Cybermetrics

What explains the Brewers scoring this year?

2026-10-17T13:00:32.939-07:00

They were 2nd in the NL in runs per game with 5.14. Yet they were last in HRs with 154 (the league average was 188). Their SLG was .403 while the league average was .405. But they did lead the NL in OBP with .341 (the league average was .321). All data is from Baseball Reference and Stathead.

Given their OBP & SLG, how many runs per game would we expect them to score? To answer that I ran a regression of all teams from 2021-2025 with runs per game as the dependent variable and OBP & SLG as the explanatory variables.

R/G = 14.67*OBP + 10.8*SLG - 4.53

Plugging in the Brewers numbers (.314 OBP * .403 SLG) gets us 4.82 runs per game, 0.32 below their 5.14.

But the Brewers hit much better with runners on base than they did with the bases empty. Their OPS with runners on was .789. Win no runners on it was just .703. That is probably an unusually big difference. For all of MLB it was .742/.701. So Brewers differential of .086 was more than double what was the norm this year, .041.

I ran another regression on the same data as before with runs per game as the dependent variable and OBP & SLG as the explanatory variables but this time OBP & SLG were broken down by situations with no runners on (NONE) and runners on (ROB).

R/G = 4.42*NONEOBP + 5.12*NONESLG + 11.14*ROBOBP + 5.89*ROBSLG - 4.90

This table shows the Brewers numbers this year

RobOBP	RobSLG	NoneOBP	NoneSLG
0.356	0.433	0.326	0.376

Plugging this data into the previous equation gets us 4.98 runs per game. This only off by 0.16 which is just half of the previous differential. So simply breaking down their performance by runners on and no runners on explains half of their differential.

One stat that amplifies this and would probably shrink the differential even more is HR% (HR/PA). The NL average HR% with no runners on was 3.14% this year. With runners on it actually went down to 2.95%. But the Brewers went up from 2.14% with no runners on to 2.77% with runners on.

With HRs, you are guaranteed runs. Hit a HR with a man on first base and you have two runs. Hit a double and maybe you only have runners on 2nd and 3rd but not necessarily any runs. So HRs tell us something above what SLG alone might tell us.

Another thing the Brewers were very good at was not hitting into DPs. They had the lowest percent in the NL this year at 7.7%. The league average was 9.0%. In 589 DP opportunities, the Brewers save 7.65 DPs. If DPs have a run value of -.44, This is another 3.37 runs saved for the Brewers (or .021 per game). That explains about 13% of the remaining gap.

So hitting well with runners on base and avoiding DPs might explain why the Brewers score more runs than expected based on OBP & SLG alone.

The Brewers also had the highest OBP this year with the bases loaded, .383. The Angels were next with .367. They also had the highest batting average with the bases loaded, .354, The Twins were 2nd with .323. Doing that well with the bases loaded will probably add some extra runs.

Players who had a six-year period when they led MLB in both OBP & SLG

2026-10-04T13:21:51.009-07:00

I had bases.chat, the AI program created by SABR member Ted Ingram, find all of the data in the table below. Then I verified the data using Stathead from Baseball Reference. The search covered the years 1901-2025.

Yesterday's post listed all the players with both 400+ HRs and a .400+ OBP. Mike Trout was one of the fourteen players. I also noticed that he led all of MLB in both OBP & SLG over the years 2014-2019.

So I wanted to find out how many different players had done this. There are 12 different guys in the table below. For most guys, I show just their first six-year period when they did it.

Most of the batters had overlapping stretches. Wagner, for example, also did it from 1904-1909 and 1905-1910. That gives him three periods, as shown in the last column. If a player is listed more than once, that means he had at least two non-overlapping six-year periods. Ruth did it over the years 1915-1920 and then did it from 1921-1926 and then from 1927-1932.

To qualify, a batter needed 1,800+ ABs for any six-year period.

Player	From	To	OBP	SLG	Periods
Honus Wagner	1903	1908	0.417	0.510	3
Ty Cobb	1906	1911	0.413	0.511	8
Joe Jackson	1908	1913	0.455	0.565	1
Ty Cobb	1912	1917	0.461	0.531	8
Babe Ruth	1915	1920	0.452	0.652	15
Babe Ruth	1921	1926	0.495	0.730	15
Babe Ruth	1927	1932	0.477	0.713	15
Ted Williams	1939	1944	0.481	0.642	17
Ted Williams	1945	1950	0.490	0.643	17
Ted Williams	1951	1956	0.487	0.632	17
Mickey Mantle	1957	1962	0.445	0.601	2
Willie McCovey	1965	1970	0.405	0.578	2
Frank Thomas	1989	1994	0.449	0.590	2
Barry Bonds	1997	2002	0.472	0.589	7
Barry Bonds	2003	2008	0.521	0.671	7
Albert Pujols	2004	2009	0.435	0.636	3
Mike Trout	2014	2019	0.425	0.597	4
Aaron Judge	2020	2025	0.423	0.645	1

Mike Trout joined the 400HR/.400OBP club in 2025

2026-10-03T14:09:17.660-07:00

Click here to see the search results from Stathead. There are only 14 guys in this club.

