Re-examining Saves – 2017 Season

The save statistic is flawed and tells us little about a closing pitcher’s effectiveness.  In order to better gauge the performance of the pitchers that populated the saves leaderboard, I developed three ways to explore their performance.  In a previous post, I examined relief pitcher performance for the 2015 and 2016 seasons.  In this post, I review the 2017 saves leaderboard using the three methods.

The first method for examining closer performance is to sort each save a pitcher earned into 5 different categories.

The criteria for each save category:

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.


Here is the 2017 Save Leaderboard sorted by total saves earned as well as a tally of saves meeting the criteria for each of the five types:


2017 Saves Leaders

Name   Saves/save Op      Ultra             Power             Plain                Ugly            Disaster

Colome 47/53                      1                      6                       6                      5                       1

Holland 41/45                     2                    11                       5                      1                       1

Jansen 41/42                        5                      9                      4                      0                        0

Osuna 39/49                         2                    12                     5                       0                       0

Knebel 39/45                        0                      7                     2                       0                       0

Rodney 39/45                       2                      9                     3                       1                       0

Kimbrel 35/39                      5                    10                     1                       1                       0

Diaz 34/39                             1                    16                     0                       0                       0

Giles 34/38                            1                     8                       2                       1                       0

Davis 32/33                           1                     6                       5                       1                       1

Allen 30/34                           0                     11                     2                       0                       0

Kintzler 29/35                      0                       3                     3                       0                       0

Iglesias 28/30                       0                       3                     2                       1                       0

Ramos 27/30                        0                       6                     1                       1                       0

Herrera 26/31                      0                       3                     4                       1                       0

Neris 26/29                           1                       6                     3                       2                       0

Doolittle 24/26                     2                        7                    0                       2                       0

Chapman 22/26                   0                         5                   2                       0                       0

Johnson 22/31                      0                         6                   3                       1                       1

Maurer 22/26                       1                         5                   3                       0                       0

Hand 21/26                           1                        10                  0                       2                       0

Rivero 21/23                         1                         6                   0                       0                       0

Oh 20/24                                0                         0                   3                       1                       0

Norris 19/23                          0                         5                   1                      0                        0

Reed 19/21                             0                         3                   2                      1                        0

colome Colome led all of baseball with 47 saves in 2017.

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 of the five possible save types are listed below:

Ultra = 3

Power = 2

Plain = 1

Ugly = -1

Disaster = -2

Utilizing the second method to examine closer performance, the Quality of Save leaderboard re-ranks the top 25 list.

Based on the new point total for Quality of Save, the 2017 leaderboard looks like this:

Saves/Save Opps.                        Quality Save Score                                               Rank Change

1 Jansen  41/42                                               37                                                                       +1

2 Osuna  39/49                                               35                                                                       +2

2 Kimbrel  35/39                                            35                                                                       +5

2 Diaz  34/39                                                   35                                                                       +6

5 Holland  41/45                                            30                                                                       -3

6 Rodney  39/45                                             26                                                                       -2

7 Allen  30/34                                                 24                                                                       +4

8 Hand  21/26                                                 21                                                                       +13

9 Giles  34/38                                                  20                                                                       -1

10 Doolittle  24/26                                         18                                                                       +7

11 Davis  32/33                                               17                                                                       -1

12 Knebel  39/45                                            16                                                                       -8

12 Neris  26/29                                               16                                                                       +3

12 Maurer  22/26                                           16                                                                       +6

15 Rivero  21/23                                             15                                                                       +6

16 Colome  47/53                                           14                                                                       -15

17 Chapman  22/26                                        12                                                                       +1

17 Ramos  27/30                                             12                                                                       -3

17 Johnson  22/31                                           12                                                                       +1

20 Norris  19/23                                              11                                                                       +4

21 Herrera  26/31                                            9                                                                         -6

21 Kintzler  29/35                                            9                                                                         -9

23 Iglesias  28/30                                             7                                                                         -10

23 Reed  19/21                                                  7                                                                         +1

25 Oh  20/24                                                      2                                                                        -2

jansen Jansen led all of baseball with a 37 quality save score in 2017.

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.  The third method is to apply a 1 point penalty for each blown save.  When this is executed, the 2017 leader board shifts again and creates a much clearer picture of closer dominance than raw save totals.

Saves/Save Opps.                        Quality Save Score                     New Point Total

1 Jansen  41/42                                             37                                           36

2 Kimbrel  35/39                                          35                                            31

3 Diaz  34/39                                                 35                                           30

4 Holland  41/45                                           30                                           26

5 Osuna  39/49                                              35                                           25

6 Rodney  39/45                                            26                                           20

6 Allen  30/34                                                24                                           20

8 Giles  34/38                                                 20                                           16

8 Davis  32/33                                                17                                           16

8 Doolittle  24/26                                           18                                          16

8 Hand  21/26                                                 21                                          16

12 Rivero  21/23                                             15                                          13

12 Neris  26/29                                               16                                          13

14 Maurer  22/26                                           16                                          12

15 Knebel  39/45                                            16                                          10

16 Ramos  27/30                                            12                                           9

17 Colome  47/53                                           14                                           8

17 Chapman  22/26                                       12                                           8

19 Norris  19/23                                             11                                           7

20 Iglesias  28/30                                           7                                             5

20 Reed  19/21                                                7                                             5

22 Herrera  26/31                                           9                                             4

23 Kintzler  29/35                                           9                                             3

23 Johnson  22/31                                        12                                              3

25 Oh  20/24                                                    2                                             -2

jansen Jansen still maintains the top slot for his work in 2017.

Another way to analyze the 2017 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 2017 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

Hand  21/26                          11 of 21                                                                             52.3%

Diaz  34/39                            17 of 34                                                                             50%

Kimbrel  35/39                      15 of 35                                                                            42.8%

Doolittle  24/26                      9 of 24                                                                             37.5%

Allen  30/34                           11 of 30                                                                            36.6%

Osuna  39/49                         14 of 39                                                                            35.8%

Jansen  41/42                        14 of 41                                                                            34.1%

Holland  41/45                      13 of 41                                                                            31.7%

Rivero  21/23                          7 of 21                                                                             33.3%

Rodney  39/45                      11 of 39                                                                             28.2%

Johnson  22/31                     6 of 22                                                                               27.2%

Maurer  22/26                       6 of 22                                                                              27.2%

Neris  26/29                           7 of 26                                                                              26.9%

Giles  34/38                            9 of 34                                                                              26.4%

Norris  19/23                         5 of 19                                                                              26.3%

Chapman  22/26                   5 of 22                                                                              22.7%

Ramos  27/30                        6 of 27                                                                              22.2%

Davis  32/33                           7 of 32                                                                             21.8%

Knebel  39/45                        7 of 39                                                                             17.9%

Reed  19/21                           3 of 19                                                                              15.7%

Colome  47/53                      7 of 47                                                                              14.8%

Herrera  26/31                      3 of 26                                                                             11.5%

Iglesias  28/30                       3 of 28                                                                             10.7%

Kintzler  29/35                      3 of 29                                                                             10.3%

Oh  20/24                                0 of 20                                                                               0%

hand Over 52 percent of Hand’s saves were of the Ultra or Power type in 2017.


