Hadoop is a fault tolerant Java framework that supports data distribution and process parallelization using commodity hardware. Based on the provided scalability and the independence of task execution, we combined Hadoop with crawling techniques to implement various applications that deal with large amount of data. Our experiments show that Hadoop is a very useful and trustworthy tool for creating distributed programs that perform better in terms of computational efficiency.
Realizing the significant effect that misprediction has on many real-world problems, our paper is focused on the way these costs could affect the sports sector in terms of soccer outcome predictions. In our experimental analysis, we consider the potential influence of a cost-sensitive approach rather than traditional machine-learning methods. Although the measurement of prediction accuracy is a very important part of the validation of each model, we also study its economic significance. As a performance metric for our models, the Sharpe ratio metric is calculated and analyzed. Seeking to improve Sharpe ratio value, a genetic algorithm is applied. The empirical study and evaluation procedure of the paper are primarily based on English Premier League’s games, simple historical data and well-known bookmakers’ markets odds. Our research confirms that it is worthwhile to employ cost-sensitive methods for the successful predictions of soccer results and better investment opportunities.
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