What types of analytics are applied in injury analysis?
Injury analysis refers to the thorough summarization of the related factors that are concerned with an injury that is sustained in an athletic activity or job. Here, injuries refer to any harm done to the body that is caused by accidents, falls, or weapons. For injury analysis, data is relied upon for the actual and successful summarization and the subsequent formulation or preparation of the report and recommendations (Loland et al., 2006). In injury analysis, the analytics used involve data collected about the type of injury, the action taken in response to the injury, the beginning and end dates of healing, the position of the player(s), the onset of the activity, and the location of the athletic activity. Usually, injury analysis is conducted stepwise following a defined layout in order to identify the root cause and to come up with a recommendation or solution. This circles around data and information collected from the witness and the victim and the analysis of the received information.
How do visualizations aid in understanding the data and delivering insights into the data?
Visualization in injury analysis assists in gaining initial insights into the collected data and the generation of ideas for the appropriate and required recommendations. Visualization is conducted based on the collected data that is analyzed to determine the cause of the injury and determine a solution to the injury. Visualization makes it easy to graph and illustrate the injury in various dimensions that are easy to understand by the audience (Manolov & Vannest, 2019). Additionally, visualization assists in making and flowing the story in a form that is easy to understand and follow. Moreover, the use of visualization is essential in highlighting trends and outliers for further continuity of the story and ease of deciphering. This is one in the form of charts, maps, graphs, and illustrations. Ideally, the human brain processes visual data faster and more effectively, which makes it easier to pass and ingrain information to the audience using visual aids.
Visual aids like maps, graphs, and charts are derived from collected data and provide insight into the collected or received data. Often, data collected may be large or wide in scope and in a form that is hard to follow. It is the responsibility of the collector or administrator to sift through the data and present it in a form that is easy to understand. Moreover, these aids make it possible for all, including the layman and an outsider, to draw information and insight from a presentation (Fisher et al., 2012). Solutions are hence easier to identify and implement when visual aids are used as the audience is in a position to draw quick information from the presentation. Moreover, some visual aids may allow for the data or variables to be tweaked, which makes it easy to input or change the values in the formulated solutions to see the intended or resulting implications.
Will you consider this a classification problem?
Yes, this is considered to be a classification problem. This is because the problem is identified and categorized, as it goes with classification problems. The injuries are analyzed and categorized depending on, for instance, location, seriousness, and healing. The type of medical attention required may also be used as a classification factor (Brzinsky-Fay & Kohler, 2010).
What can be derived by performing sequence analysis?
Sequence analysis can and is used to draw and identify the existing relationship in collected or received data. In this case, the sequence analysis provides a conclusive report on the relationship between injuries and the varied body parts where these injuries are experienced (Brzinsky-Fay & Kohler, 2010). Sequence analysis utilizes primary, secondary, and tertiary data to draw these insights from the data. It is from the results of the analysis that reports are made and appropriate recommendations made.
References
Brzinsky-Fay, C., & Kohler, U. (2010). New developments in sequence analysis. Sociological methods & research, 38(3), 359-364.
Fisher, Z., Bailey, R., & Willner, P. (2012). Practical aspects of a visual aid to decision making. Journal of Intellectual Disability Research, 56(6), 588-599.
Loland, S., Skirstad, B., & Waddington, I. (2006). Pain and injury in sport: Social and ethical analysis. Routledge.
Manolov, R., & Vannest, K. J. (2019). A visual aid and objective rule encompassing the data features of visual analysis. Behavior Modification, 0145445519854323.
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