# Using Machine Learning and Candlestick Patterns to Predict the Outcomes of American Football Games
## Methodology
### Experiment Design and Performance Evaluation  
To forecast the win/lose outcome, this study investigated two approaches, namely, the classification-based and the regression-based models. The output of the classification-based model is a binary variable representing win or loss. The output of the regression-based model is a numeric variable representing the win/lose margin. The win/lose margin is converted to the win/lose outcome. The negative margin means a win, and the positive margin means a loss. We tested the applicability of candlestick charts by using several machine learning methods for each of the classification-based and regression-based models and compared the performance of the prediction results. The choice of machine learning methods was based on existing empirical and theoretical studies. RF and RS are supervised ensemble learning algorithms that can be used for classification and regression problems. Multi-boosting (MB) and the M5 prime model tree (M5P) are boosting ensemble algorithms that attempt to create a strong classifier from many weak classifiers. MB was applied as a classification-based model, and M5P was used a model tree algorithm for the regression-based model. SVMs are supervised learning models with associated learning algorithms that analyse data used for classification and regression analysis. Sequential minimal optimisation (SMO) is an algorithm widely used for training SVMs. The SMO algorithm for the SVM classifier was selected for the classification-based model. The SMO algorithm for SVM regression (SMOReg)was selected for the regression-based model. A multilayer perceptron (MLP) is a supervised learning algorithm that has a wide range of applications in classification and regression in many domains. We performed the experiments using the Weka machine learning package [50] using the default settings without parameter adjustments. For all algorithms, the batch size was set to 100, which is the preferred number of instances to process if batch prediction is being performed. For RF, the size of each bag is set to 100% of the training set size. For further details, see [51]. For RS, a reduced error pruning tree is chosen as the base classifier. The size of each subspace is set to 50% of the number of attributes. For more information, see [52]. For MB, a decision stump is chosen as the base classifier. The approximate number of subcommittees is set to 3. The weight threshold for weight pruning is set to 100. Please refer to [53] for more information. For M5P, the minimum number of instances to allow at a leaf node is set to 4. For further details about this approach, see [54]. For SMO, the kernel is set to use a polynomial kernel and to use the logistic regression model as the calibration method. The complexity parameter C is set to 1. Reference [55] explained the concept of SVM and SMO. The SMOReg is set to use the polynomial kernel, with the complexity parameter C set to 1. The SVM learning algorithm for regression is set to use SMO, according to [PERSON], [PERSON], et al. [56]. For MLP, the number of hidden layers is set to a, which is defined by the number of (attribute + classes) /2, and the weight update is set with 0.3 learning rate and 0.2 momentum. To compare these machine learning approaches with baseline strategies, we set two comparison groups as a benchmark, namely, betting and home. Betting refers to the bookmaker's prediction, which is derived from the betting odds. Taking the winning/losing margin of a certain team in the betting market odds, a negative value implies winning and a positive value implies losing. Home is when the home team is always predicted to win the game, reflecting the well-known home advantage. The performance of the forecasting models based on different machine learning approaches was assessed by the accuracy of the win/lose outcome. The precision, recall and F-measure corresponding to the outcome were also computed. For the accuracy, precision, recall and F-measure, greater values are effectively considered better.