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題名 棒球投手投球策略的視覺化分析
Visual Analytics of Baseball Pitching Strategy
作者 徐瑄甫
Hsu, Hsuan-Fu
貢獻者 紀明德
Chi, Ming-Te
徐瑄甫
Hsu, Hsuan-Fu
關鍵詞 資料視覺化
運動視覺化
運動分析
序列分析
投球策略
Data visualization
Sports visualization
Sports analytics
Sequence analytics
Pitching strategy
日期 2020
上傳時間 3-Aug-2020 17:59:00 (UTC+8)
摘要 棒球的投手的表現在棒球比賽的勝負中佔有決定性的影響，投手在比賽中要不斷的決定投什麼球種，以及決定瞄準好球帶的什麼位置，而投出的每一球都會影響打者在對決過程中的判斷，最終形成打席的結果。當棒球球迷或專家想要去分析這些投打對決的時候，往往只能透過總結性的資料或是最原始的比賽畫面重播來分析，但總結性的資料缺少對於投打對決本身所具有的時序性的觀察，而原始影片分析不利於大量打席的分析。因此，本論文提出了一個視覺化分析架構，透過機率轉移矩陣和不同層級比賽狀態之間的互動來分析投手的表現，將每個打席內含的時序性空間資料作為佈局的基礎，搭配多層次的條件篩選以及對投手類型的分類，讓使用者可以在不同條件下的打席集合中探索投球策略的模式和成效。
Baseball pitchers have a significant role in baseball games; their performance in a game almost decides the game’s result. In the game, pitchers have to make decisions for which pitch type he should use next, and where he should pitch. Every pitch in a plate appearance(PA) will influence the batter’s mind, and cause the result of PA. When baseball fans and experts want to analyze these pitcher-batter match-ups, they just used video replay or some summary data. But summary data lacks observation of sequential property in a match-up. And video replay is not convenient for amounts of PA analysis.So in this paper, we make a visualization tool for analyzing sequential pitch data in a baseball game, using probability transition matrix and multilevel interaction to analyze pitcher’s performance and pitching patterns. We also contribute the cluster of pitchers who have similar pitching sets and let users using different game states to explore patterns and efficiency of pitching strategy.
參考文獻 [1]
Fangraphs inc., "fangraphs.com," Fangraphs Inc., 18 9 2009. [Online]. Available: https://www.fangraphs.com/. [Accessed 1 4 2020].
[2]
Sports Reference, "Baseball-Reference.com," Sports Reference, 18 4 2000. [Online]. Available: www.baseball-reference.com. [Accessed 1 4 2020].
[3]
MLB Advanced Media, LP. , "https://baseballsavant.mlb.com/," MLB Advanced Media, LP. , [Online]. Available: https://baseballsavant.mlb.com/. [Accessed 1 4 2020].
[4]
C. Perin, R. Vuillemot, C. Stolper, J. Stasko and J. Wood, "State of the Art of Sports Data Visualization," in Computer Graphics Forum, 2018.
[5]
T. Polk, J. Yang, Y. Hu and Y. Zhao, "TenniVis: Visualization for Tennis Match Analysis," IEEE TVCG, vol. 20, no. 12, pp. 2339-2348, 2014.
[6]
Y. Wu, J. Lan, X. Shu, C. Ji, K. Zhao, J. Wang and H. Zhang, "iTTVis: Interactive Visualization of Table Tennis Data," IEEE TVCG, vol. 24, no. 1, pp. 709-718, 2018.
[7]
W. Chen, T. Lao, J. Xia, X. Huang, B. Zhu, W. Hu and H. Guan, "GameFlow: Narrative Visualization of NBA Basketball Games," IEEE Transactions on Multimedia, vol. 18, no. 11, pp. 2247 - 2256, 2016.
[8]
T. Polk, D. Jackle, J. Hausler and J. Yang, "CourtTime: Generating Actionable Insights into Tennis Matches Using Visual Analytics," IEEE TVCG, vol. 26, no. 1, pp. 397 - 406, 1 2020.
