What Pitcher Stats Should I Look At?

What if I told you that I knew of a series of stats that could predict with 92% accuracy that a pitcher will finish with a sub 4.50 ERA?  What if I told you the same stats could predict with 75% accuracy that a pitcher will finish with a sub 4.00 ERA?  Would that pique your interest?

Let me back up.  Someone at Orioles Hangout asked me what stats I look for in a pitcher.  This is how I answered the question.  After posting that I realized that I didn’t set the parameters.  So I did some studying.  These are the boundaries that I came up with:

Strikeout % (Strikeouts / Batters Faced) of at least 17%

K/BB (Strikeouts / Unintentional Walks) of at least 2.5

Groundball % ( Groundballs / Balls in Play) of at least 45%

Strike % (Strikes / Pitches Thrown) of at least 64%

Swinging Strike % (Swinging Strikes / Strikes) of at least 15%

So what did I do once I settled on the parameters?  I have the traditional, batted ball, and pitch data stats for the last three years for each of the last three seasons saved in an Excel file.  First, I looked at the 378 pitcher seasons in which the pitcher faced at least 500 batters in a single season in the three years.  Next, I looked at the 380 pitchers who faced a total of at least 500 batters total in the three seasons.

The following shows how the single season pitchers fared in each category (the number meaning the number of categories in which the pitcher exceeded the minimum):

Five - 22 Pitchers - 4,654 IP - 3.54 ERA - 3.53 FIP ERA

Four - 53 Pitchers - 10,396 IP - 3.62 ERA - 3.57 FIP ERA

Three - 60 Pitchers - 11,355 IP - 4.00 ERA - 3.99 FIP ERA

Two - 65 Pitchers - 11,622 IP - 4.49 ERA - 4.37 FIP ERA

One - 110 Pitchers - 18,373 IP - 4.70 ERA - 4.65 FIP ERA

Zero - 68 Pitchers - 11,354 IP - 4.88 ERA - 4.94 FIP ERA

And the same stats for the 3-year pitchers:

Five - 21 Pitchers - 8,405 IP - 3.45 ERA - 3.46 FIP ERA

Four - 50 Pitchers - 15,095 IP - 3.51 ERA - 3.55 FIP ERA

Three - 64 Pitchers - 16,526 IP - 4.02 ERA - 4.03 FIP ERA

Two - 91 Pitchers - 23,011 IP - 4.43 ERA - 4.41 FIP ERA

One - 91 Pitchers - 26,675 IP - 4.70 ERA - 4.62 FIP ERA

Zero - 63 Pitchers - 18,427 IP - 4.86 ERA - 4.89 FIP ERA

What I liked about the above was the consistency of the ERA and FIP ERA between the 3-year and single year stats. 

Onto some other stats:

75 single season (SS) pitchers met at least four of the stat criteria.  Of these 75 pitchers, 69 (92%) finished with an ERA below 4.50, and all of them finished with a FIP ERA of 4.43 or lower.  56 of the 75 (75%) finished with an ERA of less than 4.00, while 65 (87%) finished with a sub 4.00 FIP ERA.

71 three-year (3Y) pitchers met at least four of the stat criteria.  Of these 71 pitchers, 67 (94%) finished with an ERA below 4.50, and all of them finished with a FIP ERA of 4.30 or lower.  58 of the 71 (82%) finished with an ERA of less than 4.00, while 64 (90%) finished with a sub 4.00 FIP ERA.

How about the inverse? 

178 (SS) pitchers met only one or none of the stat criteria.  Of these 178 pitchers, 114 (64%) finished with an ERA above 4.50, and 121 (69%) finished with a FIP ERA of 4.50 or higher.  71 of the 178 (40%) finished with an ERA of at least 5.00, while 49 (28%) finished with a FIP ERA of 5.00 or higher.

154 (3Y) pitchers met only one or none of the stat criteria.  Of these 154 pitchers, 104 (68%) finished with an ERA above 4.50, and 98 (64%) finished with a FIP ERA of 4.50 or higher.  66 of the 154 (43%) finished with an ERA of at least 5.00, while 41 (27%) finished with a FIP ERA of 5.00 or higher.

Food for thought.

Here’s hoping everyone has a great 2008.

Thank you

Pitch Data – What Is It Good For?

