Yogi Berra's Amazing HR-To-Strikeout Rate

He hit 358 HRs while the league average player would have hit 175 (358/175 = 2.04, which is multiplied by 100 to get the HR rate in the table below). He had 414 strikeouts while the league average player would have had 844. That gets us the 49 in the table below.

If we then do HR Rate/SO Rate we get 204/49 = 4.16. That leaves Berra 4th all-time. All data from the Lee Sinins Complete Baseball Encyclopedia.

Rank	Player	HR Rate	HR	SO RATE	SO	HR/SO
1	Ken Williams	370	190	75	237	4.93
2	Joe DiMaggio	277	361	59	369	4.69
3	Tris Speaker	145	72	33	104	4.39
4	Yogi Berra	204	358	49	414	4.16
5	Ernie Lombardi	222	190	54	262	4.11
6	Frank McCormick	151	128	39	189	3.87
7	Tommy Holmes	103	88	27	122	3.81
8	Albert Pujols	207	492	57	835	3.63
9	Ted Williams	286	521	79	709	3.62
10	Johnny Mize	306	359	86	524	3.56
11	Bill Dickey	185	202	55	289	3.36
12	Babe Ruth	622	665	189	1146	3.29
13	Stan Musial	174	475	55	696	3.16
14	Ted Kluszewski	176	279	56	365	3.14
15	Lou Gehrig	369	493	118	790	3.13
16	Mel Ott	327	511	106	896	3.08
17	Hank Aaron	237	755	77	1383	3.08
18	Barry Bonds	237	762	78	1539	3.04
19	Don Mattingly	120	222	40	444	3.00
20	Rogers Hornsby	322	274	109	492	2.95
21	Chuck Klein	291	300	99	521	2.94
22	Mickey Cochrane	147	119	52	217	2.83
23	Vladimir Guerrero	176	449	65	985	2.71
24	Gary Sheffield	176	509	67	1171	2.63
25	Billy Williams	191	426	74	1046	2.58

Now Berra did hit 210 HRs at home in Yankee Stadium, which was a great park for HR hitting if you were a lefty. But if we double his road total of 148 we get 296. If we divide that 296 by the league average of 175, we get a HR rate of 169. If that is divided by his SO rate of 49, we get 3.44. That would still leave him 11th.

Maybe Yankee stadium also cut down on his strikeouts. I don't know if it helped batters in that way. I also could have given him more than 296 HRs, since players usually hit more at home no matter what.

If we adjusted DiMaggio, who hit 213 of his HRs on the road (Yankee Stadium was tough on righties), his ratio would rise to 5.56, putting him ahead of Ken Williams.

DiMaggio hit only 41% of his HRs at home in his career while Williams hit 72%. So it is likely the case that DiMaggio would rank first, and probably by a wide margin, if HRs were park adjusted. Ted Williams hit less than 50% of his HRs at home.

See Should Joe DiMaggio's Offensive Value Be Estimated Upwards Because Of Yankee Stadium?

Astros & Rangers

This is through games of Thursday

• Astros have a team batting OPS of .733 and their pitchers have allowed .677. My regression estimates suggest the Astros should have a .573 pct or 84 wins, not 77
  
  For Rangers those are .732 & .743. So the Astros have a healthy positive differential while the Rangers are actually negative.
  
  Here are some things that might affect this and they are probably luck for the most part in favor of the Rangers
  
  Rangers allow a .666 OPS in close and late situations (much lower than what they normally allow). They are 26-20 in 1-run games and Astros are 19-26
  
  Astros Allow a .702 OPS with runners on and .660 with bases empty. Normally with runners on is higher but that is probably a bigger differential than normal. So that hurts them a bit
  
  Astros allow a .674 OPS in close and late situations, about what they normally do. So they don't gain like the Rangers do
  
  Astros hitting OPS in close and late situations is just .699. Rangers have .725. So the Astros hitters fall off more in those cases than the Rangers

  
  

Trout Joins Club With Ted Williams

They are the only two guys to get 300+ TBs at ages 20-21-22-23.

Trout has done 315-328-338-302.

So with 16 games left, he is going to clear the bar easily in all 4 seasons

Williams had 344-333-335-338

So Far, Both Leagues Have Had Their Highest OPS In September And That Is Somewhat Rare

Here are the years it happened in the AL since 1914, including this year (using the Baseball Reference Play Index-it also includes any regular season October games):

1914	0.662
1916	0.671
1918	0.768
1924	0.765
1973	0.722
1982	0.740
1985	0.750
2015	0.759

Now the NL

1939	0.733
1968	0.652
1973	0.714
1985	0.720
2007	0.784
2015	0.743

So 14 times out of 202 possible years (101 for each league, not counting 1994 when there were no games in September). So it happens about 6.9% of the time. This will be only the third time for both leagues in the same year. The AL came close in 2007 with a .770 OPS in September while May was highest with .771.

