## 2 Materials and Methods
### Statistical Analyses  
For game venue analyses, nights were categorized (\(n=5\)) as a control (neither training nor games conducted on these days or the day immediately prior and after) immediately before home games, following home games, immediately before away games, and following away games. During weeks (\(n=3\), Figure 1) where multiple away games were played on subsequent days, only the first game was used in analyses for nights immediately before away games, given that other nights surrounding games in these scenarios were categorized as following away games. For season period analyses, data were split across all nights arranged into even 4-week blocks (\(n=3\)) representing the early, middle, and late periods of the regular season [27].  
Sleep data were imported into RStudio (v4.1.3; R Core Team) from Microsoft Excel for cleaning and analyses. Data were arranged in long form, with rows representing independent observations and sleep variables presented in columns. Linear mixed-effects models (LMM) were built to assess differences in sleep variables between nights separately for game venue and season period analyses. A customized script was developed for all analyses with models built using the _lmerTest_ package [28] in RStudio. For the game venue model, 'night type' (\(n=5\)) was entered as a fixed effect, while for the season period model, 'period' (\(n=3\)) was entered as a fixed effect. 'Player' was entered as a random effect in both models to account for repeated data points taken from the same players. To assess data normality, histograms and Q-Q plots were produced from residual values and visually checked using the _see_[29] and _performance_[30] packages. No statistical assumptions for using LMM were violated, supporting their use in our analyses. [PERSON]'s Honestly Significant Difference tests were utilized for post hoc comparisons, with pairwise differences between night types (game venue analyses) and season periods determined using the _emmeans_[31] package for calculating the estimated marginal means. Furthermore,  
Figure 1: The training and game schedule for the semi-professional, female basketball players monitored in this study. _Note_: letters next to each row correspond to each day of the week from Monday (M) to Sunday (S) in order; court image indicates on-court team training session; shaded basketball indicates away game; transparent basketball indicates home game; all training sessions commenced at 18:30; all games on Fridays commenced at 18:30; all games on Saturdays commenced at 18:00; all games on Sundays commenced at 12:00.  
Hedge's \(g_{av}\) effect size with 95% confidence limits was also determined for all pairwise comparisons [32; 33]. In this regard, effect size magnitudes were interpreted according to established descriptors with accompanying thresholds: _trivial_ = \(<\)0.20; _small_ = 0.20-0.59; _moderate_ = 0.60-1.19; _large_ = 1.20-1.99; or _very large_ = \(\geq\)2.00 [34]. Descriptive data were calculated as the estimated marginal means (with 95% confidence limits) as well as the mean \(\pm\) standard deviation (SD) for all variables, with statistical significance set at \(p\leq\) 0.05 for analyses.