Have you ever checked out the Pitch Data provided by Baseball-Reference?  Have you ever wondered what exactly all the data means, or how best to use it?  I’ve spent a lot of time running the numbers and trying to see what useful information can be gleaned from it.  Hopefully this post will help to clear up some things. 

The first thing I felt I needed to know is the impact of each of the pitch stats on the performance of a pitcher.  I decided to determine this by running correlations between each stat and ERA.  Since ERA has its issues I did the same thing with FIP ERA.  Here are some correlations (ERA listed first, then FIP ERA):

Strike%                        -.42      -.47
In-Play Strike%             .35       .52
Swinging Strike%        -.35      -.53
Contact%                      .34       .53
1^st Pitch Strike%          -.34      -.36

No, the correlations aren’t that high, but they’re high enough for the purpose of this analysis.

The average correlation between HR-Rate and the two ERAs is .67. The three highest pitch stat correlations to HR-Rate are Contact% (.25), Swinging Strike% (.23), and In-Play Strike% (.23).

The average correlation between K/BB and the two ERAs is -.59. The three highest pitch stat correlations to K/BB are Strike% (.77), 1st Pitch Strike% (.63), and Swinging Strike% (.33).

The average correlation between K-Rate and the two ERAs is -.49. The three highest pitch stat correlations to K-Rate are In-Play Strike% (-.94), Contact% (-.87), and Swinging Strike% (.85).

The average correlation between BB-Rate and the two ERAs is .43. The three highest pitch stat correlations to BB-Rate are Strike% (-.87), 1st Pitch Strike% (-.75), and 0-2 Strike% (-.22).

The average correlation between BABIP and the two ERAs is .29. Interestingly enough, the correlations were pretty far apart (.55 to ERA; .03 to FIP). That ERA is so heavily influenced by a stat as inconsistent as BABIP (0.097 year-to-year correlation in this particular study) is a good indicator of the problems associated with ERA. The three highest pitch stat correlations to BABIP are 0-2 Hit% (.32), Strike% (-.08), and Called Strike 3% (-.07).

Compare the findings. The first set of pitch data stats accounted for 12 of the 15 stats listed in the next batch. These are the five pitch data stats on which I’m going to focus.

First, let me back up a bit. I have the pitch data stats and selected other stats from the last three years for every pitcher who appeared in a game for an AL team this year. I ended up with a database of 740 pitcher seasons, in which 164 of those seasons consisted of at least 500 batters faced.

Strike Percentage (Str%) is the percentage of pitches that are strikes.  The overall Str% for the 740 pitcher seasons was 63%.  The average for the 164 pitchers with at least 500 batters faced was 64%.  I determined the Standard Deviation, and then compared the stats of the pitchers at each extreme and in the middle.

                       ERA     FIP       K/9       K/BB    HR/9    BABIP
hi-Str%            3.99     3.98     6.37     3.84     1.06     .304
mid-Str%         4.27     4.33     6.30     2.34     1.03     .304
lo-Str%            5.02     4.96     6.18     1.49     1.10     .308

In-Play Strike Percentage (StIn%) is the percentage of strikes thrown that are put into play.  The overall StIn% was 31%.  The average of the pitchers in this study was 32%.  Please note that B-R.com uses StI%, while I added an “n” to make it a little easier to read.  The comparison:

                        ERA     FIP       K/9       K/BB    HR/9    BABIP
hi-StIn%           4.68     4.77     4.45     2.03     1.07     .304
mid-SIn%        4.45     4.47     6.20     2.41     1.10     .306
lo-StIn%           3.69     3.67     8.62     3.17     0.85     .304

Swinging Strike Percentage (StS%) is the percentage of strikes thrown that resulted in a swing and a miss.  The overall StS% was 14%.  The average of the pitchers in this study was 14%.  The comparison:
                        ERA     FIP       K/9       K/BB    HR/9    BABIP
hi-StS%           3.85     3.80     8.17     3.03     0.90     .304
mid-StS%        4.52     4.50     6.17     2.30     1.08     .309
lo-StS%           4.55     4.73     4.70     2.25     1.13     .299

Contact Percentage (Cntc%) is the percentage of times the batter made contact when swinging.  The overall Cntc% was 80%.  The average of the pitchers in this study was 81%.  The comparison:

                        ERA     FIP       K/9       K/BB    HR/9    BABIP
hi-Cntc%         4.59     4.80     4.31     2.17     1.13     .300
mid-Cntc%      4.52     4.53     5.98     2.31     1.09     .307
lo-Cntc%         3.85     3.60     8.17     3.03     0.90     .304

1^st Pitch Strike Percentage (1st%) is the percentage of times the first pitch to a batter was a strike.  The overall 1st% was 60%.  The average of the pitchers in this study was 59%.  The comparison:

                        ERA     FIP       K/9       K/BB    HR/9    BABIP
hi-1st%            4.01     4.00     6.50     3.63     1.03     .308
mid-1st%         4.28     4.38     6.25     2.43     1.07     .302
lo-1st%            4.88     4.71     6.24     1.60     0.99     .310

So now we have an idea of the types of numbers a pitcher might be expected to put up based on his pitch data.  

Strike%

I have a pitcher with a Strk% of less than 60%.  How likely is it that he’ll raise it to an acceptable level?  There are 82 sets of pitcher seasons in my data set in which the pitcher faced at least 450 batters in back-to-back seasons.  Of those, two pitchers were able to raise their Str% by four points the next season, and one pitcher raised his five points.  The kicker is that these pitchers were Mussina, Sabathia, and Shields – and each had a season 1 Str% of at least 62%.   

Six of the 82 pitchers had a season 1 Str% of 60% or lower.  Their season 1 and season 2 rates:

Wright (2005)              60.3     60.2
Trachsel (2006)          60.0     58.7
Meche (2005)              59.5     61.1
Santos (2005)              59.1     59.5
Cabrera (2005)           59.1     57.2
Cabrera (2006)           57.2     58.1

Yes, four of the five pitchers listed there pitched for the Orioles in 2007.  No, these numbers don’t appear to bode well for Loewen, either Cabrera, Burres, Olson, Liz, Hoey, Cherry, Leicester, or Doyne.  On the bright side, Meche was able to raise his rate to 64% last year. 

Based on my admittedly small sample size, there is a 91.5% chance that a pitcher will raise or lower his Str% by three points or less in season 2. 

In-Play Strike %

I have a pitcher with a StIn% higher than 35%.  How likely is it that he’ll lower it to an acceptable level?  Only Erik Bedard and AJ Burnett were able to lower their StIn% by four points the next season (and they had season 1 rates of 28% and 31%, respectively).   

16 of the 82 pitchers had a season 1 StIn% of 35% or higher.  Their season 1 and 2 rates:

Silva (2005)                 41        39
Wang (2005)               41        40
Wang (2006)               40        37
Silva (2006)                 39        36
Rogers (2005)             37        35
Westbrook (2006)       36        33
Buerhle (2006)            36        34
Jam Wright (2005)      36        36
Batista (2006)             35        32
Byrd (2005)                 35        34
Halladay (2006)           35        34
Ra Ortiz (2005)           35        34
Robertson (2005)        35        34
Pineiro (2005)             35        34
Blanton (2006)            35        34
Westbrook (2005)       35        36

No former Orioles on that list, which I find to be promising.  Only one of these pitchers was able to get his rate close to the league average of 31% in year two.  These numbers don’t bode well for Liz, Bradford, or Leicester.  Actually, I doubt this will prove to be an issue for Liz. 

Based on this analysis, there is a 92.3% chance that a pitcher will raise or lower his StIn% by three points or less in season 2. 

Swinging Strike %

I have a pitcher with a StS% lower than 11%.  How likely is it that he’ll raise it to an acceptable level?  Only Gil Meche has raised his StS% by more than 3 points in year 2.   

10 of 82 pitchers had a season 1 StS% of 10% or less.  Their season 1 and 2 rates:

Byrd (2005)                 10        9
Garland (2006)            10        10
Rogers (2005)             10        11
Buehrle (2006)            10        12
Meche (2005)              10        15
Trachsel (2006)          9          9
Byrd (2006)                 9          10
Wang (2005)               9          11
Silva (2006)                 8          9
Silva (2005)                 7          8

This is one reason I didn’t like the signing of Trachsel.  Only Meche on this list was able to raise his StS% to at least league average the following season.  None of the 11 pitchers with a season 1 StS% of 11% was able to post a league average rate the following season.  Bradford, Doyne, and Leicester probably won’t care too much for this stat. 

Based on this analysis, there is a 92.3% chance that a pitcher will raise or lower his StS% by three points or less in season 2. 