Click here for a look at the 2015 and 2016 MLB Saves Leaders using the same methodology.

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Big thanks to and their Play Index.


Miami Marlins: Second Verse Same as the First – A Little Bit Louder and a Little Bit Worse

When Jeffrey Loria agreed to sell the Marlins, baseball breathed a collective sigh of relief.  One of the cheapest and most poorly run franchises would finally be turned over to an ownership group that would hopefully treat the team as more than just a financial asset.   Ownership that would invest in the major and minor league roster?  A front office that might hold on to talent rather than add and then quickly divest?  Could good will develop between Miami and its team again?

The Marlins’ fan base should be extremely upset with what has played out since the new ownership group has taken over.  However, there doesn’t appear to be any Marlins fans left to care.  But for those of us who are fans of baseball in general, the off season moves that this team has made appear lack-luster at best and complete salary dumps at worst.

There are questions regarding who within the ownership group is exactly making these unpopular decisions and trades.  Derek Jeter has been the face/mouthpiece for the team and as a result he has become a lightning rod for criticism.  Regardless of whether he is the party to blame for these moves is unimportant.  What matters is that this ownership group does not seem to be charting a new course for the organization. These moves could be commended if the new owners were getting out from under bad contracts.  However, the contracts that have been moved (Gordon, Stanton, and Ozuna) were not bad contracts.  They may have been the team’s more expensive commitments but the players appeared to be well worth their price.

Compiling a talented outfield like the Marlins had is extremely difficult for a front office to accomplish. Although the former Marlins’ regime had made a number of decisions/moves that ranged from questionable to bone-headed, the one thing that they did do well was assemble an extremely talented group of starting outfielders.  There is no question that a team has to move valuable pieces in order to get value in return when making a trade, but the loss of this stacked outfield will have a crippling effect on the offensive production and defensive value of the team going forward.  The return the Marlins have to show for these moves is not worth the opportunity cost of moving on from a talented outfield core, the ill will the organization generated among hopeful fans (if there were any left), and the poison that has affected the remaining talent (Yelich and Realmuto).

stanton                         ozuna                         yelich

The disservice that the current ownership group has done to the team cannot be understated.  They have disassembled what was the most productive trio of outfielders during 2017 in all of MLB.  The pretense that these moves were made to get younger/build the farm system up/prepare for contention down the road is misleading.  It is an easy excuse for the underlying cheapness of the organization.

Here is a breakdown of 2017 outfield production around the league.  Highlighted are the number of games played by each team’s three primary outfielders, their overall OPS+ (number of games played helps put this number into better prospective), and total Wins Above Replacement (WAR) with offensive WAR an defensive WAR broken down.

2017 Outfield Performance:

Team: Marlins


Player———-Games Played—– OPS+—– WAR—– Offensive WAR—– Defensive WAR

Stanton            159                           165            7.6               6.5                             0.4

Ozuna               159                           145            5.8               4.8                             0.4

Yelich                156                           117            3.9               4.5                            -0.3

This is a relatively young outfield.  In 2017 these three players remained in excellent health.  Their strong OPS+ showing is based on a large number of games played.  WAR indicates that all three outfielders were above average (especially on offense) and that all three performed without issue in the field.  As you will see in the following comparisons, this outfield could have been the envy of the league for years to come.


Team: Red Sox


Player———- Games Played—– OPS+—– WAR—– Offensive WAR—– Defensive WAR

Betts                     153                         108            6.4               3.3                             2.6

Bradley Jr.          133                          89             2.8               1.9                             1.3

Benintendi         151                        103             2.6               1.9                             0.4

Benintendi put up a solid rookie season but Bradley Jr. missed time and came back down to his career levels of production after a great 2016.  Betts still produced on offense and in the field but his OPS+ shows that he was much closer to league average (due to the inflated offensive totals of the league) than he has been in the past.   


Team: Astros


Player———- Games Played—– OPS+—– WAR—– Offensive WAR—– Defensive WAR

Springer              140                        144            5.0                 5.2                           -0.3

Reddick               134                        134            4.4                 4.0                           -0.2

Aoki                       70                          98             0.7                 0.3                            0.1

Gonzalez              48                         150           4.3                 4.7                           -0.4

*Gonzalez played many positions for the Astros and is added to the outfield list as Aoki was traded on July 31st.  Gonzalez played the outfield 17 times after Aoki was traded.

The world champs had two regular and one rotating third member comprising their outfield.  Springer put up an extremely solid campaign and Reddick, although undervalued and mostly ignored by the media, was very good as well.  Gonzalez (see notation above) played a variety of positions and had his best offensive season ever.  However, he did not put in enough time to be considered a regular outfielder.


Team: Dodgers


Player———- Games Played—– OPS+—– WAR—– Offensive WAR—– Defensive WAR

Taylor                 140 (96 in OF)          122            4.8                    4.2                          0.7

Pederson           102                              95           -0.4                    0.9                         -1.1

Puig                    152                             118           3.7                     1.9                          1.3

*Taylor Played 96 games in the Outfield.  His OPS+ and WAR statistics are based on his entire 140 games played.

World Series runners up had mixed results in their outfield.  Puig bounced back and Taylor came out of nowhere to put up significant numbers.  However, like Gonzalez in Houston, he played a variety of positions.  His statistics above reflect his entire production for the season, not just his outfield work so on paper, the outfield looks a bit better than it actually fared over the course of the season.  Pederson’s production with the bat continued to decline and the defensive metrics did not like his glove work either.


Team: Diamondbacks


Player———- Games Played—– OPS+—– WAR—– Offensive WAR—– Defensive WAR

Pollock                 112                         99             2.9                 2.2                            0.9

Peralta                 140                         99             2.5                 1.9                            0.0

Tomas                    47                         87            -0.5                 0.2                           -0.8

Martinez                62                       168            2.6                 2.5                           -0.2

*Tomas was initially in the team’s starting OF.  Martinez was traded to the D-Backs on July 18th and his statistics are reflective only of his performance with the D-Backs.

A.J. Pollock looked good when healthy but continued to experience injuries that limited him to 112 games total.  The less that can be said about Tomas (especially his defensive work), the better.  J.D. Martinez came over in mid-July and continued to rake like he did in Detroit (and he also continued to play below average defense).  Peralta was his solid self.


