原文連結 https://goo.gl/bcVKEA
The season’s complete, which means the numbers are official. This is convenie
nt for a writer, because it means there shouldn’t be any issues anymore with
comparing 2017 to another full season in the past. A full season is a full sea
son. So how about a quick full-season review of the pitch-framing data? There
’s something interesting going on. Something dramatic, something that shakes
the foundation of the numbers themselves. I have the graphs to prove it.
隨著球季結束，這也代表有了整季的正式資料可以方便各路作家們使(唬)用(爛)、同時也
不會再有任何異議，真是太棒喏
關於Pitch-Framing，作者用季與季之間的資料來快速對照一下，發現有一些對framing的
既定印象並非是不可動搖的。
作者接下來會用幾張圖(而不是又臭又長der證明過程)來解釋&說明他發現的情況
The most advanced pitch-framing information is available at Baseball Prospectu
s. It’s long been the gold standard, and so many hundreds of hours have gone
into generating the results that get published on the sortable leaderboards. T
here are two framing metrics of note, for catchers and for entire teams. One i
s just framing runs above average, which is self-explanatory. The other is CSA
A, or called strikes above average. This is directly related to framing runs a
bove average, but it’s expressed as a rate stat. I think that’s all you need
to know. In this post, I’m going to use them both.
本篇Framing資料取自BP，BP Framing的資料可針對單一捕手以及整隊來看，資料又分為
兩個面向：
1.framing runs above average
2.called strikes above average (CSAA)
作者將一併採用來做分析
There exist 10 years of detailed information, based on the pitch-tracking tech
nology that’s been in existence. This is a plot of standard deviations, year
to year, over the course of the decade. This is on the team level, using runs
above average. This is just an examination of each year’s spread.
由於追蹤投球的技術進步，讓我們有最近十年的詳細資料可以拿來玩。下圖是以整個球隊
作為統計對象，並以RAA 作為數據統計的標準，用標準差的方式來呈現最近十年的分布情
況
https://i.imgur.com/v339xjK.jpg
There’s currently less spread than there was in the past. There hasn’t been
any real change since 2015, but there’s still less spread than in, say, 2008.
This is presumably related to a point I’ve made before: More teams than ever
are aware of the value of good framing. So more teams are prioritizing it, wh
ich makes it harder to stand out. When you raise the floor, you narrow the dis
tribution. There are still differences between the best teams and the worst, b
ut the gaps are somewhat smaller. Neat stuff.
最近三年的變化不大，但跟08年相比卻是減少了不少。
這或許和我以前曾提過的"各隊開始重視framing的價值"有關，因為大家都開始重視，與
原本領先者們的差距便開始縮小，但好的framing跟差勁的framing，差距仍然是很大的。
But that’s not really what I want to show you here. Instead, I’d like to cal
l your attention to something taking place with individual catchers. I gathere
d data for every catcher since 2008 who’s had at least 2,000 framing opportun
ities in consecutive seasons. Here’s how the numbers held up, in terms of CSA
A, between 2013 – 2016.
然而上面那個東西並不是我在本篇想要表達的重點，希望大家將目光放在捕手身上。
我將最近十年有超過2000次framing機會的捕手們的資料通通都抓了下來，底下這張圖裡
面的藍點就是那些捕手們在13~16年的 called strikes above average(CSAA)
(可惜我們並不知道到底有誰、誰又在哪個位置)
https://i.imgur.com/JqUrPCy.jpg
You see that pretty strong, linear relationship. You want that relationship to
exist; that way you can have the confidence you’re measuring something real
and sustainable. That plot comes with an R^2 value of 0.49. The slope is 0.69.
A good framer in one year was likely to be a good framer in the next year, an
d the opposite was also true.
從這張圖裡可以發現有很強的線性關係(R^2=0.49、斜率0.69)，而某些人也會希望這相關
性確實存在，因為這等於給了那些相信framing研究的人一劑強心針，也因此產生出了"好
的接球者來年也能維持好的接球表現，反之亦然"的推論。
Moving on now, here’s the same plot, except for only 2016 – 2017.
用同樣的方法來套用在16～17年間的狀況
https://i.imgur.com/p3uMxKx.jpg
Look, there’s still some relationship. This isn’t the picture of randomness.
And yet, the R^2 value is 0.20. The slope is just 0.42, which is down 40% fro
m in the earlier plot. Something just happened. Or, something is continuing to
happen. I think this last plot drives it home. Here are all the year-to-year
R^2 values, in terms of CSAA.
雖然還是看得到相關性，但已經很不顯著了。
作者認為有什麼事情已經發生了，或是正在發生。
https://i.imgur.com/kWlpgAV.jpg
The sustainability is eroding. Which means the predictability is eroding. Sure
, there was a little spike a year ago, but the last three years have the three
weakest relationships in the sample. And, actually, the last four years have
the fourweakest relationships in the sample. Which pitch-framing performance w
as first being measured, one of the things that made it so exciting was that t
he numbers held up so well, year to year. It was essentially proof of signal.
The method of measurement hasn’t meaningfully changed ever since. It’s the s
ame system. If anything, it’s more advanced now than ever. It’s had the bene
fit of time. But the year-to-year relationships are disintegrating. A good fra
mer in 2016 was still likely to look like a good framer in 2017, but that coul
dn’t be said with very much confidence. The data is getting increasingly rand
om.
