[Another work-in-progress. Your observations are welcomed, but please review the “Known shortcomings” at the end of the post.]
Taking suggestions from readers Mike L. and kds, I used the Pythagorean method to calculate the estimated career wins for all starting pitchers with at least 1,000 innings from 1893-2011 (min. 60% of career games as a SP).
Method: First, I calculated each pitcher’s Pythagorean winning percentage (P%), based on his average runs allowed per 9 innings (R/9) and the MLB scoring average per 9 innings over the length of his career (Lg R/9). Note that this is based on all runs, not earned runs. To adapt the Pythagorean formula for this purpose, “Runs Scored” is replaced by the league scoring average (Lg R/9), and “Runs Allowed” is replaced by the pitcher’s R/9. Also, I used the advanced formula’s exponent of 1.83, rather than 2. Thus, the formula is:
(Lg R/9)^1.83
————————————–
(Lg R/9)^1.83 + (R/9)^1.83
Next, I calculated two sets of estimated wins and losses: one based on their actual decisions (W:1, L:1), and one based on their estimated decisions (W:2, L:2). To figure estimated decisions, I first calculated the average IP per decision for this set of pitchers, which came out to 8.77; then I divided each pitcher’s IP by 8.77 to arrive at his estimated decisions. The columns D:1 and D:2 are the difference in wins for each method compared to the actual wins. The total estimated wins by each method is within 0.5% of the total actual wins; both are less than the actual wins.
The table is currently sorted in order of D:1, i.e., the greatest gain in estimated wins over actual wins based on actual decisions. Note that, in order to present as many relevant columns as possible, I’ve truncated the “Years” column to show only the last 2 digits of their final year. I don’t believe this should cause any confusion.
[table id=38 /]
Known shortcomings of this method:
- Because there’s no park adjustment, this method should tend to help pitchers who spent many years in low-run parks and hurt those in high-run parks.
- Estimating wins based on career totals will produce somewhat different results than doing so for each individual year. (Unfortunately, copying into Excel the year-by-year stat lines for over 700 pitchers is more work than I care to do on a volunteer basis.)
- The MLB scoring average used for each pitcher is a straight average of the MLB rates over the length of his career, rather than a weighted average. If a pitcher logged a small number of innings over a number of years in a very different run environment than the bulk of his career, the MLB scoring average used here for that pitcher will not be as accurate as a weighted average would be.
- Estimated decisions are based on the group’s average of 8.77 IP per decision. I have not adjusted for the slight variation in this figure across the eras and that may complicate the task of comparing pitchers across eras. Also, the figure of 8.77 for this group of SPs with 1,000+ IP is higher than the figure for all SPs, because the 1,000-IP standard biases the group towards good pitchers, which means more wins, which means more IP per decision. This fact is basically moot when comparing pitchers among this group, but may distort a comparison to pitchers not listed here or to some abstract standard.
This method of adjusting career W-L totals makes the HOF arguments for Rixey, Waddell, Walsh, and Drysdale look much stronger.
Kaiser Wilhelm never should have started that war back in 1914.
It also burnishes the HOF argument for Babe Ruth. Of the top ten pitchers in winning percentage, he is the only one that is estimated to have been better than his W-L record. That may be an anomaly of his not having a decline phase as a pitcher.
The great majority of the top 100 in winning percentage lose estimated wins, over half of them double digits. I don’t know if that’s indicative of anything, but it’s interesting.
The two major exceptions are Walter Johnson and Carl Hubbell.
Yes, maybe THIS will be what finally puts The Bambino over the top!,:)
File this comment under “times I miss the “Like” button.
Part of it may be an artifact of not weighing the seasons by decisions or IP. He had 5 decisions, 31 IP in 4 years with the Yankees in the high scoring lively ball era, vs 135 and 1190.1 in 6 years with the Sox in the dead ball era. This method counts those Yankee years as 40% when figuring what his league average run support would be, rather than about 3%. The increase in run scoring after the dead ball era was probably the fastest change up or down in ML history, so improper weighing of seasons in careers is most likely to have an effect there. (Might be interesting to check Cy Young, he started in a very high run scoring environment, but much of his career was in a very low one.
kds, you’re right again – and the distortion in Ruth’s case is actually worse than you described.
My “Lg R/9” figures are a simple average of the MLB scoring rate for the entire span of the pitcher’s career. Ruth last pitched in ’33, so his Lg R/9 is based on 1914-33.