Team: Rockies  


Player———- Games Played—– OPS+—– WAR—– Offensive WAR—– Defensive WAR

Parra                      115                       94             0.9                   0.3                         0.0

Blackmon              159                     142            6.0                   6.5                        -0.2

Gonzalez               136                       87            -0.2                   0.1                       -0.9

The Rockies usually sport a power-packed outfield.  One would think that their return to the post-season in 2017 would mean the outfield produced.  However, besides Blackmon, who put up impressive numbers, the two other players garnering the most outfield work failed to do much.  Gonzalez had a particularly poor year ending the season with negative value and a below average OPS+. 


Team: Brewers


Player———- Games Played—– OPS+—– WAR—– Offensive WAR—– Defensive WAR

Braun                   104                        111            1.2                  1.7                         -0.9

Broxton                143                         85              0.4                  1.2                        -0.6

Santana               151                        126             3.0                 3.5                         -1.1

The Brewers surprised the league in 2017.  They have a number of young exciting players with significant upside.  A review of their outfield however, shows that they are a butcher shop in the field.  Braun continued to battle injuries and played in only 104 games.  Broxton was streaky (to the point of a minor-league demotion) while Santana broke out in his first year as an every day player and had a big offensive year.


Team: Rays


Player———- Games Played—– OPS+—– WAR—– Offensive WAR—– Defensive WAR

Dickerson                  150                   120            2.7                    2.8                          -0.9

Kiermaier                   98                    114            5.1                    2.9                           2.5

Souza                         148                   121             4.2                    3.5                           0.2

The Rays have a quietly solid outfield.  All three regulars put up above average seasons (by OPS+ and WAR) in 2017 however, Kiermaier couldn’t stay healthy again.  This trio would be even more impressive as group if Dickerson played better defense and Kiermaier could play 145 games or more per year.  There is a lot to like about these three going into 2018.  Although they were good, they were not ‘Marlins outfield’ good.


Team: Yankees


Player———- Games Played—– OPS+—– WAR—– Offensive WAR—– Defensive WAR

Ellsbury                    112                      97            1.7                 2.1                           -0.1

Gardner                   151                     104           4.9                 2.8                            1.6

Judge                         155                     171           8.1                 7.2                           0.3

Hicks                           88                      122           3.9                 2.4                           1.5

*Ellsbury played 97 games in Centerfield.  Hicks played 52 games in Centerfield.

The addition of Stanton (from the aforementioned Marlins) will likely be a huge upgrade for the Bombers as long as he remains healthy.  How they will deploy Judge and Stanton in the same outfield should be interesting.  Ellsbury continued to be injured and came nowhere close to producing the value the team expected when he was signed as a free agent.  Hicks, when healthy, had a surprisingly good year with his bat and in the field.  Gardner remained consistent and had another solid season putting up a 20-20 campaign.  2018 will be his age 34 season.

As this look around the league shows, the Marlins have decided to squander one of the few advantages they possessed.  By choosing the break up an elite outfield unit, Jeter and company have demonstrated that saving money and trading production for lottery tickets is more important than retaining an exciting core and building around them.  If this new Marlins’ regime continues to operate like Loria’s group, losing fans and games will continue to plague Miami.      

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Line Up Construction 101 – Some Theories, Some Research, Some Thoughts

managers book

As Many Options as There are Stars in the Sky

In Bill James Guide to Baseball Managers from 1870 to Today (Scribner, 1997), James examines the multitude of options a manager has when constructing a line up.  He writes on page 20 that there are 741,354,768,000 different ways to create a nine player line up from a twenty five man roster.  Then, taking into account factors such as defensive position the options are reduced.  “If you have a roster of fourteen position players and eleven pitchers and you assume that only one pitcher will start, that reduced the options for the starting lineup from seven hundred billion to one billion.  If you assume that only certain players can catch, only certain players can play the outfield, etc., that reduces the options further; heck you can get down to a few million in no time.”

A few million options for a team’s manager to decide from seems more theoretical exercise in thinking than true game planning but there are in fact millions of options that can be picked from.  Helping to reduce the demand on one’s brain is the reality that teams often have at least one or two superstars locked in at a certain fielding position (ex. Joey Votto playing first base for the Reds) and/or a starter that is clearly the number one option when compared to other pitching options (Madison Bumgarner of the Giants).  The most ‘play’ a manager appears to have is with the batting order and the value he places on batting versus fielding options or platoon possibilities when determining his optimal line up.

bumgarner  votto-2


The Impact on Statistics Based on Where One Bats in the Order

There is a good deal of research that has been completed in recent years looking at the effect that the spot in a batting line-up has on a hitter’s statistics.  The research is clear – batting earlier in the line-up leads to more at bats over the course of a season (gaining an estimated 14 to 18 at bats per slot with each move up the order – going from batting 9th to 8th to 7th etc.).  Moving from 9th to 1st for example would lead to an estimated additional 112 to 144 at bats.  That is an extremely significant difference that is not lost on intelligent front offices and managers.

To take advantage of these additional at bats, managers have to use the right assessment tools in order to select an appropriate candidate.  For instance, using On Base Percentage (OBP) has become more widely accepted as a measure for determining who might help the team the most with an additional 100-140 at bats in a season while batting average, seen by many as a ‘noisy’ statistic that is likely to fluctuate more than the OBP statistic as well as a statistic that misses a large chunk of the ability of a batter to get on base, is used much less often.  Still, there are some managers who pay less attention to OPB when selecting a lead off man than their peers (see Dusty Baker’s use of sub.300 OBP Ben Revere in 2016).  Batting a sub .300 OBP hitter first or second in the order, regardless of the other skills that this player might possess, is to the detriment of the team’s offensive output.


Additionally, raw speed is no longer enough for a player to grab the leadoff spot.  Now, managers need to consider stolen base opportunity and success rate as well as a player’s overall ability on the base paths (ex. the ability to successfully take an extra base – such as going from 1st to 3rd on a single with regularity).  You likely won’t see a leadoff hitter with stats like Omar Moreno’s 1980 season (.306 OBP, 96 stolen bases/33 caught stealing, and striking out approximately 2 times for each walk (101 vs. 57).  96 steals look nice on paper but succeeding less than 66% of the time a steal is attempted is detrimental to a team’s ability to score runs especially when that lead off man is only on base approximately 30 percent of the time in the first place.


Research also indicates that the place where one bats in the line-up significantly impacts multiple counting statistics including Runs scored and Runs Batted In.  There seems to be some debate regarding the number of stolen bases attempted based on where a player bats (T. Cockcroft doesn’t find line up spot significant in relation to the stolen base but T. Bell who uses a more in depth calculation formula finds a significant difference – in reviewing the methodology of both I tend to give more weight to T. Bell’s results).