Contact %

How likely is it that a pitcher with a Cntc% of 85% or higher will lower it to an acceptable level the following season?  Only Meche, Bedard (twice), and Burnett have lowered their Cntc% by more than 3 points in year 2.   

12 of 82 pitchers had a season 1 Cntc% of 85% or higher.  Their season 1 and 2 rates:

Silva (2005)                 90        89
Silva (2006)                 89        88
Wang (2005)               88        86
Byrd (2006)                 88        86
Garland (2006)            87        87
Trachsel (2006)          87        87
Buehrle (2006)            86        84
Byrd (2005)                 86        88
Meche (2005)              86        79
Rogers (2005)             86        84
Garland (2005)            86        87
Wang (2006)               86        84

Does anyone wonder why I’ve never wanted Silva, Byrd, or Garland on my team, and why I’ve never been sold on Wang?  By the way, it is looking as if the Gil Meche 2005 to 2006 transformation is what we’re hoping to see out of a few Orioles pitchers.  This list is one reason I’m not a fan of Leicester, and why I’m hoping Doyne was hindered by injury. 

Based on this analysis, there is an 87.8% chance that a pitcher will raise or lower his Cntc% by three points or less in season 2. 

1^st Pitch Strike %

The stat that so many announcers allude to as being important.  How likely is it that a pitcher with a 1st% of 56% or lower will raise it to an acceptable level the following season?  Only Padilla, Wakefield, Mussina, Kazmir, Robertson, F Hernandez, and Sabathia have raised their 1st% by more than 4 points in year 2.   

10 of 82 pitchers had a season 1 1st% of 55% or lower.  Their season 1 and 2 rates:

Fossum (2005)           55        50        55
Batista (2006)             55        54
Wang (2005)               55        56        55
Padilla (2005)              55        60        58
Wakefield (2006)         55        60
Meche (2005)              54        56        60
Kazmir (2005)             53        59        57
Cabrera (2005)           52        52        55
Cabrera (2006)           52        55
Santos (2005)             51        48        53

The third column shows how each pitcher did in the third year.  None of these pitchers has been able to get his 1st% up to an acceptable level.  This is another reason I’m losing confidence in the ability of Cabrera to turn things around.  This doesn’t bode well for Loewen, Burres, Olson, Liz, Hoey, Cherry, Doyne, Leicester, and F Cabrera.  Fortunately, Kazmir and Wang are pretty good examples of pitchers having success with a fairly low 1st%. 

Based on this analysis, there is a 74.4% chance that a pitcher will raise or lower his 1st% by three points or less in season 2.  This is actually somewhat promising.  In fact, five pitchers in the survey (6.1%) actually had a 6 point increase from year 1 to year 2. 

Hopefully now you have a better idea of which pitch stats on which to place your focus. 

I would like to add one more thing.  I would recommend only using pitch data to help support conclusions as opposed to using the data to come to definitive conclusions. 

Thanks for reading. 

Thanks to Baseball-Reference for the great data.

You Have One Game to Win…

Someone over at the Orioles Hangout asked a great question the other day – if you have one game to win, who would be your starting pitcher? 

I took 20 pitchers who have each faced at least 72 batters during postseason play since 2002.  I originally used the pitchers listed in the OH thread, and then added others as their names came to me.  I only used the regular season numbers for the season in which the pitcher appeared in the postseason.  For instance, since 2002, Curt Schilling has appeared in the 2002, 2004, and 2007 postseasons.  So I compared his 2002, 2004, and 2007 regular season numbers to his 2002, 2004, and 2007 post season numbers. 

First, I want to attack the question using traditional stats, and my favorite – FIP ERA.  Overall, the 20 pitchers combined to post a 3.45 ERA in the 12,400 regular season innings in which they appeared in the playoffs.  The same 20 pitchers combined for a 3.88 ERA in 877 post season innings.  The regular season FIP ERA of the pitchers was 3.65 compared to 4.11 during post season play.  I personally found it interesting that the numbers represented a 12.4% increase in ERA compared to a 12.7% increase in FIP ERA. I will admit that I was surprised that the overall ERA actually increased.  It has always been my assumption that the overall ERA tends to go down in post season play.  Maybe that’s what I get for listening to announcers. 

