網站圖文版：http://tinyurl.com/mjrfcgl
: 前情提要(按)：原po這篇爆文的原文在米國也收到廣大的迴響
: 主要的論點就是「投射能力」應該參考「出手位置」。
:
: 計算方式為找出每位球員的出手(FG)位置及次數，
: 以全聯盟在這些出手位置的平均，算出這些出手應當的期望得分；
: ShotScore = 實際球員FG得分 - 聯盟同出手位置FG期望得分
今天介紹的這篇文則對於 ShotScore 提出了三點質疑。
LeBron James vs. Kevin Durant:
What matters when we assess the NBA's best scorers?
By Tom Ziller @teamziller on Oct 10 2013, 11:15a
(前略)
[ 1. IS THE COMPLEXITY NECESSARY? ]
有必要搞這麼複雜嗎？
The average Excel jockey cannot do what Goldsberry did. The detailed shot
data is not open source and the effort is, as with what most of Goldsberry
publishes, both extremely complex and elegant (a powerful combination). But
the mere fact of the statistic's complexity is not totally relevant. The
question is whether the complexity adds anything of value. (Because boy do I
know arty but extraneous complexity ...)
一般的 Excel 操作做不到原文做到的，詳細的出手數據也都沒有公開。
原文發表的方法是複雜中帶點優雅，但誰說這統計過程的複雜是必須的？
問題就在這其中的複雜究竟有沒有帶來價值。
To answer that, I went about creating my own "effective scoring" stat using
public data available at Basketball-Reference (and many other places) and a
Google Spreadsheet. (Here's that spreadsheet. Do with it what you will.) I
used straight theory and a common base stat one step up from field goal
percentage, effective field goal percentage (eFG). My formula gets each
player's number of expected points by multiplying the number of field goals
they attempted, league average eFG (.496) and two (the number of points a
made basic shot under eFG is worth). Then I subtracted their actual points
from the field (points minus made free throws) from the expected points. I
named it Extra Field Points.
為了找出答案，我依據 BBR 站上公開的資料創造了 "effective scoring"，
只需要 球員FG 和 聯盟eFG% 配上很直覺的理論和基本的數據。
(編按)ShotScore = 實際球員FG得分 - 聯盟同出手位置FG期望得分
XFP = 實際球員FG得分 - 聯盟同出手次數FG期望得分
= 實際球員FG得分 - (球員FGA * 聯盟eFG% * 2)
Goldsberry's complex creation resulted in a ShotScore top three of, in order,
LeBron (+231), Durant (+204) and Curry (+164). Here is the Extra Field Points
leaderboard.
ShotScore XFP
1. LeBron James, +231, +290
2. Kevin Durant, +204, +179
3. Stephen Curry, +164, +147
If the metric was primarily created because existing scoring effectiveness
metrics were lackluster