This is driven by the opening days of the 2018 baseball season, in which we have seen that Chris Sale is still a really good pitcher, and David Price also looked like a really good pitcher at least in his first two starts, while other very good pitchers have looked like they perhaps are not as good as they used to be.  There is some magic about it. . .about these opening days, but also about the fact that a player was really good last year and still is this year.  It’s hard to explain, but part of the legerdemain of baseball is the fear that a player who was really good last year will not be good this year.  A baseball fan is omnianxious.  He is afraid that the Felix Hernandez of 2015 will become the Felix Hernandez of 2016, that the Cliff Lee of 2013 will become the Cliff Lee of 2014, that the Roy Halladay of 2011 will become the Roy Halladay of 2012.  It is certain to happen sometime.  This pandemic fear that we have, that our gold will turn to lead, makes us watch the opening days of the baseball season with the hopeful dread of a banker discovering that his vault has been left open overnight.

              The purpose of this research is to examine the factors that go into sustaining excellence as a pitcher.  Of course, these issues have been examined a thousand times before in various studies and we may not learn anything new, but there is an answer to that, too.   So long as we lack perfect understanding, we must continue to do research. 

              The exact two questions we are asking are:

              1)  If a pitcher is excellent in one season, what are the odds that he will be excellent again in the next season, and

              2)  How does this vary with various inputs?

              But before we get to those, we have to start with questions like "What are we going to define as an excellent pitcher?"

              I’m going to define an "excellent" pitcher for purposes of this study in terms of Earned Runs Allowed, and specifically, I am going to say that a pitcher is excellent if:

              1)  His ERA is at least 10% better than the league average, and

              2)  He allows at least 7 runs fewer than a league-average pitcher would allow.

              The "7 runs" criteria has two purposes.   First, it excludes pitchers who pitch 3 innings and don’t allow a run, or pitchers who pitch 10 innings with a 1.80 ERA, from being included on the "excellent pitcher" list.  Second, it gives me a "slide indicator" that I can position to get the percentages I want.   I wanted to define about 20% of pitchers as excellent, and I wanted to define "10% better than the league" as "excellent".   By adding this second figure, the "runs better" figure, I can cut it off wherever I need to, to get both 20% of the pitchers and pitchers with ERAs 10% better than the league.   It happens that 7 runs better is the serendipitous point.   By these two cutoffs, 19.7% of all pitchers in the study are identified as "excellent".  

              The 20% standard is generous enough that almost every pitcher who is around for a few years will sometimes be identified as "excellent".   20% is not a Hall of Fame standard; it’s a "good pitcher that year" standard.   Don Aase, always at the top of the file, has three seasons in his 14-year career in which he is identified as "excellent".  

              It also happens that these two measurements tend often to be about the same, for the very best pitchers.   I used three figures to define the list:  M1, M2 and M3.  

              M1 is the pitcher’s ERA, divided by the league ERA, subtracted from 1.00, multiplied by 100.

              M2 is the number of earned runs a league-average pitcher would have allowed in the number of innings pitched by this pitcher, minus the number of runs that this pitcher did allow. 

              M3 is the minimum of M1 and M2. 

              My study group was "all pitchers who recorded at least one out in a season from 1920 to 2015"—eliminating pitchers who got no one out, because division by zero is messy and creates problems, and they’re not relevant to the study although they could be marginally relevant to the percentages, starting at 1920 because the game was too different before 1920 to be relevant, not including 2017 because we need to back it off a year so that we have a "look ahead year" in the data, and not including 2016 because I haven’t yet updated this Excel file I am using to include the 2017 data, so the last year I can use is 2016.   I wish I could park-adjust the data, but I don’t have the ability to do that in this file.

              Anyway, when we sort pitchers by M3, we find that the best pitchers usually have nearly identical M1 and M2 figures.  The #1 pitcher by M3 is Pedro Martinez, year 2000, whose figures are 65, 77, 65, which would not qualify as "nearly identical".  But the #2 pitcher is Bob Gibson, 1968 (62, 63, 62), #3 is Greg Maddux, 1994 (63, 60, 60), #4 is Greg Maddux, 1995 (61, 59, 59), #7 is Zack Greinke, 2015 (58, 56, 56), and #8 is Roger Clemens, 2005 (56, 55, 55).  

              An interesting feature here, right away, is that most of the very top seasons are recent. . .in your memory if you are over 50.   These are the top 25 seasons ranked by M3:

             

First	Last	Team	Lg	Year
Pedro	Martinez	Boston Red Sox	AL	2000
Bob	Gibson	St. Louis Cardinals	NL	1968
Greg	Maddux	Atlanta Braves	NL	1994
Greg	Maddux	Atlanta Braves	NL	1995
Dwight	Gooden	New York Mets	NL	1985
Pedro	Martinez	Boston Red Sox	AL	1999
Zack	Greinke	Dodgers	NL	2015
Roger	Clemens	Houston Astros	NL	2005
Kevin	Brown	Florida Marlins	NL	1996
Roger	Clemens	Toronto Blue Jays	AL	1997
Pedro	Martinez	Montreal Expos	NL	1997
Dean	Chance	Los Angeles Angels	AL	1964
Jake	Arrieta	Cubs	NL	2015
Ron	Guidry	New York Yankees	AL	1978
Lefty	Grove	Philadelphia Athletics	AL	1931
Sandy	Koufax	Los Angeles Dodgers	NL	1966
Dolf	Luque	Cincinnati Reds	NL	1923
Zack	Greinke	Royals	AL	2009
Warren	Spahn	Milwaukee Braves	NL	1953
Roger	Clemens	Boston Red Sox	AL	1990
Carl	Hubbell	New York Giants	NL	1933
Randy	Johnson	Seattle Mariners	AL	1997
Clayton	Kershaw	Dodgers	NL	2013
Lefty	Gomez	New York Yankees	AL	1937
Tom	Seaver	New York Mets	NL	1971

 

              A lot of recent seasons in there.   This doesn’t always happen.  There are a million ways to rank pitchers, but many times when I draw up a list, it seems to favor players from years ago.   For some reason the opposite happens at the top of this list. 

