# Using Machine Learning and Candlestick Patterns to Predict the Outcomes of American Football Games
## 4 Experimental Results and Discussion
### Comparison of Different Approaches  
In this study, we adopted the data on points scored and betting odds to plot the candlestick and used them as the input data to forecast the outcome of the next game. To explore whether this can be applied to the models constructed by different machine learning algorithms and achieve satisfactory accuracy, we constructed classification-based and regression-based models using a different approach. The prediction results are shown in Tables 2 and 3. In the comparison group, of the 430 testing cases, 290 were correct for betting and 254 for home. We used the comparison group as a comparison benchmark for each model. In the classification model, only MB, with 291 cases correct, was better than the comparison group. The remaining models, regardless of the algorithm used, were not as accurate as betting in the comparison group. In the regression model, RS, MSP and SMOReg with 294, 293 and 290 test cases correct, respectively, were better than the comparison group. Although the accuracy rate of these models was higher than that of the comparison group, only a slight improvement of zero to four cases can be observed. Overall, the regression model is more feasible for the prediction of the outcome than the classification model. However, among these models, the model with the highest accuracy rate does not necessarily have the smallest error measurement. For example, RS in the classification-based model performed best when measured by RMSE, but its accuracy rate was second best. In the regression-based models, although the accuracy of RS was the best, the error measurement in MAE and RMSE was inferior to that of MSP and SMOReg. RS, RF and MLP can be used for constructing classification-based and regression-based models. Only RS performs well in both types of models. However, RF and MLP did not perform better than betting in the comparison group, which may be because we did not tune the parameters of these algorithms and did not use the optimised parameter settings. We further took the best model out of the classification-based and regression-based models and examined the comparison group, as shown in Table 4. Examining all the data in the table to compare the prediction model, we found that the RS of the regression-based model was the best among the classification-based model, regression-based model and two comparison groups, and that its F-measure was also the best. The worst was home in the comparison group, which reflects the home advantage effect, but the accuracy was still at 59%, which is higher than the random neutral at 50%. We also found that the classification model is almost the same as betting in the comparison group. The maximum value in the table is 70.83%, which appears in the recall for the regression-based RS model. Therefore, when RS predicts a win, it is 70.83% correct. The kappa coefficient is a measure of inter-rater agreement involving binary forecasts, such as win-lose [57]. We used the kappa coefficient for measuring the levels of agreement among ML approaches against the comparison groups and actual outcome. Table 5 shows the results of the kappa coefficient. In Table 5, it can be seen that the level of agreement reached the degree of almost perfect (>0.81) among MB and RS against the betting. In addition, there was greater agreement in MB against the betting than in RS. \begin{table}
\begin{tabular}{c c c c c} \hline  & **Classification** & **Regression** & \multicolumn{2}{c}{**Comparison Group**} \\ \hline  & MB & RS & Betting & Home \\ Accuracy & 0.6767 & 0.6837 & 0.6744 & 0.5907 \\ Precision & 0.6791 & 0.6770 & 0.6776 & 0.5926 \\ Recall & 0.6759 & 0.7083 & 0.6713 & 0.5926 \\ F-measure & 0.6775 & 0.6923 & 0.6744 & 0.5926 \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison of different approaches.