How About Some Pop-Punk?

I’ve been a huge pop punk fan for years.  When Lookout Records went under I had some serious concerns that some of the best music would become unavailable to the next generation of fan.  Although some of the key releases have become much harder to find on CD or vinyl streaming services, Youtube, and a million other apps and platforms have kept the genre alive and kicking.

When I see the pop punk bands that are talked about today, it makes me feel old.  Good Charlotte considered ‘old school’ just makes me feel Ancient!  There is a ton of amazing pop punk out there and I feel like I wouldn’t be doing my job unless I highlighted some of my personal favorite releases from decades past.  I will do my best to not get too obscure with the recommendations, however, a few of these bands/releases may be tougher to find than others.  Additionally, I am not here to split hairs and argue with people what is punk, pop punk, emo, etc… This post isn’t to define who is or is not in the pop punk genre, its only purpose is to highlight some melodic punk rock that may be overlooked by kids today.  Kids who might really enjoy hearing some good music.  With that said, here is part one of bands, albums, and songs that are worth chasing down and checking out (Bands A-K).

  • Some extremely well known bands and bands that are still releasing music on a regular basis are not listed here: ex. Brand New, Bad Religion, Blink 182, Descendents, Fall Out Boy, and Green Day (an exception was made for Alkaline Trio because I felt like it!).


The Abducted – This D.C./ Virginia based punk band’s best songs rivals anything found on Lookout Records.  Check out Nobody Wants You Around, Why Don’t You Die, and Whatever It Is I Don’t Care.


AFI – Although this band still keeps putting music out, their style has continually changed and what they do today bears next to no resemblance to their earlier work.  The band’s perfect moment for me was the scorching track, Sacrifice Theory.

alkaline trio

Alkaline Trio – This is one of my Top 5 Favorite Bands of All Time.  Every release is fantastic in its own way.  However, the album From Here To Infirmary has it all.  It’s dark, it’s catchy, and it’s songwriting and production is exactly on point.  You can also cherry pick some of their early work, especially the songs, Radio, I Lied My Face Off, Maybe I’ll Catch Fire, and Bleeder.


Avail  – A band that combined a pop/melodic punk presentation with an underlying hard edge.  The vocals are gruff which also moves the band slightly further away from a pure pop sound.  However, check out Fifth Wheel and Observations for songs that move towards a pop punk sound.

big in

Big in Japan – Check out the song Hell Before Reno.  It’s the best Elvis Costello song that wasn’t an Elvis Costello song.


Bigwig – A great NJ punk band.  Unmerry Melodies put them on the map (check out Dylan’s Song, Girl In The Green Jacket, and Your In Sample).

bouncing souls

Bouncing Souls – Maniacal Laughter.  The Bouncing Souls led the charge of punk bands coming out of the New Brunswick scene.  Every song is a winner on here and well worth the 20 something minutes it takes to play through the 12 tracks.  A must own!  Check out one of my favorite tracks here.

catch 22

Catch 22 – This ska punk band had a ton of talent.  Compared with Operation Ivy when they first came on to the scene (although that’s a bit of a reach).  Check out their two strongest songs – As The Footsteps Die Out Forever and Keasbey Nights.


Cletus – Johnny Puke had a great under the radar pop punk band with Cletus.  Each full length was uneven in song quality but the winners really stand out.  Check out the songs Stupid After All, Joe Queer Don’t Drink No Beer, and Here Comes Your Mom.


Dillinger Four – Great punk rock for the mid-west.  Noisy with some muddy production but man, can these guys write some catchy tunes!  Check out Doublewiskeycokenoice, Get Your Study Hall Outta My Recess, and Noble Stabbings.


The Donnas – Great all female Ramones sounding band.  They went from Lookout to a major label and shifted between rock n roll, pop punk, and glam metal.  Check out Take It Off, I Don’t Wanna Go To School, and Better Off Dancing.

down by law

Down By Law – The song All American was my personal anthem for a number of years.  So much to love about that song!  They do a damn fine cover of 500 Miles too.

fab d

Fabulous Disaster – Created a pop punk masterpiece with the song, Last Big Joyride.

face to face

Face To Face – This band recently put some new tunes out for the first time in a while but you can’t go wrong with their first release (Don’t Turn Away), especially the song Disconnected.  It’s perfect melodic punk with flashes of hardcore from start to finish.  Also, be sure to seek out a few of their other songs including A-OK, Blind, and How To Ruin Everything.


Fenix*TX – A goofy pop punk band that received a major push from Mark Hoppus of Blink 182.  These are super polished sugary songs.  Check out Minimum Wage, All My Fault, and Rooster Song.


Gamits – A band heavily influenced by early Green Day.  No one song or album stands out above the others but if you like early Green Day, you may get into their releases.  Check out the song Sorry for an example of their work.

goin places

Goin’ Places – This NY band stole a page from Mr. T Experience and created song after song about girls.  Extreme pop with a nod to late 50s and early 60s rock in roll.  Check out the songs Girl Songwriting 101 and The Only Way.


Guns N’ Wankers – This band unfortunately put out only a single 8 song release on Fat Wreck Chords.  Although the entire release is an aggressive and melodic steamroller, the standout songs are Skin Deep and Raise Your Glass.  You’d be a fool not to check this out.

The Hi-Fives – The song, Transistor Sister is a short wild romp of bright pop punk goodness.


The Huntingtons – These guys are so Ramones-core that they recorded multiple Ramones releases.  They also put out a number of great originals.  I suggest tracking down I’m No Good, Aloha It’s You, Potty Mouth, and Judy Jetson (Judy Jetson isn’t an original).


Jawbreaker – A seminal punk band that many emulated but few could duplicate.  Check out Boxcar, Want, Sluttering, and The Boat Dreams From The Hill.


John Stamos Project – A great pop punk band from Long Island, New York.  Witty lyrics and tight musicianship.  For fans of the Queers, Ramones, and Screeching Weasel.  Check out Date From Hell, Living In Norway, and Hands And Knees.

kid dynamite

Kid Dynamite – Self Titled CD.  This was an amazing band that put out a diverse catalog of songs.  Many times the product was closer to hardcore than pop punk however, a few are more melodic and pop punk.  The whole album is perfection but if you are turned off from the more aggressive tunes on this disk, check out Bookworm and 3 O’Clock.

Analysis of the First Week of MLB Action (Sun – Fri)


Don’t pitch to Bryce Harper on opening day.  The dude keeps homering.  This makes his 5th opening day with a home run.  To next year’s opening day opponent of the Nats, take a hint from the Cubs of 2016 and walk this guy repeatedly.


George Springer looks like he has his homerun swing working as well.  Springer hit a game winning walk off homer in the 13th inning versus the Mariners.  Then, in their next game, Springer led off the first inning with another homer.  Not too shabby.


Jeanmar Gomez looks terrible already – 1 IP, 2 earned runs, gave up a homerun.  Pretty similar performance to how he ended up 2016.  It’s not a question of if he will lose the Philly closer job, rather, it’s a question of who takes over.  Hector Neris (young power arm but getting saves will likely inflate future arbitration salary) or Joaquin Benoit (39 year old relief pitcher in his 16th season with 51 career saves as much of his career has been spent as an 8th inning arm)?


Michael Lorenzen and Madison Bumgarner looking good.  Bumgarner jacked two homers in his opening day start.  Lorenzen gets to pinch hit homerun in the 6th inning of a game he didn’t even pitch in.  Sign these two up for the Home Run Derby!


Speaking of Madison Bumgarner: He led the league in strikeouts with 11 Ks during the first week of games.  Carlos Martinez, Justin Verlander, and Vince Velasquez are the only other pitchers with double digit Ks in their first starts (all three put up 10 each).


Mark Melancon keeps the curse of the San Francisco closer alive: 2/3 of an inning, 4 hits, 2 earned runs, blown save.  He’s one of the most consistent closers in baseball over the past few years but he doesn’t rack up the Ks and I wonder when the magic of inducing grounders wears off.  Giants hope it’s not 2017.

bryant k

Kris Bryant – Last season’s MVP is off to a slow start.  In the Cubs’ opening series against the Cardinals Bryant went 0-13 with 6 strikeouts in their first three games.  He broke into the hit category in game four going 1 for 3.  He is now 1 for 16 (.063 batting average and .273 OPS).  I assume things will get better from here.

effectively wild

A shout out to the guys who do the Effectively Wild Podcast – They highlighted a change in how pitcher velocity is being measured this season.  Prior to 2017, Pitch FX was used.  It took pitcher velocity readings at approximately 10 feet from the pitcher’s release.  In 2017 ballparks are now using Statcast.  Statcast measures pitcher velocity at the pitcher’s release point.  This doesn’t sound like a big deal but it appears that the change in where the speed of a pitch is measured has, on average, added between a half mile to a full mile per hour to a pitcher’s offering.  As the podcast mentioned, there may be cause for concerns regarding players showing a significant decrease in their velocity readings (here’s looking at you, Jake Arrieta).

