^c	147 ± 18

^a = significant difference between CAT0 and CAT1; ^b = significant difference between CAT0 and CAT2; ^c = significant difference between CAT1 and CAT2.

Regression analyses

Simple linear regression analysis showed that maturity offset explained 78.4% and 71.3% of variance in CHS for the driver and 6-iron, and 55.2% and 57.4% of the variance in driver and 6-iron carry distance, respectively (Figure 1). Handicap explained similar amounts of variance in both variables for driver and 6-iron (48.7–53.9%) (Figure 2).

Figure 1. Linear regression analysis between handicap and a) 6-iron CHS, b) 6-iron carry distance, c) driver CHS, and d) driver carry distance.

Figure 2. Linear regression analysis between handicap and a) 6-iron CHS, b) 6-iron carry distance, c) driver CHS, and d) driver carry distance.

When controlled for maturity, multiple stepwise regression analysis (Table 5) showed that 81.8% of variation in 6-iron CHS was explained by CMJ_AbsPP (R² = 79.4%) and IMTP_AbsPF (R² = 2.4%), while CMJ_AbsPP explained 82.4% of the total variance for driver CHS. For 6-iron carry distance, IMTP_AbsPF (R² = 69.6%), plank (R² = 2.5%) and SLJ (R² = 2.4) explained 74.5% of the variation, while IMTP_AbsPF (R² = 74.3%) and SLJ (R² = 3.2%) explained 77.5% of the variation in driver carry distance (Table 5).

Table 5. Multiple regression analysis on golf swing performance variables with maturity used as a covariate.

Dependent variable	Model Number	Independent variables	R^2	Adjusted R^2	p
6-iron CHS	1 2	CMJAbsPP CMJAbsPP, IMTPAbsPF 6-iron CHS = 63.779 + (0.003 × CMJAbsPP) + (0.004 × IMTPAbsPF)	0.794 0.818	0.787 0.808	<0.001 <0.001
6-iron carry distance	1 2 3	IMTPAbsPF IMTPAbsPF, plank IMTPAbsPF, plank, SLJ 6-iron carry distance = 87.244 + (0.017 × IMTPAbsPF) - (0.084 × Plank) + (0.192 × SLJ)	0.696 0.721 0.745	0.686 0.707 0.728	<0.001 <0.001 <0.001
Driver CHS	1	CMJAbsPP Driver CHS = 81.043 + (0.004 × CMJAbsPP)	0.824	0.819	<0.001
Driver carry distance	1 2	IMTPAbsPF IMTPAbsPF, SLJ Driver carry distance = 86.729 + (0.29 × IMTPAbsPF) + (0.273 × SLJ)	0.743 0.775	0.734 0.763	<0.001 <0.001

Discussion

In relation to the original aims and hypotheses, the main findings of this study were (i) when controlled for maturity status, more skilful golfers were able to hit the ball further with the driver and 6-iron, produced more isometric peak force and dynamic peak power, jumped further, and showed better movement competency in the GMS; (ii) maturity was a stronger predictor of CHS and carry distance than handicap, and (iii) IMTP_AbsPF and CMJ_AbsPP were the strongest predictors of driver and 6-iron carry distance and CHS, respectively. Considering these novel findings, we accept all of our original hypotheses.

Findings from this study showed that lower handicap golfers were significantly more mature than higher handicap golfers (CAT0 vs CAT1 and CAT2, CAT1 vs CAT2), which indicates that golfers are likely to improve in skill level as they mature. When we controlled for maturity status in our analyses, better golfers were observed to hit the ball further with the driver, which reflects previous data published in adult golfers (Pelz et al., 2008). Consequently, efforts should be made to increase the force producing capabilities of youth golfers to further develop golf swing performance by exposing them to developmentally appropriate strength and conditioning. Our data also showed that better golfers had significantly greater movement competency than less skilled golfers. Assuming and maintaining the correct posture throughout the golf swing can reduce the onset of technical faults (Gulgin et al., 2014) and allow for an efficient and repetitive technique. Notably, Gould, Oliver, Lloyd, et al. (Gould et al., 2018) highlighted those golfers who scored higher in the GMS exhibited greater spine control and x-factor stretch, which are associated with faster swing speed (Gould et al., 2018), greater ball velocities and lower handicaps (Fradkin et al., 2004). Additionally, movement competency is an important quality for youth athletes and has been deemed an essential foundation for sport-specific movements. Therefore, it is recommended that fundamental movement skills should also be incorporated from early childhood to suitably prepare young golfers for the demands of more sport-specific skills and competitive and training workloads (Lloyd & Oliver, 2012).

Linear regression analysis indicated that both driver and 6-iron CHS to be largely explained by maturity (71.3–78.4%), which underlines the potential influence asserted by maturity status on golf swing performance in young golfers. Previous findings have shown body mass to be a key predictor of driver CHS in youth golfers (Coughlan et al., 2020) and although not a true estimate of maturity status, body mass increases throughout childhood and adolescence (Faigenbaum et al., 2019). Notably, adolescence is associated with a marked increase in anabolic hormonal concentrations, which lead to natural growth in muscle mass and associated neural and architectural changes that enable heightened force production (Lloyd et al., 2014). The notion that increased maturity is associated with increased CHS likely underlines the influence of maturation on physical performance and highlights the need to account for maturity status within analyses of junior golfers. Notwithstanding this finding, examination of the beta coefficients from the linear regressions also offers practical insights. Specifically, our data indicate that driver and 6-iron CHS will increase by 5.3 mph and 3.7 mph for every year increment in maturity offset (e.g., a golfer moving from age at PHV to +1 year post-PHV), whilst a 5.3 mph and 3.7 mph increase in driver and 6-iron CHS would result in a reduction of ~2.1 in handicap, respectively. Beta coefficient analysis has also identified that a 1 mph increase in CHS would equate to a 2.6- and 2.8-yard increase in carry distance for 6-iron and driver, respectively. From a talent identification perspective, coaches should be aware that golf swing performance (i.e., CHS or carry distance) could differ markedly within a single chronological age group due to maturity status. Early and late maturing individuals of the same chronological age will be at least two biological years apart in terms of maturity (Kozieł & Malina, 2018). This would result in driver CHS being >10 mph greater for an early versus late maturing golfer of the same chronological age, and in turn, an increase in driver CHS of 10 mph would improve handicap by ~4 strokes. However, from a talent development perspective, any training-induced improvements in golf swing performance should be interpreted in relation to the reported beta coefficients and established noise in the measurement (Shaw et al., In Press).

