Creating a dynamic FIP with BaseRuns

If you’re interested in starting a fistfight at the next SABR convention (not that I’m advising this) simply start bringing up DIPS in casual conversation loudly enough and I’m sure you can get something going. Voros McCracken set up the sabermetric version of the “less filling, tastes great” argument when he wrote:

There is little if any difference among major-league pitchers in their ability to prevent hits on balls hit in the field of play.

Suffice it to say that not everyone agrees with this.

But what everyone does agree on is that pitchers have far less control over the outcome of a ball in play than they do over the so-called Three True Outcomes: the walk, the strikeout and the home run.

From this, McCracken constructed dERA, essentially a run estimation model that attempts to isolate a pitcher’s performance from that of his defense.

For those looking for a quick-and-dirty shortcut for dERA, Tom Tango’s FIP is generally relied upon:


3.2 is the league factor that puts FIP on the same scale as ERA.

FIP is also often used as a sort of component ERA, to estimate a player’s ERA from his projected component stats. There is, of course, Bill James’ Component ERA for those purposes as well. (Confoundingly enough, Component ERA is traditionally abbreviated ERC. Since "Earned Runs Created" describes what ERC is and does perfectly, that’s what I tell myself ERC stands for.)

So I decided to run a comparison of some of these run estimators.

Read more of this post


When They Were Young

Generally speaking, aging curves of players have shown that up until 28 years old someone can be expected to improve.  Once past that benchmark a skillset will begin to decline.  For some it will be more gradual than others but this is what those in the analytical community have come to expect.  On Thursday we looked at the only ten pitchers from 1956 until now that have been primarily starters from the age of 38-44, finding that Nolan Ryan has the highest average game score while Roger Clemens’ 134 ERA+ slightly outdoes the 131 of Randy Johnson for the lead.  Also of note is how all ten pitchers posted an above average ERA+, with David Wells and Tommy John’s 101 as the minimum.
I mentioned how there would be an inherent bias in that only the pitchers effective enough on the mound would be able to hang around as a starter this late into their years so it should almost be expected to find quality numbers.  We then looked at all pitchers age 45+ and saw the group whittle down to five, with Jamie Moyer emerging as the best of the bunch.  This catalyzed a new train of thought: how do the age 38+ seasons for these ten pitchers compare with their first seven or so seasons?  Were they better via these metrics as old-timers?
The goal involved comparing the first seven years to the age 38-44 years, however some players started their careers as relievers so, as long as they became a starter within a reasonable amount of time (2-3 years) I went ahead and recorded their statistics during their first seven years as a starter.  The only pitcher this could not be done for was Charlie Hough, who was a reliever for a significant portion of his career’s beginning before becoming a starter a bit later on.  I also could not use the Average Game Score as a measure because Warren Spahn’s career began prior to 1956; before that point the Retrosheet game logs are fewer and further between in terms of existence or proofing (some may exist but are not 100% sure to be correct).
Here are the comparisons, with A representing their early years and B representing elderly prformance, all in terms of their average yearly performance:

  • Nolan Ryan A: 207 IP, 1.82 K/BB, 113 ERA+
  • Nolan Ryan B: 208 IP, 2.75 K/BB, 114 ERA+
  • Randy Johnson A: 205 IP, 2.14 K/BB, 121 ERA+
  • Randy Johnson B: 158 IP, 4.59 K/BB, 131 ERA+
  • Roger Clemens A: 216 IP, 3.10 K/BB, 125 ERA+
  • Roger Clemens B: 179 IP, 2.96 K/BB, 134 ERA+
  • Warren Spahn A: 267 IP, 1.46 K/BB, 107 ERA+
  • Warren Spahn B: 246 IP, 1.90 K/BB, 105 ERA+
  • Phil Niekro A: 267 IP, 2.48 K/BB, 125 ERA+
  • Phil Niekro B: 265 IP, 1.76 K/BB, 114 ERA+
  • Charlie Hough A: 106 IP, 1.30 K/BB, 106 ERA+
  • Charlie Hough B: 221 IP, 1.31 K/BB, 107 ERA+
  • Gaylord Perry A: 281 IP, 2.79 K/BB, 122 ERA+
  • Gaylord Perry B: 213 IP, 2.27 K/BB, 106 ERA+
  • Jamie Moyer A: 122 IP, 1.64 K/BB, 93 ERA+
  • Jamie Moyer B: 210 IP, 2.20 K/BB, 107 ERA+
  • David Wells A: 176 IP, 2.52 K/BB, 109 ERA+
  • David Wells B: 162 IP, 3.45 K/BB, 101 ERA+
  • Tommy John A: 194 IP, 1.88 K/BB, 118 ERA+
  • Tommy John B: 160 IP, 1.27 K/BB, 101 ERA+

