Cybermetrics

With Murakami on the injured list, its a good time to look at how no other team has been hurt by losing HR leaders as much as the White Sox

2026-06-08T19:44:35.616-07:00

Murakami was tied for the AL HR lead when he got hurt. Bill Melton led the AL in HRs in 1971 for the White Sox yet played only 57 games the next year because he got hurt. Then Dick Allen led the AL in HRs in 1972. He got hurt and only played 72 games the next year.

The table below shows what % of a team's games HR leaders played the year after they led. I sorted them from lowest to highest. The table shows the lowest 20 (I only looked at the years 1946-2025). All data generated by bases.chat, an AI baseball program created by Ted Ingram. I did not, of course, check last year's leaders since this year is not over yet. Cal Raleigh could make the list.

Notice that both Bill Melton and Dick Allen are in the lowest 8 (I will make some comments about the guys ahead of them after the table). They are the only teammates in consecutive years (or any years close to each other) in the lowest 20. No other team has been snake bitten like they were.

The White Sox lost a close divisional race to the A's in 1972. They were in first place as late as Aug. 28 and even with 17 games left they were only 2 games out. Melton played his last game on June 23 and his performance was plagued all year by an ailing back (he only managed 7 HRs). He played only 37% of the White Sox games that year.

In 1973, Allen go hurt on June 28. The White Sox were only 1 game out. He played 3 games later in the season but only had a total of 5 PAs. He had also been the AL MVP in 1972 while having a 199 OPS+. He had a 176 OPS+ in 1973. That was higher than the league leader Reggie Jackson who had 161. Allen did not have enough PAs to qualify. He played only 44% of the White Sox games that year. CFer Ken Henderson also got hurt in May (he was hitting .311 at the time) and ended up playing only 73 games.

Player	Lead Yr	% of Games
Fernando Tatis	2021	0.00%
Marcell Ozuna	2020	29.81%
Bill Melton	1971	37.01%
Ryan Braun	2012	37.65%
Chris Carter	2016	38.27%
Jose Canseco	1988	40.12%
Luke Voit	2020	41.98%
Dick Allen	1972	44.44%
Giancarlo Stanton	2014	45.68%
Joe DiMaggio	1948	49.03%
Ken Griffey	1994	49.66%
Dave Kingman	1979	50.00%
Matt Williams	1994	52.78%
Mark McGwire	1999	54.94%
Jose Bautista	2011	56.79%
Ted Williams	1949	57.79%
Harmon Killebrew	1967	61.73%
Dave Kingman	1982	61.73%
Howard Johnson	1991	61.73%
Tony Armas	1984	63.19%

Tatis was hurt in 2022. But he got supsended on Aug. 12 for 80 games for PED use. Even without the suspension, his % would be very low.

Ozuna and Voit were leaders in the Covid year and their leadership is not on a par with other hitters. The season was only 60 games.

Ryan Braun got suspended for PED use on July 22, 2013.

Carter became a free agent and signed with the Yankees, leaving the Brewers. He had a poor season and that was his last year in MLB

Players who hit 3 HRs in a game including a walk off HR

2026-04-11T19:06:00.000-07:00

Here are the guys who hit 3 HRs in a game including a walk off HR that I found in the Stathead data base

Max Muncy LAD (he did this last nite)

Jack Suwinski

Brendan Rodgers

Eddie Rosario

Manny Machado

Albert Pujols

Todd Hollandsworth

Jack Clark

Otto Vélez

Cecil Cooper

Willie McCovey

Don Demeter

Héctor López

George Kelly

Khris Davis (walk off was a grand slam)
Joey Votto (walk off was a grand slam)
Walt Moryn (walk off against Sany Koufax)

This link has the search results:

https://www.sports-reference.com/stathead/tiny/EWynh

I searched all 3 HR games where the team won and it was a walk off. There were 30 cases and I went thru them to find all the cases where the 3 HR guy hit a walk off HR. Those are the ones I listed above. I did not list Butch Henline from 1922. He hit a game tying HR in the 9th (his third of the game) and the next guy hit a walk off HR.

Baseball Reference and Retrosheet don't have play-by-play for all games. But there probably are not too many more of these games. There were no 3 HR games between 1900-1919. I also looked at all 3 HR games from 1920-40 where the said player's team won the game and checked the box score to see if the winning run was scored in the bottom of the 9th inning or bottom of an extra inning. I found no such cases.

So we have 17 cases of this happening. It is rarer than a 4 HR game which has happened 21 times.

The correlation of runs per game and strikeouts per game

2026-04-06T19:36:00.000-07:00

Last week the announcers during a Phillies game mentioned that a couple teams that had poor records had also struck out alot. One of them said "there's got to be a correlation."

The correlation between runs per game and strikeouts per game was -0.14 for all 150 team/seasons from 2021-25. So it was negative but not very strong.

Then I ran a regression with runs per game as the dependent variable and OBP, SLG, and strikeouts per game as the independent variables. Here was the equation

R/G = .22*SO + 10.57*SLG + 15.41*OBP - 4.86

R-squared = .901 (90% of the variation across teams in runs scored per game is explained by the model)

Standard error = .147 (about 24 runs per season)

T-values & p-values

SO = 1.18 & 0.24

SLG = 12.24 & < 0.0000001

OBP = 8.29 & < 0.0000001

Strikeouts are not significant. SLG & OBP are significant. There is an extremely low chance that we could have gotten the coefficient values in the equation if the true value is zero. But the result for SO would be likely.

Strikeouts are also positive. Runs increase with strikeouts, but not much. 1 more strikeout per game increases runs by 0.22 per game.

Here is the regression equation without strikeouts. The coefficient values are about the same, so adding in strikeouts does not change things much.

R/G = 10.80*SLG + 14.67*OBP - 4.53

R-squared = .900

Standard error = .147

The number of shortstops and 2B men hitting 30+ HRs in a season has increased greatly from the 20th century to the 21st

2026-04-04T19:16:00.000-07:00

The table below shows all the shortstops who hit 30+ HRs thru 1999. All data from Baseball Reference and Stathead. Click here to see the search results for this table. There are 16 cases. But 7 came during 1995-99. So the SS power surge started a bit early (This is for guys who played 50%+ of their games at the position).

Player	HR	Season
Vern Stephens	39	1949
Vern Stephens	30	1950
Ernie Banks	44	1955
Ernie Banks	43	1957
Ernie Banks	47	1958
Ernie Banks	45	1959
Ernie Banks	41	1960
Rico Petrocelli	40	1969
Cal Ripken Jr.	34	1991
Álex Rodríguez	36	1996
Barry Larkin	33	1996
Nomar Garciaparra	30	1997
Álex Rodríguez	42	1998
Nomar Garciaparra	35	1998
Álex Rodríguez	42	1999
Tony Batista	31	1999

Now for 2000-25. Click here to see the search results for this table. There are 41 cases. That is about 2.5 times as many in the 20th century. Now there were not many HRs before 1920, but the 25 year period is far ahead of the 80 year period.