[9]
C. Dietrich, D. Koop, H. T. Vo and C. Silva, "Baseball4D: A Tool for Baseball Game Reconstruction & Visualization," in IEEE Conference on Visual Analytics Science and Technology, Paris, France, 2014.
[10]
M.Lage, J.P.Ono, D.Cervone, J.Chiang, C.Dietrich and C. Silva, "StatCast Dashboard: Exploration of Spatiotemporal Baseball Data," IEEE Computer Graphics and Applications, vol. 36, no. 5, pp. 28-37, 2016.
[11]
J. P. Ono, C. Dietrich and C. T. Silva, "Baseball Timeline: Summarizing Baseball Plays Into a Static Visualization," Computer Graphics Forum, vol. 37, no. 3, pp. 491-501, 2018.
[12]
K. Wongsuphasawat and D.Gotz, "Exploring flow, factors, and outcomes of temporal event sequences with the outflow visualization," IEEE TVCG, vol. 18, no. 12, pp. 2659-2668, 2012.
[13]
J. Zhao, Z. Liu, M. Dontcheva, A. Hertzmann and A. Wilson, "MatrixWave: Visual Comparison of Event Sequence Data," in CHI, 2015.
[14]
K. Wongsuphasawat, J. A. G. Gómez, C. Plaisant, T. D. Wang, M. Taieb-Maimon and B. Shneiderman, "LifeFlow: visualizing an overview of event sequences," in CHI, Vancouver, 2011.
[15]
D. K and M. T, "Segmentifier: Interactive Refinement of Clickstream Data," Computer Graphics Forum, vol. 38, pp. 623-634, 2019.
[16]
S. Guo, K. Xu, R. Zhao, D. Gotz, H. Zha and N. Cao, "EventThread: Visual Summarization and Stage Analysis of Event Sequence Data," IEEE TVCG, vol. 24, no. 1, pp. 56-65, 2018.
[17]
MLBAM, "statsapi.mlb.com," [Online]. Available: https://statsapi.mlb.com/api/v1/game/. [Accessed 15 9 2019].
[18]
B. Bukiet, R. E. Harold and J. L. Palacios, "A Markov Chain Approach to Baseball," Operations Research, vol. 45, no. 1, pp. 14-23, February 1997.
[19]
"sportspectator.com," [Online]. Available: http://www.sportspectator.com/fancentral/baseball/stats/linescore.html. [Accessed 30 1 2019].
[20]
J. T. Ian and J. Cadima, "Principal component analysis: a review and recent developments," Philosophical transactions Series A, Mathematical, physical, and engineering sciences, vol. 374, p. 20150202, 2016.
[21]