Rk	Player	HR	OBP
1	Barry Bonds	762	0.444
2	Babe Ruth	714	0.474
3	Jim Thome	612	0.402
4	Manny Ramírez	555	0.411
5	Mickey Mantle	536	0.421
6	Jimmie Foxx	534	0.428
7	Frank Thomas	521	0.419
8	Ted Williams	521	0.482
9	Mel Ott	511	0.414
10	Lou Gehrig	493	0.447
11	Stan Musial	475	0.417
12	Chipper Jones	468	0.401
13	Jeff Bagwell	449	0.408
14	Mike Trout	426	0.405

Close calls: Giambi (440, .399), McGwire (583, .394) and Sheffield (509, .393).

Giambi actually had 409 HRs & a .405 OBP thru 2009. But his OBP over his last 5 years was just .335.

Sheffield actually had 415 HRs & a .400 OBP thru 2004. But his OBP over his last 5 years was just .364.

McGwire never reached a .400 OBP.

If Trout gets 2000 more PAs he would need about a .381 OBP to stay at .400+.

This next table has active players with 200+ HRs and a .375+ OBP.

Rk	Player	HR	OBP
1	Juan Soto	271	0.415
2	Aaron Judge	386	0.410
3	Mike Trout	426	0.405
4	Yordan Alvarez	212	0.397
5	Bryce Harper	392	0.386
6	Freddie Freeman	383	0.384
7	Ronald Acuña Jr.	206	0.379
8	Paul Goldschmidt	390	0.375
9	Shohei Ohtani	310	0.375

Soto, Judge & Alvarez look like possibilities to make it at some point.

World Series Winners Who Had An OPS Differential Of .200+ In The Division Series, The League Championship Series And The World Series

2026-09-26T18:50:54.184-07:00

I had bases.chat, the AI program created by SABR member Ted Ingram, find all of the data in the table below. Then I verified the data at Baseball Reference.

The only two teams to pull this off are the 2005 White Sox and the 2007 Red Sox. They are both highlighted in red in the table. Also, if you add up their differentials from each series the Red Sox have 0.872 while the White Sox have .690. Those are the two highest totals.

So you could say that those two teams had the most dominant post-seasons since 1995. The Red Sox total is much higher than the one for the White Sox but the White Sox lost only one game while the Red Sox lost three. Chicago's only loss was in the ALCS while all three for Boston also came in the ALCS. Boston had two games when their opponent had a higher OPS (again, both in the ALCS) while Chicago had only one (in the division series against Boston).

The numbers in each column are the OPS differentials that each team had for each series.

Year	Team	DS	LCS	WS
1995	ATL	0.104	0.225	0.180
1996	NYA	0.022	0.172	−.101
1997	FLO	0.185	−.144	−.032
1998	NYA	0.413	0.002	0.202
1999	NYA	0.255	−.095	0.189
2000	NYA	−.053	0.131	0.138
2001	ARI	0.065	0.024	0.191
2002	ANA	0.196	0.257	−.022
2003	FLO	0.051	−.015	−.161
2004	BOS	0.181	−.057	0.32
2005	CHA	0.214	0.272	0.204
2006	SLN	0.083	0.071	0.094
2007	BOS	0.361	0.204	0.307
2008	PHI	0.272	−.008	0.277
2009	NYA	0.082	0.184	-0.057
2010	SFN	0.106	0.01	0.210
2011	SLN	0.099	0.088	0.017
2012	SFN	−.110	0.134	0.194
2013	BOS	0.137	−.050	0.049
2014	SFN	0.073	−.036	0.044
2015	KCA	0.035	0.077	0.073
2016	CHN	−.020	0.130	0.029
2017	HOU	0.189	−.056	0.082
2018	BOS	0.171	−.011	0.140
2019	WAS	−.036	0.367	−.055
2020	LAN	0.269	0.103	0.147
2021	ATL	0.170	−.012	0.15
2022	HOU	0.084	0.253	0.09
2023	TEX	0.189	0.035	−.008
2024	LAN	0.011	0.105	−.041
2025	LAN	−.100	0.424	−.087

Position players pitching over time

2026-09-20T13:05:17.836-07:00

This is something I posted on the SABR-List.

David Smith provided a Retrosheet link that has known occurrences of this happening. See Non-Pitchers on the Mound.

I sorted that table by date and then compiled the number of cases by time period. Here is what I got:

1898-99) 13
1900-09) 81
1910-19) 80
1920-29) 62
1930-39) 39
1940-49) 51
1950-59) 36
1960-69) 24
1970-79) 17
1980-89) 58
1990-99) 48
2000-09) 52
2010-19) 304
2020-25) 588

What were the most exciting games in history? Part 3

2026-09-14T18:59:46.534-07:00

In Part 1, I had bases.chat, created by SABR member Ted Ingram, add up the absolute value of all Win Probability Added events in every game that have play-by-play data. Then I had it create a list of top games. The idea was that the higher the WPA the more exciting a game was.

The top games were all regular season games. In Part 2 I did the top post-season games.

bases.chat said "WPA computed from Retrosheet play-by-play data using Tom Tango's win expectancy framework."

In this part, I show the games with the most plays that had a WPA of 0.25 or higher. The idea was that some games could just go on a long time without any big events yet still end up with a high total WPA. So other games could be considered more exciting if they had more big events.

In the table below, Inn refers to how many innings the game lasted and # refers to how many such events there were in the game. This first table has regular season games. There are many extra-inning games here but some of them were only 10 or 11 innings, so they did not last too long or have too many more chances to get high WPA events.

