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請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/93422
完整後設資料紀錄
DC 欄位值語言
dc.contributor.advisor黃景沂zh_TW
dc.contributor.advisorChing-I Huangen
dc.contributor.author何翰東zh_TW
dc.contributor.authorHan-Ton Hoen
dc.date.accessioned2024-07-31T16:14:58Z-
dc.date.available2024-08-01-
dc.date.copyright2024-07-31-
dc.date.issued2024-
dc.date.submitted2024-07-27-
dc.identifier.citationAddona, V. and Roth, J. (2010). Quantifying the effect of performance-enhancing drug use on fastball velocity in major league baseball. Journal of Quantitative Analysis in Sports, 6:6–6.
Athey, S. and Imbens, G. (2016a). Recursive partitioning for heterogeneous causal effects. Proceedings of the National Academy of Sciences, 113(27):7353–7360.
Athey, S. and Imbens, G. (2016b). Recursive partitioning for heterogeneous causal effects. Proceedings of the National Academy of Sciences of the United States of America, 113(27):7353–7360.
Athey, S., Imbens, G., and Ramachandra, V. (2015). Machine learning methods for estimating heterogeneous causal effects.
Athey, S. and Imbens, G. W. (2019). Machine learning methods that economists should know about. Annual Review of Economics, 11(Volume 11, 2019):685–725.
Britto, D. G. C., Pinotti, P., and Sampaio, B. (2022a). The effect of job loss and unemployment insurance on crime in brazil. Econometrica, 90(4):1393–1423.
Britto, D. G. C., Pinotti, P., and Sampaio, B. (2022b). The effect of job loss and unemployment insurance on crime in brazil. Econometrica, 90(4):1393–1423.
Caron, A., Baio, G., and Manolopoulou, I. (2021). Estimating individual treatment effects using non-parametric regression models: a review.
Crépon, B., Devoto, F., Duflo, E., and Parienté, W. (2015). Estimating the impact of microcredit on those who take it up: Evidence from a randomized experiment in morocco. American Economic Journal: Applied Economics, 7(1):123–50.
Gartheeban, G. and Guttag, J. (2013). A data-driven method for in-game decision making in mlb: when to pull a starting pitcher. page 973–979.
Huang, M.-L. and Li, Y.-Z. (2021). Use of machine learning and deep learning to predict the outcomes of major league baseball matches. Applied Sciences, 11(10):4499.
Jacob, D. (2021). Cate meets ml - conditional average treatment effect and machine learning. Available at SSRN: https://ssrn.com/abstract=3816558 or http: //dx.doi.org/10.2139/ssrn.3816558.
Krautmann, A. and Solow, J. (2009). The dynamics of performance over the duration of major league baseball long-term contracts. Journal of Sports Economics, 10.
Lee, Y. H. (2018). Common factors in major league baseball game attendance. Journal of Sports Economics, 19(4):583–598.
Lemke, R., Leonard, M., and Tlhokwane, K. (2010). Estimating attendance at major league baseball games for the 2007 season. Journal of Sports Economics, 11:316–348.
Lim, N., Choi, W., and Pedersen, P. M. (2021). Using hierarchical linear analysis to examine attendance determinants in major league baseball. South African Journal for Research in Sport, Physical Education and Recreation, 43(3):17–30.
McCullar, J. (2022). How has the foreign substance ban implemented by the mlb affected mlb pitcher spin rate and opponent batting statistics in 2021? was it effective? (114).
O’Neill, E. and Weeks, M. (2019). Causal tree estimation of heterogeneous household response to time-of-use electricity pricing schemes.
Qi, S.-Z., bo Zhou, C., Li, K., and yan Tang, S. (2021). The impact of a carbon trading pilot policy on the low-carbon international competitiveness of industry in china: An empirical analysis based on a ddd model. Journal of Cleaner Production, 281:125361.
