Machine learning of swimming data via wisdom of crowd and regression analysis
Every performance, in an officially sanctioned meet, by a registered USA swimmer is recorded into an online database with times dating back to 1980. For the first time, statistical analysis and machine learning methods are systematically applied to 4,022,631 swim records. In this study, we investiga...
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| Format: | Article |
| Language: | English |
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AIMS Press
2017-03-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2017031 |
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| author | Jiang Xie Junfu Xu Celine Nie Qing Nie |
| author_facet | Jiang Xie Junfu Xu Celine Nie Qing Nie |
| author_sort | Jiang Xie |
| collection | DOAJ |
| description | Every performance, in an officially sanctioned meet, by a registered USA swimmer is recorded into an online database with times dating back to 1980. For the first time, statistical analysis and machine learning methods are systematically applied to 4,022,631 swim records. In this study, we investigate performance features for all strokes as a function of age and gender. The variances in performance of males and females for different ages and strokes were studied, and the correlations of performances for different ages were estimated using the Pearson correlation. Regression analysis show the performance trends for both males and females at different ages and suggest critical ages for peak training. Moreover, we assess twelve popular machine learning methods to predict or classify swimmer performance. Each method exhibited different strengths or weaknesses in different cases, indicating no one method could predict well for all strokes. To address this problem, we propose a new method by combining multiple inference methods to derive Wisdom of Crowd Classifier (WoCC). Our simulation experiments demonstrate that the WoCC is a consistent method with better overall prediction accuracy. Our study reveals several new age-dependent trends in swimming and provides an accurate method for classifying and predicting swimming times. |
| format | Article |
| id | doaj-art-74ed5af272834eba83f9097309303252 |
| institution | Kabale University |
| issn | 1551-0018 |
| language | English |
| publishDate | 2017-03-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj-art-74ed5af272834eba83f90973093032522025-01-24T02:39:37ZengAIMS PressMathematical Biosciences and Engineering1551-00182017-03-0114251152710.3934/mbe.2017031Machine learning of swimming data via wisdom of crowd and regression analysisJiang Xie0Junfu Xu1Celine Nie2Qing Nie3School of Computer Engineering and Science, Shanghai University, 99 Shangda Road, Shanghai 200444, ChinaSchool of Computer Engineering and Science, Shanghai University, 99 Shangda Road, Shanghai 200444, ChinaUniversity High School, 4771 Campus Drive, Irvine, CA 92612, USADepartment of Mathematics, Center for Mathematical and Computational Biology, University of California, Irvine, CA 92697, USAEvery performance, in an officially sanctioned meet, by a registered USA swimmer is recorded into an online database with times dating back to 1980. For the first time, statistical analysis and machine learning methods are systematically applied to 4,022,631 swim records. In this study, we investigate performance features for all strokes as a function of age and gender. The variances in performance of males and females for different ages and strokes were studied, and the correlations of performances for different ages were estimated using the Pearson correlation. Regression analysis show the performance trends for both males and females at different ages and suggest critical ages for peak training. Moreover, we assess twelve popular machine learning methods to predict or classify swimmer performance. Each method exhibited different strengths or weaknesses in different cases, indicating no one method could predict well for all strokes. To address this problem, we propose a new method by combining multiple inference methods to derive Wisdom of Crowd Classifier (WoCC). Our simulation experiments demonstrate that the WoCC is a consistent method with better overall prediction accuracy. Our study reveals several new age-dependent trends in swimming and provides an accurate method for classifying and predicting swimming times.https://www.aimspress.com/article/doi/10.3934/mbe.2017031big datastatistical analysispredictionswimming modeltime series data |
| spellingShingle | Jiang Xie Junfu Xu Celine Nie Qing Nie Machine learning of swimming data via wisdom of crowd and regression analysis Mathematical Biosciences and Engineering big data statistical analysis prediction swimming model time series data |
| title | Machine learning of swimming data via wisdom of crowd and regression analysis |
| title_full | Machine learning of swimming data via wisdom of crowd and regression analysis |
| title_fullStr | Machine learning of swimming data via wisdom of crowd and regression analysis |
| title_full_unstemmed | Machine learning of swimming data via wisdom of crowd and regression analysis |
| title_short | Machine learning of swimming data via wisdom of crowd and regression analysis |
| title_sort | machine learning of swimming data via wisdom of crowd and regression analysis |
| topic | big data statistical analysis prediction swimming model time series data |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2017031 |
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