I have been creating sports betting algorithms for many years using Microsoft access and I am transitioning to the ML world and trying to get a grasp on determining the success of my algorithms. I have exported my algorithms as CSV files dating back to the 2013-14 NBA season and imported them into python via pandas.
The purpose of importing these CSV files is to determine the future accuracy of these algorithms using ML. Here are the algorithm records based on the Microsoft access query:
A System: 471-344 (58%) +92.60
B System: 317-239 (57%) +54.10
C System: 347-262 (57%) +58.80
I have a total of 8,814 records in my database, however, the above systems are based on situational stats, e.g., Team A fits an algorithm if they have better Field Goal %, Played Last Game Home/Away, More Points Per Game, etc...
Here is some of the code that I wrote using Jupyter to determine the accuracy:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) clf = LinearSVC(C=1.0, penalty="l2", dual=False) clf.fit(X_train, y_train) pred_clf = clf.predict(X_test) scores = cross_val_score(clf, X, y, cv=10) rfe_selector = RFE(clf, 10) rfe_selector = rfe_selector.fit(X, y) rfe_values = rfe_selector.get_support() train = accuracy_score(y_train, clf.predict(X_train)) test = accuracy_score(y_test, pred_clf) print("Train Accuracy:", accuracy_score(y_train, clf.predict(X_train))) print("Test Accuracy:", accuracy_score(y_test, pred_clf)) print(classification_report(y_test, pred_clf, zero_division=1)) print(confusion_matrix(y_test, pred_clf)) print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))
Here are the results from the code above by system:
- Train Accuracy: 0.6211656441717791
- Test Accuracy: 0.5153374233128835
- F1 Score: 0.52
- CONFUSION MATRIX: [[16 50] [29 68]]
- Accuracy: 0.55 (+/- 0.10)
- Train Accuracy: 0.6306306306306306
- Test Accuracy: 0.5178571428571429
- F1 Score: 0.52
- CONFUSION MATRIX: [[49 23] [31 9]]
- Accuracy: 0.55 (+/- 0.08)
- Train Accuracy: 0.675564681724846
- Test Accuracy: 0.5409836065573771
- F1 Score: 0.54
- CONFUSION MATRIX: [[15 29] [27 51]]
- Accuracy: 0.57 (+/- 0.16)
In order to have a profitable system, the accuracy only needs to be 52.5%. If I base my systems off of the Test Accuracy, only the C System is profitable. However, all are profitable if based on Accuracy (mean & standard deviation).
My question is, can I rely on my Accuracy (mean & standard deviation) for future games even though my Testing Accuracy is lower than 52.5%?
If not, any suggestions are greatly appreciated on how I can gauge the future results on these systems.