I’d love to be able to make the adjustments for pitcher’s who missed a year or ten. But to be honest, I was pretty pleased with myself just for figuring out how to get the simple average with just the data from B-R’s master MLB Batting page and the P-I pitcher results.
Since I’m using a P-I career search, which returns only the first and last years of the career, I can’t see how to adjust the average for skipped years. As for a weighted average, I’d have to pull each pitcher’s yearly data, which is too big a task.
(P.S. There’s something deliciously Ruthian about his estimated wins benefiting from the scoring revolution that he initiated.)
kds – Although your statement that “[t]he increase in run scoring after the dead ball era was probably the fastest change up or down in ML history” was but a small part of your point, and the precise truth of it doesn’t significantly affect the analysis of Ruth’s estimated wins, I still want to respond:
Not exactly.
A lot depends on the exact standard of comparison. Here are the biggest % changes since 1901 on a 1-year basis:
% Rise in R/G over Prior Year
19.1% – 1969
17.7% – 1911
14.3% – 1973
12.5% – 1920
11.9% – 1977
11.7% – 1993
11.3% – 1921
10.3% – 1953
9.6% – 1929
9.5% – 1934
And on a 3-year basis:
% Rise in R/G vs. prior 3-year avg.
25.8% – 1911
22.7% – 1921
17.9% – 1920
15.7% – 1970
14.3% – 1912
13.5% – 1930
13.4% – 1994
11.6% – 1922
10.2% – 1929
10.0% – 1910
Finally, on a 5-year basis, the live-ball transition bubbles to the top:
% Rise in R/G vs. prior 5-year avg.
27.6% – 1921
26.0% – 1911
20.5% – 1912
19.9% – 1922
18.0% – 1920
15.0% – 1994
13.6% – 1930
12.8% – 1970
11.6% 1950
11.5% – 1923
Thanks, I tried to weasel word it because I hadn’t done the research. Could you do the drops also? I’m betting some part of 1894-1908 beats the 60’s and 1911-1917, but I’ve been wrong before. Like, tuday.!
kds – Here are the biggest % declines in scoring since 1893:
1-Yr. Basis (% Drop in R/G vs. Prior Year)
-16.1% – 1904
-15.7% – 1898
-13.3% – 1931
-12.4% – 1988
-11.5% – 1963
-11.2% – 1902
-10.9% – 1913
-10.9% – 1895
-10.4% – 1971
-9.6% – 1926
3-Yr. Basis (% Drop in R/G vs. Prior 3-Yr. Avg.)
-19.6% – 1898
-19.4% – 1904
-13.9% – 1902
-12.7% – 1968
-12.5% – 1942
-12.0% – 1933
-11.8% – 1896
-11.8% – 1897
-11.4% – 1943
-11.3% – 1914
5-Yr. Basis (% Drop in R/G vs. Prior 5-Yr. Avg.)
-23.6% – 1898
-23.4% – 1904
-15.9% – 1906
-15.7% – 1902
-15.0% – 1899
-14.8% – 1943
-14.6% – 1905
-14.1% – 1916
-14.1% – 1942
-13.4% – 1968
Your 1894-1908 takes the top 2, top 3 and top 5 spots on the lists.
Well, there’s 1977, 1993, and 1994 on the biggest rise in R/g charts again. ’77 is rumored to be the first time Rawlings changed the ball. It probably happened again, sometime between ’92 and ’93. There is almost indisputable scientific evidence that the ball used in the mid 90s and onward was a different ball than the previous one.
Rixey, Waddell, Walsh and Drysdale?
No, Yes, Yes and Yes.
Not that anyone asked.
Drysdale benefits quite a bit on this chart because he played most of his career in a park very favorable to pitchers.
That’s a very interesting point, Howard, but more complex than it first appears. Drysdale did call Dodger Stadium home for his last 7 full seasons, accounting for about 62% of his career innings. And Dodger Stadium in the ’60s was among the very best pitcher’s parks of all time (relative to the rest of the league).
On the other hand, his first 5-1/2 years were spent in the opposite environment, Ebbets Field and the LA Coliseum. In 1957, when a 20-year-old Drysdale went 17-9, 2.69 in his first full season, Ebbets had a 1-year park factor of 113 for pitching. The Coliseum as of 1961 (its final year) had a multi-year park factor of 107 for pitching. That year, Drysdale’s 3.69 ERA equated to a 117 ERA+; 2 years later, in Chavez Ravine, his raw ERA was more than a run lower, 2.63, but his ERA+ was just 114.
