'Translated Articles'에 해당되는 글 6건
- 2008/06/05 Anatomy of a Player - Tim Lincecum Part I
- 2008/05/23 Batterbox Analysis : Adam Dunn
- 2008/04/29 The relationships between RPS and Movement of Balls
- 2008/04/27 DIPS에 관한 설명
- 2008/04/27 VORP - Value Over Replacement Player
- 2008/04/05 이가와 게이, 무엇이 문제인가?
In Part I, I'll talk about pitch classification, and the properties of each repertoires based on Pitch F/X.
I followed the methodology of Derek Carty on The Hardball Times - About Cliff Lee Part II.
- Sample data : I got 1,246 pitches and 1,197 balls had pitchF/X info.
1. Pitch classification
As far as officialy announced, Tim Lincecum throws
1. Fastball + Curve + Changeup(by Josh Kalk 2007)
2. Fastball + Curve + Changeup + Hard sliderby BA 2007)
these 3 or 4 repertoires. I'll follow the Josh way to classify Lincecum's repertoires.
- Raw Movement DATA
This is the raw state(does not have pitch_type information) movement data of Lincecum's every pitch between 4/1/08 and 6/4/08.
1) Classification by horizontal movement
As you see, there's two different groups divided by horizontal movement. We can divide these two by criteria of 90 MPH, one will be fastball+expected hard slider group, the other will be curveball+changeup group.
2) Classification by vertical movement
We can divide 3(or 4) groups by vertical movement.
Based on vertical movement, we can assume that Lincecum did not threw 'Hard slider'.
- 1st Pitch Classification
I classified Raw datas into 1st classified datas by horizontal - vertical movement.
See above, we have some 85+MPH curveballs. The problem with these curveballs are,
1. Threre are curveballs which have vertical movement value over 0.
2. There is some curveball which has the speed near 90MPH.
And, we can see some curveballs that have pfx_z value below zero. I think these should be inserted into changeup, not curveballs.
So, I classified 1st datas by conditions below,
1. If curveball has value of pfx_x below 2, change these to changeups.
2. All balls over 87MPH should be converted to fastballs.
3. Fastballs which has below 87MPH in speed and pfx_x >2, then convert to curveballs.
4. Fastballs which has below 87MPH in speed and pfx_x <2, then convert to changeups.
5. If we have other vague datas even finished 4 procedures above, remove these datas within resonable range.
- Final Pitch Classification
As you can see, we classified 3 pitches.
2. Fastball
- 2-seamer or 4-seamer?
Lincecum's fastball has big range of horizontal movement values on his fastballs. That is, Lincecum can throw balls just simillar as straight 4-seamer, but he can even throw fastballs containing wicked horizontal movements. Plus 94.5 MPH in average speed.
What do you feel, if you have to face wicked moving fastball over 95MPH?
(I don't even want to think of it.)
- Divide fastballs by -3.87 pfx_x value
The average value of pfx_x was -3.87. So I serarated fastballs by 2 groups, one is FA -3.87, the other is FA +3.87(fastballs that has pfx_x value over -3.87).
The average property values of these two was,
FA -3.87 : 95.27MPH, pfx_x -7.11, pfx_z 12.21 (356 pitches)
FA +3.87 : 93.94MPH, pfx_x -1.19, pfx_z 10.34 (431 pitches)
- Release Point
As you see above, I cannot get any special results over release point distribuion. But, we can think when Lincecum throws FA -3.87 kind of balls, the release point would be slightly right compared to FA +3.87 kind of balls.
- Side View
FA +3.87 starts higher and reaches lower than FA -3.87 balls.
It means FA +3.87 has more sinking power than FA -3.87 balls.
- Top View
FA +3.87 has just straight trajectory, like four-seam fastball.
But for FA -3.87, it has some significant change in horizontal movement. Lincecum's FA -3.87 fastball has a sort of "Boomerang" effect.(when I separated 2 groups by pfx_x -6.00, the result was same.) We already know that TV broadcasts distort original trajectory of balls, because they do not set their cameras on front position. Lincecum's FA -3.87 fastball really changes its horizontal moving direction.
Mysterious fastball : FA -3.87
But, if we think FA -3.87 as two-seam fastball and FA +3.87 as four-seam fastball, there's a problem.
Do two-seam fastballs have higher speed than four-seam fastballs in general?
As far as I have known, four-seam fastballs are faster than two-seam fastballs.
Then what? FA -3.87 is faster than FA +3.87 - two-seam fastballs are faster than four-seam fastballs? or FA +3.87 is a sort of slider?
There's big physical problems for regarding FA +3.87 as sliders.
1) FA +3.87 does not satisfy the trajectory of average sliders.
2) FA +3.87 has more pfx_z than average sliders in league.
3) and FA +3.87 has average speed over 93MPH.