Player	HR	Season	Player	HR	Season
Álex Rodríguez	41	2000	Gleyber Torres	38	2019
Miguel Tejada	30	2000	Trevor Story	35	2019
Álex Rodríguez	52	2001	Xander Bogaerts	33	2019
Rich Aurilia	37	2001	Marcus Semien	33	2019
Miguel Tejada	31	2001	Francisco Lindor	32	2019
Álex Rodríguez	57	2002	Paul DeJong	30	2019
Miguel Tejada	34	2002	Fernando Tatis Jr.	42	2021
Álex Rodríguez	47	2003	Javier Báez	31	2021
Miguel Tejada	34	2004	Corey Seager	33	2022
José Valentín	30	2004	Willy Adames	31	2022
Bill Hall	35	2006	Corey Seager	33	2023
Jimmy Rollins	30	2007	Francisco Lindor	31	2023
Hanley Ramírez	33	2008	Bobby Witt Jr.	30	2023
Troy Tulowitzki	32	2009	Gunnar Henderson	37	2024
J.J. Hardy	30	2011	Francisco Lindor	33	2024
Troy Tulowitzki	30	2011	Willy Adames	32	2024
Brad Miller	30	2016	Bobby Witt Jr.	32	2024
Francisco Lindor	33	2017	Corey Seager	30	2024
Francisco Lindor	38	2018	Francisco Lindor	31	2025
Manny Machado	37	2018	Willy Adames	30	2025
Trevor Story	37	2018

The table below shows all the 2Bmen who hit 30+ HRs thru 1999. Click here to see the search results for this table. There are 11 cases.

Player	HR	Season
Rogers Hornsby	42	1922
Rogers Hornsby	39	1925
Rogers Hornsby	39	1929
Joe Gordon	30	1940
Joe Gordon	32	1948
Davey Johnson	43	1973
Bobby Grich	30	1979
Ryne Sandberg	30	1989
Ryne Sandberg	40	1990
Jeff Kent	31	1998
Jay Bell	38	1999

Now for 2000-25. Click here to see the search results for this table. There are 40 cases. Almost 4 times as many.

Player	HR	Season	Player	HR	Season
Marcus Semien	45	2021	Jorge Polanco	33	2021
Brian Dozier	42	2016	Dan Uggla	33	2010
Robinson Canó	39	2016	Chase Utley	33	2008
Brandon Lowe	39	2021	Ian Kinsler	32	2011
Alfonso Soriano	39	2002	Ketel Marte	32	2019
Alfonso Soriano	38	2003	Jonathan Schoop	32	2017
Bret Boone	37	2001	Dan Uggla	32	2008
Jeff Kent	37	2002	Chase Utley	32	2006
Aaron Hill	36	2009	Jose Altuve	31	2021
Ketel Marte	36	2024	Jose Altuve	31	2019
Alfonso Soriano	36	2005	Jazz Chisholm Jr.	31	2025
Dan Uggla	36	2011	Ian Kinsler	31	2009
Bret Boone	35	2003	Brandon Lowe	31	2025
Max Muncy	35	2019	Dan Uggla	31	2007
Javier Báez	34	2018	Dan Uggla	31	2009
Brian Dozier	34	2017	Chase Utley	31	2009
Ozzie Albies	33	2023	Ozzie Albies	30	2021
Robinson Canó	33	2012	Rougned Odor	30	2019
Jeff Kent	33	2000	Rougned Odor	30	2017
Rougned Odor	33	2016	Brandon Phillips	30	2007

For all batters, regardless of position, here are the totals for each time period:

Thru 1999) 806
2000-2025) 757

There is no total increase. So something much different has happened for these 2 positions than the general trend.

If we take out the SS/2Bmen it would be

Thru 1999) 779
2000-2025) 676

That ratio is about .87, far lower than the 2.5 for SS or the 4 for 2Bmen.

The Problem With “Total Clutch” Hitting Statistics, Part 2

2026-03-25T18:52:00.000-07:00

This is a follow up to today's earlier post on clutch hitting (which was a follow up to yesterday's post).

Again, I use Ed Oswalt’s measure “player’s win value” (or PWV) which is like WPA or Win Probability Added.

"WPA quantifies the percent change in a team's chances of winning from one event to the next. It does so by measuring the importance of a given plate appearance."

From MLB.COM.

I ran a linear regression in which PWV/PA was the dependent variable and relative OBP and SLG were the independent variables. It used all 284 players with 5000 or more plate appearances from 1972-2002. The regression equation was

PWV/PA = -.0246 + .000149*OBP + .000097*SLG

The r-squared was .935, meaning that 93.5% of the variation in PWV/PA is explained by the model. The standard error is .00056 or about .39 wins for a 700 PA season. The correlation between OBP and SLG is about .52. Each of those has a correlation of over .8 with Wins/PA. So again, two very simple stats explain what is going on with the much more complex, clutch stat.

About 82% of the players were within .5 PWV or wins of what the equation predicts. Only 5 were more than 1 win better or worse. So at most, the best clutch hitter can add about 1.38 wins a season above what you would expect them to.

Now another stat, the Game State Victories (GSV) from the Rhoids Sports Analysis website, shows the same tendencies.

I have a data set with 191 players who have 900 or more at bats over the years 2001-2003. (this is not all of them-I'll explain later). So I ran a regression in which each hitter's GSV for the three seasons was the dependent variable and their cumulative totals for various other stats were the independent variables. The r-squared I got was .91, meaning that 91% of the variation in GSV across hitters is explained by regular counting stats that are not at all context dependent (well, not quite, again I will explain later-it concerns SACs). That is, these independent variables have nothing to do with the score or the inning. Yet they explain almost all of the variation in the context dependent variable.

Here are the coefficient estimates

SAC = -.021
SF = -.049
GIDP = -.1098
CS = -.059
SB = .0299
BB = .053
HR = .106
3B = .099
2B = .0877
1B = .0603
OUTS = -.01537
Intercept = -1.47

Outs means outs not counting the GIDP. BB includes hit by pitch. The only variables not statistically significant (T-value less than 1.96) are SAC, SF and CS. That is a surprise for CS. I believe that GSV or anything like it is a clutch stat. Yet non-clutch data does a very good job of explaining the variation in GSV. It is possible that the unexplained variation is due to chance and not any kind of clutch hitting ability.

Now the data. I chose 900 at-bats because the data given that I was able to download from the Rhoids website, listed other stats, but not walks. So I wanted a convenient cutoff for which players to count and I chose 300 for individual years, figuring anyone who gets 400 plate appearances probably has at least 300 at bats. And over three years that is 900. I also used the data from Doug Steele's website to get walks, GIDP, etc.

The data problems. In some years, players who obviously did very well had zero for their GSV. Very often they were rookies who also had a zero listed for their salary and the Rhoids people wanted to do something with runs per dollar. Maybe that is why a zero is given, since you cannot divide by zero. Also some players simply had an "NA" listed. Others clearly had the wrong number, like Juan Sosa getting the same GSV as Sammy Sosa one year. Some other players were just not listed. I did not see Edgar Martinez in the "data dump" for this year. I could not always tell which Alex Gonzalez I was looking at or if I did they were not listed for all years. Some player names were not spelled the same way each year (I went through and made the necessary corrections to allow for doing subtotals in excel). So I did not have all of the players with 900 or more at bats from this period. I think about 30 got left out.

The correlation between GSV per plate appearance for players with 300 or more at bats in both 2001 and 2002 was .5. But the correlation between OPS in 2001 and GSV per PA in 2002 was actually higher, at .519. So if you wanted to predict a player's GSV per PA in 2002, his OPS in 2001 would do a slightly better job than his GSV per PA in 2001.