Overall, I’d guess that Drysdale got a net benefit from his parks, but not as big as you’d guess just from thinking about him in Dodger Stadium.
Good points. I thought it would have been more than 62%.
I’ll add that Drysdale’s “Neutralized Pitching” on B-R plumps his ERA by almost half a run to 3.40 — but actually improves his W-L% from .557 to .584.
(To view the Neutralized Pitching tool, click the link above and then page down about 5 times.)
JA:
Thanks for the research and hard work – couldn’t help but notice Roberts and Blyleven joining the 300 win club and Maddux tying Spahn’s actual win total. Glavine, Johnson, Wynn, and Grove drop out of the 300 win club…
AND, of course, the luckiest “pitch to the score” hurler of all-time Jack Morris makes an appearance as number 774 of 778 on your chart. I guess we’ll hear about that Hall of Fame debate momentarily
The estimates also heavily penalize Andy Pettitte and David Wells. I don’t think too many people think of Wells as a HOFamer, but I think some people consider Pettitte at least marginally qualified.
If CC Sabathia signs an extension and wants to end up a Yankee for life, he should start showing up as a guy who got/gets a big boost from having a potent offense hitting for him. Will this ultimately hurt his chances or help them? I would say help, especially if he gets close to or over 300 wins. It’s certainly not hurting Andy Pettitte and Jack Morris’ case right now.
Edit: not hurting Pettitte and Morris’ case(in the minds of those who hold a vote).
I think that Wells’ HOF case is hurt a lot by the perception that he was the ultimate mercenary, going from team to team his entire career. This is only partially true; he did change teams 11(!) times, but five were because of trades.
Petite is seen as more of a True Yankee (yeah, I know about the three years with the Astros), plus he’s got all that post-season success, although Wells’ playoff record is also rather impressive (in 125 IP over 17 series, a 10-5 W-L record and 3.17 ERA).
Their WAR are almost identical (50.7/49.9), and the “Hall of Fame Statistics” favor Wells somewhat. So yes, it’s kinda strange that Petit gets marginal HOF buzz, while Wells gets none at all.
Andy Petite and David Wells are each other’s #1 Most Similar pitchers, the actual number is even the same (898). How did I miss that the first time around?
I understand your reason for using commas instead of a decimal point, but it does make it impossible to sort on WAR correctly.
Oh, geez — I did not realize that. I guess I’ll put the periods back.
Good stuff. When I click on the table it only allows me to see the top 100 at most. (I can click on a header to see the bottom 100.) Any way to see parts of the table that are not top or bottom by one of the measures listed?
One adjustment you did not mention is for the team’s defense. Probably the easiest way to do this would be to take the Rdef number from their rWAR. My guess is that this would not be as important in general as some of the items you listed above, park effects, year-by-year with proper weighing. For some pitchers, Jim Palmer and Mordecai Brown come to mind, it might make a sizable change. (There must be some who regularly pitched in front of bad defenses also.)
Doesn’t the pythag exponent change slightly with the run environment? So that 1930 or 2000 would be a bit different from 1908 or 1968.
kds — The dropdown for “Show [n] entries” controls how many entries you see per “page”. Use the left/right arrows at the bottom right of the table to scroll through more pages. Let me know if that doesn’t work for you.
Well, I discovered what the switches at the bottom right of the table are for, so now I can look at any of the 778 entries.
TZ estimates that the Oriole’s defense saved Palmer 144 runs over the years. Phil Niekro lost about 110 runs to bad defense. If you combine that with Atlanta being a good hitters park his D1 and D2 should go up considerably.
kds – I have not heard that the Pythagorean formula needs to be adjusted for run environment. Checking the 1894 Beaneaters and the 1908 Cardinals — the top scoring team in the top run environment of the modern era, and their opposite — the 1.83 exponent yields the same Pythag. wins as are shown on their B-R pages.
I don’t know what Sean uses at B-Ref. Pathaganpat is usually said to be the best and most complete variation of a runs to wins estimator. It uses (RS + RA)^.287 to get the exponent to use in the pathag equation. With per game scoring more than twice as high in 1896, than in 1908 it was easier to double your opponents scoring over a number of games, so you would expect a lower winning % than doing the same ina lower scoring time.
There were some papers that may be relevant to this in the SABR magazine “By the Numbers” about 2003.