Future Research Incorporating All of the Above

Given the above, the manager (or front office) has a good deal of influence on both player and team production when making decisions on where to bat each player.  An interesting study to examine line up construction’s impact in a pennant race would be to examine the 2007 Phillies and Mets.  The Phillies erased a 7 game deficit in the final 17 games and ultimately captured the N.L. East pennant.  Could a different line up construction for the Mets during the final 17 games have kept them atop the East?  Did the Phillies maximize their offense in just the right way as they made their final push?  It would be very interesting to explore what decisions were implemented and then evaluate that process rather than just relying on the final standings to determine whether rosters were used to maximum effectiveness or not.  Was the Mets’ line up construction sound and their failure based on atypical lack of production or did the Mets mismanage their line up and ultimately have only themselves to blame for their collapse?

Proactive and Reactive Decision Making

Second guessing line-up construction and in game decisions are two favorite activities for disgruntled fans and armchair G.M.s.  When looking at manager competency, perhaps it is best to separately examine proactive decision making abilities (line up construction) versus reactive decision making abilities (in game moves).  It seems likely that these are two very different managerial abilities and ballgames can be won or lost based on either or a combination of these two decision types.

With hundreds of thousands/millions of options available to consider, it is little wonder why each person believes their strategy makes them the smartest person in the room and why other guys/teams just don’t have a clue.  Research has shown the impact of both line-up construction and, to a lesser extent, in game decision making.  Although a good deal of line up construction research has been conducted in regards to player valuation within fantasy baseball, the results have implications that cannot be ignored by MLB teams.  A more systemic analysis of line up decisions could yield a tremendous amount of data and help better evaluate managers and front offices in the real world.

The Wrong Way Rays

This week the Tampa Bay Rays traded long time face of the franchise, Evan Longoria to the San Francisco Giants.  Longoria has had a very nice (although often below the national radar) career with the Rays.  Although it is an understandable move from a Tampa Bay franchise which is always selling off “proven” (read as expensive) players for high upside youth (read as inexpensive and under cost control for significant lengths of time), trading Longoria is painful for anyone who is a Rays fan or at least enjoys the idea of a homegrown star player remaining with his original team.


Longoria will be entering his age 32 season.  He is signed through his age 37 season at the following salary:

2018 – 13.5 million

2019 – 14.5 million

2020 – 15.0 million

2021 – 18.5 million

2022 – 19.5 million

2023 – team option for 13.0 million or a 5.0 million buyout.

Although Longoria’s best seasons are likely behind him, he is a consistent player who has put up solid numbers while playing every day.  Here is a brief look at his last few seasons with the Rays:


2014                 162                 107                3.3

2015                 160                 112                3.2

2016                 160                 127                3.9

2017                 156                 100                3.6


It is estimated that in 2017 each win a player is worth will cost a team between 8 and 9 million dollars in free agency.  Thus, on the open market, Longoria could be projected to be a 24 to 27 million dollar per year player based on being a three win player.  While his salary will increase over the course of the coming years, as long as Longoria is able to maintain around a 3.0 WAR performance, the contract is an excellent value for the Giants (especially considering the continually rising cost of free agents/buying ‘a win’ on the free agent market).  Furthermore, the upgrade of Longoria manning the hot corner when compared to the Giants’ in house options makes this trade an even stronger move for San Francisco.

Conversely, I fail to see the logic in this trade for Tampa Bay.  As stated earlier, the Rays organization consistently move higher priced established players for younger, cheaper, and more controllable players.  This is a franchise always looking to break a dollar bill into four quarters or in an even better scenario coming out ahead in the long run if one of the players they get in return happens to truly breakout.  However, as recent moves the team has made demonstrate, getting even four quarters back for a dollar is hard to pull off and getting greater value than the dollar is extremely difficult.

What’s most damaging to Tampa isn’t the decline in production that they will likely experience in losing Longoria’s bat and glove in 2018 and beyond.  It’s the continued failure to cultivate a legacy.  There is no history for this team.  Who do you think of when you envision the Rays?  Rocco Baldelli, Aubrey Huff, or Carlos Pena?  An aging Fred McGriff or Wade Boggs playing out their end days?  Keeping Longoria should have been a priority.  He is productive, signed to a fair contract, and could have been the star that would allow the franchise to finally have a player identified as completely their own.

There’s no doubt that as an organization Tampa is in a tough position.  They are stuck in a horrible stadium deal, they have a limited fan base, and the budget they are afforded is strikingly low especially when they are forced to compete in the ultra competitive and deep pocketed A.L. East.  They have to be extremely careful when deciding which players to move and when the optimal time to move a player is.  It’s unfortunate that the team has been put in a position where they are often forced to move their stars before they become too pricy to keep.  With Longoria, the team locked him up to a team friendly long-term contact.  This was the perfect opportunity for the organization to finally have the chance to keep the fan favorite from beginning to end of the player’s career.  They blew it.

Salary and stats from


Follow me on twitter: @doctordaver


Do MLB Teams Keep Their Elite Talent?

On an episode of the Effectively Wild podcast, an interesting question was raised.  The question was whether teams have increasingly held onto their top talent.  This was measured by whether a star player was still with the team that originally drafted him.  I decided to take a closer look at this question by examining the top 10 finishers in Wins above Replacement (WAR) with hitters and pitchers examined separately over a number of seasons.  To examine the current trend in holding on to elite talent, I explored the last three full seasons of the WAR leader boards (2015-2017).  I then examined the players that comprised the top 10 WAR finishers during the 2007 and 1997 seasons in order to compare whether top talent is more frequently held on to by teams of today than teams from 10 and 20 years ago.

* Wins Above Replacement (WAR) taken from


2017 WAR Leaders: Position Players

Altuve 8.3 – Age 27 – with original team.

Judge 8.1 – Age  25 – with original team.

Stanton 7.6 – Age 27 – with original team.

Votto 7.5 – Age 33 – with original team.

Arenado 7.2 – Age 26 – with original team.

Simmons 7.1 – Age 27 – with 2nd team.  Traded from Atlanta to Los Angeles/Anaheim (November of 2015).

Ramirez 6.9 – Age 24 – with original team.

Trout 6.7 – Age 25 – with original team.

Betts 6.4 – Age 24 – with original team.

Pham 6.4 – Age 29 – with original team.

Average age = 26.7 years

9 of 10 players were with their original team. 


2017 WAR Leaders: Pitchers

Kluber 8.0 – Age 31 – with 2nd team.  Traded from San Diego to Cleveland (July of 2010).

Scherzer 7.3 – Age 32 – with 3rd team.  Traded from Arizona to Detroit (2009).  Signed via free agency with Washington (January of 2015).

