Archive for June, 2004

Clutch Hits and Hope for the Braves

This quote from an article in the AJC give me some hope for the Braves,

The Braves, who led the majors with a .274 batting average in close-and-late situations in 2003, were last out of 30 teams with a .215 average in those situations before Thursday.

Chipper Jones explains why this is bad,

Most of the time last year, we had a veteran hitter up in those situations. This year a lot of times we don’t. We’ve got a lot of young guys.

Wait, but didn’t I just say this is good news? Well, the reason is that clutch hitting is not a skill, or at least no one has yet identified it as a skill. Yes, there were a lot of veterans in last year’s line-up late in the game, but it is also true that last year the line-up was a lot better in ALL hitting situations. The team hit .284 last year with more power. What is happening is that by random chance the team is not getting hits when it is close and late. Overall, the Braves batting average has been .254, and .244 in the 7-9 innings. .215 is well below that and I expect some mean reversion. As the Braves begin hitting closer to their actual ability — which is unfortunately below last year’s team — the Braves ought to win a few more games than they have been winning. It is likely that the Braves are actually a better team than the record indicates. According to Rob Neyer’s Pythagorean Standings, the Braves ought to be 35-36 and not 33-38. This is evidence that the record understates the way the team has played on the field. I know even that reacord is not all that good, but there is hope.

The Neyer/James Guide to Pitchers: A Review

I got off to a strange start with The Neyer/James Guide to Pitchers. The problem was, I couldn’t find it nor could anyone tell me if it was out or when in would be coming out. I tried Amazon, Barnes & Noble, Books-A-Million, and even those mall bookstores that only cary books about babysitter clubs and the Olsen twins. But thankfully, it finally appeared on the shelf at Barnes & Noble even as a surprise to the staff. Anyway, on to the other stuff.

The book is good, and worth buying just for the information on pitchers. I read in an ESPN chat Rob saying something to the annoying questioners “Look, it’s all in the book, stop asking if X or Y is in there. I promise.” You might think that this was a pompous exaggeration, but I find Neyer to be the opposite of pompous, so I was intrigued. You know what, he’s right. It is all in there. I have yet to think of a pitcher who has not been included. They do exist, but Neyer and James are not providing an encyclopedia of pitchers so that you can look up an old high school buddy who pitched one career inning. If the person was a serious MLB pitcher, you will find him, and that is a very nice feature.

But I did not buy the book for the encyclopedia, I bought it for the articles. The book starts off with 11 chapters on types of pitches. It includes a bit of history, science, and critical detective work on the most common pitch-types of baseball history. One thing I like about this section is a description of exactly what each pitch type is because I learned something new: it is not at all clear that we know. Pitchers, coaches, and writers seem to name different pitches the same thing and the same pitches different things. It’s quite understandable, but it never occurred to me. Neyer, who writes the chapters on the non-fastballs, does a particularly fine job of stressing this point.

The second part of the book includes 10 chapters on specific pitchers, plus the pitcher census. To be honest I have only skimmed the individual pitcher chapters, so I won’t comment on them. The census is the real meat of the book with over 300 pages of specific pitcher information. So what is in there? Well, it differs from pitcher to pitcher. At the minimum, for each pitcher there is a list of pitches in order of use. For some there are different pitch selections for different years, and sometimes there are scouting reports or other tidbits. In short, it is nice. I would really like to have seen a bit more light commentary. For example, what is the most interesting thing about Antonio Alfonseca? He has 12 fingers and 12 toes, yet the most we learn about him is his basic stats and he throws a hard sinker. No jokes? I have found at least one humorous entry about a bald kid-pitcher who gives up an unordinary number of line drives up the middle. But let me make it clear that I am NOT complaining; there is too much good stuff here to complain. The section also contains two good lists of submarine and knuckleball pitchers. Due to the wealth of information in the section, copies of this book ought to be hard-bound and chained to the bars of all sports pubs.

The third section of the book is all Bill James, with studies relating to pitchers. He takes on the Pitcher Abuse Points (PAP) model of Jazayerli and Woolner, and even allows them a chapter to respond, which is very classy. I’m not really all that familiar with this debate so forgive me if I get this wrong. I don’t like either of the approaches. I’ll leave it at that, since I don’t have any business commenting on something of which I am largely ignorant. Maybe one day I will investigate my suspicions that raw pitch counts are not comparable or very important. We get a Cy Young prediction formula and a suggested pitching code categorization method, which are largely just fun. The two chapters I really liked were the chapters “Lucky Bastards” and “Unique Records.” The former looks at pitcher Win-Loss records as compared to ERAs. This is very fun and useful. The latter is very fun, but not useful – James admits as much. In this chapter he highlights the most unique and unique Win-Loss records of baseball history. Fun but useless…I like it even more!