              So this brings up an immediate question:  Is there some sort of "recency bias" here?  I didn’t intend for there to be a bias for one era over another; that’s why I used ERA relative to the league norm; it’s supposed to make things constant over time.

              And it appears that it does, generally; for some reason there are just a lot of recent seasons at the top.  Part of it is that I am now old, so what seems recent to me is actually more than half of baseball history.    Anyway, looking at the data by decades, the percentage of pitchers who are identified as "excellent" never goes lower than 18.5% (current decade) and never goes higher than 21.0% (1970s).   This chart gives the percentage of pitchers in each decade who are identified as excellent, and the percentage of those pitchers who are identified as excellent in one season who will repeat on the list the next season:

From	To		Ex Pct	Rpt %
1920	1929		19.1%	43.7%
1930	1939		19.6%	39.3%
1940	1949		19.8%	36.8%
1950	1959		18.7%	40.5%
1960	1969		18.8%	40.9%
1970	1979		21.0%	39.0%
1980	1989		20.2%	36.8%
1990	1999		20.0%	38.6%
2000	2009		20.5%	39.8%
2010	2015		18.5%	40.8%

 

              Over time, 39.4% of pitchers who are identified as excellent in one season will also be identified as excellent in the following season.   There are 6,861 pitcher/seasons in the study which are identified as "excellent", which is 19.7% of all pitchers in the study.  Of those 6,861 pitchers, 2,703 were again identified as excellent in the following season.  Both of these figures—19.7%, and 39.4%--are essentially constant over time, or at least stable. The "repeat percentage" dropped in the 1940s for an obvious reason, World War II.  If you would like to argue with me about what constitutes an "excellent" pitcher, feel free; I will just mark you down as a moron who didn’t understand the purpose of the research, and make a mental note to ignore anything you say in the future.

              We thus can draw our first conclusion:   A pitcher’s chance of being excellent in one season is more than twice as great if he was excellent in the previous season as they it is if he was not excellent in the previous season.   39.4% is exactly twice 19.7%, not "more than twice", but my statement is true nonetheless, for a reason that you can figure out if you work on it for a moment.

              In the 1920s, 82% of pitchers who were identified as "excellent" were starting pitchers, 5% were relievers, and 13% were in a mixed role, meaning that they had between 10% and 60% of their appearances as starts, and 40% to 90% in the bullpen.   Over time, these percentages have changed enormously, so that since 1990 there are more excellent relievers than excellent starting pitchers—and pitchers who are excellent in a mixed role have virtually disappeared:

 

From	To	% Mx	% Rel	% St
1920	1929	13.2%	5.3%	81.5%
1930	1939	15.1%	7.1%	77.8%
1940	1949	10.8%	13.0%	76.2%
1950	1959	11.5%	22.6%	65.8%
1960	1969	7.0%	35.2%	57.9%
1970	1979	5.8%	38.3%	55.8%
1980	1989	5.1%	47.4%	47.4%
1990	1999	3.0%	53.8%	43.1%
2000	2009	2.5%	58.3%	39.2%
2010	2015	2.4%	57.0%	40.6%

 

              So in modern baseball 41% of excellent pitchers are starters, 57% are relievers, and 2% make a transition during the season from one role to the other.   Another thing that has changed is the number of pitchers per team:

From	To	Pitchers	Teams	Pit/Team
1920	1929	394	160	2.46
1930	1939	397	160	2.48
1940	1949	454	160	2.84
1950	1959	442	160	2.76
1960	1969	560	200	2.80
1970	1979	736	248	2.97
1980	1989	816	260	3.14
1990	1999	1027	278	3.69
2000	2009	1282	300	4.27
2010	2015	753	180	4.18

 

              Modern teams use many, many more pitchers in a season than was true a hundred years ago.  Since the percentage of all pitchers who are excellent is the same, this means that many more pitchers per team are defined as "excellent" for the season. 

              Now to get to our serious question:  How does the "repeat percentage" vary with various inputs?   Is it different for starters than relievers, for example?

              A little.  The "repeat percentage" is 42% for starting pitchers, 37% for relievers, and 30% for pitchers in a mixed role. 

              I had expected that the "mixed role" percentage would be low, since those are mainly. . .well, pitchers who are not settled into a role, not fully proven.  Before 1975, a huge percentage of league ERA champions were these mixed-role pitchers, who moved in the season from one role to the other.   In 1957 the American League leader in ERA was Bobby Shantz, in 1959 Hoyt Wilhelm, in 1960 Frank Baumann, in 1962 Hank Aguirre, in 1970 Diego Segui, in 1972 Luis Tiant, all mixed-role pitchers.  The next year their ERAs went up, in every case, by a run or something very close to a run.  What happened was, a young pitcher or a pitcher who had been fighting injuries would start the year in the bullpen, and make 25 to 35 relief appearances with an ERA of 1.00 or 1.25.   About June 1 to July 1 the team would need starting pitching, so they would move him into the starting rotation, and he would continue to pitch moderately well making 15 to 20 starts, and winding up the season with a 2.50 ERA—and just skimming over the standard of 154 or 162 innings to qualify for the ERA title.   But he’s not really an outstanding starter, to begin with, and also, he’s not properly trained to be a starting pitcher; he’s doing something that is outside his conditioning program.   By the end of the year, very often he’s just holding on, and the next year he is not the same guy. 

