Spss statistical analysis software for windows

Data are expressed as mean ± standard deviation (SD) or as individual values. The distribution of each variable was examined for the assumption of normality using the Kolmogorov–Smirnov test. Correlations were determined using Pearson’s product moment correlation coefficient (r). The magnitudes of the correlation coefficients were stratified into groups comprising trivial (r < 0.1), small (0.1 < r < 0.3), moderate (0.3 < r < 0.5), large (0.5 < r < 0.7), very large (0.7 < r < 0.9), nearly perfect (r > 0.9), and perfect (r = 1.0) [28 (link),29 ]. The entire statistical analysis was performed using SPSS Statistical Analysis Software for Windows^® (SPSS, version 25, Chicago, IL, USA).

Data are expressed as mean ± standard deviation (SD) or as individual values. Repeatability analysis was performed on a subset of 40 samples randomly chosen and assessed at two different time points by two raters (coaches). Mean estimates with 95% confidence intervals (CI) were reported for intraclass correlation coefficient (ICC). Interpretation was as follows: <0.50 poor; from 0.50 to 0.75 fair; from 0.75 to 0.90 good; and above 0.90 excellent. The ICC for inter-rater reliability between coaches was from good to excellent at 0.91 (0.78–0.96).
To test for rank order relationship in quality of reception between different reception tests and number of receptions in SSGs, we determined correlations using Spearman’s rank order correlation coefficient (r_s). The magnitudes of the correlation coefficients were stratified into groups as follows: trivial (r < 0.1), small (0.1 < r < 0.3), moderate (0.3 < r < 0.5), large (0.5 < r < 0.7), very large (0.7 < r < 0.9), nearly perfect (r > 0.9), and perfect (r = 1.0) [26 (link),27 ]. All statistical analyses were performed using SPSS Statistical Analysis Software for Windows^® (SPSS, version 25, Chicago, IL, USA).

Players’ average scores for each test were used for further analysis. The data were expressed as mean ± standard derivation (SD), and we also reported Cohen’s d effect size for sex differences in both technical tests. An effect size of 0.2 was considered small, 0.5 medium, and 0.8 large [27 ]. The difference between sexes was analysed by independent sample T-test with a significance level of p ≤ 0.05. A Pearson’s correlation was used for all participants and per sex to investigate correlations between the scores of the ball reception and long passes. Threshold values for the correlation coefficients’ interpretation as an effect size were 0.1–0.3 (trivial), 0.3–0.5 (moderate), 0.5–0.7 (large), and 0.7–0.9 (very large; Hopkins et al., 2009).
A repeatability analysis was performed on a subset of 50 randomly chosen samples and assessed at two different points in time by two coaches to evaluate the accuracy of the judgements of the test ratings. Mean estimates with 95% confidence intervals (CI) were reported for an intraclass correlation coefficient (ICC). Interpretation was as follows: <0.50 poor; from 0.50 to 0.75 fair; from 0.75 to 0.90 good; and above 0.90 excellent. The ICC for inter-rater reliability between coaches was good to excellent at 0.90. All statistical analyses were performed using SPSS Statistical Analysis Software for Windows^® (SPSS, version 25, Chicago, IL, USA).