I’m Baaack

Hello Faithful Readers (Hi, Mom and Dad).  Sorry I’ve been away for so long. 

First, thanks to Greg for your kind words in the comments section of my last post.  Also, I’d like to give a shout out to the fabulous blog, Rollie Fingers.  This blog is a refreshing change from all of those stats oriented baseball blogs (including this one, I suppose), in that he likes to take a humorous look at things.  It helps that he’s one of the more active (and better) posters on Orioles Hangout.

If you’re wondering why I disappeared for a while, I’ll tell you why.  For maybe the first time in my life, I got tired of reading, thinking, and writing about baseball.  It didn’t help that things had gotten really busy at work and at home.  I had even stopped writing columns for the Hangout – which is something I had really enjoyed doing.

Then it happened.  There were quite a few threads on the OH messageboard discussing the Orioles’ roster and Perlozzo’s handling of the roster.  Something about those discussions gave me the urge to write again.  So I wrote this column for OH on Win Expectancy and (primarily) Leverage Index. 

Probably later this evening I’m actually going to add my next post to this blog.  It will be a look at how the Orioles are doing in specific Leverage situations.

Thanks, and have a great, dry day.

ERA vs Theoretical ERAs

Okay, it has been a while since my last post on here, but things have been kind of crazy for me. 

First, let me say congratulations to the St. Louis Cardinals and their fine fans.  While I think it is safe to say that they probably weren’t the best team in baseball this year, they were the best team when it mattered most.

 

Here’s my latest article on the Orioles Hangout.

 

Yet another outstanding article from the fine folks at THT can be found here.  And yes, it is on my favorite subject.  I can hardly wait for the THT Annual.  Be sure to order your copy today.

 

One thing that has taken a lot of my time is that I had to rebuild my stats database after my hard drive crashed on me.  I now have the pitching stats for every pitcher from 2002 through 2006.  Included in these stats is batted ball data.  Having this nice database of information gives me the ability to perform a lot of studies over the winter.  I’m open to any ideas that you may have.  Just remember that I’m not a mathematician, so I may not be as thorough or as clean with my analysis as the fine folks at sites such as BP and THT.

 

One of the first studies I wanted to do was one on the consistency on specific stats such as ERA, various theoretical ERAs (think DIPS and FIP ERA), K-Rate, and so on.  I can now do that.  To do it I first had to narrow down all the instances in which a pitcher appeared in back to back seasons.  Rodrigo Lopez, for instance, ended up with four pairs of seasons (2002-2003 / 2003-2004 / 2004-2005 / 2005-2006).  I lined up each of the Year 1 and Year 2 ERAs, and then performed a simple correlation test on Excel.

I wanted to compare the correlation of ERA to those of various theoretical ERAs.  I used three versions each of four fairly common theoretical ERAs – DIPS, Component ERA (ERC), FIP, and XERA.  ERC was invented by Bill James, XERA by Ron Shandler and the folks at Baseball HQ.  This site gives the basic formula for each.  FIP ERA is sort of the cousin to DIPS. 

 

I actually use three different versions of each theoretical ERA.  The first version (ERC1, XERA1, FIP1, DIPS1) is based on the standard non-adjusted stat.  I also use DIPS 3.0 instead of either of the original two versions created by Voros McCracken (so my numbers would be different from those posted on ESPN.  For version 2 of each (ERC2, XERA2, FIP2, DIPS2) I adjusted the hits and innings pitched totals using batted ball data.  For version 3 of each (ERC3, XERA3, FIP3, DIPS3) I made the same adjustments as version 2, but took it a step further by normalizing the number of infield flyballs and line drives for each pitcher to the overall MLB average for each stat for the 2002-2006 seasons.  The next trick was to filter out pitchers based on innings pitched totals as I went along.

