The RedSox just committed the only case of illegal lineup reentry in Major League history

This comes from Dave Smith, head of Retrosheet and was posted on the SABR list, reposted with his permission.

"8/25/2017 - Although it was not actually a case of batting out of turn, the Red Sox had an amazing mistake in the 9th inning of their 16-3 loss to Baltimore on August 25, 2017. As is often the custom in such lopsided contests, the Red Sox put a position player on the mound in the top of the 9th. In this case, it was Mitch Moreland, who had played first base the entire game to this point. The Red Sox lost the DH for the remainder of the game and the new first baseman, Hanley Ramirez, entered the game in the 7th spot in the batting order, formerly occupied by DH Chris Young. They made no other changes. Moreland did well in his one inning as pitcher, allowing no runs on two hits and even collecting a strikeout.

The trouble occurred in the home 9th. The first batter was Rafael Devers, batting in the 6th spot. He made an out and the proper next batter was Ramirez. However, Chris Young came to the plate and singled - even though he was no longer in the game! This is the only case of illegal lineup reentry in Major League history. No one appeared to notice - not the umpires or either team. Since it was a 16-3 game with two outs to go, it is likely that Ramirez had not even thought about where he was batting. As for DH Young, he simply followed Devers to the plate as he had all night. The official remedy is to call Young a pinch-hitter for Ramirez, which causes all the official totals to come out right."

Is It Really A Mystery How The Royals Exceed The Expectation Of Computer Projections? (it might be timing as they perform relatively better in high leverage situations)

That is what a Wall Street Journal article says. See The Baseball Team That Computer Models Can’t Figure Out: The Kansas City Royals have exceeded the expectation of every prominent computer projection over the past five years by Jared Diamond. Excerpts:

"PECOTA, Baseball Prospectus’ sophisticated computer model for forecasting on-field performance, is impressively accurate. Half of teams have, on average, finished within 2.75 wins in either direction of PECOTA’s predictions since 2013, with nearly two-thirds of teams coming within 3.25 wins.

But there’s still one team PECOTA can’t figure out: the Kansas City Royals, the unexplained mystery of the major leagues. The Royals outperformed PECOTA by an average of 12½ games from 2013 through 2016, the most in the sport over that span, and are on pace to beat it by about that much again this season."

"The problem with predicting the Royals, prognosticators say, is that they have consistently won more games than their underlying statistics would indicate. They’ve been, as Davenport put it, “far more efficient at winning games with the number of runs they score and allow.”

That spells trouble for the models. Systems like PECOTA, which stands for Player Empirical Comparison and Optimization Test Algorithm, use piles of statistics and historical aging curves to predict how individual players will fare in a given season. After accounting for a team’s projected depth chart, the systems use that data to predict how many runs a team will score and allow, which results in a predicted record."

The article did mention their bullpen as one reason. If your relievers can come in and stop rallies consistently better than other teams, you will win more than expected.

Here is a regression generated formula to predict winning pct based on team OPS differential High, Medium and Low leverage situations. It is based on all teams from 2010-2014.

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

If applied to the Royals of 2013-2016, it predicts them winning fewer games than they actually did. But not by alot. Here are the differences

-1.88
-2.87
-3.71
-0.76

That averages out to 2.31 wins per season. So over 2013-2016, the Royals won 2.31 more games per season than predicted. Not too big of a difference. The table below shows the OPS their hitters had, OPS their pitchers allowed and the differential in the three situations for each season. Notice how they tend to do better both hitting and pitching in High leverage cases than otherwise. OPSA is OPS allowed.

Their 4 best differentials are all in High cases. Of the 8 Low & Medium cases, 6 differentials are negative.

Year	Split	OPS	OPSA	Diff
2013	High	0.733	0.672	0.061
2013	Low	0.674	0.688	-0.014
2013	Medium	0.693	0.723	-0.030
2014	High	0.712	0.636	0.076
2014	Low	0.660	0.700	-0.040
2014	Medium	0.713	0.696	0.017
2015	High	0.772	0.669	0.103
2015	Low	0.719	0.697	0.022
2015	Medium	0.734	0.748	-0.014
2016	High	0.728	0.653	0.075
2016	Low	0.712	0.778	-0.066
2016	Medium	0.704	0.767	-0.063

Trout has a good chance to be first batter to finish with a 200 OPS+ or higher since Bonds did it in 2004 (and only 19th since 1901)

Trout currently has an OPS+ of 216. He has played 68 games and the Angels have 49 left. Assuming an equal number of PAs per game played for each group of games (which might not be quite right), if he has an OPS+ of 178 the rest of the way, he would finish at 200 (his career OPS+ is 173, so that seems reasonable that he could reach 178 the rest of the way, given what he has done so far).

Three guys reached 190 since 2004: Pujols with 192 in 2008, Miguel Cabrerra with 190 in 2013 and Harper with 198 in 2015.