MLB Projections and Predictions

The start to the 2017 baseball season is just around the corner.  With the start to any season comes the countless predictions from every website, tout, and hack.  As luck would have it, I fall into all three of those categories.  So, without further delay, here is my best guess at each team’s win total and projected finish:

american league


Boston  91

Toronto 86

Baltimore 86

NY Yankees 82

Tampa Bay 80



Cleveland 93

Kansas City 82

Detroit 81

Chicago W.S. 70

Minnesota 70



Houston 90

Seattle 86

Texas 85

L.A. Angels 78

Oakland 71


national league


Washington 91

NY Mets 87

Atlanta 76

Miami 75

Philadelphia 73



Chicago C. 100

St. Louis 84

Pittsburg 82

Milwaukee 73

Cincinnati 65



L.A. Dodgers 100

San Francisco 87

Colorado 73

Arizona 70

San Diego 63


Based on my win total prediction, here are the playoff teams with my picks for the World Series and eventual champion:


AL: Cleveland, Houston, Boston, Seattle, Toronto

NL: Dodgers, Cubs, Nationals, Mets, Giants

World Series: Cubs vs. Cleveland – REMATCH!

Champion: Cubs



And now the awards.  They are all but impossible to get right.  But it can’t hurt to give it a try!

fortune teller


AL MVP: 1. Mookie Betts (Trout will likely put up better advanced stats to be a no-brainer but I just can’t see the media voting him MVP again).  2. Mike Trout


NL MVP: 1. Corey Seager  2. Anthony Rizzo


AL Cy Young: 1. Chris Sale  2. Cory Kluber


NL Cy Young: 1. Clayton Kershaw  2. Madison Bumgarner


AL Rookie of the Year: 1. Andrew Benintendi  2.Yoan Moncada


NL Rookie of the Year: 1. Manuel Margot  2. Dansby Swanson


Follow me on Twitter @doctordaver

Reexamining Saves (2015 & 16 Seasons)

The save is a diluted statistic.  It tells us much less about a pitcher’s performance than it could.  It is akin to a list of runners that completed a marathon.  All finishers are listed but no distinction between someone who finished the race in 4, 5, or 12 hours is made.  Without the crucial finish time, a good deal of information about each individual’s performance is lost.  I have created five types of saves with the goal of more clearly documenting the work of closers.  The five types are the Ultrasave, Powersave, Plainsave, Uglysave, and Disastersave (this type is extremely rare).  While the concept of ‘saves by gradation’ cuts saves data into smaller parts, it allows for more meaningful information about pitcher performance during these situations to be conveyed.

The criteria for each type of save:

Ultra Save = no walks, no hits, no HBP (no baserunners), at least 1 IP, struck out all batters faced, earned the save.

Power Save = no walks, no hits, no HBP (no baserunners), at least 1 IP, at least 1 K, earned the save.

Plain Save = no walks, no hits, no HBP (no baserunners), at least 1 IP, no Ks, earned the save.

Ugly Save = at least one hit, at least one run, no Ks, at least 1 IP, earned the save.

Disaster Save = at least two hits, at least two runs, no strikeouts, earned the save. (Rodney and Melancon are the only pitchers on the Top 35 list to earn one).

In a prior article, I examined the 2016 save leaderboard using this method.  The results can be seen here.

In this article, I look at the 2015 save leaderboard and applied the five save type method.

The Top 35 save leaders for the 2015 season with the raw tallies for each of the 5 save types:

Svs/Sv Ops.                  Ultra Sv           Pwr Sv             Pl Sv                Ugly Sv                   Dis Sv

1 Melancon 51/53           1                           9                       8                        2                               1

2 Rosenthal 48/51         4                          10                      2                        1                               0

3 Familia 43/48              0                          11                      2                        0                               0

4 Boxberger 41/47         3                          6                       3                         1                               0

5 Street 40/45                 0                         11                      2                         4                              0

6 Kimbrel 39/43             2                         12                      2                         0                              0

7 Rodriguez 38/40         0                         8                       7                         0                               0

7 Casilla 38/44                1                         4                       0                         0                               0

9 Miller 36/38                 2                         6                        1                         0                               0

9 Jansen 36/38                1                        10                       2                         0                               0

9 Britton 36/40              2                         8                        1                          2                               0

12 Tolleson 35/37           1                         6                        4                          1                               0

13 Allen 34/38                 1                        13                        1                          0                               0

13 Robertson 34/41       3                        11                        3                          2                                0

15 Chapman 33/36        2                        10                       0                         0                                0

16 Perkins 32/35           0                         3                         6                         1                                 0

16 Holland 32/37          2                         4                         4                         0                                0

16 Ramos 32/38            0                         6                         3                         0                                 0

19 Gregerson 31/36     0                         7                          3                        0                                 0

20 Ziegler 30/32          0                         5                          4                         2                                 0

20 Rondon 30 /34       0                          5                          3                        0                                  0

22 Storen 29/34          2                          3                          4                        0                                   0

23 Uehara 25/27          1                          5                          4                        0                                   0

23 Axford 25/31           0                          3                          2                        0                                   0

25 Grilli 24/26             1                           3                          1                         1                                   0

25 Papelbon 24/26     0                         4                           2                        0                                   0

25 Soria 24/30             1                           1                           3                        1                                   0

28 Osuna 20/23          1                           3                           2                       0                                   0

29 Clippard 19/25      1                           5                           2                        1                                   0

30 Davis 17/18            0                          6                           0                        0                                   0

31 Rodney 16 /23        0                          2                           0                        2                                    1

32 Jepsen 15/20          1                           3                           3                         0                                   0

32 Giles 15/20             0                          3                           2                         0                                   0

34 Wilhelmsen 13/15   1                        3                           1                         0                                    0

35 Smith 13/18            0                          3                           0                         1                                    0

My 5 save type model is based on the notion that the key characteristic of a closer is dominance.  In the 9th inning of a 1, 2, or 3 run game, any type of baserunner is a hazard to a team looking to shut the door on an opponent.  Thus, my save model rewards pitchers who do not allow balls in play (valuing the safest out – the strikeout) and avoid baserunners of any kind (HBP, walks, hits).  Greater penalties are given to pitchers based on the level that they violate these two criteria.

Keeping dominance in mind, there are a number of ways to utilize my data system to achieve a better understanding of the quality of each closer.  The first way is to award a number value to each type of save and then tally the points.  The values I gave each type of save are listed below:

Ultra = 3

Power = 2

Plain = 1

Ugly = -1

Disaster = -2


Based on the new point total for Quality of Save, the 2015 leaderboard looks like this:

Saves/Save Opps.                        Quality Save Score                       Rank Change

1 Rosenthal 48/51                           33                                                       +1

2 Kimbrel 39/43                               32                                                       +4

2 Robertson 34/41                          32                                                       +11

4 Allen 34/38                                   30                                                       +9

5 Chapman 33/36                            26                                                       +10

6 Melancon 51/53                           25                                                       -5

6 Jansen 36/38                                 25                                                       +3

8 Familia 43/48                                24                                                      -5

9 Boxberger 41/47                          23                                                       -5

9 Rodriguez 38/40                           23                                                      -2

11 Britton 36/40                              21                                                       -2

12 Street 40/45                               20                                                       -7

13 Miller 36/38                                19                                                       -4

14 Tolleson 35/37                           18                                                       -2

14 Holland 32/37                             18                                                      +2

16 Gregerson 31/36                       17                                                        +3

16 Uehara 25/27                             17                                                        +7

18 Storen 29/34                              16                                                       +4

19 Casilla 38/44                               15                                                      -12

19 Ramos 32/38                               15                                                       -3

21 Clippard 19/25                           14                                                       +8

22 Rondon 30 /34                           13                                                       -2

23 Ziegler 30/32                              12                                                       -3

23 Davis 17/18                                 12                                                       +7

23 Jepsen 15/20                              12                                                       +9

26 Perkins 32/35                             11                                                      -10

26 Osuna 20/23                               11                                                       +2

28 Papelbon 24/26                         10                                                       -3

28 Wilhelmsen 13/15                     10                                                       +6

30 Grilli 24/26                                  9                                                        -5

31 Axford 25/31                              8                                                          -8

31 Giles 15/20                                  8                                                          +1

33 Soria 24/30                                 7                                                         -8

34 Smith 13/18                                5                                                          +1

35 Rodney 16 /23                            0                                                         -4

This rating system gives a more accurate picture of player performance than using only save totals.  However, there is another adjustment that makes the rankings even more descriptive.  When a 1 point penalty for each blown save is applied, the 2016 leader board shifts again:

