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

1. Spread Football
Analysis
Tim Hoolihan
tim@hoolihan.net
@thoolihan

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. Goals
• Come back from a horrible start
in my league
• Pet project in R that motivates
learning more

4. Track
• Google Spreadsheet
• Download as CSV
• Blank copy you can edit:
http://bit.ly/1xLvg64

5. Functions For Game Types

6. …continued

7. League Trends
• rmarkdown
• convert to html

8. Home vs Away
Favorite vs Underdog
• First meaningful insight
• It appears away favorites are a
better pick
• Why?

9. Testing The Math

10. Spread Performance By
Team

11. My Trends

12. Upcoming Games

13. My Results & Progress

14. Residuals

15. Packages
• rmarkdown
• scales
• dplyr
• ggplot2
• gridExtra

16. dplyr
• Workshop exposure
• chaining, functional
• Linq in .Net
• Closures (JavaScript, Ruby)
• Domain Specific Language - like (see Residuals.R)

17. ggplot2
• Nice, but challenging
• qplot vs ggplot

18. Have I Learned Anything?
• Yes, a lot more R
• League position improved from
basement to basement stairs
• Don’t pick home underdogs

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. Your Turn
• https://github.com/thoolihan/FootballPicks
• Google Docs http://bit.ly/1xLvg64
• tim@hoolihan.net