 

This list shows the correlation for each stat for all pitchers.  The totals for the 1,848 pitchers were 156,276 IP in Year 1, and 152,186 IP in Year 2:

 

ERA                 .068

ERC1               .075

ERC2               .084

ERC3               .204

DIPS1              .199

DIPS2              .185

DIPS3              .265

XERA1            .118

XERA2            .120

XERA3            .271

FIP1                .139

FIP2                .202

FIP3                .251

 

The highest correlation for any of these was only.271, which isn’t very high.  It is telling that ERA was so low when compared to anything other than ERC1 or ERC2.  Note that XERA3 had the highest correlation.

 

This list shows the correlation of each stat for all pitchers with at least 25 IP in each season.  The totals for the 1,250 pitchers were 137,732 IP in Year 1, and 135,054 IP in Year 2:

 

ERA                 .263

ERC1               .301

ERC2               .482

ERC3               .578

DIPS1              .549

DIPS2              .562

DIPS3              .593

XERA1            .321

XERA2            .503

XERA3            .623

FIP1                .402

FIP2                .576

FIP3                .587

 

The correlation for ERA became a little better this time, even though it was still less than it was for XERA3 in the first chart.  Once again, XERA3 had the highest correlation – with a very respectable .623. 

 

One last list – this one showing all pitchers with at least 75 IP in each season.  The totals for the 557 pitchers were 90,998 IP in Year 1, and 89,583 IP in Year 2:

 

ERA                 .381

ERC1               .430

ERC2               .595

ERC3               .680

DIPS1              .665

DIPS2              .681

DIPS3              .688

XERA1            .452

XERA2            .601

XERA3            .706

FIP1                .553

FIP2                .687

FIP3                .688

 

Once again we see improvement for ERA – even though it was still lower than every single theoretical ERA.  XERA3 was also the king once again, even though ERC3. DIPS1, DIPS2, DIPS3, FIP1, and FIP2 also did quite well, and weren’t far behind XERA3.

 

So now we have an idea of the consistency of each theoretical ERA.  In the next installment I plan to evaluate the success rate of each at predicting whether Year 2’s ERA will go up or down.  We’ll also look at whether the difference can be used to predict Year 2’s ERA.

 

After I had begun typing this article I stumbled upon this posting on another site.  This guy essentially argues that the theoretical stats are too busy and that we should just focus on K:BB ratio (or Command Rate, or whatever else you might want to call it) as they are more consistent.  So I decided to do a quick correlation test.  Using pitchers with at least 75 IP in each season, K:BB had a correlation of .598.  Pretty good, but eight of the theoretical stats were higher.  Having said that, I think he makes a pretty good argument, and I plan to study it further.

 

One final set of correlations to mention.  This list shows the correlations of various rate stats.  The columns are 25 IP, and 75 IP.

 

H/9                  .392     .471

HR/9                .207     .356

HB/9                .330     .408

BB/9                .521     .653

K/9                  .720     .768

GB%                .756     .821

FB%                .722     .734

IFFB%             .187     .241

LD%                .088     .066

HR/OFFB%     .083     .199

BIP%               .735     .779

 

Most of the above is not a surprise, but there is one big surprise – at least to me.  I was floored that the Year 1 to Year 2 correlation for H/9 was higher than the Year 1 to Year 2 correlation for HR/9 – and it wasn’t really even close. 

 

I was concerned that my methodology may have been wrong, but the data available in the 2006 THT Annual was consistent with my numbers, so I’m pretty confident they are right.

 

I’ve seen it written many times that pitchers have a lot of control over whether a batter hits a home run, but not nearly as much control over whether a batted ball becomes a hit.  The above tells me we’re either giving pitchers too much or too little credit (depending on your point of view).  One thought that crossed my mind is that home run data was skewing the data, so I ran correlations for (H-HR)/9.  I came up with a correlation of .447 – not much less than H/9, but still higher than HR/9.

 

Interesting.

Predicting the Future

This is the first of two companion articles to an article I wrote for the Orioles Hangout. 

That article shows tools we can use to perform a quick analysis of whether a pitcher should figure to see an increase or decrease in his ERA either in the near future or the following season.

