NFL Predictive Model

 

In this analysis, I seek to predict who will win by running a Monte Carlo model simulation on the 2020-2021 dataset of 2 given NFL teams’ offensive and defensive position group data. I compare statistics between position groups, simulate 1000 times, and determine who wins the matchup. I do this with four different position groups, WRs vs CBs, Dline vs Oline, RBs vs Front 4, and RBs vs LBs. I then weight these total outcomes, with WRs vs CBs getting 20%, Dline vs Oline getting 35%, RBs vs Front 4 getting 25%, and RBs vs LBs getting 20% to predict an overall winner.

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Monte Carlo Overview: https://www.ibm.com/cloud/learn/monte-carlo-simulation
Monte Carlo in Sports: https://medium.com/@lloyddanzig/quantitative-sports-betting-6976e1ceaf0f
Learning Monte Carlo: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0002-introduction-to-computational-thinking-and-data-science-fall-2016/lecture-videos/lecture-6-monte-carlo-simulation/