Gonzalez 6.6 – Age 31 – with 4th team.  Traded from Chicago to Philadelphia, from Philadelphia to Chicago, from Chicago to Oakland, and from Oakland to Washington (December of 2011).

Strasburg 6.5 – Age 28 – with original team.

Verlander 6.4 – Age 34 – with 2nd team.  Traded mid-season from Detroit to Houston (2017).

Greinke 6.0 – Age 33 – with 5th team.  Drafted by Kansas City, traded to Milwaukee, traded from Milwaukee to Los Angeles/Anaheim.  Signed via free agency with Los Angeles (2012).  Signed via free agency with Arizona (2015).

Sale 6.0 – Age 28 – with 2nd team.  Traded from Chicago to Boston (December of 2016).

Stroman 5.8 – Age 26 – with original team.

Carrasco 5.4 – Age 30 – with 2nd team.  Traded from Philadelphia to Cleveland (July of 2009).

Severino 5.3 – Age 23 – with original team.

Average Age: 29.6 years

3 pitchers remained with original team. 


2016 WAR Leaders: Position Players

Trout 10.5 – Age 24 – with original team.

Betts 9.5 – Age 23 – with original team.

Bryant 7.7 – Age 24 – with original team.

Altuve 7.6 – Age 26 – with original team.

Donaldson 7.5 – Age 30 – 3rd team.  Drafted by Chicago, traded by Chicago to Oakland, Oakland traded to Toronto (November of 2014).

Cano 7.3 – Age 33 – with 2nd team.  Signed with Seattle via free agency (2013).

Seager 6.9 – Age 28 – with original team.

Machado 6.7 – Age 23 – with original team.

Arenado 6.6 – Age 25 – with original team.

Dozier 6.5 – Age 29 – with original team.

Average age: 26.5 years

8 of 10 players were with their original team.  1 hitter was traded and 1 hitter moved on to his second team via free agency.   


2016 WAR Leaders: Pitchers

Verlander 6.6 – Age 34 – with original team.

Kluber 6.4 – Age 30 – with 2nd team.  Traded from San Diego to Cleveland (July of 2010).

Scherzer 6.2 – Age 31 – with 3rd team.  Traded from Arizona to Detroit (2009).  Signed via free agency with Washington (January of 2015).

Cueto 5.6 – Age 30 – 3rd team.  Signed by Cincinnati, traded to Kansas City (July of 2015), signed with San Francisco via free agency (2015).

Kershaw 5.6 – Age 28 – with original team.

Roark 5.5 – Age 29 – with 2nd team.  Traded by Texas to Washington (July of 2010).

Tanaka 5.4 – Age 27 – with original team.

Martinez 5.4 – Age 24 – with original team.  Originally signed with Red Sox but league voided and then signed with St. Louis.

Lester 5.3 – Age 32 – with 3rd team.  Drafted by Boston, traded to Oakland (July of 2014).  Signed with Chicago via free agency (2014).

Syndergaard 5.3 – Age 23 – with 2nd team.  Drafted by Toronto and traded to New York (December of 2012).

Average age: 28.8 years

 4 of 10 pitchers were with their original team.


2015 WAR Leaders: Position Players

Harper 9.9 – Age 22 – with original team.

Trout 9.4 – Age 23 – with original team.

Donaldson 8.8 – Age 29 – 3rd team.  Drafted by Chicago, traded by Chicago to Oakland, Oakland traded to Toronto (November of 2014).

Goldschmidt 8.8 – Age 27 – with original team.

Votto 7.6 – Age 31 – with original team.

Pollock 7.4 – Age 27 – with original team.

Kiermaier 7.3 – Age 25 – with original team.

Cain 7.2 – Age 29 – with 2nd team.  Drafted by Milwaukee.  Traded to Kansas City (December of 2010).

Machado 7.1 – Age 22 – with original team.

Heyward 6.5 – Age 25 – with 2nd team.  Drafted by Atlanta and traded to St. Louis (November of 2014).

Average age: 26.0 years

7 of 10 players were with their original team.


2015 WAR Leaders: Pitchers

Greinke 9.3 – Age 31 – with 5th team.  Drafted by Kansas City, traded to Milwaukee, traded from Milwaukee to Los Angeles/Anaheim.  Signed via free agency with Los Angeles (2012).  Signed via free agency with Arizona (2015).

Arrieta 8.7 – Age 29 – with 2nd team.  Drafted by Baltimore and traded to Chicago (July of 2013).

Kershaw 7.5 – Age 27 – with original team.

Keuchel 7.2 – Age 27 – with original team.

Scherzer 7.1 – Age 30 – with 3rd team.  Traded from Arizona to Detroit (2009).  Signed via free agency with Washington (January of 2015).

Price 6.0 – Age 29 – with 3rd team.  Drafted by Tampa Bay and traded to Detroit (mid 2014) and then traded to Toronto (mid 2015 season).

Gray 5.8 – Age 25 – with original team.

Lackey 5.7 – Age 36 – 3rd team.  Drafted by Los Angeles/Anaheim.  Signed with Boston via free agency (2009).  Traded by Boston to St. Louis (July of 2014)

Bumgarner 4.8 – Age 25 – with original team.

deGrom 4.7 – Age 27 – with original team.

Average age: 28.6 years

5 of 10 pitchers were with their original team. 


2007 WAR Leaders: Position Players

Rodriguez 9.4 – Age 31 – with 3rd team.  Left Seattle for Texas via free agency (2001).  Traded from Texas to New York February of 2004).

Pujols 8.7 – Age 27 – with original team.

Wright 8.3 – Age 24 – with original team.

Utley 7.8 – Age 28 – with original team.

Jones 7.6 – Age 35 – with original team.

Granderson 7.6 – Age 26 – with original team.

Ordonez 7.3 – Age 33 – with 2nd team.  Left Chicago for Detroit via free agency (February of 2005).

Pena 7.2 – Age 29 – with 6th team – Drafted by Texas, traded to Oakland, traded from Oakland to Detroit (2002).  Signed as a free agent with New York (2006), signed as a free agent with Boston (2006), and signed as a free agent with Tampa Bay (January of 2007).

Tulowitzki 6.8 – Age 22 – with original team.

Cano 6.7 – Age 24 – with original team.

Average age: 27.9 years

7 of 10 hitters were still with their original team. 


2007 WAR Leaders: Pitchers

Oswalt 6.7 – Age 29 – with original team.

Beckett 6.5 – Age 27 – Traded from Florida to Boston after 2005 season.

Webb 6.4 – Age 28 – with original team.

Sabathia 6.3 – Age 26 – with original team.

Lackey 6.3 – Age 28 – with original team.

