网站图文版：http://tinyurl.com/mjrfcgl
: 前情提要(按)：原po这篇爆文的原文在米国也收到广大的回响
: 主要的论点就是“投射能力”应该参考“出手位置”。
:
: 计算方式为找出每位球员的出手(FG)位置及次数，
: 以全联盟在这些出手位置的平均，算出这些出手应当的期望得分；
: ShotScore = 实际球员FG得分 - 联盟同出手位置FG期望得分
今天介绍的这篇文则对于 ShotScore 提出了三点质疑。
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? ]
有必要搞这么复杂吗？
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，详细的出手数据也都没有公开。
原文发表的方法是复杂中带点优雅，但谁说这统计过程的复杂是必须的？
问题就在这其中的复杂究竟有没有带来价值。
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.
为了找出答案，我依据 BBR 站上公开的资料创造了 "effective scoring"，
只需要 球员FG 和 联盟eFG% 配上很直觉的理论和基本的数据。
(编按)ShotScore = 实际球员FG得分 - 联盟同出手位置FG期望得分
XFP = 实际球员FG得分 - 联盟同出手次数FG期望得分
= 实际球员FG得分 - (球员FGA * 联盟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