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dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/93422-
dc.description.abstract2023 賽季,大聯盟實施了近年來最重大的規則變更,包括增設投手計時器、限制防守佈陣和加大壘包尺寸等。除了基於商業考量提升比賽觀賞性外,這些變 更的主要目的是改善近年來投手相對於打者更具優勢的局面。本文著重於探討限 制守備佈陣政策實施後對大聯盟打者表現的影響。
首先探討限制守備佈陣對大聯盟打者整體的影響。結果顯示,左打者和左右 開弓型打者在政策實施後的打擊率均有所上升,說明限制守備佈陣政策有效地減 輕了這兩類打者在打擊時面對的防守壓力。
隨後,本文進一步探討了政策效果的異質性。我們從多個打者的擊球特徵中 找出受影響幅度最大的類型,結果發現,內野安打率高、內野飛球率低、拉打比 率高、強擊球百分比高的打者在政策實施後的打擊率提升更大。說明即使所有打 者受到相同的政策影響,由於打者的習慣和擅長的擊球方式不同,他們對政策的 反應存在差異。
zh_TW
dc.description.abstractIn the 2023 season, Major League Baseball introduced significant rule changes, including a pitch clock, restrictions on defensive shifts, and larger bases. These changes aimed to enhance game viewership and counteract the recent dominance of pitchers over hitters. This study examines the impact of defensive shift restrictions on MLB hitters.
Overall, the restrictions led to increased batting averages for left-handed and switch- hitters, indicating reduced defensive pressure on these hitters. Further analysis revealed that hitters with high infield hit rates, low infield fly ball rates, high pull rates, and high hard-hit percentages saw the most significant improvements in their batting averages. This suggests that the policy’s impact varies based on individual hitting styles and strengths
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dc.description.tableofcontents口試委員審定書i
摘要ii
Abstract iii
目錄iv
圖目錄vi
表目錄vii
第一章緒論1
第二章文獻探討5
2.1 大聯盟研究綜述. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 機器學習方法於棒球中的應用. . . . . . . . . . . . . . . . . . . . . 6
2.3 因果森林模型. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.3.1 異質性處理效果. . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.3.2 因果樹. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.3.3 因果森林. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
第三章資料說明與敘述統計11
3.1 資料來源. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.2 敘述統計. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
第四章實證模型與結果16
4.1 限制守備布陣的整體影響. . . . . . . . . . . . . . . . . . . . . . . . 16
4.1.1 簡介. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
4.1.2 平行趨勢驗證. . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
4.1.3 模型. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
4.1.4 結果分析. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
4.1.5 穩健性測試. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
4.1.5.1 隊伍. . . . . . . . . . . . . . . . . . . . . . . . . . . 22
4.1.5.2 是否為新秀. . . . . . . . . . . . . . . . . . . . . . . 22
4.1.5.3 是否為自由球員. . . . . . . . . . . . . . . . . . . . 23
4.1.5.4 是否入選全明星. . . . . . . . . . . . . . . . . . . . 23
4.1.5.5 以低佈陣率打者為對照組. . . . . . . . . . . . . . . 25
4.2 守備布陣限制對不同打者類型的影響. . . . . . . . . . . . . . . . . 26
4.2.1 內野安打率. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.2.2 內野飛球率. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.2.3 拉打比率. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
4.2.4 強擊球百分比. . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
4.3 藉因果森林挖掘打者特徵. . . . . . . . . . . . . . . . . . . . . . . . 31
4.3.1 簡介與實證設定. . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4.3.2 結果. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
第五章結論35
5.1 實證結果. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
5.2 後續影響分析. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
參考文獻38
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dc.language.isozh_TW-
dc.subject大聯盟zh_TW
dc.subject限制守備佈陣zh_TW
dc.subject打擊率zh_TW
dc.subject異質性處理效果zh_TW
dc.subject差異中之差異法zh_TW
dc.subjectDifference-in-Differences methoden
dc.subjectMLBen
dc.subjectDefensive Shift Limitsen
dc.subjectBatting averageen
dc.subjectConditional Average Treat- ment Effect (CATE)en
dc.title2023 大聯盟政策之影響探討zh_TW
dc.titleThe Impact of 2023 Major League Baseball Policiesen
dc.typeThesis-
dc.date.schoolyear112-2-
dc.description.degree碩士-
dc.contributor.oralexamcommittee楊睿中;朱建達zh_TW
dc.contributor.oralexamcommitteeJui-Chung Yang;Jian-Da Zhuen
dc.subject.keyword大聯盟,限制守備佈陣,打擊率,異質性處理效果,差異中之差異法,zh_TW
dc.subject.keywordMLB,Defensive Shift Limits,Batting average,Conditional Average Treat- ment Effect (CATE),Difference-in-Differences method,en
dc.relation.page40-
dc.identifier.doi10.6342/NTU202402247-
dc.rights.note未授權-
dc.date.accepted2024-07-30-
dc.contributor.author-college社會科學院-
dc.contributor.author-dept經濟學系-
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