2007 NL Starting Pitching Analysis

When it comes to analyzing and comparing pitchers, those conducting the comparisons will often find themselves in a tricky situation.  Sure, certain pitchers are better than others, but what are they specifically better at? 

How can we conduct an honest analysis when there are so many variables to consider?  And how can we truly determine which pitchers were better than others when some are on terrible teams with no run support and others are on tremendous teams with tons of run support?
The first step is to determine what we are measuring.  If we want to know who the best strikeout pitcher is, we should look at the raw total for strikeouts and also an average of K/IP, since some guys will make less starts than others.  To figure out who walks the least, we measure the number of walks each pitcher gives up and a walk-IP ratio.
These measurements are contingent on one category, though, and cannot tell us who is better or more effective than the rest.  All of the research and ideas presented in this article are designed to measure the “effectiveness” of a pitcher. 
In order to determine this effectiveness, a whole heck of a lot of numbers need to be measured and properly weighted/scaled so that everybody has a fair shot – whether or not they are on a great team.
I took the 1-3 best pitchers from each National League team and entered their statistics into a database, measuring everything from their raw Innings Pitched totals to their Adjusted Quality Start % (you’ll read more on that below).  After entering all of the statistics, and crunching numbers until my brain turned to mush, I came up with my weighted points system.  I assigned the corresponding point totals and added everything up to determine what I feel is a very accurate measurement of pitching effectiveness amongst the NL’s best. 
This was not applied to every single NL Pitcher in 2007 (I will do that another time) but rather amongst these 30 selected #1, #2, or #3 starters.  For instance, a guy like Jeff Suppan may have been more effective than Jason Bergmann but I wanted to have at least one person from each team.
The system is not 100% perfect and does not take into account every single statistic (do you know how many statistics there are??), but it definitely levels the playing field between those on good or bad teams, those injured/called up or just plain bad, and those who got lucky or unlucky with run support.  The points are assigned based on the areas I, as an intense student of the game, feel are most important to determine true effectiveness. 
The basic idea of this system is to measure the true quality of a pitcher over his season – IE, what would happen if a pitcher was rewarded every time he pitched well and discredited every time he pitched poorly – something that happens perfectly just about 0% of the time. 
We will begin by going over the statistics involved, what their points scale was, and why they are used.  The idea behind these corresponding point totals is to properly weight the areas in which most people intuitively attribute to success and quality.
The points given to each statistical subset are designed to separate the aces from the workhorses and the workhorses from the seemingly replacement level pitchers.  They may seem arbitrary and could be replaced with different numbers, or fractions/decimals, however the difference between the points in subsets was based on the amount of pitchers who fall into certain categories.
GAMES STARTED
In order to be as effective as possible, a pitcher needs to make as many starts as he can.  How can we say that a pitcher with 14 starts is more effective than one with 34-35, even if his numbers in those 14 starts are tremendous and the numbers of the one with 34-35 are a bit worse?  His numbers may be better than the pitcher with 35 starts, however the latter pitcher was involved in 21 more games and proved to be durable enough to pitch an entire season, and solid enough to maintain his SP status for 162 games. 
This does not mean that a pitcher with 35 starts is necessarily “better” than one with 14-16, but rather he is more effective because he is involved in more of his team’s season. 
If the pitcher with 14-16 starts posted the same numbers in 32 starts, it would not be a contest.  But, he didn’t – it was only 14-16.  You cannot have as much of an effect on your team (actual play, not motivational or anything) unless you are out there as often as possible.
***What the end result of this effectiveness points system showed is that those with average numbers, over 30+ starts, were equally as effective, or slightly better/worse, than those with good numbers over 16-20 starts.***
If somebody makes only 14 starts in a season, it could be because he was injured for half of the season or was called up from the minors during the season, so he should not be penalized with negative points for that – he just should not be rewarded as highly as someone with 30+ starts.

  • if over 30 starts, +5
  • if 25-29 starts, +3
  • if 20-24 starts, +2
  • if under 20 starts, 0

INNINGS PITCHED
Just like Games Started, IP can only get you positive numbers, because the low raw number of IP can be attributed to injury or a midseason call-up.  Those with more IP get higher point totals, though.  The reason for 0 points for under 100 innings is because you were not necessarily a bad pitcher, but the lack of innings (whether due to injury or a call-up) limits the effectiveness.

  • if 230+, +8
  • if 220-229, +7
  • if 200-219, +5
  • if 150-199, +3
  • if 100-149, +2
  • if under 100, +1

IP/GAME
This is where negative numbers can begin.  If you were hurt, or called up from the minors, you are not penalized with negatives for the raw number of innings pitched or games started, but if you posted a high number of starts and low number of innings, this statistic will bite you in the rear.  IP/Game separates the hurt or called up from the downright below average or bad.  It also helps reward those with a couple less starts than others but with more raw innings pitched.  These types of pitchers were in the same GS range but some went deeper into games than others.  Nobody averaged over 7 IP/gm, so we start lower.