              We don’t do that in modern baseball; we don’t rush guys from the bullpen into the starting rotation in mid-season.   But anyway, by 1972 I recognized the syndrome, so I expected the "repeat percentage" for mixed-role pitchers to be low.   In checking the facts, I see that in 1959 Hoyt Wilhelm was not technically a mixed-role pitcher, he was a starter.  If you point out things like this to me, I will also mark you down as a moron; I don’t care.  I’m addressing the general question, not the question of whether Hoyt Wilhelm was a starter or a mixed-role pitcher in 1959.  

              Probably the percentage for relievers is lower than the percentage for starters because there are more pitchers there who are closer to the margins of the definition.  Remember the "M3" definition—the minimum of the two numbers that define excellence.  Later we will study this, but there is no doubt that pitchers whose M3 figure is 30 are going to be more likely to repeat as excellent pitchers than pitchers whose M3 figure is 8.  Because starting pitchers pitch more innings, they have larger M3 figures, thus more "safe" pitchers who are not near the margins of the definition. 

              In the 1970s, when I started doing baseball research, it was an article of faith among baseball writers that relievers were much less consistent than starters.   Older writers would wax grand and eloquent about the phenomenal consistency of Rollie Fingers, as a reliever, when most relievers were good one year and not the next.   There were two reasons that this was generally believed.  One was that, in the 1950s, when these sportswriters were born in the business, most relievers were guys with second-line stuff, junkballers, and they may well have been somewhat less consistent.  Guys like Jim Konstanty and Luis Arroyo came out of nowhere in their 30s, had monster seasons, and returned the next season to the mists of mediocrity.  The other reason was that if you don’t study things, you form all kinds of beliefs that turn out, with research, just to be wrong.  These guys didn’t study anything, so they believed a lot of nonsense.  Even if you go back to the relievers of the 1930s, 1940s, if you study it with modern methods and adjust for the instability of the data in smaller samples, you’ll find that there’s not a lot of difference between the consistency of starters and that of relievers.  It’s actually always been pretty much the same.  Also, I know there is one of you out there who wants to argue with me about Rollie Fingers’ phenomenal consistency.  Feel free to make your argument; I’ll make a note.

              While we’re here. . . it’s not really what we’re here for, but I suppose I should give you a list of the pitchers who have the most seasons which are considered excellent by our present definition.  Roger Clemens leads the list, with 19 seasons, followed by Tommy John and Mariano Rivera, 17, Warren Spahn with 16, Nolan Ryan, Tom Seaver, Hoyt Wilhelm, John Smoltz, Bob Feller and Whitey Ford, 15, Bert Blyleven, Tom Glavine, Curt Schilling and Gaylord Perry with 14, and Don Sutton, John Franco, Billy Wagner, Lefty Grove and Pedro Martinez with 13.   Mostly Hall of Famers. 

              Now to the issue of age. . . .64% of teenagers who had excellent seasons were able to repeat as excellent pitchers the next year, or 9 out of 14. (You are probably wondering who the teenagers were who qualified as "excellent" pitchers.   The list is Bob Feller in 1936, 1937 and 1938, Ralph Branca in 1945, Don Drysdale in 1956, Wally Bunker and Billy McCool in 1964, Larry Dierker in 1966, Gary Nolan in 1967, Don Gullett and Bert Blyleven in1970, Fernando Valenzuela in 1980, which was the year before Fernandomania, Dwight Gooden in 1984, and Felix Hernandez in 2005.)

              This chart summarizes the percentage of excellent pitchers at each age who are able to repeat at the next age:

 

Age	#	Repeat	Pct
<19	14	9	64%
20	33	19	58%
21	104	37	36%
22	189	80	42%
23	322	119	37%
24	444	178	40%
25	574	248	43%
26	690	255	37%
27	626	245	39%
28	600	225	38%
29	558	243	44%
30	537	209	39%
31	451	182	40%
32	386	149	39%
33	315	132	42%
34	251	79	31%
35	186	79	42%
36	173	65	38%
37	119	44	37%
38	101	41	41%
39	70	31	44%
40	51	13	25%
41	26	7	27%
42+	41	14	34%

 

              The numbers go up and down a little, but there is no obvious pattern.   This conclusion is re-inforced when we group the numbers at age 25 with those at age 24 and 26:

Age	#	Repeat	Pct	#	Repeat	Pct
<19	14	9	64%	47	28	60%
20	33	19	58%	151	65	43%
21	104	37	36%	326	136	42%
22	189	80	42%	615	236	38%
23	322	119	37%	955	377	39%
24	444	178	40%	1340	545	41%
25	574	248	43%	1708	681	40%
26	690	255	37%	1890	748	40%
27	626	245	39%	1916	725	38%
28	600	225	38%	1784	713	40%
29	558	243	44%	1695	677	40%
30	537	209	39%	1546	634	41%
31	451	182	40%	1374	540	39%
32	386	149	39%	1152	463	40%
33	315	132	42%	952	360	38%
34	251	79	31%	752	290	39%
35	186	79	42%	610	223	37%
36	173	65	38%	478	188	39%
37	119	44	37%	393	150	38%
38	101	41	41%	290	116	40%
39	70	31	44%	222	85	38%
40	51	13	25%	147	51	35%
41	26	7	27%	118	34	29%
42+	41	14	34%	67	21	31%

 

              The percentage of excellent pitchers repeating as excellent is the same at age 38 as it is at age 25.   Age is not an obvious variable in a pitcher’s chance of repeating a successful season.

