Learning Data Mining with Python

Predicting Sports Winners with Decision Trees
There are many possible features we could use, but we will try the following
questions:
• Which team is considered better generally?
• Which team won their last encounter?
We will also try putting the raw teams into the algorithm to check whether the
algorithm can learn a model that checks how different teams play against each other.
Putting it all together
For the first feature, we will create a feature that tells us if the home team is generally
better than the visitors. To do this, we will load the standings (also called a ladder in
some sports) from the NBA in the previous season. A team will be considered better
if it ranked higher in 2013 than the other team.
To obtain the standings data, perform the following steps:
1. Navigate to http://www.basketball-reference.com/leagues/NBA_2013_
standings.html in your web browser.
2. Select Expanded Standings to get a single list for the entire league.
3. Click on the Export link.
4. Save the downloaded file in your data folder.
Back in your IPython Notebook, enter the following lines into a new cell. You'll need
to ensure that the file was saved into the location pointed to by the data_folder
variable. The code is as follows:
standings_filename = os.path.join(data_folder,
"leagues_NBA_2013_standings_expanded-standings.csv")
standings = pd.read_csv(standings_filename, skiprows=[0,1])
You can view the ladder by just typing standings into a new cell and running
the code:
Standings
[ 50 ]