Now we know that the overall ERA goes up, but do the numbers give us a clue as to why?  BABIP stayed about the same (it increased from .288 to .292).  LOB% is relatively unchanged (73.6% up to 73.8%).  WHIP goes up from 1.18 to 1.26.  K% dropped from 19.7% down to 18.1%, while BB% goes from 6% up to 6.7%.  Command Rate dropped from 3.31 down to 2.72.  So the only relatively big changes seen thus far involved walks and strikeouts.  I suppose this really shouldn’t be a surprise considering these pitchers are facing better hitters.  Want another indicator that the pitchers are facing better hitting?  HR/OFFB% went up a good amount (10.6% up to 12.2%) – this explains the increased ERA as much as anything.   

Of the 20 pitchers in the survey, only six had a better post season ERA than regular season ERA.  This caused me to think maybe the first comparison I made was negatively impacted by those at the bottom.  So I decided to do another comparison, this one taking a look at the pitchers in the survey with the 10 best regular season ERAs.  The ERA change this time was 2.99, compared to 3.13 in the post season.  The FIP ERA change was 3.38 up to 3.67.  Those represent a 5% and 7% increase, respectively.   

We’ve looked at the overall numbers, how’d the pitchers do individually?   

Curt Schilling’s postseason ERA during the time span is 3.17, compared to his 3.39 regular season ERA in the affected seasons. 

The pitcher in the survey with the 5^th best ERA improvement was Andy Pettitte (3.44 down to 3.13 in the postseason). 

Chris Carpenter’s post season ERA was 2.53 compared to 2.95 in the affected regular seasons. 

John Smoltz had the third best improvement in the survey – a 2.76 regular season ERA compared to a 1.96 post season ERA. 

Mariano Rivera’s post season ERA since 2002 was an amazing 0.70 compared to 2.06 in the regular season. 

The biggest ERA improvement from the regular season to the post season?   

Drum roll, please. 

Raise your hand if you guessed Josh Beckett.  His regular season ERA during the 2003 and 2007 seasons was 3.18, compared to 1.78 during those two post seasons.  Before someone says that it’s only two seasons, only three pitchers in the survey have faced more post season batters than Beckett since 2002. 

The three pitchers with the biggest increase in ERA?  Glavine (3.36 to 5.84), R Johnson (3.60 to 7.11), and Wang (3.73 all the way up to 7.58). 

So how did they rank in overall post season ERA since 2002? 

Rivera (0.70), Beckett (1.78), Smoltz (1.96), Carpenter (2.53), Pettitte (3.13),Schilling (3.17), Lackey (3.63), Oswalt (3.66), Santana (3.97), Wells (4.08),Zito (4.11), Mussina (4.19), Pedro (4.39), Clemens (4.50), Morris (4.96),Hudson (5.10), Glavine (5.84), Wakefield (5.91), R Johnson (7.11), Wang (7.58). 

If you have one game to win, who would be your starting pitcher? 

Based on this survey, Josh Beckett seems to be the best bet to me. 

In my next post I’ll take a look at the question from a different angle. 

Thanks. 

Go Rockies!

The Schilling Theory

During Boston’s first trip to Baltimore, Gary Thorne talked about Curt Schilling’s theory of which team would win the division title.  According to Schilling, the opening day five-man rotation which made the most combined starts would win the division.  So, is Curt Schilling on to something? 

The opening day rotation for the Blue Jays made a total of 85 starts.  Their overall total was hurt by the early loss of Gustavo Chacin, who made only five starts.   

The Orioles rotation made 96 starts, with the early exits of Loewen and Wright hurting the overall total. 

The Yankees?  105 starts, with Pavano’s two starts bringing down the total. 

The big surprise to me was Tampa Bay’s rotation, which actually combined for 116 starts.  They actually had three pitchers (Kazmir, Shields, and Jackson) with at least 31 starts. 

For those scoring at home, it looks as if Schilling was right, at least in terms of which team would win the division.  The opening day Red Sox rotation combined for an amazing 140 starts, including at least 23 starts from each member of the rotation. 

The cases of Jeremy Guthrie and Roger Clemens caused me to take it a step further and add the sixth starter to each rotation.  How much of an effect did this have? 

