Effective Mechanism to Mitigate Injuries During NFL Plays

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.

Reviews and author responses:

The paper aims to improve injury prediction and modeling in the National Football League (NFL) using machine learning. Unfortunately, the lack of clarity in the paper and poor writing prevents me from writing a thorough review. Given that the authors seem to apply off-the-shelf machine learning algorithms to NFL data, and spend a significant amount of time discussing low-level details of data storage, preprocessing, and underlying hardware/software pipelines, this paper does not appear to be a good fit for ICLR and would be better served at either a data science conference, or alternatively a sports medicine/science conference.

This paper introduces a machine learning pipeline for injury prediction in NFL events. The paper discusses several system settings on data streaming and processing, along with model selection and other hyper-parameter tuning details. The problem itself is very important. However, there are several disadvantages of the current status of the paper.

The writing of this paper needs to be largely improved. The description is very redundant and the texts are very hard to read. Therefore, it makes the paper much less clear to the reader.

Second, the proposed system is not related to the focus of ICLR and it lacks the novelty. Both the data processing and model selection methods are already well-known practicies. Experiments are also not established well enough to demonstrate the advantage of the proposed solution to this problem.

Due to the above issues, I think the paper is not ready for publication.

The study analyzes NFL data to mitigate injuries of NFL players. Methods such as K-NN, XGBoost, and SVM are used for the analysis and predictive modeling.
Although the research is important for NFL and sports industries, there are several problems as an ICLR paper: 
First, it is not formatted correctly. I guess using single column, the content may be overlength. Second, the methods used in the paper are conventional and there is no novelty from the algorithmic perspective. Third, the findings is useful within the sports industry, but does not conveys much insight for the ICLR community.


Meta-review: All reviewers recommend reject, and there is no rebuttal.

Decision: reject