I also calculated a predicted GSV per PA for these players in both 2001 and 2002 using the coefficient values from the regression which used 1Bs, 2Bs, 3Bs, HRs, BBs, SBs, CSs, Outs and GIDPs. Then I calculated the difference between the actual GSV per PA in each year and the predicted GSV per PA in each year. Then I found the correlation between the differences or residuals for the two years and it was .081. That seems very low. I think this means that players who were especially good in the clutch in 2001 (who had a higher GSV per PA than predicted) were not likely to again, in 2002, have a higher GSV per PA than predicted in 2002 (I think this is the kind of analysis that Dick Cramer performed on the Player Win Average of the Mills brothers).

Sources

These are the sources that I listed 20 years ago. Some links might no longer work.

"Ballpark Figures to Bet On," Nov. 21, UPFRONT section BusinessWeek magazine. Author was Brian Hindo.

“What's a Ball Player Worth?” can be found at:

http://www.businessweek.com/print/bwdaily/dnflash/nov2003/nf2003115_2313_db016.htm?db

Player Win Averages by Eldon G. and Harlan D. Mills. 1970. A.S. Barnes, publisher.

Curve Ball: Baseball, Statistics, and the Role of Chance in the Game by Jim Albert and Jay Bennett. Revised 2003. Copernicus Books.

Rhoids Sports Analysis: http://www.rhoids.com/

Ed Oswalt’s site is at: http://www.livewild.org/bb/playervalues/index.html

The Nov. 7, 2004 NY Times article is at

http://query.nytimes.com/gst/abstract.html?res=F30A1EFA39580C748CDDA80994DC404482

But you will probably have to pay to read all of it.

Other sites where you might find it are

http://www.iht.com/articles/2004/11/07/sports/base.html

http://redsox.mostvaluablenetwork.com/wp-content/sites/schwarzWRAP.html

The Problem With “Total Clutch” Hitting Statistics, Part 1

2026-03-25T14:47:00.000-07:00

This is the follow up to yesterday's post on clutch hitting that I mentioned. It is also from articles I posted about 20 years ago. I will probably do a Part 2 very soon.

Introduction

A recent article (late 2003) appeared in BusinessWeek magazine called “Ball Park Figures You Can Bet On” which described a “new statistic” developed by Benjamin Polak and Brian Lonergan of Yale University which measures “wins contributed” by major league hitters. (another article on their work appeared in Nov. 2004 in the NY Times-see sources below) From the online version:

“Here's how their method works: Let's say the home team is down by two runs in the bottom of the fifth inning, with no outs and a runner on second base. At that moment, the home team has a 39% chance (or 0.39 probability) that it will win. If the batter grounds out, and the runner at second fails to advance, the team's chance of winning falls to 33%. The difference between the two, -0.06, is assigned to the batter who just grounded out.”

Now they add this up for the whole season, every plate appearance and get wins contributed for each player (see link in sources).

The problem with this approach is that it is not new and that it really tells us nothing about what a ball player is worth since in the long run this “total clutch” stat is highly correlated with normal hitting statistics, as I will demonstrate(my critique is not new-See the book "The Hidden Game of Baseball" by John Thorn and Pete Palmer. They discuss what Dick Cramer had to say about the Mills brothers) By “total clutch” stat, I mean one that takes into account every plate appearance and each one is weighted by its importance according to the score and inning. Hits when the game is late and close will count for more than hits when the game is early and the score is lopsided.

History

This is definitely not a new stat. It goes back at least as far as 1970 when Eldon G. and Harlan D. Mills published their book Player Win Averages. Polak and Lonergan’s “wins contributed” stat is similar. So is the “Player Game Percentage” in the book Curve Ball by Jim Albert and Jay Bennett. So is the “Game State Victories (or Wins) found at the Rhoids Sports Analysis website (see sources). So is “player's win value” by Ed Oswalt (his link is also in sources). So what Lonergan and Polak have done is definitely not new.

Analysis

Let’s start with the Ed Oswalt’s measure “player’s win value” (or PWV) since he uses thirty years of data, covering the years 1972-2002. The best hitters on his list will not surprise you and his stat divided by plate appearances (or PA) is highly correlated with stats like on-base percentage (OBP) and slugging percentage (SLG) as well as OPS (OBP + SLG).

First, I looked at the top 100 players in plate appearances from 1972-2002. I then correlated relative OPS (relative to the league average for each player) with Oswalt’s PWV/PA. The correlation was 0.948. This is very close to a one-to-one relationship. If you square this (called r-squared), you get 0.898, meaning that 89.8% of the variation across hitters in PWV/PA is explained by relative OPS. This is important because it shows that a very simple, non-clutch, non-situational, non-context stat like OPS pretty much explains a much more complex context dependent stat that is supposed to tell us the value of hitters.

The linear regression equation is PWV/PA = .00022*OPS - 022

In the figure below, you can see the relationship where PWV/PA is a function of relative OPS.

In the table below, you can see each player’s PWV/PA and his relative OPS. Bonds, for example has 137, which means his OPS was 37% higher than the league average for the 1972-2002 period. The top ten or twenty hitters will not surprise you.

Rank	Player	PWV/PA	Relative OPS
1	Barry Bonds	0.008	137
2	Mark McGwire	0.0068	131
3	Jeff Bagwell	0.006	127
5	Mike Schmidt	0.0049	126
6	Ken Griffey Jr.	0.0048	124
7	George Brett	0.0045	119
13	Fred McGriff	0.004	119
14	Rafael Palmeiro	0.0039	119
4	Will Clark	0.005	118
8	Rod Carew	0.0045	118
10	Jack Clark	0.0041	118
11	Reggie Jackson	0.004	118
26	Jim Rice	0.0032	118
49	Sammy Sosa	0.0023	118
19	Dwight Evans	0.0037	117
23	Fred Lynn	0.0035	117
32	Ellis Burks	0.003	117
12	John Olerud	0.004	116
20	Wade Boggs	0.0036	116
9	Tony Gwynn	0.0042	115
25	Jose Canseco	0.0033	115
55	Bobby Bonilla	0.002	115
15	Kirby Puckett	0.0038	114
17	Rickey Henderson	0.0038	114
18	Eddie Murray	0.0037	114
36	Dave Winfield	0.0027	114
42	Dale Murphy	0.0024	114
30	Don Mattingly	0.003	113
33	Andres Galarraga	0.0029	113
27	Dave Parker	0.0031	112
43	Al Oliver	0.0024	112
53	Bobby Grich	0.0021	112
16	Mark Grace	0.0038	111
21	Ken Singleton	0.0036	111
28	Harold Baines	0.0031	111
34	Tim Raines	0.0028	111
37	Cecil Cooper	0.0026	111
50	Wally Joyner	0.0023	111
51	Paul O'Neill	0.0022	111
56	Ron Cey	0.0019	111
58	Carlton Fisk	0.0019	111
61	Andre Dawson	0.0018	111
22	Keith Hernandez	0.0036	110
24	Darrell Evans	0.0033	110
39	Paul Molitor	0.0025	110
41	Ted Simmons	0.0025	110
44	Roberto Alomar	0.0024	110
45	Brian Downing	0.0024	110
47	Craig Biggio	0.0023	110
52	Barry Larkin	0.0022	110
59	Ryne Sandberg	0.0019	110
72	Chet Lemon	0.0009	110
29	Ken Griffey Sr.	0.0031	109
48	Dusty Baker	0.0023	109
54	Steve Garvey	0.002	109
31	Toby Harrah	0.003	108
35	Lou Whitaker	0.0027	108
40	Gary Matthews	0.0025	108
62	Chili Davis	0.0018	108
69	George Hendrick	0.0012	108
71	Don Baylor	0.0011	108
38	Pete Rose	0.0026	107
46	Jose Cruz	0.0024	107
60	Robin Ventura	0.0018	107
65	Robin Yount	0.0014	107
73	Gary Carter	0.0008	107
66	Cal Ripken	0.0014	106
67	Julio Franco	0.0013	106
63	Alan Trammell	0.0016	105
64	Chris Chambliss	0.0014	105
68	Graig Nettles	0.0013	105
70	Brett Butler	0.0011	104
74	Brady Anderson	0.0008	104
76	Ruben Sierra	0.0007	104
77	Carney Lansford	0.0007	104
78	Buddy Bell	0.0004	104
79	Joe Carter	0.0004	104
82	Todd Zeile	0.0003	103
85	Steve Finley	0	103
93	Lance Parrish	-0.0009	103
84	Jay Bell	0.0001	102
57	Tony Phillips	0.0019	101
75	Bill Buckner	0.0007	101
81	Tony Fernandez	0.0004	101
88	Tim Wallach	-0.0005	101
80	Willie Randolph	0.0004	100
86	Willie McGee	0	100
87	Gary Gaetti	-0.0003	100
83	B.J. Surhoff	0.0001	99
91	Devon White	-0.0007	99
89	Terry Pendleton	-0.0005	97
90	Dave Concepcion	-0.0006	96
92	Steve Sax	-0.0009	96
95	Willie Wilson	-0.0013	96
97	Garry Templeton	-0.0016	93
98	Frank White	-0.0018	93
94	Omar Vizquel	-0.0012	92
96	Ozzie Smith	-0.0015	92
99	Bob Boone	-0.0025	91
100	Larry Bowa	-0.0035	87