Looking at the results for each of these ten pitchers we see that nine of them tended to post better numbers in their younger years.  Sure, there are instances of these nine posting better K/BB ratios as elder statesmen, but their durability and ERA+ would decrease.  Nolan Ryan’s ERA+ and IP stayed the same but his K/BB shot up when older.  Randy Johnson actually had a better K/BB and ERA+ while older despite many more IP as a youngster.  Roger Clemens had better IP when young, a better ERA+ when old, and the same K/BB essentially.
Warren Spahn had better IP when young, a better K/BB when old, and essentially the same ERA+.  Phil Niekro was better at everything when young.  Charlie Hough is tough because his early years were as a reliever, but, still, he posted virtually idential K/BB and ERA+ numbers.  Gaylord Perry joined Niekro as posting better counts in all three categories when young.  David Wells had better IP and ERA+ numbers when young but a much better K/BB later on.  And Tommy John posted better numbers of these three metrics when young.
That leaves just Jamie Moyer, who was the only one here to post better numbers in all three categories when older.  He was below average as a youngster and increased that to 7% above average as an old-timer.  Not only is Jamie Moyer potentially the best 45+ pitcher in the Retrosheet era (1956+) but he is the only one of these ten to legitimately improve in all three of these areas (durability, strikeouts to walks, ERA+) from his first seven seasons to his age 38-44 seasons.

Recapping the BIP

Before even getting into the meat of this article, no, the title does not refer to Bip Roberts… so I’ll understand if hardcore fans of his are now turned off.  What the title does refer to, however, is balls in play and how they pertain to the statistics BABIP, FIP, and ERA.  I have written a lot here and on my other stomping grounds of late about how some of these statistics are affected and, seeing as it is a holiday weekend with not much interweb usage, it seemed like the logical time to recap everything into one neat package.  For starters, what are these three statistics?
BABIP: Batting Average on Balls In Play is a statistical spawn of the DIPS theory discovered by Voros McCracken at the turn of the century.  Essentially Voros found that pitchers have next to no control over balls put in play against them, which is why certain pitchers would surrender a ton of hits one year and much less the next.  From a control standpoint, the goal of the pitcher would be to get an out.  Once a ball is put in play, unless it is hit right back to the pitcher many defensive aspects have to coincide for an out to result.  Take a groundball for instance, one between shortstop and third base: both fielders have to understand whose territory the ball occupies and that fielder has to have the proper range in order to field it, all in a very short amount of time. 
There are plenty of other variables as well but what should be clear is that the pitcher has no control over them.  He may have control over sustaining a certain percentage of balls in play each year but the hits that result are almost entirely out of his hand.  In fact, the only aspects of pitching over which he has any type of control are walks, strikeouts, and home runs allowed.  Everything else is dependant on the fielding and luck.
BABIP is calculated by dividing the Hits minus Home Runs by the Plate Appearances excluding Home Runs, Walks, Strikeouts, and Sacrifice Flies.  If Player A has 30 hits out of 90 at-bats he will post a .333 batting average.  But if 8 of those 30 hits are home runs and 8 of the outs are strikeouts, in BABIP terms he would be 22 for 74, or .297.  This explains that, of all balls put in play–any hit or batted out other than a home run–29.7% fell in for hits.
FIP: a creation of Tom Tango’s, Fielding Independent Pitching takes the three controllable skills of walks, strikeouts, and home runs allowed, properly weights them, and then scales the result similar to the familiar ERA.  The end result explains what a pitcher’s skillset suggests his ERA should be around.  Someone with an ERA much lower than their FIP is usually considered to be lucky while the inverse is also true.  The statistic is kept at Fangraphs and ERA-FIP was recently added as well in order to allow readers a glimpse at those under- or overperforming their controllable skills.
ERA: arguably the most popular pitching barometer, ERA can be calculated by multiplying the earned runs of a pitcher by nine and dividing that product by the total number of innings pitched.  While not a terrible stat it suffers from some pretty drastic noise.  For starters, what are earned runs?  The surname ‘earned’ implies there are other runs that can be given up and that these must satisfy a specific criteria.  For instance, if a fielder botches a routine play with two outs, and the pitcher then gives up seven runs, none will be earned because the inning was extended by the poor play of the fielder.  This gets into all sorts of questions regarding exactly what an error is and how that factors into a pitcher’s performance.
Earned runs are also a direct result of hits, which have been proven to be largely accrued through chance via the DIPS theory.  So, if pitchers cannot control the percentage of hits they give up on balls in play, then fluctuations in hits can either inflate or deflate an ERA regardless of the pitcher’s skill level.  Therefore the FIP is more indicative of performance level because it only measures the three aspects of pitching he has control over which should not suffer from much fluctuation at all, as Pizza Cutter showed not too long ago that these skills were some of the quickest to stabilize.
Controlling BABIP
At Fangraphs we occasionally call upon a statistic we titled xBABIP, which refers to what the BABIP of a pitcher can be expected to be given his percentage of line drives.  Dave Studeman found a few years back that the general range of BABIP could be predicted with very good accuracy by adding .12 to the LD%; if a pitcher surrendered 22.1% line drives his xBABIP would be ~.341.  Using this for predictive purposes would not be correct due to the fact that the general baseline for pitchers is .300.  What we can do is evaluate performance at a given time and attribute line drives to a rather high or low BABIP.  For instance, saying that Player B’s BABIP of .275 as of today primarily due to his ultra-low 14-15% LD rate would be correct; saying that it will continue like this would not.  The line drive percentage may change as the season goes on.  In summation, we can use something like this when evaluating the past for pitchers but not the future.
David Appelman showed not too long ago that, in 2007, 15% of flyballs fell in for hits, 24% of grounders turned into hits, and a whopping 73% of line drives also followed suit.  Due to this, the ideal xBABIP calculation would be .15(FB) + .24(GB) + .73(LD).
I have done studies here recently, and Jonathan Hale at Baseball Digest Daily has done others in the past as well, that show how aspects like velocity, movement, and location can all affect the BABIP of a given pitcher.  It also been shown, again by Studeman, that elite relievers have the ability to consistently post lower BABIPs than others.  More studies have shown that pitchers, if any, have very weak control over their BABIP but instead of deeming it control I would be more inclined to say that these pitchers are merely taking advantage of “cold spots.” 
If just 15% of flyballs result in hits and such a large number of line drives do, then we could intuitively expect someone with consistently low LD rates and higher FB rates to post lower BABIPs.  From a movement perspective, I found that those with above average vertical movement in different horizontal movement subgroupings post lower BABIPs as well.  Higher vertical movement usually correlates to flyballs, and voila, flyballs have the lowest percentage of hits.
This was just a recap of the three statistics and explanations pertaining to their usage.  Based on this, if we see someone like Carlos Zambrano, whose ERA consistently beats his FIP, based on consistently posting lower BABIPs, we could somewhat safely assume that he might not be controlling anything persay but rather taking advantage of all the aspects proven to result in lower BABIPs.  His controllable skills may not be as good as his ERA would suggest but movement, velocity, and location may have combined to greatly aid his efforts.

