## 2 Methods
### Statistical Analysis  
Initially, all the data were recorded as means and SD in Microsoft Excel. The normality of the data was confirmed using the Shapiro-Wilk test (\(p>0.05\)). To assess the intra-session reliability of all tests, a two-way random intraclass correlation coefficient (ICC) with absolute agreement and 95% confidence intervals (CI), the coefficient of variation (CV%) with 95% CIs, and the standard error of the measurement was used. The interpretation of the ICC values was in accordance with previous research by [PERSON] and [PERSON] [29], in which values of <0.5 = poor, 0.5-0.75 = moderate, 0.76-0.9 = good, and >0.9 = excellent. The CV values were considered to be acceptable if <10%, in accordance with the findings of [PERSON] et al. [30]. Specifically, the process was the same for all the data, but this involved utilising 3 trials for the fitness testing data and 10 trials for the golf shot data. [PERSON]'s \(r\) correlation analysis was utilised to assess the magnitude of associations between the golf performance metrics and physical assessment data. In line with prior research, correlations were categorised as follows: 0-0.09 = trivial, 0.1-0.29 = small, 0.3-0.49 = moderate, 0.5-0.69 = large, 0.7-0.89 = very large, and \(\geq\)0.9 = nearly perfect [31]. Finally, a median split analysis was performed creating higher (\(n=13\)) and lower (\(n=13\)) groups for (i) peak force in the IMTP, (ii) peak force in the isometric bench press, (iii) jump height in the CMJ, and (iv) distance in the medicine ball throw. Given that  
Figure 2: Set-up of the isometric bench press assessment. these splits were performed for each individual test, it should be acknowledged that the athletes in each group may be different, depending on the test in question. Following this, between-group differences were then assessed for golf shot data with a driver only. Given the volume of physical capacity metrics reported in the present study, these metrics were selected for the median split analysis because they exhibited the best reliability (Table 2), showed the strongest associations with the golf shot data in the present study (Figure 3), and are most commonly used in day-to-day practice in sport science. Due to the data being normally distributed, the differences between groups were assessed using paired sample \(t\)-tests, with statistical significance set at \(p<0.05\). Hedges' \(g\) effect sizes (ES) with 95% CIs were also used to determine the magnitude of the differences between the groups. These were interpreted as follows: \(g<0.35=\) trivial; 0.35-0.80 = small; 0.81-1.50 = moderate; and \(>\)1.5 = large [32]. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{_Variables_} & \multicolumn{4}{c}{**Driver**} & \multicolumn{4}{c}{**6-Iron**} \\ \cline{2-10}  & _Mean_\(\pm\)_SD_ & _ICC (95\% CIs)_ & _CV (95\% CIs)_ & _SEM_ & _Mean_\(\pm\) SD & _ICC (95\% CIs)_ & _CV (95\% CIs)_ & _SEM_ \\ \hline Clubhead Speed (mph) & 109.24 \(\pm\) 8.43 & 0.99 (0.99, 1.00) & 0.64 (0.55, 0.77) & 0.84 & 92.92 \(\pm\) 7.12 & 0.99 (0.98, 0.99) & 0.75 (0.65, 0.90) & 0.71 \\ \hline Ball Speed (mph) & 155.06 \(\pm\) 15.29 & 0.98 (0.96, 0.99) & 1.64 (1.42, 1.96) & 2.16 & 122.01 \(\pm\) 10.80 & 0.90 (0.82, 0.95) & 3.00 (2.58, 3.59) & 3.42 \\ \hline Carry Distance (yards) & 239.15 \(\pm\) 31.33 & 0.94 (0.90, 0.97) & 3.54 (3.05, 4.24) & 7.67 & 169.81 \(\pm\) 19.64 & 0.87 (0.77, 0.93) & 4.65 (4.00, 5.58) & 7.08 \\ \hline Smash Factor & 1.42 \(\pm\) 0.06 & 0.80 (0.61, 0.90) & 1.71 (2.06, 2.60) & 0.03 & 1.32 \(\pm\) 0.06 & 0.65 (0.27, 0.83) & 2.84 (2.44, 3.40) & 0.04 \\ \hline \hline \end{tabular} ICC = intradax correlation coefficient; CV = coefficient of variation; CI = confidence interval; SEM = standard error of the measurement; mph = miles per hour.  
\end{table} 
Table 2: Mean \(\pm\) standard deviation (SD) and reliability data for driver and 6-iron shot metrics. Figure 3: A heatmap showing Pearson’s \(r\) correlations between physical assessments and golf shot data with a driver and 6-iron.