# Using Convolutional Neural Network and Candlestick Representation to Predict Sports Match Outcomes
## 1 Introduction
Similar to the stock market behavior resulting from the psychological reaction of the investing public, the behavior of a sports team is influenced by the psychological reaction of several players. In team sports, the outcome of a match is easily influenced by the main powerful player on the field and can greatly vary across each player [25]. Identifying these implicit behavioral patterns of sports remains extremely challenging. This paper proposes an approach for sports match outcome prediction with two main components: the convolutional neural network (CNN) classifier for implicit pattern recognition and the logistic regression model for matching outcome judgment. This novel prediction model cleverly transforms the prediction problem, which is often solved using numerical parametric models, into a time series classification and prediction problem to exploit the strengths of deep learning techniques in visualizing behavior recognition. The significance of this study lies in the interdisciplinarity of integrating sports, econometrics, computer science, and statistics to develop a hybrid predictive model. The most important contributions of this paper are the framework of the hybrid machine learning approach and the design of each processing procedure, as well as the empirical results from real data. With respect to the state of the art, the following three main features are summarized. First, we propose an alternative approach based on well-established betting market forecasts without considering too many sport performance indicators, thereby ensuring consistency in practice across various sports types. Similar to many previous studies on sports outcome prediction, we use machine learning as our basis for developing predictive models. However, previous studies have mostly focused on the selection of sports performance indicators, their feature capture approach, and the input variables for the prediction models. In this paper, we argue that such models are highly specific to a certain type of sport and that the parameters adopted in different competitions also vary. Second, our predictive model is capable of learning the behavioral patterns of sports teams from historical scores and betting market odds. To achieve this, we employ CNN to leverage its recent significant success in computer vision and speech data classification. For the 2D matrix data required for image recognition, in which CNN operates excellently, we represent the behavioral characteristics of the team as an image encoded from OHLC time series in candlesticks, which are widely used in financial market forecasting. Unlike candlesticks in the stock market that demonstrate well-known patterns, the behavioral patterns of sports teams in preceding and following games are implicit and not easily detected. CNN has the potential to identify unknown patterns by classifying them through image recognition. Combining candlestick charts with CNN for sports prediction is a novel contribution of this study. Third, we adopt a two-stage strategy to design our prediction model following a heuristic approach within the domain knowledge of sports. Initially, we assess the current status and recent performance trends of each team, and then adjust its assessment by plus or minus based on the strengths and weaknesses of the opponents in a match and judge the outcome. In this two-stage approach, the CNN classifier in the first stage recognizes the graphical behavioral pattern and computes the classification probability of each team to win in the next match. Meanwhile, the logistic regression in the second stage learns the CNN winning probabilities and the actual results of the two teams by pair in past matches to build the outcome judgment model. The rest of this paper is organized as follows: Section 2 reviews the relevant literature; Section 3 defines the proposed two-stage approach; Section 4 provides a thorough experimental evaluation of US National Football League (NFL) matches from seasons 1985/1986 to 2016/2017; Section 5 concludes the paper.