              Well, then. . .what DOES age mean, for a pitcher?   It’s not that easy to say.   There is a narrative established in my head, so I see it the way I see it.   It’s basically a game of attrition.  If you have 1,000 successful pitchers at age 32, 30% of them will get hurt at age 33, leaving 700, 30% will get hurt at age 34, leaving 490, etc., until they’re all gone.  But IF a pitcher is still successful, it’s pretty much irrelevant to a third party whether the pitcher is 33 or 40—not absolutely, but pretty much.

              What changes is his ability to come back.  A 26-year-old pitcher gets hurt, he may have time to re-build his career.  A 33-year-old has less time to come back, and a 40-year-old is out of time.

              Let’s turn our attention now to the strikeout rate.   The strikeout rate can be looked at either in absolute terms, strikeouts per nine innings, or relative to the league.   We’ll start with strikeouts in absolute terms.   I sorted the 6,861 pitchers according to their strikeouts per nine innings; the highest strikeout rate in the group was by Aroldis Chapman in 2014 (17.67 strikeouts per nine innings) and the lowest was by Ernie Wingard in 1924 (0.95 strikeouts per nine innings).   Then I divided the pitchers into ten deciles of 686 pitchers each, except that the fifth group from the top had 687 pitchers.  

              Of the top 686 pitchers in strikeout rate, 376 repeated as successful pitchers in the following season.   Of the bottom 686 pitchers, only 223 repeated as successful pitchers in the following season:

 

 

Highest Strikeout Group	55%
2nd Highest	47%
	43%
	42%
	37%
	36%
	34%
	33%
2nd Lowest	33%
Lowest Strikeout Group	33%

             

              Obviously the strikeout rate influences the "repeat success" rate, as we would expect that it would based on hundreds of previous studies.   But what if, instead, we sorted the pitchers by strikeout rate relative to the league strikeout rate?  Would that improve the sorting power of this factor?

              It does, yes:

Highest Strikeout Group	50%
2nd Highest	50%
	49%
	44%
	40%
	39%
	36%
	33%
2nd Lowest	30%
Lowest Strikeout Group	25%

 

              We sort the data by deciles and then measure the standard deviation of the decile measurements.  If the measurement becomes more effective, the top groups move further away from the bottom ones, and the standard deviation of the decile measurments increases.   It does increase, and markedly, from .074 to .089.   I refer to this, in the rest of this article, as "74 to 89". 

              If strikeouts sort the pitchers by the likelihood of future success, what about walks?   This chart sorts the pitchers in the same way based on raw walk rates:

 

Lowest Walk Rate	48%
2nd Lowest	45%
	43%
	45%
	39%
	38%
	36%
	34%
2nd Highest	37%
Highest Walk Rate	31%

 

              Here the standard deviation of the decile measurements is .054 (54).  The walk rate has significant effectiveness as an indicator of the ability to remain successful, but less power than the strikeout rate.  The wildest pitcher who qualified as successful in a season was Tommy Byrne in 1949, who walked 179 men in 196 innings, but posted a 3.72 ERA.  The low-walk guy was Carlos Silva in 2005, who walked only 9 in 188 innings. Let’s do walks per nine innings compared to the league norm:

 

Lowest Walk Rate	49%
2nd Lowest	44%
	44%
	44%
	40%
	39%
	34%
	35%
2nd Highest	36%
Highest Walk Rate	30%

 

              Here again the effectiveness of the measurement increases when we adjust for the league norm, although the increase is not as large; our standard deviation here is 57.  Probably adjusting for the league walk rate has less impact than adjusting for the league strikeout rate because there is much less historic variation in walk rates than there is in strikeouts.   Let’s do strikeout/walk ratio, raw:

 

Best Strikeout/Walk Ratio	57%
2nd Best	49%
	46%
	41%
	38%
	37%
	34%
	35%
2nd Worst	31%
Worst Strikeout/Walk	26%

 

              This is the most effective predictor of the ability to remain effective that we have found, with a standard deviation as discussed above of 91. 

              Now I have two directions I can go in.  I have a method that I developed probably ten years ago of comparing the strikeout to walk ratio to the league norm, called the strike zone winning percentage, and I’ll look at that in a moment.   But Tom Tango argues that strikeout to walk ratio is not the optimal evaluator of strikeouts and walks, for this reason.   Suppose that two pitchers pitch 200 innings each.  One pitcher strikes out 100 batters and walks 25, a 4-to-1 ratio.   The other pitcher strikes out 150 batters and walks 75, a 2-to-1 ratio.   Are these pitchers the same in their impact, or different?

              Tom argues that these pitchers have different strikeout to walk ratios, but essentially the same impact on the runs allowed column, since, he says, the benefit of a strikeout is more or less equal to the cost of a walk.   He thus argues that what matters is the strikeout to walk margin.  

              So that sub-divides, too; we could study strikeout to walk margin, raw, which we will do later, or strikeout to walk margin per inning, which we will do now.   Strikeout to walk margin, per inning:

Best K-W Margin	58%
2nd Best	50%
	42%
	42%
	40%
	32%
	33%
	37%
2nd Worst	34%
Worst K-W Margin	27%

 

Ted Wingfield in 1925 had a better-than-league ERA although he had 30 strikeouts and 92 walks, a margin of 2.91 more walks than strikeouts per nine innings.   Anyway, this does in fact score higher on our test than the strikeout to walk ratio, 92 to 91 (.092 to .091, standard deviation of the deciles.)   The best 10% of these pitchers, in their strikeout margin per inning, are 58% likely to remain excellent in the following season, which is the best percentage we have yet seen.

