The forth in this series takes a look at Mariners SS.  The previous posts have some info about how I am going about this (Catcher, 1B, 2B).  I also have added polls to the Catcher, 1B and 2B posts so go back and vote on these if you want to.  I'll make this post extra concise because this one is fairly obvious and it is about to be Friday evening.  On to the data...

As with the past posts, the top chart has data for the players over their whole career and the bottom one has their value at the time they were Mariners. (click on chart to enlarge)

It doesn't take long to look at these tables and figure out Alex Rodriquez is and was really good.  He had an 11 win season as a Mariner which is amazing.  Absolutely amazing.  He is in the top 20 all time for a positional player ranked by WAR and he is still playing.  Realistically if he keeps playing for a few more years he could pass guys like Gehrig, Ted Williams and Micky Mantle and we got to see this guy have his career year as a Mariner.  Whatever you feel about ARod now, it was awesome when he was on our team. 

Omar scores well over his whole career and he ranks #2 as a Mariner on the list but there is a huge gap between him and Alex.  I really thought Omar's career was almost done when he joined the Indians and I remember thinking it was a good time to get rid of him.  Couldn't have been more wrong.  He probably won't make the HoF but if you are going to induct amazing defensive SS, he should be able to get in.

The bottom of the list has no surprises with Medoza and Fermin at the bottom of the list.  Not too much to say about these guys besides they couldn't hit much and weren't actually that good in the field which adds up to bad no matter how you look at it.

Overall the Mariners have gotten 54 WAR from the SS position for an average of 1.6 WAR per season.  Alex Rodriguez's contribution works out to be 68% of the M's total WAR production from the position.  Without him the M's would have averaged ~ 1/2 a WAR per season.

Best M's SS of All Time: Alex Rodriguez

Worst M's SS of All Time: Mario Mendoza

Continuing the series (Catcher, 1B) I chose to work my way around the diamond to 2B.  I used the same methods as I did with 1B.  To recap the methods, I require the player to play at least 30 games at 2B and then I weight their WAR production based on how many games they played at that position.

The first chart has all the information of 2B for their whole career.  The second chart has the data for players when they played as Mariners.  Looking at this chart, we can see the Mariners have had a long list of decent 2B that have provided value to the team and not too many atrocious players.  Besides for a couple seasons by Bret Boone there has not been much spectacular production from the position.  Overall the Mariners have generated around 63.3 WAR from 2B which works out to about 1.86 WAR per season which isn't special but also isn't too bad.  I don't know what the final numbers will tell us but I bet 2B will be one of the better performing positions in M's history because we have pretty consistently been able to fill the spot with above replacement level players.

The best M's secondbaseman is easily Bret Boone.  He had a couple seasons that were truly amazing (2001 and 2003).  These are probably a couple of the best seasons ever by a Mariner and coming from 2B gives it a lot of value.  Those two seasons from Boone produced 16.5 WAR.  Its a little amazing that he was only able to total a little over 19 WAR which tells you how the rest of his years as a Mariner worked out.  Overall Bret Boone contributed to around 30% of the Mariner's total WAR at 2B in his 7 seasons on the team.  Besides Bret Boone, Reynolds and Julio Cruz were solid producers over their careers but there are a ton of guys with shorter Mariners careers that did just as well.  At the bottom of the list, there weren't really any players who were really horrible and played 2B much.  Larry Milbourne produced around -0.75 WAR in around 100 games at 2B as a Mariner.  While he wasn't good, he wasn't godawful.

I'm not surprised to see Bret Boone be so high on the list but I am a little surprised he totaled only barely more than 20 WAR over his career which is not that impressive.  I also thought Harold Reynolds would be more valuable and had never heard of Julio Cruz before doing this.

Best M's 2B of All Time: Bret Boone

Worst M's 2B of All Time: Larry Milbourne

This is the second post using Rally's WAR database to quantify the best Mariners of all time.  The first post looked at Mariners catchers.  This one will focus on Mariners 1B.

Doing this for catchers was particularly simple for one reason.  A player who played catcher tended to only play catcher.  Once I moved on to looking at firstbasemen I had to decide how to deal with players who only played 1B occasionally or even just a few times.  I decided to set a minimum of 30 games played at first base as a mariner as my cutoff.  Its pretty low but actually worked pretty well.  There were still tons of players who only occasionally played 1B or only played 1B for a couple of seasons.  In order to account for this I looked up how many games the player has played at 1B vs how many games he had played elsewhere and only gave him credit for the fraction of games he played at 1B.  Therefore if a player earned 10 WAR over his career and played 25% of his games at 1B he was credited with 2.5 1B weighted WAR.  This might have its drawbacks but its simple to do and for most players I feel like it should even out.  I also decided to do my WAR rate stat on the basis of PA since it is simple and straightforward even though defense is weighted per inning and it really just doesn't matter if I try more complicated stuff.

This first chart has all of the data for all the M's 1B for their WAR totals over their whole career.  Olerud is clearly leading the pack and its not even close.