L. v. d. Maaten and G. Hinton, "Visualizing Data using t-SNE," JMLR, vol. 9, pp. 2579-2605, 2008.
描述 碩士
國立政治大學
資訊科學系
105753037
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0105753037
資料類型 thesis
dc.contributor.advisor 紀明德zh_TW
dc.contributor.advisor Chi, Ming-Teen_US
dc.contributor.author (Authors) 徐瑄甫zh_TW
dc.contributor.author (Authors) Hsu, Hsuan-Fuen_US
dc.creator (作者) 徐瑄甫zh_TW
dc.creator (作者) Hsu, Hsuan-Fuen_US
dc.date (日期) 2020en_US
dc.date.accessioned 3-Aug-2020 17:59:00 (UTC+8)-
dc.date.available 3-Aug-2020 17:59:00 (UTC+8)-
dc.date.issued (上傳時間) 3-Aug-2020 17:59:00 (UTC+8)-
dc.identifier (Other Identifiers) G0105753037en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/131114-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊科學系zh_TW
dc.description (描述) 105753037zh_TW
dc.description.abstract (摘要) 棒球的投手的表現在棒球比賽的勝負中佔有決定性的影響，投手在比賽中要不斷的決定投什麼球種，以及決定瞄準好球帶的什麼位置，而投出的每一球都會影響打者在對決過程中的判斷，最終形成打席的結果。當棒球球迷或專家想要去分析這些投打對決的時候，往往只能透過總結性的資料或是最原始的比賽畫面重播來分析，但總結性的資料缺少對於投打對決本身所具有的時序性的觀察，而原始影片分析不利於大量打席的分析。因此，本論文提出了一個視覺化分析架構，透過機率轉移矩陣和不同層級比賽狀態之間的互動來分析投手的表現，將每個打席內含的時序性空間資料作為佈局的基礎，搭配多層次的條件篩選以及對投手類型的分類，讓使用者可以在不同條件下的打席集合中探索投球策略的模式和成效。zh_TW
dc.description.abstract (摘要) Baseball pitchers have a significant role in baseball games; their performance in a game almost decides the game’s result. In the game, pitchers have to make decisions for which pitch type he should use next, and where he should pitch. Every pitch in a plate appearance(PA) will influence the batter’s mind, and cause the result of PA. When baseball fans and experts want to analyze these pitcher-batter match-ups, they just used video replay or some summary data. But summary data lacks observation of sequential property in a match-up. And video replay is not convenient for amounts of PA analysis.So in this paper, we make a visualization tool for analyzing sequential pitch data in a baseball game, using probability transition matrix and multilevel interaction to analyze pitcher’s performance and pitching patterns. We also contribute the cluster of pitchers who have similar pitching sets and let users using different game states to explore patterns and efficiency of pitching strategy.en_US
dc.description.tableofcontents 1 緒論 1
1.1 研究動機與目的 1
1.2 問題描述 2
1.3 主要貢獻 2
2 相關研究 4
2.1 運動資料視覺化 4
2.2 棒球資料視覺化 92.3 事件序列分析與資料視覺化 10
3 需求及設計目標 16
3.1 需求分析 16
3.2 設計目標 19
3.3 任務設計 21

4 研究方法 22
4.1 資料收集與分析 22
4.2 投手型態分佈視覺化 24
4.3 狀態轉移矩陣 25
4.4 打席細節視覺化 28
5 實驗結果與討論 30
5.1 投手型態分佈 30
5.2 投球模式分析 32
5.3 比賽中投球模式演變分析 37
5.4 專家訪談與意見回饋 38
6 結論與未來展望 41
參考文獻 43		zh_TW
dc.format.extent 6284110 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0105753037en_US
dc.subject (關鍵詞) 資料視覺化zh_TW
dc.subject (關鍵詞) 運動視覺化zh_TW
dc.subject (關鍵詞) 運動分析zh_TW
dc.subject (關鍵詞) 序列分析zh_TW
dc.subject (關鍵詞) 投球策略zh_TW
dc.subject (關鍵詞) Data visualizationen_US
dc.subject (關鍵詞) Sports visualizationen_US
dc.subject (關鍵詞) Sports analyticsen_US
dc.subject (關鍵詞) Sequence analyticsen_US
dc.subject (關鍵詞) Pitching strategyen_US
dc.title (題名) 棒球投手投球策略的視覺化分析zh_TW
dc.title (題名) Visual Analytics of Baseball Pitching Strategyen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) [1]
Fangraphs inc., "fangraphs.com," Fangraphs Inc., 18 9 2009. [Online]. Available: https://www.fangraphs.com/. [Accessed 1 4 2020].
[2]
Sports Reference, "Baseball-Reference.com," Sports Reference, 18 4 2000. [Online]. Available: www.baseball-reference.com. [Accessed 1 4 2020].
[3]
MLB Advanced Media, LP. , "https://baseballsavant.mlb.com/," MLB Advanced Media, LP. , [Online]. Available: https://baseballsavant.mlb.com/. [Accessed 1 4 2020].
[4]
C. Perin, R. Vuillemot, C. Stolper, J. Stasko and J. Wood, "State of the Art of Sports Data Visualization," in Computer Graphics Forum, 2018.
