# Developing a Model of Risk Factors of Injury in Track and Field Athletes
## 4 Discussion  
This work aimed to develop a model to assess the likelihood of injury in track and field athletes, and to establish which factors determine the probability to the greatest extent. Our primary focus was on severe injuries with extensive consequences and require the help of specialists as well as significant changes in the training program [28]. There were also large discrepancies between the group of respondents (a) and the representative group (b) in females and males. This was due to the fact the questionnaire was filled mainly by athletes with higher sports classes. According to [PERSON] [11], the injury risk factors are mostly divided into two main categories: internal--athlete-related risk factors, often recognized as intrinsic factors; and external--environmental risk factors. When we try to model the risk of injury in sport, all these factors can be divided into modifiable and nonmodifiable factors [10]. In our studies, most of the factors can be classified into the above categories; however, our analysis is based on three categories: demographic factors, training/competition factors, and health/regeneration factors. The more precise the division and  
Figure 4: WoE (weight of evidence) graph for the variable _previous injuries._ \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Case Classification**} \\  & \multicolumn{3}{c}{**Odd. Ratio 19.531250**} \\  & \multicolumn{3}{c}{**Log Odd. Ratio2.972016**} \\ \cline{2-4}  & **Predicted: 1** & **Predicted: 0** & **Correct \%** \\ \hline Observed: 1 & 125 & 12 & 91.24 \\ Observed: 0 & 24 & 45 & 65.22 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The classification matrix for model M.  
grouping of factors, the greater the likelihood of classifying a particular factor as modifiable. Such an assumption may indicate a more effective modeling process, and thus help to determine the most critical factors affecting sports injuries. Nonmodifiable risk factors, such as gender and age, may be of interest. Because age and gender are nonmodifiable, they were excluded from the model. Most factors are training factors that are modifiable through behavioral approaches [10]. Therefore, such a multifactorial approach to the determination of risk factors of sports injuries requires a dynamic model, which takes the entire sequence of events preceding or directly affecting the occurrence of an injury. According to [PERSON] [34], the sum of these risk factors and the interaction between them \"prepares\" the athlete for an injury to occur in a given situation. Studies considering only a single variable and its impact on injury risk may be too simplistic. Therefore, in order to better understand the etiology of injuries, the collective involvement of many factors in the risk of injury should be analyzed [35; 36]. According to [PERSON] [35] and [PERSON] [36], injuries occur as a result of complex and nonlinear interactions between multiple factors. [PERSON] [37] stated that it is unlikely that a single, isolated factor is capable of providing enough information to predict injuries at the individual level. Our model includes seven predictors: previous injuries, sleep, age, blood, training load, atmospheric conditions, and competition. Although age belongs to the unmodified category of risk factors [10], in our study, age was included in the modeling process because the athletes were divided into age categories. Training and sports level, as well as competitions, are related to age categories, including the number of competitions in the season. Training factors are modifiable through behavioral changes, which can alter blood parameters. Training and competition periods occur in an annual training cycle. The training period was the critical predictor creating the strongest relationship with the risk of injury, for which the IV value was 0.326. Track and field athletes are at particularly high risk of injury due to their involvement in many events.