This second chart has all the information for the players while they were Mariners.  Olerud is still in the lead with the highest raw WAR totals as well as highest WAR/PA numbers as a firstbaseman.  Alvin Davis has a slightly higher total WAR total and higher peak year total but played far less at 1B than Olerud did.  Tino Martinez, Bruce Bochte and Richie Sexson sort of round out the top 1B of the Mariners history however there is a large gap between these three and Olerud and Davis and a small gap between the rest of the pack below them.  

The bottom of the list is dominated by some truly horrible performances by Dan Meyers and Pete O'Brien.  Not only were these guys bad but they got lots of at bats somehow.  Dan Meyers somehow had a season worth -3.2 WAR!  This has to be one of the worst performances ever.  Below average defense plus 477 PA of 227/264/327 is absolutely pathetic from 1B.  (Oh my god, I just looked and the DH in 1978 did worse than this.) Bryan Lahair and Ben Broussard (sorry Jeff) are also near the bottom of the list.

Overall the Mariners 1B have produced ~50 WAR over the entire franchise's history which is worth about 1.5 WAR per season which is decent enough.  Olerud and Davis represent about 60% of the overall franchise production from this position.  I was surprised to see Olerud so high and had forgotten Davis had such a productive career.  I don't know why but I thought Tino might be a little bit higher up on this list.

Best M's 1B of All Time: John Olerud

Worst M's 1B of All Time: Dan Meyers

Everybody around here hates Rob Johnson and probably rightly so (unless his walk rate is sustainable.  Every passed ball causes another M's fan's head to explode.  That said, I started thinking about Mariners catchers and how Rob Johnson would rank amongst them.  Besides Kenji, Dan Wilson and Dave Valle I couldn't come up with any Mariners catchers worth anything.  I went to Rally's historic WAR database and hunted down all the players who ever played catcher for the Mariners (baseball reference).  Then I made myself a little spreadsheet where I have broken down each player by the amount of WAR produced over their whole career as well the amount of WAR accumulated while they were a Mariner.  I also included WAR/season as well as the best (Peak) and worst (Valley) seasons they had as Mariners.  Rally's database only goes up until 2009 so I updated active players by adding their contributions from this season using Fangraphs.

(Click on table to enlarge)

What we see from this is that there really have only been a few M's catchers worth anything and none of them have ever been an elite player.  One thing that goes into this is that the Mariners haven't been around that long and there haven't been that many starting catchers.  From this list, catchers have contributed 29.6 WAR and over 34 seasons this works out to 0.87 WAR per year which is pretty bad but not too surprising since the Mariners historically have been a below average team and before Dave Valle came around had basically nothing at catcher.  Also from this chart we see that the franchise has has only 3 catchers who have really been "good" catchers.  Dan Wilson, Kenji Johjima and Dave Valle contribute exactly 29.6 WAR which happens to be exactly the same number as the overall M's total (I double checked this and this is a weird coincidence).  Without those three we would see that overall the contribution from all other catchers has been at replacement level. 

We also see Wilson has the highest overall total but Kenji has higher per year numbers.  I think we can make an argument that one of these two is the best mariners catcher however its difficult to pick one over the other.  They both had good peak years where Dan had 2 years ('96-'97) with WAR totals of 3.3 and Kenji had one year with a WAR total of 3.4.  Dan always had a great defensive reputation and his success over his career was tied to above average defense.  Kenji did not have this reputation while he was here but still scored very highly (+14 runs) during his peak year which is very surprising to me.  On the other end of the spectrum Scott Bradley (others were also really bad) somehow stuck around for half a decade with horrible production both with the glove and the bat.  Scott Bradley literally had 200 good PA in 1986 and rode on the coat tails of this for the rest of his career.

I thought this was cool.  I might do this for other positions if people are interested.  I'll end with a bold statement.

Best M's Catcher of All Time: Dan Wilson

Worst M's Catcher of All Time: Scott Bradley

Playing with numbers led me to make these two charts.  The top one is a plot I made where the vertical axis is a pitcher's strikeout to walk ratio and the horizontal axis is the ratio of the number of strikes thrown to balls thrown.  The size of the dot represents the average number of pitches thrown per at bat.  The dashed line represents what you would expect the relation of K/BB and strikes/balls where a pitcher's K/BB would be 1 if they had an average ratio of 3 strikes to 4 balls. 

The second chart has most of the same information as the top one but in a different format.  One thing that is important to remember is that strikes include fouls so weird things like Aardsama has 3.1 strikes per batter faced.

Its not really useful or telling analysis but I thought the graph was something to think about.

via i271.photobucket.com

First off, this is not a completely novel or creative idea.  It has been tossed around here and there for a while.  Graham and I had exchanged a few comments about it a while back which ended with me saying it would be cool and Graham saying he didn't have the time to do it at that time.  From there I expected it to fade away and not be thought about again in the near future.

The basic idea is that Baseruns is a very good runs estimator.  Some would probably consider it the best for a variety of reasons.  The most important part is that can account for basically any run environment.  For example, if we use it analyze a single game where one of the teams gets one hit and its a home run then Baseruns predicts 1 run instead of the 1.4 runs linear weights predicts.  If you want to read more about it, I suggest looking at The Book Blog or ask if you want more details.