              Strike zone winning percentage is the pitcher’s strikeouts, multiplied by the league walks (or walks per game, it doesn’t matter), divided by the same plus the league strikeouts multiplied by the pitcher’s walks.  In other words, using P-SO for pitcher strikeouts and L-SO for League strikeouts:

              K Zone Winning Pct  = (P-SO * L-BB) / [(P-SO * L-BB) + (L-SO * P-BB)]

              The only pitcher in the data who has a K Zone winning percentage of .900 is Dennis Eckersley, who was at .916 in 1989—and again in 1990, the same percentage.  The best by a starting pitcher is .849, by Bret Saberhagen in 1994.    Previous studies have shown that the strike zone winning percentage correlates very well with the pitcher’s actual winning percentage.

              Anyway, the strike zone winning percentage—which is the strikeout/walk ratio, but league-adjusted—blows everything else we have seen so far out of the water in terms of its effectiveness at predicting continued excellence:

Best K Zone Win Pct	59%
2nd Best	54%
	48%
	47%
	41%
	35%
	33%
	33%
2nd Worst	27%
Worst K Zone Win Pct	20%

              That’s a score of 122—by far the best we have seen.  We have the highest retention percentage on one end, the lowest on the other.

              Well.  . .let’s see.  If the "Score" of Strikeout to Walk Ratio is 91, Strikeouts Minus Walks per inning is 92, and league-adjusted strikeout to walk ratio is 122, then how about league-adjusted Strikeouts Minus Walks per 9 innings?  Do I need to write out the formula, or is it self-‘splanatory?   Better safe than sorry. . . .

              [(P-SO -– P-W) /  P-IP ]  /   [(L-SO -– L-W) / L-IP]

              Well (skipping chart), that scores at just 92. . .it turns out that there is an obvious problem with that.   There are some leagues in which the difference between the strikeout rate and the walk rate is only .03 to .05 per nine innings.   A pitcher with an advantage of 1.00 strikeouts above walks per game, in such a league, comes out at 20 or 30.  Ewell Blackwell in 1947 comes out at 107.69.   This causes that formulation to lose its predictive power.

              We can solve that problem in one of two ways.   First, we could switch to SUBTRACTING the league average from the pitcher’s data, rather than dividing by it. . ..I’ll try that first:

Largest Relative Margin	61%
2nd Largest	54%
	48%
	45%
	40%
	34%
	34%
	31%
2nd Lowest	30%
Worst Relative Margin	18%

 

              Oooh. . .that scores at 127.   Nice. 

              The other option is to divide the pitcher’s strikeout margin by the league strikeout margin, PLUS X.   In other words, before we divide the pitcher’s strikeouts minus walks per nine innings by the league data, we add something to the league data so that it can’t be near zero.  

              What is X?   What is the number that you add to the league data to maximize the result?   It is easy to experiment.   It turns out that the maximum result is achieved by adding 1.20.   1.20  works better than 1.10 or 1.30; 1.20 works better than 1.19 or 1.21 This is the data for that:

Best Relative K Margin	63%
2nd Best	54%
	49%
	43%
	39%
	34%
	30%
	32%
2nd Worst	27%
Worst Relative K Margin	24%

 

              That scores at 126. . . basically the same as subtracting the league number, but not better than it.   I promised earlier to check the impact of just using raw strikeouts minus walks, which would be +311 for Sandy Koufax in 1965 (382 and 71) and -42 for Dick Fowler in 1949 (43 strikeouts, 115 walks.)  This is the data for that sort:

Best Strikeout Margin (Raw)	62%
2nd Best	50%
	47%
	45%
	38%
	37%
	33%
	33%
2nd Worst	26%
Worst Strikeout Margin (Raw)	24%

 

              That scores at 117, which is a very good number.  Let’s check the impact of the Home Run Rate. Raw Home Run rate:

 

Lowest HR allowed rate	37%
2nd Lowest	43%
	45%
	40%
	40%
	40%
	41%
	39%
2nd Highest	37%
Most HR Allowed	33%

 

              Nothing there so far. . .that scores at 33.  Let’s move on to Home Run Rate compared to league norm.   Slick Castleman in 1937 was very effective despite a home runs allowed rate that was six times the league average:

 

Lowest HR allowed rate	39%
2nd Lowest	42%
	43%
	40%
	41%
	38%
	42%
	39%
2nd Highest	36%
Most HR Allowed	35%

 

              Well, that’s even worse.  There doesn’t appear to be anything there that helps the prediction.   I’m moving on. 

              Let’s look at excellence retention by innings pitched.   The left-hand column below is the average innings pitched by the pitchers in the group:

 

282	Most Innings Pitched	59%
240	2nd Most	50%
216		41%
192		36%
157		33%
118		35%
93		35%
78		38%
67	2nd Fewest	40%
47	Fewest Innings Pitched	26%

             

              That scores at 93, which means that it actually has considerable value as a predictor of the ability to sustain excellence, although not as much as the strikeout/walk data.  59% of the pitchers who were excellent in 255.1 innings or more were able to repeat as excellent the next season, whereas only 26% of those who were excellent in less than 58 innings were able to repeat. 

              OK, and here’s what I expect to be the biggest separator.  Margin of excellence.   Remember the M1, M2, M3 figures, from the start of this article 13 pages ago?