Rank	Date	Visitor	Score	Home	Inn	#
1	5/24/1988	Padres	6–7	Expos	13	10
2	7/17/2000	Athletics	9–10	Rockies	10	9
3	7/10/1932	Athletics	18–17	Indians	18	9
4	7/23/1930	Pirates	16–15	Phillies	13	9
5	7/18/1949	Yankees	5–6	White Sox	10	9
6	8/11/2020	Orioles	10–9	Phillies	10	9
7	8/12/1966	Pirates	14–11	Reds	13	8
8	6/20/1977	Cubs	10–9	Giants	12	8
9	7/3/1951	Phillies	8–9	Giants	13	8
10	7/24/1998	Marlins	6–7	Phillies	12	8
11	8/4/2011	Blue Jays	6–7	Rays	12	8
12	5/7/1920	Robins	6–7	Giants	11	8
13	8/17/2019	Brewers	15–14	Nationals	14	8
14	6/15/1912	Cardinals	4–5	Dodgers	11	8
15	6/9/2023	Astros	9–10	Guardians	14	8
16	9/16/1991	Reds	5–6	Dodgers	12	8
17	6/21/1927	Cubs	5–6	Cardinals	13	8
18	7/7/1985	Expos	6–3	Astros	19	8
19	7/26/1993	Padres	6–9	Cubs	11	8
20	8/21/1954	Yankees	9–10	Red Sox	12	7
21	4/26/1995	Mets	9–11	Rockies	14	7
22	5/19/1990	Phillies	15–12	Dodgers	11	7
23	7/17/2012	Mets	4–5	Nationals	10	7
24	7/25/2019	Orioles	10–8	Angels	16	7
25	6/24/2011	Nationals	9–5	White Sox	14	7

Now the top 25 post season games.

Rank	Date	Round	Visitor	Score	Home	Inn	#
1	10/3/1995	NLDS	Braves	5–4	Rockies	9	7
2	10/29/2017	WS G5	Dodgers	12–13	Astros	10	6
3	10/24/2020	WS G4	Dodgers	7–8	Rays	9	5
4	10/3/1995	ALDS	Red Sox	4–5	Indians	13	5
5	10/9/1912	WS G8	Giants	6–6	Red Sox	11	5
6	10/25/2017	WS G3	Astros	7–6	Dodgers	11	5
7	10/4/1995	ALDS	Mariners	5–7	Yankees	15	5
8	10/9/1985	ALCS	Royals	5–6	Blue Jays	10	5
9	10/6/1995	NLDS	Rockies	7–5	Braves	10	5
10	10/27/2011	WS G6	Rangers	9–10	Cardinals	11	4
11	10/12/1986	ALCS	Red Sox	7–6	Angels	11	4
12	11/1/2025	WS G7	Dodgers	5–4	Blue Jays	11	4
13	10/17/2024	ALCS	Yankees	5–7	Guardians	10	4
14	10/9/1988	NLCS	Dodgers	5–4	Mets	12	4
15	10/12/2009	NLDS	Phillies	5–4	Rockies	9	4
16	10/10/2004	NLDS	Braves	6–5	Astros	9	4
17	10/21/1975	WS G6	Reds	6–7	Red Sox	12	4
18	10/9/2009	ALDS	Twins	3–4	Yankees	11	4
19	10/26/2018	WS G3	Red Sox	2–3	Dodgers	18	4
20	10/11/1986	ALCS	Red Sox	3–4	Angels	11	4
21	10/10/1995	NLCS	Braves	2–1	Reds	11	4
22	9/30/2020	Wild Card	Yankees	10–9	Indians	9	4
23	10/20/1972	WS G4	Reds	5–4	Athletics	9	4
24	10/8/2010	NLDS	Braves	5–4	Giants	11	4
25	10/8/1995	ALDS	Yankees	5–6	Mariners	11	4

What were the most exciting games in history? Part 2

2026-09-02T18:49:17.580-07:00

The top games were all regular season games. Here I will do the top post-season games. This table has the top 10 post-season games that went 9 innings. Excitement is just the sum of all the WPA events. If you click on the date you will go to the Baseball Reference box score for the game.

Rank	Date	Visitor	Score	Home	Round	Excitement
1	10/3/1995	Braves	5–4	Rockies	NLDS	6.273
2	10/24/2020	Dodgers	7–8	Rays	World Series	6.257
3	9/30/2020	Yankees	10–9	Indians	Wild Card	5.888
4	10/12/2009	Phillies	5–4	Rockies	NLDS	5.307
5	10/11/2009	Phillies	6–5	Rockies	NLDS	5.009
6	10/12/1997	Orioles	7–8	Indians	ALCS	4.957
7	10/19/1980	Phillies	4–3	Royals	World Series	4.937
8	10/20/1993	Blue Jays	15–14	Phillies	World Series	4.894
9	10/12/1976	Phillies	6–7	Reds	NLCS	4.889
10	10/13/2016	Dodgers	4–3	Nationals	NLDS	4.873

Now for the extra-inning post-season games. The number in parentheses is how many innings the game went.

