Skip to content

Applying statistics, machine learning, and deep learning techniques to predict how many yards a team will gain on rushing plays using data provided by NFL’s Next Gen Stats.

Notifications You must be signed in to change notification settings

CornellDataScience/Insights-Databowl

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

68 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Insights-Databowl

William Bekerman, Tushar Khan, Sam Fuchs

Objective: Determine how many yards will an NFL player gain after receiving a handoff. Perhaps, we can try to answer additional questions in the future.

Goals: Develop statistical, machine learning, and deep learning models to predict how many yards a team will gain on given rushing plays. Visualize how our models work and the outcome of each play. Simulate, analyze, and visualize live data. Input data for any play and visualize the outcome.

Past work done: Data compilation and some cleaning

Milestones:

  1. Get familiar with dataset, data cleaning
  2. Get visualization of plays working (essential dynamics)
  3. Work on simple models — simple regression, basic machine learning techniques. Feature engineering
  4. Set baseline accuracy for future models
  5. Cool models. Neural nets, advanced machine learning, statistical modeling
  6. Visualize the model, upgrade visualization of plays
  7. Live data simulation, analysis, visualization
  8. Input data for any plan and visualize the outcome

About

Applying statistics, machine learning, and deep learning techniques to predict how many yards a team will gain on rushing plays using data provided by NFL’s Next Gen Stats.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Contributors 3

  •  
  •  
  •  

Languages