## 2 Materials and Methods
### Statistical Analysis  
A priori power analysis was conducted using G*Power (version 3.1.9.7) to determine the required sample size, ensuring adequate statistical power for detecting meaningful differences [31; 32]. Two separate analyses were performed: one for validity assessment using correlation analysis and another for reliability assessment using repeated measures ANOVA. The results indicated that a minimum of 8 participants was required for the validity analysis, while at least 15 participants were necessary for the reliability assessment to achieve sufficient statistical power. However, it was agreed to include a sample size exceeding the minimum requirement of 15 participants in order to enhance the precision of estimates between MyJump 2 and Chronojump for both countermovement jump (CMJ) and squat jump (SJ) assessments in soccer players across different age groups.  
All statistical analyses were conducted using IBM SPSS Statistics (version 29 for Windows), Jamovi (version 2.6.23.0 for Windows) and R (version 4.4) [33; 34; 35]. Agreement and reliability analyses were performed using the SimplyAgree package [36], while additional statistical procedures and model diagnostics were supported by the car package for applied regression [37] and the emmeans package for estimating marginal means [38]. Descriptive statistics, including mean and standard deviation (SD), were calculated for countermovement jump (CMJ) and squat jump (SJ) performance, as measured by Chronojump and MyJump. Normality was assessed using the Shapiro-Wilk test, which confirmed that all variables followed a normal distribution (\(p>0.05\)).  
To evaluate the validity of MyJump in comparison to Chronojump, Bland-Altman analysis was performed to assess the systematic bias and 95% limit of agreement (LoA). The mean bias between the two methods was computed, along with confidence intervals (CIs) for the LoA, to determine the extent of agreement and possible measurement discrepancies. Additionally, the association between MyJump and Chronojump measurements was analyzed using [PERSON]'s correlation coefficient (r) and [PERSON]'s rho (\(\rho\)) to assess both linear and rank-order relationships. Moreover, a linear regression model was applied to examine the predictive validity of MyJump against Chronojump measurements.  
To determine the reliability of MyJump measurements, test-retest reliability was assessed by comparing MyJump test and retest values. Intraclass correlation coefficients (ICCs) were computed to evaluate absolute and average agreement across trials. Specifically, one-way and two-way random models (ICC1, ICC2) were used for absolute agreement, while the two-way fixed model (ICC3) assessed consistency. Average agreement across repeated trials was estimated using ICC1k, ICC2k and ICC3k. Further, measurement variability was quantified using the coefficient of variation (CV%), standard error of measurement (SEM), standard error of the estimate (SEE), minimum detectable change (MDC)--calculated using the mathematical formula MDC = 1.96 \(\times\)\(\surd\)2 \(\times\) SEM--and standard error of prediction (SEP). A Bland-Altman analysis was also conducted to assess agreement between the MyJump test and retest measurements, providing mean bias, 95% LoA and confidence interval (CI) to evaluate systematic differences and test-retest consistency.  
The intraclass correlation coefficients (ICCs) for both validity and intra-rater reliability [39] values ranging from 0.90 to 1.00 were considered very high correlations, those between 0.70 and 0.89 indicated a high correlation, values within 0.50 to 0.69 reflected a moderate correlation, ICCs between 0.26 and 0.49 were classified as low correlations and values from 0.00 to 0.25 suggested minimal to no correlation. Statistical significance was set at \(p<0.05\).