Forecasting National Football League Game Outcomes Based on Fuzzy Candlestick Patterns

In this paper, a sports outcome prediction approach based on sports metric candlestick and fuzzy pattern recognition is proposed. The sports gambling market data are gathered and processed to form the candlestick chart, which has been widely used in financial time series analysis. Unlike the traditional candlestick is composed of the price for financial market analysis, the candlestick for sports metric is determined by the point spread, total point scored, and the gambling shock which measures the bias of gambling line and real total point scored. The fluctuation behaviors of sports outcome are represented by the fuzzification of candlestick for pattern recognition. The decision tree algorithm is applied on the fuzzified candlesticks to find the implicit knowledge rules, and used these rules to forecasting the sports outcome. The National Football League is introduced to our empirical study to verify the effectiveness of forecasting.