His path might be easy. The Angels have only 3 games left against any of the top 4 teams in the AL in ERA+ (Bos, Clev, NY, KC).  That team is Clev. Here is the OPS+ of those teams

Bos 124
Clev 124
NY 122
KC 109

The next highest ERA+ the Angels will face is the 102 of Texas & Tampa Bay and Texas traded Darvish. The Angels have 15 games left against 3 of the 4 lowest ERA+ teams in the AL.

Bal 89 (5)
Oak 90 (6)
Chi 92 (4)

The Angels have 2 games left against one NL team, Wash. They have a 107 ERA+ and some good starters: Scherzer, Stasburg (if he is not on the DL) and Gonzalez.

Here are the players who have done it and the number of times. I used the Baseball Reference Play Index and set the minimum PAs as what qualifies for the batting title. If I lower that to 400 PAs (Trout might not make 502 PAs since he was hurt), McGwire and Williams would each get one more.

Player	Count
Babe Ruth	11
Ted Williams	6
Barry Bonds	6
Rogers Hornsby	4
Ty Cobb	3
Lou Gehrig	3
Mickey Mantle	3
Jimmie Foxx	2
Frank Thomas	1
Jeff Bagwell	1
Norm Cash	1
George Brett	1
Stan Musial	1
Nap Lajoie	1
Willie McCovey	1
Sammy Sosa	1
Honus Wagner	1
Mark McGwire	1

Astros Had The 2nd Highest Team SLG For A Month In July Since 1913 At .568 (minimum of 20 games)

Data from the Baseball Reference Play Index.

Team	Month	Year	G	SLG	SLG
NYY	June	1930	28	0.593	0.592522
HOU	July	2017	24	0.568	0.567816
STL	April/March	2000	25	0.568	0.567599
BOS	June	2003	26	0.556	0.556468
ATL	July	2006	24	0.554	0.554007
SEA	May	1999	27	0.549	0.549428
CLE	July	1936	33	0.546	0.546252
TEX	Sept/Oct	2011	25	0.544	0.543624
CLE	April/March	1997	25	0.543	0.543430
SFG	June	2000	26	0.539	0.539037

Now the OPS leaders

Team	Split	Year	G	OPS	R	BA	OBP	SLG
NYY	June	1930	28	1.035	261	0.366	0.442	0.593
STL	April/Mar	2000	25	0.959	180	0.301	0.392	0.568
HOU	July	2017	24	0.948	174	0.323	0.38	0.568
BOS	June	2003	26	0.945	190	0.315	0.388	0.556
CLE	April/Mar	1997	25	0.942	167	0.305	0.399	0.543
CLE	July	1936	33	0.94	244	0.352	0.394	0.546
SEA	May	1999	27	0.935	191	0.308	0.386	0.549
SFG	June	2000	26	0.932	175	0.309	0.393	0.539
STL	July	1928	29	0.931	188	0.32	0.401	0.529
NYY	May	1936	28	0.929	218	0.309	0.395	0.534

Here are the stats for the Yankees in June 1930. They were 20-8, allowing 160 runs. Ruth and Gehrig are the only teammates to have an SLG of at least .900 in the same month, 75+ PAs. First guy not named Ruth to do it is Chick Hafey in 1928 (July) at .925.

Player	HR	RBI	BA	OBP	SLG	OPS	PA	AB
Babe Ruth	15	35	0.392	0.560	0.918	1.477	137	97
Lou Gehrig	10	42	0.495	0.582	0.919	1.501	137	111
Ben Chapman	2	18	0.346	0.421	0.514	0.936	123	107
Tony Lazzeri	3	34	0.352	0.424	0.562	0.986	121	105
Earle Combs	2	17	0.440	0.505	0.692	1.197	105	91
Harry Rice	1	18	0.366	0.398	0.505	0.903	101	93
Bill Dickey	1	23	0.439	0.483	0.610	1.093	92	82
Lyn Lary	0	9	0.185	0.264	0.277	0.541	76	65
Samuel Byrd	1	9	0.474	0.545	0.632	1.177	44	38
Jimmie Reese	1	6	0.238	0.238	0.333	0.571	44	42
Dusty Cooke	3	9	0.343	0.395	0.714	1.109	38	35
Yats Wuestling	0	1	0.231	0.231	0.308	0.538	27	26
Bubbles Hargrave	0	2	0.286	0.348	0.429	0.776	24	21
George Pipgras	0	4	0.158	0.238	0.263	0.501	22	19
Red Ruffing	0	2	0.474	0.500	0.579	1.079	20	19
Herb Pennock	0	4	0.467	0.529	0.467	0.996	18	15
Ed Wells	0	1	0.333	0.375	0.333	0.708	17	15
Billy Werber	0	2	0.286	0.412	0.286	0.697	17	14
Benny Bengough	0	1	0.308	0.357	0.538	0.896	15	13
Roy Sherid	0	2	0.167	0.286	0.167	0.452	15	12
Hank Johnson	1	6	0.385	0.429	0.923	1.352	14	13
Ownie Carroll	0	1	0.333	0.333	0.333	0.667	7	6