 

 

              One would expect pitchers who are HIGHLY excellent to be more likely to retain excellence than pitchers who just scraped by the qualifying standard, right?   The minimum M3 figure to be described as "excellent" is 7.   In the chart below the left-hand figure is the average M3 of the group:

34	Highest M3 figure	62%
24	2nd Highest	53%
20		43%
17		41%
14		40%
13		35%
11		36%
10		28%
9	2nd Lowest	31%
7	Lowest M3 Figure	25%

 

              That scores at 113.  It turns out that the M3 figure is a good indicator for the retention of excellence—but not the best we have seen.  I thought it would be the best indicator we had seen, but it isn’t.   Also, it turns out that the M1 figure (which is simply the ERA relative to the league norm) is a very poor indicator of the ability to retain excellence (41), whereas the M2 figure is a very good indicator (118), so that when you mix M1 and M2 together into M3, it’s actually a poorer indicator than M2 by itself.  This is M2:

38	Highest M2 figure	65%
25	2nd Highest	51%
20		45%
17		40%
15		39%
13		35%
11		36%
10		28%
9	2nd Lowest	31%
7	Lowest M2 Figure	25%

 

              Before we move into the wrapup stages here, another thing I should look at is consecutive seasons of excellence.   Obviously, it would seem, a pitcher coming off of five consecutive seasons of excellence must be a better bet to repeat as excellent than a pitcher coming off of one good year, right?   So let’s look at that. . .

              Mariano Rivera had 16 consecutive seasons of excellence, the most ever.   In the chart below, "10" means "10 or more":

Consecutive	Count	Repeat	Pct
10	36	21	58%
9	23	15	65%
8	34	23	68%
7	49	35	71%
6	80	51	64%
5	138	84	61%
4	283	140	49%
3	568	293	52%
2	1417	592	42%
1	4233	1449	34%

 

              Pitchers coming off their first (consecutive) season of excellence are 34% likely to repeat as excellent the next year.   Although the data is irregular because the numbers get to be small, this increases generally until a pitcher having his 7^th consecutive excellent season is 71% likely to have an eighth. 

              OK, so the best indicators I have found are the M2 figure and pitcher’s strikeouts minus walks margin, minus the league margin.   The final question, then, is how can we combine these two into one number which is more effective than either one of them by itself?

              M2 operates on a scale of 7 to 77, with a standard deviation of 9.49.   The Strikeout Margin operates on a scale of -5.1 to +10, with a standard deviation of 1.78.  If we simply add the two together, then, the M2 figure will dominate, and we won’t make any progress over M2 by itself. 

              Since the strikeout margin is the better predictor of the two (127 to 118), we want the strikeout margin to have more impact on the combination than the M2 figure.   If we multiply the Strikeout Margin number by six before adding it to the M2, that would make a standard deviation ratio of 10.7 to 9.5, about the right ratio.   So let’s try that. . . a newly created figure, M2 plus six times the pitchers strikeout margin minus the league norm:

Best Manufactured Number	71%
2nd Best	55%
	45%
	48%
	40%
	34%
	31%
	26%
2nd Worst	27%
Worst Manufactured Number	18%

 

              The predictive power of this manufactured number by the method I have been using is 158—much higher than any previous number we have seen.

              So a pitcher who has a manufactured number in the top 10% of all excellent pitchers has a 71% chance to repeat as excellent, while a pitcher who has a manufactured number in the bottom 10% has only an 18% chance to repeat as excellent—which is to say, less than a randomly selected pitcher.  A randomly selected pitcher has a 20% chance to be excellent this season; a pitcher who was excellent last year but who did not have a large margin of excellence (M2) and did not have a good strikeout to walk margin has only an 18% chance to repeat as excellent. 

              The final question I addressed was whether I could improve the predictive power of this manufactured number by factoring in the number of consecutive excellent seasons the pitcher has had.   I was not able to do so.   All of my attempts to improve this number by factoring in the number of consecutive excellent seasons made the predictor less powerful, rather than more, and so I abandoned the effort.  

              OK, then; our "Excellence Repeat Predictor" is:

              The pitchers strikeouts minus his walks, per nine innings,

              Minus the league average of the same,

              Times 6,

              Plus the number of runs the pitcher has saved compared to a league-average pitcher, as measured by ERA.  

              In all of baseball history, the ten pitchers most likely to repeat as excellent were:

             

First	Last	Year
Pedro	Martinez	2000
Pedro	Martinez	1999
Randy	Johnson	2001
Randy	Johnson	1999
Randy	Johnson	2002
Randy	Johnson	2000
Pedro	Martinez	2002
Pedro	Martinez	2003
Lefty	Grove	1931
Mariano	Rivera	2008

 

              All ten of them did in fact repeat as excellent, except for Randy Johnson in 2002.  In tomorrow’s article I will review the excellent pitchers of 2017, and assign "likely repeat scores" to each one. 

 

Tomorrow’s Article

              I was going to publish these two articles on Monday and Tuesday of next week, but it occurred to me that some people might have fantasy drafts over the weekend and might be angry with me if I held off publishing this.  

              Suppose that we use the method above to

              1)  Identify everyone who qualifies as an excellent pitcher in 2017, and

              2)  Estimate, based on the method explained here, that pitcher’s probability of repeating as excellent again in 2018.

 

              We have to extrapolate the method just a little bit, but it’s pretty straightforward.   I think there were 137 "excellent" pitchers in 2017.   These are detailed below, with an estimate for each pitcher of his likelihood of repeating excellence in 2018.   Pitchers are listed by the team that they were with at the end of the 2017 season.

              I am sure I must have included here some pitcher who has since had Tommy John surgery, and consequently has no chance at all to be excellent in 2018.   My apologies. 

