## 2 Materials and Methods
### Statistical Analysis  
Medical attention injuries (time loss/non-time loss) were recorded as a binary variable where no occurrence was noted as 0 and an occurrence was noted as 1. A lag period of 7 days was calculated for every training monitoring variable. A 7-day lag period was chosen to overcome the potential of a time loss event creating an artificial low load on the week the event occurred. A time lag was also pertinent as there has been a suggestion of a delayed effect between training load exposure and injury (2019). One week prior to injury would also represent the latest period of adjustment a coach could make to their pre-planned training week, thus making it practically relevant and impactful. All training load data were scaled as shown in Table 2. sRPE was scaled as per [PERSON] et al. (2015). Descriptive analyses of athlete training loads are presented in Table 4 to investigate the occurrence of time loss and non-time loss injuries over a 7-day period, mixed-effect Bayesian logistic regression models were fitted with different predictor variables of interest. A Bayesian approach was used to circumvent the widely reported issues with the misinterpretation of traditional \(p\)-values ([PERSON] et al., 2017) and confidence intervals (CIs) ([PERSON] et al., 2017), to provide probabilistic interpretation of parameters and quantify uncertainty in predictions. Importantly, Bayesian models provide direct evidence for null hypotheses, given that the probability in these methods is calculated under the assumption that the null hypothesis is true.  
The structure of all the models was similar, with either time loss injury or non-time loss injury as the dependent variable. This dependent \(Y_{i}\) (below) represents a binary outcome of time loss or non-time loss injury for the \(i\)th swimmer. The likelihood for each observation is given by a Bernoulli distribution. This is a binary probability distribution of a random variable which takes a binary, Boolean output: 1 (time loss or non-time loss injury) with probability \(p\), and 0 (no time loss or non-time loss injury) with probability \(1-p\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **Max** & **Min** & **Mean** & **Stdev** \\ \hline Weekly Pool Volume (km) & 63.20 & 0.00 & 33.54 & 12.88 \\
4-week Rolling Pool Volume (km) & 217.00 & 0.00 & 115.99 & 58.64 \\ Total Weekly Training Load (AU) & 12,280.00 & 0.00 & 3838.02 & 1616.13 \\
4-week Rolling Total Training Load (AU) & 29,980.00 & 0.00 & 13,162.08 & 6535.19 \\ ACWR (AU) & 3.16 & 0.14 & 1.23 & 0.39 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Descriptive summary of the key training load variables for the athletes.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Term** & **Definition** \\ \hline Injury & Tissue damage or other derangement of normal physical function, resulting from rapid or competitive transfer of kinetic energy ([PERSON] et al., 2017). \\  & A physical complaint where a qualified clinician assessed the athlete’s physical complaint or medical condition. A qualified clinician is anyone who is involved in the health care of athletes, reviews medical or physiological information and/or implements an action plan to improve the athlete’s health, where health is considered in a broad sense but must be more than performance enhancement ([PERSON] et al., 2017). \\ Time Loss & A health problem which leads to the athlete being unable to take full part in FINA activities. If the athlete misses the rest of the training or competition session but returns for the next training/competition, this should be recorded as a time-loss incident ([PERSON] et al., 2017). \\ Severity & Mild: 0–7 days missed; moderate: 8–28 days missed; severe: \textgreater{}29 days missed ([PERSON] et al., 2017). \\ \hline \hline \end{tabular}
\end{table}
Table 3: Definitions of key terms used within the injury surveillance system.