I knew Ryan would be near the top. I would guess that there’s still perhaps something about being a high K, high BB, low H guy that is hard to account for, but I remember going year by year through Ryan’s career and figuring just from the records of the other starting pitchers on his team and his ERA (not scientific at all) that he probably lost a win a year or thereabout and said to myself he shoulda been +25 in wins and -25 in losses. Funny that your analysis here is about the same. Not that it proves my suspicion true. It’s just that going year by year like that he seemed to be unlucky more than lucky.
This is near and dear to my heart: I’ve compiled 600+ pitchers with annual win-loss and run data. I like to use ERA+ to produce “park neutral” LeagueR, which removes the smoothing of career-span average league factors. (And then i prefer negative binomial to Pythagorean, but that’s just me.) I’d put up another list, but I’ll refrain for now — suffice it to say that Eppa Rixey (Go ‘Hoos!) looks like a HOFer to me, too.
“The total estimated wins by each method is within 0.5% of the total actual wins”
Known shortcomings not withstanding, that level of accuracy seems sufficient for using this method to identify lucky and unlucky pitchers.
Remaining question would be whether luckiness or unluckiness is a peculiar trait of certain pitchers. I’m guessing the pitchers at either end of your list probably did better or worse than their estimated wins on a fairly consistent basis. Begs the question whether the something that is being measured really is luck or is it something else if some number of pitchers enjoy or lack this “luck” on a consistent, year-by-year, basis.
Great work, John, on a new analysis tool.
When judging a pitcher by his Won/Loss record I think it is fair to call everything that can effect it that is outside his pitching to be luck. And the biggest thing we are looking at here is his teammates offensive abilities. So, I think it is fair to say that almost any Yankee pitcher was lucky to have such a good offense. (Ruffing, Gomez, Ford, Guidry, Pettit, etc.) And some have been quite unlucky in that respect, starting with Walter Johnson, on may bad Senator teams.
Lefty Gomez once said “I’d rather be lucky than good”.
It’s results like Ed Brandt that give me pause: career ERA+ of 101 (6 seasons above average and 5 below), career K/BB of 1.13 . . . I don’t see him as “unlucky” to this extent. My NegBin “team-neutral” record for him is 134-133, so 13 games “unlucky.” My top 25(gotta have a list!):
Pitcher W L bW bL Luck
Johnson, Walter 417 279 477 219 -60
Garvin, Ned 57 97 95 59 -38
Young, Cy 511 316 546 281 -35
Rixey, Eppa 266 251 299 218 -33
Garver, Ned 129 157 162 124 -33
Friend, Bob 197 230 230 197 -33
Leonard, Dutch 191 181 222 150 -31
Raffensberger, Ken 119 154 150 123 -31
Trout, Dizzy 170 161 201 130 -31
Blyleven, Bert 287 250 318 219 -31
Lyons, Ted 260 230 290 200 -30
Lee, Thornton 117 124 147 94 -30
Nichols, Kid 361 208 389 180 -28
Gaston, Milt 97 164 125 136 -28
Rush, Bob 127 152 155 124 -28
Ramsey, Toad 113 124 140 97 -27
Mercer, Win 132 164 159 137 -27
Perry, Gaylord 314 265 341 238 -27
Faber, Red 254 213 280 187 -26
Luque, Dolf 194 179 220 153 -26
Baldwin, Mark 154 165 179 140 -25
Dickson, Murry 172 181 197 156 -25
Niekro, Phil 318 274 343 249 -25
Ehret, Red 139 167 163 143 -24
Scott, Jim 107 113 131 89 -24
And on the other end, Clark Griffith. Should over 3000 innings of 123 ERA+ result in only two games over .500? I like the idea, but maybe there’s just too much smoothing by using the career endpoints.
Griffith’s ER% (66%) was about 2 points below the weighted average for his career, which boosts his ERA+ a smidge.
And it’s easy to see some luck in his record on a yearly basis. For instance, in his 2 years with the AL White Sox, he went 39-16 but with a 103 ERA+.
Funny that Clark Griffith (the biggest loser by the 1st method) and Ned Garvin (3rd-biggest gainer) were teammates on the 1899-1900 Chicago NLers.
Griffith, 127 ERA+, 36-27 record
Garvin, 141 ERA+, 19-31 record
In 1900, the two had virtually the same IP and runs allowed (earned or not). Griffith was 14-13, Garvin 10-18.
One factor in Brandt’s gains in estimated wins is his high rate of earned runs. For his career, 90% of Brandt’s runs allowed were earned, compared to a MLB average of 85% over his career. That high ER% depresses his ERA+.