Vasquez 6.2 – Age 30 – with 4th team.  Traded from Montreal to New York, New York to Arizona, and Arizona to Chicago.

Hernandez 6.2 –  Age 26 – with original team.

Peavy 6.2 – Age 26 – with original team.

Buehrle 6.1 – Age 28 – with original team.

Harang 6.0 – Age 29 – with 3rd team.  Traded from Texas to Oakland (2000) and Oakland to Cincinnati (mid-2003 season).

Average age: 27.7 years.

7 of 10 pitchers were with their original team. 


1997 WAR Leaders: Position Players

Walker 9.8 – Age 30 – with 2nd team.  Signed by Montreal (1984).  Signed with Colorado via free agency (April of 1995).

Biggio 9.4 – Age 31 – with original team.

Griffey 9.1 – Age 27 – with original team.

Piazza 8.7 – Age 28 – with original team.

Bonds 8.2 – Age 32 – with 2nd team.  Drafted by Pittsburg (1985).  Signed with San Francisco via free agency (1992).

Bagwell 7.7 – Age 29 – with 2nd team.  Drafted by Boston (1989) and traded to Houston (August of 1990).

Thomas 7.3 – Age 29 – with original team.

Knoblauch 6.7 – Age 28 – with original team.

Garciaparra 6.6 – Age 23 – with original team.

Rodriguez 6.5 – Age 21 – with original team.

Average age: 27.8 years

7 of 10 hitters were with their original team. 


1997 WAR Leaders: Pitchers

Clemens 11.9 – Age 34 – with 2nd team.  Drafted by Boston.  Signed by Toronto via free agency (December of 1996).

Martinez 9.0 – Age 25 – 2nd team.  Signed by Los Angeles (1988) and traded to Montreal (November of 1993).

Pettitte 8.4 – Age 25 – with original team.

Johnson 8.0 – Age 33 – with 2nd team.  Drafted by Montreal (1985).  Traded to Seattle (1989).

Maddux 7.8 – Age 31 – with 2nd team.  Drafted by Chicago (1984).  Signed with Atlanta via free agency (1992).

Thompson 7.7 – Age 24 – with original team.

Brown 7.0 – Age 32 – with 3rd team.  Drafted by Texas (1986).  Signed with Baltimore via free agency (April of 1995).  Signed with Florida via free agency (December of 1995).

Cone 6.8 – Age 34 – with 4th team.  Drafted by Kansas City, Traded to New York Mets (1987).  New York traded to Toronto (August of 1992).  Signed with Kansas City via free agency (1992).  Traded from Kansas City to Toronto (April of 1995) and from Toronto to New York Yankees (July of 1995).

Schilling 6.3 – Age 30 – with 3rd team.  Drafted by Boston, traded to Baltimore (July of 1988).  Baltimore traded to Houston (January of 1991).  Traded from Houston to Philadelphia (April of 1992).

Hentgen 5.8 – Age 28 – with original team.

Average age: 27.1 years

3 of 10 pitchers were with original team. 



Lists containing 10 hitters and 10 pitchers are small samples thus it is dangerous to conclude much.  However, there are some data trends that appear interesting.

  1. The number of top pitchers that remained with their original teams was lower than the number of hitters that remained with their original teams. This trend is more pronounced in the 2015-2017 groups. Examining the transactions that led to top pitchers no longer remaining with their original teams during the last three seasons indicated the following – trades prior to a pitcher gaining any, or extremely limited MLB experience (Cory Kluber, Noah Syndergaard, and Carlos Carrasco).  Additionally, established pitchers found on the lists were often traded as part of trade deadline deals (Lester, Greinke, and Cueto) or as part of their original team jettisoning high priced talent during tanking/rebuilding efforts (Verlander and Sale).  Compare this with the pitching WAR leaderboard for 2007 and 1997.  These two lists show a different story of pitcher movement.  In 2007, 7 of 10 pitchers were still with their original team (whereas 7 pitchers on the WAR leaderboard were with their original teams in 2016 and 2017 combined).  The 1997 group is populated by a number of pitchers age 30 or older who left their original teams via free agency.
  2.  Teams appear much less willing to part with offensive talent. In 4 of the 5 seasons examined, more hitters than pitchers remained with their original team. The only year this was not the case was in 2007 when the same number of hitters and pitchers were still with their original ball clubs (7).  The pattern of hitters remaining with their club of origin and pitchers being moved was most extreme in 2017.  Here, 9 of 10 hitters remained with their original team while only 3 pitchers remained with their team.

3a. The current hitter WAR leaders (2015-17) trend younger than their counterparts of 2007 and 1997.  In the last three seasons the average age of the hitter top 10 were 26.7, 26.5, and 26 years while the average age from 2007 and 1997 were 27.9 and 27.8 years.

3b. Conversely, modern WAR pitching leaders trend older than their 1997 and 2007 peers.  The 2017, WAR pitcher leaders had an average age of 29.6, 28.8 in 2016, and 28.6 in 2015.  In 2007, the WAR pitching leader average age was 27.7 and in 1997 it was 27.1.

3c. Same season WAR leaders: The average age of hitters and pitchers on the 2007 and 1997 leader boards are closer than the hitter and pitcher leaders from 2015-2017.

3d. The average age of hitters from the 1997 and 2007 sample is older than the pitcher lists from 97 and 07.  The reverse is true in 2015-2017.  Here, the average pitcher age is significantly older than the average age of the hitter.


Although the samples are small, there is still much to think about.  Some areas I wonder about include:

The impact PED use and current testing has on the leader board demographics.  Possibly related to this, why do older players appear to provide less elite production/value than they did 10 and 20 years ago?

The data indicates that teams are more willing to deal top pitching prospects than hitting prospects.  This may be related to pitching development being more variable/harder to predict than hitting ability, the increased potential for career stalling or career threatening pitcher injury, and the typical aging curve for hitting and pitching development and performance.  Based on the data from the last three years though, it appears that it takes longer for most pitchers to reach elite levels when compared to one or two decades ago.

The impact of free agency and the impact of tanking and rebuilds may be an area worth examining more closely.  The two great motivators for original teams moving elite talent appear to be (1) highly paid player on a team re-tooling and/or stripping assets or (2) players that will be paid more than the original team can afford when the player is a free agent and the team determines that it is better to move the player for future assets than to let the player walk away for nothing.  As teams appear to be more willing to tank in order to rebuild, this may become the most common way an elite talent leaves his original team.

Below is the summary of the Top 10 WAR leaders for each season (age, number of players with original team, and average WAR).



Average age = 26.7 years

9 of 10 players were with their original team.

Average WAR: 7.22


Average Age: 29.6 years

3 pitchers remained with original team.

Average WAR: 6.33




Average age: 26.5 years

8 of 10 players were with their original team.  1 hitter was traded and 1 hitter moved on to his second team via free agency.