  • if 6.5-7 IP/gm, +7
  • if 6.0-6.49 IP/gm, +5
  • if 5.5-6 IP/gm, +3
  • if 5.0-5.5 IP/gm, 0
  • if below 5.0 IP/gm, -5

If you cannot average over 5 innings per game, or exactly 5 innings per game, you should not be a starting pitcher.  Even Adam Eaton averaged over 5 IP/gm in 2007.
ADJUSTED QUALITY STARTS
Quality Starts can be an inaccurate statistic because it takes into account games in which a pitcher goes 6+ innings and gives up no more than 3 earned runs… and nothing else.
If a pitcher goes 8.1 innings and gives up 4 runs, it is arguably the same ratio and an equal game in terms of quality, but does not get counted as a quality start.
With that in mind, I came up with the stat of Adjusted Quality Starts, which takes into account all regular quality starts as well as games in which someone goes 7.2-9 innings and gives up no more than 4 runs.  This measures the true number of games in which a pitcher had a good-great performance.
***If you wonder why it is 7.2 IP, instead of 8, the number was derived from the amount of times a pitcher was lifted after 7.2 IP for a specialist, or other sort of reliever, and from the sheer low average of innings pitched/game by a starter this year.  Reaching the 7th inning is now a great feat, let alone coming within one out of finishing the 8th.  Though the previous ratio for a QS was 2:1, due to the data mentioned above, going an extra 1.2 IP to get to 7.2 IP merits being able to give up one more run.***
I used the percentage of AQS to the total number of Games Started to measure effectiveness in this area.  Someone over 75% almost always pitches a good-great game, whereas someone under 50% only pitches a good game less than half of the time – not very effective.

  • if AQS % is above 75%, +5
  • if AQS % is 67-74%, +3
  • if AQS % is 50-66%, 0
  • if AQS % is below 50%, -3

If you’re keeping score at home, AQS= 6+IP with ER =< 3, AND, 7.2+IP with ER =< 4, where =< is the blog version of greater than/less than or equal to. 
COMPLETE GAMES & SHUTOUTS
In addition to AQS, something that needs to be taken into account is how often a pitcher went for a complete game, since they are so rare.  We also need to take into account a shutout, since they occur even less. 

  • For every CG, +2
  • For every SHO, additional +1

***NOTE: Aaron Harang had two games in 2007, one where he went 9 IP, and one where he went 10 IP, when he did not get a decision.  Even so, I am counting these 2 as a combined 1 CG, since he went 9+ innings.***
WINS AND LOSSES (ADJUSTED)
W-L Records are the most deceiving statistics because they do not take into account the true quality of the games pitched.  Just because a pitcher goes 14-7 does not mean he was necessarily a great pitcher.  He could have pitched terribly and had great run support in 10 of 14 wins, but brilliantly with terrible run support in the 7 losses.
The whole point of the adjusted W-L records is to get an AQS, since that means you pitched well and should be rewarded, even if your team (offense or bullpen) does not help you. 
After all, Ian Snell cannot control the Pirates’ offense.  It is not his fault that 4 of his 12 losses were “Tough Losses” and all 11 of his No-Decisions were games in which he pitched brilliantly and had an AQS, yet he received little to no offense to help garner him a ‘W’.
With that in mind, I changed W-L to the following 5 stats:

  • Cheap Wins: wins in which one does not get an AQS (-1)
  • Tough Losses: losses in which one does get an AQS (+2)
  • Legit Wins: wins in which one does get an AQS (+2)
  • Legit Losses: losses in which one does not get an AQS (-2)
  • ND-AQS: no-decisions in which one gets an AQS (+1)

I received some questions for how these numbers came to be, and to keep it simple, the statistics that actually have an effect on the W-L record are valued higher (negatively and positively) than the statistics like ND-AQS, which prevent a pitcher from winning but do not hurt him with a loss.
ND-non AQS is not used here for the same reason that Cheap Wins is only negative one, which is that not every Cheap Win or ND-non AQS was a terrible start.  A large bulk of them were games in which a pitcher had a good outing but only went 5 or 5.1 innings.   Cheap Wins loses you a point (not two, only one) because you do not get an AQS but it does effect your win-loss record.  ND-non AQS means you do not get an AQS but it does not effect your win-loss record, which is why I decided to just leave it out.
WHIP
Though I am not too fond of this statistic and originally tinkered around with separately evaluating H/IP and BB/IP, using WHIP just seemed to make things easier.  Though it does not tell us which pitchers walk less and give up more hits, or vice versa, or tell us how many “empty innings” a pitcher had (innings where no baserunners got on), it does provide a valid average of baserunners to expect in a given game since it does not equate to a per-9 inning scale.