The Most Important Pitch: A Look at Greg Maddux and 1-1 Counts

There are twelve possible ball-strike counts in a given plate appearance.  Ranging from the initial 0-0 to the dramatic 3-2, these counts shift in favor of either the batter or the pitcher.  A 2-0 count favors the hitter; if the pitcher misses the count will run to 3-0.  Along similar lines, an 0-2 count favors the pitcher because the batter will theoretically be more likely to swing at junk in an effort to protect himself.
Of all twelve, Greg Maddux considers the 1-1 count to be of utmost importance.  Though some may spot the identical numbers and deem the count neutral, the linear weights run expectancy shows it favors the pitcher.  Missing on a 1-1 count shifts the momentum back towards the hitter whereas a successful 1-1 pitch can move the count’s favor further in the direction of the pitcher.  The 1-1 count brings with it a run expectancy of -0.012 from the batter’s perspective; a ball shifts it to +0.037 whereas a strike causes a jump to -0.079.  Maddux is right.
This is the third and final (for now) look at Greg Maddux’s theory and selection in certain situations using Pitch F/X data.  Previously, we have looked at Maddux’s “playbook” vs. Bengie Molina as well as his selection, location, and results in 0-2 counts, in which he does not like to throw waste pitches.  Here we are going to conduct a similar analysis to the 0-2 article but with regards to his 1-1 counts.  Be sure to note that not all of his starts were recorded by the Pitch F/X system last year.
Maddux primarily throws his two-seam fastball, a changeup, and a cutslide. Though “slutter” sounds funnier for the combo cutter/slider, this blog has a PG rating… though nowadays even PG allows naughty words and innuendos.. anyway, back to baseball. Here is a breakdown of Maddux’s pitches and results to lefties and righties:
Since he has thrown more pitches to righties, seeing the percentages of pitches thrown to each batting handedness can help show discrepancies in either approach or selection. To righties, Maddux has thrown 58.9% fastballs, 25.2% changeups, and 15.9% cutslides; to lefties, 55.3% fastballs, 32.5% changeups, and 12.3% cutslides. Clearly, he uses the cutslide sparingly. Maddux has thrown three percent less fastballs to lefties, as well as three percent less cutslides; the difference has been made up with over six percent more changeups.
Here is a location chart of his fastballs thrown to both lefties and righties, with lefties always on the left:
The biggest difference between results here is the amount of called strikes. When facing righties, Maddux has gotten many called strikes on 1-1 counts whereas he has just four when pitching to lefties. Though he clearly favors the outside corner to both types of batters, lefties have made contact with the corner pitches while righties seem to be more inclined to take the pitch. Due to his fastball having the tailing movement, righties tend to think pitches like this are outside; when it tails back to the outside corner for a strike it catalyzes many glances back at the umpire.
Here is a location chart of the changeups thrown:
The results of his changeups thrown to each batting handedness do not differ too much; even if they did it is too small of a sample to garner anything worthwhile from. Despite this, the visualization helps us see that he has thrown a higher percentage of changeups in the strike zone and down the middle to righties; to lefties he continues to hit the outside corner. Regardless, the pitches that have worked the best for him in these situations have been changeups to lefties and all offspeed pitches to righties. Essentially, throwing it in the general vicinity of down the middle has not yet hurt him in 1-1 counts in the Pitch F/X era.
Location Results
No, I didn’t just combine the headings of the previous two sections no matter how much it may seem like that. Maddux’s fastball has not been particularly effective to lefties or righties in these counts. Therefore, I want to look at the nine zone sections–up and away, down and in, etc–and see what types of results his fastballs have produced. Unfortunately, small sample size syndrome has forced me to combine the nine sections into three: away, middle, in. Here are the results:
These are not large samples either but we can still discern some potential strategies to watch for over the remainder of the season. He has had his most success with the fastball away, to both types of hitters, even though righties have still done well with the balls in play. I hate even attempting to draw conclusions from these small samples, but based on the non-BIP results and the BIP results, it seems Maddux’s best chance at getting the 1-2 as opposed to the 2-1 would be to stick to his offspeed stuff (cutslide or changeup) but if he had to throw the fastball, make sure it is away to lefties and, more specifically, down and away to righties.