Rank	Date	Visitor	Score	Home	Round	Excitement
1	10/27/2025	Blue Jays	5–6	Dodgers	World Series	9.443 (18)
2	10/4/1995	Mariners	5–7	Yankees	ALDS	8.294 (15)
3	10/25/2005	White Sox	7–5	Astros	World Series	7.64 (14)
4	10/3/1995	Red Sox	4–5	Indians	ALDS	7.61 (13)
5	10/9/2005	Braves	6–7	Astros	NLDS	7.591 (18)
6	10/17/1999	Braves	3–4	Mets	NLCS	7.518 (15)
7	10/18/2004	Yankees	4–5	Red Sox	ALCS	7.471 (14)
8	10/26/2018	Red Sox	2–3	Dodgers	World Series	7.385 (18)
9	10/27/2011	Rangers	9–10	Cardinals	World Series	7.183 (11)
10	10/15/2022	Astros	1–0	Mariners	ALDS	7.097 (18)

What were the most exciting games in history? Part 1

2026-08-27T13:16:29.296-07:00

I had bases.chat, created by SABR member Ted Ingram, add up the absolute value of all Win Probability Added events in every game that have play-by-play data. Then I had it list the top 25. This is in the table below.

All of these games were extra inning (and mostly high scoring) so I will also show the table it came up with for 9 inning games. And all of these games are regular season games so I will do a Part 2 blog post for post-season games.

I also plan to do a Part 3 that has a list of games with the most WPA events of 0.25 or higher (in absolute value).

Again, this first table is regular season games. Excitement is just the sum of all the WPA events. The number in parentheses is how many innings the game went. If you click on the date you will go to the Baseball Reference box score for the game.

Rank	Date	Visitor	Score	Home	Excitement
1	7/10/1932	Athletics	18–17	Indians	14.668 (18)
2	5/8/1984	Brewers	6–7	White Sox	14.201 (25)
3	5/21/1977	Padres	11–8	Expos	12.508 (21)
4	6/24/1962	Yankees	9–7	Tigers	11.860 (22)
5	7/4/1985	Mets	16–13	Braves	11.810 (19)
6	9/11/1974	Cardinals	4–3	Mets	11.739 (25)
7	5/12/1972	Brewers	4–3	Twins	11.486 (22)
8	7/7/1922	Giants	9–8	Pirates	11.245 (18)
9	7/1/1934	Cardinals	8–6	Reds	11.193 (18)
10	6/17/1967	Athletics	6–5	Tigers	11.13 (19)
11	6/27/1939	Dodgers	2–2	Bees	11.084 (23)
12	5/25/2008	Reds	9–12	Padres	11.029 (18)
13	6/1/1919	Phillies	10–9	Robins	10.879 (18)
14	6/3/1989	Dodgers	4–5	Astros	10.833 (22)
15	4/30/1919	Robins	9–9	Phillies	10.786 (20)
16	7/5/1940	Dodgers	6–2	Bees	11.741(20)
17	8/22/1917	Pirates	5–6	Robins	10.706 (22)
18	8/31/1993	Indians	4–5	Twins	10.704 (22)
19	4/16/1967	Red Sox	6–7	Yankees	10.636 (18)
20	5/26/1973	Indians	3–6	White Sox	10.612 (21)
21	9/3/1981	Mariners	8–7	Red Sox	10.603 (20)
22	9/7/1951	Cubs	6–7	Reds	10.578 (18)
23	5/24/1973	Mets	7–3	Dodgers	10.471 (19)
24	7/19/1924	Browns	10–9	Senators	10.469 (16)
25	4/13/1982	Mariners	3–4	Angels	10.451 (20)

The first game listed is an 18 inning game. So that doubles the number of events to add up compared to a 9 inning game, which helps give it a high score (but if we divide 14.668 by 2 we get 7.334 and that would be the 2nd highest if it were a 9 inning game as the next table will show). Also, bases.chat might not use the same win expectancy tables that BB Ref uses so the total WPAs might be different than what you might get if you added this up yourself. It said that "WPA computed from Retrosheet play-by-play data using Tom Tango's win expectancy framework."

Here are the top 9 inning games.

Rank	Date	Visitor	Score	Home	Excitement
1	6/30/1996	Dodgers	15–16	Rockies	8.637
2	6/24/2016	Diamondbacks	10–9	Rockies	7.323
3	9/8/1998	Marlins	10–11	Rockies	7.262
4	7/7/1930	Giants	13–12	Phillies	7.228
5	8/31/1967	Orioles	9–10	Twins	7.069
6	8/22/1933	Senators	8–10	Tigers	7.056
7	9/25/1929	Robins	9–10	Phillies	7.013
8	7/25/1959	Yankees	9–8	Tigers	6.935
9	5/14/2000	Cubs	15–16	Expos	6.902
10	5/10/1922	Tigers	10–9	Red Sox	6.825

HBPs relative to walks and wild pitches over time

2026-08-15T08:19:48.867-07:00

All data from Stathead. This first graph shows HBP/(PA - IBB) in MLB since 1949. There has been an upward trend since the mid 1980s.

But what if the trend is just part of an overall increase in wildness? The next graph shows MLB HBP as a fraction of non-intentional walks since 1949. That is still going up. So HBPs are going up faster than walks. It might not just be overall wildness.

MLB HBP as a fraction of wild pitches since 1949

MLB HBP as a fraction of wild pitches since 1989

The decline of players aged 38+ since 1998

2026-08-13T19:08:59.954-07:00

All data from Stathead. The first graph shows the year by year total of the number of guys aged 38+ who had 400+ PAs and at least a 100 OPS+

The next table is the same thing but for guys 22 and younger.