  • if WHIP 1.00-1.15, +3
  • if WHIP 1.16-1.25, +2
  • if WHIP 1.26-1.30, +1
  • if WHIP 1.31-1.40, 0
  • if WHIP above 1.40, -2

K:BB RATIO
Instead of using K’s, I wanted to use the ratio of strikeouts to walks, since not every pitcher is a strikeout pitcher.  Even so, you do not have to be a strikeout pitcher to be an accurate one, and because of this I rewarded those with high K:BB ratios.  Greg Maddux only struck out 104 in 34 starts, but only walked 25 – a K:BB of 4.16.  This meant that Maddux kept more runners off-base by striking them out and not walking them.

  • if K:BB above 4, +7
  • if K:BB above 3, +5
  • if K:BB above 2, +3
  • if K:BB above 1, 0
  • if K:BB 1 or below, -3

EXAMPLE OF USAGE
Now that we have the points, let’s test it out and put it to use.  We will use Ian Snell and Carlos Zambrano.
The table below shows Ian Snell’s 2007 numbers and points he receives for each in my points system.

Starts 32 +5
Innings 208.0 +5
Cheap W 0 0
Tough L 4 +8
Legit W 9 +18
Legit L 8 -16
ND-AQS 11 +11
AQS % 75% +5
IP/Game 6.52 +7
WHIP 1.33 0
K:BB 2.60 +3
CG 1 +2
SHO 0 0

When we add up all eleven of these numbers, we get Snell’s Effectiveness #, which comes to: +48.
Now, let’s look at Carlos Zambrano’s season numbers in the table below and add his point totals up.

Starts 34 +5
Innings 216.1 +5
Cheap W 0 0
Tough L 2 +4
Legit W 18 +36
Legit L 11 -22
ND-AQS 0 0
AQS % 53% 0
IP/Game 6.36 +5
WHIP 1.34 0
K:BB 1.75 0
CG 1 +2
SHO 0 0

We look at his numbers and add up the totals to get his Effectiveness #: +35.
Zambrano had more legit wins but also more legit losses, and of Zambrano’s 3 no-decisions, none were ND-AQS, whereas of Snell’s 11 no-decisions, all were ND-AQS. 
That tells us that if each player got a win for every game he pitched well, and a loss for every game he did not pitch well (did not get an AQS), and the only no-decisions they received came from no-decisions that they pitched poorly in or did not go a full 6 IP, their records would look like this -

  • Carlos Zambrano (18-13) would actually be 20-11
  • Ian Snell (9-12) would actually be 24-8

Snell went further into his games, had a better K:BB ratio, and had that higher AQS %.  It also tells us that of Snell’s 32 starts, 24 of them were of great quality, whereas Zambrano had 18 good-great starts and 16 average-bad starts.
This essentially tells us that while Zambrano’s good-great starts may have been better than Snell’s good-great starts, when Zambrano had his bad starts, Snell was still having good-great ones.
RESULTS
As mentioned before, I used this points system to evaluate 30 National League pitchers.  I compiled a group of spreadsheets, ranking the pitchers in order in different categories to show that certain stats we rely on do a bad job of proving effectiveness.
To view all of my results, click on the links below.  You can use this data in other areas, but please credit my work.

  • To see the list of pitchers and their statistics used to assign points, click here.
  • To see the list of pitchers in order of effectiveness points, click here.

I do not want to post a ridiculously long table on this article, so you will need to look at the linked files to see the results, but I will list the top 15 pitchers and their effectiveness points.

  1. Jake Peavy, +74
  2. Aaron Harang, +69
  3. John Smoltz, +69
  4. Brandon Webb, +67
  5. Cole Hamels, +65
  6. Brad Penny, +64
  7. Tim Hudson, +63
  8. Ted Lilly, +60
  9. Matt Cain, +52
  10. Roy Oswalt, +50
  11. Ian Snell, +48
  12. Bronson Arroyo, +47
  13. Derek Lowe, +47
  14. Greg Maddux, +45
  15. Adam Wainwright, +45
  16. Jeff Francis, +45