圖中顯示CSAA的可持續性正在衰退，可預測性也因此衰退。縱使前一比較年度有個稍回反
彈的小高峰，但依然是在十年線以下(拖走...)。
"一個framing好的捕手，來年也可能依舊是framing好的捕手"
這句話依然有點道理，但說這話的人，信心指數已經不若以前那麼高了。
Welington Castillo is currently a free agent. He just spent the year with the
Orioles. When he became an Oriole, he had the record of being a below-average
framer. Last year, he performed like an above-average framer. Ditto J.T. Realm
uto. Ditto Stephen Vogt. Buster Posey, meanwhile, got a lot worse, and so did,
say, Tony Wolters. I have a sample of 393 catcher season pairs. Of the 24 big
gest year-to-year changes in CSAA, seven of them just happened between 2016 an
d 2017. I don’t even know how to explain what’s happened with Chris Iannetta
.
我們都知道Wellington Castillo(以下簡稱威卡)的framing評價一向不好，然而今年球季
他的CSAA數據卻是反常的高於平均，同樣詭異的事情也發生在馬林魚隊的柳木、轉戰我酒
的Stephen Vogt身上。
Framing評價頂級的巨人Buster Posey，今年則是反常的差，山脈的捕手Tony Wolters亦
然。
我甚至不知道Chris Iannetta究竟是花生省魔術。
Of those 393 catcher season pairs, Iannetta is responsible for nine of them. B
ut of the seven largest year-to-year changes, whether for better or worse, Ian
netta’s been responsible for three of them. All three are from the most recen
t seasons. From 2014 to 2015, Iannetta got dramatically better. From 2015 to 2
016, he got dramatically worse. And from 2016 to 2017, he got dramatically bet
ter again. Iannetta might be the current face of pitch-framing uncertainty. Or
maybe it’s Jonathan Lucroy, who just keeps on declining. I don’t know. Thin
gs are just weird.
在393組的跨季資料中，有九組是Iannetta的。此外無論好壞，數據變動最大的七組，Ian
netta就佔了其中第三組，而且分別是近四年的三組跨季資料。從14年到15年，Chris Ian
netta的接球數據從負轉正，15到16卻由正轉負，16-17卻又再次由負轉正。
Iannetta的數據所代表的意義可能是指現在的framing數據所面臨到的問題－不確定性。
又或者這問題呈現的是在另一位捕手 Jonathan Lucroy 身上，他的framing數據是逐年下
滑的
There are a few possible explanations. One, it’s all a blip. I don’t know. M
aybe. Numbers do funny things sometimes. Two, there’s something wrong with th
e actual data, which might be related to the recent switch-over from PITCHf/x
to Trackman/Statcast. That wouldn’t explain what already seemed like a trend
before 2017. And all this information comes from Pitch Info, which takes delib
erate care to make all necessary adjustments and corrections.
對於上述的問題，幾個可能的解釋是：
(1)：這全部的數據或許只是短時間之內呈現出來的現象而已，數字這種東西有時候是很
有趣的
(2)：資料來源有問題，因為資料蒐集的系統從原本的 PITCHf/x 換成了今年的 Trackma
n/Statcast，但這不能解釋2017年以前就開始產生的趨勢。所有這些信息都來自Pitch In
fo，它需要仔細考慮做出一切必要的調整和修正
And three, get used to this. This could be the new normal, the consequence of
more teams caring about how their catchers catch. Maybe framing is easier to t
each and learn than we thought. Maybe more catchers than ever know what they’
re supposed to do, and so the baseline for everyone is so high that random vol
atility plays a larger-than-ever role. Even if everyone were exactly the same,
there would still be variation, because the baseball season isn’t infinitely
long. If every team, for example, were a true-talent .500 ballclub, a season
would still end up with 90-win teams and 90-loss teams. You could detect this
volatility because, in subsequent years, you’d observe further randomness. Pe
rformances wouldn’t correlate so well year to year. That’s what we’re seein
g with framers.
(3)：這篇文章反映出來的情況有可能是新的常態。
更多隊伍開始重視捕手framing的重要性，而framing也可能比我們想的還要來的容易被教
導學習。也因為捕手的技術有所提升，整體水平被拉高，所以數據的隨機性開始明顯了。
就算每個捕手能力都一樣，當數據拿出來看，每個捕手數據都不會相等。
In a sense, you could see this all coming. It’s been possible to forecast, an
d I’ve written about this on multiple occasions. But it’s still pretty stark
to look at that 2016 – 2017 R^2. A far weaker relationship than ever. Seemin
gly far more randomness than ever. Is Welington Castillo actually a good pitch
-framer now? I never would’ve believed it, but the data says what it says. I
don’t know what to think about Castillo, and I don’t know what to think abou
t a lot of different guys. Pitch-framing has entered a strange new era. An era
in which it still matters, but an era in which it’s not easy to tell who’ll
actually stay good at it. It’s difficult to justify a heavy investment in so
mething that now comes with such a high degree of uncertainty. But that same u
ncertainty is what the league has to reckon with.
球員表現依然有可能被預測， 但是看看16-17年的R^2：0.42， 一個比以往任何時候都更
弱的關係。數據比以往更多了隨機性。
威卡現在是一個framing好的球員？ 我從來不會相信，但數據卻這麼說。 我不知道該怎
麼想威卡，我不知道該怎麼去評價跟威卡一樣數據波動的球員們。
Framing已經進入了新的時代，光憑數據已經難以分辨誰能夠保持好表現了。