The primary tool for predicting future ERA is line drive percentage (LD%).  Someone asked if I had any historical data to show that a high LD% is a good predictor of a lower ERA in the future. I made a list of every pitcher with enough IP to qualify for the ERA title since 2004 (83 in 2004, 92 in 2005, and 86 in 2006). I then looked for back to back seasons and paired these seasons together. Barry Zito made all three lists giving him two pitcher comparisons (2004/2005 and 2005/2006). I ended up with 117 pitcher comparisons total.

I ranked each player by his Season 1 LOB% and then compared his Season 1 ERA to his Season 2 ERA. I ended up with the following data.  The average LD% is 71%.  Each difference grouping listed below is the difference above and below 71% (so a difference of 2% is >73% and <69%).– As stated in the article, the average LOB% is 71%. There were 16 comps in which the pitcher had a LOB% between 70% and 72% (within 1 point above and below the average). The ERA went in the same direction as predicted in 6 of these 14 comps (two pitchers were right at 71%). 1 of 7 above and 5 of 7 below.

All LOB% > 1%

22 of 32 (68.8%) below 70% correctly predicted an increased ERA the following season.

53 of 69 (76.8%) above 72% correctly predicted a decreased ERA the following season.

75 of 101 (74.3%) correctly predicted an increased or decreased ERA the following season. 

All LOB% > 2%

14 of 20 (70.0%) below 69% correctly predicted an increased ERA the following season.

41 of 52 (78.8%) above 73% correctly predicted a decreased ERA the following season.

55 of 72 (76.4%) correctly predicted an increased or decreased ERA the following season.

 

All LOB% > 3%

8 of 13 (61.5%) below 68% correctly predicted an increased ERA the following season.

33 of 38 (86.8%) above 74% correctly predicted a decreased ERA the following season.

41 of 51 (80.4%) correctly predicted an increased or decreased ERA the following season. 

All LOB% > 4%

7 of 11 (63.6%) below 67% correctly predicted an increased ERA the following season.

26 of 29 (89.7%) above 75% correctly predicted a decreased ERA the following season.

33 of 40 (82.5%) correctly predicted an increased or decreased ERA the following season.  

All LOB% > 5%

6 of 9 (66.6%) below 66% correctly predicted an increased ERA the following season.

17 of 20 (85.0%) above 76% correctly predicted a decreased ERA the following season.

23 of 29 (79.3%) correctly predicted an increased or decreased ERA the following season. 

All LOB% > 6%

4 of 6 (66.6%) below 65% correctly predicted an increased ERA the following season.

13 of 15 (86.7%) above 77% correctly predicted a decreased ERA the following season.

17 of 21 (81.0%) correctly predicted an increased or decreased ERA the following season. 

All LOB% > 7%

2 of 2 (100%) below 64% correctly predicted an increased ERA the following season.

11 of 11 (100%) above 78% correctly predicted a decreased ERA the following season.

13 of 13 (100%) correctly predicted an increased or decreased ERA the following season.  

Every pitcher who had a LOB% of 77.9% or greater in the last three years, saw an increase in his ERA the following year.

Also, this is not a new idea from me.  I believe Baseball HQ has been running similar studies with similar results in the past.

NOTE: when reading the remainder of this post, keep in mind this is all a small sample size.If the last couple of years can be used as our guide, then the following pitchers will see an increased ERA next season: Verlander, Arroyo, Young, Zito, Zambrano, Carpenter, and Oswalt; while  Maddux and Randy Johnson will see an improved ERA. 14 of the following 18 will see an increased ERA next season:
Jennings, Kazmir, Schilling, Josh Johnson, J Santana, Smoltz, Schmidt, Penny, Trachsel, Webb, Lilly, Haren, Lowry, Robertson, Capuano, Glavine, Myers, and Halladay.Also note that I’m not predicting how much of an increase or decrease in ERA each season. Hope that helps and makes sense.  So what about the other indicators?  The OH article also talked about using LD% and IFFB% as indicators.  So I ran similar results with this data.  The purpose of these tests is to see if high percentages in these categories might lead to lower percentages the following season. 