So now that I have described the book and told you I liked it I will list a few other things. One thing I really liked was the decision to separate the chapters by authors. This was a good decision, and probably a no-brainer. James and Neyer are both very good writers, but they have very different writing styles. Trying to make a Bill Neyer or a Rob James out of this book would have been a disaster. James is Bill and Neyer is Rob, and this makes the book very readable. Plus, you know each has commented on the other’s work so you feel a bit more confident in the quality of the work. I do have one HUGE criticism: where is DIPS!? There is no mention of the development of defense independent pitching statistics anywhere that I have found in the book. I admit there maybe something buried in the census, but I have not found it yet. Even if this is the case I expected a grand discussion of the topic. The big problem with evaluating pitching is the separating the individual input of pitchers from the joint product of preventing runs. Thanks to Voros McCracken and others working with and off his work we now have a tool to minimize this problem. Why not use it? As some readers may have seen I made somewhat of an attempt to do this myself (first try, second try), but I wish Neyer and James would have taken the opportunity to do it themselves. I have tried to ask Neyer this in ESPN web chats, but I keep joining in too late. If you have the inclination to submit a question to him during his weekly chat, please ask him. I want to know, as both authors know of DIPS and seem to think highly of it.

In summary, it’s a good book that is worth buying.

Prediction: The Norfolk Expos

Why? Well, I think DC is out. It has historically been a bad baseball city. But certainly things have changed there, right? Yes, it is even more of a crime-ridden hell-hole with traffic nightmares. Honestly, traffic circles are not a good idea. If it takes an hour to travel from the suburbs, why not go to Camden? The Northern Virginia Dulles idea is just silly. Talk about putting a team in the middle of nowhere. Las Vegas? Please. Norfolk wins by default. It is a top-30 MSA with no other distractions for fans, which will cause this team to be loved like no other. For example, dee the Charlotte Hornets before George Shinn’s yellow hair dye seeped through his skull. One thing I know about this is that the media never seems to have a clue about these things. Remember when Jacksonville was awarded the Jaguars. I think even the mayor of Jacksonville was quoted as saying “we have no chance to get the team” on the day of the announcement.

Other evidence? I have none. Just thinking on it after reading this.

Part II of DIPS: The Best, the Lucky, and the Not-So-Lucky

Thanks for all of the responses to Part I. Sorry for the delay. I was ready to post this in the morning until I found an error in my data, so I had to redo it all. Now I am back with a second round of numbers. The main critique of my first round was that I did not control for different seasons. I knew this would be a problem, but I really wanted to see a list that had two qualities. First, it must express ERA like we read everyday ERA. I really wanted to avoid an AERA or ERA+ number, just because it would be hard to translate, especially with DIPS-corrected numbers. Second, I wanted to avoid distorting DIPS ERA numbers due to differing values of runs across seasons. This is why I wanted to run one regression to generate the coefficients. However, the main thing I am missing is some sort of component to capture the quality of hitters in a given season. I think I found a good way to do what I want. And the results are different from what I posted earlier.

Here is the solution. I developed deflators for ERA, walks, strikeouts, and home runs based on the league averages for that year. I was then able to convert pitcher statistics into a single ERA as if all pitchers were pitching in 2003. For example, in 1968 the average ERA for pitchers in this sample was 3.03, compared to 2003′s average ERA of 4.51. The ratio of 2003 to 1968 ERAs is about 1.48. I can take this number and multiply it by all pitcher ERAs of 1968 to make them comparable to 2003 stats. This is similar to the method used for calculating price deflators (such as the CPI) to adjust money values for inflation. I use deflator indices to convert all past pitcher stats (walks, Ks, HRs, and ERA) to values comparable to the 2003 averages. Now, I’m ready to estimate the regression using the fielding independent components per 9 innings.

pERA = 2.80 + 0.46*BB – 0.17*K + 1.14*HR

Comparing these values to the previous estimates, I would say the adjusting the components for each year is an important step. I had figured it might just wash out, but that does not seem to be so. First, I present the top-25 pERA seasons from 1921-2003.

