# Using Machine Learning and Candlestick Patterns to Predict the Outcomes of American Football Games
## 2 Related Work  
### Sports Forecasting and Machine Learning  
Based on previous research, methods of predicting the outcomes of matches can be divided into two categories: namely, result-based and goal-based approaches. Result-based approaches aim to predict the result class, whereas goal-based approaches aim to predict the goals scored by each team in a certain match. In this context, predicting the winner and loser involves two approaches. Result-based approaches are usually conducted using classification-based models, where the predicted outcome is a categorical variable, such as win, loss or draw. Goal-based approaches are achieved using regression-based models, where the predicted outcome is a numerical variable of a score or a difference in score from that of the opponent, and the score is used to ascertain win, loss or draw. Other approaches can be used to predict the outcome of a match, such as using the probability distributions model to estimate win probability [1; 2] or rating teams based on past performance and then predicting based on rankings [3; 4; 5].  
Regardless of whether the model is classification-based or regression-based, the data used to construct the model are mostly performance indicators, which are usually retrieved and calculated from the offensive and defensive records of each team or each player. These performance indicators produced on a statistical basis have been explored in many studies in various sports [6; 7], and are often used to predict the outcome of the game [8; 9; 10] and obtain explainable results.  
In addition to using performance indicators, situation variables, also known as conditional factors, have been considered in many studies. Situational variables, such as match location, who scores first and the quality of the opponent, can potentially affect the structure of a match and eventually the outcome of the match; evidence has been presented to support the idea that situational variables are worth including when analysing performance from a behavioural perspective [11; 12]. Moreover, a play's position in the field is commonly considered in basketball [13; 14] and football studies [9]. Additionally, in international competitions among different national teams, the gross domestic production per capita, population size and other relevant factors for each country have been considered [15; 16].  
[PERSON] et al. [17] systematically reviewed the variables commonly used in many football match analysis studies and recommended the adoption of methodologies that include the general description of technical, tactical, physical performance, situational, continuous and sequential aspects of the game to make the science of match analysis easy to apply in the field. Studies have considered many variables to increase the complexity of the model. For example, [PERSON] et al. [PERSON] et al. (2018) used 33 player-related variables, seven player performance indicators and the position of a player or a role. The explosion of a large amount of input data accompanied by many variables has caused the traditional approach based on the statistical model to have many dimensions. Thus, many studies have instead used machine learning to process a large amount of data to construct black-box prediction models.  
Utilising machine learning for predictive analysis has been studied in various sports tournaments ([PERSON] and [PERSON], 2017), such as basketball ([PERSON] and [PERSON], 2017; [PERSON], 2018), baseball ([PERSON] and [PERSON], 2018), cricket ([PERSON] and [PERSON], 2018), ice hockey ([PERSON] and [PERSON], 2018) and football ([PERSON] and [PERSON], 2018, 2018). Many kinds of machine learning methods, such as artificial neural networks (ANNs), support vector machines (SVMs), random subspace (RS), random forests (RFs) and hybrid modelling approaches combining multiple methods, have been developed and used for comparison in match result prediction.