LD%

As stated in the article, the average LD% is 20.4%.  Each difference listed will be the difference from 20.4.  The methods used for this study are essentially the same as used above. 

Overall

There were 115 Year 1 instances in which the LD% was above or below 20.4%.  With the theory being that any time the Year 1 LD% is below 20.4%, it will rise the following season, and vice verse.  This theory was correct 82 (or 71.3%) times. 

All LD% > 1%

44 of 59 (74.6%) below 19.4% correctly predicted an increased LD% the following season.

18 of 21 (85.7%) above 21.4% correctly predicted a decreased LD% the following season.

62 of 80 (77.5%) correctly predicted an increased or decreased LD% the following season. 

All LD% > 2%

28 of 35 (80.0%) below 18.4% correctly predicted an increased LD% the following season.

8 of 9 (88.9%) above 22.4% correctly predicted a decreased LD% the following season.

36 of 44 (81.8%) correctly predicted an increased or decreased LD% the following season. 

All LD% > 3%

14 of 18 (77.8%) below 17.4% correctly predicted an increased LD% the following season.

3 of 3 (100%) above 23.4% correctly predicted a decreased LD% the following season.

17 of 21 (81.0%) correctly predicted an increased or decreased LD% the following season. 

IFFB%

The average IFFB% is 13.3%.  Each difference listed will be the difference from 13.3.  The methods used for this study are essentially the same as used above. 

Overall

There were 116 Year 1 instances in which the IFFB% was above or below 13.3%.  With the theory being that any time the Year 1 IFFB% is below 13.3%, it will rise the following season, and vice verse.  This theory was correct 80 (or 69.0%) times. 

All IFFB% > 1%

40 of 61 (65.6%) below 12.3% correctly predicted an increased IFFB% the following season.

28 of 30 (93.3%) above 14.3% correctly predicted a decreased IFFB% the following season.

68 of 91 (74.7%) correctly predicted an increased or decreased IFFB% the following season. 

All IFFB% > 2%

36 of 51 (70.6%) below 11.3% correctly predicted an increased IFFB% the following season.

19 of 20 (95.0%) above 15.3% correctly predicted a decreased IFFB% the following season.

55 of 71 (77.5%) correctly predicted an increased or decreased IFFB% the following season. 

All IFFB% > 3%

26 of 35 (74.3%) below 10.3% correctly predicted an increased IFFB% the following season.

17 of 18 (94.4%) above 16.3% correctly predicted a decreased IFFB% the following season.

43 of 53 (81.1%) correctly predicted an increased or decreased IFFB% the following season. 

LOB%

The average LOB% is 71%.  Each difference listed will be the difference from 71.  The methods used for this study are essentially the same as used above. 

Overall

There were 116 Year 1 instances in which the LOB% was above or below 71%.  With the theory being that any time the Year 1 LOB% is below 71%, it will rise the following season, and vice verse.  This theory was correct 79 (or 68.1%) times. 

All LOB% > 1%

23 of 32 (71.9%) below 70% correctly predicted an increased LOB% the following season.

50 of 69 (72.5%) above 72% correctly predicted a decreased LOB% the following season.

73 of 101 (72.3%) correctly predicted an increased or decreased LOB% the following season. 

All LOB% > 2%

16 of 22 (72.7%) below 69% correctly predicted an increased LOB% the following season.

38 of 52 (73.1%) above 73% correctly predicted a decreased LOB% the following season.

54 of 74 (73.0%) correctly predicted an increased or decreased LOB% the following season.  

All LOB% > 3%

10 of 13 (76.9%) below 68% correctly predicted an increased LOB% the following season.

32 of 40 (80.0%) above 74% correctly predicted a decreased LOB% the following season.

42 of 53 (79.2%) correctly predicted an increased or decreased LOB% the following season. 

All LOB% > 4%

9 of 12 (75.0%) below 67% correctly predicted an increased LOB% the following season.

25 of 30 (83.3%) above 75% correctly predicted a decreased LOB% the following season.

34 of 42 (81.0%) correctly predicted an increased or decreased LOB% the following season. 

