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Spread Football 
Analysis 
Tim Hoolihan 
tim@hoolihan.net 
@thoolihan
What Is A Spread? 
• Point Differential 
• Added to favored team to determine adjusted score 
• Attempts to get 50% of bet...
Goals 
• Come back from a horrible start 
in my league 
• Pet project in R that motivates 
learning more
Track 
• Google Spreadsheet 
• Download as CSV 
• Blank copy you can edit: 
http://bit.ly/1xLvg64
Functions For Game Types
…continued
League Trends 
• rmarkdown 
• convert to html
Home vs Away 
Favorite vs Underdog 
• First meaningful insight 
• It appears away favorites are a 
better pick 
• Why?
Testing The Math
Spread Performance By 
Team
My Trends
Upcoming Games
My Results & Progress
Residuals
Packages 
• rmarkdown 
• scales 
• dplyr 
• ggplot2 
• gridExtra
dplyr 
• Workshop exposure 
• chaining, functional 
• Linq in .Net 
• Closures (JavaScript, Ruby) 
• Domain Specific Langu...
ggplot2 
• Nice, but challenging 
• qplot vs ggplot
Have I Learned Anything? 
• Yes, a lot more R 
• League position improved from 
basement to basement stairs 
• Don’t pick ...
What Comes Next? 
• Calculate Correct, other columns 
• Clustering by spread size 
• Model training (machine learning… car...
Your Turn 
• https://github.com/thoolihan/FootballPicks 
• Google Docs http://bit.ly/1xLvg64 
• tim@hoolihan.net
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Presentation from 11/19/2014 at Cleveland R User Group on using R to improve and track spread NFL picks throughout the season.

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Football Spreads

  1. 1. Spread Football Analysis Tim Hoolihan tim@hoolihan.net @thoolihan
  2. 2. What Is A Spread? • Point Differential • Added to favored team to determine adjusted score • Attempts to get 50% of betters on each side of the bet* • Different than trying to be accurate. For example: large fan-base skew • Rule of Thumb: Home Team starts with -3
  3. 3. Goals • Come back from a horrible start in my league • Pet project in R that motivates learning more
  4. 4. Track • Google Spreadsheet • Download as CSV • Blank copy you can edit: http://bit.ly/1xLvg64
  5. 5. Functions For Game Types
  6. 6. …continued
  7. 7. League Trends • rmarkdown • convert to html
  8. 8. Home vs Away Favorite vs Underdog • First meaningful insight • It appears away favorites are a better pick • Why?
  9. 9. Testing The Math
  10. 10. Spread Performance By Team
  11. 11. My Trends
  12. 12. Upcoming Games
  13. 13. My Results & Progress
  14. 14. Residuals
  15. 15. Packages • rmarkdown • scales • dplyr • ggplot2 • gridExtra
  16. 16. dplyr • Workshop exposure • chaining, functional • Linq in .Net • Closures (JavaScript, Ruby) • Domain Specific Language - like (see Residuals.R)
  17. 17. ggplot2 • Nice, but challenging • qplot vs ggplot
  18. 18. Have I Learned Anything? • Yes, a lot more R • League position improved from basement to basement stairs • Don’t pick home underdogs
  19. 19. What Comes Next? • Calculate Correct, other columns • Clustering by spread size • Model training (machine learning… caret?) • My performance vs recommendation • Break out web pages further (league, my performance, next week) • Shiny?
  20. 20. Your Turn • https://github.com/thoolihan/FootballPicks • Google Docs http://bit.ly/1xLvg64 • tim@hoolihan.net
  • WilliamWhite32

    Sep. 15, 2015
  • alexsbresler

    Nov. 27, 2014

Presentation from 11/19/2014 at Cleveland R User Group on using R to improve and track spread NFL picks throughout the season.

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