Analyzing Athletic Injuries: Any athletic activity is prone to injuries. If the
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Question
Analyzing Athletic Injuries:
Any athletic activity is prone to injuries. If the inju- ries are not handled properly, then the team suffers. Using analytics to understand injuries can help in deriving valuable insights that would enable coaches and team doctors to manage the team composition, understand player profiles, and ultimately aid in bet- ter decision making concerning which players might be available to play at any given time. In an exploratory study, Oklahoma State University analyzed American football-related sports injuries by using reporting and predictive analytics. The project followed the CRISP-DM methodology (to be described in Chapter 4) to understand the problem of making recommendations on managing injuries, understanding the various data elements collected about injuries, cleaning the data, developing visualizations to draw various inferences, building PMs to analyze the injury healing time period, and drawing sequence rules to predict the relationships among the injuries and the various body part parts afflicted with injuries. The injury data set consisted of more than 560 football injury records, which were categorized into injury-specific variables—body part/site/laterality, action taken, severity, injury type, injury start and healing dates—and player/sport-specific variables— player ID, position played, activity, onset, and game location. Healing time was calculated for each record, which was classified into different sets of time periods: 0–1 month, 1–2 months, 2–4 months, 4–6 months, and 6–24 months. Various visualizations were built to draw infer- ences from injury data set information depicting the healing time period associated with players’ positions, severity of injuries and the healing time period, treatment offered and the associated healing time period, major injuries afflicting body parts, and so forth. Neural network models were built to pre- dict each of the healing categories using IBM SPSS Modeler. Some of the predictor variables were current status of injury, severity, body part, body site, type of injury, activity, event location, action taken, and position played. The success of classifying the healing category was quite good: Accuracy was 79.6% percent. Based on the analysis, many busi- ness recommendations were suggested, including employing more specialists’ input from injury onset instead of letting the training room staff screen the injured players; training players at defensive posi- tions to avoid being injured; and holding practice to thoroughly safety-check mechanisms.
Questions for discussion
1. What types of analytics are applied in the injury analysis?
2. How do visualizations aid in understanding the data and delivering insights into the data?
3. What is a classification problem?
4. What can be derived by performing sequence analysis?
Explanation / Answer
1. There are two types of analytics namely Reporting Analytics and Predictive Analytics which are applied in the analysis of an injury. 2. Visualizations help in getting data regarding various aspects by monitoring the injury and also predict a time period that is needed to heal the injury. The data collected was of football players and visualization was used which formulated the time of healing depending on the various factors like the severity of injury, position of the player in the game, treatment offered and also the parts that are getting affected due to the injury. 3. The actual issue needs to be analyzed and depending on that the time period required for healing is ascertained which are in different sets 0–1 month, 1–2 months, 2–4 months, 4–6 months, and 6–24 months. Classification problem is generally classifying the problem or injury that the player has got and based on the specification the respective approach of treatment needs to be applied. 4. A sequence analysis of the injury needs to be done based on the parameters like current status of the Injury, severity of the injury, body part injured, type of injury, activity through which injury happened, action taken and the playing position of the player. The above mentioned parameters needs to analyzed sequentially which will provide the actual status of the player and the expected time taken to revive.