# Using Convolutional Neural Network and Candlestick Representation to Predict Sports Match Outcomes
## 4 Experiments
### Comparison of Different Approaches in the Outcome Judgment Model  
We used the best learning strategy for each team to learn individually based on their own data. After building the CNN classifier in the first stage, we calculated the CNN prediction values of all experimental data, including those for the training and test set. In the second stage of the judgment model, we used the CNN output values of both teams in the training set to learn the judgment model and then verified the final prediction performance in the test period. In addition to logistic regression and the simple judgment model, we adopted sequential minimal optimization (SMO) for support vector machines (SVM), Naive Bayes, Adaboost, J48, random forests, and multi-objective evolutionary algorithms (MOEA) for fuzzy classification, which are commonly used in machine learning, in our experiments to compare the prediction accuracy of the judgment model. The results are shown in Table 2.  
Table 2 also presents the accuracy of two single-stage models, and these values were used as benchmarks for comparison with two-stage models. As shown in Table 2, the accuracies of the first-stage CNN classification and the first-stage logistic regression were 60.11% and 61.42%, respectively. It revealed that when using OHLC time series to build a prediction model for sports matches, the CNN classifier with graphical classification to process the time series was not as accurate as the logistic regression model that took into account the strengths and weaknesses of the two teams. Although these values of the single-stage model were higher than the 58.05% accuracy of home in the comparison group, they were not as good as the 63.67% accuracy of betting. After incorporating the second stage to the output values of the first stage CNN classifier, the accuracy of random forest decreased, whereas that of [PERSON] grew slightly larger than that of simple judgment. The rest of the machine learning algorithms increased the prediction accuracy above that of betting (63.67%), and the best-performing logistic regression judgment model reported an accuracy of 69.29%. Table 2 also lists the kappa coefficients for comparing the level of agreement with the actual outcome and facilitating a fine-grained comparison. The kappa coefficients of all approaches were below 0.4, with only the logistic regression and Adaboost obtaining coefficients above 0.3. We conducted McNemar tests to examine whether the logistic regression model in the second stage significantly improved the first-stage results and outperformed the betting model. This nonparametric test is designed for two related samples and is particularly useful for the before-and-after measurements of the same subjects [66]. Table 3 presents the results. As shown in the table, the logistic regression judgment model significantly differed from the simple judgment model and the first-stage CNN classifier in that both of them rejected the null hypothesis. This proved that by adding the logistic regression judgment model in the second stage, it is significantly different from the one-stage only