Sources

These are the sources that I listed 20 years ago. Some links might no longer work.

"Ballpark Figures to Bet On," Nov. 21, UPFRONT section BusinessWeek magazine. Author was Brian Hindo.

“What's a Ball Player Worth?” can be found at:

http://www.businessweek.com/print/bwdaily/dnflash/nov2003/nf2003115_2313_db016.htm?db

Player Win Averages by Eldon G. and Harlan D. Mills. 1970. A.S. Barnes, publisher.

Curve Ball: Baseball, Statistics, and the Role of Chance in the Game by Jim Albert and Jay Bennett. Revised 2003. Copernicus Books.

Rhoids Sports Analysis: http://www.rhoids.com/

Ed Oswalt’s site is at: http://www.livewild.org/bb/playervalues/index.html

The Nov. 7, 2004 NY Times article is at

http://query.nytimes.com/gst/abstract.html?res=F30A1EFA39580C748CDDA80994DC404482

But you will probably have to pay to read all of it.

Other sites where you might find it are

http://www.iht.com/articles/2004/11/07/sports/base.html http://redsox.mostvaluablenetwork.com/wp-content/sites/schwarzWRAP.html

How Many Games Do Clutch Hitters Really Win?

2026-03-24T14:58:00.000-07:00

This is an article I wrote for the "Beyond the Boxscore" site about 20 years ago.

This might seem like an easy question to answer. Just check how many times a player drove in the winning run in his team’s last AB (like David Ortiz, who seems to do it regularly). But very quickly problems arise. What about the guy who got on base and who scored the winning run? Should he get part of the credit? And what about times when a player fails to get a hit that would have won a game? Should that count as some kind of loss or be subtracted from the games he did win? None of these concerns even deal with whether or not players really can hit better than they normally do when the game is on the line. But I assume, for the sake of argument, that whatever difference in performance in the clutch that we find for players is real. Then I estimate how many wins (or losses) this brings to his team.

First, I start with a a somewhat imprecise method and also a somewhat crude measure of clutch performance (since I am reposting an article from 20 years ago the next part will be a separate post soon using a stat like win probability added). The clutch stat is hitting in “close and late” or CL situations. These are situations when the game is in the 7th inning or later and the batting team is leading by one run, tied, or has the potential tying run on base, at bat or on deck. If a player hits better (or worse) in these situations, it will raise (or lower) his team’s overall performance. I use that increase (or decrease) to estimate the increase (or decrease) in wins over a full season due to a given hitter’s CL performance.

Here is an example. Suppose a player has an OPS of .750 in non-CL situations (OPS is on-base percentage + slugging percentage). Let’s say he raises it by .072 when its CL (that is a pretty big increase). Spread over nine players in the lineup, it raises his team’s CL OPS by .008. But how many wins does that add over the course of a season? For that, I will use a regression generated equation from some previous research I have done called “Does Team Clutch Matter?” A team’s winning percentage is estimate by

(1) PCT = 0.501 + 0.918*NONCLOPS + 0.345*CLOPS - 0.845*OPPNONCLOPS - 0.421*OPPCLOPS

where CLOPS means how the team hit in CL situation and OPP refers to how their opponents did. NONCLOPS is how the team hit in non-CL situations. If a team’s CL OPS goes up by .008, its winning percentage rises by .345*.008 or .00276. Over 162 games, the team wins .447 more games (.00276*162). I then found all the players from 1987-2001 who had 6,000 or more plate appearances and found out how much their OPS went up or down in CL situations as compared to non-CL situations. Then I estimated how much their team OPS went up and then calculated its impact on team wins over a season. There was one additional step. I raised each player’s CL OPS by 4.5% because that is about the normal drop off in those situations (this might be due to not having the platoon advantage or just facing better than average pitchers when the game is on the line). If a player simply mainted his normal OPS win CL situations, he would essentially be a clutch hitter since OPS normally falls.

The table below shows how the players ranked.

Let’s take Tino Martinez. His non-CL OPS was .812 while his CL OPS was .889. But increasing that CL OPS by 4.5%, we get .929. This is .117 higher than normal and spread over 9 hitters in a lineup, it raises the team OPS when it is close and late by .013. How many wins does this add? We first multiply .013 by .345. This is .00449. That is the increase in team winning percentage. Then multiplying that by 162 we get about .727. So the best CL performer added .727 wins a year for his team. Also, there were only 5 players who added or subtracted even .5 or more wins (and well over half made a difference, whether positive or negative, of less than .25 wins per season).

So it is rare to find a player whose clutch performance will have much impact. That is something GMs should probably keep in mind when making personnel decisions. It is unlikely that clutch performance would be so different between two players that you would clearly pick one over the other. Even the biggest difference is only 1.286 (between Martinez and Carter). If two players were identical in every way, sure, you pick the better clutch player. But in most cases, there is not much difference. In fact, only about 20% of the differences between any two given players are .5 wins or more. If a GM was thinking about using clutch performance to help decide which player to sign, trade or trade for, it is not likelys it would matter much. And this assumes that these differences are real, not due to chance.