And even so, the expected W% for a career 101 ERA+ is about .520, based on the median of 135 SPs since 1901 with 100+ decisions and an ERA+ from 99-103. Applying that .520 to his 267 decisions yields 139 wins, or 18 more than his actual wins. That’s still short of the 151 or 147 estimates in the table, but it’s easy to see that Brandt should have been a winning pitcher.
Great point, JA — those unearned runs occasionally rear up and throw a wrench into my reams of spreadsheets.
And great work to do all this! I have Excel spreadsheets with annual data for 678 starters(and 37 relievers), if you’re interested and/or crazy. . . .
John A. This is terrific. Looks like it shows something else. If you have a decent amount of talent and durability it’s good to be a Yankee pitcher. They score and have had great bullpens.
I wonder what the worst W% is for a Yankee career? (Excluding pre-Ruth years, of course.)
To answer my own question, here are the worst SP winning percentages in a Yankees uniform since 1920:
25+ decisions – .333, Chuck Cary (11-22)
50+ decisions – .340, Tim Leary (18-35)
75+ decisions – .480, Scott Kamieniecki (36-39)
100-215 decisions – .507, Fritz Peterson (109-106)
yeesh. Brings back those bad memories. Leary, BTW, was the second pick of the 1979 draft. 13 years, career ERA+ of 90.
Yeah, not that most people have much sympathy for “suffering” Yankees fans, but can everybody just agree on this blog to not post anything that refers in any way to River Ave, 1989 – 1992 ?
As a quick sniff test for the extremes on the list, I’ll check their estimated wins by this method against an alternative method of applying the median W% of all SPs with 100+ decisions and ERA+ very similar to the target pitcher.
Top net gainers:
Walter Johnson, est. 463 wins (+46) – There aren’t nearly enough pitchers in his stratum to make a significant comparison. But Johnson had a 147 ERA+. There are 8 others since 1901 with ERA+ of 140-154. Their median W% is .658, which would yield 458 wins for Johnson (+41).
Ed Brandt, est. 151 wins (+31), 101 ERA+ – Median W% of the 135 SPs since 1901 with ERA+ from 99-103 is .520, which yields 139 wins for Brandt (+19).
Ned Garvin, est. 86 wins (+28), 125 ERA+ – Median W% of the 50 other pitchers since 1893 with ERA+ from 121-130 is .605, which yields 94 wins (+36).
Bob Friend, est. 223 wins (+26), 107 ERA+ – Median W% of the 81 live-ball SPs with ERA+ of 105-109 was .529, which yields 226 wins (+29).
Ken Raffensberger, est. 145 wins (+26), 110 ERA+ – Median W% of the other 68 SPs since 1901 with ERA+ of 108-112 was .539, which yields 147 wins (+28).
Net losers:
Clark Griffith (from 1893 only), est. 181 wins (-42), 123 ERA+ – Median W% of the other 58 pitchers since 1893 with ERA+ of 119-127 is .588, which yields 212 wins (-11). We have our first major discrepancy; but at least both methods point in the same direction. (See #47 below for further analysis of Griffith’s numbers.)
Kenny Rogers, est. 187 wins (-32), 108 ERA+ – Median W% of the other 82 pitchers since 1901 with ERA+ of 106-110 is .531, which yields 199 wins (-20).
Andy Pettitte, est. 209 wins (-31), 117 ERA+ – Median W% of the other 67 pitchers since 1901 with EA+ of 114-120 is .555, which yields 210 wins (-30).
David Wells, est. 208 wins (-31), 108 ERA+ – Median W% of the other 82 pitchers since 1901 with ERA+ from 106-110 is .532, which yields 211 wins (-28).
Jack Morris, est. 224 wins (-30), 105 ERA+ – Median W% of the other 121 pitchers since 1901 with ERA+ of 103-107 is .526, which yields 231 wins (-23).
Out of 10 comparisons, all 10 pointed in the same direction, and only 1 showed a major difference in the estimated change. I think it passes the smell test.
Interesting stuff.
I do find it interesting that four of the five biggest net losers are all from the past generation (1980s forward), and three of the four were contemporaries, pitching within the last 15 years.
Don’t know what it means, but I’m guessing it means something.
Not just contemporaries — Pettitte was a teammate of Rogers in 1996-97, and of Wells in 1997-98 and 2002-03. Most likely, being on the Yankees in those years boosted their W%. Wells was 69-28 as a Yankee (.708), 170-129 elsewhere (.569, which ain’t bad either).
