# Using Convolutional Neural Network and Candlestick Representation to Predict Sports Match Outcomes
## 4 Experiments
### Comparison of Candlestick Pattern Recognition with CNN
Based on the average accuracy obtained in all prediction cases, we found in both experiments that the accuracy is lowest if only the first-stage CNN was used. However, such accuracy improves when the second-stage judgment model was added. This model considers the CNN prediction values of both teams in the match. Only slight improvements in accuracy were observed when the simple judgment model was used, and the best accuracy was obtained when using logistic regression. Second, we compared the effect of introducing overall and individual behaviors into the prediction model. The prediction accuracy of using overall behavior was less acceptable than that of using individual behavior, and the average accuracy of the best logistic regression judgment model was only 60.30%, which was not meaningful. Meanwhile, the average accuracy of using individual behavior, whether CNN, simple judgment, or logistic regression judgment, was high, with the best logistic regression judgment model reaching an acceptable value of 69.29%. Third, we addressed the differences across each team in the prediction models. We plotted the accuracies of the CNN prediction classification and judgment model according to the overall and individual behaviors in Figures 7 and 8, respectively. The x-axis denotes the ranking of actual win percentage, which represents the strength of the team, and an x-value of 1 is ranked first. Meanwhile, the y-axis shows the accuracy with values ranging from 0% to 100%. The curves of accuracy were added to the figure with trend lines to highlight the tendency of each team's strength and weakness rankings. Figure 8 presents the results of the overall behavior experiment. The teams on the left and right sides of the graph reported higher accuracies than those at the center. The trend line showed a smile-like curve, thereby suggesting that the strong and weak teams had fixed behaviors in terms of winning and losing, thereby facilitating predictions. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**All Teams**} & \multicolumn{3}{c}{**Each Team**} \\ \cline{3-8}  & \multirow{2}{*}{\begin{tabular}{c} **Actual Win** \\ **Percentage** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **CNN** \\ **Classifier** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Simple** \\ **Judgment** \\ **Model** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Logistic** \\ **Regression** \\ **Classifier** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Simple** \\ **Judgment** \\ **Model** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Logistic** \\ **Regression** \\ **Model** \\ \end{tabular} } \\ \hline New Orleans & 43.75\% & 43.75\% & 62.50\% & 50.00\% & 31.25\% & 50.00\% & 62.50\% \\ Philadelphia & 43.75\% & 43.75\% & 37.50\% & 50.00\% & 62.50\% & 50.00\% & 75.00\% \\ Carolina & 37.50\% & 43.75\% & 50.00\% & 50.00\% & 56.25\% & 25.00\% & 25.00\% \\ Cincinnati & 37.50\% & 37.50\% & 75.00\% & 62.50\% & 62.50\% & 62.50\% & 50.00\% \\ N.Y. Jets & 31.25\% & 31.25\% & 75.00\% & 62.50\% & 50.00\% & 62.50\% & 50.00\% \\ San Diego & 31.25\% & 56.25\% & 37.50\% & 37.50\% & 68.75\% & 62.50\% & 75.00\% \\ St. Louis & 25.00\% & 75.00\% & 87.50\% & 87.50\% & 75.00\% & 62.50\% & 62.50\% \\ Chicago & 18.75\% & 81.25\% & 50.00\% & 25.00\% & 68.75\% & 75.00\% & 50.00\% \\ Jacksonville & 18.75\% & 75.00\% & 75.00\% & 62.50\% & 75.00\% & 62.50\% & 87.50\% \\ San Francisco & 12.50\% & 87.50\% & 62.50\% & 50.00\% & 87.50\% & 75.00\% & 75.00\% \\ Cleveland & 6.25\% & 75.00\% & 87.50\% & 62.50\% & 93.75\% & 87.50\% & 75.00\% \\ _Overall average_ & **49.63\%** & **55.99\%** & **59.55\%** & **60.30\%** & **60.11\%** & **62.17\%** & **69.29\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._Figure 9 presents the results of the individual behavior experiment.