今年2024年春訓時，台鋼雄鷹團隊導入了運動科學，與統一獅和樂天桃猿 一樣，選擇同一家外部棒球學校進行專案合作。根據網路上查到的資訊，春訓期間，他們為球員進行了數據收集檢測和球路設計訓練。
從YouTube 影片中可以看到，張奕、賴鴻誠 、王柏融 分別在做投球和揮棒，旁邊的儀器進行資料收集。應用這些數據，可以讓選手了解自己的問題，接下來將為每位選手制定個人化訓練課表。
據棒球學校的投手訓練教練曾浩哲的說法，通過球路設計訓練，原本投手 #許育銘 的球種（滑球、曲球、直球和變速球）變化量不大，經過調整後，直球和變速球的變化量增加，而滑球和曲球變得更穩定。
我認為導入運動科學的最大挑戰是教練團對新觀念的接受程度。前穀保家商總教練周宗志曾在專訪中提到，導入運動科學後期，他本人不再提供投手技術上的建議，完全交由運動科學團隊協助。而富邦悍將最近三位日籍教練不續留，據PTT鄉民說法，是因為觀念較為傳統。不知道台鋼教練團對這些新概念的看法如何？
無論如何，希望2025新的球季，球員能夠透過運動科學有更進一步的成長
We could review our organization’s performance by using analytical data. If we see the performance report, but don’t take any further action. Nothing will change.
The book author of “Competing On Analytics”, Tom Davenport, suggests readers apply data carefully and gain an advantage through it.
The hedgehog knows one big thing, but the fox knows many little things.
If an original method is not surely working, the hedgehog is reluctant to change, but the fox is tolerant of complexity and is adaptable to find a new approach. That is why the author suggests being foxy is a right attitude toward a good forecaster.
We live in a world in which information is pervasive so that the gap between what we know and what we think we know is widening. As the study has shown, even the experts usually make incorrect predictions. For example, the probability of the skyscraper being crashed into by the terrorists is 0.05%. The possibility would rise to 38% given that the first building is under attack.
If we could use one of the principles, “Today’s Forecast is the First Forecast of the Rest of Your Life” in this book, we could make a better forecast possible today—regardless what we said yesterday, last month, or last year– and prevent the formidable catastrophe from happening.
This book is a little long but readable, not a formula-heavy, general science book. It consists of four sections.
The first section considers the failures of predictions in finance, baseball, and politics. Then, the author gives the readers some advice about how we can apply our judgment to the data without succumbing to the biases. The second section focuses on dynamical systems (weather, economy, earthquake, and economy) that make forecasting more difficult. Following the third section, it turns toward a solution by an introduction of Bayer’s theorem. Finally, the discussion of applying Bayer’s theorem to more existential types of problems.
If you’re interested in general science books or statistically forecasting, please enjoy it. However, if you are the audience who need depth in measuring and making data-driven decisions, you might not appreciate this book as much. I would suggest to read “How To Measure Anything” by Douglas W. Hubbard.