Name	CL OPS	CL OPS*1.045	NONCL OPS	Diff	Team Change	Win Value
Tino Martinez	0.889	0.929	0.812	0.117	0.013	0.727
Tony Gwynn	0.906	0.947	0.853	0.094	0.010	0.581
Gary Gaetti	0.782	0.817	0.732	0.085	0.009	0.530
Mark Grace	0.867	0.906	0.826	0.080	0.009	0.494
Tony Fernandez	0.765	0.799	0.746	0.054	0.006	0.334
Omar Vizquel	0.708	0.740	0.688	0.052	0.006	0.321
Luis Gonzalez	0.857	0.896	0.845	0.050	0.006	0.311
Dante Bichette	0.844	0.882	0.833	0.049	0.005	0.302
Harold Baines	0.842	0.880	0.831	0.048	0.005	0.301
Tim Raines	0.812	0.849	0.804	0.044	0.005	0.274
Edgar Martinez	0.951	0.994	0.956	0.038	0.004	0.235
Tony Phillips	0.783	0.818	0.781	0.037	0.004	0.229
Paul Molitor	0.848	0.887	0.850	0.037	0.004	0.228
Mark McLemore	0.691	0.722	0.688	0.034	0.004	0.210
Rickey Henderson	0.816	0.853	0.820	0.033	0.004	0.205
Cal Ripken	0.763	0.797	0.771	0.026	0.003	0.164
Ozzie Guillen	0.628	0.657	0.631	0.026	0.003	0.160
Brett Butler	0.752	0.785	0.760	0.026	0.003	0.159
Gary Sheffield	0.900	0.941	0.923	0.018	0.002	0.109
Wally Joyner	0.785	0.820	0.805	0.016	0.002	0.097
Mark McGwire	0.961	1.004	0.989	0.016	0.002	0.097
Jeff Bagwell	0.946	0.989	0.973	0.015	0.002	0.094
Delino DeShields	0.718	0.750	0.736	0.015	0.002	0.092
Jay Bell	0.746	0.780	0.767	0.013	0.001	0.078
Ruben Sierra	0.752	0.786	0.775	0.011	0.001	0.067
Andres Galarraga	0.835	0.873	0.862	0.010	0.001	0.063
Bobby Bonilla	0.815	0.852	0.842	0.010	0.001	0.059
Gregg Jefferies	0.744	0.777	0.769	0.008	0.001	0.051
Rafael Palmeiro	0.864	0.903	0.898	0.005	0.001	0.032
Terry Pendleton	0.705	0.737	0.733	0.004	0.000	0.026
Bernie Williams	0.858	0.897	0.893	0.004	0.000	0.022
Roberto Alomar	0.805	0.841	0.838	0.003	0.000	0.021
John Olerud	0.849	0.887	0.886	0.001	0.000	0.005
David Justice	0.852	0.890	0.891	0.000	0.000	-0.003
Barry Larkin	0.799	0.835	0.839	-0.004	0.000	-0.024
Chili Davis	0.799	0.835	0.843	-0.008	-0.001	-0.053
Barry Bonds	0.972	1.016	1.025	-0.009	-0.001	-0.056
Ken Griffey Jr.	0.902	0.943	0.952	-0.010	-0.001	-0.060
Fred McGriff	0.852	0.890	0.903	-0.013	-0.001	-0.080
Eric Karros	0.748	0.782	0.796	-0.014	-0.002	-0.087
Will Clark	0.840	0.878	0.893	-0.015	-0.002	-0.095
Steve Finley	0.731	0.764	0.779	-0.016	-0.002	-0.097
Marquis Grissom	0.682	0.713	0.731	-0.018	-0.002	-0.111
Ron Gant	0.760	0.794	0.813	-0.019	-0.002	-0.118
Benito Santiago	0.669	0.699	0.724	-0.025	-0.003	-0.155
Eddie Murray	0.754	0.788	0.816	-0.028	-0.003	-0.175
Devon White	0.688	0.719	0.748	-0.029	-0.003	-0.179
Jay Buhner	0.794	0.830	0.862	-0.033	-0.004	-0.203
Jose Canseco	0.816	0.853	0.886	-0.033	-0.004	-0.207
Paul O'Neill	0.774	0.809	0.843	-0.034	-0.004	-0.212
Kenny Lofton	0.741	0.774	0.812	-0.038	-0.004	-0.237
Dave Martinez	0.678	0.709	0.747	-0.039	-0.004	-0.242
Juan Gonzalez	0.847	0.885	0.924	-0.039	-0.004	-0.243
Robin Ventura	0.748	0.782	0.823	-0.041	-0.005	-0.254
Matt Williams	0.743	0.776	0.818	-0.041	-0.005	-0.257
Larry Walker	0.899	0.939	0.981	-0.041	-0.005	-0.258
Albert Belle	0.863	0.902	0.944	-0.042	-0.005	-0.260
Ray Lankford	0.783	0.818	0.863	-0.045	-0.005	-0.277
Todd Zeile	0.711	0.743	0.791	-0.048	-0.005	-0.298
Craig Biggio	0.747	0.781	0.831	-0.050	-0.006	-0.310
Sammy Sosa	0.811	0.847	0.900	-0.052	-0.006	-0.323
Brady Anderson	0.717	0.749	0.802	-0.052	-0.006	-0.325
Wade Boggs	0.759	0.793	0.853	-0.060	-0.007	-0.370
Chuck Knoblauch	0.713	0.745	0.806	-0.060	-0.007	-0.375
Ken Caminiti	0.714	0.746	0.809	-0.063	-0.007	-0.389
B.J. Surhoff	0.666	0.696	0.762	-0.066	-0.007	-0.409
Greg Vaughn	0.731	0.764	0.831	-0.067	-0.007	-0.419
Ellis Burks	0.785	0.820	0.890	-0.070	-0.008	-0.433
Travis Fryman	0.692	0.723	0.803	-0.080	-0.009	-0.497
Frank Thomas	0.905	0.946	1.034	-0.089	-0.010	-0.550
Joe Carter	0.665	0.695	0.785	-0.090	-0.010	-0.559

Quantifying the Eduardo Rodriguez vs. USA match up (and the USA's lineup might have been better than the 1927 Yankees)

2026-03-18T15:33:00.000-07:00

Eduardo Rodríguez's stats the last 2 years with league average in parentheses. He has been a mediocre pitcher at best the last 2 years over 204 IP (all numbers are from Baseball Reference and Stathead)

2024
5.04 ERA (4.13)
4.57 FIP (4.06)
84 ERA+ (101)

2025
5.02 ERA (4.22)
4.57 FIP (4.14)

86 ERA+ (101)

This year he is projected to have a 4.56 ERA in 144 IP

Here are the starters for the USA with their OPS+ from last year. This is a lineup that is far above average

Judge 215
Schwarber 160
Buxton 136
Witt 136
Harper 129
Turang 121
Bregman 128
Smith 152
Anthony 140

The simple average is 146. That would have made you the 8th best hitter in baseball last year. And it is far higher than the highest in the AL/NL since 1920. That belongs to the 1927 Yankees who had 127.

Rodriguez is a lefty and the USA had 4 lefties in the lineup. In his career Rodriguez has allowed a higher OPS against lefties than righties. Last year he allowed over .800 to both lefties & righties. No team last year had an .800 OPS. Yankees were highest at .787. So normally, facing Rodriguez would turn you into the best hitting team in baseball.

Here is how the USA starters did in OPS vs. lefties last year

Judge 1.279
Schwarber .964
Buxton 1.050
Witt .879
Harper .815
Turang .752
Bregman .855
Smith .836
Anthony .770

None of them had any trouble with lefties. The simple average of their OPS vs. lefties was .911. The highest OPS ever is .872 by the 1927 & 1930 Yankees.

Here are the overall OPS numbers for Team USA in 2025 (includes both LHP & RHP):

Judge 1.144
Schwarber 0.928
Buxton 0.878
Witt 0.852
Harper 0.844
Turang 0.794
Bregman 0.821
Smith 0.901
Anthony 0.859

The simple average is .891, again, better than the 1927 Yankees.

Now maybe we should look at what these guys are projected to do this year.

