Learning Data Mining with Python

Predicting Sports Winners with Decision Trees
Next, we use the cross_val_score function to test the result. First, we extract
the dataset:
X_homehigher = dataset[["HomeLastWin", "VisitorLastWin",
"HomeTeamRanksHigher"]].values
Then, we create a new DecisionTreeClassifier and run the evaluation:
clf = DecisionTreeClassifier(random_state=14)
scores = cross_val_score(clf, X_homehigher, y_true,
scoring='accuracy')
print("Accuracy: {0:.1f}%".format(np.mean(scores) * 100))
This now scores 60.3 percent—even better than our previous result. Can we
do better?
Next, let's test which of the two teams won their last match. While rankings can give
some hints on who won (the higher ranked team is more likely to win), sometimes
teams play better against other teams. There are many reasons for this – for example,
some teams may have strategies that work against other teams really well. Following
our previous pattern, we create a dictionary to store the winner of the past game and
create a new feature in our data frame. The code is as follows:
last_match_winner = defaultdict(int)
dataset["HomeTeamWonLast"] = 0
Then, we iterate over each row and get the home team and visitor team:
for index, row in dataset.iterrows():
home_team = row["Home Team"]
visitor_team = row["Visitor Team"]
We want to see who won the last game between these two teams regardless of which
team was playing at home. Therefore, we sort the team names alphabetically, giving
us a consistent key for those two teams:
teams = tuple(sorted([home_team, visitor_team]))
We look up in our dictionary to see who won the last encounter between the two
teams. Then, we update the row in the dataset data frame:
row["HomeTeamWonLast"] = 1 if last_match_winner[teams] ==
row["Home Team"] else 0
dataset.ix[index] = row
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