臺灣博碩士論文加值系統

相較於其他球類運動，棒球受到團隊交互作用影響較小，更適合以數據統計來分析，也因此，數據統計對於棒球運動而言一直是極重要的一部分；各種預測球隊與球員成績的預測系統已相當成熟，能夠有效地以各種年度統計數據來預測下一個球季的成績，成為安排下個球季球員名單或是與球員簽約的重要依據；然而，球員在球季中的成績起伏一直讓球團管理層感到困擾，球季中總會有些球員突然陷入低潮而導致球隊戰績不佳，目前的預測系統仍未能夠在球季中及時追蹤球員狀況。
2015年，美國職棒大聯盟建立了STATCAST系統，以雷達與光學追蹤技術結合的先進量測方法，從統計「結果」變成統計「因素」，改變了棒球的統計學。STATCAST記錄了比賽中每一球的數據，令逐球預測成為可能，本研究以2015至2019年STATCAST系統量測的美國職棒大聯盟數據，以二元羅吉斯迴歸法建立預測模型，不同於主流的預測模型以預測球季成績為主，本研究開發場內安打率之「逐球」監控系統，來監控球員在球季中每一次擊球的擊球品質狀況與實際表現之場內安打率是否受運氣影響，以減少球員在球季中的表現起伏對球隊戰績的影響。
根據模擬2019年球季的逐球監控結果，球員的場內安打率表現確實會隨著球季的進行，朝向本研究模型所預測的趨勢變化，並可以讓數據使用者根據本研究的預測結果與實際表現的異常訊號，即時做出對策，能夠有效協助球團、教練、球員本身甚至遊戲玩家做出更正確的判斷及調度，即時改變戰術或訓練方針，進而提升球員本身及球隊的表現。

Compared with other ball games, baseball is less affected by team interaction. Therefore, data analysis has always been an important part of baseball. Many prediction systems for teams and players are able to predict their states during the season effectively. Prediction systems become an important basis of team rostering and contracts. However, players’ streakiness always makes the front office confusing. It always affects team records when some player’s performance falls into a slump suddenly, but current prediction systems are still not able to track whether players are in hot or cold states during the season.
The Major League Baseball (MLB) has imported the STATCAST system, an advanced measurement using radar and optical tracking technologies. The statistics of baseball is changed by changing collected data from “results” to “factors”. The STATCAST records the states of every play in every game, and makes the play-by-play prediction to be possible. Different from the mainstream baseball prediction, this study develops a play-by-play prediction system to monitor batting quality by using two-class logistic regression. The results can also monitor whether the BABIP performance was affected by luck or other reasons.
According to the results of simulation of the 2019 season, the BABIP performance of players will evolve toward the trend, which the system predicts, as the season progresses. This study analyzes abnormal signals of prediction and performance which can help team managers, coaches, players, and even game players to make correct decisions and set strategies and training, to improve players as well as team performance.

摘 要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 v
表目錄 vi
第一章、 緒論 1
1.1 研究背景 1
1.2 研究動機 5
1.3 研究目的與流程 7
第二章、 文獻回顧 9
2.1 棒球統計文獻回顧 10
2.2 機器學習方法文獻回顧 17
2.3 分類模型評估文獻回顧 25
第三章、 研究方法 29
3.1 問題描述 29
3.2 變量分析 31
3.3 建模步驟 35
第四章、 研究結果與個案分析 38
4.1 預測模型分析與比較 38
4.2 個案研究 41
4.3 表現起伏大之球員成績回顧 48
4.4 預測結果不佳之個案研究 51
4.5 結果分析 55
第五章、 結果與後續研究 61
5.1 研究結論 61
5.2 後續研究建議 62
參考文獻 64