Judge 1.061
Schwarber 0.853
Buxton 0.812
Witt 0.885
Harper 0.847
Turang 0.726
Bregman 0.784
Smith 0.801
Anthony 0.833

The simple average here is .845. That would be tied for 9th best OPS in the AL/NL since 1920.

That .845 is about 94.8% of the .891 simple average from their overall OPS numbers in 2025. If we multiply the 146 OPS+ they had last year times .948 we get about 138. So a projected OPS+ for Team USA is still higher than for the 1927 Yankees (not sure if that adjustment makes sense or not).

Here are the ERAs last year for the guys who pitched for Venezuela last nite followed by their projections for this year

E. Rodriguez 5.02 4.56
E. Bazardo 2.52 3.63
J. Butto 3.90 3.65
A. Zerpa 4.18 4.08
D. Palencia 2.91 3.74

A mix but no one here is unhittable. And the projections overall, the second set of numbers, are just okay.

The other guy was Machado (not listed) who has not pitched in MLB since 2023 when he had a 5.22 ERA. His career ERA in MLB is 4.48 in 148 IP.

Here are the spring training OPS numbers for the starters for the USA last nite. Not alot of PAs, around 15 each and no more than 20. And I don't know how good the pitchers were that they faced

Judge 1.285
Schwarber 0.833
Buxton 0.796
Witt 1.3
Harper 1.5
Turang 1.212
Bregman 1.481
Smith 1.273
Anthony 0.616

So it seems like they were doing well

Did Lefty Grove Lead the AL in Championship Win Probability Added (cWPA) in 1931 as Both a Starter and a Reliever?

2026-03-13T19:16:00.000-07:00

What is cWPA? See Single-Season Leaders & Records for Championship WPA (cWPA) from Baseball Reference:

"Given average teams, this is the change in probability, displayed in percentage points. A change of +/- 100% would indicate one world series win added or lost."

If a pitcher or batter does well in close games late in the season, they will probably have a high cWPA. It also helps to do well late in games and be on a contender.

Click here to see the leaders in cWPA in the 1931 AL. You will have to click on the cWPA column heading to get the pitchers ranked accordingly.

I looked at the top 16, which includes every guy with 1.0 or higher. Then I went to the Baseball Reference game logs for each guy and determined how much cWPA they had has as a starter and then as a reliever. cWPA is listed for each game and games as starters and relievers can easily be separated.

The first table shows those 16 pitchers ranked by cWPA as starters.

Player	Starter	Reliever
Lefty Grove*	16.4	4.1
Rube Walberg*	8.5	-1.3
George Earnshaw	4.1	0.8
Firpo Marberry	3.9	2.3
Roy Mahaffey	3.2	0.9
Waite Hoyt	2.1	-0.2
Herb Pennock*	1.9	0
Clint Brown	1.7	-0.2
Earl Whitehill*	1.6	0
Pat Caraway*	1.3	0
Lefty Stewart*	1.2	0
Bump Hadley	1.1	0.5
Bobby Burke*	1.0	2.0
Lefty Gomez*	0.9	2.9
Carl Fischer*	0.3	2.4
Wilcy Moore	-0.8	3.1

This does not mean that these were the top 16 in cWPA if we only counted starts, but how the top 16 overall would rank amongst themselves if we only looked at starts. Grove is the clear leader.

Now if they are ranked by relief appearances.

Player	Starter	Reliever
Lefty Grove*	16.4	4.1
Wilcy Moore	-0.8	3.1
Lefty Gomez*	0.9	2.9
Carl Fischer*	0.3	2.4
Firpo Marberry	3.9	2.3
Bobby Burke*	1.0	2.0
Roy Mahaffey	3.2	0.9
George Earnshaw	4.1	0.8
Bump Hadley	1.1	0.5
Herb Pennock*	1.9	0
Earl Whitehill*	1.6	0
Lefty Stewart*	1.2	0
Pat Caraway*	1.3	0
Waite Hoyt	2.1	-0.2
Clint Brown	1.7	-0.2
Rube Walberg*	8.5	-1.3

I don't think it is likely that any other pitcher could have beaten Grove in either category. Any pitcher with less than 1.0 cWPA overall would need to have a very large negative in one of the roles. For example, to beat Grove's 4.1 in relief he would need to have had -3.3 as a starter (no one in the league was worse than 2.9 overall). So if he had 4.2 as a reliever and -3.3 as a starter, he would be 0.9 overall (and 0.9 was the next highest overall after the top 16 that I looked at here).

There might be some pitchers in other years who led in both roles. In Grove's time, regular starters pitched fairly often in relief. See my post Starting Pitchers As Relievers Over Time.

Grove had a 31-4 record with a 2.04 ERA and 5 saves. Here are his stats in both roles:

Split	W	L	ERA	G	IP	OPS
as Starter	27	3	1.94	30	260.1	0.574
as Reliever	4	1	3.18	11	28.1	0.644

To put these numbers in perspective, the league ERA was 4.38 while the league OPS was .740. Grove, of course, was also helped by being on a first place team

If we combine both hitting and pitching, where does Babe Ruth's 1918 season rank in Championship Win Probability Added?

2026-02-10T19:01:00.000-08:00

Here is how Baseball Reference defines it.

"Championship Win Probability Added (cWPA): for Offensive Player Given average teams, this is the change in probability, displayed in percentage points. A change of +/- 100% would indicate one world series win added or lost."

To do well in this a player or pitcher most likely needs to be on a team that was in a close pennant race and perform well.

The Red Sox won the pennant by just 2.5 games and Ruth performed very well in both hitting and pitching in the last 2 months (there will be details on this later to show how good Ruth was). As I will also show, adding Ruth's cWPA from both hitting and pitching will rank him very high all-time.

All data used is from BB Ref and Stathead.

Here are the top 10 batters in cWPA in the AL in 1918:

1	Ruth • BOS	19.70
2	Hooper • BOS	17.10
3	Bodie • NYY	5.60
4	Baker • NYY	4.90
5	Roth • CLE	4.90
6	Speaker • CLE	4.70
7	Mayer • BOS	3.90
8	Wood • CLE	3.60
9	Pipp • NYY	3.20
10	Collins • CHW	3.10

Notice the top two, by far, are from the champion Red Sox. Ruth actually had 19.67 as shown on the Single-Season Leaders & Records for Championship WPA (cWPA) for hitters.

Here are the top 10 pitchers in cWPA in the AL in 1918:

1	Jones • BOS	11.80
2	Johnson • WSH	10.20
3	Ruth • BOS	10.00
4	Coveleski • CLE	9.40
5	Bush • BOS	6.80
6	Mays • BOS	5.30
7	Bagby • CLE	4.50
8	Harper • WSH	4.40
9	Thormahlen • NYY	4.30
10	Morton • CLE	3.90

Ruth actually had 9.96 as shown on the Single-Season Leaders & Records for Championship WPA (cWPA) for Pitchers.

So Ruth has a total of 9.96 + 19.67 = 29.63. That is higher than all but 9 hitting seasons and all but 7 pitching seasons. So, overall, that would be the 17th highest season by any man whether a pitcher or hitter.

But since we are adding Ruth's hitting and pitching cWPAs together, let's add in the cWPA on offense for the pitchers. Most of them are negative. I looked at each pitcher in the top 30 of cWPA. Waite Hoyt is number 30 with 22.84. For him to reach 29.63, he would need 6.79. It is not likely a pitcher would have that much (see technical notes).