Boston             150 starts

Tampa Bay     138 starts

New York         122 starts

Baltimore         122 starts

Toronto            112 starts 

Kudos to Schilling, at least in terms of forecasting the division winner.  The thing I can’t remember is if he said the final division standings would be determined in the same manner.  If he did, then he wasn’t so right.  I’m probably in the minority here, but seeing Tampa Bay rank so high should make the rest of the division real nervous going into 2008. 

Hearing Thorne talk about the Schilling Theory gave me an idea.  I made a list I called “You’ll Know the 2007 Orioles Had a Bad Season If…”  For instance, one item on my list was “if Rob Bell winds up facing at least 250 batters.” 

I never posted the list because I realized that there was no way so many things could go wrong with one pitching staff.  So I held on to the list until now. Here goes: I’ll know the 2007 Orioles are in trouble if …

• Jon Leicester, Victor Zambrano, Kurt Birkikns and someone named Victor Santos combine for more combined starts (12) than opening day rotation members Adam Loewen and Jaret Wright (9). 

• Jeremy Guthrie starts 26 games.  (Of course, if he somehow gives the team a 3.70 ERA over 175 innings pitched, I would consider this one to be a good thing.)

• Daniel Cabrera shows signs of regression by posting a 5.55 ERA with a 23% lower K-Rate.

• Erik Bedard is not able to start at least 30 games or garner 200 innings pitched.

• Steve Trachsel is actually allowed to start 25 games.  (Of course, if he pulls off the miracle of all miracles and posts a 4.48 ERA, then this would be a good thing.)

• Rob Bell ends up facing at least 250 batters, while Chris Ray faces less than 180.

• Chris Ray and Danys Baez combine for a 5.52 ERA with only 19 saves and only 93 innings pitched.

• Scott Williamson is released following 16 appearances and 14.3 innings pitched.

• Someone or something named Rocky Cherry is a key cog in the bullpen by September.

• Paul Shuey is needed to appear in 25 games.

• The team would need to give starts to 13 pitchers.

• The team would need to use 27 pitchers.

• 19 of the 27 pitchers used posted an ERA higher than 5.00.  (In fact, I would have bet this could not possibly happen.)

• The final overall team ERA is 5.19.

 As I said, I had to keep this list hidden because it was just too far out there.  It’s a good thing none of those things actually happened.

Okay, you got me.  I didn’t make this list five months ago.  No one has an imagination like that.

“The Great Orioles Teams Did Just Enough Offensively to Get By.” Really?

The great Orioles teams won because of “pitching, defense, and the three-run homer.”   

How many times have you heard this or a similar statement?  Yes, those teams had great pitching.  Yes, they typically played some darn good defense.  Yes, they hit quite a few home runs.  The one thing that seems to be overlooked when talking about pitching, defense, and the three-run homer is that you can’t hit a three-run homer if you don’t have people on base.  This, to me, is the hidden gem in the Earl Weaver philosophy.  

So I did some research.  Below is how the Orioles ranked in certain categories in the years they played in a World Series (the number in parentheses is the average rank). 

R/G > 1^st - 2^nd - 1^st - 1^st - 6^th - 2^nd (2.2)

OBP > 1^st - 1^st - 1^st - 1^st - 7^th - 1^st (2.0)

SLG > 1^st - 2^nd - 3^rd - 2^nd - 5^th - 3^rd (2.7)

OPS+ > 1^st - 1^st - 1^st - 1^st - 4^th - 3^rd (1.  

ERA > 4^th - 1^st - 1^st - 1^st - 1^st - 2^nd (1.7)

ERA + > 4^th - 1^st - 1^st - 1^st - 1^st - 3^rd (1.

Def Eff > 4^th - 1^st - 3^rd - 2^nd - 1^st - 5^th (2.7) 

Yes, pitching was a key part of the equation.  Those were obviously well balanced teams.  Notice that they led the AL in OBP in each of those seasons except for 1979.  These numbers also tell us something else about those teams that often seems to get overlooked.  They were very good offensive teams (finishing 1^st or 2^nd in runs 5 of 6 seasons, and 1^st in OPS+ 4 times).   

I bring up the offensive prowess of these teams because of something that was recently posted on an Orioles internet message board.  Someone stated something to the effect that the great Orioles teams had great pitching, played great defense, and did enough to get by on offense.  The above numbers tell us they did quite a bit more than just get by. 

Nowick Gray wrote a very good column for The Orioles Hangout on this subject, and how the current front office seems to have forgotten a very important part of the equation.

Bedard a top 5?

