Effective Mechanism to Mitigate Injuries During NFL Plays

NFL (American football), which is regarded as the premier sports icon of America, has been severely accused in the recent years of being exposed to dangerous injuries that proves to be a bigger crisis as the players' lives have been increasingly at risk. Concussions, which refer to the serious...

Full description

Saved in:

Bibliographic Details
Published in:	2019 14th Conference on Industrial and Information Systems (ICIIS) pp. 350 - 355
Main Authors:	Ahamed, Arshad A.A., Arulanantham, Arraamuthan, Rajalingam, Gowshalini, Ingran, Krusanth Sivagnana, Haddela, Prasanna S.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-12-2019
Subjects:	Concussions Data models Defense Forestry Formation pairs FP-Matrix Games Information technology Injuries NFL Plays Offense Predictive model Predictive models
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	NFL (American football), which is regarded as the premier sports icon of America, has been severely accused in the recent years of being exposed to dangerous injuries that proves to be a bigger crisis as the players' lives have been increasingly at risk. Concussions, which refer to the serious brain traumas experienced during the passage of NFL play, have displayed a dramatic rise in the recent seasons concluding in an alarming rate in 2017/18. Acknowledging the potential risk, the NFL has been trying to fight via NeuroIntel AI mechanism [3] as well as modifying existing game rules and risky play practices to reduce the rate of concussions. As a remedy, we are suggesting an effective mechanism to extensively analyse the potential concussion risks by adopting predictive analysis to project injury risk percentage per each play and positional impact analysis to suggest safer team formation pairs to lessen injuries to offer a comprehensive study on NFL injury analysis. The proposed data analytical approach differentiates itself from the other similar approaches that were focused only on the descriptive analysis rather than going for a bigger context with predictive modelling and formation pairs mining that would assist in modifying existing rules to tackle injury concerns. The predictive model that works with Kafka-stream processor real-time inputs and risky formation pairs identification by designing FP-Matrix, makes this far-reaching solution to analyse injury data on various grounds wherever applicable.
DOI:	10.1109/ICIIS47346.2019.9063267