Average WAR: 7.68


Average age: 28.8 years

4 of 10 pitchers were with their original team.

Average WAR: 5.73




Average age: 26.0 years

7 of 10 players were with their original team.

Average WAR: 8.0


Average age: 28.6 years

5 of 10 pitchers were with their original team.

Average WAR: 6.68




Average age: 27.9 years

7 of 10 hitters were still with their original team.

Average WAR: 7.74


Average age: 27.7 years

7 of 10 pitchers were with their original team.

Average WAR: 6.29




Average age: 27.8 years

7 of 10 hitters were with their original team.

Average WAR: 8.0


Average age: 27.1 years

3 of 10 pitchers were with original team.

Average WAR: 7.87


Follow me on Twitter: @doctordaver

2017 Baseball – Predictions and Reality

The 2017 season is in the books.  Baseball fans were given a very entertaining regular and post-season.  Although many favored teams heading into the season performed well, there were enough surprises, both good and bad, that kept things interesting.

It is now time to take stock of how I did projecting team wins and which teams would make the playoffs.  Below are my predictions on the left and the actual results on the right for each team.  The number of wins (above or below my prediction are in parentheses).

american league

AL EAST Prediction          AL EAST Results

Boston 91                            Boston 93 (+2)

Toronto 86                          NY Yankees 91 (+9)

Baltimore 86                       Tampa Bay 80 (0)

NY Yankees 82                   Toronto 76 (-10)

Tampa Bay 80                     Baltimore 75 (-11)

Looking back at the pre-season projection – I was right about Boston but they did not have the season I envisioned.  The pitching never got on track and the loss of Ortiz clearly affected the lineup, especially in the power department.  The Yankees arrived a year earlier than many (including me) expected and Judge’s production more than made up for the loss of offense that Greg Bird’s injury appeared ready to inflict upon the offense.  Gregorius had a tremendous season, especially in the power department and the pitching, even with starters going out with injury and Chapman and Betances both struggling at times, held things down well enough to challenge the Sox for the A.L. East crown all the way to the end of the regular season.  I was more bullish on the Orioles than I probably should have been.  I know many projections didn’t like their chances to challenge for a playoff spot but the same projection systems tend to under-rate them year after year.  This was the year the projection systems got things right.  Toronto dug themselves an early season hole and although they played better at times, the damage was done and the 2017 season was lost.  Tampa Bay played as I expected and even a little better than I expected during parts of 2017.  My pre-season projection had them as a last place 80 win team.  The reality was that those 80 wins made them a third place finisher in the division.   

AL CENTRAL Prediction          AL CENTRAL Results

Cleveland 93                               Cleveland 102 (+9)

Kansas City 82                            Minnesota 85 (+15)

Detroit 81                                    Kansas City 80 (-2)

Chicago W.S. 70                         Chicago W.S. 67 (-3)

Minnesota 70                              Detroit 64 (-17)

Cleveland cruised along and then hit the gas blowing away the competition as they headed into the post season.  Making the post season surprised no one but their exit and the hands of the Yankees sure did.  The Twins were a great A.L. Central story.  They had a surprisingly good 2015 (83 wins), went backwards in 2016 (59 wins), and many thought their struggles would continue in 2017.  They started out hot and played good enough ball down the stretch to hold onto the wild card and proved just about every baseball fan living outside of Minnesota wrong.  The 2017 season was not very exciting for the three other AL Central teams.  Detroit bottomed out, the White Sox traded every player being paid over ten bucks, and the Royals treaded water in what may have been their last attempt at a run at the post season for a while (due to the impending free agencies of many of their best/most productive players).  

AL WEST Prediction          AL WEST Results

Houston 90                          Houston 101 (+11)

Seattle 86                             L.A. Angels 80 (+2)

Texas 85                              Seattle 78 (-8)

L.A. Angels 78                    Texas 78 (-7)

Oakland 71                          Oakland 75 (+4)

 The Astros started out cold in 2016 which ultimately sunk their season.  I didn’t think that this would happen again and the team played as I expected them to throughout 2017.  The Astros looked good from beginning to end and were a first place team in the A.L. West the whole year.  The Angels pitchers couldn’t stay healthy which crippled their chances all season long.  The Halos were in the wild card hunt for a while and stayed relevant even when Trout went on the disabled list.  Once Trout returned, there was optimism that the team would make a final push and catch the Twins for the final wild card.  Trout did not perform in his typical superhuman fashion and neither did the team (even with the Justin Upton addition to the outfield).  The Angels ultimately finished 5 games back of the Twins for the last wild card spot.  I thought the 2017 Mariners had an aging core and a suspect pitching staff.  Jerry Dipoto made a number of moves in an attempt to bolster an aging team and keep the Mariners in the playoff discussion.  Although James Paxton took a big leap forward in his development, not much else broke right for the Mariners and they ended 2017 in typical fashion, watching playoff baseball on TV.  The Rangers were a team that vastly outperformed their underlying metrics by many games in 2016.  This may have given fans a false hope for 2017.  Not me.  The team struggled early on (especially the bullpen) and the Rangers were left on the outside looking in at the post season.  Oakland was expected to finish poorly… and they did….again.    


national league

NL EAST Prediction          NL EAST Results

Washington 91                    Washington 97 (+6)

NY Mets 87                         Miami 77 (+2)

Atlanta 76                            Atlanta 72 (-4)

Miami 75                             NY Mets 70 (-17)

Philadelphia 73                   Philadelphia 66 (-7)

I predicted the Nationals and the Mets would beat up on the rest of their N.L. East rivals and I was 50% right.  The Nationals seemed to never break a sweat.  Even with the loss of Adam Eaton (for almost the entire season) and Trea Turner (for a sizable chunk of time), the team just kept abusing the other four teams in the East and ran away with the division. I expected the Mets to continue their winning ways.  The pitching staff looked to be maturing and developing into a dominating machine that could feasibly have more quality starters than they would know what to do with.  Everything broke wrong for the team, especially health.  If not for the Giants in the N.L. West, the Mets would have been the biggest disappointment of 2017.  The Marlins are a strange team.  The pitching staff seemed quite suspect heading into the season.  Conversely, the Stanton, Ozuna, Yelich outfield may be the best overall trio of starting outfielders in the game.  Most baseball fans will tell you that this team sucked in 2017 yet they still managed to finish second in the division.  That tells you all you need to know about the N.L. East in 2017.  The Braves were expected to have a re-building year.  They started off looking OK but quickly fell to Earth as Freeman was injured and Swanson struggled all year.  I was not hopeful about the Phillies chances in 2017 as they remained in perpetual rebuild mode.  It looked like a completely lost year for the team until a few of their in-season call ups caught fire and saved the team the embarrassment of losing 100 or more games in the season (however they kept their streak of 90 plus loss seasons going – it is now at 3 years).   