There’s a lot of talk right now as to whether Erik Bedard’s current string of great outings makes him one of the best pitchers in baseball right now.  So where does he rank?

There are a lot of ways to look at this.  What I don’t get are those people who like to pull up the numbers from 3 or 4 years ago and use the overall numbers since then to prove that he isn’t.  Yes, if you look at the overall numbers since 2004, Roy Halladay has been a better pitcher than has Erik Bedard.  However, I say that anyone who really thinks Roy Halladay is a better pitcher right now than is Erik Bedard doesn’t know what he’s talking about.  Having said that, I can also understand why we need to look at more than just this year’s numbers.

So I decided to go back to June 5 of last year.  How does Bedard rank since then?  There have been 78 pitchers who have at least 200 innings pitched since that date.  Those are the 78 pitchers used for the following comparison.

Erik Bedard is third in ERA at 2.91 (behind Santana and Chris Young).

Bedard is fifth in H/9 at 7.44 (behind Young, Santana, Maine, and Zambrano).

Bedard is 41st in BB/9 with a very respectable 2.78 (the top three are Byrd, Maddux, and Sheets).

Bedard is 1st in K/9 at 10.05 (followed by Peavy, Hamels, and Santana).

Bedard is 13th in K/BB at 3.61 (the top three are Sheets, Sabathia, and Schilling).

Bedard is 12th in HR/9 at 0.74 (the top three are Wang, Lowe, and Peavy).

You probably know that one of my favorites measures of a pitcher is FIP ERA.  Here are the top five:

1. Peavy - 2.96
2. Bedard - 3.03
3. Smoltz - 3.25
4. Santana - 3.31
5. Escobar - 3.34

And Bedard’s ranking in some of the counting stats: 

• tied for 7th with 42 starts.
• tied for 17th with 19 wins.
• 12 pitchers have fewer than his 10 losses (but only Santana and Harang match his 42 starts among those with fewer losses).
• Not a counting stat, but he is 13th with a .655 winning %.
• He is one of 34 with a shutout (Sabathia, Hernandez, Lackey and Contreras have two each)
• 15th in IP (he’s averaged 6.4 IP per start)
• 1st in strikeouts with 300 (Santan has 296, Peavy 273, and Harang 264).

That’s a lot of information.  Bedard is in the top five in ERA, K/9, FIP ERA, and Strikeouts.  Compare the top five in ERA and FIP ERA:

1. Santana ( 2.58) / Peavy (2.96)
2. Young (2.64) / Bedard (3.03)
3. Bedard (2.91) / Smoltz (3.25)
4. Escobar (3.15) / Santana (3.31)
5. Smoltz (3.16) / Escobar (3.34)

Santana, Bedard, Escobar and Smoltz appear in both lists.  If you were to give points based on rankings, Bedard ties with Santana for the most points (3 for 3rd, 4 for 2nd for a total of 7 points). 

Part of the reason for determining who is the best pitcher is predicting future performance.  Of the four pitchers who made both lists, only Smoltz has a smaller difference in FIP ERA and ERA than does Bedard.

I think Erik Bedard has a darn good argument as one of the best five pitchers in MLB right now.  In fact, the only one I see who is really better (when taking things such as league and park into effect) is Johan Santana.

So I’ll say it:

Erik Bedard is the second best pitcher in baseball.

One of my newest, favorite toys is the Pitch Data supplied by Baseball-Reference.  The problem with the data is that there is so much of it that it is hard to decipher the importance of each stat contained in the data.  I’m sure there have been extensive studies of the data, but I haven’t seen them.  I did see a pretty good article on The Hardball Times website about it, but that’s about it.  I decided to do a small study of my own. To do this, I needed some data.  I transferred the 2005 through 2007 data of every pitcher who has appeared in a 2007 American League game through May into an Excel spreadsheet.  The approach I decided to take is to first determine which of the stats a pitcher has the most control over.  (I would be remiss if I didn’t warn you up front that the data used in this study potentially suffers from the dreaded “small sample size.”)   I did this via year-to-year correlation of each stat (filtering out any pitcher who had faced less than 100 batters in either year one or year two – leaving me with 224 pairs of seasons).  The results: 

Cntc%             .732

StS%               .721

StI%                .719

Sw/In%           .700

K%                  .685

Strk %             .668

P/PA                .620

StL%               .594

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