The major problem with Baseruns as a run estimator is that it is difficult to apply to individual players because it is a team runs estimator.  A great player on an average team is a great player living in an average offensive environment.  If you take that great player's stats and enter them into a Baseruns equation, you will overestimate the production of the great player because Baseruns thinks he is a great player playing in a great offensive environment.  Likewise, a poor hitter will be undervalued by Baseruns.  One of the ways to use Baseruns to calculate the value of a hitter is to use Baseruns to calculate the team production and then calculate the team production without the hitter on it.  Linear weights is a more commonly used offensive metric because you can calculate the production of a hitter, independent of the run environment (team) they play on.  Instead, linear weights assumes that the hitter plays in an average offensive environment which is fair and makes a lot of sense for a hitter.

The great thing for Baseruns is that pitchers play in a run environment determined by mostly just themselves (as well as defense, park and competition level).  Felix Hernandez has a run scoring environment which is much different than Livan Hernandez and this changes the run value for different outcomes.  I will be ignoring the effect of the defense or park for now although these are important.  This means that we can take the stats from a pitcher's performance and input them into a Baseruns equation and it will spit out the expected runs while accurately taking into account the value for strikeouts, walks, and every hit type in that specific run environment.  This is something that pretty much none of the metrics out there do (FIP, xFIP, tRA, etc). For example, Felix's home runs are less harmful than average because he allows less baserunners than average and Livan's strikeouts are worth more than average since he likely has baserunners on.  Overall this is a pretty small effect because almost all pitchers have roughly similar run environments except for a few exceptional and exceptionally bad pitchers.  To sum up this paragraph, pitchers performance (runs) is not linearly related to our measured variables (K, BB, etc).  Baseruns takes this into account and FIP, tRA, etc don't.  Since most pitchers are actually very similar with small absolute differences in abilities these non-linearities only show themselves in the extremes.

Interestingly, I stumbled across a thread over at Tango's blog where he was discussing BP's new pitching metric, SIERA.  There, I found Patriot (a well known Saber/blogger) had recently done something pretty similar to what I was thinking about using some data (Comment #28 - Colin Wyers) that gave 1B, 2B, etc probabilities for batted ball types.  Patriot was trying to recreate SIERA using this sort of data so his aims were a little different than what I was looking for.

I was interested was using this data to convert a player's batted ball profile (FB, GB, etc) along with BB and K rates to calculate the expected runs allowed using the Baseruns equation.  The data posted by Colin Wyers gave me all the batted ball outcomes I needed.  With this data I could take a pitcher that gave up 10 groundballs, 10 flyballs, 2 popups and 5 line drives and estimate how many singles, doubles, triples and home runs he would give up in an average park in front of an average defense.  The next step is to take the projected number of 1B, 2B, 3B and HR along with the number of K and BB the pitcher allowed and plug all this info into the Baseruns equation.  Baseruns then estimates the number of runs allowed by the pitcher based on his batted ball profile independent of park or defense. (Not completely independent but its close. More work could fix bias.)

I think this is pretty interesting and we will probably see this sort of thing pop up in some form however the differences between using Baseruns and a linear run estimator turns out to be pretty small.  Graham has talked about trying to use Baseruns with tRA as a way to improve it but really the improvement would be small and its tough to motivate coding and gathering all the data to implement it but it would still be cool.

A couple small notes about my implementation.  The values Colin Wyers gave were for a few years ago and appear to overpredict hits and home runs which is probably because offense has declined since then.  To take care of this I just applied a fudge factor to push down the hit and home run totals to push them in line with league performance last year.  The fudge factor isn't ideal but I haven't taken the time to master play-by-play databases to be able to calculate this sort of stuff for myself.  I am posting the spreadsheet right here if people are interested.  I'm intending on trying to post a couple of things building on this after this post but I wanted to get this out there to hear any thoughts.  If we think this sort of thing is valid and works then I'll throw up a post with some more analysis.

The spreadsheet (2009 data) including leaderboards (Pitchers w/ 50+ IP) and all the data can be downloaded here (I hope you can download this).  There are lots of numbers and I haven't explained too much of the details so let me know if you have questions about what is going on.

There are many ways to slice the same pie.  I was playing around on Fangraphs when I should have been working or sleeping and eventually had a bunch of numbers and an idea.  Its an idea which I'm sure has been done before but I think its useful just because I think it will show how much of an impact defense can have on a pitcher's stats.

The basics behind the idea is that we know a pitcher's R/9 which is easy and straightforward.  We also know how valuable our infield defense and outfield defense is.  Finally, we know the batted ball profile of each pitcher.  Using the team defense numbers and the batted ball profiles we can adjust a pitcher's R/9 to make it defense neutral.  Its simple and doesn't require DIPS theory like most other stats.  I am about 99% sure I have seen this done before but I was curious and went ahead and calculated the Mariners adjusted R/9 (I'm gonna call it xR/9).

Working from numbers from Fangraphs, I went through some simple math and divided up the defensive impact.  I'll try to just state what I did right here.  If its boring, skip it and go to the stuff later on.