And, again, these points were assigned to statistics based on how important they corrolate to effectiveness.  The points system essentially covers the statistics and averages from all angles.
CHRIS YOUNG
The most shocking part of this was how low Chris Young of the Padres came out.  Young went 9-8, with a 3.12 ERA, in 30 starts.  He should have been more effective, I thought, based on those numbers.  After looking at his game logs, though, I changed my mind and realized it made sense.
Of his 30 starts, he was essentially two different people.  In the 19 starts in which he went for 6+ innings, he was 9-1 with a 1.64 ERA, averaging 6.6 IP/gm, with a 0.85 WHIP and 129 K’s in 126.1 innings.
In the other 11 starts, he was 0-7, with a 7.14 ERA, only going 4.2 IP/gm, with a 1.76 WHIP, and 38 K to his 36 BB, in 46.2 innings.
After analyzing his situation and the points system I realized that my effectiveness model favors consistency and lower standard deviations (the average of how far someone strays from his average).  To me, that truly defines effectiveness.
I would much rather have a guy who I knew would amass an AQS 67% or more of the time than a guy who might strikeout 20 batters and pitch a two-hitter in one game, but give up 5 runs in 6 innings for the next three, before again pitching a brilliant game.
As long as the consistency is of a good nature, consistency in this model proves effectiveness.
CONCLUSION
I know, we’re finally at the end of the article, right?  I apologize for the length but it took this long to get everything across. 
Looking at Jake Peavy, the most effective NL pitcher at +74, we see that the only counted statistic in which he led was AQS.  Peavy had the most good-great starts of any NL pitcher.  While he may not have led in IP, IP/gm, K:BB ratio, or least losses (Brad Penny only had 1 legit loss), he led in consistency and being consistently good-great.
These results also show that Cole Hamels, with 6 more starts that he missed due to injury, would likely challenge Peavy for #1 in effectiveness – however, as my model dictates, the fact that he missed those 6 starts and Peavy did not shows that Peavy was more effective.
Yes, there were more stats we could add to this, and more variables to account for, but I feel this accurately levels the field of play between pitchers in distinctly different playing situations, and levels the difference between 2007 reputation and 2007 actual performance.
I must remind you before I come to a close, though, that this is only a measure of effectiveness, not the end-all solution to determining who the “best” pitchers are.
However, for this Sabermetrician, effectiveness directly corrolates with quality and value.

