Please use this identifier to cite or link to this item: https://ah.lib.nccu.edu.tw/handle/140.119/136831
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dc.contributor.advisor翁久幸zh_TW
dc.contributor.advisorWeng, Chiu-Hsingen_US
dc.contributor.author林澤慶zh_TW
dc.contributor.authorLin, Tse-Chingen_US
dc.creator林澤慶zh_TW
dc.creatorLin, Tse-Chingen_US
dc.date2021en_US
dc.date.accessioned2021-09-02T07:41:20Z-
dc.date.available2021-09-02T07:41:20Z-
dc.date.issued2021-09-02T07:41:20Z-
dc.identifierG0108354015en_US
dc.identifier.urihttp://nccur.lib.nccu.edu.tw/handle/140.119/136831-
dc.description碩士zh_TW
dc.description國立政治大學zh_TW
dc.description統計學系zh_TW
dc.description108354015zh_TW
dc.description.abstract技能評分系統是根據參賽隊伍的比賽戰績與其他相關資料,以評估隊伍實力與預測獲勝機率的一套計算方法。以職業網球比賽為例,選手的實力發揮往往受到比賽場地因素的影響,近年有學者針對網球單打提出一個將場地因素納入考量的模型,研究結果顯示納入場地因素的模型可以更準確地預測獲勝機率。本論文參考文獻中的相關討論,將此模型推廣至職業網球雙打比賽。本研究結果顯示納入場地因素於雙打比賽可以提高預測準確率,此外更新技能變數的變異程度也可提高預測準確率。zh_TW
dc.description.abstractA skill-rating system is a system that evaluates players’ skills and predict the winning probability based on competition records and relevant information. Take Association of Tennis Professionals as an example. Player’s performance is often affected by the surfaces. In recent years, some researchers have proposed models that take surfaces into consideration for tennis singles. Research results show that models incorporating surfaces can more accurately predict the probability of winning. The present thesis aims to extend this model to tennis doubles matches. The experimental results show that the inclusion of surfaces in doubles matches can improve the accuracy of prediction.en_US
dc.description.tableofcontents第1章 緒論 7\n1.1 研究動機 7\n1.2 研究目的 8\n第2章 文獻回顧 9\n2.1 Bradley-Terry Model 9\n2.2 Elo 10\n2.3 Glicko 11\n第3章 研究方法 14\n3.1 GenElo Surface Model 14\n3.2 多組別多組員之比賽 16\n3.3 結合場地於雙打比賽 18\n第4章 資料介紹 23\n4.1 ATP雙打資料集 23\n第5章 分析結果 25\n5.1 參數設定 25\n5.2 評估準則 28\n5.3 模型預測結果 28\n5.3.1 多組別多組員之比賽 28\n5.3.2 結合場地於雙打比賽 29\n5.3.3 整體 32\n第6章 結論與建議 33\n參考文獻 34zh_TW
dc.format.extent2134092 bytes-
dc.format.mimetypeapplication/pdf-
dc.source.urihttp://thesis.lib.nccu.edu.tw/record/#G0108354015en_US
dc.subject技能評分系統zh_TW
dc.subject職業網球雙打zh_TW
dc.subject場地zh_TW
dc.subjectSkill-rating Systemen_US
dc.subjectProfessional Tennis Doublesen_US
dc.subjectSurfacesen_US
dc.title網球雙打選手的技能評分模型探討zh_TW
dc.titleA Study on Skill-Rating Models for Doubles Tennis Playersen_US
dc.typethesisen_US
dc.relation.referenceBradley, R. A. and Terry, M. E. (1952). Rank analysis of incomplete block designs: I. the method of paired comparisons. Biometrika, 39(3/4):324–345.\nElo, A. E. (1978). The rating of chessplayers, past and present. Arco Pub., New York.\nGlickman, M. E. (1999). Parameter estimation in large dynamic paired comparison experiments. Journal of the Royal Statistical Society: Series C (Applied Statistics), 48(3):377–394.\nGlickman, M. E., Hennessy, J., and Bent, A. (2018). A comparison of rating systems for competitive women’s beach volleyball. Statistica ApplicataItalian Journal of Applied Statistics, 30(2):233–254.\nIngram, M. (2021). How to extend elo: a bayesian perspective. Journal of Quantitative Analysis in Sports.\nKovalchik, S. (2020). Extension of the elo rating system to margin of victory. International Journal of Forecasting, 36(4):1329–1341.\nNelder, J. A. and Mead, R. (1965). A simplex method for function minimization. The Computer Journal, 7(4):308–313.\nThurstone, L. (1927). A law of comparative judgment. Psychological Review, 34(4):273–286.\nWeng, R. C. and Lin, C.-J. (2011). A bayesian approximation method for online ranking. Journal of Machine Learning Research, 12(9):267–300.zh_TW
dc.identifier.doi10.6814/NCCU202101154en_US
item.openairetypethesis-
item.openairecristypehttp://purl.org/coar/resource_type/c_46ec-
item.grantfulltextopen-
item.fulltextWith Fulltext-
item.cerifentitytypePublications-
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