Frailty Scores are calculated based on various factors including injury history,
player statistics, field conditions, and player influence.
Injsur.ai uses machine learning to create a comprehensive assessment of a player's risk of injury and potential performance.
Adjusting the importance of these factors helps refine the Frailty Scores to better predict the probability of injury of players. A high Frailty Score reflects a higher chance of player injury and lower expected ovrall perfromance.
Set the importance of various injury types to refine the Frailty Scores. Players who are more prone to these specific injuries will receive higher Frailty Scores. This helps in identifying players who are more likely to get injured based on their history and susceptibility to certain types of injuries.
Set the importance of specific player statistics. Players who excel in certain statistics will receive higher Frailty Scores. This allows you to evaluate players based on their performance metrics and how these stats influence their overall health and performance.
Set the importance of different field conditions. Players whose performance is significantly affected by various field conditions will receive higher Frailty Scores. This helps in understanding how environmental factors like weather, turf type, and field quality impact player safety and performance.
Set the importance of player influence by selecting a specific position and adjusting its impact on other players. If a key position performs poorly, it can increase the Frailty Scores of other players who depend on that position. This highlights the interconnected nature of player performance and injury risk within a team.