Introduction

We were interested to understand what predictor was the best for each ability level. As the grade increases in climbing, the complexity increases. The demands of the problems become more strenuous with them requiring climbers to use smaller holds, fewer fingers, climb longer sustained sequence or short more powerful sequences. This creates a unique environment which a single test cannot capture. With the increase in demands as the grade increases we wanted to understand does the best predictor changes as ability level increases. Therefore, we conducted a mini-test on our remote assessment data to 1) understand how a specific metric predicted bouldering performance and 2) how this changed across ability levels.

Methodology

Participants

Nine hundred and one participants were used in our analysis. These were a small subset of our remote assessment data pool. 773 of the participants were male, 98 were female and the remaining 40 did not define their sex. Grades ranged from V1 to V15. The table below details the anthropometrics of the groups.

Procedure

A small pool of data was taken from the remote assessment data pool and analysed. We looked at the 4 key tests used in our Climbing Training Plans assessment: finger strength test, power endurance test, pulling strength test and flexibility test. These tests were conducted in the athlete’s own time and at a facility of the athlete’s choice.

Finger Strength Test

Participants completed a progressive dead-hang test on a fingerboard to determine their maximum hanging load for a duration of seven seconds. The test was performed using both arms while maintaining a standardized half-crimp grip position. Prior to testing, participants completed a warm-up to prepare for maximal effort attempts.

The test began with a moderate-intensity hang, followed by progressively harder hangs by increasing the additional load. Participants aimed to reach their maximum load within 4-8 attempts. If more than eight attempts were required, they were instructed to rest for 24 hours before retrying. A test was considered unsuccessful if the grip position changed during the hang, in which case the last successful attempt was recorded.

The maximum loading was calculated as the sum of the participant’s body weight and the additional load. The following data were recorded:

• Fingerboard used
• Depth of the hold (18 mm, 20 mm, or 22 mm edge)
• Total load (kg) (body weight plus additional weight added or removed)
• Percentage bodyweight (%BW – ((Total load/ bodyweight) x 100))

Power Endurance Test

Following the finger strength test, participants completed a power endurance assessment by performing repeated dead-hangs using 60% of their previously determined maximum load. Each repetition consisted of a seven-second hang followed by a three-second rest, repeated until failure. Failure was defined as the inability to complete a seven-second hang or a change in grip position.

Participants were required to maintain an elbow angle between 150 and 180 degrees throughout the test and avoid locking off at any point. The 60% load was calculated as: (Bodymass+max test additional load) ×0. 6. If required, a pulley system was used to provide assistance.

The following data were recorded:

• Total time (s) of successful hangs
• Total load (kg) (body weight plus additional weight added or removed)

Pulling Strength Test

Participants performed a two-repetition maximum (2RM) pull-up test using additional weight to assess their maximal pulling strength. Weight was added using a harness or sling. Participants rested for three minutes between each attempt to progressively determine their maximum load.

The pull-ups were performed with a shoulder-width grip, palms facing away. Each repetition was executed from nearly straight arms to the chin reaching the bar, in a controlled manner without lower body movement.

The following data were recorded:

• Total load (kg) (body weight plus additional weight added or removed)
• Percentage bodyweight (%BW – ((Total load/ bodyweight) x 100))

Flexibility Test

Participants assessed their lower body flexibility using a standing box split test against a wall. Standing with their toes touching the wall, participants gradually moved their feet wider while maintaining contact between their toes, stomach, and the wall. Only upper body support was permitted for entry and exit from the position.

The maximum distance achieved between the heels at the widest stance was measured and recorded in centimeters.

The following data were recorded:

• Distance between the heels (cm)

Statistical Tests

Statistical Analysis

Before the analysis was conducted the data was cleaned for outliers. Further, the analysis was cleaned a second time to remove sport climbing specialist from the analysis. The athlete’s best boulder worked grade and best boulder worked grade were compared and if there was a difference of 2 in favour of boulder climbing, the athlete was reviewed. This reduced the dataset from 942 to 901.