I re-calculated the cWPA of each of the top 30 pitchers by adding in their cWPAs as hitters. For most of them this meant lowering their total cWPA for the year since most of them had a negative cWPA as hitters.

This table shows the top 15 all-time (since 1912-that is how far back BB Ref goes on this):

Rank	Player	Year	cWPA	Role
1	Carl Yastrzemski	1967	52.14	Pos. Player
2	Bobby Thomson	1951	50.19	Pos. Player
3	Willie Mays	1962	45.64	Pos. Player
4	Babe Ruth	1921	44.90	Pos. Player
5	Al Wickland	1915	38.87	Pos. Player
6	Claude Hendrix	1914	34.70	Pitcher
7	Tommy Davis	1962	34.57	Pos. Player
8	Vic Raschi	1949	34.14	Pitcher
9	Carl Hubbell	1933	32.48	Pitcher
10	Ellis Kinder	1949	31.50	Pitcher
11	Babe Ruth	1924	31.07	Pos. Player
12	Gene Bearden	1948	30.66	Pitcher
13	Jackie Robinson	1951	30.51	Pos. Player
14	Ken Keltner	1948	30.08	Pos. Player
15	Babe Ruth	1918	29.63	Both

So Ruth's 1918 season is 15th. Just on hitting it was 57th. On pitching alone it was 265th.

Now let's look at how Ruth did in the last 2 months of the 1918 season (they won the pennant by just 2.5 games). Here are all the Red Sox batters with 50+ PAs in each of the last two months of the season ranked by OPS:

Player	July OPS	July PA	Player	Aug. OPS	Aug. PA
Ruth	0.931	108	Ruth	0.782	98
Hooper	0.735	128	Strunk	0.729	117
McInnis	0.728	89	Shean	0.665	121
Strunk	0.629	104	Hooper	0.607	122
Shean	0.548	82	McInnis	0.586	112
Schang	0.472	60	Scott	0.404	105
Scott	0.421	106	Cochran	0.303	55
Stansbury	0.414	54

Ruth was clearly the best hitter on the Red Sox in each of the last two months of the season. The AL had a .644 OPS in July and .637 in Aug. The Red Sox only played 2 games in Sept. The season was only 126 games long due to World War I. They actually clinched on Aug. 31. So Ruth hit much better than the rest of the league down the stretch.

Ruth was 3-0 with a 1.88 ERA in 24 IP in July (the league average was 2.81). In Aug. he was 6-2 with a 1.73 ERA in 73 IP (the league average was 2.74). Here is what Red Sox pitchers did in Aug.

Player	IP	W	L	ERA
Ruth	73	6	2	1.73
Mays	69	4	4	3.13
Bush	51	1	6	3.00
Jones	44	4	1	1.43
Kinney	11	0	0	2.45
Pertica	3	0	0	3.00

Although Sad Sam Jones beats Ruth in ERA Ruth had alot more IP. Ruth had a cWPA of 10.0 in Aug. and Jones had 2.1. So it is reasonable to say Ruth was the key pitcher for the Red Sox in Aug.

Technical notes:

I looked at all pitchers with at least an .800 OPS with 100+ PAs since 1912 (the year cWPA data starts). The highest cWPA year was 4.6 by Walter Johnson who had a 1.033 OPS in 1925. Then I looked at all the cWPA events of 2.0 or higher by pitchers thinking that maybe some one like that might have racked up a high season cWPA. But none of the 22 pitchers were as high as 4.6. So its unlikely that any pitcher ever had as high as 6.79 cWPA as a hitter.

Top 5 year HR spans by difference between 1st and 2nd

2026-02-02T19:08:00.000-08:00

All data from Baseball Reference and Stathead searches.

Ruth had the most HRs from 1920-24 with 235. Cy Williams had the next highest in the AL/NL with 124. So Ruth's differential was 111. This was the highest I found for the #1 and #2 guys for any 5 year period in baseball history.

The highest by anyone not named Ruth is 76 by Ralph Kiner from 1947-51. Then the next highest by anyone not named Ruth or Kiner was 53 by Sammy Sosa from 1998-2002. So Kiner's best mark is well above the best mark of the next guy on the list, Sosa (20 higher).

Player	HR	From	To	Diff	2nd
Babe Ruth	235	1920	1924	111	CyWilliams
Babe Ruth	232	1924	1928	110	Hornsby
Babe Ruth	219	1923	1927	93	CyWilliams
Babe Ruth	232	1925	1929	87	Gehrig
Babe Ruth	256	1926	1930	79	HWilson
Ralph Kiner	234	1947	1951	76	TWilliams
Babe Ruth	206	1921	1925	62	Hornsby
Babe Ruth	194	1922	1926	60	Hornsby
Babe Ruth	255	1927	1931	59	Gehrig
Ralph Kiner	215	1949	1953	57	Hodges
Ralph Kiner	220	1948	1952	55	Sauer
Babe Ruth	236	1928	1932	53	Gehrig
Sammy Sosa	292	1998	2002	53	Bonds
Ralph Kiner	215	1946	1950	49	TWilliams
Mark McGwire	284	1995	1999	43	Sosa

Update Feb. 3:

Kiner hit 141 of his 234 HRs from 1947-51 at home (60.3%). He was right handed and his HR% at home in these years was 10.38% (141HRs/1359ABs).

All other RHBs in Forbes Field had a HR% of 2.88% (I found all the HRs by Pirate RHB at Forbes and all the HRs allowed to RHBs by Pirate pitchers at Forbes using the Stathead Batting Event Finder and Pitching Event Finder Functions). I subtracted out Kiner's stats to get all other RHB stats.

Kiner's HR% at Forbes was 3.6 times higher than it was for other all RHBs.

For all games from 1947-51 (both home and road), Kiner had a HR% of 8.52%. The rest of the NL (and that means Kiner's stats are removed) had a HR% of 2.17%.

Kiner's HR% for all games from 1947-51 was 3.9 times higher than it was for other all batters. Maybe Forbes helped Kiner. But if it did it seems like his ratio compared to other RHBs there would be higher, not lower, than his overall ratio compared to all batters during this period.

Another interesting thing is that both Johnny Mize (who was 4th in this period) and Ralph Kiner hit 51 HRs in 1947 and 40 in 1948. Mize only hit 54 HRs over the next three years in 1148 PAs (so he was less than a full-time player). He was 36-37-38 years old then while Kiner was 26-27-28.

Stan Musial was 3rd with 154 but Kiner only edged him 93-86 in road games. Ron Selter of SABR published an article in the 2003 Baseball Research Journal called Sportsman’s Park’s Right-Field Pavilion and Screen.

A very tall screen was in place in RF every year except 1955. Selter says that without it Musial could have hit 676 career HRs. That means he would have been much closer to Kiner from 1947-51. I want to thank my friend and fellow SABR member Monte Cely for pointing this issue out to me.

The 2026 Dodgers will have 3 of the top 10 hitters in OPS+ from 2021-25

2026-01-30T19:14:00.000-08:00

I used Stathead from Baseball Reference to call up the top 10s in OPS+ in the AL/NL for every 5 year period starting in 1901. Minimum was 2,000+ PAs.

This link is has the Stathead search results for 2021-25:

Leaders from 2021-25.

Ohtani is 2nd with 171, Freeman 6th with 148 and Tucker 9th with 145.

So the Dodgers enter the 2025 season with 3 guys on their roster who are in the top 10 in OPS+ over the previous 5 years.

What other teams have had 3 of the top 10 hitters in OPS+ over the previous 5 years?