 

 

ANGELS
Name	IP	ER	SO	BB	ERA	Estimate
Bridwell,Parker	121.0	49	73	30	3.64	16%
Petit,Yusmeiro	91.3	28	101	18	2.76	58%
Parker,Blake	67.3	19	86	16	2.54	64%
ASTROS
Name	IP	ER	SO	BB	ERA	Estimate
Verlander,Justin	206.0	77	219	72	3.36	50%
Morton,Charlie	146.7	59	163	50	3.62	38%
Keuchel,Dallas	145.7	47	125	47	2.90	35%
Peacock,Brad	132.0	44	161	57	3.00	53%
Devenski,Chris	80.7	24	100	26	2.68	57%
Giles,Ken	62.7	16	83	21	2.30	61%
ATHLETICS
Name	IP	ER	SO	BB	ERA	Estimate
Blackburn,Paul	58.7	21	22	16	3.22	15%
BLUE JAYS
Name	IP	ER	SO	BB	ERA	Estimate
Stroman,Marcus	201.0	69	164	62	3.09	41%
Happ,J.A.	145.3	57	142	46	3.53	31%
Leone,Dominic	70.3	20	81	23	2.56	45%
Osuna,Roberto	64.0	24	83	9	3.38	63%
BRAVES
Name	IP	ER	SO	BB	ERA	Estimate
Ramirez,Jose	62.0	22	56	29	3.19	17%
Freeman,Sam	60.0	17	59	27	2.55	23%
Vizcaino,Arodys	57.3	18	64	21	2.83	33%
BREWERS
Name	IP	ER	SO	BB	ERA	Estimate
Davies,Zach	191.3	83	124	55	3.90	17%
Nelson,Jimmy	175.3	68	199	48	3.49	54%
Anderson,Chase	141.3	43	133	41	2.74	48%
Suter,Brent	81.7	31	64	22	3.42	20%
Swarzak,Anthony	77.3	20	91	22	2.33	58%
Knebel,Corey	76.0	15	126	40	1.78	73%
Hughes,Jared	59.7	20	48	24	3.02	17%
Hader,Josh	47.7	11	68	22	2.08	56%
CARDINALS
Name	IP	ER	SO	BB	ERA	Estimate
Martinez,Carlos	205.0	83	217	71	3.64	39%
Lynn,Lance	186.3	71	153	78	3.43	23%
Nicasio,Juan	72.3	21	72	20	2.61	37%
Lyons,Tyler	54.0	17	68	20	2.83	43%
Brebbia,John	51.7	14	51	11	2.44	38%
Tuivailala,Samuel	42.3	12	34	11	2.55	21%
CUBS
Name	IP	ER	SO	BB	ERA	Estimate
Arrieta,Jake	168.3	66	163	55	3.53	32%
Hendricks,Kyle	139.7	47	123	40	3.03	36%
Montgomery,Mike	130.7	49	100	55	3.38	18%
Edwards Jr.,Carl	66.3	22	94	38	2.98	41%
Duensing,Brian	62.3	19	61	18	2.74	31%
Strop,Pedro	60.3	19	65	26	2.83	29%
Davis,Wade	58.7	15	79	28	2.30	48%
DIAMONDBACKS
Name	IP	ER	SO	BB	ERA	Estimate
Greinke,Zack	202.3	72	215	45	3.20	65%
Ray,Robbie	162.0	52	218	71	2.89	69%
Walker,Taijuan	157.3	61	146	61	3.49	28%
Godley,Zack	155.0	58	165	53	3.37	42%
Bradley,Archie	73.0	14	79	21	1.73	56%
Hernandez,David	55.0	19	52	9	3.11	32%
DODGERS
Name	IP	ER	SO	BB	ERA	Estimate
Darvish,Yu	186.7	80	209	58	3.86	39%
Kershaw,Clayton	175.0	45	202	30	2.31	77%
Wood,Alex	152.3	46	151	38	2.72	61%
Hill,Rich	135.7	50	166	49	3.32	52%
Ryu,Hyun-Jin	126.7	53	116	45	3.77	21%
Jansen,Kenley	68.3	10	109	7	1.32	80%
Watson,Tony	66.7	25	53	20	3.38	18%
Baez,Pedro	64.0	21	64	29	2.95	23%
Fields,Josh	57.0	18	60	15	2.84	35%
Avilan,Luis	46.0	15	52	22	2.93	26%
Morrow,Brandon	43.7	10	50	9	2.06	51%
GIANTS
Name	IP	ER	SO	BB	ERA	Estimate
Bumgarner,Madison	111.0	41	101	20	3.32	34%
Gearrin,Cory	68.0	15	64	35	1.99	24%
Strickland,Hunter	61.3	18	58	29	2.64	21%
INDIANS
Name	IP	ER	SO	BB	ERA	Estimate
Kluber,Corey	203.7	51	265	36	2.25	81%
Carrasco,Carlos	200.0	73	226	46	3.28	67%
Clevinger,Mike	121.7	42	137	60	3.11	33%
Shaw,Bryan	76.7	30	73	22	3.52	27%
Allen,Cody	67.3	22	92	21	2.94	60%
Miller,Andrew	62.7	10	95	21	1.44	74%
McAllister,Zach	62.0	18	66	21	2.61	34%
Otero,Dan	60.0	19	38	9	2.85	19%
Goody,Nick	54.7	17	72	20	2.80	49%
Olson,Tyler	20.0	0	18	6	0.00	26%
MARINERS
Name	IP	ER	SO	BB	ERA	Estimate
Leake,Mike	186.0	81	130	37	3.92	20%
Paxton,James	136.0	45	156	37	2.98	62%
Diaz,Edwin	66.0	24	89	32	3.27	40%