Rank First Last Year ERA Defl_ERA pERA
1 Pedro Martinez 1999 2.07 1.91 1.66
2 Dazzy Vance 1925 3.53 3.76 1.85
3 Pedro Martinez 2000 1.74 1.58 2.14
4 Pedro Martinez 2001 2.39 2.37 2.14
5 Lefty Grove 1930 2.54 2.48 2.18
6 Dazzy Vance 1924 2.16 2.43 2.19
7 Greg Maddux 1995 1.63 1.64 2.37
8 Harry Brecheen 1948 2.24 2.52 2.43
9 Cy Blanton 1935 2.58 2.84 2.43
10 Greg Maddux 1997 2.20 2.23 2.45
11 Kevin Brown 1998 2.38 2.34 2.46
12 Greg Maddux 1994 1.56 1.56 2.46
13 Dwight Gooden 1984 2.60 2.95 2.49
14 Carl Hubbell 1933 1.66 2.00 2.50
15 J.R. Richard 1980 1.90 2.15 2.53
16 Pedro Martinez 2002 2.26 2.31 2.55
17 Randy Johnson 1998 1.28 1.26 2.56
18 Roger Clemens 1997 2.05 2.08 2.57
19 Randy Johnson 1995 2.48 2.49 2.58
20 Pedro Martinez 2003 2.22 2.22 2.59
21 Lefty Grove 1928 2.58 2.94 2.61
22 Bill Gullickson 1981 2.80 3.42 2.62
23 Babe Adams 1922 3.57 3.99 2.62
24 Randy Johnson 2001 2.49 2.46 2.63
25 Dazzy Vance 1928 2.09 2.38 2.64

Though pitchers of recent history still dominate this list, it is not as extreme as the previous list. Pedro is still the king of pERA with 5 top-25 seasons with 3 in the top-5. Though he must be in decline since last season was only the 20th best pERA season ;-). Greg Maddux, Randy Johnson, and Dazzy Vance have 3 seasons a piece.

Again, for comparison here are the top-25 Deflator-Adjusted ERAs of the sample.

Rank First Last Year ERA Defl_ERA pERA
1 Randy Johnson 1998 1.28 1.26 2.56
2 Greg Maddux 1994 1.56 1.56 2.46
3 Doyle Alexander 1987 1.53 1.58 3.52
4 Pedro Martinez 2000 1.74 1.58 2.14
5 Greg Maddux 1995 1.63 1.64 2.37
6 Bob Gibson 1968 1.12 1.66 3.04
7 Dwight Gooden 1985 1.53 1.73 2.74
8 Red Munger 1944 1.34 1.77 3.48
9 Kevin Brown 1996 1.89 1.81 2.69
10 Steve Rogers 1973 1.54 1.81 3.90
11 Pedro Martinez 1999 2.07 1.91 1.66
12 Pedro Martinez 1997 1.90 1.93 2.76
13 George Witt 1958 1.61 1.93 3.93
14 Carl Hubbell 1933 1.66 2.00 2.50
15 Joel Pineiro 2001 2.03 2.01 3.18
16 Dean Chance 1964 1.65 2.05 3.38
17 Nolan Ryan 1981 1.69 2.07 3.15
18 Jim Hearn 1950 1.94 2.07 3.72
19 Ron Guidry 1978 1.74 2.08 2.85
20 Roger Clemens 1997 2.05 2.08 2.57
21 Cal Eldred 1992 1.79 2.10 3.33
22 Sandy Koufax 1966 1.73 2.13 3.09
23 J.R. Richard 1980 1.90 2.15 2.53
24 Sandy Koufax 1964 1.74 2.17 3.08
25 Tiny Bonham 1940 1.90 2.17 3.13

Now to the top-25 luckiest pitchers, as denoted by the pRatio.

Rank First Last Year Defl_ERA pERA pRatio
1 Doyle Alexander 1987 1.58 3.52 2.23
2 Steve Rogers 1973 1.81 3.90 2.15
3 Randy Johnson 1998 1.26 2.56 2.03
4 George Witt 1958 1.93 3.93 2.03
5 Leo Dickerman 1924 2.71 5.49 2.03
6 Red Munger 1944 1.77 3.48 1.96
7 Bob Gibson 1968 1.66 3.04 1.83
8 Jim Hearn 1950 2.07 3.72 1.80
9 Andy Benes 2002 2.84 5.02 1.77
10 Brian Bohanon 1998 2.36 4.18 1.77
11 Roger Craig 1959 2.38 4.17 1.75
12 Al Benton 1949 2.38 4.13 1.73
13 Freddie Fitzsimmons 1941 2.44 4.20 1.72
14 Steve Sundra 1939 3.00 5.14 1.72
15 Jim Konstanty 1944 3.70 6.31 1.70
16 Ken Holtzman 1967 3.38 5.70 1.69
17 Floyd Youmans 1985 2.77 4.66 1.68
18 Woody Williams 2001 2.26 3.79 1.68
19 Rube Melton 1946 2.70 4.50 1.66
20 Joey Jay 1958 2.57 4.27 1.66
21 Greg Harris 1991 2.48 4.12 1.66
22 John Candelaria 1977 2.56 4.25 1.66
23 Dean Chance 1964 2.05 3.38 1.64
24 Hal Dues 1978 2.82 4.63 1.64
25 Pete Smith 1992 2.41 3.95 1.64