Svs/Sv Ops.                         New Point Total

1 Rosenthal 48/51                           30

2 Kimbrel 39/43                              28

3 Allen 34/38                                   26

4 Robertson 34/41                          25

5 Chapman 33/36                           23

5 Melancon 51/53                           23

5 Jansen 36/38                                23

8 Rodriguez 38/40                         21

9 Familia 43/48                              19

10 Boxberger 41/47                       17

10 Britton 36/40                            17

10 Miller 36/38                               17

13 Tolleson 35/37                           16

14 Street 40/45                               15

14 Uehara 25/27                             15

16 Holland 32/37                           13

17 Gregerson 31/36                       12

18 Storen 29/34                              11

18 Davis 17/18                                 11

20 Ziegler 30/32                            10

21 Casilla 38/44                              9

21 Ramos 32/38                              9

21 Rondon 30 /34                            9

24 Clippard 19/25                           8

24 Perkins 32/35                             8

24 Osuna 20/23                               8

24 Papelbon 24/26                         8

24 Wilhelmsen 13/15                     8

29 Jepsen 15/20                              7

29 Grilli 24/26                                7

31 Giles 15/20                                  3

32 Axford 25/31                              2

33 Soria 24/30                                 1

34 Smith 13/18                                0

35 Rodney 16 /23                            -7

Another way to analyze the 2015 save leaders performance using my 5 save type method is adding the number of Ultra and Power saves together.  This gives a snapshot of how dominant a closer has been while in save situations.  Using just these two categories, the 2015 dominant save leaders were (total and % of saves that were dominant are both reported below):

Name                   Ultra & PowerSvs Combo Total                   % of overall svs Ultra+Power

Rosenthal 48/51                           14                                                                           29.1%

Kimbrel 39/43                                14                                                                           35.8%

Allen 34/38                                     14                                                                           41.1%

Robertson 34/41                          14                                                                           41.1%

Chapman 33/36                            12                                                                           36.3%

Familia 43/48                                  11                                                                           25.5%

Street 40/45                                   11                                                                           27.5%

Jansen 36/38                                  11                                                                           30.5%

Britton 36/40                                  10                                                                         27.7%

Melancon 51/53                           10                                                                            19.6%

Boxberger 41/47                          9                                                                             21.9%

Rodriguez 38/40                           8                                                                            21.0%

Miller 36/38                                    8                                                                           22.2%

Tolleson 35/37                               7                                                                            20.0%

Gregerson 31/36                          7                                                                             22.5%

Holland 32/37                                6                                                                           18.7%

Ramos 32/38                                  6                                                                            18.7%

Uehara 25/27                                 6                                                                            24%

Clippard 19/25                               6                                                                            31.5%

Davis 17/18                                     6                                                                            35.2%

Casilla 38/44                                   5                                                                           13.1%

Ziegler 30/32                                  5                                                                            16.6%

Rondon 30 /34                               5                                                                             16.6%

Storen 29/34                                  5                                                                             17.2%

Grilli 24/26                                     4                                                                            16.6%

Papelbon 24/26                            4                                                                             16.6%

Osuna 20/23                                   4                                                                            20%

Jepsen 15/20                                 4                                                                              26.6%

Wilhelmsen 13/15                        4                                                                              30.7%

Perkins 32/35                                 3                                                                              9%

Axford 25/31                                  3                                                                               12%

Giles 15/20                                      3                                                                              20%

Smith 13/18                                    3                                                                              23%

Soria 24/30                                      2                                                                             8.3%

Rodney 16/23                                2                                                                              12.5%

Adding the 2015 save leaders to the previous 2016 save analysis yields the following infomation: It appears the raw totals of Ultra and Power Saves significantly increased from 2015 and 2016.  This increase appears to have occurred across the leaderboard.  Also, the percentage of ultra and power saves relative to overall save totals also increased significantly between 2015 and 2016.  Were pitchers who held the closer jobs in 2016 more dominant than in 2015?  It appears so.  Interestingly, this increase was not just a function of newly anointed save specialists.  Rather, save leaders from 2015 who made the 2016 list also showed significant increases in the number of dominant appearances.

It’s likely that multiple factors have led to greater closer dominance.  Hitters may be increasing their willingness to selling out for power.  Thus, batters take a greater number of huge swings leading to more strikeouts and fewer hits.  Next, the role of the closer may be shifting towards even greater specialization and usage – teams may see a power arm as a requirement for pitching in the 9th.  They may also encourage their closers to avoid pitching to contact and instead try to maximize strikeouts.  A third possibility is just year to year variation in data/trends.  I would love to hear from anyone about how they would interpret the data from the 2015 and 2016 seasons so please get in touch if you have any ideas.

The predictive value of save quality using this five save type model needs to be further evaluated.  This can be done by looking longitudinally over more than just the 2015 and 2016 seasons in order to see if prior year(s) performance had any significant predictive power for the following year(s).  Closer performance is difficult to evaluate due to the small sample size of data each pitcher generates annually.  However, there is likely enough information to chart predictive value of this model if someone had the time to chart and score each season’s leaderboard and then use formal statistical analysis to compare save earners over many seasons of work.  Anyone who owns a good statistics program and wants to collaborate, please get in touch.

Follow me on Twitter: @doctordaver

Let’s Go Streaking!

Baseball streaks are funny.  Some take on mythological qualities such as Joe D’s 56 game hitting streak or Cal Ripken’s Iron Man streak of games played (2,632).  These feats give fans pause and resonate with us somewhere deep inside our baseball souls.  Then, there are other streaks that don’t land on anyone’s radar.  I like to dig around the Play Index on and find streaks like these.  Using 1986 through 2016 as search years, here were some of my interesting findings:

For Pitchers:

Starting pitchers with consecutive starts of 10 or more Ks from 1986-2016:


Pedro Martinez had a stretch of 10 straight starts (from 8/19/1999 through 4/9/2000) where he racked up at least 10 Ks.  He struck out 130 batters in 76.1 innings.  Second on the list is Chris Sale who had an 8 game streak (5/23/15 through 6/30/2015).  He struck out 97 batters in 60 innings.  Pedro makes the Top 10 of this list two more times, once with a 7 game streak and once with a 6 game streak.  Randy Johnson makes multiple Top 10 appearances as well.  Johnson once had a 7 game streak and also had four 6 game streaks.  The only other pitcher to rank in the top ten was Clayton Kershaw.  He had a 6 game streak during the 2016 season.

Pitchers with consecutive games of at least 1 wild pitch from 1986-2016:


Before my search, I predicted that the leader would have four.  I was not expecting this result.  The leader, Jason Grimsley, threw a wild pitch in 9 consecutive appearances (from 9/18/1990 through 4/28/1991).  He was followed closely by Ricky Romero (8), Matt More Jason Johnson, William VanLandingham, and Joey Hamilton who each threw a wild pitch in 7 straight appearances.  While Romero took 56.1 innings to throw his 8 wild pitches (2010), VanLandingham needed only 31.2 innings to throw 7 (1997).

Pitchers with consecutive games of hitting at least one batter from 1986-2016:

fossum     wright

Keeping with the wild pitcher search, I looked at pitchers who had consecutive appearances with hitting at least one batter.  Two pitchers were tied with 10 straight games of hitting a batter.  The two were Casey Fossum (2005) and Jamey Wright (2001).  Chan Ho Park racked up 8 consecutive games where he hit someone (2002).  How many of these led to bench clearing brawls?  Unfortunately, I couldn’t find a search that could supply an answer.  The most surprising name I found on this list was Pedro Martinez.  He hit a batter in 7 consecutive appearances during the 2004 season.

Pitchers with consecutive appearances with at least 1 intentional walk 1986-2016:


Kirk Rueter is the leader with 6 straight appearances.  There are 11 players that issued an intentional walk in 5 straight appearances.  Interestingly, Greg Maddux is one of these illustrious 11 pitchers.  What caught my eye even more was that during these 5 appearances, Maddux walked only 9 batters, meaning more than half of the walks he gave up were intentional.  The intentional walks moved his strikeout to walk rate during this stretch from 6:1 down to less than 3:1.  Ouch.


I then looked at starting pitchers who issued at least 2 intentional walks in consecutive starts from 1986-2016.  Rick Sutcliffe holds this record.  In 1987, Sutcliffe had 3 consecutive starts where he issued at least 2 intentional walks.  This was somewhat topped by Jimmy Anderson.  He issued at least 3 intentional walks in back to back starts during the 2001 season.

For Hitters:

Hitters with consecutive games of hitting two or more homeruns 1986-2016:


Jeff DaVanon hit two homeruns in three consecutive games in June of 2003.  He’s the king of the multi-game multi-homer streak.

Although this next streak is a little better known that the ones I have previously mentioned, I’ll include it here: Hitters with consecutive games of at least one home run from 1986-2016:

grif     mattingly

Ken Griffey and Don Mattingly are your leaders with 8 consecutive games with a homerun (Griffey had 8 homers in total while Mattingly jacked 10).  They are followed by Kevin Mench, Barry Bonds, and Jim Thome who each hit home runs in 7 consecutive games.  Even more impressive than seven straight games with a homer may be Bonds’ .722 batting average and 2.988 OPS.

Hitters with consecutive games with at least one walk 1986-2016:


Probably not surprising, Barry Bonds is first on the list.  He earned a walk in 19 straight games.  He’s also second on the list with 17.  Nick Johnson and Chipper Jones drew walks in 16 straight games.  Bonds and Lenny Dykstra follow next with 15 straight games with a walk.