Vincent,Nick	64.7	23	50	13	3.20	22%
Zych,Tony	40.7	12	35	21	2.66	16%
MARLINS
Name	IP	ER	SO	BB	ERA	Estimate
Urena,Jose	169.7	72	113	64	3.82	16%
Barraclough,Kyle	66.0	22	76	38	3.00	24%
Steckenrider,Drew	34.7	9	54	18	2.34	55%
METS
Name	IP	ER	SO	BB	ERA	Estimate
deGrom,Jacob	201.3	79	239	59	3.53	59%
Blevins,Jerry	49.0	16	69	24	2.94	44%
NATIONALS
Name	IP	ER	SO	BB	ERA	Estimate
Gonzalez,Gio	201.0	66	188	79	2.96	47%
Scherzer,Max	200.7	56	268	55	2.51	78%
Strasburg,Stephen	175.3	49	204	47	2.52	75%
Kintzler,Brandon	71.3	24	39	16	3.03	16%
Albers,Matt	61.0	11	63	17	1.62	46%
Madson,Ryan	59.0	12	67	9	1.83	65%
Doolittle,Sean	51.3	16	62	10	2.81	54%
ORIOLES
Name	IP	ER	SO	BB	ERA	Estimate
Givens,Mychal	78.7	24	88	25	2.75	43%
Brach,Brad	68.0	24	70	26	3.18	27%
Bleier,Richard	63.3	14	26	13	1.99	16%
PADRES
Name	IP	ER	SO	BB	ERA	Estimate
Chacin,Jhoulys	180.3	78	153	72	3.89	18%
Stammen,Craig	80.3	28	74	28	3.14	25%
Hand,Brad	79.3	19	104	20	2.16	70%
PHILLIES
Name	IP	ER	SO	BB	ERA	Estimate
Nola,Aaron	168.0	66	184	49	3.54	46%
Neris,Hector	74.7	25	86	26	3.01	40%
Garcia,Luis	71.3	21	60	26	2.65	22%
Milner,Hoby	31.3	7	22	16	2.01	15%
PIRATES
Name	IP	ER	SO	BB	ERA	Estimate
Rivero,Felipe	75.3	14	88	20	1.67	66%
Schugel,A.J.	32.0	7	27	14	1.97	17%
RANGERS
Name	IP	ER	SO	BB	ERA	Estimate
Cashner,Andrew	166.7	63	86	64	3.40	15%
Claudio,Alex	82.7	23	56	15	2.50	27%
RAYS
Name	IP	ER	SO	BB	ERA	Estimate
Cobb,Alex	179.3	73	128	44	3.66	22%
Faria,Jake	86.7	33	84	31	3.43	25%
Colome,Alex	66.7	24	58	23	3.24	20%
Hunter,Tommy	58.7	17	64	14	2.61	44%
Cishek,Steve	44.7	10	41	14	2.01	28%
RED SOX
Name	IP	ER	SO	BB	ERA	Estimate
Sale,Chris	214.3	69	308	43	2.90	79%
Pomeranz,Drew	173.7	64	174	69	3.32	37%
Reed,Addison	76.0	24	76	15	2.84	42%
Price,David	74.7	28	76	24	3.38	29%
Kimbrel,Craig	69.0	11	126	14	1.43	82%
Kelly,Joe	58.0	18	52	27	2.79	18%
Maddox,Austin	17.3	1	14	2	0.52	29%
REDS
Name	IP	ER	SO	BB	ERA	Estimate
Castillo,Luis	89.3	31	98	32	3.12	35%
Iglesias,Raisel	76.0	21	92	27	2.49	52%
ROCKIES
Name	IP	ER	SO	BB	ERA	Estimate
Gray,Jon	110.3	45	112	30	3.67	30%
Rusin,Chris	85.0	25	71	19	2.65	31%
Neshek,Pat	62.3	11	69	6	1.59	68%
ROYALS
Name	IP	ER	SO	BB	ERA	Estimate
Duffy,Danny	146.3	62	130	41	3.81	25%
Minor,Mike	77.7	22	88	22	2.55	50%
Alexander,Scott	69.0	19	59	28	2.48	21%
Buchter,Ryan	65.3	21	65	26	2.89	26%
TIGERS
Name	IP	ER	SO	BB	ERA	Estimate
Fulmer,Michael	164.7	70	114	40	3.83	18%
Greene,Shane	67.7	20	73	34	2.66	28%
TWINS
Name	IP	ER	SO	BB	ERA	Estimate
Santana,Ervin	211.3	77	167	61	3.28	36%
Berrios,Jose	145.7	63	139	48	3.89	24%
Rogers,Taylor	55.7	19	49	21	3.07	19%
Busenitz,Alan	31.7	7	23	9	1.99	17%
WHITE SOX
Name	IP	ER	SO	BB	ERA	Estimate
Infante,Gregory	54.7	19	49	20	3.13	19%
Giolito,Lucas	45.3	12	34	12	2.38	19%
YANKEES
Name	IP	ER	SO	BB	ERA	Estimate
Severino,Luis	193.3	64	230	51	2.98	71%
Gray,Sonny	162.3	64	153	57	3.55	30%
Montgomery,Jordan	155.3	67	144	51	3.88	23%
Sabathia,CC	148.7	61	120	50	3.69	20%
Green,Chad	69.0	14	103	17	1.83	76%
Robertson,David	68.3	14	98	23	1.84	71%
Kahnle,Tommy	62.7	18	96	17	2.59	72%
Betances,Dellin	59.7	19	100	44	2.87	47%
Warren,Adam	57.3	15	54	15	2.35	32%

 

 

 

 

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