This graph has Number of players aged 22 or younger each year since 1998 with 400+ PAs and at least a 100 OPS+ MINUS the same for guys aged 38+. The younger guys are starting to outnumber the older guys.

Red Sox are 32-5 in their last 37 games. Here are the other teams that won 32+ in a 37 game span since 1898

2026-08-08T10:10:01.815-07:00

The Red Sox 32-5 in their last 37 games. Here are all the teams since 1898 that had a 37 game span with 32+ wins. Some of these teams had multiple spans that overlap. If a team had a 37 game span with more than 32 wins that number is in parentheses and in red. Their regular season record is the last thing. After the table is the Stathead link for the search.

1906 Cubs (35) won pennant 116-36
1912 Giants won pennant 103-48
1913 Giants won pennant 101-51
1914 A's won pennant 99-53
1928 Yankees won World Series 101-53
1936 Giants won pennant 92-62
1941 Yankees won World Series 101-53
1942 Cardinals won World Series 106-48
1947 Yankees won World Series 97-57
1977 Royals (33) won division 102-60
1984 Tigers won World Series 104-58
2017 Indians (33) won division 102-60
2017 Dodgers won pennant 104-58
2022 Dodgers won division 111-51

Click here to see the Stathead search results

Players who were hitting .400+ while reaching the batting title qualifier at some point in a year they ended up below .400

2026-08-04T17:35:51.980-07:00

I used bases.chat, the baseball AI program created by fellow SABR member Ted Ingram, to create this list. The qualifiers for different time periods were used since this rule has changed a few times. See Rate Stats Qualifiers from mlb.com.

For Rogers Hornsby in 1921 there were 2 games left in the season. After 584 ABs & 664 PAs he had a .402 batting average. The Cardinals had no chance to make the post season. Yet he played the last 2 games of the season. He went 0 for 8 and his average fell below .400 to .397.

Player	Qualifier	Season	Final AVG	Peak AVG	Last Day at .400
Burkett	60% team G	1899	0.395	0.405	9/4/1899
McGraw	60% team G	1899	0.391	0.404	8/26/1899
Speaker	60% team G	1912	0.383	0.405	8/31/1912
Joe Jackson	60% team G	1913	0.373	0.409	8/4/1913
Ty Cobb	60% team G	1915	0.369	0.400	8/3/1915
Speaker	60% team G	1916	0.388	0.401	8/13/1916
Speaker	100 G	1920	0.388	0.421	8/16/1920
Heilmann	100 G	1921	0.394	0.419	9/24/1921
Hornsby	100 G	1921	0.397	0.415	10/1/1921
Ruth	100 G	1923	0.394	0.405	9/1/1923
Ruth	100 G	1924	0.378	0.408	8/16/1924
BaHerman	100 G	1929	0.383	0.414	9/1/1929
Klein	100 G	1930	0.385	0.407	8/16/1930
BaHerman	100 G	1930	0.393	0.405	8/18/1930
Vaughan	100 G	1935	0.385	0.407	9/10/1935
Medwick	100 G	1937	0.374	0.402	8/19/1937
DiMaggio	100 G	1939	0.381	0.409	9/10/1939

The decline of players aged 35+ since 1998

2026-07-31T13:38:27.710-07:00

All data was from Stathead.

This chart shows the number of players aged 35+ with 400+ PAs and at least a 100 OPS+ each year since 1998. There were 0 in 2020 due to Covid.

Now the same thing for 25 and younger

This graph shows the ratio of guys 35 and older to guys 25 and younger The ratio has been .25 or lower since 2017. It was at least .4 every year from 1998-2014 except one.

What batters had the largest gain in their OPS in the 2nd half of a season compared to the 1st half?

2026-07-29T19:07:51.253-07:00

I used bases.chat, the baseball AI program created by Ted Ingram.

The minimum PA requirement was 200 or more in each half. The program considered the the first half ended on June 30, no matter what the year. Notice that Bobby Thomson (1951) is in the leader list. The search covered all years from 1898-2025.

I initially did this query because I wanted to see where Christian Yelich of 2018 would rank. He had a tremendous second half and won the MVP award. His gain was .304 (.835 to 1.139). Very good but well below the top 10 here. It did not even make the top 10 from 2001-2025 (10th was .352).

Rank	Player	Season	1H OPS	2H OPS	Gain
1	Johnny Mize	1938	0.756	1.219	0.463
2	Jose Ramirez	2019	0.634	1.078	0.444
3	George Brett	1990	0.656	1.089	0.433
4	Mike Hargrove	1979	0.572	0.984	0.412
5	Bobby Thomson	1951	0.724	1.130	0.406
6	Luis Rengifo	2023	0.590	0.980	0.390
7	Albert Belle	1998	0.863	1.251	0.388
8	Willson Contreras	2023	0.672	1.059	0.387
9	Luke Easter	1952	0.659	1.044	0.385
10	Eugenio Suarez	2024	0.591	0.974	0.383

The Phillies lost to the Dodgers on Tuesday with the final play having the two base runners on 2nd and 3rd getting tagged out resulting in a 0.558 Win Probability Added for the Dodgers. Where does this defensive WPA rank all-time?

