Tuesday, May 31, 2016

Which teams' actual ERAs were the farthest below their FIP ERAs since 1900?

Data used here comes from Fangraphs and Baseball Reference. In the table below, DER is defensive efficiency rating. That is the percentage of balls in play converted into outs. Rank is where the team ranked in DER in their league for that year. Lg Avg is the league average DER for that year.


Team Year ERA FIP Diff DER Rank Lg Avg
Yankees 1939 3.31 4.26 -0.95 0.694 1 0.685
Giants 1933 2.71 3.49 -0.78 0.719 1 0.702
Giants 1934 3.19 3.95 -0.76 0.704 1 0.685
Reds 1999 3.99 4.74 -0.75 0.722 1 0.687
Giants 1954 3.09 3.83 -0.74 0.722 1 0.707
Athletics 1929 3.44 4.16 -0.72 0.703 1 0.687
Giants 1936 3.46 4.18 -0.72 0.693 2 0.684
Indians 1948 3.22 3.94 -0.72 0.731 1 0.704
Braves 2002 3.13 3.83 -0.70 0.712 2 0.695
Twins 1965 3.14 3.84 -0.70 0.724 3 0.715
Robins 1930 4.03 4.72 -0.69 0.693 1 0.669
Cubs 1906 1.75 2.43 -0.68 0.736 1 0.698
Cubs 1935 3.26 3.93 -0.67 0.696 2 0.686
Reds 1939 3.27 3.94 -0.67 0.708 1 0.695
Yankees 1955 3.23 3.89 -0.66 0.733 1 0.710
Orioles 1973 3.08 3.74 -0.66 0.731 1 0.701
Yankees 1935 3.60 4.26 -0.66 0.713 1 0.687
Bees 1937 3.22 3.88 -0.66 0.714 1 0.689
Athletics 1990 3.18 3.84 -0.66 0.732 1 0.699
Braves 1956 3.10 3.75 -0.65 0.726 2 0.716
White Sox 1967 2.45 3.10 -0.65 0.735 1 0.718
Reds 1940 3.05 3.69 -0.64 0.730 1 0.701
Giants 1931 3.30 3.93 -0.63 0.706 1 0.687
Yankees 1957 3.00 3.62 -0.62 0.727 1 0.713
Tigers 1982 3.81 4.43 -0.62 0.725 1 0.704

Twenty of the top 25 teams ranked first in DER. That makes sense, since if you turn alot of balls in play into outs, you will give up fewer runs (and fewer than expected based on walks, strikeouts and HRs, which figure into FIP ERA). The 1939 Yankees had a 106-45 record and the all-time best run differential of 411.

Teams might also see their actual ERA go below their FIP ERA if they pitch well with runners on base. But my post last week showed that although that matters, DER probably matters more.

Twelve of these 25 teams came between 1929 & 1940 (I wonder what the reason is). Many of the 25 were pennant winners or made the playoffs.

Four Giants teams from the 1930s are on the list. They led the NL in DER 4 times in the decade and were 2nd three times. They also led in fielding pct. 4 times with one 2nd. They had the highest DER for the decade of 0.7001 (2nd place had 0.6960). Maybe they are one of the great fielding teams in baseball history. But it is not anything I have ever heard about.

Wednesday, May 25, 2016

David Ortiz's Historically Great Season (so far) For A Guy 40+ Years Old

Using the Baseball Reference Play Index, here is the top 10 in OPS+ for qualifiers 40+ years old:


Rk Player OPS+ Year Age
1 David Ortiz 188 2016 40
2 Willie Mays 158 1971 40
3 Edgar Martinez 141 2003 40
4 Dave Winfield 138 1992 40
5 Stan Musial 137 1962 41
6 Darrell Evans 135 1987 40
7 Cap Anson 135 1894 42
8 Carlton Fisk 134 1990 42
9 Ty Cobb 134 1927 40
10 Rickey Henderson 128 1999 40
11 Deacon White 128 1888 40

Okay, I did the top 11 since we don't know where Ortiz will end up. If Ortiz has an OPS+ of 110 the rest of the way, he would end up with 132, still in the top 10. If he can do 140 the rest of the way (140 was his OPS+ in each of the last two seasons) he would finish at 153, which would still be the 2nd best ever. If he does 148 the rest of the way, he finishes with 159 and that would still beat Mays.

