[This article was first published on Fantasy Football Analytics in R, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
About This Blog
My name is Isaac and I’m a Ph.D. student in Clinical Psychology. Why am I writing about fantasy football and data analysis? Because fantasy football involves the intersection of two things I love: sports and statistics. With this blog, I hope to demonstrate the relevance of statistics for improving your performance in fantasy football. In particular, I will use a statistical software package called R.Why R?
R is free and open source, and has great flexibility for advanced statistical techniques and graphics. You can download it here: http://www.statmethods.net/. I strongly recommend the RStudio text editor for working with R scripts: http://www.rstudio.com/ide/download/. R scripts and data files for this blog are located in the following GitHub repository: https://github.com/dadrivr/FantasyFootballAnalyticsR.
How Can I Learn R?
- Use this intro to R: http://www.statmethods.net/
- Post to the R mailing list or forums if you have questions
- Read other blogs on R-bloggers
- Read this blog!
About The Author
Everyone has biases. For full disclosure, here are mine.
I tend not to believe in the following:
- The “Hot Hand“
- Momentum in the context of player or team performance
- The Madden, ESPN, or Sports Illustrated curse
- Clinical judgment (e.g., picking players by judgment alone)
Instead, I prefer the following:
- Previous performance does not affect future performance, yet our brains perceive order out of randomness and streaks out of nothing (known as cognitive biases)
- Random variation around the central tendency (e.g., mean)
- Regression to the mean
- Actuarial formulas
Future Posts
These assumptions will serve as an important conceptual building block for the analytical approaches that I will discuss in the future. In future posts, I will show you how to download and calculate fantasy projections, how to determine the riskiness of a player, and how to determine the best possible players to pick in a snake and auction draft to maximize your team’s chances of winning your league championship. Thanks for reading, and I would appreciate your ideas, comments, thoughts, and suggestions below!
References
- Dawes, R. M., Faust, D., & Meehl, P. E. (1989). Clinical versus actuarial judgment. Science, 234, 1668-1674.
- Gilovich, T., Vallone, R., & Tversky, A. (1985). The hot hand in basketball: On the misperception of random sequences. Cognitive Psychology, 17, 295-314. doi: 10.1016/0010-0285(85)90010-6
To leave a comment for the author, please follow the link and comment on their blog: Fantasy Football Analytics in R.
R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.