Multiple regression analysis has identified that CMJ_AbsPP and IMTP_AbsPF explained a large amount of variation in CHS and carry distance, which highlights the importance for practitioners to prescribe youth golfers’ resistance training interventions that aim to enhance strength and power qualities in order to maximise golf swing performance. Typically, the ability to maximally produce force can be reflected by the rates of muscle activation, differential motor unit recruitment, reduced agonist-antagonist co-contraction and conduction velocity (Dotan et al., 2012; Falk et al., 2009). It has been shown that as youth athletes mature, they are able to recruit higher threshold motor units which is accompanied by significant increases in maximal force producing capabilities and an enhanced ability to produce force rapidly (Tumkur Anil Kumar et al., 2021). Given the magnitude of changes in muscle activation as youths mature, practitioners may wish to take advantage of this opportunity and prescribe training programmes that are complementary to the natural adaptive processes. For example, pre-PHV youth have shown to respond more favourably in jump performance when taking part in plyometric training whilst post-PHV youth have shown to respond better to combined plyometric and traditional strength training (Lloyd et al., 2016). This relationship between specific adaptations induced by a training programme and the simultaneous growth and maturation-related adaptations has been termed as “synergistic adaptation” (Lloyd et al., 2016). Despite previous apprehensions, it is widely accepted that resistance training is a safe practice for children within a supervised environment conducted by qualified professionals (Lloyd & Oliver, 2012). Although some athletic development models have suggested a window of opportunity for strength development 12–18 months post-PHV (Balyi & Hamilton, 2004) and during the onset of adolescence for power (Beunen & Malina, 1988), it has also been accepted that youth in their prepubertal years can also develop strength and power levels due to accelerations in the neuromuscular system and therefore should be incorporated in long-term athletic development programmes (Lloyd & Oliver, 2012). Not only should strength and power development be a priority in resistance training programmes for the performance-based enhancements inferred from this study but also due to the reduction in apparent risks that early specialisation sports like golf may present (e.g., overuse injuries, overtraining, reduced motor skill development, and early withdrawal from sport) (DiFiori et al., 2014; LaPrade et al., 2016; Lauersen et al., 2014).

Despite the novel findings of this study, certain limitations should be noted. Due to the sample size and the distribution of maturity status of the participants within this study, it was not feasible to evenly group participants based on maturity status. However, this study utilised a cross-sectional design with maturity offset as a covariate to explore the impact of maturation on golf swing performance, providing useful information for practitioners. However, developmental rates need to be confirmed with longitudinal monitoring to provide greater insight on the trajectories of physical fitness and golf swing performance. Although this study analysed the underpinning kinetics that influence golf swing performance, it did not measure kinematics. Given that consistency and centredness of strike have been shown to play a contributing factor on CHS and carry distance, certain kinematic variables may help explain a larger amount of variance in golf swing performance (Sweeney et al., 2013). Future research may wish to investigate the kinematics of the golf swing (e.g., 3D motion analysis) and establish key biomechanical variables that influence golf swing performance. Consideration should also be given to individual’s training history (i.e., training age) and other factors which may influence physical performance, such as baseline fitness levels. Nonetheless, this study has provided novel findings which contribute to the limited literature surrounding the physical development of youth golfers and the data can be used to help support the design and assessment of future strength and conditioning programmes.

Conclusions

The current study shows that more skilful golfers were able to hit the ball further with the 6-iron and driver, produced more isometric peak force and dynamic peak power, jumped further, and showed better movement competency. The evidence of physical-related differences in golfers of differing skill levels offers national governing bodies and professional sporting associations the opportunity to develop relevant testing batteries to identify emerging talent and recruit golfers onto talent development pathways. Given the identified importance of strength and power kinetics for golf swing performance in youth golfers, practitioners should administer tests that assess these variables as opposed to relying solely on performance-based variables when access to equipment allows.

When maturation was accounted for, measures of strength and power were shown to be key predictors of 6-iron and driver CHS and carry distance. These findings should provide practitioners with confidence that increases in measures of strength and/or power should result in improved golf swing performance (i.e., CHS and carry distance). Therefore, it is imperative for practitioners to prescribe training programmes that are designed to enhance force producing capabilities. Given the relatively large amount of explained variance of maturation in CHS and carry distance, it is imperative that practitioners measure and monitor maturity offset to help prescribe adequate training programmes that complement the natural adaptations associated with growth and maturation. Additionally, assessing maturation is also important for interpreting the effectiveness of a given training programme, ensuring that any changes in physical or golf swing performance are a result of the intervention and not solely due to growth and maturation.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article was originally published with errors, which have now been corrected in the online version. Please see Correction (https://doi.org/10.1080/02640414.2024.2312670)

Additional information

Funding

The author(s) reported that there is no funding associated with the work featured in this article.