※ [本文转录自 MLB 看板 #1H9IQY1A ] 作者: tanaka0826 (田中鬪莉王) 看板: MLB 标题: [闲聊] The 20-80 Scale, SABR Style 时间: Thu Feb 21 03:40:01 2013 The 20-80 Scale, SABR Style 以数据观点探讨球探报告之给分方式 http://www.fangraphs.com/blogs/index.php/the-20-80-scale-sabr-style/ When scouts evaluate the players on the field, they use a 20-80 scale as shorthand to describe a player’s tools and/or his overall ability. Receiving a 50 on the scale means that one is major-league average, and for every 10 points up or down the scale, the scout believes the player is one more standard deviation above or below major-league average. An 80 is incredibly rare because one would have to be 3 standard deviations above the mean (or in the top 0.1-0.2 percent of players), and it’s a representation of the truly, truly elite. But the question becomes what those grades represent. When someone says that a player is an [insert grade], what should we actually expect them to do statistically at the major-league level? Armed with some advanced statistics and z-scores, I went to find out. 球探评量一名球员时，会以20-80之间的分数来速记球员的tools和(/或)整体的能力。50 分代表大联盟平均，每往上十分与往下十分都代表着相差一个标准差。拿到80分是相当难 能可贵的，因为80分代表着该球员的能力比平均值高出三个标准差(大约在前0.1%~0.2%之 间)，是个菁英中的菁英。 问题在於这些分数代表什麽？如果某个球员拿下某个分数，代表球探预期他会在大联盟拿 出甚麽样的数据呢？ 以下我们将从进阶数据以及 z-scores (得分-平均)/标准差 来下手。 (Note: I used statistics from 2010-2012 because the timeframe is large enough to get a sample and small enough to stay within the recent run environment, and I used Russell Carleton’s measurements for each statistic to get a sample at least indicative of skill.) (注：以下采用的数据为2010-2012，因为时间长度得超过「小样本」；同时也得取得够短 ，才比较能看出一位球员在近期的表现。 以下参考自 http://www.baseballprospectus.com/article.php?articleid=17659 ，以 取出足够看出某项技能之等级的样本。) Hit Tool Hit Tool z BA Player BABiP Player 80 3 .336 Miguel Cabrera .383 - 70 2 .313 Josh Hamilton .357 Dexter Fowler 60 1 .290 Martin Prado .332 David Wright 50 .267 Rafael Furcal .306 Asdrubal Cabrera 40 -1 .244 Vernon Wells .280 Shane Victorino 30 -2 .221 Brendan Ryan .254 Mark Teixeira 20 -3 .199 - .228 - H - HR ( BABiP = Batting average on balls in play， ──────── ) AB - K - HR + SF You can certainly argue with the statistics I choose for each of these tools, but I preferred to use statistics with which you are already familiar to simply give you an idea of the spread. In regard to the hit tool, I could have used Contact%, but while the hit tool is defined as the ability to make contact, we usually imply some semblance of production with it -it doesn’t matter that the player makes a lot of contact if he doesn’t do anything with it. Looking at the chart specifically, Cabrera was the only 80 hitter, and he was the only hitter within 15 points of the .336 mark. All the way at the bottom, you see a lack of examples for a 20 grade, but it shouldn’t be too surprising that a 20 hitter wouldn’t get enough PA (1000 for BA, 1500 for BABiP) to be on the list. It certainly doesn’t mean 20 hitters don’t exist. 