3. Curveballs
Slow curve and Power curve?
I separated curveballs into 2 groupes, by speed of 80 MPH - CU -80, CU +80.
CU -80 : 77.66 MPH, 6.11 pfx_x, -5.35 pfx_z(65 pitches) - I thought this would satisfy normal curveball.
CU +80 : 82.50 MPH, 5.69 pfx_x, -3.77 pfx_z(74 pitches) - and this would be power curve.
Release Point
Nothing special, but Lincecum has almost same release point distribution on both curveballs and fastballs.
Side View
CU +80 and -80 has very different trajectories. CU +80 has splitter-like trajectory, but CU -80 has somewhat weak movement compared to average curveballs.
Top View
CU +80 bends toward LHB, CU -80 has 'hook' power on it.
Overall
CU -80 kind is the slowest balls thrown by Lincecum and has slow-hook like trajectory.
CU +80 kind is 5 MPH faster than CU -80 kind(in average start speed), and has splitter or power-curve like trajectory.
4. Changeup
Average value of properties
82.83 MPH, -3.85 in pfx_x, 5.28 in pfx_z(216 pitches)
Release Point
Lincecum throws all of his pitches on almost exact same point.(In other words, Lincecum's release point distribution has no big difference in each pitches.)
Thus,
"Batters cannot judge what Lincecum has pitched by his release point."
Side View
Blue is the trajectory of changeup. Changeups are similar to CU +80 balls in average start speed, but changeups have almost straight trajectory.
Top View
As you see, changeups and CU +80 curveballs have mirror relationship in their horizontal movements.
So I dare say, Lincecum's changeup is more powerful when combined with his powercurves. Because they are same in start speed, but very different in movements.
5. See you again!
These are all for today. Thank you for reading, and any comments/questions are welcomed.
If you have any problems with writing comment on my blog, just send me a e-mail.
landor82 at gmail.com.
special thanks to : Josh Kalk, Mike fast, Derak Carty, Jonathan Hale, Harry Pavlidis and Ike hall.
| Current Status: Active |
이 글은 이번에 제가 이글을 쓰는데 많은 도움을 주신 몇몇 외국 분들께 감사의 표시를 하고자 없는 영어실력에 따로 영어로 써놓은 글입니다. 한글을 이해할 능력이 있으신 분들은 http://bronxbomber.tistory.com/51 의 글을 읽으시기 바랍니다.
Today, I'm going to focus on Adam Dunn, the OF in Reds. He is the symbol of "Power Hitter", isn't he? He hit 40 homeruns for 4 consecutive years, and has extremely good plate discipline. Recently, the newbie named R.Howard gets rid of Dunn's strike records and earns a honor as "another power hitter", but still Adam Dunn is the first symbol of "Power hitter".
Look at the current season record above. He follows the exact stat of his careeer. But the good part is the decrease in number of his strikeouts.(In his whole major career, he recorded 1.5+ K/BB ratio. But this season, his K/BB ratio hits near 1)
So let's get into Adam Dunn 2008.
Zone Selection
I used the sz_top/sz_bottom variables to calculate Dunn's strikezone height, and used the range of "-10 inch to 10inch" in wide.
In order to divide strike zone into 9 sections, I divided width and height by 3.
In this article, I'll use range of 21.36inch to 43.10inch for the Dunn's strikezone height.
BABIP
I used some of BABIP concept to analyze Dunn's Zone discipline. If you have no idea of BABIP, go to the link and read it. The original concept of BABIP, HR cannot be included, but in this article, I included HR for ease. (This article is just beta version of my batter analysis, and I have not much idea on sabermetric words.)
For the sake of getting IN PLAY situation data, I used type "X" datas.
Swing tendency of Dunn
Which course(of course, in the zone) did Dunn most like? Yes, every major batters like center, but I thought batters have at least 1 more 'favored' course. So I did this. Picture below is the swing rate of Adam Dunn in each sections.
Maybe you are thinking of outside of the zone. However Dunn pulled his bat on chance of 3.13% for the balls located outside of strikezone.
(Swingrate includes all X situations and Swinging strike. And they were divided by total balls located on specific section.)
As you see above, Dunn pulled his bat more on out-course than these of in-course balls. I thought power hitters would like in-course balls, but Dunn was not. Dunn has pulled his bat mercilessly on out-course balls.
Flyouts and Groundouts?
Pictures below are the FO/GO portion of each sections.
I didn't get any particular thing on FO datas. Let's see GO data.
As I wrote above, pitchers would have nearly 50% chance to get groundballs if he throw out-course balls for Adam Dunn. But it can destroy the pitcher on the other hand. Let me explain why this is stupid.