2007 Sabermetric Year In Review: New York Yankees

Because it’s Christmas-time, I’ll refrain from referring to the Yankees as “evil.”  Our next two stops (#12 and #13) are in New York for a look at the Mets (coming soon) and today, the Yankees.  It’s also the longest I’ve ever stayed in New York.
Record: 94-68, 2nd in AL East (Wild Card, lost ALDS 3-1 to the Indians…. awww yeah!  Sorry… momentary lapse…)
Pythagorean Projection (Patriot formula):  98.34 wins (968 runs scored,  777 runs allowed)
Team Statistical Pages:
Baseball Reference
Baseball Prospectus
FanGraphs
MVN Blog:
The Bronx Block
More Yankees Resources:
Latest News
Contract Status
Trade Rumors
Overview: The Yankees scored the most runs in the American League, had the most hits, and led the league in OBP, SLG, and home runs.  They made the playoffs for the 12th straight year, despite the fact that this looked like the year that they would finally fall apart.  It seems that way every year.  After all, aren’t a lot of their key guys about 38?
What went right: Well, A-Rod.  He did win the MVP award with no competition.  (Oh that’s right, the two guys from Detroit voted for Magglio Ordonez…)  It grates on my ears when I hear people complain about A-Rod.  No one else puts up the goods like that.  True, he’s a specific type of player.  He puts the ball in the air and it goes far far away.  He doesn’t hit a lot of singles and he strikes out in 20% of his plate appearances.  It would be great if he hit more line drives, struck out less, walked a little bit more, etc.  But, as a clinical psychologist, I have a saying: Don’t argue with success.  If you want to poke holes in his game, they’re there, but perhaps you might try sitting back and relaxing and enjoying one of the best hitters ever to play the game.  Yeah, I said it.
Chien-Ming Wang won 19 games.  Simple recipe: put the ball on the ground and let gold glove fielders like Derek Jet… I almost got through that one without laughing… let your infield do the rest.  You don’t give up many HR that way (Wang was third in the majors in not giving up HR).  Occasionally, you get singled to death, but you have to put together three singles in a row to score a run, while you just need one home run.  During the playoffs, there was a lot of talk about how the Yankees hadn’t signed Wang to be an “ace”, I suppose with the assumption that an ace must be someone who has blinging strikeout numbers.  Another way that fantasy baseball has blinded folks.  No, Wang is not a strikeout machine, but there’s more than one way to pitch successfully.
What went wrong: That Roger Clemens thing didn’t work, but it did make for one of the best baseball-related commercials of the year.  The fastball is registering at 91 mph, the change at 86.  The strikeouts are starting to go and the walks are starting to climb.  And this whole “Will he or won’t he?” un-retire at the beginning of each year is getting old.  Roger, please, for the good of the game (and I won’t even touch the steroid thing), retire.  Go home.  Become a roving instructor for someone.  We’ll see you in five years in Cooperstown.
Then there was Kei Igawa.  I have to imagine it was awful to see the Red Sox’s Japanese import work out just nicely, when Igawa just seemed to fall apart.  Igawa ended up having some good outings in AAA, why couldn’t he do it in the majors?  First off, take a look at his pitch profile.  He’s got a 90 mph fastball with some amazing sink.  Sure enough, in AAA, his strikeout rate was about the same as in the majors, but his walk rate was much lower in the minors.  In AAA, more guys will chase that pitch.  In the majors, guys will lay off that sort of pitch.  Plus, Igawa gives up a lot of flyballs, and that probably had something to do with his 2 HR per 9 innings.  The Yankees just bought a Japanese lemon.
Finally, there was Jason Giambi.  Part of his problem was that he was hurt.  But what suffered?  Well, there was no notable drop in pitches per PA (4.38 to 4.30), but there was a small jump in his strikes/pitches ratio.  His batted ball profile was almost an exact replica of his 2006 profile.  Almost exact.  The biggest drop was in Giambi’s HR/FB (and ISO).  Could very well be the signs of a power outage, which at 37 isn’t likely to return.
Yeah, that about sums it up: I’ve gone on the record as saying that managers really don’t matter all that much, but will someone tell me what Joe Torre did last year in actually managing the game that Joe Girardi, Buck Showalter, or my brother-in-law would have done differently?  Don’t kid yourself.  Torre’s firing had absolutely nothing to do with his decisions in the dugout.  Sure, it’s tempting to say “Well, he shouldn’t have started Wang on three days rest,” although the assumption is always that had he started Mussina, the Yankees would have won Game 4.  The extended fantasy version of that line of logic is that they would have won Game 5 as well.  Then, the ALCS.  Then the World Series.  Sure, if Torre had gone with Mussina (or reverse any of his decisions that you didn’t like, this is just the most obvious), the Yankees might have won.  They might also have still lost that game.  Sometimes you do everything right and it doesn’t work.
A manager not only runs the game strategy, but he’s also the chief psychologist in charge of player morale, the head media liaison, and in case of emergency, he’s the paperweight that management can throw overboard to make people believe that the ship isn’t sinking.  The real reason that Joe Torre got fired was that the Yankees went into the ALDS with a two-man bullpen (Joba and Mariano).  The Yankees had a pretty good team, but they ran into a better one.  Where’s the shame in that?  But, when your organization views anything other than complete world domination as shameful, it’s time to publicly throw a paperweight over the side.  That’s life in the Big Apple.
How horrible was A-Rod’s post-season?: I know, I know.  He’s so un-clutch.  He was banished to the 8-spot in the line up in the 2006 playoffs.  He doesn’t hit in the playoffs, right?  Let’s see what the numbers say.  A-Rod had an on-base percentage of .422 during the year.  Let’s say that’s his true talent level going into the playoffs.  The nice thing about A-Rod is that it’s a measure of the most important skill in the game, avoiding making an out.  A-Rod came to bat 17 times, had four hits and walked twice.  In 17 random PA, with a guy who has a “true” .422 OBP, how often would he only get on 6 times (or worse?)  About 37% of the time.  So, if he had it to do over again, A-Rod would probably be just as bad or worse 37% of the time and better 63% of the time.  A-Rod had a slightly sub-par (for him) post-season this year. 
Should I be excited about Shelley Duncan?: Of course you should.  It’s the off-season.  Seeing as there’s not that much really going on, now’s the time of year to get excited about guys like Shelley Duncan.  It’s cold outside, and it will warm your heart to think about how Shelly Duncan is going to lead the Yankees to the World Series.  Then again, you probably got excited and had all sorts of dreams about another Shelley Duncan (maybe it was Kelly Gruber) in junior high and those never happened either.  Duncan will be 28 on Opening Day and just made his debut last year?  Why?  Because he was a middling outfield prospect who put up an OPS around .750-.800 or so in the minors at AA.  Not bad.  Not great.  Then in 2007, he put up an OPS of .957 at AAA and got called up where in 83 plate appearances, he hit 7 home runs.  And one double.  Keep that in mind.
The good news is that Duncan does hit a lot of line drives.  Line drives generally make good things happen.  He also strikes out 27% of the time.  That’s forgivable if he’s hitting for power to go with it.  And he has shown some pop in the minors.  But let me show you how a little illusion can be made with a little luck.  Duncan’s amazing OPS in AAA was driven mostly by his OBP, which just happened to be well-above what he had done in the past.  It also corresponded to a jump in his BABIP, which was well-above his career average.  When he got to the majors, his BABIP dropped, and so did his OBP, but his OPS stayed high thanks to a rather high slugging percentage.  Duncan had always been a home run hitter, but in his at-bats in the majors, 31.8% of his fly balls went for home runs (which would make him more prolific in that category than guys like A-Rod.)  Methinks he caught a few good gusts of wind that pushed what would have been a double into the stands where it became a home run.  The fact that he only had one double (and no triples) indicates that he wasn’t exactly an extra-base hit machine.  Suppose that a few of those balls had landed for doubles rather than home runs.  His SLG would have gone down.  When his BABIP levels off and the small sample size issue is solved, Duncan will probably be discovered to be a true .750-.800 OPS guy.  Not bad.  Not outstanding.
Outlook: Let’s talk about a man named Johan.  The Yankees want Santana, and they look ready and willing to trade some pretty high test prospects to get him.  (At last check, they’re trying to talk the Twins down from both Phil Hughes and Ian Kennedy, plus Melky Cabrera!)  Trading away the Melk man (he’s only 22?) would leave the Yankees with one regular under the age of 30 (Robinson Cano), but then again, this is Yankee-land where problems can be solved by signing another over-priced, aging veteran.  Hughes and Kennedy are both real.  (Joba is even real-er…)  It looks like the Yankees will end up with some combination of three of Santana, Hughes, Kennedy, and Joba around which to build a rather nice starting rotation.  There are plenty of over-priced outfielders out there who will put up good numbers, and there isn’t a lot position-wise in the Yankees minor-league cupboard.  The Yankees are built to have an ever-escalating payroll with which they will buy free agents and thus they will continue to be the most-hated team in baseball.