How about players who drew at least two walks in consecutive games?  Adrian Gonzalez drew multiple walks in 8 straight in 2009 as did Barry Bonds in 2004.  Bonds, David Justice, and Jack Clark each compiled 7 games in a row of drawing multiple walks.

Hitting a triple in consecutive games 1986-2016:

nomar     sosa

Nomar Garciaparra and Sammy Sosa are tied for first place.  They both were able to hit triples in four straight games.

Hitting a double in consecutive games 1986-2016:

molina     lee

Yadier Molina and Derrek Lee both hit at least one double in 8 straight.  Molina also hit a double in seven straight games too.  Others with seven straight games with a double include Brian Roberts, Carlos Delgado, Jeff Kent, Gary Disarcina, Todd Walker, and Jim Presley.

Consecutive games of stealing at least one base 1986-2016:

crawford     patterson     henderson

Carl Crawford (2009), Corey Patterson (2006), and Rickey Henderson (1986) each stole bases in nine straight games.  Crawford ended up with 14 steals in these 9 games while Patterson and Henderson both ended up with 11 total steals during their nine game streaks.  Kenny Lofton had two separate streaks of 8 games with a steal and Vince Coleman had one 8 game streak as well.

Conversely, here are the leaders for consecutive games of being caught stealing at least once per game 1986-2016:

pod     biggio

Scott Podsednik and Craig Biggio were both caught in five straight games combining for 1 success and 10 failures.  Chone Figgins, Carl Everett, Shane Mack, Ray Lankford, Delino DeShields, Lenny Dykstra, and Chili Davis all compiled four game caught stealing streaks of their own.  When these nine player streaks are totaled, the level of futility is astounding.  This group stole 8 bases and was caught 39 times (17% success rate).

To end on a more positive note, here is a list of hitters with the most consecutive games of racking up 5 or more total bases in each game 1986-2016:

blalock     walker

Hank Blalock (2008) and Larry Walker (1999) each put a six game streak together.  Nolan Arenado, Nelson Cruz, Alex Rodriguez, Frank Thomas, Jason Bay, and Jose Cruz all had steaks of five games.  These streaks appear to be fueled by a barrage of home runs as six of these eight players hit 5 homers during their streak, one player hit 6, and one (Rodriguez) hit seven homers.

Many thanks to and the great Play Index located there!

Follow me on Twitter: @doctordaver

Saves and Relief Pitcher Usage Over 56 Years

The role of the save artist has morphed over time.  In the past, it was not uncommon for a reliever to work multiple innings in an appearance in order to earn a save.  However, it’s been said that the save statistic appears to have had a strong influence on the shift from a multi-inning fireman to a highly specialized designated game ender.  Sabermetrics advocates for moving the pendulum away from a single 9th inning closer who is responsible for the save opportunity and instead advocates for using the team’s best arm (for argument’s sake, the best arm = current closer) in the highest leverage situations (for example, Francona’s use of Andrew Miller, especially in the 2016 post season).  Whether this will lead to a significant shift in usage has yet to be determined.

I decided to review relief pitcher usage over the decades to get a better sense of the way managers have deployed their relief specialists over the past half century plus.  Jerome Holtzman developed the criteria for awarding a save in the early 1960s and tracked it for The Sporting News until the save became an official MLB statistic starting in 1969.  Thus, I looked at 1960 (save data retroactively applied as per baseball-reference) for a ‘pre-save’ baseline for pitcher usage where the power of the save statistic had no influence upon managerial decision making.  I was concerned about relying too heavily on 1960 data however because pitching trends have a tendency to change over the span of a decade so looking at 1960 and then comparing the data to 1969 might lose some of the descriptive power.  Conversely, I was also concerned about using a season close to 1969 because the save was likely entering public consciousness by this time (although not yet formally used by MLB).  Thus, I have included 1960, 1968, and 1969 data to give a more balanced examination of early data.



Save of 1 inning or less = 191

Save of 2 or more innings = 180


1968: (the year prior to the save becoming an official MLB statistic)

Save of 1 inning or less = 291

Save of 2 or more innings = 223


1969: (the first season that the save statistic was tracked by MLB)

Save of 1 inning or less = 335

Save of 2 or more innings = 287


I then looked at save data for each decade.

I started with 1970 to see if keeping track of the save statistic had a noticeable impact in year two of MLB keeping save data.


Save of 1 inning or less = 398

Save of 2 or more innings = 335


Data for 1980, 1990, 2000, and 2010 indicated the following save totals:



Save of 1 inning or less = 356

Save of 2 or more innings = 404



Save of 1 inning or less = 594

Save of 2 or more innings = 320



Save of 1 inning or less = 899

Save of 2 or more innings = 128



Save of 1 inning or less = 1087

Save of 2 or more innings = 18


I then looked at the most recently completed season of MLB (2016).  The most recent save totals were:



Save of 1 inning or less = 1,165

Save of 2 or more innings = 29

Based on this cursory analysis, it appears as though the addition of the save statistic had an influence on the way relief pitchers were utilized.  The effect in the 1960s and 1970s appears to be less influenced by the save statistic and perhaps is more accurately a reflection of managers using starting pitchers for less innings per start and their relief corps more frequently.  1980 is a strange year.  It is the only year where there were a greater number of two or more inning saves than saves of one inning or less.  I did a little more investigating and looked at 1981.  1981 is difficult to assess as the season was shortened due to a player strike (5o days and 713 games missed).  However, again, the two inning save was more plentiful than the one inning save (280 vs. 233).  What to make of the early 80s?  Anyone have a theory?

By 1990, the one inning save begins to gain momentum as the one inning/9th inning closer becomes defined more regularly employed.  This is the first time the one inning or less save almost doubles the two or more inning save (594 vs. 320).  The one inning or less save becomes the norm for teams by 2000 (899 saves of one inning or less) and this trend rapidly increases.  By 2010, there are 1,087 saves of one inning or less and only 18 saves of two or more innings.  This pattern continued to hold in 2016 as well (1,165 vs. 29).

Screenshot (1)

The last 16 years of data point to the manager’s extreme reliance on the one inning closer model.  It will be interesting to see if teams begin to shift usage away from what has become the norm and utilize the best bullpen arms in the highest leverage situations regardless of save opportunity.  I would love to hear what people think regarding bullpen and ‘closer’ usage in 2017 and beyond.  Please feel free to leave a comment.

You can follow me on Twitter: @doctordaver

Coffee is for Closers – Evaluating the 9th Inning Guy


The save statistic has an interesting backstory.  Although some teams tracked the pitchers who finished games, little was done to standardize the measurement for a number of years.  That is, until Jerome Holtzman developed the criteria for awarding a save in the early 1960s and tracked it for The Sporting News through the 1960s.  The save entered public consciousness by being regularly written about and finally became an official statistic tracked by Major League Baseball starting in 1969.

Major League Baseball’s rule 10.19 states the official scorer shall credit a save when a pitcher meets all four of the following conditions:

  1. he is the finishing pitcher in a game won by his team
  2. he is not the winning pitcher
  3. he is credited with at least 1/3 of an inning of work
  4. he satisfies one of the following – (he enters the game with a lead of no more than three runs and pitches at least one inning, he enters a game, regardless of the count, with the potential tying run either on base, at bat, or on deck, he pitches at least three innings.


Saves are difficult to evaluate. There are baseball players and fans who believe that it takes a player who possesses a special make-up to consistently go out in the 9th inning and record the final three outs of a game.  Other fans and players don’t believe there is a difference between the final three outs or any other inning within a game.  The philosophical question of the save becomes, what is the value of a pitcher who gets the final three outs of a game and does that ability warrant a statistical acknowledgement that has far reaching implications on things like arbitration-driven salary, free agent price, and trade value?

gossage          sutter         eck         rivera         kimbrel

The role of closer has morphed over time which may make the save statistic less meaningful when tracking totals over decades.  In the past, it was not uncommon for a closer to work multiple innings in an appearance (see Gossage or Sutter).  For example, in 1982 there were 439 instances of a player earning a save based on an appearance of 2 or more innings.  As the 1980s progressed, teams like LaRussa’s Oakland A’s began using a specified 9th inning pitcher and workloads moved towards one inning (see Eckersley).  By 1989, there were 325 instances of 2 plus inning saves.  The one inning appearance took hold throughout the 1990s (1997 had 95 instances of 2 or more inning saves) and became even more ingrained during the 2000s (2008 had 35 saves of 2 or more innings).  The one inning closer came to define the role (see Rivera and Kimbrel).  Conceptually speaking, the save statistic has created a single anointed closer and the shift in thinking from a multi-inning fireman to a highly specialized designated game ender appeared to be the natural evolution of the job.  That is, until recently.