It is interesting that this list is not all that different from the first list. Finally, here is the unlucky top-25.

Rank First Last Year Defl_ERA pERA pRatio
1 Dazzy Vance 1925 3.76 1.85 0.49
2 Chris Zachary 1971 6.72 3.57 0.53
3 Ramiro Mendoza 1996 6.50 3.53 0.54
4 Seth Morehead 1958 7.03 3.97 0.57
5 Lefty Grove 1934 7.02 3.97 0.57
6 Camilo Pascual 1955 6.95 3.99 0.57
7 Bobo Newsom 1942 6.42 3.70 0.58
8 Herman Besse 1942 8.02 4.63 0.58
9 Jon Lieber 1995 6.35 3.71 0.58
10 Johnny Babich 1935 7.32 4.31 0.59
11 Slim Harriss 1926 5.13 3.06 0.60
12 Benny Frey 1935 7.53 4.51 0.60
13 Micah Bowie 1999 9.20 5.52 0.60
14 Ken Holloway 1926 5.89 3.54 0.60
15 Dutch Leonard 1949 4.66 2.80 0.60
16 Ted Blankenship 1924 5.63 3.39 0.60
17 Bob Muncrief 1946 6.78 4.10 0.60
18 Milt Pappas 1968 8.32 5.05 0.61
19 Rick Wise 1968 6.76 4.10 0.61
20 Mike Parrott 1981 6.21 3.83 0.62
21 Roy Halladay 2000 9.64 5.94 0.62
22 George Murray 1926 6.49 4.00 0.62
23 Brad Havens 1983 9.36 5.78 0.62
24 Milt Gaston 1928 6.28 3.88 0.62
25 Hal Gregg 1947 7.03 4.36 0.62

Again, I see some familiar faces. And Dazzy Vance’s second best pERA of all time is also the most unlucky season. I guess that is not surprising.

Anyway, this has been a fun exercise, and I have appreciated all of the suggestions and comments that I have received. I am happy to listen to more.

DIPS: The Best, the Lucky, and the Not-So-Lucky

I’ve been enjoying The Neyer/James Guide to Pitchers in my spare moments over the past few days. I hope to post a review by next week, but one of the chapters in the book intrigued me. In the chapter “Lucky Bastards” Bill James rates the luckiest and unluckiest pitcher seasons and careers of all-time. When I first saw the chapter I got overly excited, because I thought it would use Defense Independent Pitching methods (DIPS or FIP) to analyze pitchers. As an economist, I find DIPS to be a fascinating tool for untangling the joint product of preventing runs. Unfortunately, although not all that unfortunate, James estimates the luck in Wins and Losses versus what the ERA would predict. Certainly, it is still an interesting exercise, but not what I was looking for. To me, pitchers Wins and Losses are so irrelevant that it is almost not worth discussion. Certainly, Wins are correlated with good pitching, so they contain some good information. But, they also contain bad information: the quality of the offense of the team, which is largely irrelevant to how a pitcher pitches. Since we have ERA, why bother looking at Wins? [Insert Joe Morgan jab here]

Rather than fret, I decided to develop my own list of best pitching seasons using DIPS as a theoretical motivator. The idea behind DIPS is to analyze how good pitchers are at preventing runs without relying on defense. We simply remove the balls put into play by hitters and judge pitchers on events that involve only the pitcher and batter. The original DIPS ERA, as discovered by Voros McCracken, involves a complicated formula that is a bit cumbersome for what I want to do. It includes calculations for handedness, knuckleballers, hit batters, etc. Tangotiger has developed FIP (or Fielding Independent Pitching), which focuses on the three main fielding independent statistics, walks, strikeouts, and HRs. However, Mr. Tiger calculates his number via linear weights, which I am not going to do here (and probably cause me a much deserved scolding). So here is what I have done.