2026-07-26T07:32:46.674-07:00

With 1 out in the bottom of the 9th the Dodgers were ahead 2-1. Phillies had runners on 2nd & 3rd. The batter hit a ground ball to the 3Bman. He throws home as the runner on 3rd went home.

But the runner ran back to 3rd seeing that the catcher had the ball. The catcher runs him back to 3rd. But Betts ran in from SS. He took a throw from the catcher and tagged out the runner.

By this time the runner on 2nd had run to third but then ran back to 2nd base when he saw the original runner retreat to 3rd base.

As soon as Betts tagged the original man on 3rd he turned and threw the ball to the 2Bman who tagged the runner trying to get back. Game over.

Here is a video

Here is a video posted by Todd Zolecki

This link from Stathead has the results from the search For year 2026, Involving Philadelphia Phillies, Game-Ending, Sorted by Championship Win Probability Added.

If you sort by WPA, this play is the highest listed.

This link from Stathead has the results from the search In years 1903 to 2026, requiring WPA >= .5, Sorted by Win Probability Added.

I had selected "non-SO outs->(to Pos) as the Event Type. I also used the "All Outs" option under "Double/Triple Plays." Here are the top plays:

Rk	Date	WPA	Bat Tm	Def Tm
1	7/15/1930	0.572	PHI	STL
2	9/25/1948	0.571	BSN	NYG
3	9/10/1973	0.571	BAL	BOS
4	4/8/2018	0.57	NYY	BAL
5	5/1/2000	0.57	CLE	NYY
6	8/4/1921	0.568	BOS	CHW
7	5/11/1997	0.566	MIN	TOR
8	9/24/2000	0.566	BOS	BAL
9	7/1/1939	0.564	STL	PIT
10	8/27/1996	0.564	COL	CIN
11	9/13/1982	0.564	BOS	CLE
12	9/16/1930	0.564	BRO	STL
13	7/2/2021	0.563	OAK	BOS
14	7/21/1999	0.558	CLE	TOR

So it looks like this play by the Dodgers ties for the 14th highest WPA ever in the Stathead databse by a defensive team. Also, the search results will allow you to go to to the box scores of the games and it also tells you what the play was and all the relevant situational details.

This most recent play from the Dodgers-Phillies game did not show up on these search results. I asked BB Ref about this and they told me

"It looks like the event is coded as a "Fielder's Choice", and for some reason, that doesn't show up when filtering for "Non-SO outs". That seems like an error in the codebase, so I'll have our engineering team look into it."

Also, the actual top 4 results you will see if you click on the link are WPAs greater than one (which makes no sense). I omitted those in the table above. BB Ref told me that "I'll have our engineering team look into the problem" about this issue as well. The 5th one is actually a play in which a run scored on an error on the final play of the game and the batting team won.

Interesting Game Score trends

2026-07-25T09:26:43.394-07:00

There are 52 pitcher starts with an 85+ Game Score in 7 IP or less in the Stathead database (earliest is 1993). 51 of them are since 2000, 40 are since 2016. 4 this year, all since 6-10. 7 had 0 hits & all of those are since 2016.

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

There are 118 games when the starter pitched 6 or 7 innings with no hits allowed in the Stathead database. 80 since 2010. 69 are from 2017-2026

https://www.sports-reference.com/stathead/tiny/sGV5l

Stathead has 170 games when a starter had a Game Score of 62+ in 4 IP or less. 142 of them since the year 2000. 14 this year. Only 14 before 1980

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

What happens in games when one team has a higher OPS and the other has a higher AVG?

2026-07-15T14:38:30.491-07:00

I don't recall seeing this specific question raised before. So I asked bases.chat, the AI program created by fellow SABR member Ted Ingram. If there is a way to search this easily on Stathead or somewhere else I don't know what that is.

In almost every year I looked at, the teams that have a higher OPS than their opponent in games when the other team had a higher AVG win at least 66% of the time and sometimes over 70%. The lowest year was just 64%.

I had it check several different years (At first I just did 2021-25 but Jessica Brand suggested I look at earlier periods to see if there was a trend). The table below has the results. After that will be a table summarizing the three periods.

Season	Divergent Games	Wins	Win %
1982	298	218	73.20%
1983	270	185	68.50%
1984	270	183	67.80%
1985	279	197	70.60%
1986	304	203	66.80%
2001	321	206	64.20%
2002	313	212	67.70%
2003	335	229	68.40%
2004	328	233	71.00%
2005	344	227	66.00%
2021	311	218	70.10%
2022	313	225	71.90%
2023	347	239	68.90%
2024	289	205	70.90%
2025	301	203	67.40%

Divergent games just means how many such games happened in each year.

Now the summary table.

Season	Divergent Games	Wins	Win %
1982-86	1421	986	69.39%
2001-05	1641	1107	67.46%
2021-25	1561	1090	69.83%
Total	4623	3183	68.85%

Earlier I said I did not know how to search this without bases.chat. But I did ask it which team in 2025 played in the most games like this and it said it was the Orioles and that they were 14-3 in those games.

I called up the batting game logs for the Orioles at Baseball Reference and then used Stathead to find the same logs for their opponents. By sorting, matching games and finding differentials I could see that the Orioles were 14-3 in such games. So that helps show that bases.chat was doing this right.