The use of sabermetrics has slowly moved the pendulum away from a single 9th inning closer who is responsible for the save opportunity regardless of whether a team is leading by one, two, or three runs.  The use of statistical analysis and game theory have indicated that it may be more efficient to utilize a team’s best arm (for argument’s sake, let’s consider the best arm the closer) in the highest leverage situations (For example, Francona’s use of Andrew Miller after his arrival to Cleveland during 2016, especially the post season).  Similarly, Showalter’s failure to use Britton in this fashion during the 2016 playoffs may have cost Baltimore moving forward in the playoffs and at the very least created a talking point that is still discussed as the league prepares for the 2017 season.

jansen          britton

In statistics, larger sample sizes lend themselves to more meaningful analysis and less chance for statistical noise and error.  In the case of closers, data is restricted due to their typical workload.  Take Kenley Jansen for example.  In 2016, he made 71 appearances, finished 63 games, and earned 47 saves in 68 and 2/3 innings pitched.  Zach Britton is similar.  In 2016, he appeared in 69 games, finished 63, and earned 47 saves in 67 innings pitched.  Limited appearance totals, limited innings pitched, and limited batters faced per appearance make the results of statistical analysis tenuous at best.  By focusing solely on saves, the amount of data being considered shrinks even more.

In 2016, there were 1061 saves that were based on 1 inning of work.  An additional 44 saves were based on 2/3 of an inning of work and 60 saves were based on just 1/3 of an inning of work.  This means that in addition to 1061 instances of one inning saves, there were an additional 104 saves awarded based on an appearance of less than one inning.  Conversely, there were only 29 saves in 2016 of 2 or more innings and almost all of these saves were earned by ‘non-closers’.  Clearly, all saves are not created equal and the data pool for analysis, especially using a pitcher’s performance per appearance (since an appearance can be as short as one batter faced) difficult to assess.


It is my contention that the save is a diluted statistic and tells us much less about a pitcher’s performance than it could.  It is akin to a list of runners that completed a marathon.  All finishers are listed but no distinction between someone who finished the race in 4, 5, or 12 hours is made.  Without the crucial finish time, a good deal of information about each individual’s performance is lost.  I have created five types of saves with the goal of more clearly documenting the work of closers.  The five types are the Ultrasave, Powersave, Plainsave, Uglysave, and Disastersave (this type is extremely rare).  While the concept of ‘saves by gradation’ cuts the data into smaller parts, it allows for more meaningful information about pitcher performance to be conveyed.


Below are the five types of saves and the criteria for each:

UltraSave = no walks, no hits, no HBP (no baserunners), at least 1 IP, struck out all batters faced, earned the save.

PowerSave = no walks, no hits, no HBP (no baserunners), at least 1 IP, at least 1 K, earned the save.

PlainSave = no walks, no hits, no HBP (no baserunners), at least 1 IP, no Ks, earned the save.

UglySave = at least one hit, at least one run, no Ks, at least 1 IP, earned the save.

DisasterSave = at least two hits, at least two runs, no strikeouts, at least 1 IP, earned the save.  This is the rarest save type (thankfully).  Of the Top 50 Save Leaders of 2016, only Kintzler and Gomez each earned one in 2016.


2016 Saves Leaders (Top 50)

Name              Sv/Op        BlnSv          UltraS          Pwrsv             Plnsv      Ugsv

Familia            51/56             5              0                   22                        1              1

Britton            47/47            0               1                    15                       5              0

Jansen             47/53             6              3                   24                        2              1

Melancon       47/51             4               1                    13 (7 Pt 6 Wa) 5              3

Rodriguez       44/49            5               1                    11                        7              1

Ramos             40/43             3              0                   13                         0             1

Dyson              38/43              5              1                    8                          6             2

Colome           37/40              3              1                    11                         2            0

Gomez            37/43               6              0                   5                          9             4

Robertson      37/43              7              2                  12                          2             0

Chapman        36/39              3              2                  13 (8 NY 5 Ch)    1             1

Osuna              36/42              6              0                 11                           5             0

Allen                32/35               3              0                 13                          2              1

Casilla             31/40               9              0                  7                           3              1

Kimbrel           31/33               2              4                  15                         0              0

Madson          30/37               7              0                  10                         5              2

Davis               27/30              3               1                    4                         1               0

Jeffress           27/28              1              0                    4                         1                1

Cishek             25/32             7              0                    8                          1               0

Rodney           25/28              3              1                    7 (1 Mia 6 SD)  2               0

Ziegler            22/28            6              0                   2 (1 Bos 1 AZ)     1               0

Johnson          20/23            3               1                    6                          1               1

Oh                     19/23             4              0                    8                           0              0

Papelbon        19/22            3              1                     4                           2              0

Diaz                 18/21             3              1                     9                           1               0

Rondon           18/23            5              1                     9                           2               0

Cingrani         17/23           6              0                    4                            2               0

Kintzler          17/20          3              0                    3                             2               3

Giles                15/20            5              0                    5                           0              2

Gregerson     15/21            6             1                     7                            2              0

McGee            15/19             4             0                     3                          2               1

Watson           15/20            5             0                    4                           1               2

Rosenthal      14/18           4             1                     4                            1              0

Maurer           13/19           6              0                    4                            2              1

Thornburg    13/21           8             0                     5                             1              0

Betances       12/17            5             1                      4                           0              0

Harris            12/15            3             0                     4                            0              3

Herrera         12/15            3             0                     7                            2              0

A Miller         12/14           2              2                    4 (3 NY 1 Cle)     1               0

Estevez         11/18            7             0                     5                            2               0

Tolleson       11/15            4             0                     2                            1                0

Vizcaino       10/14           4            0                      3                            0                0

Street             9/12              3            0                      3                          1                 0

Jepsen            7/11              4             0                     0                         0                 1

Kelley             7/9               2             0                      1                         0                0

Ottavino        7/12             5              1                      4                        0                0

Uehara           7/9               2             1                       4                        0                0

Bailey             6/7               1             0                       2                        1                 0

Cabrera          6/7               1             0                        1                       2                 0

Iglesias          6/8              2             1                         5                        0                 0


As previously stated, the save statistic is diluted and tells us little about a pitcher’s performance.  I have created five types of saves with the goal of more clearly documenting the work of closers.  The five save types are the Ultrasave, Powersave, Plainsave, Uglysave, and Disastersave.  Although ‘saves by gradation’ cuts the already lean relief pitcher data into smaller parts it allows for more meaningful information about pitcher performance than merely using a save total as an approximation for quality of a pitcher/quality of closer performance.

Ask managers, front office employees, or even fans of the game and most (everyone?) will tell you that the key characteristic of a closer is dominance.  In the 9th inning of a 1, 2, or 3 run game, any type of baserunner is a hazard to a team looking to shut the door on an opponent.  Thus, my save model rewards pitchers who do not allow balls in play (valuing the safest out – the strikeout) and avoid baserunners of any kind (HBP, walks, hits).  Greater penalties are given to pitchers based on the level that they violate these two criteria.

There are a number of ways to utilize my data system to achieve a better understanding of the quality of each closer.  The first way is to award a number value to each type of save and then tally the points.  The values I gave each type of save are listed below:

Ultra = 3

Power = 2

Plain = 1

Ugly = -1

Disaster = -2


Based on the new point total for Quality of Save, the 2016 leaderboard looks like this:

Name                   Point Total

Jansen                  58

Familia                 44

Kimbrel                42

Britton                 38

Robertson           32

Chapman             32

Melancon            31

Rodriguez            31

Colome                27

Osuna                  27

Allen                    27

Ramos                 25

Dyson                  23

Madson               23

Rondon                23

Diaz                      22

Rodney                19

Gregerson           19

Cishek                  17

Casilla                  16

Oh                         16

Herrera                16

A Miller                15

Johnson               15

Papelbon             13

Gomez                 13

Iglesias                13

Davis                    12

Rosenthal           12

Estevez                12

Thornburg          11

Betances             11

Ottavino              11

Uehara                 11

Cingrani              10

Maurer                 9

Jeffress                 9

Giles                      8

McGee                  7

Watson                 7

Street                    7

Vizcaino               6

Harris                    5

Tolleson                5

Bailey                    5

Cabrera                 4

Ziegler                   3

Kintzler                 3

Kelley                    2

Jepsen                  -1


This rating system gives a more accurate picture of player performance than using traditional save totals.  However, there is another adjustment that makes the rankings even better.  When a 1 point penalty for each blown save is applied, the 2016 leader board shifts again:

Name                   Point Total

Jansen                  52

Kimbrel                40

Familia                 39

Britton                 38

Chapman             29

Melancon            27

Rodriguez            26

Robertson           25

Colome                24

Allen                     24

Ramos                  22

Osuna                   21

Diaz                      19

Rondon                18

Dyson                   18

Madson               16

Rodney                16

Herrera                13

A Miller                13

Gregerson           13

Johnson               12

Oh                         12

Iglesias                 11

Cishek                  10

Papelbon             10

Davis                     9

Uehara                  9

Rosenthal             8

Jeffress                 8

Gomez                  7

Casilla                   7

Betances              6

Ottavino               6

Estevez                 5

Street                    4

Bailey                    4

Cingrani               4

Giles                      3

McGee                   3

Maurer                  3

Thornburg            3

Cabrera                 3

Watson                 2

Harris                    2

Vizcaino                2

Tolleson                1

Kelley                    0

Kintzler                 0

Ziegler                  -3

Jepsen                  -5


Even within this system, closers that held the job all season and made more appearances still get a positive bump due to their increased usage/opportunities.  However, less dominant compilers take a small hit while the dominators get a positive nudge.  Rank movement for each player from the original 2016 saves leaders list is listed below (basing the present order on my formula).

