# Using Convolutional Neural Network and Candlestick Representation to Predict Sports Match Outcomes
## 1 Introduction  
Sports forecasting research has developed rapidly in recent years and begun to cover different sports, methods, and research questions. Previous studies from various disciplines have extensively investigated various predictive aspects of sports match outcomes. Several review articles [1; 2; 3; 4; 5; 6; 7] have synthesized and organized existing predictive models with different perspectives based on human heuristics, quantitative models, ratings and rankings, betting odds, machine learning, and hybrid methods and compared their prediction capabilities. For sports betting, which is a billion-dollar industry, accurate predictive models are necessary to set the baseline for betting odds [8; 9]. Given that an improper setting of betting odds will bring huge losses to betting companies, the win/loss predictions implied in the odds are highly credible. Therefore, these odds are frequently adopted by researchers in economic and financial contexts and manipulated via a time series method to examine efficiency [10; 11], study irrational behavior [12], and predict wins-losses [13; 14] in betting markets. However, such odds-based models mostly focus on how to beat bookmakers and measure performance based on the returns obtained from the predicted outcome in conjunction with the betting strategy [15], not pursuing the accuracy of the predictions [16]. Similar to assessing risky financial market behavior, sports competition outcome predictions are assessed based on systemic and non-systemic effects [7]. Systematic effects are mainly related to the strength of teams, whereas non-systematic effects are some random factors. In general, systematic effects are relatively mature to analyze. By using the past performance of each team, various quality measures for teams or players and statistical-based metrics for building prediction models have been introduced. Experienced practitioners often use heuristics to measure the strengths and weaknesses of teams and to determine the winner of a match. By contrast, non-systematic effects are usually difficult to predict. Some predictive models that have been comprehensively tested and applied in stock markets over the past decade, such as stochastic processes, probability distribution models, and Monte Carlo simulations, have also been applied in sports forecasting. However, some scholars argue that stock price fluctuations do not completely follow the stochastic process but rather show preceding and succeeding relationships that highlight behavioral patterns. Consequently, the behavioral patterns of these relationships have been widely explored in recent years through pattern recognition based on technical indicator analyses and knowledge graphs, such as candlesticks for prediction modeling. Candlesticks can clearly show the degree of fluctuation and are commonly used in stock market analysis and prediction, as well as in a few novel studies for the prediction of teens' stress level change on a micro-blog platform [17], prediction of air quality changes [18], analysis of the teaching model effectiveness [19], and prediction of sports match outcomes [20]. While the results of sports games are traditionally considered independent events that do not affect one another, some scholars have recently proposed that these games are not entirely independent events and that certain trends or regularities may occur as a result of previous events [6]. Some scholars have also suggested to exclude many factors that affect winning and losing, and considered only the scores and the match results as the raw data, using the score-driven time series to build prediction model of football match outcomes [21; 22] In addition, in professional sports with intensive schedules, a win or loss in the previous match can bring anxiety for the player and generate a certain impact on the next match, such as the \"hot hand\" effect [23; 24]. Therefore, behavioral patterns may exist in sports matches.