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題名 基於貝氏技能更新與深度神經交互模型的體育分析
Sports Analytics with Bayesian Skill Updates and Deep Neural Interaction Models
作者 李永濬
Li, Yong-Jun
貢獻者 翁久幸
Weng, Chiu-Hsing
李永濬
Li, Yong-Jun
關鍵詞 深度學習
貝式定理
神經網路
非遞移性
對決預測
Deep learning
Bayes' theorem
Neural network models
Intransitivity
Matchup prediction
日期 2025
上傳時間 4-Aug-2025 15:11:09 (UTC+8)
摘要 本研究提出一套專為體育對決預測任務設計的深度學習架構，結合貝式技能更新機制、特徵交互建模與時序特徵處理，有效強化模型對選手能力動態變化與非遞移性效應的表徵能力。核心方法包括貝氏後驗更新以追蹤選手能力浮動與不確定性，特徵交互網路結合指數移動平均（EMA）特徵，以捕捉非遞移性效應並強化模型對當下賽局的判斷能力。 為進一步提高模型的穩健性與泛化能力（generalization ability），本研究採用預訓練凍結骨幹網路（frozen backbone）策略，以獲取穩定表徵後進行整合層微調，降低對特定模組的依賴。實驗結果顯示，所提方法在多項體育競技對決資料集上顯著優於傳統對決模型，展現了貝式推論與深度神經網路在體育對決預測上的整合潛力。
This study proposes a deep learning framework specifically designed for sports matchup prediction tasks. The framework integrates Bayesian skill updating, feature interaction modeling, and temporal feature processing to improve the model’s capacity to capture dynamic variations in athlete performance and intransitivity effects. Methods include Bayesian posterior updates to capture fluctuations and uncertainty in player states, and a feature interaction network augmented with exponential moving average (EMA) features to capture intransitivity effects while enhancing the model’s judgment in current matchups. To further improve model robustness and generalization ability, we adopt a frozen backbone training strategy. This allows stable representation learning before fine-tuning the integration layers, thereby reducing dependency on specific components. Experimental results demonstrate that the proposed method significantly outperforms traditional matchup models across multiple sports datasets, highlighting the integration potential of Bayesian inference and deep neural networks in sports prediction tasks.
參考文獻 Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D. (2015). Weight uncertainty in neural networks. In Proceedings of the 32nd International Conference on Machine Learning, pages 1613–1622. PMLR. Bradley, 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. Chen, S. and Joachims, T. (2016a). Modeling intransitivity in matchup and comparison data. In Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, pages 227–236. Chen, S. and Joachims, T. (2016b). Predicting matchups and preferences in context. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 775–784. Glorot, X. and Bengio, Y. (2010). Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pages 249–256. JMLR Workshop and Conference Proceedings. Graves, A. (2011). Practical variational inference for neural networks. Advances in neural information processing systems, 24. Gu, Y., Liu, Q., Zhang, K., Huang, Z., Wu, R., and Tao, J. (2021). Neuralac: Learning cooperation and competition effects for match outcome prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 4072–4080. Herbrich, R., Minka, T., and Graepel, T. (2006). Trueskill™: a bayesian skill rating system. Advances in neural information processing systems, 19. Li, M., Wu, J., Wang, X., Chen, C., Qin, J., Xiao, X., Wang, R., Zheng, M., and Pan, X. (2023). Aligndet: Aligning pre-training and fine-tuning in object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6866–6876. Loshchilov, I. and Hutter, F. (2017). Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101. Lowe, Z. (2017). Why the lebron-kyrie pick-and-roll is the deadliest weapon in the nba. Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E. Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S. (2019). Pytorch: An imperative style, high-performance deep learning library. CoRR, abs/1912.01703. Rusu, A. A., Rabinowitz, N. C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., and Hadsell, R. (2016). Progressive neural networks. arXiv preprint arXiv:1606.04671. Seabold, S. and Perktold, J. (2010). Statsmodels: Econometric and statistical modeling with python. In Proceedings of the 9th Python in Science Conference.