Place – Name – Total Spots Moved

  1. Jansen +1
  2. Kimbrel +8
  3. Familia -2
  4. Britton -2
  5. Chapman +2
  6. Melancon -4
  7. Rodriguez -4
  8. Robertson -2
  9. Colome -3
  10. Allen -1
  11. Ramos -6
  12. Osuna -4
  13. Diaz +4
  14. Rondon +3
  15. Dyson -8
  16. Madson -4
  17. Rodney -2
  18. Herrera +6
  19. A Miller +6
  20. Gregerson +3
  21. Johnson -2
  22. Oh -1
  23. Iglesias +9
  24. Cishek -6
  25. Papelbon -3
  26. Davis -8
  27. Uehara +6
  28. Rosenthal -1
  29. Jeffress -9
  30. Gomez -15
  31. Casilla -12
  32. Betances -1
  33. Ottavino +3
  34. Estevez -1
  35. Street 0
  36. Bailey +2
  37. Cingrani -7
  38. Giles -7
  39. McGee -7
  40. Maurer -5
  41. Thornburg -5
  42. Cabrera +1
  43. Watson -8
  44. Harris -5
  45. Vizcaino -3
  46. Tolleson -5
  47. Kelley -3
  48. Kintzler -11
  49. Ziegler -16
  50. Jepsen -5


There is another way to analyze the 2016 save leaders performance using my 5 save type method.  Adding the number of Ultra and Power saves together gives a snapshot of how dominant a closer has been while in save situations.  Using just these two categories, the 2016 dominant save leaders were (% of saves that were dominant in parentheses):


Name                   Ultra & PowerSvs Combo              Percentage of overall save total that were Ultra+Power

Jansen                                 27                                         (57%)

Familia                                22                                         (43%)

Kimbrel                               19                                         (61%)

Britton                                16                                         (34%)

Chapman                            15                                         (42%)

Robertson                          14                                         (38%)

Melancon                           14                                         (30%)

Allen                                    13                                         (41%)

Ramos                                 13                                         (33%)

Colome                               12                                         (32%)

Rodriguez                           12                                         (27%)

Osuna                                  11                                         (31%)

Diaz                                     10                                         (56%)

Rondon                               10                                         (56%)

Madson                              10                                         (33%)

Dyson                                   9                                          (24%)

Gregerson                           8                                         (53%)

Oh                                         8                                          (42%)

Cishek                                 8                                          (32%)

Rodney                                8                                          (32%)

Herrera                                7                                          (58%)

Johnson                               7                                          (35%)

Casilla                                  7                                          (23%)

Iglesias                                6                                         (100%)

A Miller                               6                                          (50%)

Ottavino                              5                                          (71%)

Uehara                                 5                                          (71%)

Estevez                                5                                          (45%)

Betances                             5                                          (42%)

Thornburg                          5                                          (38%)

Rosenthal                           5                                          (36%)

Giles                                     5                                          (33%)

Papelbon                            5                                          (26%)

Davis                                    5                                          (19%)

Gomez                                 5                                          (14%)

Harris                                   4                                          (33%)

Maurer                                 4                                          (31%)

Watson                                4                                          (27%)

Cingrani                              4                                         (24%)

Jeffress                                4                                          (15%)

Street                                   3                                          (33%)

Vizcaino                              3                                         (30%)

McGee                                 3                                          (20%)

Kintzler                               3                                          (18%)

Bailey                                   2                                          (33%)

Tolleson                              2                                          (18%)

Ziegler                                 2                                          (9%)

Cabrera                                1                                          (17%)

Kelley                                   1                                          (14%)

Jepsen                                  0                                          (0%)


Based on these results it appears the more dominant closers fare much better than their less dominant brethren.  A quick examination of the average number of Ultra/Power saves earned by the Top 10 on the list as compared to the Bottom 10 on the list yields the following information:

Top 10 performers averaged 16.5 Ultra/Power saves in 2016.

Bottom 10 performers averaged 2 Ultra/Power saves in 2016.


Interestingly, if the Top 50 Saves Leaders of 2016 are examined based on total save number (i.e. the traditional list) the averages look like this:

Top 10: 14.4 Ultra/Powersave total

Bottom 10: 2.8 Ultra/Powersave total


Examining these two lists indicates that the ability to dominate during a save outing likely positively correlates with retaining a closer job, earning more chances at save opportunities that present themselves to a team, and ultimately compiling more saves. Conversely, a player put in a closer role with a lower level of dominance is more likely to lose a closer job, have fewer chances at save opportunities for their team, and fail to compile as many saves as the more dominant closers given the same number of save chances.

A big thanks to and the play index.

Follow me on twitter: @doctordaver

If you haven’t already, take the Machado vs. Harper poll here.

You might also like: My two part (Part 1  / Part 2) examination of Bill James’ Top 100 First Basemen of All Time list and an evaluation of candidates since the list was last updated in 2001.


A Little Game of You’re the G.M.


Pitchers and catchers have reported.  Spring Training 2017 is heating up.  Here’s a mental exercise to get you thinking about two of today’s best players.

Baseball fans are blessed with the amount of young talent that is present in the game today.  The 2010 amateur draft  has generated more than its fair share of stars.  Bryce Harper went 1st, Manny Machado 3rd, Matt Harvey 7th, Chris Sale 13th, and Christian Yelich 23rd.  This doesn’t even take into account players like Drew Pomerantz, Yasmani Grandal, or Jameson Taillon who are just now building their big league resume or teams like the Mariners or Tigers who didn’t have a first round pick that year but still landed Taijuan Walker and Nick Castellanos with their first picks during round two.

mach-harperphoto credit: R. Carr

Harper and Machado have cemented their places as top ten players, some would even argue top five.  Often times, comparing player value is difficult as one player may be much older or much more expensive than another.  That’s not the case with Harper and Machado though which allows for the following thought experiment.  As a thought experiment: Does  Machado or Harper have more trade appeal?  Here is a short summary of both players.


Manny Machado – Plays third base and shortstop.  He’s 24 years old.  Machado was called up in August of 2012 and played 51 games.  Since then he has totaled 156, 82, 162, and 157 games.  His Baseball Reference WAR season by season has been 1.6, 6.7, 2.4, 7.1, and 6.7 (24.4 total).  Hardware: 3 All Star selections and 2 Gold Glove awards along with two top-five MVP finishes.


Bryce Harper – Plays outfield.  He’s 24 years old.  His Baseball Reference WAR season by season has been 5.1, 3.7, 1.0, 9.9, and 1.6 (21.5 total).  Harper has played over 150 games in a season only one time (139, 118, 100, 153, 147).  Hardware: NL Rookie of the Year, NL MVP, 4 All Star selections, and 1 Silver Slugger award.

Harper put up one of the most statistically impressive seasons during the 2015 season but fell back to Earth in 2016.  Many believe the slippage was at least in part due to his playing through an injury (in addition to a general regression to the mean).  Machado continued to deliver elite performance at bat and in the field.  Interestingly, although he maintained his power (37 homers in 2016 as compared to 35 in 2015) he went from stealing 20 bases in 2015 to zero based in 2016.

Both players are in similar contractual positions as they remain arbitration eligible until both enter free agency in 2019.

Given the information above, here are some questions:

Follow me on Twitter: @doctordaver

Apocalypse Rock


Direct Hit!, a punk band from Milwaukee, released Brainless God in September of 2013.  Why would anyone in his or her right mind review a CD thee plus years later?  My answer: because I suck.  I broke the 40 year old barrier, I have a wife and two kids, and I now have about 1/50th of the time to spend on music that I used to have back in my heyday.  Unfortunately for me, Direct Hit! did not get onto my music radar until recently but since the band entered my awareness, I can’t get enough of them, especially this CD.

Brainless God is a concept album released on Red Scare Industries.  In an interview with Dan Ozzi for Noisey, guitarist and vocalist Nick Woods summed up the record succinctly.  He said, “It’s about how love, the apocalypse, a serial killer, and a suicide cult help this battered woman find herself.”   Clearly, this story line is not typical lyrical fodder and that alone should be enough to catch your attention.  However, for those of you visually inclined, the band also created a mini-movie and additional chapter by chapter videos to accompany each song on this CD.  The gory action of the film ties together the story line and brings the listener/viewer face to face with the Stephen King-esque horror as it unfolds.

The 12 songs on Brainless God are all extremely well written.  Although each track has its own unique modus operandi, there is an over-arching sound and style that ties this package of tracks into a unified presentation.  The opener, On & On, begins as a piano driven dirge which quickly morphs into a street punk sing along.  This is followed by The World Is Ending (No One Cares) which contains a melodic hook that is as good as anything Green Day has ever crafted.  Track three (Buried Alive) is the most pop-punk sounding track on Brainless God.  However, unlike most pop punk which deals with love, girls, or a broken heart, Buried Alive focuses on a male serial killer who is in the process of abducting a female to replace the rotting carcass that currently resides in his basement.  This eclectic approach continues throughout the entirety of the album and never lets up.  Although the entire album has no filler, there are two tracks that I can’t get my fill of no matter how many times I play them; White Robes and Bank of Elevators.  White Robes has a funky synth running through the background of the verse, unique drum work, and an extremely catchy chorus.  Bank of Elevators has a chorus reminiscent of Rocky Horror’s ‘Time Warp’ but veers into much darker territory than the campy dance routine of “It’s just a step to the left.”


