Football Spreads

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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.

Technologie

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 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

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

League Trends
• rmarkdown
• convert to html

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

Packages
• rmarkdown
• scales
• dplyr
• ggplot2
• gridExtra

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

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 home underdogs

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?

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

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Football Spreads

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

Notes de l'éditeur

Welcome User Group Restrooms Doors Any specific requests? Questions?
then show Picks.R to show all the permutations we’re calculating
Describe.R demo
TestMath.R
credit to R Workshop (Robert Kabacoff) Teams.R (needs improvement) PlotTeams.R
Very useful I’m doing poorly overall, but… highlight home underdogs highlight away favorites
Teams.R
Plot.R
Not terribly useful predicting my results going forward summary(bt_model) shows not significant summary(bw_model) is mixed week not a sig factor but model is?
show git stash
Questions? Follow up? Topics they would like covered?

Football Spreads

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Notes de l'éditeur