描述 碩士
國立政治大學
統計學系
112354018
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0112354018
資料類型 thesis
dc.contributor.advisor 翁久幸zh_TW
dc.contributor.advisor Weng, Chiu-Hsingen_US
dc.contributor.author (Authors) 李永濬zh_TW
dc.contributor.author (Authors) Li, Yong-Junen_US
dc.creator (作者) 李永濬zh_TW
dc.creator (作者) Li, Yong-Junen_US
dc.date (日期) 2025en_US
dc.date.accessioned 4-Aug-2025 15:11:09 (UTC+8)-
dc.date.available 4-Aug-2025 15:11:09 (UTC+8)-
dc.date.issued (上傳時間) 4-Aug-2025 15:11:09 (UTC+8)-
dc.identifier (Other Identifiers) G0112354018en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/158712-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 統計學系zh_TW
dc.description (描述) 112354018zh_TW
dc.description.abstract (摘要) 本研究提出一套專為體育對決預測任務設計的深度學習架構，結合貝式技能更新機制、特徵交互建模與時序特徵處理，有效強化模型對選手能力動態變化與非遞移性效應的表徵能力。核心方法包括貝氏後驗更新以追蹤選手能力浮動與不確定性，特徵交互網路結合指數移動平均（EMA）特徵，以捕捉非遞移性效應並強化模型對當下賽局的判斷能力。 為進一步提高模型的穩健性與泛化能力（generalization ability），本研究採用預訓練凍結骨幹網路（frozen backbone）策略，以獲取穩定表徵後進行整合層微調，降低對特定模組的依賴。實驗結果顯示，所提方法在多項體育競技對決資料集上顯著優於傳統對決模型，展現了貝式推論與深度神經網路在體育對決預測上的整合潛力。zh_TW
dc.description.abstract (摘要) This study proposes a deep learning framework specifically designed for sports matchup prediction tasks. The framework integrates Bayesian skill updating, feature interaction modeling, and temporal feature processing to improve the model’s capacity to capture dynamic variations in athlete performance and intransitivity effects. Methods include Bayesian posterior updates to capture fluctuations and uncertainty in player states, and a feature interaction network augmented with exponential moving average (EMA) features to capture intransitivity effects while enhancing the model’s judgment in current matchups. To further improve model robustness and generalization ability, we adopt a frozen backbone training strategy. This allows stable representation learning before fine-tuning the integration layers, thereby reducing dependency on specific components. Experimental results demonstrate that the proposed method significantly outperforms traditional matchup models across multiple sports datasets, highlighting the integration potential of Bayesian inference and deep neural networks in sports prediction tasks.en_US
dc.description.tableofcontents 第一章 緒論 1 第二章 文獻探討 3 第一節 Bradley-Terry 模型3 第二節 TrueSkill 模型 3 第三節 Blade-Chest 模型 4 第四節 NeuralAC 模型：合作與競爭效應 5 第五節 Bayes by Backprop 6 第三章 研究方法 10 第一節 基於變分推論的技能分佈模組 13 第二節 特徵交互學習建模 18 第三節 Frozen Backbone Training（凍結骨幹訓練）策略 22 第四章 實驗 25 第一節 資料來源與結構 25 第二節 特徵工程 26 第三節 實驗結果 30 第五章 結論 44 參考文獻 45 附錄 A：不確定性參數 σi 變化推導 47 第一節 背景與定義 47 第二節 先驗 KL 項探討 50 第三節 概似項探討 51 第四節 總結 53zh_TW
dc.format.extent 2072274 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0112354018en_US
dc.subject (關鍵詞) 深度學習zh_TW
dc.subject (關鍵詞) 貝式定理zh_TW
dc.subject (關鍵詞) 神經網路zh_TW
dc.subject (關鍵詞) 非遞移性zh_TW
dc.subject (關鍵詞) 對決預測zh_TW
dc.subject (關鍵詞) Deep learningen_US
dc.subject (關鍵詞) Bayes' theoremen_US
dc.subject (關鍵詞) Neural network modelsen_US
dc.subject (關鍵詞) Intransitivityen_US
dc.subject (關鍵詞) Matchup predictionen_US
dc.title (題名) 基於貝氏技能更新與深度神經交互模型的體育分析zh_TW
dc.title (題名) Sports Analytics with Bayesian Skill Updates and Deep Neural Interaction Modelsen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D. (2015). Weight uncertainty in neural networks. In Proceedings of the 32nd International Conference on Machine Learning, pages 1613–1622. PMLR. Bradley, 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. Chen, S. and Joachims, T. (2016a). Modeling intransitivity in matchup and comparison data. In Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, pages 227–236. Chen, S. and Joachims, T. (2016b). Predicting matchups and preferences in context. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 775–784. Glorot, X. and Bengio, Y. (2010). Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pages 249–256. JMLR Workshop and Conference Proceedings. Graves, A. (2011). Practical variational inference for neural networks. Advances in neural information processing systems, 24. Gu, Y., Liu, Q., Zhang, K., Huang, Z., Wu, R., and Tao, J. (2021). Neuralac: Learning cooperation and competition effects for match outcome prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 4072–4080. Herbrich, R., Minka, T., and Graepel, T. (2006). Trueskill™: a bayesian skill rating system. Advances in neural information processing systems, 19. Li, M., Wu, J., Wang, X., Chen, C., Qin, J., Xiao, X., Wang, R., Zheng, M., and Pan, X. (2023). Aligndet: Aligning pre-training and fine-tuning in object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6866–6876. Loshchilov, I. and Hutter, F. (2017). Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101. Lowe, Z. (2017). Why the lebron-kyrie pick-and-roll is the deadliest weapon in the nba. Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E. Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S. (2019). Pytorch: An imperative style, high-performance deep learning library. CoRR, abs/1912.01703. Rusu, A. A., Rabinowitz, N. C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., and Hadsell, R. (2016). Progressive neural networks. arXiv preprint arXiv:1606.04671. Seabold, S. and Perktold, J. (2010). Statsmodels: Econometric and statistical modeling with python. In Proceedings of the 9th Python in Science Conference.zh_TW