As far as concept records go, Brainless God is on par with Green Day’s American Idiot and NOFX’s The Decline.  Unlike Green Day and NOFX, whose rock operas focused on slice of life issues and political discord, Direct Hit! creates an apocalyptic nightmare that follows a story line and characters that are anything but slice of life.  Direct Hit! have fashioned a masterpiece by crafting a unique story and backing it up with some of the best punk rock songwriting and musical execution in recent memory.  Brainless God needs to be a part of any punk rock fan’s music collection.



You can follow me on Twitter: @doctordaver


You may also like reading the following articles:

A book review about the history of Lookout Records

NOFX wrote a book and I reviewed it

An interview with Dr. Frank of the Mr. T Experience

A Screeching Weasel album retrospective

Blink 182 – Their California CD is reviewed

Examining the Bill James Greatest First Basemen List in 2017 (Part 2)


In his book, the Bill James Historical Baseball abstract, James created a Top 100 Players of All Time list and a Top 100 Players of All Time for each position.  The problem with James’s extraordinary book is that it was last updated in 2001.  Because James has been employed by the Red Sox since 2003, he has been unable to share statistical information and developments or even his personal thoughts and opinions about professional baseball players.  Sabermetrics has continued to evolve and has changed the way the game is measured and played as well as what is valued by teams.  Additionally, there are a large number of modern players whose career trajectories substantially changed since the 2001 publication/rankings.  Similarly, many players that were unknown in 2001 are now perennial all-stars.  This makes the Top 100 positional lists in James’s book in need of a major overhaul.

Rather than attempt a complete historical re-rank, I thought it would be more interesting to examine players that were not included on James’s published lists and give a rough estimate regarding whether they should be included on a positional Top 100 list circa 2017.  There are a number of ways to measure a player’s career which makes this task, by itself, difficult enough.

I used the JAWS rankings on to help narrow down the prospective candidates.  JAWS, created by Jay Jaffe, attempts to measure a player’s Hall of Fame worthiness by applying statistical measures to smooth out variations between eras and then evaluate the player’s accomplishments against already enshrined players.

In PART 1 of this article, I started with 28 candidates from Jaffe’s list:

Albert Pujols

Jim Thome

Miguel Cabrera

Todd Helton

Jason Giambi

David Ortiz

Mark Teixeira

Joey Votto

Carlos Delgado

Adrian Gonzalez

Kevin Youkilis

Paul Goldschmidt

Justin Morneau

Edwin Encarnacion

Travis Hafner

Tino Martinez

Paul Konerko

Carlos Pena

Prince Fielder

Freddie Freeman

Anthony Rizzo

Aubrey Huff

Chris Davis

Ryan Howard

Brandon Belt

Lyle Overbay

Eric Hosmer

Mark Trumbo

This list was reduced to 14 candidates for various reasons (see PART 1 for detailed analysis).  The remaining candidates for consideration on an updated All Time Greatest First Basemen list are:

Albert Pujols

Jim Thome

Miguel Cabrera

Todd Helton

Jason Giambi

Mark Teixeira

Joey Votto

Carlos Delgado

Adrian Gonzalez

Justin Morneau

Tino Martinez

Paul Konerko

Prince Fielder

Ryan Howard

Before the resume for each of the remaining 14 players is examined, I thought it would be important to take a brief look at a section of Bill James’s Top 100 First Basemen of All Time list (2001).  Specifically, the players that populate the slots 90 through 100.  This may provide context as to the qualifications of a player that populated the final slots on James’s 2001 list.  Here are the players from the end of that list in sequential order:

90. Whitey Lockman: 771 games at first and 752 games in the outfield. 15 seasons.  OPS+ average of 95, 18.2 WAR.

91. Jason Thompson: 1314 games at first. 11 seasons.  OPS+ average of 122, 24.8 WAR.

92. Dan McGann: 1377 games at first.  12 seasons.  OPS+ average of 117, 34.3 WAR.

93. Tommy Tucker: 1670 games at first.  13 seasons.  OPS+ average of 102, 25.1 WAR.

94. Jim Gentile: 854 games at first.  9 seasons.  OPS+ average of 136, 17.0 WAR.

95. Wes Parker: 1108 games at first. 9 seasons.  OPS+ average of 111, 22.9 WAR.

96. Pete O’Brien: 1377 games at first.  12 seasons.  OPS+ average of 104, 19.1 WAR.

97. Don Mincher: 1138 games at first. 13 seasons.  OPS+ average of 127, 23.0 WAR.

98. Deron Johnson: 880 games at first, 332 at third, 287 at designated hitter, and 249 in the outfield.  16 seasons.  OPS+ average of 102, 6.2 WAR.

99. Joe Pepitone: 953 games at first and 496 games in the outfield.  12 seasons.  OPS+ average of 105, 9.7 WAR.

100. Ripper Collins: 894 games at first.  9 seasons.  OPS+ average of 126, 23.5 WAR.

A majority of these players will likely fail to remain on the All Time First Basemen list if all player seasons through 2016 are included in the general analysis.  Eyeballing some of the other names that were slotted higher on James’s 2001 list may be found wanting as well. While this may very well be the case, I fear falling victim to potential recency bias.  I worry about opening the door for too many modern day players only because I had the chance to see them play, have more knowledge about them than players from the past, and/or because I am subconsciously swayed by their fame or persona.

With that caveat, let’s move forward and examine the remaining 14 players from the list:


Ryan Howard – Howard played 13 seasons with the Phillies.  Although he broke in during 2004, he didn’t play more than 100 games with the team until the 2006 season.  That was his age 26 season.  He led the league in homers twice, RBI three times, and strikeouts twice.  He won the NL Rookie of the Year in 2005, the NL MVP in 2006, finished 2nd in ’08, and 3rd in ‘09.  He also made 3 All Star appearances, won 1 Silver Slugger, and was on 1 World Series winner.  He has an average OPS+ of 125, totaled 382 home runs, and earned a surprisingly low 14.9 career WAR.  Getting a late start to his major league career hurt Howard as his decline was fast and severe.  His inability to hit lefties became catastrophic while his fielding was always rated to be beyond sub-par.  His 2006-2009 prime was tremendous but four years of super-human production does not make Howard an all-timer.


Prince Fielder – Fielder appeared in 1324 games at first over his 12 year career.  Injuries led to poor production and few games played in two of his last three seasons and ultimately forced him into retirement.  Fielder led the league in homers once, RBI once, walks once and games played 4 times.  He’s a 6 time All Star and won a Silver Slugger 3 times.  He ended his career with 319 homers, an average OPS+ of 134, and 23.8 WAR.  Defensive metrics trashed his glove work annually.  He had some great seasons offensively but his defense and health issues keep him from making the All Time list.


Justin Morneau – Morneau has made 1324 appearances at first base during his 14 seasons in the big leagues.  He’s been the league leader in games played once and batting average once.  Morneau won the 2006 AL MVP, won a Silver Slugger twice, and was a 4 time All Star.  He has 27.3 career WAR and an average OPS+ of 120.  He had a 5 year stretch where he looked destined to be an all-timer but unfortunately Morneau’s career was derailed by a series of concussions.  He’ll be 36 in 2017 and is currently a free agent.  Morneau’s played a total of 107 games during the past two seasons so even if he finds a job, he’s not likely to play many games or generate elite production.  He doesn’t crack the list.


Paul Konerko – Konerko retired after the 2014 season after appearing in 1904 games at first.  He played 18 seasons and amassed 439 homers, averaged a 118 OPS+, and 27.6 WAR.  He was a 6 time All Star and was on the World Champion 2005 White Sox team.  Konerko never led the league in any category (unless you count grounding into double plays in 2003) and the defensive metrics indicate negative contributions for his first base work.  He had some nice seasons and was an important part of a few good White Sox teams however his overall numbers (especially when defense is accounted for) leave him an interesting candidate but not an all-time great.


Jason Giambi – Giambi played 20 seasons and amassed 1307 games at first (62% of all appearances), 595 at designated hitter, 113 in the outfield, and 70 games at third.  He compiled 50.4 WAR, averaged a 139 OPS+, and hit 440 homers.  He was a 5 time All Star, won the AL MVP in 2000, and won two Silver Slugger awards.  He led the league in walks 4 times, OBP three times, doubles once, strikeouts once, slugging once, and OPS once.  His defense was a completely different story.  He never posted a positive dWAR in any of his 20 seasons.  Giambi had a strong peak but his peak came in the midst of PED use (although it has never been determined how many of his best seasons occurred while he was using).  He’s not an All Timer at first base.