From Fangraphs I was able to compute UZR runs per BIP for the OF and IF.  The Mariners OF was +62.1 runs and IF was +23.4 runs or 0.036 runs/BIP and 0.013 runs/BIP.  Then I could just look at how many GB and FB were  allowed by each pitcher to figure out how to divide up the defensive contribution.  The final equation looked something like this:

xR/9 = 9*(Runs Allowed - xFB - xGB)/IP

xFB = (FB BIP) * (team OF UZR / team FB BIP) [runs saved on flyballs by defense]

xGB = (GB BIP) * (team IF UZR / team GB BIP) [runs saved on groundballs by defense]

Its pretty simple.  Basically all we are doing here is saying that a pitcher benefits from a defense based on how many balls in play he allows and where these balls in play go.  I ignored line drives basically because it was hard and not totally clear how to treat them.  Lets say I'm ignoring line drives because they usually go for hits even for good defenses (this might be false).

The other simple thing I tried was regressing each player's HR/FB numbers to the team average.  Then I could use the linear weights value for a home run to correct the pitcher's total runs allowed for the number of home runs they should have allowed.  Since I lack much creativity, I'm going to call this xR*/9.  I don't want to say xR/9 is equivent to FIP but if xR/9 was FIP then xR*/9 is like xFIP.

Now to the results. 

The Mariners benefited from some great defense last year.  The Mariners outfielders were freaking amazing though being 62.1 runs above average compared to the Mariners infielders who were more good than divine at +23.4 runs.  The question is how much did this defense help our pitchers' numbers and who did it help the most.  If the defense helped all pitchers equally, each pitcher should see their R/9 decrease by 0.53 runs (11%).  We know that defense doesn't benefit all pitchers equally though because some pitchers limit balls in play and others don't.  Obviously Felix Hernandez with high strikeout and groundball rates will benefit less than Jarrod Washburn with low strikeouts, walks and high flyball totals.

I know its a big chart so I also posted a shortened chart and graph below to try to pick out the major pitchers you are probably interested in.  I just wanted to point out the column labeled "tdiff" is how the pitcher's xR/9-R/9 differs relative to the team average.

The major thing I think jumps out to me is that almost all pitchers affected relatively evenly by a good defense.  Although Washburn is a contact hitter and others aren't they all roughly benefit by around the same amount.  Only looking at the major pitchers on the team, Ryan Rowland-Smith appears to have been helped the most and either Felix Hernandez or Erik Bedard were helped the least by the defense.

I think I like this approach of looking at how defense affects a pitcher's R/9 because its fairly simple although it definitely isn't as useful as metrics like FIP.  I think its mostly just an interesting way to quantify how much defense can help different types of pitchers.  If you would have asked me, I would have guessed Washburn would have been helped much more than Felix but its really only a tenth of a run between the two.

So I thought it would be interesting to do something a little different and review the moves that have taken place this offseason.  I'm not going to do a detailed post with in depth analysis but I want to highlight how the moves this front office has made this offseason have improved the M's chances at making the playoffs.  I'm not worrying about salary or the future.  I'm only thinking about how it affects our production this year.  To do this, I've chosen to use an equation Jeff highlighted a little while back that is from Athletic Nation.

To be clear with my methods, I simply used the equation at Athletic Nation to use my projected win totals to compute our chances at making it to the playoffs.  I loosely used Matthew's projected win and WAR totals.  To understand how the moves affected the team, we are interested in wins above bench (or how much better they were than the player they replaced) and not WAR so I had to fudge stuff a little.  There are also a little fudging numbers (personal opinion) here or there so there is always some questions.  If you are interested in the exact numbers I used, I supplied a table at the end of the post.

This chart shows the chance of the Mariners making the playoffs after each successive move.  It is made in chronological order (I did it by day so the Bradley/Silva and Langerhans moves are combined since they occurred on the same day).  You can see that the Lee move basically propelled our team in one day into the playoff picture and then soon after we were in contention.  Since then we have seen our odds slowly increase to about the 35% mark.

I also made another plot with the same data looking at the change in our playoff chances.  This plot shows better that the Mariners playoff chances were basically made in two days at the start of the offseason.

I thought this was a little different way of looking at the success of the offseason and which moves were important.  It doesn't really say much specifically about the offseason but I thought it was an interesting way to make a timeline.  Also, don't put too much faith in the absolute numbers because of a ton of factors.  I think its a little interesting to think about it this way.  It shows the Figgins move didn't really improve our chance of making the playoffs since at that time the team was not projected to be in contention.  I'll end this with one final plot showing how a win added now is worth much more for this team than it was at the end of the season since this team's expected win total is approaching that magic value of around 90 wins that teams shoot for to get in the playoffs.

With that I'll leave with a final graph showing the increase in playoff odds divided by wins added for each move this offseason.

We projected Ichiro last week, Felix the other day and Beltre  and Bedard earlier today.

You know the deal.  How do you think Lopez will do this year?  Take the offensive step forward we have been waiting for and maintain his defensive abilities.  See regression in either offensive or defensive production.  Or repeat 2007's hitting with defense slipping.  You chose.