A simple linear regression analysis was conducted to examine the relationship between individual predictor variables (7 second max hang % Bodyweight, Pull-Up 2RM % Bodyweight, Flexibility (Box Split), and 60% Time to Exhaustion) and climbing performance, measured as the highest boulder grade worked within a two-year period. The analysis was performed separately for each predictor to evaluate its independent contribution to performance across different ability levels (Combined, Advanced, Elite, and Higher Elite). Due to the size of the intermediate group they were removed.

For each regression model, the grade (Y) was the best-worked boulder grade, and the ingrade (X) was the selected predicto: Finger strength, pulling strength, power endurance and flexibility.

Model performance was assessed using several regression metrics. The coefficient of determination (R) was reported to indicate the proportion of variance in climbing grades explained by the predictor, while adjusted R² was used to correct for sample size differences and prevent overestimation of explanatory power. The regression coefficient (β1) and its standard error were calculated to estimate the magnitude and uncertainty of the predictor’s effect. The p-value were used to assess statistical significance, with thresholds set at p<0.05 for significance and p<0.001 for highly significant relationships. In addition, 95% confidence intervals (CIs) were computed to provide a range within which the true effect size is likely to fall. The F-statistic was used to test the overall significance of the model, and the Durbin-Watson statistic was included to assess autocorrelation in residuals.

To ensure that regression assumptions were met, multiple diagnostic tests were performed. The normality of residuals was evaluated using Q-Q plots and Shapiro-Wilk tests to confirm that errors were normally distributed. Homoscedasticity, or constant variance of residuals, was checked using residual plots to ensure that error variance remained stable across fitted values. Linearity between predictors and the grade was verified through scatterplots of residuals against fitted values. Additionally, Cook’s Distance and Leverage Plots were used to detect influential data points that could unduly affect model estimates.

Results/ Discussion

Finger strength – 7-second Max Hang test.

This test represents the maximum amount of weight an athlete can hold on 2 arms on a 20mm edge for 7 seconds. The score is presented as a percentage of bodyweight. Typically this test is a good indicator of finger strength.

A regression analysis was conducted using the combined dataset, with best boulder worked as the grade and 7-sec Max Hang (%BW) as the predictor. The analysis yielded a correlation coefficient (R) of 0.704, indicating a moderate-to-strong relationship between the variables. The model explained 49.6% of the variance in the grade (R² = 0.496). A significant effect was observed, F(1, 899) = 885.74, p < 0.001, demonstrating that the model provides a statistically significant explanation of variation in the best boulder worked.

Pulling Strength – Pull up 2 Rep Max Test.

This test represents the maximum amount of weight an athlete can pull for 2 repetitions in the prontated pull-up position. The score is presented as a percentage of body weight. Typically this test is a good indicator of upper body pulling strength.

A regression analysis was conducted using the combined dataset, with best boulder worked as the grade and 2-rep max pull-ups (%BW) as the predictor. The analysis yielded a correlation coefficient (R = 0.582), indicating a moderate positive relationship between the variables. The model explained 33.7% of the variance in best boulder worked (R² = 0.337), suggesting that while 2-rep max pull-ups (%BW) are a significant predictor, other factors contribute substantially to performance. A significant effect was observed, F(1, 899) = 459.36, p < 0.001, confirming that the model provides a statistically significant explanation of variation in best boulder worked.

Power Endurance – 60% Time to Exhaustion.

This test represents the time to exhaustion @60% of the 7-second max hang. Till failure, participants will complete 7 second on, 3 second off repeaters. Typically this test is a good indicator of power endurance..

A regression analysis was conducted using the combined dataset, with best boulder worked as the grade and 60% Time to Exhaustion (Seconds) as the predictor. The analysis yielded a correlation coefficient (R = 0.007), indicating little to no relationship between the variables. The model explained -0.001% of the variance in best boulder worked (R² = -0.001). A non-significant effect was observed, F(1, 899) = 0.046, p = 0.830, confirming that the model does not provide a statistically significant explanation of variation in best boulder worked.

Flexibility – Box Splits.

This test represents the maximum distance between the two heels when completing a box split. The distance is measured in cm and is an indicator of hip flexibility. 

