An example of an injury data set containing minimum required injury
information as well as other further injury-related variables. It includes
Liverpool Football Club male's first team players' injury data. Each row
refers to player-injury. These data have been scrapped from
https://www.transfermarkt.com/ website using self-defined R code
with rvest and xml2 packages.
Format
A data frame with 82 rows corresponding to 23 players and 11 variables:
- player_name
Name of the football player (factor)
- player_id
Identification number of the football player (factor)
- season
Season to which this player's entry corresponds (factor)
- from
Date of the injury of each data entry (Date)
- until
Date of the recovery of each data entry (Date)
- days_lost
Number of days lost due to injury (numeric)
- games_lost
Number of matches lost due to injury (numeric)
- injury
Injury specification as it appears in https://www.transfermarkt.com (character)
- injury_acl
Whether it is Anterior Cruciate Ligament (ACL) injury or not (NO_ACL)
- injury_type
A five level categorical variable indicating the type of injury, whether Bone, Concussion, Ligament, Muscle or Unknown; if any, NA otherwise (factor)
- injury_severity
A four level categorical variable indicating the severity of the injury (if any), whether Minor (<7 days lost), Moderate ([7, 28) days lost), Severe ([28, 84) days lost) or Very_severe (>=84 days lost); NA otherwise (factor)
Note
This data frame is provided for illustrative purposes. We warn that they might not be accurate, there might be a mismatch and non-completeness with what actually occurred. As such, its use cannot be recommended for epidemiological research (see also Hoenig et al., 2022).
References
Hoenig, T., Edouard, P., Krause, M., Malhan, D., Relógio, A., Junge, A., & Hollander, K. (2022). Analysis of more than 20,000 injuries in European professional football by using a citizen science-based approach: An opportunity for epidemiological research?. Journal of science and medicine in sport, 25(4), 300-305.