Play Money Ball Machine Learning projects

Node : This Project on Github and Open Source Project

In the movie / book Moneyball - data analysis is employed to discover that teams that had a roster containing players with a high on base percentage (OBP) did very well in the regular season. Using this knowledge the General Manager was able to create a very successful team on a shoestring budget with players that had a high OBP.

First get the most recent complete baseball statistics dataset from Sean Lahman's website. Unzip it into a data/ directory inside your repository.

This notebook puts together the starting 9 player roster for a single season. How? You need to find the players with the highest OBP and the lowest salary in any specific year. Make sure you are removing outliers (an OBP of 1.0 is not an indicator of a perfect player, more like they possibly only played 4 or 5 games and had good luck, alternatively an OBP of 0 is pretty bad). Your 9 player roster should include:

(1st, 2nd, 3rd) Baseman
(Left, Center, Right) Fielders
Short Stop
Pitcher
Catcher

A player that historically played multiple positions can not account for 2 places on your roster.

To View This Notebook

Just click on the moneyball.ipynb file above.

To Run This Notebook

System Requirements / Installation

You will need to have python 3 installed on your machine. See python's site for details.
Clone this repo onto your machine.
You will need to make sure that you have a virtual environment running in the folder that you intend to work from. See this site for details if you're not familiar. Complete this step before attempting the below.
In your command-line program (such as Terminal on Mac OS X), navigate into the newly created repo. By default, this will be called moneyball. Install the requirements file by runnning pip install -r requirements.txt.

Opening the Notebook

Using a command-line program, navigate to the folder containing the downloaded file and run the following line: ipython notebook moneyball.ipynb
Note: This will open in a browser window and take over the command-line program's window until you close out of IPython Notebook. If you have closed your browser window, but your command line is still running the notebook, kill the process by pressing Ctrl+C or quitting the program entirely.

Note

You will have to calculate the on base percentage for each player. Here is the formula you might want to use On Base Percentage