A regression analysis was conducted using the combined dataset, with best boulder worked as the grade and flexibility box splits (cm) as the predictor. The analysis yielded a correlation coefficient (R = 0.140), indicating a weak positive relationship between the variables. The model explained 2% of the variance in best boulder worked (R² = 0.020).  A significant effect was observed, F(1, 899) = 17.08, p < 0.001, confirming that the model provides a statistically significant explanation of variation in best boulder worked.

Ability level

Advanced

Among Advanced climbers, Finger Strength and Pulling Strength are the most influential factors.

Finger Strength has a moderate predictive power with R = 0.411 and an adjusted R² of 0.167, meaning about 17% of the variation in grade can be explained by Finger Strength. The p-value is <0.0001, confirming its high statistical significance. The regression coefficient (0.0233) indicates that for each unit increase in Finger Strength, the grade increases by 0.0233 units.

Pulling Strength is another significant factor, with R = 0.346 and an adjusted R² of 0.117. This means 12% of the variation in performance is explained by this variable. The regression coefficient (0.0213) suggests that an increase in Pulling Strength results in a 0.0213-unit increase in the grade. The p-value <0.0001 confirms statistical significance.

Flexibility and 60% Power Endurance have low R² values (0.072 and 0.058, respectively), meaning they explain very little of the variation in the grade. Additionally, 60% Power Endurance (P = 0.1386) and Flexibility (P = 0.2329) are not statistically significant, indicating these factors are not strong predictors of performance at this level.

Summary for Advanced Climbers

• Finger Strength and Pulling Strength are statistically significant and explain moderate variation in performance.
• Flexibility and 60% Power Endurance have minimal predictive power and are not statistically significant.

Elite

For Elite climbers, the influence of Finger Strength and Pulling Strength declines.

Finger Strength remains the strongest predictor but has a lower R of 0.319 and an adjusted R² of 0.099. This means only 9.9% of the performance variation can be explained by Finger Strength. The regression coefficient (0.0154) is smaller than in Advanced climbers, indicating a weaker effect size. However, the p-value <0.001 still confirms statistical significance.

Pulling Strength also shows a decline in influence, with R = 0.219 and an adjusted R² of 0.046, explaining 4.6% of the performance variation. The regression coefficient (0.0119) indicates a smaller impact, though it remains statistically significant (P < 0.001).

Flexibility and 60% Power Endurance have R values close to 0 (0.026 and 0.014, respectively), and their p-values (0.6235 and 0.7848) indicate no significant effect on performance.

Summary for Elite Climbers

• Finger Strength and Pulling Strength remain statistically significant but explain less variation than in Advanced climbers.
• Flexibility and 60% Power Endurance have no predictive value for performance.

Higher Elite

At the Higher Elite level, the predictive power of all variables weakens significantly, reinforcing the idea that factors beyond strength and endurance play more of a role at this stage.

Finger Strength has R = 0.225 and an adjusted R² of 0.042, meaning it explains only 4.2% of the variation in performance. The regression coefficient (0.0188) is still positive, and statistically significant ( p = 0.0187).

Pulling Strength becomes statistically insignificant (P = 0.1640), with an adjusted R² of only 0.009, meaning it explains less than 1% of the variance.

60% Power Endurance and Flexibility remain insignificant predictors, with p-values of 0.0956 and 0.9093, respectively.

Summary for Higher Elite Climbers

• Finger Strength is significant but explains very little variation.
• Pulling Strength, 60% Power Endurance, and Flexibility are not significant predictors.
• Overall, strength-based variables lose predictive power, suggesting other factors such as more specific strength metrics, technique, efficiency, and mental strategies maybe more critical at this level.

Conclusion

• Finger Strength and Pulling Strength are the most important predictors for Advanced and Elite climbers, but their influence declines in Higher Elite climbers.
• 60% Power Endurance and Flexibility are not significant predictors at any level.
• At the Higher Elite level, strength and endurance lose their predictive power, meaning other performance factors become more important.

These results indicate that while physical strength is a key factor in early climbing progression, high-level climbers likely depend more on specific training, advanced movement skills, route reading, mental endurance, and efficiency rather than just raw physical attributes.