中文部分
王濟川、郭志剛(2005)。Logistic 廻歸模型－方法及應用，台北市：五南圖書。
吳明隆(2008)。SPSS 操作與應用-多變量分析實務。五南書局：台中市。
邱皓政(2019)。量化研究法（一）研究設計與資料分析，二版，雙葉書廊，臺北市。
簡禎富，許嘉裕(2018)。大數據分析與資料挖礦，二版，前程文化，新北市。
Michael Lewis(2014)，魔球：逆境中致勝的智慧，游宜樺（譯），初版，早安財經，臺北市。
英文部分
Albert, J. (2016) “Improved component predictions of batting and pitching measures,” Journal of Quantitative Analysis in Sports, 12, pp. 73–85.
Davenport, T.H. (2014) “Analytics in sports: The new science of winning,” International Institute for Analytics, 2, pp. 1–28.
Fast, M. (2009) “What the heck is PITCHf/x,” The Hardball Times Annual, 2010, pp. 153–158.
Kagan, D. and Nathan, A. M. (2017) “Statcast and the Baseball Trajectory Calculator,” The Physics Teacher, 55, pp. 134.
Quinlan, J.R. (1986) “Introduction of Decision Trees,” Machine Learning, 1, pp. 81-106.
Barnes, J. (2015) “Azure Machine Learning, Microsoft Azure Essentials,” 1st edition, Microsoft Press, California.
Johnson, R.A. and Wichern, D.W. (2007) “Applied Multivariate Statistical Analysis,” 6th edition, Prentice Hall, New Jersey.
Bailey, S.R. (2017) “Forecasting Batting Averages in MLB,” University of the Pacific United States CA, Unpublished thesis.
Taylor, N.C. (2017) “Forecasting Batter Performance Using STATCAST data in Major League Baseball,” North Dakota State University United States ND, Unpublished thesis.
Alamar, B. and Mehrotra, V. (2012) “Beyond Moneyball: The Future of sports analytics. Analytics Magazine,”
Retrieved from http://analytics-magazine.org/beyond-moneyball-the-future-of-sports-analytics
Anderson, R.J. (2017) “How Statcast has changed MLB and why not everybody seems all that happy about it”
Retrieved from https://www.cbssports.com/mlb/news/how-statcast-has-changed-mlb-and-why-not-everybody-seems-all-that-happy-about-it/
Arthur, R. (2015) “Chase Utley is the Unluckiest Man in Baseball,”
Retrieved from https://fivethirtyeight.com/features/chase-utley-is-the-unluckiest-man-in-baseball/
Brousell, L. (2014) “8 Ways Big Data and Analytics Will Change Sports,”
Retrieved from http://www.cio.com/article/2377954/data-management/data-management-8-ways-big-data-and-analytics-will-change-sports.html
Birnbaum, P. (2017) “A guide to sabermetric research. Society for American Baseball Research,”
Retrieved from https://sabr.org/sabermetrics
Druschel, H. (2016) “A Guide to the Projection Systems,”
Retrieved from https://www.beyondtheboxscore.com/2016/2/22/11079186/projections-marcel-pecota-zips-steamer-explained-guide-math-is-fun
Pesca, M., (2009) “The Man Who Made Baseball's Box Score A Hit,”
Retrieves from https://www.npr.org/templates/story/story.php?storyId=106891539
Schoenke, P., (1999) “How To Play Fantasy Baseball,”
Retrieved from https://www.rotowire.com/baseball/advice/how-to-play.php
Slowinski, S., (2011) “DIPS” Retrieved June 20,2020,
Retrieved from https://library.fangraphs.com/principles/dips/
Vice Sports (2015) “Future of the Game: Baseball's Latest Statistical Revolution,”
Retrieved from https://www.vice.com/en_ca/article/kbdmyz/future-of-the-game-baseballs-latest-statistical-revolution
Sprankton (2018) “The Curious Case of Paul Goldschmidt: Examining A Slump” Retrieved from https://www.azsnakepit.com/2018/5/7/17325992/the-curious-case-of-paul-goldschmidt-examining-a-slump-dbacks-astros-dodgers
Shaw, S., (2015) “Updated MLB Statcast Data (July 2015),”
Retrieved from http://www.banishedtothepen.com/updated-mlb-statcast-data-july-2015/
Tango, T., (2004) “Marcel,”
Retrieved from http://www.tangotiger.net/marcel/
Weinberg, N., (2015) “Batted Ball Direction,”
https://library.fangraphs.com/offense/batted-ball-direction/
Wolfson, E. and Yanofsky, D. (2017) “Why calculating home run distances is like measuring sea level,”
Retrieved from https://qz.com/1025800/home-run-derby-2017-how-home-runs-are-measured-has-changed-in-the-last-decade/
網頁部分
Baseball Reference https://www.baseball-reference.com/
Baseball Savant https://baseballsavant.mlb.com/
FanGrapgs https://www.fangraphs.com/