# Using Convolutional Neural Network and Candlestick Representation to Predict Sports Match Outcomes
## 2 Related work  
### Sports Outcome Prediction with CNN  
Most applications of CNN in the field of sports are based on images, videos, or sensor data as data sources. The application examples include classifying six categories of cricket shots from 3600 images [26], recognizing tennis actions based on CNNs with historical long short-term memory network (LSTM) from sports videos [27], and classifying wearable sensor data from beach volleyball players to monitor activity and prevent injury [28]. However, only a few studies have transformed raw sports competitions data to exploit the benefits of CNN. The main reason is that when using CNNs for sports competition data, the greatest challenge is to appropriately convert the non-graphical numerical data into a multidimensional matrix that complies with the CNN input norm.  
[PERSON] et al. [29] and [PERSON] et al. [30] used CNN to predict the outcome of NBA games. They used data of more than 4000 games covering three seasons and coded the score of each player from both two teams as 1, 0, and -1 to represent the contributions of each player toward winning. Both teams, each with a maximum of 16 players, form a 16 \(\times\) 16 2D matrix by cross-comparison and are used for CNN prediction modeling.  
[PERSON] et al. [31] applied CNN with NBA data from seasons 2007 to 2014 for match outcome prediction. The convolutional layer takes advantage of a large number of player-related statistics as its input. In the input matrix, the rows represent the players, whereas the columns represent the player-level features grouped by two matrices, namely, home team and away team.  
[PERSON] and [PERSON] [32] utilized CNN to predict the outcome of cricket matches by learning the end-to-end mapping between the performance of players and the outcome of matches. The input data are used to build a representation matrix of two match teams as obtained by encoding the feature of each player and then aggregating each player to the team-level data.  
[PERSON] and [PERSON] [33] used CNNs in predicting the outcomes of Major League Baseball matches. However, instead of exploiting the advantages of 2D CNN in computer vision recognition, they used a 1D CNN model with a total of about 30 statistical values for batting and pitching as variables, which were treated as signals and time series to analyze the data.  
The aforementioned studies on match result prediction have mainly considered the advantages of CNN in analyzing large amounts of data and represented the performance of each player in the team as an encoded feature matrix. As a CNN input, the 2D matrix merely represents multiple features after encoding and is not treated as an image in computer vision. Each sports match is considered a sequence of discrete-time data, including spatiotemporal or statistical information, that lack a consensual visual representation. Even if consistent knowledge graphs are available for representation, the implicit patterns in these graphs are difficult to realize. To exploit the power of CNN in image-based classification for sports outcome prediction, we visualize sports competition data as candlesticks and leverage the time series classification technique.