All LOB% > 5%

8 of 9 (88.9%) below 66% correctly predicted an increased LOB% the following season.

18 of 20 (90.0%) above 76% correctly predicted a decreased LOB% the following season.

26 of 29 (89.7%) correctly predicted an increased or decreased LOB% the following season.   

All LOB% > 6%

5 of 6 (83.3%) below 65% correctly predicted an increased LOB% the following season.

15 of 15 (100%) above 77% correctly predicted a decreased LOB% the following season.

20 of 21 (95.2%) correctly predicted an increased or decreased LOB% the following season.   

All LOB% > 7%

2 of 2 (100%) below 64% correctly predicted an increased LOB% the following season.

11 of 11 (100%) above 78% correctly predicted a decreased LOB% the following season.

13 of 13 (100%) correctly predicted an increased or decreased LOB% the following season.

Batted Ball Data

There have been some concerns raised about the Orioles’ 6.02 ERA (as of July 27) for the month of July.  While I can understand these concerns, I tend to look at things a little differently.   

The problem with ERA is that there are so many factors that affect it, especially over short periods of time.  That’s one reason I prefer to look at other stats, particularly theoretical stats, such as one that I call True Performance ERA (yes, it is a borrowed name). TPE uses Component ERA, except my version adjusts hits and home runs based on batted ball data, and I make an adjustment on walks and strikeouts.  The idea is that this formula will take out flukes such as poor fielding or “luck” impacting the number of hits allowed, as well as some other things that impact ERA.

Here is how the Orioles pitchers fare by month:

TPE – 5.01 / 5.09 / 4.52 / 5.10
ERA – 5.54 / 5.54 / 4.49 / 6.02

TPE indicates that things haven’t been quite as bad this month overall.  In fact, take out the “contributions” of Russ Ortiz, and the July TPE drops to 4.72.  Also, even with Ortiz, the staff’s BB% (11.1 > 10.1 > 9.2 > 9.1) and K% (14.2 > 14.4 > 16.9 > 16.4 [but 17.3 without Ortiz]) represent continued improvement.

I’m sure some are wondering what other factors have impacted actual ERA.  These things have played a role:

HR/OFFB.  It is pretty much accepted that pitchers typically don’t have much control over the percentage of flyballs that become home runs. Typically, you can expect to see a rate of about 11%.  The Orioles rate by month is 14.1 > 12.6 > 13.4 > 16.5.  Yes, this is the second straight month it has increased, but a rate of over 16% is extremely high.  Lower that number to something closer to normal, and they would have allowed fewer HRs this month (11% = 22, compared to an actual total of 33).  Fewer HRs would likely have lead to a lower ERA.

LOB%.  The percentage of baserunners left on base.  The typical league average is about 71% (both leagues are at exactly 71% in 2006).  The Orioles by month – 68.4 > 68.9 > 72.5 > 67.0.  In other words, 33% of all runners who reach base score – as opposed to a league average of 29%.

H%/BABIP.  I track a stat I call H%, which is essentially the same as BABIP-A.  H% represents the percentage of balls put in play that become hits.  The Orioles by month: .298 > .296 > .297 > .327.  This tells me that their combination of “luck” (variance), defense, and yes, pitching just hasn’t been all that great this month.

I’m sure some will look at the above and see excuses. I don’t intend for them to be looked at as such. Yes, the pitcher still plays a role in each of these.

The point of this post is that the rise in ERA isn’t nearly as bad as it appears to be at first glance. 

MiLB Batted Ball Data 

I’ve been asked a lot about minor league batted ball data.  One recent question had to do with a pitcher’s ability to control whether a fly ball becomes a home run.  The person who asked made the assumption that this applies to both major and minor league pitchers.
I have seen arguments that some pitchers have an ability to limit home runs on outfield fly balls, but I haven’t seen the evidence that “proves” it (this evidence may exist, I just haven’t seen it).  For instance, a couple of people have pointed to Erik Bedard as one of these pitchers – pointing to his rates of 8.2 and 7.5% the previous two seasons.  The problem here is that his rate is up to 11.7% this year (even though it has been down to 9.1 and 8.3 in June and July, respectively).