A visit from St. James

(With my most sincere apologies to Clement Clark Moore… and for my rather loose use of rhyme.) 
‘Twas the night before Christmas and all through the sport,
Not a player was stirring (‘cept those in the Mitchell Report)
The Red Sox were hung by the chimney with care
Next to their two World Series trophies, now how’d those get there?
The GM’s were nestled all snug in their beds
While visions of Johan Santana danced in their heads
And Scott Boras in his kerchief and A-Rod still in his Yankees cap
Had just settled in for a long winter’s nap.
When out on the internet their arose such a clatter
I ran to my computer to see what was the matter
Went to MLB Trade Rumors and The Hardball Times
To greeted there by the most heinous of crimes.
And what to my wandering eyes should appear
But 3 years and 12 million for a lefty reliever.
I sighed at the news that had disturbed my rest,
And mumbled in pain as my head hit my desk.
But then on the roof, another sight came my way
The guys from Baseball Prospectus in a miniature sleigh
With a little old driver, who would school those in the game
I knew in an instant, it must be St. James
More rapid than Ichiro his coursers they came,
And he whistled, and shouted, and called them by name
On OPS, On Leverage, On RZR, On VORP!
On PECOTA, On WPA, and Pythagenport!
To the top of the standings, they shot like a rocket
Yet, took considerably little money out of the owner’s pocket.
To the top of the stadiums, the coursers they flew
With a sleigh full of stats and St. James with them too
And then with a twinkling, I heard o’er my Indians hat
The clicking and clacking of spreadsheets and stats.
As I drew in my head, and was turning around,
Down the chimney St. James came with a bound.
A bundle of abstracts he had clutched in his hand,
And he looked like a security guard rather than a baseball man.
His eyes how they twinkled! his dimples how merry!
His cheeks were like roses, his nose like a cherry…
Wait that’s someone else
A wink of his eye, and a twist of his head,
(Which may have actually been the sign to steal third)
Soon gave me to know I had nothing to dread.
He spoke not a word, but went straight to his chore,
And filled all the stockings with statistics and more!
Crazy acronyms for BP, and asterisks for Barry*
And to the traditionalists and anti-statheads out there, an explanation of why, really, we swear, we’re not all that scary
For Adam Everett, a glove that’s gold-en
And the absence of Joe Morgan from ESPN
A little understanding of stats and “regression to the mean”
And a little common sense for the salary structure of each team.
And then laying his finger aside of his nose,
And giving a nod, up the chimney he rose.
He sprang to his sleigh, to his team gave a whistle,
And away they all flew like the down of a thistle;
But I heard him StatSpeak, ere he drove out of sight,
“Sabermetrics to all, and to all a good night!”

Can we classify every pitch?

What if we knew what type of pitches every major league pitcher threw? What if we had detailed pitch-by-pitch data about how he used those pitches in every game situation? What if this information was accurate and freely accessible to baseball researchers?
Let’s begin with some history. Since Sportvision’s PITCHf/x system was unveiled during the 2006 playoffs, people have been thinking about using the detailed pitch data to classify pitches by type. Reference this comment by MLBAM’s Director of Stats, Cory Schwartz:

“When the system is installed in all 30 ballparks, it will provide unprecedented accuracy, consistency and depth of data to the measurement of speed and trajectory of each pitch,” Schwartz said. “Ultimately we’ll be able to use this data to determine the pitch type in real time and with greater accuracy than ever before. By recording all of this data in real time, we can provide it to broadcasters such as FOX, in-stadium scoreboards, fans via Enhanced Gameday, clubs and other business partners.