当然各项挑选出的数据并不是正确解答，但我偏好使用大家熟悉的数据以便大家了解。 关於hit tool，我其实也能选用Contact%，但是hit tool的定义是make contact的能力 ，而Contact%可能会被假产物影响─例如常常碰到球但是都打不好。 回到上表，Cabrera是唯一一位hit tool拿到80分的球员，打击率.336甚至领先第二名.015 。至於最底下的20分那栏为什麽没有人呢？因为20分的球员通常活不久，无法拿到足够的 打席数；而并不代表世界上没有20分的人。(注：BA最低PA数需要1000；BAPiP则是1500) Power Tool Power Tool z ISO Player HR/PA Player 80 3 .294 Jose Bautista 6.6% Jose Bautista 70 2 .242 Joey Votto 5.2% Edwin Encarnacion 60 1 .191 Buster Posey 3.8% Kevin Youkilis 50 .140 Nick Markakis 2.4% Shane Victorino 40 -1 .089 Ryan Hanigan 1.0% Jose Altuve 30 -2 .038 Ramiro Pena -0.4% - 20 -3 -.013 - -1.8% - ( ISO = Isolated Power，SLG - BA ) ISO was the stat of choice here as it is the most commonly used power metric, and I used HR/PA to give a look with a stat that didn’t involve speed (SLG, and therefore ISO, give singles, doubles, and triples different weights when speed could be the deciding factor between getting one or the other). Again, these are here to give you an idea of what a certain grade would merit in the majors. Back to the list, Bautista is the only player in either list to get an 80, but Giancarlo Stanton is so close in both (.282 and 6.2%) that you could go ahead and throw an 80 on it. As for the negative numbers, it’s mainly just a glitch in the numbers (those negatives obviously aren’t possible). Emmanuel Burriss had the least amount of power with a .007 ISO and a 0.0% HR/PA. Again, 20 power guys don’t stick on MLB rosters very long (180 PA – I took out pitchers – was the restriction here). ISO是量测力量方面最广为使用的数据，因此这里采用ISO。我也在此使用HR/PA，藉此 排除速度的影响。(ISO会用到SLG，而SLG的是由一垒安打、二垒安打、三垒安打以及全 垒打组成，并各自给予加权─因此，能否多跑一个垒也会成为一项因素。)总而言之，这 些东西主要是要让大家了解某成绩的球员之後在大联盟的表现如何。 上表中的Jose Bautista是唯一一位拿下80分的球员，不过Giancarlo Stanton也相当的接 近(.282、6.2%)，所以给他80分也是很合理的。至於表中的ISO出现负值，主要是来自於 统计上的小瑕疵(与平均值与标准差的大小有关；最烂应该是0)。 Emmanuel Burriss是ISO最差的球员，只有.007；同时，HR/PA是糟糕的0.0%。与Hit tool 相同，20分的球员通常无法站稳大联盟，一下子就再见了。(注：扣掉投手後，至少 180 PA) Speed Tool Speed Tool z SB/3 Player BsR/3 Player 80 3 38 Ichiro Suzuki 9 Michael Bourn 70 2 29 Elvis Andrus 6 Rajai Davis 60 1 19 Chris Young 3 Andrew McCutchen 50 9 Jon Jay Yunel Escobar 40 -1 0 - -3 Jason Kubel 30 -2 -10 - -5 Adrian Gonzalez 20 -3 -20 - -8 Ryan Howard ( BsR = Base Runs ) Speed isn’t much easier to isolate outside of the traditional 60-yard dash and home-to-first times, but as I said earlier, we expect a certain production from the tool by the time a player reaches the majors. BsR/3 (I divided the cumulative BsR by 3 to give you an idea of what it would take per season) gives you a better spectrum of players in this instance as it can go into the negative range (0 SB players are slow, but they aren’t necessarily equally slow), and it incorporates other instances involving speed, such as going first-to-third. 最能独立出速度的数据就属传统的60码冲刺与跑到一垒的时间罗。不过如同前面所说的， 我们要的是预测球员站上大联盟之後该tool的能力等级。