This Week in News and Sabermetrics: 4/6-4/12

Welcome to the first edition of TWINS – This Week in News and Sabermetrics.  This will be a weekly article recapping the goings on in the baseball world, ranging anywhere from top games of the week or oddest stats to frontrunners for awards based on my formulas and links to great articles.  Expect one of these bad boys every Saturday.  If anybody has suggestions for additions they would like to see feel free to post them in the comments.  Without further delay:
Interesting Bits of Tid
Well, the Tigers finally won a game after starting the season 0-7 and worrying the moustache off of Jim Leyland (not in a literal sense).  Unfortunately, any hope of a winning streak was put to rest when Tim Wakefield took the mound the next night.  Two weeks into the season the team expected to score 1,000 runs in 162 games (6.17/gm for anyone wondering) has scored 28 runs in 9 games (not 6.17/gm for anyone wondering).  To show how bad things have been Placido Polanco even made errors in consecutive nights.
Staying in the AL, Travis Buck of the Athletics started the season by going 0-21, with 9 strikeouts, and a .043 OPS… out of the leadoff spot.  He was about as effective as Travis Buckley–the other guy that appears when you type “Travis Buck” into Baseball Reference–but then remembered how to hit.  In his next three games Buck went 7-16, with 6 doubles, 4 RBIs, and a 1.284 OPS.
MVP Predictor
I came up with a pretty simple formula to see who would win the MVP should the season end at any given point.  The formula is: (OPS+ / 2) + VORP + VB.
OPS+ compares production to the rest of the league; VORP offers how important a player proved to be in accounting for runs than a replacement level player; VB is a Victory Bonus, just like in the James Cy Young Predictor, that awards points to a division leader.  In this case, +10 for first place and +6 for second place.  It’s simple but effective in determining how important a player statistically performed.  It does not take into account the more human factors of the game but the MVP is usually awarded to the best hitter on the best team; this formula measures that. 
I will be revising this throughout the season I am sure but for now it will work fine.  Here are the top five in the NL:

  1. Kendall, MIL, 135.7
  2. Ramirez, FLA, 130.9
  3. Burrell, Phi, 123.5
  4. Pujols, StL, 120.9
  5. Upton, Ari, 107.2

And the AL:

  1. Pierzynski, CHW, 131.4
  2. Scott, BAL, 126.7
  3. Crede, CHW, 123.2
  4. Dye, CHW, 120.0
  5. Drew, BOS, 114.8

Cy Young Predictor
In The Neyer/James Guide to Pitchers Bill James presented a formula that could, with pretty good accuracy, predict the eventual Cy Young Award.  For a description of the formula, click here.  Though I altered his formula in previous articles to account for old-time players, his works great here.  Here are the top five in the NL:

  1. Jake Peavy, SD, 23.3
  2. Brandon Webb, ARI, 19.6
  3. Micah Owings, ARI, 18.8
  4. Ben Sheets, MIL, 18.6
  5. Jason Isringhausen, StL, 18.4

And the AL:

  1. Daisuke Matsuzaka, BOS, 23.2
  2. Zach Greinke, KC, 22.2
  3. Edwin Jackson, TB, 22.0
  4. Chien-Ming Wang, NYY, 20.3
  5. Brian Bannister, KC, 19.9

Beane Count
Over at Rob Neyer created a really cool stat I had never heard of until earlier this month, titled Beane Count.  The stat measures all of the contributions Athletics GM Billy Beane looks for in players and evaluates the teams that best fit his desires.  The total is found by adding the team rank in home runs hit, walks, home runs allowed, and walks allowed.  Interestingly enough, as of right now, both the Chicago White Sox and Chicago Cubs lead their respective leagues–and by significant margins.
Cain Watch
Many readers here should know that I have some crazy manlove for Matt Cain, despite having no allegiances to the Giants, and really cannot stand how unlucky he gets on the mound.  In 2007 he went 7-16, though my Adjusted W-L system had him pegged at 16-7; my SP Effectiveness System even scored him a +50, just meeting the cutoff for a #1 pitcher.  Each week I will look at his starts and see if the unlucky trend continues.

  • #1, 4/1/08, 5.2 IP, 3 H, 0 R, 0 ER, 4 BB, 5 K, ND.  Records an AQND because it was an Adjusted Quality Start.  Game Score of 64.  From what I saw and heard he was squeezed and really should have only walked two batters.
  • #2, 4/7/08, 4.1 IP, 7 H, 5 R, 4 ER, 5 BB, 5 K. Loss.  Does not record an AQS and legitimately deserved to lose.  Unlike his first start he was not terribly squeezed and this was not a good start by any means.

Game Scores of the Week
Bill James created the Game Score statistic to measure the exact quality of a pitched game.  Info on the easy to calculate figure can be found here.  For the record, a GSC of 50 or higher is good.  Below are the top three game scores of the week of 4/6-4/12.

  • Ben Sheets, April 6th: 9 IP, 5 H, 0 R, 0 ER, 0 BB, 8 K – 85 GSC
  • Edwin Jackson, April 10th: 8 IP, 2 H, 0 R, 0 ER, 4 BB, 6 K – 80 GSC
  • Wandy Rodriguez, April 7th: 7.1 IP, 3 H, 0 R, 0 ER, 0 BB, 6 K – 78 GSC

Weekly Oddibe Award
The Oddibe Awards are given to the hitter with the slash stats (BA/OBP/SLG) closest to the league average and are named after Oddibe McDowell, whom RJ Anderson of Beyond the Box Score determined to have the career slash line closest to the league average from 1960-2006.  As of this week the league average slash line is .257/.327/.403.  Should the season for some odd reason end today, the 2008 Oddibe Award recipient would be – Orlando Hudson, Ari: .270/.325/.405.
If the Season Ended Today
Speaking of whether or not the season ended today I think it will be interesting to look at the playoff matchups each week if it did end.  This way we can see which teams were in it all year as opposed to burning out or surging in. Note – this was done at 11:16 PM EST, so the As had played while the Angels were still playing.

  • Baltimore Orioles (AL East) vs. Chicago White Sox (Wild Card)
  • Kansas City Royals (AL Central) vs. Oakland Athletics (AL West)
  • Arizona DBacks (NL West) vs. Winner of Tiebreaking Game between CHC/MIL
  • Florida Marlins (NL East) vs. St. Louis Cardinals (NL Central)

In Case You Missed It
Here are some great sabermetrics articles from this past week:

The Oceanic Six

Yes, I’m a LOST fanatic and, depending when this article is read, I cannot wait for/really enjoyed this week’s episode.  For those unaware, the show centers around a group of plane crash survivors that are stranded on an island with no outside communication.  Hell, there’s even some baseball involved as two episoded eluded to, or showed video of, the 2004 Red Sox World Series title.
With the plot of the show in mind, I decided to find some of the best pitched seasons in the Wild Card era (1995-now) that have either been forgotten, or never realized to have been so good thanks to poor evaluative statistical barometers.  My method involved parsing the Baseball-Reference Play Index in order to find seasons that I deemed forgotten while also meeting this criteria:

  1. Minimum of 25 GS
  2. ERA+ over 120
  3. OPS+ under 85
  4. Game Score average of at least 54
  5. Nothing after 2003 qualifies as it was too recent

