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Hall of Merit— A Look at Baseball's All-Time Best
Sunday, July 29, 2007
2002 Results: Two Shortstops (Trammell and Smith) and One Pitcher (Stieb) Are Now Hall of Meriters!
In his first year of eligibility, Tiger All-Star Alan Trammell scored an impressive 86% of all possible points for induction into the Hall of Merit.
Close behind him was fielding legend Ozzie Smith with a very strong 85% of all possible points in also his ballot debut.
Last but not least was Blue Jay great Dave Stieb as he became our third inductee this “year.” In his fifth year of eligibility, he received 27% of all possible points.
Rounding out the top-ten were: Pete Browning (almost looks like a sure bet in 2003!), Roger Bresnahan, Charley Jones, Bob Johnson, Andre Dawson (fine debut!), Cannonball Dick Redding and Tony Perez (back in the top-ten!).
RK LY Player PTS Bal 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
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1 n/e Alan Trammell 1011 48 18 14 4 2 1 4 1 2 2
2 n/e Ozzie Smith 999 48 18 14 3 1 2 3 2 2 2 1
3 5 Dave Stieb 314 24 1 3 3 1 1 2 3 1 1 4 2 2
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4 4 Pete Browning 300 19 2 4 2 1 1 1 3 1 1 1 1 1
5 8 Roger Bresnahan 271 19 1 2 2 1 2 3 2 1 1 1 2 1
6 6 Charley Jones 270 18 4 1 2 2 1 1 1 2 1 1 1 1
7 9 Bob Johnson 250 19 2 4 3 2 2 1 2 2 1
8 n/e Andre Dawson 235 18 1 2 1 4 2 1 1 2 1 1 2
9 7 Cannonball Dick Redding 232 14 1 2 2 2 1 2 1 2 1
10 14 Tony Perez 230 17 1 2 4 1 1 1 1 1 1 1 3
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11 10 Hugh Duffy 222 17 1 3 2 1 2 1 1 3 2 1
12 12 Kirby Puckett 214 16 1 1 5 2 1 3 1 1 1
13 17 Tommy Leach 211 15 1 2 3 1 1 2 1 2 1 1
14 11 Bucky Walters 210 15 3 3 3 2 1 1 2
15 13 Gavvy Cravath 206 18 2 1 1 1 2 2 2 1 2 1 3
16 15 Alejandro Oms 186 15 1 1 2 1 1 2 2 2 1 2
17 16 George Van Haltren 181 12 1 1 1 1 1 1 2 1 1 1 1
18 21 Graig Nettles 161 15 1 1 1 3 2 1 1 2 1 2
19 19 Luis Tiant 155 13 1 1 1 1 2 2 1 1 1 2
20 18 John McGraw 149 8 5 1 1 1
21 25T Mickey Welch 145 10 1 1 2 1 1 2 1 1
22 25T Lou Brock 144 10 2 1 1 1 1 1 2 1
23 24 Bus Clarkson 144 9 1 2 2 1 1 1 1
24 23 Reggie Smith 140 12 1 3 3 2 1 1 1
25 28 Burleigh Grimes 134 12 1 1 1 2 1 1 2 3
26 33 Norm Cash 130 11 1 1 1 1 1 1 1 2 1 1
27 31T Rusty Staub 129 11 1 1 2 1 1 1 1 1 1 1
28 20 Tommy Bridges 129 9 2 2 1 2 1 1
29 36 Vic Willis 124 9 1 1 1 1 1 1 1 2
30 29 Orlando Cepeda 122 11 1 1 2 1 1 2 2 1
31 22 Dizzy Dean 111 7 2 1 1 1 2
32 27 Phil Rizzuto 110 9 2 3 1 1 1 1
33 34 Larry Doyle 106 8 1 3 1 1 2
34 31T Ken Singleton 103 10 1 1 1 3 1 2 1
35 30 Dave Concepción 97 8 1 1 1 1 1 1 1 1
36 35 Dale Murphy 91 9 1 1 2 1 2 2
37 37 Bobby Bonds 89 9 3 2 2 2
38 39 Bob Elliott 87 9 2 1 2 1 2 1
39 44T Tommy John 87 6 1 1 1 1 2