If I use 300+ PAs as a qualifier, here are the leaders. Only Ted Williams is higher than what Ortiz has right now.


Rk Player OPS+ PA Year Age
1 Ted Williams 190 390 1960 41
2 Barry Bonds 169 477 2007 42
3 Willie Mays 158 537 1971 40
4 Barry Bonds 156 493 2006 41
5 Edgar Martinez 141 603 2003 40
6 Brian Downing 138 391 1992 41
7 Dave Winfield 138 670 1992 40
8 Moises Alou 137 360 2007 40
9 Stan Musial 137 505 1962 41
10 Harold Baines 136 486 1999 40
11 Carlton Fisk 136 419 1989 41

Tuesday, May 24, 2016

What Might Explain The Difference Between A Team's ERA And Their FIP ERA?

To see a good explanation of FIP (fielding independent ERA), go to this Fangraphs link

FIP.

The idea is to estimate what a pitcher's ERA might be based only on his walks, strikeouts and HRs allowed. It was inspired by research by Voros McCracken. Pitchers might not have much control over what happens on balls in play (which excludes HRs). I thought that the FIP ERA formula they give there was created by Tangotiger, but they don't mention him. Data used here comes from Fangraphs and the Baseball Reference Play Index.

The first thing I did was to correlate (ERA - FIP ERA) with a team's defensive efficiency rating (DER). That is the percentage of balls in play converted into outs. If teams are good at this, then the number of runs they allow is affected less by walks, strikeouts and HR. I looked at all teams from 2013-2015, so 90 teams.

The correlation was -.786. As DER rises, a team's actual ERA falls farther below the ERA predicted by it's walks, strikeouts and HRs. It is probably not a big surprise to see a fairly high correlation here. Teams that have a high DER will allow fewer runs, so the actual ERA will be lower than for a team with a low DER that allows the same number of walks, strikeouts and HRs.

Then I ran a regression where the ERA - FIP ERA differential (DIFF) depended on DER and the difference between the OPS allowed with runners on base and the OPS allowed with none on (OPSDIFF). Here is the regression equation

DIFF  = 2.17*OPSDIFF - 17.6*DER + 12.1

The r-squared is .709, meaning that 70.9% of the variation in the dependent variable (DIFF) is explained by the two independent variables. If we squared the original correlation of -.786, we get .617. So there is some improvement in the model by adding the OPSDIFF variable. The standard error of the regression was .13.

The t-values for the variables are

OPSDIFF) 5.26
DER) -13.59

So they are both significant.

I also calculated the impact of a one standard deviation (SD) change in each variable on DIFF

OPSDIFF) .07
DER) -.19

As OPSDIFF increases, it means that teams are getting hit harder with runners on than otherwise, so we expect the ERA - FIPERA difference to rise. A one SD increase in OPSDIFF raises DIFF .07 (here I am talking about a team getting 1 SD worse). A team will have a higher ERA than expected if they have bad timing and get hit unusually hard with runners on base.

For DER, a one  SD improvement leads to the actual ERA falling .19 farther below the ERA predicted by walks, strikeouts and HR (here I am talking about a team getting 1 SD better).

But there is still about 30% of the variation in DIFF that this model does not explain. I thought also taking into account OPS allowed with runners on and none on would make a bigger difference than it did.

Tuesday, May 17, 2016

Jack Morris, Pitching To The Score, And The Hall Of Fame

Last night Harold Reynolds said on the MLB network that Morris should be in the Hall of Fame and mentioned pitching to the score (or something like that). The idea is that if you have a big lead you ease off a bit and throw strikes so you don't walk anyone.

I know others have analyzed this in detail but I don't have any links handy.

Did Morris pitch better in key situations? Here is his OPS allowed in High, Medium and Low leverage situations:

High .695
Medium .692
Low .694

(data from the Baseball Reference Play Index-the higher the leverage, the closer the score, the more runners on and the later the game) His career OPS allowed is .693. So it looks like he has about the same results, no matter what the situation. But how does this compare to the AL average for the years 1977-94? Those are the years he pitched.

High .736
Medium .731
Low .724

The overall OPS was .729. So notice that the league OPS was .012 higher in High leverage situations than Medium. Morris was only .001 higher. The difference is .011. So in that way, Morris may have been better at elevating his game when it mattered (his OPS went up less than the average pitcher).