Half Ground Half extrabasehits
I calculated BABIP*(I added * for the modified. Because BABIP in this article does not mean that of Hardballtime says.) and SLGIP(just think this as slugging percentage. Values are exactly same)
As you see, Dunn produced 50% of in plays as hits on the section of middle-outside. So, if pitchers cannot make groundball-inplay situations by middle-outside on Dunn, that has to be at least single.(more likely to be extra base hit.)
And Dunn made the highest slgip value on the center of the strikezone. because he made 5 of his 11 HRs on this section.
Finalle
Although Dunn has low batting avg, he doesn't even pull his trigger on the outside of the zone. If Dunn had more contact abilty, he could re-write all the batting records existing now. I hope you enjoyed my article. If you have any further question, e-mail me.(landor82 at gmail.com)
Thanks to Jonathan Hale, Harry Pavlidis, and Josh Kalk.
This is the beta version of my Hot/cold zone analysis.
The relationships between RPS and Movement of Balls

I picked 22starters for my analysis, and if there was no pfx logs, I excluded them.
(for example, there is no pfx logs in Tyoko Dome Opener)
The criteria for these 22 starters are,
1. Starters who has the most Ks
2. Starters who showed impressive perfomance in April 2008.
3. Starters who was the Cy-young nominees(or got Cy-young award)
I got 9818 sample balls from these starters and 5965 of them were fastballs, and 5800 balls were remained when I excluded datas that was located out of the standard deviation(understable ranges, not the theoritical sigma range). And These are the results of their fastball average values.
| Name | Team | SPEED(KMH) | STDEV | DIFF | PFX | BRK | RPS | FA# | Balls# | FA% |
| J. Weaver | ANA | 90.4(145.46) | 1.83 | 8.48 | 13.01 | 2.93 | 41.02 | 169 | 497 | 34.00% |
| D. Haren | ARI | 90.34(145.36) | 1.74 | 7.82 | 12.99 | 4.48 | 41.79 | 203 | 416 | 48.80% |
| D. Cabrera | BAL | 93.94(151.15) | 1.62 | 9.10 | 12.34 | 4.62 | 40.70 | 399 | 489 | 81.60% |
| D. Matsuzaka | BOS | 91.07(146.53) | 1.46 | 9.37 | 12.51 | 4.29 | 39.83 | 207 | 394 | 52.54% |
| J. Beckett | BOS | 95.17(153.13) | 1.90 | 9.01 | 12.52 | 4.91 | 42.09 | 207 | 268 | 77.24% |
| C. Zambrano | CHC | 90.34(145.36) | 2.05 | 6.44 | 11.30 | 5.77 | 36.95 | 346 | 471 | 73.46% |
| J. Cueto | CIN | 93.04(149.7) | 1.39 | 7.30 | 11.23 | 3.09 | 37.63 | 264 | 439 | 60.14% |
| A. Harang | CIN | 88.59(142.54) | 1.97 | 6.95 | 12.81 | 3.84 | 40.72 | 282 | 460 | 61.30% |
| C. Hamels | PHI |
88.26(142.02) | 2.08 | 6.63 | 12.93 | 3.74 | 41.19 | 281 | 525 | 53.52% |
| C.C. Sabathia | CLE | 93.6(150.61) | 1.28 | 7.93 | 11.77 | 4.34 | 39.27 | 320 | 458 | 69.87% |
| C. Lee | CLE | 89.95(144.73) | 1.35 | 7.76 | 13.82 | 4.15 | 44.24 | 349 | 412 | 84.71% |
| J. Verlander | DET | 93.31(150.14) | 2.00 | 8.99 | 15.58 | 5.59 | 51.15 | 195 | 489 | 39.88% |
| R. Oswalt | HOU | 92.19(148.33) | 1.35 | 7.98 | 10.80 | 4.53 | 35.47 | 274 | 467 | 58.67% |
| B. Sheets | MIL | 92.16(148.28) | 1.19 | 7.36 | 12.11 | 3.28 | 40.11 | 217 | 378 | 57.41% |
| J. Santana | NYN | 90.64(145.84) | 1.56 | 7.29 | 11.23 | 4.63 | 36.47 | 246 | 440 | 55.91% |
| J. Peavy | SDN | 93.24(150.02) | 1.27 | 9.10 | 12.48 | 4.78 | 40.86 | 253 | 547 | 46.25% |
| F. Hernandez | SEA | 94.89(152.69) | 1.35 | 8.74 | 11.14 | 4.80 | 37.35 | 319 | 542 | 58.86% |
| M. Cain | SFN | 92.91(149.5) | 1.64 | 8.62 | 12.89 | 3.16 | 42.31 | 346 | 474 | 73.00% |
| J. Sanchez | SFN | 90.12(145) | 2.26 | 7.97 | 12.13 | 5.16 | 38.83 | 319 | 383 | 83.29% |
| T. Lincecum | SFN | 95.47(153.62) | 1.72 | 8.72 | 12.73 | 2.93 | 42.99 | 335 | 493 | 67.95% |
| R. Halladay | TOR | 92.08(148.16) | 1.73 | 8.11 | 10.41 | 6.23 | 34.04 | 222 | 403 | 55.09% |
| D. McGowan | TOR | 94.89(152.68) | 1.66 | 9.48 | 12.77 | 3.84 | 42.43 | 212 | 373 | 56.84% |
Speed is described in MPH and numbers in ( ) is the the unit of KMH(Kilometers per Hour, the Korean unit). Stdev is the standard deviation of start speed, DIFF is the diffrerence between average speed of start and end speed. And maybe you already know what pfx and brk(break) means. FA# is the number of fastballs, and Balls# is the number of all balls in their log. FA% is the portion of fastballs. RPS is the Rotation per Second(not RPM, R per minute)
I tried to get relationships between start speed and RPS of fastballs. My hypothesis was,
"The Faster the ball is, the more spin it has to be"
But I found it was silly when I saw my results. See the graph below.