I can’t imagine that being on the Yankees didn’t boost their W%! Excellent offense and a man named Rivera to help close out games.
I didn’t mention they were all teammates at various times since I wasn’t sure how much that impacted the career records of Wells and Rogers as shown by your charts. Pettitte has spent the bulk of his career with the Yankees, so it’s easy to see how he benefited, but Wells and Rogers spent a smaller portion of their careers with the Yankees. Rogers the least amount of the three and I seemed to remember it was ugly. I just went back and checked and it was. 5.11 ERA, although he still managed an 18-15 record, confirming the benefit. The question is how much did that impact his overall career.
Interesting stuff.
Note also that Whitey Ford, Allie Reynolds, and Ron Guidry are also big net beneficiaries. It’s not just the 1995-2012 era.
John – Griffith had 5 seasons near the end of his career where he pitched less than 10 innings. I’m guessing that’s why there’s a discrepancy with him?
Ed, you’re right about Griffith’s est. wins being severely distorted by his continuing to pitch through 1914, but with very few IP.
The average Lg R/9 for the span of Griffith’s career – from his first year to his last, counting every year equally (even when he didn’t pitch at all) – comes to 4.85, and his own R/9 for that span is almost the same, 4.83.
But if I cut it off at 1903, his last year of more than 102 IP, the Lg R/9 shoots up to 5.80, almost a full run higher, while his own R/9 only creeps up to 4.91.
His actual record through 1903 was 205-123. His estimated wins based on that number of decisions is 189, or -16. That’s a far cry from the -42 shown in the study, and is likely a much truer measure of his actual quality.
The last time we had talked about estimated wins I mentioned an old Bill James study comparing Rick Reuschel and Jack Morris. He took the extra step and actually had Reuschel pitching in Morris’ environment and vice versa and was able to prove you could almost flipflop their W-L records doing so.
Though to a lesser extent, John’s study of adjusting a player’s win total based on estimated wins shows a similar result. Here’s the players actual W-L record compared to their W1-L1:
Jack Morris actual W-L 254-186 W1-L1 224-216 ERA+ 105
Rk Reuschel actual W-L 214-191 W1-L1 224-191 ERA+ 114
Geez. Typo again. Reuschel’s adjusted W1-L1 should be 224-181. Sorry.
I want to come back to this “Yankee” distortion, because it’s not likely a coincidence, and may point to a wrinkle in the evaluation. There are two kinds of luck-the first is how good is the team you are pitching on, and the second is how lucky you are in getting the marginal win. Yankee pitchers are “lucky” systemically because they have pitched on good hitting teams with deep bullpens. So, using method 1, if you compare the average runs allowed by a Yankee pitcher to the league average, that shows what he should be doing if he were on a league average hitting team. But he’s not on a league average hitting team-he’s on the Yankees, so being a 20% better pitcher than league average would be amplified in his winning percentage. A really “lucky” Yankee pitcher would be Phil Hughes a couple of years ago, who was 18-8 with an ERA+ of 103. If you want to figure out how empirically lucky (or unlucky) a pitcher is, you would have to compare his average runs allowed to his team’s batting runs over the course of his career. That would tell you how much better than he is than the environment he’s pitching in. Sort of a “Wins over Replacement Yankee Pitcher”.
Walter Johnson looks pretty darn good.
“Wins Over Replacement Yankee” … a.k.a. WOAJB. 🙂
Since I started this project partly to check on claims that Tom Seaver was criminally undersupported, I will belatedly note the following:
1. By the 2nd method (based on estimated rather than actual decisions), Seaver “should” have 29 more wins, the 6th-largest gain by that method.
2. Seaver had 17 no-decisions of 9+ IP and no more than 2 runs allowed. In the game-searchable era (1919-), no one else had more than 14. Although just 61% of his career starts came with the Mets, 15 of these 17 games occurred in his first Mets tenure; three came in July ’76. Eight of the 17 were 10+ IP, including a pair of 12-inning efforts.
3. Seaver had 6 no-decisions of at least 9 scoreless innings, all with the Mets. (Only Don Sutton (7) had more; Jim Perry had 5, P.Niekro and Koosman 4 each, no one else more than 3.) Of those 6 for Seaver, 4 were 10 IP, and 5 turned into 1-0 Mets losses, including a famous 24-inning game in 1968.
John A, just a thanks if I hadn’t expressed it well enough. Whatever the methodology’s minor flaws, it’s really interesting and the data seem intuitively correct (at least for those pitchers I’ve observed). Well worth scrolling through 700 plus results.