But that is a pretty slight difference. The following regression equation shows the relationship between team runs per game and team OPS:

R/G = 13.02*OPS - 5.04

I used all teams from 1996-2009. 13.02*. 011 = .14. That is a very slight difference. Now runs might matter more in high leverage situations. In High cases, his average Leverage Index  (LI) was about 2.1. In Low cases, it was about .41.

But it takes about 10 runs to add a win over the course of a season. So .14/10 = .014 more victories per year due to his High leverage performance. Now maybe we could multiply that by 5 to get .07 (since 2.1/.41 is about 5). .07 extra wins per year seems pretty slight (one thing I left out here is how many PAs he had in each of these situations-here I am simply applying this on a per game analysis-I should probably make a proportional adjustment but then these numbers might be even smaller).

Another thing to notice is that if we compare Medium situations to High situations, Morris goes up .003 and the league average goes up .005. So Morris is only .002 better than average there.

In his career, Morris walked & hit 8.42% of the batters he faced (with IBBs taken out). With a lead of more than 4 runs, it was 8.07%. So he did improve there and probably threw more strikes. But this is also a very slight difference.

He is only 138th in WAR for pitchers with two 5th place finishes, 8th and a 9th. The 5th placers were not back to back although the 8th followed one of them. That is just not much career or peak value for a Hall of Famer.

His best finishes in ERA+ were a 4th, a 6th, an 8th and a 10th (that is ERA adjusted for the league average and park effects). Good, but not great. He had one top 10 finish (6th) in FIP ERA (that is an ERA estimated just using walks, strikeouts and HRs allowed).

He had two top 10 finishes (4th, 7th) in WPA added (that is win probability added-the idea is that you get more credit for what happens in key situations).  His career rank among pitchers here is 136th.

There is also a WPA/LI stat where LI is the average leverage index. He had four top 10 finishes (the best was a 4th). That is good, but certainly not Hall of Fame caliber. His career rank in that is 121st among pitchers. So although he may have pitched a bit better the more important the situation, it was nothing unusual.

Saturday, May 14, 2016

Hank Greenberg vs. The Yankees

I got interested in this recently when David Ortiz became the sixth player to hit 50+ career HRs vs. the Yankees. It turns out that Greenberg is the only one of the six who never played for the Red Sox (Hat tip to Ryan Pollack for that info). Also, if anyone asks who hit the most career HRs by a guy who attended NYU, Greenberg is the answer.

Greenberg hit 331 career HRs and 1/7 of those would be 47.29. He had 53 against the Yankees. So a bit more than you would expect. The Yankees allowed a HR% of 1.52% over the years 1933-46 (he was in the military from 42-44 and good chunks of 41 & 45 so this is not quite right, but I hope it is close). The league average was 1.56%. So the Yankees were about average in terms of allowing HRs. Also, the Yankees and Tigers were just about tied for the lowest SLG allowed in the AL over these years, .351 (Tigers were 0.00064 lower-what teams allowed is in a table at the end of the post). They Yankees allowed the lowest average of .254. So against what might have been the best pitching staff of these years in the AL, Greenberg upped his game. Of course, the Yankee fielders might have helped.

He had a career HR% of 6.37% but against the Yankees it was 7.26%. So he did especially well against them. Here are his AVG-OBP-SLG against them: .333-.409-.667. His whole career was .313-.412-.605 (if we take out 1947, his year in the NL, they were .319-.412-.616). So he really hit alot better against them than other teams (probably walked less frequently)

In Yankee Stadium he had .319-.375-.608. Pretty good for a righty with that death valley they had, over 400 to left center. His HR% there was 5.45%. He probably had an SLG of over .700 against them at Tiger Stadium.

Why did he hit so well against them? Was it because he was from New York? Who knows.

Here are the top 25 guys in SLG vs. the Yankees since 1913 with 250+ PAs (from the Baseball Reference Play Index). SLGtot is their career SLG. Then the difference between their career SLG and what they did against the Yankees. Then how many PAs vs. the Yankees along with their BA and OBP.


