# Using Machine Learning and Candlestick Patterns to Predict the Outcomes of American Football Games
## 2 Related Work
### Analysis and Prediction of American Football  
American football, such as NFL games, operates with a highly complex scoring system, making it highly demanding to model the points scored using standard modelling approaches. NFL players can score in five ways: an unconverted touchdown (six points), a touchdown with a one-point conversion (seven points), a touchdown with a two-point conversion (eight points), a safety (two points) or a field goal (three points). Other sports, such as football, baseball and basketball, follow relatively simple rules for giving scores under limited conditions. Consequently, American football predictions are highly challenging, regardless of whether traditional statistical models, modern machine learning models or even bookmakers' predictions are used, with lower accuracy than in other sports. However, this challenge reveals opportunities for improvement in prediction models, leading to many studies being devoted to it.  
[PERSON] et al. [41] compared the prediction of the outcomes of NFL games by experts, statistical models and opening betting lines on two NFL seasons. The findings showed that the distinctions between experts and statistical systems in predicting the accuracy of contest winners were not statistically significant and that betting lines performed better than the other two. [PERSON] et al. [42] analysed the ability of a neural network model to predict the outcome of NFL games. This model only adopted readily available statistics, such as rushing yards, passing yards, fumbles lost and scoring, and proposed the inclusion of differences in statistics between teams for comparison. [PERSON] and [PERSON] [43] presented a point process model for forecasting end-of-match exact scores in NFL games. Historical tournament information, including the teams' scores, the field of play, records for each game, various game statistics and data given by bookmakers, such as the margin (point spread) and the over/under for each game, was obtained for the study. With a set of simple covariates based on past match statistics, the model performed as well as bookmakers in predicting match outcomes and exact scores. [PERSON] and [PERSON] [44] collected play-by-play data from the past seven seasons of the NFL and built a descriptive model for the probability of the home team winning a game. A football prediction matchup engine was provided by combining this descriptive model with a statistical bootstrap module. It achieved an overall accuracy of 63.4%, which outperformed the baseline prediction based on win-loss standings every season. [PERSON] et al. [45] examined the application of techniques from technical charting used in stock price analysis to sentiment gathered from social media for NFL game outcome prediction. The sentiment polarity was analysed as a time-series signal,examining the position and magnitude of signal change between two temporal windows. The 50- and 200-day moving averages, which are popular techniques to analyse price movements in stock technical charts, can be adopted to find patterns, such as golden and death crosses.  
These NFL outcome prediction studies have employed different variables and methods. The experiments within them were designed and measured in different ways. They used different datasets to ensure that the criteria for accuracy were calculated differently and were not easy to compare. However, most studies have compared their models with betting market predictions; some models were better, and some were worse. Therefore, we suggest the use of interdisciplinary predictive models that combine stock market technical analysis, betting market behaviour, sports prediction and machine learning in data science. They have the potential to improve on previous approaches and lead to a new research direction.