(Table stolen from Fangraphs...hopefully ok?)

As always, honesty and intelligence is appreciated.

We projected Ichiro last week, Felix the other day and Beltre earlier today.

You know the deal.  How do you think Bedard will do this year?  Repeat the year he had with Baltimore before we brought him over because he is healthy this year.  Experience a season like last year with injury and mediocriocy.  Or have his arm blow up and become completely ineffective.  You chose.

(Table stolen from Fangraphs...hopefully ok?)

As always, honesty and intelligence is appreciated.

We projected Ichiro last week and Felix the other day.

You know the deal.  How do you think Beltre will do this year?  Continue his godly defense and have the offensive year we have always wanted now that he is healthy.  Field a little more like a human and maintain his underappreciated hitting.  Or revert back to 2005 Beltre who swung at every outside slider given to him.  You chose.

(Table stolen from Fangraphs...hopefully ok?)

As always, honesty and intelligence is appreciated.

I've fallen behind on my schedule for posting these so I'll be trying to throw them up over the next week or so.  We projected Ichiro last week.

You know the deal.  How do you think Felix will do this year?  Make the next step to finally become an elite SP.  Continue to be good but not amazing.  Or regress significantly.  You chose.

(Table stolen from Fangraphs...hopefully ok?)

Upon realizing today that LL/USSM have dropped the community projections, I figured I'd try to take up an abridged version of what they have been doing.  It was discontinued because they are busy people and its really just a fun thing and not really anything quantitative. 

I also agree it takes way too much time to do it in full justice so I'm going to try an scaled back version that will be easier to keep track of.  Instead of asking you to project a ton of categories, I'm gonna make it easy for you.  I will put up a poll that will simply ask you how valuable a player will be this year.  I'll break it down into increments and you can place your vote.

If you need an idea of how WAR works please go to fangraphs.  When projecting value please take into account the player's offensive value, defensive value, position and playing time.  I'm assuming most of the readers here have had this mantra beaten into their heads enough but if you need background on it Dave wrote some great "how to" articles that are posted under the glossary at fangraphs.

I will give a simple example of projecting a player.  Take Randy Winn for example assuming he plays a full season:

Offensive Value = 0 - 5 runs

Defensive Value = 5 - 15 runs

Replacement Value = 22 runs

Positional Adjustment  = -7.5 runs (RF/LF)

Total Value = 27 runs = 2.7 WAR (because 10 runs = 1 WAR)

In a little change from what fangraphs does, we will include SB/CS in the offensive value.  It shouldn't make much of a difference but if you think a player is a really good base stealer feel free to add a few fractions of a win on your projection.  Keep in mind that even the best base stealers rarely get more than ~0.5 WAR or so for their efforts.

Anyway, on to the poll.  I won't discuss my opinions on the players here in an attempt to avoid biasing your vote but feel free to discuss what you think he will do in the upcoming year.

We are starting off with Ichiro because he is awesome and if you are going to start talking about the Mariners, he's the guy you always start with.  I'll probably do a new guy every few days and try to cover the main guys on the team.

Apparently Smoltz may be going to the Red Sox in one of the more unpredictible moves of the offseason.  Reported by ESPN so its a little more credible than your typical rumor.

Smoltz has been great lately and looked to be doing well last year if it wasn't for the injury he sustained.  I'm pretty surprised Boston went after Smoltz because their rotation should be pretty set although I guess everybody could find room for him in their rotation.  Although it kills Smoltz's value, it would be interesting to see if he is used in Boston's rotation or the bullpen. 

This really hurts the Braves since they were looking for a pitcher. 

Apparently the contract is 5 mil base and up to 10.5 mil total.

(Olney's post doubled in size and details right as I posted this)

At the bottom is some speculation of trading young pitching (Clay Buchholz) for a catcher.  We have a lot of catchers....Lets make a deal!

Interesting little article over at BBTF.  Its probably written by a Red Sox fan so there is that small hint of irrational thinking.  Still its interesting.  More or less, its talking about what the Red Sox should do to get a good catcher.

One of his suggestions is to trade for a young catcher.  To avoid giving away too many prospects to do this he suggests taking on bloated contracts.  In the M's case he suggests trading for Jeff Clement and Carlos Silva.  You lose Clement who projects to be a good/great catcher if he can stick at the position and stay healthy.  Carlos Silva sucks both as a person and a player.  He is also making 36 million over the next 3 years.  Clement projects to be pretty good and Silva isn't quite as bad of a player as last year shows (although my hatred for him will burn strongly for the next three years) so I don't think this plan makes sense if its a pure salary dump. 

If you say that Clement is a 3 WAR player (4 seasons including arb? I suck at service time.) and Silva is a 1.5 WAR player then we are trading 36 million for Silva and 15 million in Clement arb salary.  We are trading 15.5 WAR * ~6 mil/WAR = 93 million in value at a price of only 51 million effectively giving them 42 million in value.  This means we need to get 42 million in value in return to come out even.  All this is really rough but this pretty clearly shows that this type of trade doesn't make sense if its just a salary dump.  Clement has too much value.  Clement might be around longer than 4 years if I messed his service time up too which would make him even more valuable.