It wasn’t long before Baseball Analysts’ Joe Sheehan was leading the public research down that path, too, publishing articles in the spring of 2007 about pitch classification for pitchers like Jeff Weaver, Mike Mussina, and Kenny Rogers, using the data from the 2006 playoffs.
In April 2007, the PITCHf/x system was installed in nine ballparks, and this produced a wealth of data that encouraged more people to join the analysis fun. Dan Fox, Bill Ferris, and Steve West were among the leading PITCHf/x researchers in the first half of 2007, and although the work in the field covered a number of topics, pitch classification was often at the forefront.
Soon the quest turned toward developing a set of rules to classify pitches for many pitchers, perhaps for every major league pitcher. John Walsh published the early definitive article on this topic. In August, the analysis really began to heat up; for example, see these articles from John Beamer and Joe Sheehan. The quest for a pitch classification algorithm was on.
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2007 Sabermetric Year in Review: Oakland A's

Moneyball.

2007 Sabermetric Year in Review: Oakland A’s

Moneyball.  There, I said it.  Stop #11 on the tour: Oakland.
Record: 76-86, 3rd in AL West
Pythagorean Projection (Patriot formula):  79.97 wins (741 runs scored,  751 runs allowed)
Team Statistical Pages:
Baseball Reference
Baseball Prospectus
FanGraphs
MVN Blog:
Athletics Supporters 
More A’s Resources:
Latest News
Contract Status
Trade Rumors
Overview: Somewhere the wheels fell off the cart.  The much-vaunted A’s approach of on-base percentage Uber alles clearly didn’t filter its way down to the players.  A look through the A’s numbers show that a bunch of key players ended up with unfortunately low OBPs.  Maybe they didn’t get the memo.  Perhaps Mr. Beane is looking at a new stat?  Perhaps it was just a bad year in Oakland.
What went right: Some interesting facts about Jack Cust.  This was not his first season tasting the Bigs.  Cust has actually appeared in an MLB uniform with the Diamondbacks, Rockies, Orioles, and Padres.  He was acquired by the A’s on May 3rd for a PTBNL(!) from the Padres.  Oddly enough, he was in San Diego after spending the 2005 season in the A’s minor leauge system.  The A’s swung and missed on that one too.  He also struck out in 41.5% of his plate appearances, walked in 21.0%, and hit a HR in 5.1%.  That means that in 67.6%, more than 2/3rds of his plate appearances, the outcome was one of those three “true” outcomes.  For some perspective on this, I opened the Lahman database and calculated whether any other player had as high a percentage since 1959, with a minimum of 250 AB, just to keep it honest.  Nope.  In fact, Mark McGwire’s 1998 season (the 70 HR one) was the closest, and he was 10 percentage points behind.  And oh yeah, for a guy who wasn’t on the roster on Opening Day (or even in the organization), he was the best offensive weapon that the A’s had all year.
Travis Buck.  If you knew who he was before this year, you win a cookie.  (Note: no actual cookies will be awarded.)  What have we here?  Strikes out a little too much, but he’s on the right end of the age spectrum for that to be solved in the next few years.  Any 23 year old with an .850 OPS needs another look.  But a reminder, he had 334 PA in 2007.  For a lot of stats, that’s not really a big enough sample to get a good read on him.  It’s not to say that he won’t keep going, just that it’s still an open question.  The thing is that he’s not a good fantasy pick, because he likes to hit doubles rather than HR, but there’s a difference between a good fantasy player and a good player in reality.  I encourage everyone reading this to learn the difference.
What went wrong: Congratulations go out to Jason Kendall, who was the Least Valuable (or perhaps Least VORPy) Catcher of the year in the American League.  I can’t really blame Mr. Beane for this one.  In the two years before signing with Oakland, Kendall put up a nifty .399 OBP in each season and has proceeded to drop off over the last few years.  What’s the problem?  Well, being a 33-year-old catcher isn’t a good idea.  (Maybe a move to the outfield is in order?)  I’d love to say that there was some big reason for Kendall’s downfall this past season, but to look at his diagnostic stats, he’s seen a steady deline in walks and a bump up in his strike outs.  He did have a sudden drop in BABIP this year, but that doesn’t explain the previous two years.  He’s just… getting worse.  It’s amazing that the A’s got a somewhat promising lefty (Jerry Blevins) for him.  Kendall is in a downfall, but still has some name value to him.  So does Britney Spears.
But perhaps nothing can sum up Oakland’s dashed dreams than poor Eric Chavez.  Remember a few years ago when the A’s were trying to figure out whether to sign Jason Giambi, Miguel Tejada, or Eric Chavez long term?  According to RZR, Chavez had the worst range for a third baseman in the American League.  Then there was his offense.  His .752 OPS puts him in league with Melvin Mora and Jose Bautista.  Chavez might have the excuse of the shoulder injury that kept him out of the lineup for the last two months of the year and he’s still only 29.  He’s another case where his medical records will be a better predictor of his stat line than any algorithim, but ummm… I suppose it’s also possible that he just already peaked.