Show the value of R square. The tendency line(I don't know the exact terms to decribe this, maybe trend line?) has no meaning to this graph.
BECAUSE, each pitcher has their unique movement in their fastball and each pitcher has their unique RPS in their fastball(regardless of the speed)
For example, there is the pitcher who has average start speed of 93 miles and has RPS of high 30s(the case of Cueto), there is pitcher like average speed of 93 MPH and RPS over 50(case of Verlander)
Let's see this in picture.
As you see, there is many pitchers located in speed of 90 to 95 mph, and 35 to 45 in rps. What I want to say is, the rps of ball IS NOT AN ABSOLUTE CRITERIA FOR SPEED, there's somewhat other varibles effect on the value of rps.
So, I merger 5800 fastballs in to one excel sheet and separated them to 1 mile criteria.(85 to 97 in unit of 1mile per hour)
TO 85 to 97 MPH, there was 12 intervals and 5500 samples, and their relationship was very interesting to me. Let's see that.
The value of Y axis is RPS, ans value of x-axis is decribed in the picture.
As you see above, the value of R square has very siginificnat meaning compared to speed-rps relationships. And the relationship was decribed in every 12 intervals.
| MOVEMENT - RPS Relationship | |||
| speed range | Formula | R^2 | Sample# |
| 85-86 | 3.1525x-3.67 | 0.8348 | 101 |
| 86-87 | 3.0805x-2.7342 | 0.9030 | 115 |
| 87-88 | 3.3946x-6.5701 | 0.9138 | 192 |
| 88-89 | 3.2884x-4.5288 | 0.9187 | 370 |
| 89-90 | 3.1882x-2.662 | 0.9244 | 486 |
| 90-91 | 3.249x-3.0762 | 0.9329 | 571 |
| 91-92 | 3.3829x-4.3772 | 0.9146 | 745 |
| 92-93 | 3.3303x-3.2576 | 0.9054 | 803 |
| 93-94 | 3.3855x-3.6724 | 0.9192 | 741 |
| 94-95 | 3.2175x-1.1906 | 0.9486 | 672 |
| 95-96 | 3.0155x+0.9232 | 0.9696 | 448 |
| 96-97 | 3.2262x-0.315 | 0.9582 | 270 |
I got this formulas but I don't know how to apply these into speed-rps-movement relationship.
But I got one important lessons from today's analysis.
"RPS is not an absolute guideline for ballspeed. But we can expect that the more rps it has, the more movement it has to be."
And in datas above, horizontal movement value has the big portion on them, so I have to correct or revise on sinking 2-seamers like fastballs by Halladay, Wang.
Thank you for reading.
Mingu, Song.
If you have any questions about this article, contact me landor82 at gmail.com or write comments below in the blue box(click the blue button when you finished writing your comment.)
DIPS - Defense Independant Pitching Stats
by Voros McCracken
다음 두 투수의 성적을 보자.
(BFP : 상대한 타자 수)
|
투수 |
승 |
패 |
방어율 |
투구이닝 |
피안타 |
피홈런 |
볼넷 |
사구 |
삼진 |
HBP |
BFP |
|
애런 실리 |
18 |
9 |
4.79 |
205.0 |
244 |
21 |
70 |
3 |
186 |
12 |
920 |
|
호세 로사도 |
10 |
14 |
3.85 |
208.0 |
197 |
24 |
72 |
1 |
14 |
5 |
882 |
자, 누가 더 잘 던졌다고 할수 있을까?
내가 무슨말을 하려 하는지, 당신은 대충 예상하고 있을 것이다.
‘아마도 승/패라는게 별 의미없는 거라고 할 또다른 녀석이군..’
그렇다. 내가 말하고자 하는 바는 이렇다.
more..