The second highest month in the AL so far was August with .739. In the NL, it is also August with .734. So the AL is a much better bet to have its highest OPS in September, since that month is currently .020 ahead of the next highest while it is only .009 higher for the NL.

Cardinal's Pitching Has Been Great This Year, Especially With Runners On Base And Unusually So

With no runners on, they have allowed a .318 OBP and a .390 SLG but with runners on, those numbers are .295 and .329. This differential is much better than any team over the years 2101-14. We can measure this by using OBP and SLG to estimate wOBA or weighted on-base average, a stat from Tangotiger. See this definition at Fangraphs. All data from Baseball Reference.

The idea is that each event has a run value and once that is found it is divided by something like plate appearances. The following estimator is a good approximation:

wOBA = (1.7*OBP + SLG)/3

So that gives the Cards a .310 wOBA with no runners on (NONE) and .277 with runners on (ROB). So, they are .033 better with ROB. The team with the best differential from 2010-2014 was the 2011 Phillies, who allowed a .296 wOBA with NONE and a .276 with ROB. So they were only .020 better, not even close to the Cards this year. The 2013 Red Sox were next being .018 better.

I ran a regression to estimate runs per game allowed using this wOBA estimator for all teams (in all situations) from 2010-2014. Here is the equation:

R/G =  30.136*wOBA - 5.20

The standard error was 23.94 per 162 games. The Cards have about a .296 wOBA allowed in all situations this year. The equation predicts they would allow about 3.72 runs per game while it is actually 3.21. So they are allowing about half a run per game less than expected. Their performance with ROB helps out here.

I also ran the regression with wOBA in both NONE and ROB situations. Here is the equation:

R/G = 11.72*NONE + 18.52*ROB - 5.32

The standard error was 21.1 per 162 games.  It predicts the Cards would allow 3.44 runs per game (only .23 above the actual). So over half the difference between their actual runs allowed per game and that predicted by wOBA is eliminated if we break things down into wOBA allowed in NONE and ROB situations.

Some of the remaining differential is probably explained with their wOBA with runners in scoring position (RISP). That is just .269. 

Big Jump So Far In OPS In Both Leagues In September

AL	PA	BA	OBP	SLG	OPS
April/March	12432	0.251	0.319	0.395	0.713
May	16107	0.252	0.312	0.402	0.715
June	15109	0.255	0.314	0.407	0.721
July	14230	0.258	0.316	0.416	0.733
August	15956	0.259	0.319	0.420	0.739
Sept/Oct	4735	0.262	0.324	0.438	0.763
NL	PA	BA	OBP	SLG	OPS
April/March	12276	0.249	0.311	0.385	0.696
May	16051	0.254	0.315	0.395	0.710
June	15429	0.256	0.314	0.391	0.706
July	14108	0.252	0.315	0.390	0.705
August	16077	0.256	0.319	0.414	0.734
Sept/Oct	4952	0.271	0.334	0.439	0.773

Decade Distribution Of Top Defensive War Seasons

Baseball Reference Rankings.

The first table shows the number of seasons from a decade that are in the top 100 (actually the top 106 due to ties). 1900 means the decade of 1900-09. The 1950s had only one such season, Ernie Banks, 3.5 in 1959, which is tied for 62nd place. From 1945-58, there was not a single season in the top 106.

The percent of the total that each decade has is listed next. Then the total defensive WAR from the guys in that decade and that decade's percentage of all the WAR from the top 100 (actually 106).

Decade	Count	Percent	Defensive WAR	Percent
1900	11	0.104	40.5	0.102
1910	6	0.057	23.7	0.060
1920	5	0.047	19.8	0.050
1930	2	0.019	7.9	0.020
1940	9	0.085	31.6	0.080
1950	1	0.009	3.5	0.009
1960	15	0.142	55.4	0.140
1970	11	0.104	42	0.106
1980	11	0.104	40	0.101
1990	9	0.085	32.9	0.083
2000	11	0.104	40.2	0.102
2010	15	0.142	58.2	0.147

The 1930s also had only 2 seasons while the 2010s have already had 15, the most since the 1960s. Some of this has to do with how many games get played and teams there are, but the 2010s are still doing very well.

Here is the same table for the top 500 seasons (actually the top 546 due to ties)

Decade	Count	Percent	Defensive WAR	Percent
1880	13	0.024	31.8	0.020
1890	8	0.015	20.5	0.013
1900	40	0.073	117.5	0.076
1910	33	0.060	97.8	0.063
1920	23	0.042	68.7	0.044
1930	28	0.051	76.2	0.049
1940	31	0.057	89.5	0.058
1950	26	0.048	70	0.045
1960	48	0.088	140.4	0.090
1970	69	0.126	194.9	0.126
1980	62	0.114	172.3	0.111
1990	54	0.099	149.4	0.096
2000	58	0.106	165.5	0.107
2010	53	0.097	157.9	0.102