First, I gathered all pitcher seasons (using the Lahman database) from 1921-2003 for pitchers who started more than 10 games. From this, using linear regression I estimated the impact of walks, strikeouts, and HRs (all normalized per 9 innings pitched) on ERA. Here is the estimate.

pERA = 2.45 + 0.38*BB – 0.19*K + 1.59*HR

I will call this stat pERA rather than dERA or FIP, to avoid confusion with these already established numbers. I only went back to 1921, because that is the year in which the spitball was abolished and HRs became a dominant part of the game. I can’t imagine what an 1880s DIPS ERA would even mean. I could have done different estimates for each year, because the variables may have slightly different impacts on ERA in different seasons. I decided instead on treating each pitcher’s numbers the same. I have done several estimates on different seasons and I find very little difference across seasons. Plus, I am curious in identifying good/bad DIPS seasons, and I don’t want to punish a pitcher for having a great strikeout year in a year when runs are scarce. Quibble if you want, but now it is on to step two.

Next, I ranked the pitcher-seasons by pERA. Here is the list of the top-25 pERA seasons in the modern era.

Rank Last First Team Year ERA pERA
1 Martinez Pedro BOS 1999 2.07 1.11
2 Martinez Pedro MON 2001 2.39 1.38
3 Johnson Randy ARI 1998 1.28 1.80
4 Martinez Pedro MON 2000 1.74 1.81
5 Brown Kevin TEX 1998 2.38 1.82
6 Gooden Dwight NYN 1984 2.60 1.86
7 Maddux Greg ATL 1995 1.63 1.88
8 Clemens Roger NYA 1997 2.05 1.90
9 Johnson Randy SEA 1995 2.48 1.92
10 Maddux Greg CHN 1994 1.56 1.92
11 Johnson Randy SEA 2001 2.49 1.94
12 Martinez Pedro BOS 2003 2.22 1.94
13 Maddux Greg CHN 1997 2.20 1.98
14 Martinez Pedro MON 2002 2.26 2.00
15 Halladay Roy TOR 2001 3.16 2.09
16 Richard J.R. HOU 1980 1.90 2.09
17 Clemens Roger BOS 1990 1.93 2.11
18 Gibson Bob SLN 1968 1.12 2.14
19 Martinez Pedro BOS 1997 1.90 2.16
20 Gullickson Bill CIN 1981 2.80 2.20
21 Koufax Sandy LAN 1963 1.88 2.21
22 Brown Kevin TEX 1996 1.89 2.24
23 Clemens Roger BOS 1988 2.93 2.27
24 Maddux Greg ATL 1996 2.72 2.27
25 Prior Mark CHN 2003 2.43 2.27

A few things jump right out and surprise me. First, this seems like a who’s who of very recent pitchers. Only two of the seasons on the list occurred before I was born, and I’m 30. Only five of these seasons occurred before 1990. Why? That is a puzzle I will leave alone for now, but I think it has to do with the rise of the strikeout. But there is more. Pedro has thrown six of the top-25 pERA seasons ever, and 3 of the top-5. Greg Maddux comes in second with 4 seasons. Randy Johnson and Roger Clemens tie for third with 3 seasons. Four men have pitched 16 of the top-25 pERA seasons ever.

For comparison here is the list of the top-25 ERA seasons of all-time for this sample of pitchers.

Rank Last First Team Year ERA pERA
1 Gibson Bob SLN 1968 1.12 2.14
2 Johnson Randy ARI 1998 1.28 1.80
3 Munger Red SLN 1944 1.34 3.06
4 Alexander Doyle NYA 1987 1.53 3.08
5 Gooden Dwight DET 1985 1.53 2.30
6 Rogers Steve MON 1973 1.54 3.40
7 Maddux Greg CHN 1994 1.56 1.92
8 Tiant Luis BOS 1968 1.60 2.53
9 Witt George PIT 1958 1.61 3.30
10 Maddux Greg ATL 1995 1.63 1.88
11 Chandler Spud NYA 1943 1.64 2.54
12 Chance Dean CLE 1964 1.65 2.58
13 Hubbell Carl NY1 1933 1.66 2.37
14 Ryan Nolan CAL 1981 1.69 2.57
15 Koufax Sandy LAN 1966 1.73 2.41
16 Guidry Ron NYA 1978 1.74 2.46
17 Koufax Sandy LAN 1964 1.74 2.37
18 Martinez Pedro MON 2000 1.74 1.81
19 Pollet Howie SLN 1943 1.75 2.72
20 Seaver Tom NYN 1971 1.76 2.33
21 Cooper Mort BSN 1942 1.78 2.80
22 Eldred Cal ML4 1992 1.79 2.73
23 Newhouser Hal CLE 1945 1.81 2.71
24 McDowell Sam DET 1968 1.81 2.72
25 Blue Vida SFN 1971 1.82 2.62