Only ten seasons from 1995-2002 met this, and my mental “forgotten-ness” criteria, and I narrowed it down into a group of six.  The other four will be Honorable Mentions at the end.  These are in order by their Game Score average but nothing else; I don’t necessarily consider any of these to be more forgotten than the rest.
JUAN GUZMAN, 1996, TOR:  27 GS, 11-8, 2.93 ERA, 1.12 WHIP, 6.95 IP/gm
Guzman began a series of career-shortening injuries at the end of the 1996 season and, thanks in large part to teammate Pat Hentgen’s 1996 Cy Young Award, Guzman’s campaign has largely been forgotten as anything memorable or noteworthy from that season.  In 27 starts, Guzman had a GSC (Game Score Average) of 60, but only posted an 11-8 record.  The Adjusted W-L method says he pitched well enough to go 13-6.  He held opponents to an incredible OPS+ of 65 while posting a tremendous 171 ERA+.  He also earned a +56 in the SP Effectiveness System, deeming him a true #1 in that season.
JUSTIN THOMPSON, 1997, DET: 32 GS, 15-11, 3.02 ERA, 1.14 WHIP, 6.98 IP/gm
Once a promising up-and-comer in the Tigers organization, Thompson’s severe inactivity from the end of 1999 until his two-game stint in 2005 rendered him one of the forgotten.  His 1997 campaign, however, earned him an All-Star spot, and saw him finish in the AL top ten in ERA, ERA+, GS, IP, CG, IP/GM, WHIP, and H/9.  In those 32 starts, his GSC was 58, ERA+ was 151, and OPS+ was 68.  His 15-11 record translates to an Adjusted W-L of 21-5, as in the decisions he received, he recorded an AQS 21 out of 26.  His SP Effectiveness was a tremendous +76.
ISMAEL VALDEZ, 1997, LAD: 30 GS, 10-11, 2.65 ERA, 1.11 WHIP, 6.57 IP/gm
The 2000-2005 seasons of Ismael Valdez were so average or poor that many people forget how solid his 1995-1999 seasons were.  During those first five years, he averaged just about 200 innings per season, had a max ERA of 3.98, max WHIP of 1.36, and posted K:BB ratios over 2.00 four out of five times, just missing it for the fifth time.  Then, it all seemingly went downhill.  His innings and strikeouts lowered while his walks and hits trended upwards.  His 1997 season, however, was a bright spot that little light gets shone upon.  In that season, Valdez’s GSC was 58, ERA+ was 146, and OPS+ 76.  His 10-11 record translates to a 13-8 Adjusted W-L.  Though that is not much of a difference, he was extremely unlucky in the sense that, in eight of his nine no-decisions, he went 6+ innings and gave up no more than 2 ER.  His Net Luck Rating (NLR) was -6.5 in 1997 which is rather high; the highest since 2000 belongs to, guess who, 2007 Matt Cain, at -12.5. 
DUSTIN HERMANSON, 1998, MON: 32 G (30 GS), 14-11, 3.13 ERA, 1.17 WHIP
The most recent sight of Hermanson Munster saw him as the-closer-that-lost-his-job-to-Bobby-Jenks on the 2005 World Series champion Chicago White Sox.  Prior to his relief pitching years, though, Hermanson actually came up in the bigs as a starter, averaging 31 GS/yr from 1997-2001.  Though his last three years as a starter were nothing to write home about, his 1998 season featured some pretty solid, yet oft-forgotten, numbers.  Dustin made 30 starts, averaged 6.13 IP/gm, while posting a K:BB of 2.75 (154 K to 56 BB).  His ERA+ was 134 while his OPS+ came in at 78.  His GSC during the season was 56.  The 14-11 record becomes a 15-10 in the Adjusted W-L system and there were an additional three games wherein Hermanson pitched extremely well but failed to record a decision.
FRANCISCO CORDOVA, 1998, PIT: 33 GS, 13-14, 3.31 ERA, 1.24 WHIP, 6.68 IP/gm
Francisco Cordova’s brief major league career reads like some sort of statistical haiku, wherein his numbers increase to the maximum and then decrease back to where they originated.  Seriously, look at his Baseball-Reference page.  It’s quite odd as just about every statistic does this.  During his 1998 campaign, his 132 ERA+ and 82 OPS+ only resulted in a 13-14 record.  Of course, this was most likely a direct result of playing for the Pirates.  His Adjusted W-L would have been 18-9.  That’s a pretty significant difference.  In a 9-game stretch from 4/23 to 6/6, Cordova went 64.2 IP, 55 H, 14 ER, 8 BB, 41 K, 1.95 ERA, 0.95 WHIP, Opp. OPS of .599, and a GSC of 62.  Some of these were cancelled out by a few <5 IP starts and two consecutive 6 ER surrendered performances on 7/31 and 8/5, but Cordova’s season really could have resulted in serious Cy Young Award consideration had he been on a team with good run support.
JOEY HAMILTON, 1995, SD: 30 GS, 6-9, 3.08 ERA, 1.19 WHIP, 6.78 IP/gm
Hamilton’s 1995 season proved to be a forgotten one simply due to its statistical oddities.  The year prior, his rookie, season, he received 15 decisions in 16 starts; a year later he received 15 decisions in 30 starts.  How does this happen?  Not because he didn’t pitch late into games; as mentioned above, he averaged 6.78 IP/gm and only went under 6 IP six times out of thirty starts.  There were nine games in which he went 6+ IP and gave up 3 or less ER, or 7.2+ IP with 4 or less ER (AQS requirements) wherein he received a no-decision.  Of his 15 decisions, though, his Adjusted W-L should have been 9-6.  Even if he went 5-4 in the nine no-decisions he should have won, we would have had a 14-10 record to work with; 15 decisions in 30 starts is just ridiculous.  What’s even more ridiculous is Odalis Perez’s 13 decisions in 31 starts in 2004.  Back to Hamilton.  His GSC was 54, and he had an ERA+ of 132 with an OPS+ of 80.  Greg Maddux was literally in a league of his own in 1995, but Hamilton’s GSC, ERA+ and OPS+ were extremely comparable when stacked up next to Pete Schourek, Tom Glavine, Hideo Nomo, and Ramon Martinez, all four of whom received Cy Young Award votes.
These were the seasons that qualified for inclusion but just missed the cut, not necessarily due to quality but due to “forgotten-ness”:

  1. Rick Ankiel, 2000, StL: 31 GS, 11-7, 134 ERA+, 76 OPS+, 55 GSC
  2. Ken Hill, 1996, Tex: 35 GS, 16-10, 145 ERA+, 76 OPS+, 53 GSC
  3. Jaime Navarro, 1995, CHC: 29 GS, 13-6, 125 ERA+, 82 OPS+, 55 GSC
  4. Todd Ritchie, 1999, Pit: 26 GS, 15-9, 132 ERA+, 86 OPS+, 53 GSC

If anybody has more seasons they feel qualify for inclusion, definitely post them in the comments section.  Let’s have some fun with this.

The Santana Hypocrisy

Before getting into the article I wanted to mention that my personal website, is now back up and running. The site holds information for all of my endeavors, including sabermetrics, magic, and my professional screenwriting.
DISCLAIMER: This will not truly be a statistical piece but rather more along the lines of psychology and opinion. And yes – the title sounds like a Matt Damon movie title.
I was watching Freaks and Geeks the other day and an incident in the episode sparked a metaphor in my mind. In the show, Sam really liked Cindy Sanders, a girl who was dating a jock and only wanted to be his friend. At dinner Sam told his mother about Cindy’s lack of interest. His mother, trying to keep her son optimistic, told him she was making a mistake/dumb decision and that it would be “her loss.”
I wondered, though, would Sam’s mother have been as “down” on Cindy if Sam came home with news that Cindy did like him?
As in, is it okay to “diss” or find flaws in something not yours if you would be ecstatic if said thing was yours?
Even though I would love to continue talking about one of my favorite television shows the purpose of this post is to direct the above question towards the recent trade of Johan Santana.
Unequivocally, I am a die-hard Phillies fan. Though I seem to adopting the Rays as a second team the Phillies are the sole owners of the baseball-area in my heart. Even though they are my favorite team, and the Mets are in their division, I am really excited about the Johan trade.
Yes, a Phillies fan excited that the Mets improved their team.
Johan has been a favorite of mine since 2002 when, via the MLB digital cable package, I watched him routinely make relief appearances. I always noted how “cool” or “funky” his windup and delivery were and loved watching him on the mound. He has also been the only non-Greg Maddux player that I like to exclusively follow.
Now he is in the same division as the team I root for and I cannot wait to see these games. I cannot wait to see a Hamels/Santana battle of the changeups, or Santana facing off against Jimmy Rollins in the 8th inning of a (hopefully) meaningful September game. I am greatly anticipating a Santana/Peavy Sunday Night Baseball matchup or even just simply watching the guy bat!
Unfortunately, I am mostly alone in my thoughts when it comes to non-NYM NL East fans. You see, a stark contrast exists between the definitions of “die-hard fans” and that is the main reason I am mostly alone in my thoughts. There are fans whose personal lives are so effected by sports that it borders on sick obsession, and there are fans like me, fans who give so much of their heart and mind to the game but can continue their regular lives when the game ends.
I am a die-hard Phillies fan but, when the Mets landed Johan, I did not cry, pop pills, seek therapy, or curse on message boards. I grinned. I grinned as if to say – “Oh, you rascal Metropolitans!” I grinned because this is going to be a very exciting season.
In an initial reactive conversation with my brother Corey, though, he caught me doing the same thing I had been complaining about to him – falling into The Santana Hypocrisy.
I made a comment to him along the lines of – “I mean, honestly, how many games is he going to personally improve?”
Corey called me on it and I admitted fault. After all, this is such an easy hypocrisy to fall victim to but it becomes a problem when fans become so entrenched in it that they lose touch with reality.
DISCLAIMER 2: This is not means to bash any fan of any team, so Mets, Phillies, Braves, and Twins fans, please do not scream down my throat. I am merely investigating the human nature and seemingly programmed response that falls into this hypocrisy.
I have read a plethora of reactions on this trade and, while most are valid or provide some semblance of a reasonal response, some are ridiculous in their hypocritical nature. The hypocrisy does not stem from the reactions, themselves, but rather the fact that these reactions would be completely reversed if the circumstances were different (IE – if Santana was on their team).
The reactions to this trade seem to come in three forms – excited, disappointed, and angered. You’ll never guess which bunch are excited.
The disappointed department houses some Twins fans, Phillies fans, Braves fans, Manny Acta and Felipe Lopez, 12 of the 32 Marlins fans, some Red Sox/Yankees fans, and one Royals fan (Joe Posnanski). The angered department holds the rest of the Twins fans and some very opinionated Phillies and Braves fans.
Some of those in the angered department have lost some sense of reality. I have read so many posts that point out flaw after flaw after flaw about Johan, be it his home run total of last year, his decline in W-L record (useless stat), his high ERA (yeah, 3.33 in the AL is ridiculously high, right?), his potential arm troubles, how “overrated” he is, or anything else along those lines. These fans are finding everything they can to serve the dual roles of –