Todd Helton – Helton played 2178 games at first base over 17 seasons in Colorado.  He generated 61.2 career WAR, averaged a 133 OPS+ and hit 369 homers.  He was an All Star 5 times, won 3 Gold Gloves, and 4 Silver Slugger awards.  He led the league in OBP twice, hits once, doubles once, RBI once, batting average once, slugging once, OPS once, and total bases once.  He also walked more times that he struck out which is quite a feat for someone who had as many extra base hits as he did.  Helton had an 8 year peak and then fell to earth as injury and age caught up to him.  Helton’s resume should be taken with a grain of salt as he played half his games in the friendly confines of Coors Field.  His home and road splits tell the story:

Home: 1084 games – 329 doubles, 28 triples, 227 homers, 859 RBI, .345 batting average, .441 OBP, .607 slugging, and 1.048 OPS.

Road: 1052 games – 271 doubles, 9 triples, 142 homers, 547 RBI, .287 batting average, .386 OBP, .469 slugging, .855 OPS.

Helton had some great years but the overall numbers are definitely inflated thanks to the Colorado altitude and spacious dimensions of his home park.  I saw Helton as a no brainer for the All Time list when I started this project.  After closer inspection, I am not as sure.  He was a great player who was consistently among the better first basemen for a better part of a decade but if his road stats were doubled to create a hypothetical career, I think we would be looking at a very good, but not All Time great.  If he resides on the All Time list, he lives on the edge of town.


Mark Teixeira – Teixeira played 1769 games at first over 14 seasons.  He compiled 51.8 WAR, 409 homers, and averaged 126 OPS+.  Teixeira led the league in games played twice, runs once, homeruns once, RBI once, and total bases twice.  He was an All Star twice, won three Silver Sluggers, five Gold Gloves, and was a member of one world championship with the Yankees.  Teixeira was a well rounded player and consistently contributed with his glove and his bat.  The fast start to his career and strong middle of his career helped solidify his reputation in the game.  Unfortunately, Teixeira’s last few seasons were cut short by various injuries and as a result his overall level of play suffered especially his power and batting average which were once a hallmark of his game.  Although he was a 2015 All Star selection during this time frame, the final third of his career was mostly non-descript and keeps him from being a no brainer for the All Time list.  Like Helton, Teixeira lives on the periphery and depending on the measures being used he may fall just on or just off the All Time list.


Tino Martinez – Martinez played with 5 organizations over 16 seasons (although his first two years consisted of 60 total games played).  He appeared as a first baseman in 1869 games, tallied 28.8 WAR, averaged an OPS+ of 112, and hit 339 home runs.  He was a two time All Star and won a Silver Slugger award once but he never led the league in any category (unless you count sacrifice flies once).  His glove work was solid – not great but nowhere near as suspect as some of his contemporaries.  His arrival in New York coincided with the team winning four world championships in five years.  Martinez is a borderline candidate on a Best First Basemen list but being a fan favorite and respected clubhouse presence who was a contributing member of a dynasty gives him a slight bump.  He’d be #100 on my Top 100 First Basemen of All Time list.


Joey Votto – Votto will enter his 11th season with the Reds.  He’s played 1240 games at first base, averaged a 157 OPS+, and has earned 47.3 WAR.  He’s won the NL MVP, has been an All Star 4 times, and has won 1 Gold Glove.  Votto has led the league in games played once, doubles once, walks 4 times, OBP 5 times, slugging once, and OPS once.  Throughout his career Votto has been somewhat polarizing among the fans and analysts.  Old schoolers have knocked him for not trying to connect for more homers and RBIs while the stat-heads believe his best skills are often overlooked and undervalued.  Votto lost most of 2014 to injury which robbed him of one peak season and his presence on a Reds team that has been in rebuild mode for the past few seasons likely depressed some of his counting stats.  As he enters the 2017 season, Votto’s resume puts him on the cusp for the All Time distinction.  If he is able to put up another year or two of typical Votto production, he solidifies his place on the All Time First Basemen list.  I have little doubt he gets there.


Adrian Gonzalez – 2017 will be Gonzalez’s 14th pro season.  He has 1728 appearances as a first baseman.  During his career, Gonzalez has compiled 308 homers, averages a 133 OPS+, and has amassed a 43.8 WAR.  He’s a 5 time All Star, a 4 time Gold Glove winner, and has won a Silver Slugger twice.  He’s led the league in hits once, RBI once, walks once, and games played once.  Gonzalez is a consistently good batter even if his power tool never fully emerged.  Additionally, the fielding metrics often rate his work at first as quite strong.  He’s done enough to be included on the All Time list already.  If he can put up a few more good years with the bat while maintaining solid glove work (adding a world championship wouldn’t hurt either), he’ll continue to climb up the All Time list of first basemen.  It will be interesting to see if he can stave off a steep decline period and put together enough additional above average seasons to become a potential Hall of Fame candidate.


Carlos Delgado – Delgado made 1767 appearances at first base during his 17 year playing career.  He led the league in doubles once, RBI once, OPS once, and games played twice.  Delgado amassed 473 homers, 44.3 WAR, and averaged a 138 OPS+.  He was a two time All Star and won three Silver Slugger awards.  The defensive metrics indicate that his play at first base was well below average.  Although his collection of award hardware is sparse, he garnered MVP votes in 7 seasons.  Delgado was likely underappreciated as he spent a good deal of his career in Toronto.  Additionally, although he put up excellent numbers, his teams didn’t make the post-season which denied him a larger stage to showcase his talents (His lone post season came in 2006 with the Mets where he hit extremely well in the NLDS and NLCS.).  He was able to maintain strong offensive play for over a decade and separated himself from his peers with his bat alone.  Even getting dinged for poor defense, Delgado gets a spot (albeit, towards the bottom) on the All Time First Basemen list.


Jim Thome – Thome played 22 seasons.  He tallied 1106 games at first base, 818 at designated hitter, and 493 at third base.  Thus, Thome played only 45% of his games as a first baseman.  He earned 72.9 WAR, averaged a 147 OPS+, and his 612 homeruns ranks 7th all time.  He made five All Star teams and won 1 Silver Slugger.  He led the league in homers once, walks 3 times, strikeouts 3 times, slugging once, and OPS once.  Thome is clearly an all time great of the game, but he played only 45% of his games at first (although this is the position he played most frequently).  If Thome is going to be considered a first baseman, he is included on the All Time Best First Basemen list.  However, since he didn’t play at least 50% of his games at first, I am not sure that he should be represented on a positional list and instead may be better included on an ‘All Time list’ that doesn’t take position into account.


Miguel Cabrera – 2017 will be Cabrera’s 15th season in MLB.  He’s appeared in 978 games as at first, 697 games at third base, 347 in the outfield, and 78 as a designated hitter.  This means Cabrera has only played approximately 47% of his games at first.  He’s an 11 time All Star, 7 time Silver Slugger, won 1 World Series, and won the triple crown in 2012.  He’s led the league in games played once, doubles twice, homers twice, RBI twice, batting average 4 times, OBP 4 times, Slugging twice, OPS 2 times, and total bases two times.  In 14 years, Cabrera has compiled 446 homers, a .321 lifetime batting average, 69.6 WAR, and has averaged a 155 OPS+.  Let’s just not talk about his defense…   Cabrera’s case is similar to Thome’s.  If he is to be considered a first baseman, his inclusion as an all-timer is a no brainer.  However, if the percentage of games played needs to reach a certain threshold (ex. 50% or greater) then Cabrera will have to toil at first a while longer to get himself over the 50% mark or be excluded from a positional list and reside only on a non-positional All Time list.


Albert Pujols – Pujols burst onto the scene in 2001 and never looked back.  2017 will be his 17th year in the bigs.  He’s played 1728 games at first base and although age and injuries have taken their toll on his performance, Pujols continues to add to an already incredible career.  He has led the league in runs 5 times, hits once, doubles once, homers twice, RBI once, batting average once, total bases 4 times, slugging 3 times, OBP once, and OPS 3 times.  He enters 2017 with 591 homers, an average OPS+ of 157, and a WAR of 101.1.  His hardware is also impressive.  He’s won two championships with the Cardinals, was NL Rookie of the Year, NL MVP 3 times (with 4 second place finishes as well), has been an All Star 10 times, won 6 Silver Sluggers, and 2 Gold Glove awards.  So, yeah… he’s at or right near the top of any All Time First Basemen list.

In summary:

“No” – Howard, Fielder, Morneau, Konerko, and Giambi

“Depends on how you slice it” – Helton and Teixeira

“Just over the hump” – Martinez and Votto

“Yes” – Gonzalez and Delgado

“Yes, of course! (as long as they qualify as first basemen)” – Thome and Cabrera

“Without a doubt” – Pujols

Any list is bound to stir up strong feelings about particular players getting onto or being left off.  There are no right or wrong answers, just opinions.  I hope this article can generate some thoughtful conversation.  I would love to hear your opinion about who you feel qualifies and who doesn’t.  Thanks for taking the time to read.

You can follow me on Twitter: @doctordaver