Rk Player SLGvNY SLGtot Diff PA BA OBP
1 Hank Greenberg 0.667 0.615 0.052 826 0.333 0.409
2 Miguel Cabrera 0.665 0.570 0.095 255 0.335 0.412
3 Alex Rodriguez 0.651 0.581 0.070 374 0.334 0.386
4 Manny Ramirez 0.617 0.589 0.028 861 0.322 0.413
5 Albert Belle 0.611 0.564 0.047 503 0.301 0.366
6 Ted Williams 0.608 0.633 -0.025 1351 0.345 0.495
7 Ken Griffey 0.595 0.549 0.046 572 0.311 0.392
8 Curt Blefary 0.592 0.410 0.182 257 0.313 0.414
9 Jim Rice 0.582 0.506 0.076 714 0.330 0.387
10 Mike Napoli 0.580 0.482 0.098 299 0.300 0.418
11 Rafael Palmeiro 0.579 0.519 0.060 820 0.311 0.396
12 Jay Buhner 0.578 0.496 0.082 409 0.283 0.379
13 Jimmie Foxx 0.577 0.616 -0.039 1260 0.303 0.400
14 Jose Bautista 0.576 0.509 0.067 425 0.253 0.402
15 David Ortiz 0.576 0.550 0.026 1003 0.307 0.395
16 Mo Vaughn 0.569 0.526 0.043 484 0.285 0.380
17 Nomar Garciaparra 0.556 0.544 0.012 404 0.326 0.360
18 Ramon Hernandez 0.553 0.420 0.133 300 0.333 0.403
19 Paul Konerko 0.551 0.491 0.060 443 0.306 0.378
20 Ken Williams 0.544 0.543 0.001 763 0.303 0.380
21 Edgar Martinez 0.542 0.516 0.026 594 0.317 0.423
22 Larry Parrish 0.542 0.453 0.089 314 0.301 0.354
23 Magglio Ordonez 0.541 0.502 0.039 407 0.301 0.369
24 Rusty Staub 0.541 0.437 0.104 255 0.320 0.369
25 Earl Averill 0.540 0.535 0.005 1070 0.323 0.395


It was not until the 1990s and 2000s that anyone came close to what Greenberg did (Rodriguez and Cabrerra). Tiger's pitcher Frank Lary (from the 1950s and 60s) was called "The Yankee Killer." But maybe Greenberg should have had that nickname.

Here are the top 10 in terms of how much better they did against the Yankees


Rk Player SLGvNY SLGtot Diff PA
1 Curt Blefary 0.592 0.410 0.182 257
2 Jim Spencer 0.516 0.379 0.137 335
3 Ramon Hernandez 0.553 0.420 0.133 300
4 Ernie Whitt 0.530 0.418 0.112 274
5 Rusty Staub 0.541 0.437 0.104 255
6 Jonny Gomes 0.531 0.428 0.103 290
7 Mike Napoli 0.580 0.482 0.098 299
8 Miguel Cabrera 0.665 0.570 0.095 255
9 Larry Parrish 0.542 0.453 0.089 314
10 Dick Wakefield 0.530 0.447 0.083 349

Now those leaders with 400+ PAs


Rk Player SLGvNY SLGtot Diff PA
1 Jay Buhner 0.578 0.496 0.082 409
2 Ruben Sierra 0.529 0.453 0.076 531
3 Jim Rice 0.582 0.506 0.076 714
4 Jose Bautista 0.576 0.509 0.067 425
5 Paul Konerko 0.551 0.491 0.060 443
6 Rafael Palmeiro 0.579 0.519 0.060 820
7 Chili Davis 0.526 0.471 0.055 476
8 Charlie Maxwell 0.505 0.452 0.053 549
9 Hank Greenberg 0.667 0.615 0.052 826
10 Vernon Wells 0.516 0.467 0.049 730

Here are all the right-handed batters with a .500+ SLG and 200+ career PAs in Yankee Stadium (I think this includes only the original park but both before and after the renovations of the 1970s when LF center was brought in from like 450 to 400 or so). Again, Greenberg is up there and no one passed him until Jim Rice in the 1970s and 80s, after the wall got closer.