I will say that my gut tells me that eventually someone will be able to show that pitchers do have more control over this than what is currently believed. 

The problem with the MLB/MiLB batted ball data discussion is that we are comparing Major League rates and expectancies to Minor League data.  For instance, we know that 11% of all OFFBs become home runs, we know that 18.3% of all line drives become doubles (based on 2002 through 2005 data), but we don’t know how often these things occur within the various minor leagues.  What we need is for someone to do that for each level and for each league.

The person that asked me the question was asking primarily about Astros farmhand Jason Hirsh – whom he thought had an ability to “miss bats.”  The thing to do (in my mind) is to compare his stats to the stats for his team and league (PCL).(Hirsh/Team/League – team and league through Friday):

GB%: 39 / 47 / 46
FB%: 47 / 37 / 37
IFFB%: 28 / 20 / 20 (% of flyballs that become popups)
LD%: 13 / 16 / 17

MLB stats tell us that IFFB% and LD% are not easily controlled by pitchers.  If that is true about minor league stats, then Hirsh may be in trouble – in that he can expect to start allowing more line drives and fewer popups.  FWIW, I’ll give an educated guess that minor league pitchers (especially the good ones) have much more control over these than do ML pitchers.  This would explain what would be his absurdly low (by MLB standards) HR/OFFB rate (3.8% compared to a PCL rate of 10.6%).

There are two other stats to compare:

BB%: 9.9 / 9.0 / 9.0
K%: 21.4 / 16.4 / 18.2

You have to love his K%, but should be a little worried about his BB%. 

By the way, the MiLB data comes from what is now one of my favorite stat sites.  (See the link to the right.) 

Clutchiness 

Thanks to the good folks at THT I have found yet another great blog – Clutchiness (see the link to the right).  I’m not even going to try to explain how this new stat works.  For one, I haven’t read enough about it to give a good explanation.  Besides, he does a great job of explaining it.  Do yourself a favor, take the time to read this – it is definitely good stuff.

By the way, let’s hope the Angels, Astros, and whatever other team that might be interested in Miguel Tejada doesn’t check out the link on the Orioles.  And to think that the primary reason given for going after Tejada a few years ago by a prominent OH poster was his abilities in the clutch.  Oh well, maybe that’s a fluke.

My Most Recent OH Article…

can be found here.

A Few Random Thoughts

Second Half Pitching Expectations? 

First, here’s the link to my most recent article on OH.  It basically talks about what to expect from the pitching staff during the second half.  Unfortunately, one of my comments about Chris Britton proved to be prescient.

…..

Miguel Tejada Missing Protection? 

Jim Palmer made comments the other night about Miguel Tejada’s production being hurt by not having the “protection” of Jay Gibbons behind.  This, of course, brings up the long time discussion on whether protection a lineup really exists. 

Is the whole protection thing a myth? I have no idea, I’ll let people who are a heckuva lot smarter than me figure that one out. My guess is that, like a lot of these types of arguments, the truth lies somewhere in between.

Some additional info on Tejada.  The following is the percentage of flyballs hit by him that became HRs:

2002: 16.7
2003: 13.4
2004: 16.7
2005: 13.6
2006: 19.3

And before someone chimes in with something along the lines of “he’s probably hitting more infield flyballs,” nope, his IFFB% (the percentage of flyballs that are popups) is at its lowest since they started tracking the data.  His IFFB% the last three years has consistently been about 12%, this year it is at 5.7%.

So why is he hitting fewer home runs?  Pretty simple answer actually – he’s hitting fewer flyballs.  Exhibit A would be his GB/FB rate:

2002: 1.29
2003: 1.26
2004: 1.36
2005: 1.41
2006: 1.98

Exhibit B would be his GB% versus his FB%:

2002: 45.2 / 35.1
2003: 44.1 / 34.9
2004: 46.5 / 34.2
2005: 47.2 / 33.4
2006: 51.6 / 26.1

The above numbers also account at least partially for the increase in DPs from Tejada.

