# Using Convolutional Neural Network and Candlestick Representation to Predict Sports Match Outcomes
## 4 Experiments
### Comparison of Candlestick Pattern Recognition with CNN  
Our first task was to examine whether the patterns implied by sports candlesticks can reflect past behavior and if they are useful in predictive models. Several patterns in stock market candlesticks have been proven to be useful in explaining investor behavior and forecasting purposes. These patterns are not limited to a particular stock but can be applied to any stock. Therefore, we checked for common behavioral patterns among different teams based on sports candlesticks. We conducted two experiments by using the data of all teams and the data of each team as our data source for CNN learning. The experiment results reveal that either all teams share a common pattern or that each team is suitable for the prediction based on their historical behavioral patterns. The results are shown in Table 1. \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{2-9}  & \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** \\ **Modent** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Simple** \\ **Judgment** \\ **Model** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Logistic** \\ **Regression** \\ **Modent** \\ \end{tabular} } \\ \hline New England & 89.47\% & 84.21\% & 60.00\% & 70.00\% & 89.47\% & 80.00\% & 80.00\% \\ Dallas & 76.47\% & 64.71\% & 66.67\% & 77.78\% & 23.53\% & 55.56\% & 77.78\% \\ Kansas City & 70.59\% & 58.82\% & 55.56\% & 66.67\% & 64.71\% & 77.78\% & 77.78\% \\ Oakland & 70.59\% & 64.71\% & 87.50\% & 87.50\% & 82.35\% & 75.00\% & 75.00\% \\ Atlanta & 68.42\% & 57.89\% & 54.55\% & 45.45\% & 31.58\% & 36.36\% & 36.36\% \\ Pittsburgh & 68.42\% & 68.42\% & 66.67\% & 77.78\% & 68.42\% & 66.67\% & 88.89\% \\ N.Y. Giants & 64.71\% & 47.06\% & 62.50\% & 75.00\% & 64.71\% & 87.50\% & 87.50\% \\ Green Bay & 63.16\% & 47.37\% & 22.22\% & 55.56\% & 57.89\% & 44.44\% & 66.67\% \\ Seattle & 61.11\% & 77.78\% & 88.89\% & 77.78\% & 66.67\% & 88.89\% & 100.00\% \\ Miami & 58.82\% & 70.59\% & 62.50\% & 75.00\% & 58.82\% & 100.00\% & 87.50\% \\ Denver & 56.25\% & 50.00\% & 37.50\% & 50.00\% & 56.25\% & 62.50\% & 75.00\% \\ Tampa Bay & 56.25\% & 43.75\% & 62.50\% & 50.00\% & 43.75\% & 37.50\% & 37.50\% \\ Tennessee & 56.25\% & 37.50\% & 25.00\% & 37.50\% & 75.00\% & 62.50\% & 50.00\% \\ Houston & 55.56\% & 38.89\% & 55.56\% & 55.56\% & 27.78\% & 33.33\% & 66.67\% \\ Detroit & 52.94\% & 23.53\% & 50.00\% & 62.50\% & 47.06\% & 37.50\% & 100.00\% \\ Baltimore & 50.00\% & 62.50\% & 25.00\% & 50.00\% & 37.50\% & 75.00\% & 75.00\% \\ Indianapolis & 50.00\% & 62.50\% & 75.00\% & 75.00\% & 62.50\% & 75.00\% & 75.00\% \\ Minnesota & 50.00\% & 37.50\% & 50.00\% & 62.50\% & 68.75\% & 25.00\% & 62.50\% \\ Washington & 50.00\% & 37.50\% & 62.50\% & 37.50\% & 62.50\% & 62.50\% & 75.00\% \\ Arizona & 43.75\% & 43.75\% & 62.50\% & 62.50\% & 43.75\% & 50.00\% & 62.50\% \\ Buffalo & 43.75\% & 56.25\% & 75.00\% & 75.00\% & 62.50\% & 87.50\% & 75.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the accuracy of modeling with all teams’ data and each team’s data. Table 1 lists the teams in a descending order according to their actual win percentage; that is, the percentage of wins out of all matches played during the test period. The prediction accuracy of the two experiments was grouped into three measures: the CNN prediction classification in the first stage, the simple judgment in the second stage, and the logistic regression judgment models in the second stage. First, we investigated the utility of our two-stage approach by comparing the CNN classifier prediction results in the first stage with the judgment model results in the second stage.