40 38 Ben Taylor 80 7 1 1 1 1 1 1 1
41 41 Dave Bancroft 80 6 1 1 1 1 1 1
42 40 Pie Traynor 78 7 1 1 2 1 2
43 46 Carl Mays 76 7 2 1 1 1 2
44 43 Wally Schang 71 6 1 1 1 1 1 1
45 42 Elston Howard 67 6 1 1 1 1 1 1
46 44T Chuck Klein 64 5 1 1 1 2
47 47T Vern Stephens 62 5 1 2 1 1
48 59 Frank Tanana 59 4 1 2 1
49 49 Don Mattingly 54 5 1 1 2 1
50 50 Sal Bando 49 5 1 1 1 1 1
51T 47T Bill Monroe 46 4 1 1 1 1
51T 51 Lance Parrish 46 4 1 1 1 1
53 54 Frank Chance 45 4 1 1 1 1
54T 52 Addie Joss 45 3 1 1 1
54T 68 Rick Reuschel 45 3 1 1 1
56 55 Wilbur Cooper 43 3 1 1 1
57T 58 Buddy Bell 42 4 1 1 1 1
57T 57 Lefty Gomez 42 4 1 1 1 1
59 61 Ernie Lombardi 41 3 1 1 1
60 60 Tony Oliva 40 2 1 1
61 62T Thurman Munson 36 3 1 2
62T 65T Tony Mullane 33 3 2 1
62T 65T Urban Shocker 33 3 1 1 1
64 62T Don Newcombe 32 3 1 1 1
65 53 Ed Williamson 32 2 1 1
66 64 Sam Rice 31 3 1 1 1
67 56 George J. Burns 30 3 1 1 1
68 69 Rabbit Maranville 28 3 1 1 1
69 82 Ron Cey 24 3 1 1 1
70T 86 Jim Kaat 24 2 1 1
70T 70 Jimmy Ryan 24 2 2
72 73T Jack Quinn 23 2 1 1
73 76 Ed Cicotte 23 1 1
74 75 Jack Clark 22 2 1 1
75 84T Bill Mazeroski 22 1 1
76 73T Bruce Sutter 21 2 1 1
77T 77 Frank Howard 20 2 1 1
77T 67 Johnny Pesky 20 2 1 1
79 84T Dizzy Trout 19 2 1 1
80 92T Fred Dunlap 17 2 1 1
81 83 Brian Downing 17 1 1
82T 71T Jim Rice 16 2 1 1
82T 78 Dave Parker 16 2 2
82T 79 Al Rosen 16 2 1 1
82T 71T Bobby Veach 16 2 2
86 80 Luis Aparicio 16 1 1
87 87T Sam Leever 13 1 1
88 87T Hack Wilson 11 1 1
89T 90T Fielder Jones 10 1 1
89T 90T Leroy Matlock 10 1 1
89T 92T Jack Morris 10 1 1
92T 94T Jim Fregosi 9 1 1
92T 87T Tony Lazzeri 9 1 1
94T n/e Charlie Hough 8 1 1
94T 94T George Kell 8 1 1
94T 96T Mickey Vernon 8 1 1
97T n/e Dutch Leonard 7 1 1
97T 96T Bill Madlock 7 1 1
99T 99T Dick Lundy 6 1 1
99T 99T Al Oliver 6 1 1
99T n/e Gene Tenace 6 1 1
Dropped Out: Levi Meyerle(96T), Carlos Morán(81), Mike Tiernan(99T).
Ballots Cast: 49
Thanks to OCF and Ron Wargo for their help making sure that the tally is correct.
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Has anyone checked, however, to see whether the fit to run scoring is better when you weight OBP at 1.7*SLG? I think here that you want the model with the best fit, not necessarily the one with the highest correlation.
-- MWE
Yes. OPS+ already gives OBP a bit of a boost simply because OBP is a smaller number. OPS+ is the equivalent of using:
(lgSLG/lgOBP)*OBP + SLG
the coefficient lgSLG/lgOBP is going to vary, but in 2007 its about 1.28 or so.
1.7 is quite high. I've seen it, but I've also seen 1.4.
That last sentence needs to be in bold, because it's extremely important.
Its not an all or nothing deal the other way around either. If Joe Mauer were to swing at those strikes right down the middle, he's not always going to get a hit out of it.