Update 7-30-2026:

I had bases.chat do this for all World Series games going back to 1903. It said the record was 66-16 for an .805 pct. My own analysis from compiling things using Stathead got 65-16. Not sure what the reason is for the difference. It probably is not tie games. Maybe some game that had a very low differential in OPS or AVG like .0000001 got counted by bases.chat but not by me. I simply used the AVG & OPS that Stathead presented to me which is just to 3 decimal points. So if there was a game where each team had the same 3 digit AVG or OPS then there was no difference and that would not be a qualifying game. But maybe at more decimal places it would and bases.chat might have counted it.

So for some reason the pct was much higher in the World Series.

In other post season games (non-World Series games) bases.chat said the record was 83-48 for a .634 pct. Why it would be so much lower than the World Series, I don't know.

Last year Grok cited my research on Twitter (X)

2026-07-06T07:23:09.605-07:00

Click here to see the thread.

Someone asked Grok:

The reply was

The research Grok mentions is called And the Winning Number is…OPS!. Here is that article.

Well, okay, maybe it is not THE WINNING NUMBER, but it is a pretty good one. Here’s why.

Most of you probably know that OPS is on-base percentage (OBP) plus slugging percentage (SLG). OPS is highly correlated with winning. The correlation between team winning percentage & OPS differential is .933. OPS differential is the difference between a team’s OPS and the OPS they allow their opponents. A perfect correlation is 1.00. The teams I looked at were from 2001-03 (data from ESPN).

The graph below illustrates the relationship.

As you can see, most teams are pretty close to the trend line. As your OPS differential rises, so does your winning percentage.

The correlation is often referred to as r. Another statistic is the r-squared, or square of the correlation. In this case it is .933*.933 or .87. This means that 87% of the difference in winning percentage across teams is explained by the OPS differential. How does this compare to other stats? The r-squared for batting average differential is .738, on-base percentage is .849 and slugging percentage is .786. So OPS tops them all in its ability to explain team winning percentage. This tells us that getting on base and hitting for power (and stopping your opponents from doing so) are very important in baseball.

One problem with OPS, however, is that it adds together two numbers that are highly correlated. In fact, the correlation between OBP and SLG here (only on the hitting side) is .806. So, I looked at an alternative measure. Branch Rickey developed it. It adds OBP plus .75*ISO (Isolated Power, which is slugging percentage minus batting average). The correlation between OBP and ISO was .643, not as high as it is for OBP & SLG. I then found the r-squared between winning percentage and the Rickey stat. It was .863. So again, a measure similar to OPS, which takes into account power hitting and getting on base, does a good job of explaining winning percentage.

Now it is possible that other factors, like stealing, affect a team’s OPS. This is not likely to be true. See “Other Factors” below.

The linear relationship between team winning percentage and OPS differential is

Pct = .496 + 1.3*OPSDIFF

Where OPSDIFF is each team’s OPS differential. This formula predicted about two-thirds of the teams to within 5 wins of their actual total. I then looked at which teams won more games than expected according to the formula. Five of the top ten on this list had less than .57 SBs per game, which was the major league average for the three years. I also looked at which teams won fewer games than expected according to the formula Four of the bottom ten had less than .57 SBs per game, almost the same. The top ten averaged .65 SBs per game while the bottom averaged .52. No big difference here. Perhaps some other reason explains why some teams won more than expected while others won less. It might be luck or it might be the bullpen.

NOTE: THESE NEXT TWO PARAGRAPHS WERE ADDED ON JULY 10, 2005. I also looked at all teams from 1989-2002. The regression formula for that data set was

Pct = .49996 + 1.26*OPSDIFF

This is virtually the same as the regression for the 2001-03 period. Then I determined how many more (+) or fewer (-) games each team won for each season. Then all of those plusses and minuses were added up. The A’s won 45 more games than the formula predicted over the 14 year period. That is just a little over 3 a year. All but four of the 30 major league teams won within 2 games a year of what the formula predicted. Only one team was off by more than 4, the Red Sox, who won about 4.06 fewer games a year than predicted.

If a team happens to win more games one year than expected, then next year they likely win fewer games. The Twins won 13.5 more games than expected in 1994, but won 1.9 FEWER the next (and just 4.6 more games more than predicted in 1993). So one could argue that a team wins more games than OPS would indicate because they play “small ball,” like stealing and moving the runners along through “productive outs.” But this is not likely to be true. If a team is supposedly good at moving runners along one year, it should do so the next. This is not happening. See also Productive Outs Are Not Productive.

One last thing to remember here. I am basically saying that if you “out-OPS” your opponents, you will do very well. This could be done with better hitting, better pitching, better fielding or some combination of the three. But this analysis shows that comparing two players based on OPS is not a bad way to go.

Other Factors

Take base stealing for example. If a man is on, this distracts the pitcher. Maybe he throws more fastballs and the batter can be ready for this. Or a hole opens up that makes it easier to get a hit. So some of the 87% I attributed to OPS might, in fact, actually come from another source. So I looked at how both good and bad stealing teams hit with a runner on first base only.

I looked at the top 10 teams in SBs from 1982-92 and the bottom ten. Then I determined how much their AVG, SLG, OBP and OPS differed between having a runner on 1st or no runners on at all. I determined the runner on first data by finding the difference between the runners on base and the runners in scoring position data (from Retrosheet).