Is he now getting fewer good pitches to hit?  There’s a stat we can look at to maybe get an idea about this.  His LD% between 2002 and 2005 was between 19.2% and 20.9% each year (and was at 19.2% and 19.4% in his two years in Baltimore).  This year, his LD% is 22.3%.  These numbers seem to indicate that he hasn’t been pitched around as a whole this season.

…..

Torii Hunter Interview 

Did anyone else hear the Torii Hunter interview on the John Thompson Show?

What a pleasant, well spoken man. He is also extremely passionate about the game of baseball. In case you can’t tell, I was impressed.

The reason for the interview is that he’s concerned about the low percentage of African-American baseball players in MLB. So he’s trying to do something about it. They didn’t get into specifics, but apparently he’s recruiting African-American players to represent different teams of young players in a baseball jamboree next June.  Each young player who participates will be put up in a nice hotel and will be shown “the good life.” He wants to show them the upside of being a MLB player.

As someone who believes in the value of numbers I can see the arguments against having him on a team. After hearing his interview, I can now see the definite positives (primarily in the form of his passion) to having him on a team.

…..

The Nationals / Reds Trade

Jim Bowden has been the butt of quite a few jokes (goodness knows he’s given me plenty of laughs).  If this is the type of trade made by “joke GM’s”, I certainly wish the Orioles’ GM were a joke (be easy now).

Analysts are saying some surprisingly good things about Gary Majewski.  Why?  A pitcher with his numbers can normally expect to have an ERA in the mid 4’s range.  In his career, only 4.7% of all flyballs have become HR’s (roughly 10-11% is normal) – I don’t suppose RFK has helped him there.  You’d be hard pressed to find another pitcher more likely to see a pretty good sized increase in ERA.

Austin Kearns seems to be one of those guys who is either overrated or underrated, but mostly underrated of late.  The Fielding Bible rates him pretty highly.  “Kearns is an excellent defender and the best defensive outfielder on the Reds.  He has very good range, takes good routes on the ball and has a very strong arm.  He has enough range to play centerfield if necessary and his arm is good enough to play in right.”  Me thinks Bowden just found his replacement for Soriano and/or Guillen.

As good as the Spears/whoever for Patterson trade has been for the Orioles thus far, this trade has the potential to be an even better one for the Nationals.

Bedard vs the White Sox

Wow, was that a heckuva performance by Bedard or what?  I have long been an advocate of both keeping Bedard in Baltimore and keeping him the rotation.  It looks my patience is being rewarded.

He is now 9-6 with a 4.44 ERA (which is amazing considering where his ERA was about two weeks ago).  His 4.13 FIP ERA (adjusted by THT), 4.27 TP ERA (I’ll explain this stat in a later poste), and 4.14 adjusted XERA all suggest that there are still better things to come for Bedard.  His 70.7% LOB Rate and and 12.7% HR/OFFB Rate are also indicators that there’s been nothing lucky about his game.  More importantly in my eyes, his 3.22 BB-Rate, 8.3 BB% and 2.22 Command Rates serve as career bests up to this point.  His 7.14 K-Rate, 18.39 K% and 0.96 are all close to his normal ranges.

Prior to the season I wrote an article for OH praising Daniel Cabrera for his combination of a high K-Rate and a high ground ball percentage (GB%).  I showed that at least over the last four years pitchers with that combination have the most success.  Well, in addition to his now high K-Rate (which is an even higher 9.93 over his last four starts), Bedard now has a GB% of 48.26.

Mark my word, Erik is now well on his way to being one of the best pitchers in the American League.

Welcome to Cookin’ With Gas

Hi everyone,

My name is Ted Cook.  I am an avid Orioles fan who writes a regular column for Orioles Hangout.  I ‘m very much into the statistical analysis part of baseball (yep, just what we need, another “analyst”).  I will make every effort to bring you at least one article per week focusing on an analysis of the Orioles and/or baseball in general.

Hang on for the ride.

Hello world!

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