I'd love to get a guy like Lars Anderson if that was possible but maybe we could grab one of their pitchers like Masterdon since they already have too many good SP.  There are ways that this could make sense because we need to get rid of some catchers and Clement has the most value by far and has his fair share of question marks.  With NYY and Tampa looking good next year the Red Sox have a lot of pressure to improve their team.  Their most obvious hole is at catcher and it makes sense in their case if they can get somebody who will help them out.  That motivation plus a pretty tight catcher market might make this the time to sell Clement.

There are a ton of reasons to hold on to Clement and if the return wouldn't be high enough then you definitely hold onto him.  I think it is an interesting thing to think about though.

Link

To sum up Dan Szymborski's projection of our team...We should have seen last year coming and the 2009 team is going to be pretty pathetic for a lot of reasons.

Kenji is projected to be our worst catcher behind Clement, Moore, Johnson, and Burke.  I think Moore's projection is actually much closer to Clement's than I thought.  If you haven't already jumped on the Adam Moore bandwagon, this is probably a good time to do it.

Russell Branyan is going to be one of our best players....

2B has three options all which look to give very similar production (Lopez, Tug, and Valbuena).

Beltre is expected to repeat last year without improvement/decline.

Yuni is the team's 3rd best option.  The best, Mike Morse...god help us all.  (Tug is probably a better option though).

Our OF is gonna suck unless we bring in some people.  Ichiro is good athough ZIPS has a tough time predicting a good year for him.  The rest are either not good or too young to help. 

Starting pitching should be good and the bullpen might be mediocre (especially if Putz is traded).

If this depresses you too much look at the Marlins projections.  They suck too although they have young exciting players.  All and all, these projections are pretty depressing to me.  The worst part is I think they might be pretty accurate.

Stumbled across this nice write up.  Goes through and shows a list of the league's best position players.  Combines all the saber stats to give total value for the players.  Also has links to each position so you can look at how bad our team is.  Moral of the story is Beltre is borderline amazing.

List of Positional Players

Anyway...check it out.  I don't think the climax will be a surprise.

(Saw this link on tango's blog.  Definitely a good spot to stop by.)

Random thought while I was watching the Twins vs. Soxs game. 

We have WPA which tells us how much of each victory or loss can be credited to a player for his actions.  This is easy.

We also have ways to predict the chance of making the playoffs and winning the world series based on a team's current record (as well as some info about ability).  There many different ways to do this and usually they give similar answers.  We can use these projections to figure out what each game is valued by considering each win to have a "change in % chance at winning the WS".

It would be cool to combine the two.  Each player's goal is to win the world series.  WCPA (World Champion Probability Added) would be much like WPA but have even more context wrapped up in it.  Would be less "useful" but I think pretty interesting.

All of the M's recent games would have no value in their quest for the WS since we don't have a chance.  At some point early on in the season we did and some players would accumulate some WCPA but most would go to teams that have a chance at the WS.

Not only would WCPA tell us which players were "clutch" with well timed hits in games.  WCPA would tell us which players were "clutch" with well timed hits in games that matter.

I just thought the idea was a little cool.  I will never try to implement it cause it seems hard and more time consuming which means maybe its not such a good idea.  Anyway, stop watching the mariners guys and start watching baseball that matters for the rest of the season.

Just was surfing the web and stumbled along this.  I think its pretty cool and I'm happy to see that somebody has already done it.  Josh Kalk (writer for THT) has posted the WE by count on his blog.Here is the link to his blog and the WE table.

For example:

Tie game in the bottom of the 9th and bases are loaded with 2 outs

Count / WE

0-0 / 65.8

0-2 /  60.1

3-0 / 76.1

Thats a pretty big difference just based on the count.  I was hoping the difference would be bigger than it is for more situations.  In most cases the count would only change the WE by fractions of a point within an AB.

I know we've all got our own mancrushes on the team.  Who are they?

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Over at THT Sean Smith has been going through old data and putting together historic defensive stats.  You can go download the spreadsheet that has all the info.

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In an attempt to make a diary that relates to baseball, I figured I'd try to go through the FA that are left.  I think we all agree that we probably need at least a bench bat and/or maybe a 4th OF.  We could use a little insurance because if the four headed monster of Ibanez/Wilkerson/Sexson/Vidro has the collapse they are doomed to have them we'll be screwed.  I also wouldn't be surprised if we signed a RP. With the retarded Cairo signing I'm guessing we won't be looking at any infielders.

So here we go in no particular order:

1B/DH
Tony Clark - Padres (1yr/900k)
Doug Mientkiewicz
Mike Piazza
Mike Sweeney
Russell Branyan
Jeff Cirillo
Corey Koskie?
Barry Bonds
Shea Hillenbrand

IF
Tony Graffanino

OF
Kenny Lofton
Kevin Mench
Trot Nixon
Corey Patterson
Shannon Stewart
Reggie Sanders
Sammy Sosa
Preston Wilson

RP
Jeff Weaver
Armando Benitez
Shawn Chacon
Rudy Seanez
Bob Wickman

I wasn't gonna do a bunch of analysis of all the options but there are still a fair number of guys out there that could help the club.  