Next, I want to identify the luckiest and unluckiest ERA seasons in terms of pERA. At the heart of DIPS is the idea that batting average on balls-in-play is random. One of McCracken’s most shocking findings was that the previous season’s DIPS ERA is a better predictor of ERA and the previous season’s ERA because the in-play average distorts a raw ERA. In addition, some pitchers may be helped out by good defense. I simply want to see what pitchers have been able to accomplish on their own relative to their ERA, which is helped or hurt by random chance and defense. To do this I calculate the ratio of the pERA to actual ERA in a season, which I label as pRatio. As the number falls below 1 the pitcher has been unlucky with outside factors, while as the number rises above one the pitchers has been more lucky. Maybe I shouldn’t call this measure “luck” but what it does tell us how much the actual ERA is helped or hurt by non-DIPS factors. So here is the list of the top-25 “luckiest” pitchers in the sample.

Rank Last First Team Year ERA pERA pRatio
1 Munger Red SLN 1944 1.34 3.06 2.280
2 Rogers Steve MON 1973 1.54 3.40 2.211
3 Witt George PIT 1958 1.61 3.30 2.048
4 Alexander Doyle DET 1987 1.53 3.08 2.012
5 Melton Rube BRO 1946 1.99 3.90 1.958
6 Gibson Bob SLN 1968 1.12 2.14 1.911
7 Hearn Jim SLN 1950 1.94 3.66 1.884
8 Benton Al DET 1949 2.12 3.95 1.862
9 Craig Roger BRO 1959 2.06 3.81 1.851
10 Holtzman Ken CHN 1967 2.53 4.61 1.823
11 Jay Joey ML1 1958 2.14 3.83 1.789
12 Fitzsimmons Freddie NY1 1941 2.07 3.64 1.760
13 Dickerman Leo SLN 1924 2.41 4.22 1.749
14 Mahaffey Art PHI 1960 2.31 4.04 1.747
15 Candelaria John PIT 1977 2.34 3.99 1.706
16 Beggs Joe CIN 1946 2.32 3.93 1.695
17 Antonelli Johnny SFN 1954 2.30 3.89 1.692
18 Dues Hal MON 1978 2.36 3.99 1.691
19 Benton Al DET 1945 2.02 3.41 1.687
20 Chandler Spud NYA 1942 2.38 4.00 1.679
21 Pierce Billy CHA 1955 1.97 3.31 1.678
22 Pollet Howie SLN 1946 2.10 3.50 1.668
23 Howard Bruce CHA 1966 2.30 3.82 1.659
24 Benes Andy ARI 2002 2.78 4.58 1.647
25 Bearden Gene CLE 1948 2.43 3.98 1.639

Five of these men appear on the top-25 ERAs of all-time in the sample (Munger, Rogers, Witt, Alexander, and Gibson), four of them with pERAs of greater than three. Here are the unlucky top-25.

Rank Last First Team Year ERA pERA pRatio
1 Mendoza Ramiro NYA 1996 6.79 3.33 0.491
2 Grove Lefty BOS 1934 6.50 3.42 0.526
3 Martinez Pedro BOS 1999 2.07 1.11 0.536
4 Lieber Jon PIT 1995 6.32 3.41 0.540
5 Rodriguez Frank MIN 1998 6.56 3.61 0.550
6 Frey Benny CIN 1935 6.85 3.79 0.553
7 Babich Johnny BRO 1935 6.66 3.72 0.559
8 Zachary Chris SLN 1971 5.32 2.99 0.562
9 Bowie Micah CHN 1999 9.96 5.63 0.565
10 Rusch Glendon NYN 2003 6.42 3.67 0.571
11 Berenyi Bruce CIN 1984 6.00 3.46 0.577
12 Martinez Pedro MON 2001 2.39 1.38 0.577
13 Elliott Hal PHI 1930 7.67 4.44 0.579
14 Leverett Dixie CHA 1929 6.36 3.73 0.587
15 Donohue Pete CIN 1930 6.13 3.63 0.592
16 Smith Zane ATL 1995 5.61 3.33 0.593
17 Kolp Ray CIN 1924 5.68 3.40 0.599
18 Irabu Hideki NYA 2000 7.24 4.35 0.601
19 Halladay Roy TOR 2000 10.64 6.41 0.602
20 Gubicza Mark KCA 1991 5.68 3.45 0.607
21 Pascual Camilo WS1 1955 6.14 3.76 0.612
22 Pruett Hub PHI 1927 6.05 3.71 0.613
23 Burkett John SFN 1998 5.68 3.49 0.614
24 Blankenship Ted CHA 1924 5.01 3.08 0.615
25 Holloway Ken DET 1926 5.12 3.15 0.615