The top ten teams in SBs had the following increases when there was a runner on first compared to no runners on (the average across the teams)

AVG-.025

SLG-.030

OBP-.008

OPS-.038

That is, with a runner on first, these teams had a .025 higher batting average than they did when there were no runners on base. Slugging went up .030, OBP .008 and OPS .038.

The bottom ten teams in SBs had the following increases

AVG-.019

SLG-.028

OBP-.011

OPS-.039

The top ten teams averaged about 240 SBs and the bottom around 40. The one difference that is big is the AVG difference (.025-.019=.006). But in general, the best stealing teams had little additional benefit over what the worst stealing teams.

The best stealing teams had in the 900 range of ABs with a runner on first. The bottom in the 1100 range. This makes sense because the best steal and they won’t be on first as often. Also, who is most likely to be left on first base on those teams? The few guys who don’t steal, like Jack Clark (5 of the teams were Cards). But those bottom teams must have rarely had a good base stealer on, a lot less often than the best. I think if the runners bother the pitcher, we should see a bigger effect here. After all, we are comparing the best stealing teams to the worst.

The change in OPS for both teams is just about the same. I am still skeptical that having a good stealer on first helps a lot. Maybe the change in AVG is simply a result of the hole opened up at first. There is little change in SLG. Maybe the fast guy bothering the pitcher and making it easier for the hitter is not happening.

Sources: Retrosheet and ESPN

Hank Aaron's amazing combination of durability and hitting excellence (part 2)

2026-07-05T07:40:17.871-07:00

In Hank Aaron's amazing combination of durability and hitting excellence from January of 2025, I showed that Aaron has the most seasons with at least a 140 OPS+ & 140+ games and the most seasons with at least 150+ games played and at least a 150 OPS+.

Here I look at how he was consistently in the top 10 of 8 different stats for 19 straight years. All data is from Baseball Reference.

The first table has his year by year numbers for the 8 different stats.

Red stats are when he led the league

Blue stats are when he ranked 2nd thru 5th

Green stats are when ranked 6th thru 10th

As you can see, he was among the leaders quite often. There will be some summary stats on this after the second table.

Season	H	HR	BA	OBP	SLG	OPS	OPS+	TB
1955	189	27	0.314	0.366	0.540	0.906	141	325
1956	200	26	0.328	0.365	0.558	0.923	151	340
1957	198	44	0.322	0.378	0.600	0.978	166	369
1958	196	30	0.326	0.386	0.546	0.931	153	328
1959	223	39	0.355	0.401	0.636	1.037	183	400
1960	172	40	0.292	0.352	0.566	0.919	156	334
1961	197	34	0.327	0.381	0.594	0.974	163	358
1962	191	45	0.323	0.390	0.618	1.008	170	366
1963	201	44	0.319	0.391	0.586	0.977	179	370
1964	187	24	0.328	0.393	0.514	0.907	153	293
1965	181	32	0.318	0.379	0.560	0.938	161	319
1966	168	44	0.279	0.356	0.539	0.895	142	325
1967	184	39	0.307	0.369	0.573	0.943	168	344
1968	174	29	0.287	0.354	0.498	0.852	153	302
1969	164	44	0.300	0.396	0.607	1.003	177	332
1970	154	38	0.298	0.385	0.574	0.958	149	296
1971	162	47	0.327	0.410	0.669	1.079	194	331
1972	119	34	0.265	0.390	0.514	0.904	147	231
1973	118	40	0.301	0.402	0.643	1.045	177	252

This next table shows all the ranks he had in these stats with the same color code.

Season	H	HR	BA	OBP	SLG	OPS	OPS+	TB
1955	2	10	5		9	9	10	6
1956	1		1		3	5	3	1
1957	2	1	4	9	3	3	3	1
1958	3	5	4	6	3	4	3	3
1959	1	3	1	2	1	1	1	1
1960	6	2			2	5	4	1
1961	3	6	5	8	3	3	2	1
1962	6	2	5	5	2	2	2	3
1963	2	1	3	2	1	1	1	1
1964	8	9	3	3	8	6	6
1965	10	6	2	5	2	2	2	4
1966		1			6	8	8	4
1967	6	1			1	3	3	1
1968	10	5			4	5	5	2
1969		2		7	2	2	2	1
1970		5			7	6	6
1971		2	5	3	1	1	1	2
1972		4		4	5	5	7
1973		4			2	2	6

Aaron had a total of 124 top 10s out of a possible 152 (8*19 = 152). That is about 81.6% of what was possible. Yes, if a hitter is good at HRs it will help his SLG and OPS. We could say this about a few other stats. But this is still very impressive.

He was in the top 10 of all 8 stats in 7 different years and he had 7 top 10s in 2 other years. He was never in fewer than 4 top 10s.

He was in the top 5 96 times. That is about 63.2%. He was in the top 5 in at least 1 stat every year and he was in the top 5 in at least 2 stats in 18 of the years.

Baseball Reference shows him as 2nd in SLG in 1973. He actually did not qualify for the batting title that year but if we give him an extra 37 ABs to get to 502 PAs without any additional hits, his SLG would still have been .587 and that would have been 2nd in the NL that year.

I show him with a 177 OPS+ in 1973 and I rank him 6th. I estimated his OPS+ assuming those additional 37 ABs with no hits and came up with 148. That would tie him for 6th with Ken Singleton. Maybe it would be 7th but I did not redo Singleton's OPS+ to additional decimal points to see who would be ahead. But 7th is still in the top 10.