Yeah, that about sums it up: Total number of BB for the team in 2007: 664, good for second in the AL.  Yep.  Good to know that some things in the universe never change.
The obligatory “What I think of the Dan Haren trade” paragraph: Dan Haren.  Scarily consistent.  Pretty good pitcher.  27 on Opening Day 2008.  There’s nothing there to unearth in terms of unknown quantities there.  What did the A’s get back?  Brett Anderson, 19, about a 6:1 K/BB ratio in A-ball.   Carlos Gonzalez, 21, and a pretty consistent .800 or so OPS at all levels, most recently AA for a full season.  Aaron Cunningham, 21, around .900 OPS numbers and some wheels.  Seem like good bets.  It’s the classic future for present trade.  Billy Beane’s playing the multiply by trading game.  Trade one guy now and turn him into 3 or 4 good ones down the road.  An old trick.
I can only wonder what goes on in that man’s mind: Presented, for your consideration.  A 23-year-old lefty submariner who had been a 25th round pick and is festering in A-ball in the White Sox organization.  No one knows that he exists, not even his own mother.  No one, that is, except for one man.  A man named William Lamar Beane.  Mr. Beane sees that in A-ball, our submarining friend strikes out very many hitters and walks very few.  Mr. Beane selects this anonymous lefty with a pick in the Rule 5 draft.  Consistent with requirements, he proceeds to keep the young left-hander on the roster throughout the whole season.  This despite the fact that the young man does not have a discernible fastball, nor does he function above replacement level during his year in the big leagues.  However, instead of jettisoning this left-armed pitcher, Mr. Beane instead assigns him to LOOGY duties hoping that he will find the strike zone.  Mr. Beane has now wandered into an area we like to call The Twilight Zone.
Jay Marshall is a puzzle, but also a clue.  Billy Beane had to know before the season started that Jay Marshall was not going to be an amazing reliever and probably understood deep down that Marshall was a below-replacement-level proposition.  Had The. Smartest. GM. Ever! been playing for the immediate future, he could have signed some random lefty (there’s always a million washed-up lefties out there) or promoted from within, but instead he stuck with this guy.  Why?  This has all the earmarks of a sneaky devious Billy Beane move where he “calls” a guy way before anyone else.  Marhsall is now an Oakland A for the foreseeable future and he’s only 24, which means he’ll hopefully learn a few things in the next few years.  But, projects like that are for re-building teams.  I suppose then, had we been paying attention, we might have seen the trade of Dan Haren coming.  Marshall may or may not work out in the long-run, but it looks like Billy Beane, like a good chess player, has been thinking two or three moves ahead.
If Billy Beane is so smart, why hasn’t he won a World Series?: I made a promise to myself that I wouldn’t reference Moneyball during this post (the oblique reference above notwithstanding).  But, one of the big anti-Sabermetric arguments revolves around the very question above.  In theory, every team has a 1/30 (3.33%) chance of winning the World Series on Opening Day.  In reality, some teams have a better chance than that and some have no chance.  Any way you slice it, winning a World Series takes a lot of luck, no matter how good a team you are or how you approach the problem of team construction.  But let’s say that I’m a Sabermetrician who actually gets to be the GM of a team.  I will look for ways to increase my team’s chances, and let’s say I take a team which usually has a 5% chance of winning to a team that has a 7% of taking home the big prize.  Over five years, what are the odds that in at least one of those years, we’ll be celebrating a title?  About 30%.  Far from certain.  What were those chances before?  About 22%.  So, it’s possible that they’d win without me and it’s not certain that they’ll win with me.  Over a few hundred years, the team is better off with me at the helm, but most fans want results within 5 to 7 years.  It just doesn’t work that way.  If fans understood statistics, they’d understand why that’s far too small of a sample size.  Then again, a Sabermetrician is just a fan who understands the basics of statistics and the scientific method.
Outlook: Re-building.  And it looks like Billy Beane is cashing in his chips to get young kids who look like they have a chance to become something big.  With that said, I’m guessing that the Bonds-to-Oakland rumors aren’t actually real.  The A’s have Jack Cust to DH and why in a rebuilding year should they pay the price that even a steroid-tainted Bonds (assuming he’s a free man) would demand for his services?  Nah, the A’s are basically aiming for a few years down the road.

The 2007 All "Paid By My Former Employer" Team

When a baseball team decides to release a player, one of two things can happen.

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