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The best tool for Fantasy Football analytics: Excel or R?

In the early days, I used Excel for basic stuff, but I’ve been playing with R for some fun and funky data crunching. It’s a nice tool to know how to use.

It can be hard, though, to keep up with all the extensions that are being written and released almost daily. But that’s a good problem to have. And for the stuff I do, I definitely prefer R to Excel or an Excel-related application.

For anaylzing Fantasy Football data, somebody who obviously takes Fantasy Football seriously and knows a thing or two about R, seems to agree with me and has written up a good number of reasons why R is the best tool for Fantasy Football data analysis.

Here are a few of his reasons:

1. Data Manipulation. R allows you to manipulate (e.g., subset, recode, merge) data quickly. Some R packages have been designed specifically for these purposes, e.g., plyr. Typically, a majority of the time spent on an analysis project is spent before the analysis—preparing the data. R is much more adept and efficient in data preparation than Excel. Fantasy data scraped from websites often require many steps in data processing to be ready for analysis, so R is ideal.

2. It Supports Larger Data Sets. Excel has restrictions for how large your data can be. Even if if your data don’t exceed this maximum size, Excel can become slow with large data sets (especially after you add tabs, formulas, and references). R supports larger data, and can support big data with packages such as Hadoop.

3. It’s Open Source. Unlike Excel and other proprietary software used for data analysis, R is not a black box. You can examine the code for any function or computation you perform. You can even modify and improve these functions by changing the code.

You may read the rest at Fantasy Football Analytics.

Fantasy Football Analytics: R or Excel

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