I'd love to see the team sign Mike Sweeney but that's probably me being irrational.  As sad as it is, Mientkiewicz might be as good as Sexson next year (and have the hardest name to spell ever).  Bonds would be great but we'll never sign him.  Branyan and Piazza would be interesting if nothing else.  I'd also really like so get Koskie but from what it sounds like he might never play again (or at least not until part way through the season) because of the concussion issues.

In the OF, I like Patterson the most probably (if Bonds doesn't count).  After that its basically everyone else.  Half of them will probably do fine but the others will probably suck.  The bar has to be set by asking if they are better than Wlad/Reed.  It would be nice to have somebody who could be a defensive sub (Patterson).

I wouldn't be extremely pissed if we signed a RP.  With Sherrill gone it isn't the worst move as long as we don't overpay and don't sign somebody who isn't actually any good.

Anyway, thoughts?  Since this Bedard trade went down, a push for this year needs to be made by the front office.  If I took over Bavasi's job right now I'd probably try to grab somebody useful with a trade.

If you need something else to talk about here is a random name: Hee Seop Choi.

So a while back I was wondering how the variance of OPS+ has changed with time.  The average of OPS+ is set at 100 by definition but there is nothing in the formula that makes sure the stat falls under the same distribution all the time.  An OPS+ of 120 is always 20% better than average but it doesn't tell you what percentile you were.

My first thoughts about this came from trying to compare OPS+ from different generations.  A fairly wide feeling is that as the competion in the league increases we should see the variance in the league decrease.  Ty Cobb might have been much like Ichiro but he played in a drastically different league for example.

Not surprisingly, I don't have amazing database skills and the info wasn't availible instantly.  After dreading it for a while, I went through baseball reference's database using their PI tool and took down all the players with more than 100 PA for a bunch of different years.  Then I found the PA weighted OPS+ which came out to be about 100 for every case (good sign!).  After that I found the PA weighted standard deviation in OPS+.  I also found the skewness but I don't think that tells me a bunch.

So anyway, what did I see?  

Well, looking at the graphs you see that it looks surprisingly noise free.  I was pretty shocked.  I didn't expect this.  I was basically expected to not see anything interested.  Oh yeah, don't complain about me not having every year.  It takes a while to strip the info for me and I got lazy.  So anyway, we see that the variance looks to decrease slowly which is what we expect.  I kinda would have thought the increase might have been larger but I've been wrong plenty of times before.

The most interesting features of the graph is the blip around 1970 and 1999.  I'm not 100% sure why there is the bump around 1970.  I think a small error I made when I was collecting data was that I did not exclude pitchers and they tend to have pretty horrible OPS+.  I don't think it caused this spike but it might explain a little of it.  This wasn't smart to do but I don't think it destroys everything (hopefully).  From 1990 to 2007 there were only 6 times that 100 PA was broken by a pitcher so the recent times should not be affected by this at least.

And to the blip I think is most interesting.  The one that goes from around 1990 to 2005 fits amazingly well to the steroids era.  The most interesting part I think is that the variance is back down to the level it was before 1990.  The first question I asked myself is what happens when you take a few of the top hitters out for those years?  Maybe its just that a couple players like Barry Bonds are just skewing it.  Well I tried that and it doesn't get rid of the whole peak.  Does this mean that that the effects of steroids and performance enhancers are gone or does it mean that everybody is using HGH now?  Does it mean anything at all?  I'm not really sure.  I'm guessing that the testing baseball has now has really done something.

For a little while I wanted to try to use WE to quantify how "exciting" a game was.  It should be pretty simple.  In general, the more spiky the graph, the better the game and blowouts tend to be big yawnfests.

Using some data Jeff gave me (thanks), I was able to do a little number crunching and come up with some simple formulas that give you some idea of how good a game was.

The first we'll call Blowout Rating (BR).  This tells you how big of a blowout a game was (big shocker!).  It ranges from 100 to -100.  100 represents a "perfect" blowout where we win.  -100 represents the opposite.  0 tells us the WE was 50% the whole game.

The formula:  BR = 100*[2*average(WE)-1]

This basically uses the area under the WE curve to figure out whether the game was mostly hopeless or a guaranteed win.

The next two formulas are a little more difficult (not too bad) and use a little calculus (oh no!).  They are the average absolute 1st and 2nd derivatives of the WE curve.

The average absolute 1st derivative (AA1D) mathematically is the slope of the WE curve.  Absolute means if the slope is negative then we'll make it positive.  This gives us the average change in WE with each plate appearance.  In other words, if it is 10%, then whenever there was a batter up the WE changed on average 10% (in either direction).  I'd say this would signify how exciting the game is because it shows us how much the WE changes.

The average absolute 2nd derivative (AA2D) is a little bit tougher to nail down.  Its the absolute derivative of the 1st derivative (which was not absolute).  When the slope of the WE changes directions this results in a large value for the 2nd derivative.  This tries to tell us how much the slope of the WE varies and gives us a percent change in the slope of the WE.  In other words, this shows us how much the momentum varies in a game.

