# Using Machine Learning and Candlestick Patterns to Predict the Outcomes of American Football Games
## 1 Introduction  
Many people focus their attention on the outcomes of sports events. A match result has a significant impact on players, coaches, sports fans, journalists and bookmakers. Thus, many people attempt to predict match results before games. In recent years, detailed data gathered during games and the box scores of every competition in various sports have been systematically recorded and stored in databases. Given the vigorous development of machine learning technology, these databases have gradually gained attention, and classic sports analysis and prediction, such as technical and tactical analysis, offensive/defensive strategy analysis and opponent scouting, have extended to the field of the application of big data.  
Many historical game performances of teams and players, as well as a wide variety of game-related data, have been used as a feed for machine learning modelling to enable prediction. However, the tournament systems, competition rules and point scoring systems of the various competitions are different. Thus, the indicators used to measure performance are different. The process of extracting characteristics to represent competition data is a challenge. Applying machine learning modelling and different data processing methods to various sports leads to different predicting accuracies. The adaptability of machine learning for predicting the outcome of a given sports competition is an important research topic.  
This study proposes the use of candlestick charts and machine learning to predict the outcome of sports matches. Candlestick charts have been applied to financial market analysis for decades. The pattern of candlestick charts has been empirically proven to reveal adequately the behaviour of financeand is highly suitable for use in conjunction with machine learning in predicting the price fluctuations of financial commodities. As with the stock market, various analyses, such as fundamental, chip and technical analyses, exist. For sports competitions, this study's innovative proposal was to borrow the technical analysis of the stock market and use candlesticks instead of indicators of sports performance to make predictions.  
The purpose of this study is to present a methodology that combines the points scored and the odds of the betting market to create a candlestick chart of a sports tournament. The study also aims to extract the characteristics of machine learning modelling to predict match outcomes. To explore the feasibility of our proposed classification and regression approaches based on machine learning, data from a professional American football league (i.e., the National Football League, NFL) are used as empirical evidence. Only a few studies with weak prediction accuracy are available as reference.  
The main contributions to the literature are as follows. First, we develop a consistent approach that incorporates candlestick charts and machine learning for sports predictions without domain knowledge in various sports. Second, we explore the impact of various candlestick features on the predicted outcomes. Third, we compare the different approaches of machine learning models to reveal which one can provide a more precise prediction. Fourth, we analyse in detail the differences in the predictions between teams and between home and away teams.  
The rest of this paper is structured as follows. Section 2 contains an overview of related work. Section 3 describes the dataset, proposed methodology and experimental design. Section 4 conducts a performance analysis. Section 5 provides the concluding remarks and an outlook on our future work.