## 4 Discussion  
This study explored the relationship between training load and injury in competitive swimmers. Fundamentally, numerous analyses examining the relationship between training load variables and medical attention-requiring injuries (time loss/non-time loss); returned ORs of 1.0 or close to 1.0 with Bayes Factor\({}_{10}\) suggest the data for all variables were more likely under the null hypothesis of no relationship. Therefore, these findings suggest that no relationship between the training load metric and medical attention-requiring injuries is highly likely. Typically, Weekly Pool Volume (km), has been commonly used by swim coaches to plan and design a swim programme periodisation strategy and monitor athletes' responses to training [11]. However, using training load monitoring for injury reduction purposes has been reported as less common in an applied setting [11]. In research to date, the relationship between pool volume and injury rate has been described as \"questionable\" [36]. Much of the ambiguity surrounding a consensus on the relationship between these variables has been attributed to methodological inconsistencies and limitations. This study sought to address these issues. The effect of external training load was investigated using both Weekly Pool Volume (km) and 4-week Rolling Pool Volume (km). The findings of this study suggest that neither variable had a meaningful association with injury (time loss/non-time loss). This may be related to the fact that these metrics solely consider the impact of external training load and do not incorporate internal training load nor quantify S&C training load. The investigation of the sRPE-derived metrics was a crucial aspect of the study analyses. However, the findings conclude that there is really weak evidence for any relationship with injury (time loss/non-time loss). A key finding surrounding ACWR suggested a 70% chance of positive association with time loss injury and a 97% chance of a negative relationship with non-time loss injury. However, our analyses show these findings to be highly uncertain, which, when coupled with the conclusions of [PERSON] et al. [37] surrounding the foundations of the ACWR models, suggest that this metric should not be used as a predictor of injury. These findings contrast with previous studies in other sports which have found a positive relationship between training load metrics and injury. Regarding elite Australian Footballers, [PERSON] [38] found a reduction in absolute training load (sRPE-TL) resulted in a corresponding reduction in injuries, while [PERSON] et al. [39] found that an increase in 1-2-week accumulated training load resulted in a higher risk of injury. It is very difficult, however, to compare these studies, as not only are the sports vastly different (weight bearing/non-weight bearing), but so too are the weekly training loads, and the statistical analyses make it difficult to compare findings. This issue is acknowledged by [PERSON] and [PERSON] [13], who noted that endurance-based sports typically display training loads with  
Figure 2: Spaghetti plots of the conditional effects of the relationships between injury types and Acute Chronic Workload Ratio (ACWR). a longer duration at a lower intensity, while other sports tend to have higher-intensity training with lower durations, making comparisons difficult. In a research context, this current study has addressed previous limitations [14,40] investigating the relationship between training load and injury. The study design presents a strong framework for future research but also for applied practitioners to understand and transfer to their environments. The findings of this study show that despite improving the methodological structure of the data collection procedures, understanding the mechanism for injury is complex and multifactorial [41]. The Bayesian approach enables direct quantification of the odds of the null hypothesis based on the data.