I tried to make a model game where we'd expect to see the WE jump around as much as is possible.  It probably isn't the theoretical maximum but its probably close enough.  I found that the AA1D(max) = 22% and AA2D(max) = 19%.  Once again, these are a little too low so maybe we'll just say that they are both 25%.

These are the games Jeff supplied me with data for.
Sep 08
Aug 15
Jul 7
May 09
Apr 21

I think the games I was given gave me a pretty good mix to work with.  So what did I get?

Blowout Rating (BR)
4_21    -72
5_9      43
7_7      69
8_15    -19
9_8     -41

So what does this tell us?  Well the April game pretty much sucked.  It sucked so much Jeff didn't write anything up for it.  On the other hand, the June game was also a pretty big blowout in our favor as the game was in hand the whole time.  The best of the bunch according to this was the August game which was back and forth a little until the last few innings where it slipped away.  The biggest shortcomings of this is that it doesn't take into account the timing of the game.  It would be kinda nice if it was weighted so that the last 1/3 of the game was more important but for now I think it is pretty decent and shows what you want in one number.  Once again, this doesn't really show us how "exciting" a game was, just how big of a blowout it was.

Absolute Average 1st Derivative (AA1D)
4_21    2.1%
5_9     2.2%
7_7     1.7%
8_15    4.4%
9_8     3.6%

Absolute Average 2nd Derivative (AA2D)
4_21    2.6%
5_9     3.2%
7_7     2.4%
8_15    6.1%
9_8     5.2%

I kinda lumped these two together because they kind of show similar things in the end.  They show that the last two games were clearly more exciting than the first three.  Looking at the WE graphs this isn't surprising.  Also, again as with the BR, there is no extra weighting for the time things occur but I think WE takes care of this since it varies much more near the end of the game which increases both metrics.  One nice tidbit you can tease out from this that isn't in the BR metric is that the September game was a more exciting game than the May game even though the BR is roughly the same magnitude for both.  

One thing I didn't look into just because I didn't have the info that would be interesting is looking at the average leverage index for a game.  This would be a pretty easy way to show how many close situations there were in a game.  I'd like to look at this one and see how it'd compare to the other metrics I came up with.

Oh I also thought it'd be good to include this one that Jeff says he likes.  As a quick and dirty way he says he looks at a game to see how many plays change the WE by more than 10%.

Plays where WE changed my more than 10%
4_21    2
5_9    2
7_7    1
8_15    5
9_8    6

Appears as if Jeff's method is pretty good and goes along right along with my derivatives.  This method appears to be surprisingly good.  Once again, Jeff appears to be pretty smart.

This stuff is pretty rough but I kind of wanted to get the idea out there.  It should be pretty easy to formula a good rating system and be able to come up with a list of the most exciting games ever.

I was debating whether to put this up but since Bill James' projections are availible on fangraphs (how I got them).  Go over and take a look.

I guess I'll just quickly summarize them.

Ichiro: repeat of 2006
Beltre: nice progression
Sexson: big rebound
Lopez: I guess its good, kinda...
Yuni: Same old
Jones: I'm happy with it.
Ibanez: Look at LL/USSM projections for 2007
Guillen: Predictable
Kenji: WOW!!!! All star??
Wlad: Adam Jones light

SP: Boring with Felix looking better but not great

So I don't know if much is amazing beside Kenji. (.301/.357/.497    really?)

I know, everyone and their mother has made a RC formula or two.  I was just messing around in some huge spreadsheet quite a while ago and kinda just stumbled across this.  The thing that started me messing around is that I don't like complicated equations and OPS is nice because its so simple and uses numbers I already have a good sense for.

As we all know OPS is a decent judge of runs created but it isn't as good as we would like.  We also know it undervalues OBP.

OPS = OBP + SLG = (AVG + ISOpa) + (AVG + ISOpo) = 2*AVG + ISOpa + ISOpo

So my idea was basically to take a bunch of data and fit the expression RC/PA = a*AVG + b*ISOpa + c*ISOpo and do a pretty straightforward regression.  This actually gave pretty good results. RC/PA = 7.674*AVG + 6.357*ISOpa + 14.945*ISOpo but the R^2 was only 0.8 which isn't very good when compared against team RS results from the past 40 years.  After looking at some of the residuals it was apparent that it was too simple to represent the AVG term as just a single order term.

To improve it I changed it to RC/PA = a*AVG + b*AVG^2 + c*ISOpa + d*ISOpo.  I know this is getting away from the simplicity and that made me sad.  This gave great results though.  RC/PA = 1.4088*AVG^2 - 0.1412*AVG + 0.3446*ISOpa + 0.2701*ISOpo and has a R^2 of 0.96 when compared against team RS results.  Also, when compared against Baseruns it has an R^2 of 0.999 which basically means that it gives you baseruns without the complex formula that baseruns has.

I like it.  I don't know if I'll use it.  I just thought it was surprising that it did so well when compared against something as advanced as Baseruns.

Oh, I looked at including things like SB or CS but when using team RS to create a correlation the impact of SB and CS is just not significant and gives pretty strange results sometimes.