Rk Player SLG PA
1 Jim Rice 0.661 308
2 Albert Belle 0.645 258
3 Jay Buhner 0.623 235
4 Hank Greenberg 0.608 421
5 Manny Ramirez 0.605 440
6 Alex Rodriguez 0.597 1878
7 Mike Stanley 0.560 877
8 Harry Heilmann 0.557 315
9 Joe DiMaggio 0.547 3789
10 Edgar Martinez 0.545 303
11 Gary Sheffield 0.532 856
12 Al Simmons 0.532 646
13 Red Kress 0.520 439
14 Mariano Duncan 0.509 247
15 Ben Paschal 0.506 389
16 Shane Spencer 0.506 570
17 Roy Sievers 0.503 426

Here is what each AL team allowed opposing hitters from the years 1933-46. The league averages were BA .270, OBP .342, SLG .368


Team BA OBP SLG OPS
DET 0.263 0.335 0.351 0.686
NYY 0.254 0.325 0.351 0.676
CLE 0.265 0.339 0.359 0.699
BOS 0.271 0.344 0.365 0.709
WSH 0.273 0.344 0.365 0.709
CHW 0.269 0.336 0.371 0.706
SLB 0.285 0.357 0.386 0.743
PHA 0.278 0.353 0.397 0.750

Friday, May 6, 2016

Is Mike Trout Mickey Mantle Reborn?

Tim Kurkjian said he was on ESPN this week (or something like that). Trout, like Mantle, has both great speed and power to go along with a high OBP. Let's look at the record of each of these players through age 24

First Mantle


Mantle/Age PA OPS+ WAR
19 386 117 1.4
20 626 162 6.5
21 540 144 5.3
22 649 158 6.9
23 638 180 9.5
24 652 210 11.2
Age 19-23 Totals 2839 155 29.6

Now Trout


Trout/Age PA OPS+ WAR
19 135 89 0.7
20 639 168 10.8
21 716 179 9.3
22 705 168 7.9
23 682 175 9.4
24 120 183 1.9
Age 19-23 Totals 2877 169 38.1

Trout has better numbers thru age 23. Mantle's highest OPS+ was 180 while Trout's was 179. But Trout's next best was 175 while Mantle's was 162. Trout has a big edge in career OPS. That might be misleading since Mantle had many more PAs at age 19, and we would not expect too much from a player so young. That might depress Mantle's averages.

If we look at ages 20-23, Trout leads in OPS+, 173-162. So he still has a solid edge in hitting.

But at age 24, Mantle won the triple crown and had an OPS+ of 210. Trout is 24 this year and he is at 183 so far. To finish with 210 this year, he would need to be close to 220 the rest of the season.

Mantle got off to a sensational start that year (at age 24), batting over .400 in both April (11 games) and May (31 games). His SLG was over .800 in both months as well.

At both age 23 and 24, Mantle had big gains in OPS+ (22 and 30, respectively). If Trout has a gain of 30 this year, that would give him a 205, not too far off from Mantle's 210.

Mantle went on to have two other seasons in his career with OPS+ over 200, plus one at 195. He finished with 172. Trout is at 169 in his career so far, not too far behind. But he will have a decline phase later in his career. So to stay close to Mantle, he will have to have some high OPS+ numbers at some point. Can Trout also have three seasons over 200? If he does, that would put him on a par with Mantle.

Monday, May 2, 2016

April Hitting, 2010-2016

From Baseball Reference. First table is AL, then NL. Biggest story seems to be NL gain in SLG of .025 over last year.

American League


Year  PA AB HR BA SLG OBP HR%
2010 12399 10952 304 0.255 0.406 0.330 0.028
2011 14148 12624 349 0.249 0.394 0.319 0.028
2012 11962 10742 342 0.252 0.409 0.319 0.032
2013 14825 13282 417 0.255 0.411 0.322 0.031
2014 15303 13611 344 0.252 0.392 0.324 0.025
2015 12432 11120 317 0.251 0.395 0.319 0.029
2016 13142 11836 375 0.245 0.399 0.311 0.032
National League


Year  PA AB HR BA SLG OBP HR%
2010 14207 12476 344 0.257 0.406 0.332 0.028
2011 16236 14436 375 0.252 0.389 0.320 0.026
2012 13615 12149 296 0.247 0.383 0.314 0.024
2013 14980 13380 383 0.247 0.391 0.313 0.029
2014 15714 14115 376 0.246 0.386 0.310 0.027
2015 12276 11061 275 0.249 0.385 0.311 0.025
2016 13613 12052 365 0.253 0.410 0.326 0.030