Effective Mechanism to Mitigate Injuries During NFL Plays 
Authors: Arraamuthan Arulanantham, Ahamed Arshad Ahamed Anzar, Gowshalini Rajalingam, Krusanth Ingran, Prasanna S. Haddela
Keywords: Concussion, American football, Predictive modelling, Injuries, NFL Plays, Optimization
TL;DR: Mitigate concussions in American Football using Machine learning and Optimization techniques
Abstract: 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 prove 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 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.