# Using Convolutional Neural Network and Candlestick Representation to Predict Sports Match Outcomes
## 4 Experiments
### Discussion  
By using CNN to learn the data of all teams, the binary prediction performance was not satisfactory when used to predict the classification of each team in the next match. The second stage of the judgment model, which simultaneously considers both teams in the match, is necessary to improve accuracy. Each team should learn individually with their own data and build their own CNN classifier. A total of 32 CNN classifiers were associated with 32 teams instead of using all data to construct a common CNN classifier. This approach is in line with the concept of some prediction methods in sports, where teams are initially grouped according to their performance and specific models are built for each group [67]. In the second stage, the logistic regression model is significantly more effective than the other machine learning methods probably due to the fact that when only the names and CNN prediction values of the two teams (total of four variables) are used to predict the victory or defeat of the home team, the logistic regression can simply yield different weights and either add or subtract points to adjust the prediction value for each team from the historical data. Other methods may require additional data, such as past performance indicators, and additional variables in the composition of players to be equally effective. The two-stage approach proposed in this study, incorporating the CNN classifier in the first stage and the logistic regression in the second stage, would yield several benefits. The combination of the two results in a considerable improvement in prediction accuracy over the CNN classifier and logistic regression alone and outperforms the betting market prediction. Another benefit is that the use of match scores and betting odds as raw data is a universal approach to predicting without restrictions on the type of sport. There is no need to think about which performance metrics, such as earned run average and batting average in baseball, or real plus-minus and pace in basketball, to use as features in the model. In contrast to recent two studies on machine learning for NFL game outcome prediction, [PERSON] [20] similarly used features derived from candlestick charts based on betting odds and scores as input variables to makes one-step ahead predictions. These features include the difference and the proportion of change between the preceding and following match data. A total of 19 and 28 features were selected as the input variables for the classification-based model and the regression-based model, respectively. Both models were examined using five different methods of machine learning. Another study by [PERSON] et al. [68] tested the nine different machine learning classification techniques using a total of 42 features for each team. These 42 features were derived from scores and performance metrics, using the current season average up to that game and the average across the most recent completed season. The total number of features of the two teams in a match was 84, and with considering the home advantage, a total of 85 parameters were used as input values for the prediction model. Although both studies are able to outperform the bookmaker's prediction accuracy, they are accompanied by a number of features that need to be considered as input variables in the prediction model. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Betting** & **CNN Classifier** & **Simple Judgment Model** \\ \hline McNemar value & 0.468 & 48.188 & 57.017 \\ \(p\) value & 0.494 & \textless{}0.0001 & \textless{}0.0001 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: McNemar test results for the pairwise comparison of the predictive outcome with the logistic regression. However, such time series-based models that we adopt are inevitably challenged as overly simplistic and difficult to represent sports matches. Complementarily, the complicated deep learning algorithms processed in the CNN are used to learn the features of different sports matches and different teams within the time series.