2025 Dodgers. The 3 players are Ohtani 4th (163), Freeman 6th (153), Betts 7th (145). Again, that shows that those 3 guys were in the top 10 for the years 2020-24.

2024 Dodgers. Freeman 4th (151), Ohtani 6th (148), Betts 7th (142)

2004 Yankees. Giambi 3rd (171), Sheffield 8th (156), Alex Rodriguez 10th (153)

1984 Angels. Lynn 7th (140), Grich 8th (136), Reggie Jackson 9th (136)

1966 Giants. Mays 2nd (171) Cepeda 7th (148), McCovey 8th (143) (Cepeda only played 19 games for the Giants in 1966 before being traded to the Cardinals where he played 123 games)

1956 Dodgers. Snider 4th (152), Robinson 8th (137), Hodges 10th (137)

1955 Dodgers. Snider 6th (146), Robinson 7th (143), Hodges 10th (137)

1954 Dodgers. Robinson 5th (146), Campanella 7th (141), Snider 8th (137) (Hodges was 11th)

1954 Indians. Rosen 3rd (150), Doby 4th (148), Wertz 9th (134) (Wertz started the year with Orioles and had 106 PAs there and then 337 with Clev.)

1950 Yankees. DiMaggio 3rd (158), Mize 5th (153), Henrich 7th (137)

1934 Giants. Ott 5th (156), O'Doul 8th (149), Terry 10th (146)

1928 A's. Speaker 5th (147), Cobb 9th (139), Simmons 10th (138)

1923 Tigers. Cobb 3rd (165), Heilmann 6th (145), Veach 10th (131)

1922 Tigers. Cobb 2nd (174), Veach is 8th (138), Heilmann 10th (135)

1912 Tigers. Cobb 1st (186), Crawford 5th (153), Jim Delahanty 10th (130)

The 1942 Dodgers had Dolph Camilli and Joe Medwick in the top 10 of the previous 5 years. They also had Arky Vaughan who Stathead lists as 11th with a 133 OPS+. In 10th was Rudy York with 133. My guess is that York was slightly ahead. His name would come after Vaughan alphabetically if BB Ref sorted that way. So maybe if they showed a decimal point York would come out ahead.

Do teams with several good base stealers hit better with a runner on first base?

2026-01-23T17:44:00.000-08:00

I looked at all teams since 1946 that had 4 or more players with 25+ SBs (I tried using 30+ SBs first but there were only 5 teams with 4 or more). The idea was that if a team had many base stealers the opposition will hold the runner on first base, opening up a hole that might help the batter. All data from Baseball Reference and Stathead.

There were 12 teams that had 4 or more players with 25+ SBs. I looked at how much better those teams hit with a runner on first base (and first base only) compared to having no one on base. But that difference was then compared to the league average difference.

Here are the 12 teams along with how many of their players stole 25+ bases and the average for the other teams in their league:

Team	Season	Count	Lg Avg.
OAK	1976	6	1.36
STL	1985	5	1.09
OAK	1974	4	0.83
TEX	1977	4	1.00
PIT	1977	4	1.27
HOU	1979	4	1.36
SDP	1982	4	1.55
STL	1983	4	1.82
STL	1984	4	1.36
CIN	1988	4	1.82
STL	1990	4	1.64
SDP	1999	4	1.20

Now it is possible that these teams played in a league that had many players with 25+ SBs, so that would make it harder to be better than the league average. So the last column supplies us with that information.

The 1974 A's employed a "designated pinch runner," track star Herb Washington (29 SBs). He appeared in 92 games yet never came to bat or took the field. The 1976 A's did pretty much the same thing with Larry Lintz (31 SBs) who appeared in 68 games with only 4 plate appearances. He played just 13 innings in the field.

The table below shows how much higher (or lower) each team's batting average was with a runner on first base than with no runners on base. Then the difference for the entire league. Then whatever edge each team had over the league average.

Team	Season	Team Diff	Lg Diff	Team Edge
OAK	1976	0.028	0.026	0.002
STL	1985	0.003	0.012	-0.009
OAK	1974	-0.003	0.033	-0.036
TEX	1977	0.048	0.024	0.024
PIT	1977	0.022	0.023	-0.001
HOU	1979	0.037	0.029	0.008
SDP	1982	0.071	0.026	0.045
STL	1983	0.034	0.028	0.006
STL	1984	0.04	0.028	0.012
CIN	1988	0.023	0.025	-0.002
STL	1990	0.021	0.032	-0.011
SDP	1999	0.012	0.012	0

If we look at the 1976 A's they hit .028 higher with a runner on first base than with no runners on. The league hit .026 higher, so their edge was .002.

5 of the 12 teams did worse than the league average. 4 others did less than .010 better. So no clear pattern of teams with several good base stealers doing better. And these are teams that would have had a hole on the right side of the infield more often than average. The average edge across the 12 teams was .00317.

The one team that had a very large edge was the 1982 Padres. They batted .242 with no runners on and .313 with a runner on first. So .071 higher. For the NL that year it was .253 with no one on and .279 wit a man on first, .026 higher. That means the Padres had an edge of .045 (.071 - .026).

The Padres had 873 ABs with a runner on first. That would mean an extra 39 hits or so (873*.045 is about 39). Not all of those extra hits would necessarily have been singles through the big hole between first base and second base. Their percentage of hits that went for 2Bs, 3Bs and HRs was about the same with runners on first as it was for no runners on.

About 76% of their hits in either case were singles. So maybe they gained about 30 hits from having more runners held on first base than the average team. But either way, 39 or 30, no other team would be even close to that kind of impact.

The next highest team was the 1977 Rangers with an edge of .024. They had 952 ABs with a runner on first. That would mean about 23 extra hits (.024-952 is about 23). And then there is the 1974 A's who were .036 worse than the league average. That would mean about 34 fewer hits (.036-932 is about 34).

Another thing that might be important is what percentage of ABs with a runner on first were by left handed batters, who presumably would have an easier time hitting a ball in the hole.

Team	Season	% LHB	Lg Avg	% LHB Edge	Avg Edge
SDP	1982	0.611	0.402	0.209	0.045
TEX	1977	0.432	0.428	0.004	0.024
STL	1984	0.517	0.404	0.113	0.012
HOU	1979	0.392	0.364	0.028	0.008
STL	1983	0.594	0.418	0.176	0.006
OAK	1976	0.254	0.397	-0.143	0.002
SDP	1999	0.417	0.384	0.033	0
PIT	1977	0.457	0.349	0.108	-0.001
CIN	1988	0.404	0.407	-0.003	-0.002
STL	1985	0.562	0.386	0.176	-0.009
STL	1990	0.449	0.431	0.018	-0.011
OAK	1974	0.289	0.355	-0.067	-0.036

61.1% of the 1982 Padres' ABs with a runner on first were by a LHB. The league average was 40.2%. So their %LHB edge was 20.9%. It is not a surprise that a team that must have had alot of LHBs hit so much better with a runner on first (they were the best relative to the league average, .045 batter from the last column).

But the next best team in Avg Edge, the 1977 Rangers, only had %LHB edge of .004. And 3 teams that had a positive %LHB edge had a negative Avg Edge. The 1983 Cardinals had a %LHB edge of .176 yet their Avg Edge was only .006.

So it does not look like there is much of pattern or evidence that having alot of LHBs raises batting average with a base stealer on first.

When I get a chance I might lower the SB level to 15 and do the same analysis.