Using Data Mining Method to Predicting Winning Percentage for Dual Meet Team Sport – Using NBA Regular Season as a Case Study
| Autor: | Szupei Wang, 王斯霈 |
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| Rok vydání: | 2017 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 105 The purpose of this study is to find out the key factors that affect the outcome of the confrontational team competition, and to build the model for the NBA data, and then build the data model of the opposing team competition to predict the outcome of the future competition. The use of the 1996-1997 season to 2015-2016 season data, and produce the forecast 2016 - 2017 season, the score and the outcome, and then compared with the actual results to determine the applicability of the model. Based on the results after modeling, the two points score and free throw score is the key factor affecting the outcome of the team. The results show that the error rate of these four models (rpart, cubist, randomforest, svm) is less than 12%, and the prediction accuracy fluctuates at 60%. Further, the Cubist and randomforest models have less than 10% error rates in the three seasons of 2013-2016 and 2012-2016, indicating that the two models have high accuracy. In the prediction of the outcome, the results show that the fourth test accuracy than other test results are good, the accuracy rate is between 56 to 60%. From this study found that in the analysis or prediction of data to use more models to compare each other in order to make more objective judgments. In addition, according to the data of this study, the numerical predictive ability is stronger than the predictive ability. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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