https://github.com/javorraca/nflsuperbowlprediction

Data sets and R code to predict the Super Bowl 53 winner, team scores, and visualizations.
https://github.com/javorraca/nflsuperbowlprediction

Last synced: 5 days ago
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Data sets and R code to predict the Super Bowl 53 winner, team scores, and visualizations.

• Host: GitHub
• URL: https://github.com/javorraca/nflsuperbowlprediction
• Owner: JavOrraca
• Created: 2019-02-01T02:09:34.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2019-02-01T05:31:42.000Z (almost 6 years ago)
• Last Synced: 2023-08-22T08:56:54.576Z (over 1 year ago)
• Language: Jupyter Notebook
• Size: 9.54 MB
• Stars: 1
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# NFL Super Bowl LIII

## Predictive Modeling and Game Simulation via R

Attached you will find the NFL data sets and R code used to run a Poisson regression, game simulation, with code that can help you animate your charts via Plotly and gganimate (both wrapped around the R's powerfull ggplot2). If you have any questions or find areas for improvement, feel free to reach out.

**Conclusions**

* Our predictions indicate that Super Bowl 53 will be an extremely close game

* Even with the New England Patriots having the home-team advantage (due to heavier fanbase predicted to be Super Bowl 53), they're projected to lose

* Our regression analysis and simulations favor the Los Angeles Rams narrowly leading with a final score of 15-14

* The Rams have a 53.6% chance of winning based on the game simulation output

**Acknowledgements**


The backbone for this game simulation came from one of David Sheehan's GitHub repos, [Predicting Futbol Results With Statistical Modelling](https://dashee87.github.io/data%20science/football/r/predicting-football-results-with-statistical-modelling/)