BsR/3(除以三的意义是把三季 总合变成单季平均)更能让你看出球员的速度对比赛造成的影响；同时结合了其它与速度 有关的因素─例如从一垒冲到三垒。(备注：零盗垒的球员不快，但是不一定代表零盗垒 的人通通都一样慢。) Defense Tool Defense z UZR/150 Player Fld/3 Player 80 3 22.8 - 17.9 Brett Gardner 70 2 15.7 Adrian Beltre 12.4 Michael Bourn 60 1 8.7 Giancarlo Stanton 6.9 Carlos Gomez 50 1.6 Casey McGehee 1.5 Drew Stubbs 40 -1 -5.4 Dan Uggla -4.0 Rickie Weeks 30 -2 -12.5 Asdrubal Cabrera -9.5 Michael Morse 20 -3 -19.6 - -14.9 - UZR = Ultimate Zone Rating in runs above average (Arm+DPR+RngR+ErrR) (150：每150场) Fielding = Fielding Runs Above Average based on UZR (3：三年) Defense might be the hardest tool to look at in this situation, and while it might have been better to look at this position-by-position, the sample (needing 2500 innings) was already pretty small. According to UZR/150, there are no 80 defenders in the game, and although Fld/3 names Gardner, he was the only one on the list. Perhaps 80 defenders are usually bad enough at offense that they don’t get the playing time necessary for this query, or we may simply need a bigger sample. 防守应该是最难用这种方式分等级的tool。虽然说每个位置分开来看比较好，不过我们取 的样本数已经够小了(2500局)。根据UZR/150，没有任何80分的防守者。而从Fld/3来看的 话也只有Brett Gardner一个而已。或许80分的防守者通常攻击力都不怎麽样，无法拿下 足够的打席。也或许我们的样本(年数)需要扩大。 Arm Tool Arm Tool z rARM/3 Player ARM/3 Player 80 3 8.8 Alex Gordon 8.5 Jeff Francoeur 70 2 5.9 Jose Bautista 5.7 Alex Gordon 60 1 3.0 Jayson Werth 3.0 Jayson Werth 50 0.2 Austin Jackson 0.2 Matt Kemp 40 -1 -2.7 Ichiro Suzuki -2.5 Matt Holliday 30 -2 -5.6 - -5.3 Ryan Braun 20 -3 -8.4 - -8.1 - Arm - Outfield Arm runs above average (UZR) rARM - Outfield Arm Runs Saved runs above average (OF) I added this in just to show you I wasn’t ignoring it. I used these arm ratings, but they only exist for outfielders and include accuracy as well as arm strength. Radar gun measurements and/or FIELD f/x velocity measurements would probably be more helpful for objectively measuring this tool. 我列入这个表格只是要告诉大家我没有忽略这部分。我会用这些Arm tool的数据，不过 这只适用於外野手，而且同时牵涉到力量与准度。用测速枪和(或)球场的追踪系统来进行 调整的话可以让这个数据更客观的呈现外野手的Arm tool。 Fastball Velocity FB Velo z SP Velo Player RP Velo Player 80 3 97 - 100 - 70 2 95 Stephen Strasburg 97 Daniel Bard 60 1 93 Mat Latos 95 Drew Storen 50 91 Adam Wainwright 92 Ramon Ramirez 40 -1 88 Mike Fiers 89 Michael Wuertz 30 -2 86 Mark Buerhle 86 Pat Neshek 20 -3 84 Jamie Moyer 84 Livan Hernandez This one is the easiest to isolate. While none of the pitchers have 80 velocities on average, several of them are obviously able to touch or even sit in that range for a period of time. As for how I split up the data, I originally did it for both SP vs. RP and RHP vs. LHP. Using SP and RP demonstrated the differences between the velocities necessary for starting and relieving, and while I hoped the LHP and RHP would show the differences between the two, I got some weird results. The mean for the two were 91.2 (RHP) and 90.5 (LHP), which was expected, but when I applied standard deviations, the SD for LHP was larger (probably due to the much smaller sample). A 60-80 necessitated a higher velocity from a LHP than a RHP, which didn’t seem to make sense. Using the mean velocity difference of about 1 mph, you can dock the grades shown above by 1 mph and use that for lefties if you so choose. 