I would call this the list of guys you could have stolen for your fantasy team after these years. I seem to recall my grandfather telling me how he jobbed everyone in his league by picking up Lefty Grove after the 1934 season when everyone thought he was finished ;-).

I have a few final words. I wanted to at least make the HRs park-neutral, but I do not have easy access to such a large number of HR park-factors over this time period. Also, I did nothing to modify ERA for years, such as calculating ERAs relative to the average. I did this, well, because I wanted to look at raw ERA numbers. I am more interested in the comparison to of the pERA to the raw number that we are oooing and ahhhing over. Anyway, so these are the lists I wanted to see. I hope you enjoy, and please feel free to send thoughts and suggestions. I am certainly not married to the list.

Cuba Sucks

David Pinto posts some of the sad story of the Jose Contreras family.

That is all.

What’s Up with Turner South?

For many years the Braves have been a staple on TBS. When the Braves played, you could watch them on TBS. But over the past few seasons the ownership of the team has decided to put some games on TBS’s sister network Turner South. Right now the Braves are doing something really weird by putting the Braves on Turner South for nearly two weeks strait. Why?

I think the official AOL reasoning for this is to hype TBS’s new image with shows like “Sex in the City” and “Outback Jack.” But, I think there is something else at play here. The Braves are a boon to TBS. While “Sex in the City” will get ratings, so will the Braves. Why banish the Braves to new home which fewer cable packages provide? Well, I think the main reason has to do with hyping Turner South. If you put the Braves on a channel off the basic package, Braves fans are going to complain to their cable company. This is a good way for AOL to get Turner South, and its shows like “Junkin’,” “Off the Menu,” and “Liars and Legends” some advertising. This is consistent with last year’s banishing of Skip and Joe to Turner South. The network stated that Skip was somehow too much of a homer for TBS’s national image. I never bought it. Almost all sports announcers are homers, and fans want Skip Carey. He was the bait.

So why not just put “Sex in the City” on Turner South? Fewer people are going to call their cable companies to demand a channel for a show they have never seen (“Sex” was on HBO). And why now? Why not do this at the start of the season? Well, the network wanted fans to develop a taste for this team.

Anyway, just some random thoughts. The switch doesn’t bother me at all, because I get both channels with Direct TV.

Travis Smith Again

Avkash Patel at The Raindrops has an interesting theory as to why Travis Smith seems to have no problems with his strikeouts and walks in the Majors while his homers have ballooned. He e-mailed me the following:

Smith’s troubles with the long ball aren’t all that surprising. Major League hitters are simply better than minor leaguers, with the biggest gap distinguishing them plate discipline and power. The BBs, Ks, and HRs are not independent of each other. Smith needs to throw strikes, and a lot of them, to keep his BB:K numbers where they are; however, he simply doesn’t have the stuff or ability to do so without paying the price in homers.

It also isn’t surprising if you look at his minor league numbers, all the way from a ball in Stockton in 1996 through 2002 in Triple-A. Every time he’s moved up a level, he’s either kept his BB:K numbers the same while his homers have gone up, or he’s kept the home run rate stable while the Ks go down.

Here is how I view Avkash’s theory. I suspect many non-pitchers could go on the mound and throw nothing but strikes and never walk anyone. However, despite their low walk rates, they would give up a zillion hits and homers. To get Ks and avoid walks, you have to throw strikes. Travis Smith does this. Unfortunately, while his stuff is good enough to avoid walks and get some Ks it also increases the likelihood that he will give up the long-ball.