  • Raining on the parade of Mets fans
  • Making themselves feel better about not acquiring Johan

There is no way in hell these fans would search for these flaws if their teams landed Santana. If the Twins signed Johan to an extension he would have a great year and would be applauded for staying. If the Phillies got him then it would seem very likely that a team with the NL’s best offense, the MLB’s best pitcher, and arguably the best young pitcher would perform VERY well. If the Braves were able to line him up alongside Smoltz and Hudson, something tells me that his “flaws” would be forgotten more quickly than Mark Lemke’s pitching career.
Why do we all allow ourselves to criticize someone we would shower with love if in our presence? It is jealousy? Fear? Ignorance? Probably all three.
Johan is the best pitcher in baseball and makes a significant difference on any team he plays for. He did not single-handedly will the Twins to the playoffs during his tenure there but I would love to see how many of those Twins teams would have made the playoffs without his services.
To not acknowledge the difference he makes is to be an ignorant baseball fan.
To go as far as to say he is not that great, has a ton of flaws, or is overrated is to be a fan completely detached from reality. I can guarantee that every other pitcher on the teams that these fans root for has many more flaws than Johan.
There are reasons this guy has finished either #1 or in the top five in Wins, ERA, ERA+, WHIP, K, K:BB, SHO, GS, and IP over the last four years. The primary of those reasons is that he is extremely dominant and talented. In my SP Effectiveness System, where you need a +50 or higher to be considered a #1 SP, Johan has averaged a +71.3 in in the last four years. That is clearly the most from 2004-2007 and the only four-year spans since 2000 that were higher were the 2000-2003 seasons of Curt Schilling and Randy Johnson, both of whom are at the end of their careers now.
He has made 134 starts since 2004, and 97 of them have been AQS, which is 72 %, more than anyone else in that span.
Looking even further, if we want to use W-L records as a barometer, we are going to use my Adjusted W-L. Johan has gone a recorded 70-32 in the last four years (an average of 18-8 per season), but by my calculations, his Adjusted W-L would be 78-24 (an average of 20-6 per season).
I have no problem with people being upset that Johan now plays for the Mets. I have no problem with people not personally liking Johan Santana. I have no problem with people not personally liking the Mets (hey, I don’t like them!). I also have no problem with fans questioning the opinions of other fans.
I do, however, have a problem with no middle ground of opinion existing.
It seems that Mets fans believe they have already won the world series and, based on numerous message boards I have read, Phillies and Braves fans think Johan stinks. The Mets fans overexaggerate and the other fans have to do the polar opposite to compensate. There are very few people, relative to those who express opinions, who can be fans of other teams effected by the trade and be able to acknowledge that the Mets did something positive by gaining a great player. It’s either Johan is the messiah or Johan is overrated.
If a player, who when on your team, would increase a bulge in your pants worthy of Ron Burgundy’s thumbs-up, there is absolutely no justifiable reason to legitimately criticize said player and point out his flaws just because he is on another team. It is equivalent to really wanting a toy truck and, when you find out you can’t have it, calling that truck stupid or pretending like you don’t want it. In other words, it’s very childish.