[5]
T. Polk, J. Yang, Y. Hu and Y. Zhao, "TenniVis: Visualization for Tennis Match Analysis," IEEE TVCG, vol. 20, no. 12, pp. 2339-2348, 2014.
[6]
Y. Wu, J. Lan, X. Shu, C. Ji, K. Zhao, J. Wang and H. Zhang, "iTTVis: Interactive Visualization of Table Tennis Data," IEEE TVCG, vol. 24, no. 1, pp. 709-718, 2018.
[7]
W. Chen, T. Lao, J. Xia, X. Huang, B. Zhu, W. Hu and H. Guan, "GameFlow: Narrative Visualization of NBA Basketball Games," IEEE Transactions on Multimedia, vol. 18, no. 11, pp. 2247 - 2256, 2016.
[8]
T. Polk, D. Jackle, J. Hausler and J. Yang, "CourtTime: Generating Actionable Insights into Tennis Matches Using Visual Analytics," IEEE TVCG, vol. 26, no. 1, pp. 397 - 406, 1 2020.
[9]
C. Dietrich, D. Koop, H. T. Vo and C. Silva, "Baseball4D: A Tool for Baseball Game Reconstruction & Visualization," in IEEE Conference on Visual Analytics Science and Technology, Paris, France, 2014.
[10]
M.Lage, J.P.Ono, D.Cervone, J.Chiang, C.Dietrich and C. Silva, "StatCast Dashboard: Exploration of Spatiotemporal Baseball Data," IEEE Computer Graphics and Applications, vol. 36, no. 5, pp. 28-37, 2016.
[11]
J. P. Ono, C. Dietrich and C. T. Silva, "Baseball Timeline: Summarizing Baseball Plays Into a Static Visualization," Computer Graphics Forum, vol. 37, no. 3, pp. 491-501, 2018.
[12]
K. Wongsuphasawat and D.Gotz, "Exploring flow, factors, and outcomes of temporal event sequences with the outflow visualization," IEEE TVCG, vol. 18, no. 12, pp. 2659-2668, 2012.
[13]
J. Zhao, Z. Liu, M. Dontcheva, A. Hertzmann and A. Wilson, "MatrixWave: Visual Comparison of Event Sequence Data," in CHI, 2015.
[14]
K. Wongsuphasawat, J. A. G. Gómez, C. Plaisant, T. D. Wang, M. Taieb-Maimon and B. Shneiderman, "LifeFlow: visualizing an overview of event sequences," in CHI, Vancouver, 2011.
[15]
D. K and M. T, "Segmentifier: Interactive Refinement of Clickstream Data," Computer Graphics Forum, vol. 38, pp. 623-634, 2019.
[16]
S. Guo, K. Xu, R. Zhao, D. Gotz, H. Zha and N. Cao, "EventThread: Visual Summarization and Stage Analysis of Event Sequence Data," IEEE TVCG, vol. 24, no. 1, pp. 56-65, 2018.
[17]
MLBAM, "statsapi.mlb.com," [Online]. Available: https://statsapi.mlb.com/api/v1/game/. [Accessed 15 9 2019].
[18]
B. Bukiet, R. E. Harold and J. L. Palacios, "A Markov Chain Approach to Baseball," Operations Research, vol. 45, no. 1, pp. 14-23, February 1997.
[19]
"sportspectator.com," [Online]. Available: http://www.sportspectator.com/fancentral/baseball/stats/linescore.html. [Accessed 30 1 2019].
[20]
J. T. Ian and J. Cadima, "Principal component analysis: a review and recent developments," Philosophical transactions Series A, Mathematical, physical, and engineering sciences, vol. 374, p. 20150202, 2016.
[21]
L. v. d. Maaten and G. Hinton, "Visualizing Data using t-SNE," JMLR, vol. 9, pp. 2579-2605, 2008.		zh_TW
dc.identifier.doi (DOI) 10.6814/NCCU202001005en_US