The only problem that I see with this theory is that Smith’s Ks ought to fall along with his HR rise if the better competition is the reason for his new-found love for the HR ball. That has not happened yet, though it has happened with past minor league jumps. However, the sample size here is very small. If Smith continues to pitch — and I suspect he will considering the Braves other options — his Ks ought to fall if the theory is correct. Better hitters ought hit both more HRs and strikeout less, not do one or the other. Hopefully for Braves fans, it is his HRs — not his Ks — that will fall. We will just have to wait and see.

Thanks again to Avkash for excellent idea. Too bad he’s a Mets fan (bleh). ;-)

A Review of Harry Potter and the Prisoner of Azkaban

Ok, this is way off topic, especially considering my prolonged absence from blogging, but I just have post my thoughts on this. I haven’t seen many reviews that mirror my thoughts on this.

Last week the grandparents babysat so my wife and I could see the new Harry Potter. We are both fans of the books and liked the first two movies. It had been a few years since I had read the book so I reread it to be ready for the movie. I am glad I did because otherwise I think I would have been lost. I kept hearing this movie was different “in a good way.” I didn’t know what that meant until about 15 minutes into the movie. This is a movie directed by an art-house goon who had a very different view of the books than I do. To me Harry Potter lives in a fairy tale world that though similar to reality, it is most definitely a fantasy. The stories of JK Rowling are very good and central to the enjoyment of the series, but the world and its little delights are just as enjoyable. Alfonso Cauron seemed to like neither the story nor the world as both are different from the previous movies and the book. The Whompimg Willow and Hagrid’s hut have mysteriously and unnecessarily changed locations and looks. The children are dressed in American clothes and rarely wear their required school wizard robes. Add to these minor annoyances, we get only a brief glimpse of quidditch and the obsessive team captain Wood is not seen at all. This really annoys me, because not only is Harry in love with quidditch, but one of the most important and funny scenes takes place in the match against Slytherin. Malfoy, Crabbe, and Goyle dress up as dementors and Harry, who is fooled, scares the pants off his enemies while overcoming his fears.

The lighting used in the movie made me feel like I was watching Braveheart. One of the things I like about the Potter stories is the escape from reality. My wife once said to me, “I wish I could go to Hogwarts,” and I agree. I wasn’t sure Harry was not mistakenly sent to St. Brutus’s in this movie. The Hogwarts charm is notably absent. The teachers are mean, the students are bullies, and even the portraits on the wall insult you…not to mention the hideous guards who suck out your happiness but can’t seem to keep a crazed killer out of the dormitory. And I don’t like the new old-hippie Dumbledore.

And then there is the editing. The first three-fourths of the movie is just a highlight reel of the book. A few choice scenes, some differing from the book, were pasted together as if only to appease those who had read the book. It is very choppy. Once they were out of the way we could proceed with the ending, which was the best part of the movie. We finally get some dialog driven scenes in the shrieking shack, although I think more explaining is needed for those who did not read the book.

Overall, I liked the movie, though I would have preferred the Christopher Columbus true-to-the-book formula. Luckily, the story is too good to let a poor artistic interpretation ruin it.

What is Wrong With Travis Smith?

The other day I posted my support for the call-up of Travis Smith. The guy was smoking in Richmond and seemed to be primed for success. In fact, the funny thing is that he has almost continued his performance at Richmond with the big club. With Richmond is BB/9 and K/9 were 2.3 and 7.8 respectively. With Atlanta those numbers have been 2.1 and 7.9. Smith’s divergence in MLB from the minors is with the HR ball, and I find this very weird. In Richmond Smith gave up 0.35 HR/9; in Atlanta he had given up 3 HR/9. Is this normal for a pitcher to continue his success in walking and striking out batters while completely losing it with respect to HRs? I wonder if he is just nervous and having some bad luck.

Unfortunately, Smith seems to have done this before. Smith’s career minor league numbers for the main Fielding Independent Pitching statistics (FIP or DIPS) are 2.6 BB/9, 6 K/9, and 0.75 HR/9. In 2002 he pitched for the Cardinals in 12 games where he posted 3.3 BB/9, 5.3 K/9, and 1.66 HR/9. I have never heard of such a strange quality. I would not be surprised if his HR-rate is what it is if his walks and strikeouts also became worse, but they have not. Why wasn’t Smith prone to the long ball in the minors or why does he strikeout and walk Major League batters as if they were in the minors?

I am very puzzled by this. This might be a common phenomenon of which I am unaware. If so please let me know. I am open to explanations, because I can’t think of any. Though Smith is struggling, I am not ready to jump off his bandwagon just yet. He is doing something right and I don’t see any reason why he cannot get his HRs under control, especially with the help of coach Leo.