The recent growth in the field of data mining and machine learning has remitted into more recognition of outcome prediction and classification. However, the application of these techniques in the field of sports is still unexplored. This paper presents the implementation of data mining and machine learning in sports particularly. Here, machine learning based algorithm to predict the outcome of the badminton tournament has been proposed. We have employed three classifiers, Naïve Bayes with Correlation Based Feature Weighting (NB-CBFW), Composite Hypercubes on Iterated Random Projections (CHIRP) and Hyper Pipes to predict the outcome of Australian Open 2019, Malaysian Open 2019, German Open 2019 and Singapore Open 2019 Badminton tournaments. The outcome prediction is measured in terms of Accuracy, Root Mean Square Error (RMSE), True Positive Rate (TPR), True Negative Rate (TNR), Positive Predicted Value (PPV), Negative Predicted Value (NPV) and Receiver Operating Characteristics (ROC). After implementing the classifiers, it has been observed that NB-CBFW shows excellent accuracy in match outcome prediction as compared to CHIRP and Hyper Pipes.

数据挖掘和机器学习领域的最新发展使人们更加认识到结果预测和分类。但是，这些技术在体育领域的应用仍未探索。本文特别介绍了数据挖掘和机器学习在体育中的实现。在此，提出了一种基于机器学习的算法来预测羽毛球比赛的结果。我们采用了三个分类器，即具有基于相关特征权重的朴素贝叶斯（NB-CBFW），基于迭代随机投影的复合超立方体（CHIRP）和超管道，以预测2019年澳大利亚公开赛，2019年马来西亚公开赛，2019年德国公开赛和新加坡公开赛的结果2019羽毛球锦标赛。根据准确度，均方根误差（RMSE），真正速率（TPR），真负速率（TNR），正预测值（PPV），负预测值（NPV）和接收器工作特性（ROC）。实施分类器后，已观察到与CHIRP和Hyper Pipes相比，NB-CBFW在比赛结果预测中显示出极好的准确性。