球速是最容易独立出来的数据。虽然没人的「平均球速」能站上80分，不过有些投手至少 能在一段时间之内碰到或保持在80分的速度。至於我怎麽分开这些数据呢？分成先发 vs. 牛棚以及左投vs.右投。前者的分法能看出先发与牛棚所需要的球速不同；蛋我希望能从 後者的分法看出差异─我得到了奇怪的数据。右投手91.2mph、左投手90.5mph(这很符合 预期)，但是在我带入标准差後发现左投手的标准差较大(也有可能是因为样本数较小才会 这样)─因此，左投手在60分-80分的球速是比右投手还要高的，感觉不大合理。或许你可 以利用平均球速的差距(约1mph)进行调整，先将数据降低。 Control Control z SP BB% Player RP BB% Player 80 -3 1.7% - 2.0% - 70 -2 3.7% Roy Halladay 4.4% Sergio Romo 60 -1 5.7% Rick Porcello 6.8% Jason Motte 50 7.8% Derek Holland 9.2% Guillermo Mota 40 1 9.8% C.J. Wilson 11.6% Manny Parra 30 2 11.8% Danny Duffy 13.9% Tim Collins 20 3 13.9% Jonathan Sanchez 16.3% Carlos Marmol I considered using Strike% here, but there are strategic reasons to throw balls and BB% is more commonly used when talking about pitchers. Again, I split up relievers and starters, and as you might expect, starters walk fewer hitters than relievers, which is probably at least one reason why they’re starting as opposed to relieving. You’ll note that there are no 80 control guys, and no one was even within 1% of reaching that grade. Perhaps 80 control (in its literal definition) is too high of a standard here, but there ’s also the possibility that there is such a thing as “throwing too many strikes”, where pitchers somewhat choose to walk a guy even when they theoretically could avoid doing so. 其实我也有考虑过要用Strike%，但是Strike%会受投球策略的影响(i.e.配坏球)，而BB% 也是较常用来讨论投手Command的数据。一样的，这里也成先发与牛棚两边。跟大家所想 的一样，先发投手的坏球率低於牛棚。从上表中可以看出并没有Command 80分的投手─ 事实上，最接近的投手也比80分的BB%高出1%以上。大概是因为80分的标准过於严苛(以 标准差来计算的话)；也有可能是因为所谓的「丢太多好球了」，选择闪开而让对手获得 保送机会才造成这个结果。 *** The point of all of this was simply to give us all an idea of what it would actually take to reach a certain scouting grade. How rare is a literal 80? How hard is it to sustain such elite performance? What does it mean to be “ plus” (60) in something at the major-league level? And how bad does one actually have to be to receive a 20? As prospect lists continue to roll out, you’ll hear these grades used frequently, and I just thought it was interesting and necessary to look at what it actually means to receive these scouting grades in our current environment. 这篇文章得主要目的在於告诉大家你在球探报告上看到的分数大概会是什麽样子。80分有 多难得？一直保持超高水准表现有多难？在大联盟等级之下的"plus"(60分)是甚麽样子？ 20分有多烂？ 新秀排名不断的出现，也常常会看到谁的哪个tool几分。所以我觉得告诉大家在这个时空 背景之下拿几分代表什麽意思应该蛮有趣的，同时也是大家该知道的。 -- "HARD WORK BEATS TALENT WHEN TALENT FAILS TO WORK HARD." --

※ 发信站: 批踢踢实业坊(ptt.cc) ◆ From: 140.114.207.153 ※ 编辑: tanaka0826 来自: 140.114.207.153 (02/21 12:23) ※ tanaka0826:转录至看板 Prospect 02/21 16:52

※ 发信站: 批踢踢实业坊(ptt.cc) ※ 转录者: Aroyo (118.169.109.132), 时间: 02/22/2013 13:34:24