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.

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

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.

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.

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

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.

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

White Sox A Bit Lucky So Far This Year

The White Sox OPS in high-medium-low leverage situations .874-.642-.654 Their pitchers allow .510-.629-.673. Thru yesterday. So a bit luck.

They had a team OPS of .688 and allowed .622 for a differential of .066. Using all teams from 2010-14, the relationship between OPS differential and winning pct is

Pct = .5 + 1.3246*OPSDIFF

That estimates the Sox to have a .587 pct (much lower than their actual of .680 thru yesterday). In 25 games, that is about 14.7 wins or 2.3 fewer than they actually have (they may have increased their OPS differential with today's game).

.587 is not bad, but it is far below .680.

Using the same years, here is the equation when breaking things down by leverage

Pct = .5 + .306*LOW +.420*MED + .564*HIGH

Estimating the Sox pct here would be

Pct = .5 + .306*(-.0058) + .420*(.013) + .564*(.364) = .705

That is a bit high, but much closer to .680 than .587. It estimates the Sox win total to be 17.6. That is only off by .6 wins, much less than the 2.3 from using overall OPSDIFF. That differential in high situations is probably not going to last very long. From 1960-2014, the biggest differential in high leverage situations belongs to the 1995 Indians, with .208.

The lowest OPS allowed in high leverage situations was .552 by the 1965 Dodgers. The Sox .854 OPS by their hitters would rank tied for 25th out of 1,432 teams. So they have been incredible both ways in high leverage situations.