For people out there who don't actually *watch* Twins games, there's a current frustration amongst Twins fans concerning Joe Mauer's plate discipline. It seems too easy for the opposing pitcher to get ahead in the count by grooving the first pitch right down the middle. Any negative outcome can then be easily blamed on this hole he had to work himself out of. When given time to reflect, its hard to complain too much about a .299/.389/.435 hitter, but *in the moment* the called strikes cause some consternation amongst offense-starved Twins fans.
Justin's a bad example though. His OBP is fine. He's just having a better year at the plate than Joe is. All the metrics agree.
The 'classic' example is Robbie Alomar/Joe Carter. Joe got a little too much RBI-credit for Robbie's OBP abilities.
And some of it is what Esteban said back in #92. There's been a campaign amongst saber-types to convert people's thinking away from AVG and RBI and this has often focused on demonstrating how players with a low-walk rate are "overrated". I think the campaign has been won for the most part. OBP and SLG are plastered everywhere now whereas 20 years ago it was almost impossible to even find. The victory means that the campaign needs to stop. There's a generation of fans who don't know that a player used to be almost completely defined by his batting average. They've now created players who are now "so overated they're now underrated". :-) Guys like Gwynn and Suzuki can still be worth a ton.
Remember that what we are talking about here is a linear regression of the form
y = a + bx + R
where R is the residual error - the difference between what the regression line predicts for "y" and what the actual value of "y" is.
Correlation is a measure of the relationship between y and x - if x and y are positively correlated, y increases as x increases. It says nothing about the magnitude of the residual errors - you could be wildly overestimating or underestimating actual run scoring, and it wouldn't matter one bit as long as the variables move in tandem.
Fit, on the other hand, is a measure of how small the residual errors are. Since what you are presumably trying to figure out is the actual impact of OBP/SLG on run scoring, I'd think you'd want a model that gets you as close to the actual runs scored as possible - which may or may not be the one that has the highest correlation between OBP/SLG and runs.
-- MWE
Mike Emeigh--but isn't the square of the correlation equal to the percentage of variance accounted for? The higher your r-squared, the lower your residuals...no?
yes
The higher your r-squared, the lower your residuals...no?
the lower your sum of squared residuals, yes.
The residuals should be examined for patterns. Is there any pattern to "where" the residuals are unusually large in absolute value? any pattern to where the residuals are negative or positive?
"Where" means where in the data set. One might say "when" because residual patterns by date are likely to be important in this data. But there may be an important residual pattern by on-base-plus or slugging-plus, for example, which might reveal that any linear model is a poor one.
I wonder whether there is a good way to display the residuals graphically in a file that is not too large.
Yes.
Not necessarily.
The formula for r is:
cov(x,y)/sqrt(var(x)*var(y))
r (and therefore r-squared) does NOT address the magnitude of variance in the residuals - which I think should be the area of interest here - only the relationship between variances in the dependent and independent variables.
-- MWE
This is absolutely correct - but not done very much, at least in terms of applied statistical analysis of baseball. More often, players/teams that don't fit the model are dismissed as outlyers rather than being subjected to the type of analysis that Paul suggests.
-- MWE
Runs = a + b1*(hits/PA) + b2*(OB-not-hit/PA) + b3*(EB/PA) + R
where OB-not-hit is times on base that are not the result of a hit (I would include ROE, if you have it), and EB is extra bases.
The other option that you have is to do what Mark Pankin did. This, too, has some issues - the underlying assumption that a player's performance is relatively constant across any specific situation being the biggest one - but it's more appropriate for an individual player in context.
-- MWE
Bridges 3685
I've got Bridges with 3675.
1. Bill Monroe, 17.6%
2. Tommy Leach, 8.3%
3. Quincy Trouppe, 6.8%
4. Spotswood Poles, 6.4%
5. Mike Griffin, 6.4%
6. Dickey Pearce, 5.8%
7. Dick Redding, 5.5%
8. Lip Pike, 5.0%
9. Joe Sewell, 4.9%
10. Jim McCormick, 4.4%
11. Wes Ferrell, 4.1%
I don't think it means anything (I haven't voted for Poles, Griffin or McCormick since the early '40s), but what the heck. I think I might have tabbed my Monroe % as even higher than that, but I didn't actually guess.
1 n/e Alan Trammell 1011 48 18 14 4 2 1 4 1 2 2 ; and one off ballot2 n/e Ozzie Smith 999 48 18 14 3 1 2 3 2 2 2 1 ; and one off ballot
My statistical instinct says That's incredible.
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