NL CENTRAL Prediction          NL CENTRAL Results

Chicago C. 100                           Chicago C. 92 (-8)

St. Louis 84                                 Milwaukee 86 (+13)

Pittsburg 82                                 St. Louis 83 (-1)

Milwaukee 73                             Pittsburg 75 (-7)

Cincinnati 65                              Cincinnati 68 (+3)

Entering 2017, I thought the Cubs would be firing on all cylinders and have no World Series hangover.  I was wrong.  The team took a long time to finally get going and didn’t take over first place for what felt like forever.  Even when they took over the division lead, they never “caught fire”.  Although 92 wins is nothing to cry about, this team underachieved.  The Brewers on the other hand overachieved.  What was supposed to be a rebuilding year turned into one of the best story lines.  Like most baseball fans and writers, I sorely underestimated this team.  Although they were not able to hold off the Cubs for the division or the Rockies for the last wild card spot (ultimately 1 victory less than the Rockies), their season has to be seen as major success. It will be interesting to see if their success was a one season wonder or if the front office has a knack for putting together a competitive team much more quickly than many expected.  The Cardinals are always a solid organization and even in a down year, they managed to hang around the playoff race.  Injuries and down years from guys like Piscotty, Diaz, and Grichuk hurt their chances.  The Pirates were another team that I thought could break either way in 2017.  I knew which direction they were going once Marte earned a PED suspension.  I didn’t expect much from the Reds heading into the season.  The team began hot, cooled off quickly, and was irrelevant after the first month or two.  This is a shame because Joey Votto had a terrific season that was largely ignored by most of the baseball world (although he is a MVP finalist).  

NL WEST Prediction          NL WEST Results

L.A. Dodgers 100                 L.A. Dodgers 104 (+4)

San Francisco 87                   Arizona 93 (+23)

Colorado 73                          Colorado 87 (+14)

Arizona 70                            San Diego 71 (+8)

San Diego 63                       San Francisco 64 (-23)

Although the N.L. Central was supposed to be the powerhouse division in the National League, the West was where the best teams resided.  The Dodgers were expected to win a ton of games and during their hot streak it seemed like had a chance to set the all time mark for wins in a season.  However, an equally extreme cold streak brought their win total back in line with many pre-season estimates (although some systems had them projected for around 110 wins).  Arizona completely changed their front office personnel and the payoff was immediate (69 wins in 2016 to 93 in 2017).  Great pitching performances and excellent offense carried this team to a level of success that I was not anticipating.  Colorado was another team I underestimated. All season long I expected their pitching to finally give out and although closing games became a slight issue as Holland struggled towards the end of the season, the team hung on to the final wild card.  The Padres were expected to continue their never ending re-build.  Many of their higher rated prospects started the season with the big league team.  I had them pegged for an extremely poor season and although their season won’t be remembered for generations to come, it wasn’t a complete dumpster fire either.  The Giants’ season on the other hand was a complete dumpster fire.  Bumgarner had an off the field injury that limited him to only 17 starts.  Outside of Bumgarner, no Giants starter with more than 9 starts had an ERA+ over 100.  The team also lost their big free agent acquisition, closer Mark Melancon for most of the year and the bullpen, even when Melancon was closing, was shaky at best.  The team’s decision not to upgrade the outfield hampered the offense as did losing Belt (he played just over 100 games) and a down year from Crawford.  In a nutshell, 2017 SUCKED for the Giants.  I completely missed on this projection as I had them making the playoffs when in reality they ended the season with the worst record in baseball.        

Based on my win total prediction, here were the playoff teams with my picks for the World Series and eventual champion as compared to how things played out:


AL Playoff Representatives Prediction: Cleveland, Houston, Boston, Seattle, Toronto

Actual AL Playoff Representatives: Cleveland, Houston, Boston, N.Y. Yankees, Minnesota

(The 3 division winners were predicted correctly.  The two wild card predictions were incorrect.)

NL Playoff Representatives Prediction: Dodgers, Cubs, Nationals, Mets, Giants

Actual NL Playoff Representatives: Dodgers, Cubs, Nationals, D-Backs, Rockies

(The 3 division winners were predicted correctly.  The two wild card predictions were incorrect.)

World Series: Cubs vs. Cleveland (Zero for two in predicting the World Series representatives from each league).

Champion: Cubs (Nope!)

Entering the 2017 Playoffs, I felt good about my Cleveland pre-season pick.  The only other AL team that I thought had a legitimate chance to knock them out was Houston. It turned out that the Yankees did the Astros’ dirty work for them. 

In the National League I felt less confident with my Cubs pre-season pick as the post season began.  The Cubs never really caught fire at any time during the season.  Some analysts believed that the Cubs played just enough solid baseball to get to the post-season and once there, last year’s juggernaut would reappear.  That did not happen and the Cubs went home courtesy of the Dodgers in the NLCS. 

Unlike the 2017 Cubs, the Dodgers did catch fire.  However, their hot streak occurred after the All-Star break and the momentum they had built evaporated in the final few weeks.  Although a win total that would rival the all-time great teams was not in the cards for them, the Dodgers looked primed for a deep playoff run.  If given a chance to change my pre-season prediction, I would have dumped the Cubs and jumped on the L.A. bandwagon.   

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5 Homers in the World Series: Examining Jackson and Springer

Reggie Jackson set the record for most home runs in a World Series when he launched 5 homers in a 6 game series versus the Dodgers in 1977.  This record held until 2009 when Chase Utley matched the feat against the Yankees in a losing effort.  In this past World Series George Springer tied the mark with 5 of his own against the Dodgers in a seven game series win.  It’s impressive when a post season record stands for forty years, especially when considering the current playoff format.  The current playoff system allows for more games to be played in the post season than the system that was in place years ago.  More games equal more chances.  More chances equal more opportunities to rack up counting stats – and that’s why many post season records fall and why the current post season record book is filled with players from the past 20 years or so.

The 2017 regular season featured unprecedented power as home runs came from the usual suspects like Giancarlo Stanton (59) but also from surprising sources like Francisco Lindor (33).  This trend continued into the post season as homers continued raining from the skies.  The year of the homer culminated with George Springer scaling the mountain of power and planting his flag next to the one Jackson placed four decades prior.

With these two players separating themselves from the countless other power hitters that have appeared post season history, I wanted to take a closer look at the production of each player given the environment of the World Series in which each player accomplished the 5 homer feat.


In 1977, Jackson’s Yankees faced off against the Los Angeles Dodgers.  The Yankees ultimately defeated the Dodgers 4 games to 2.  During the 6 game series, a total of 17 home runs were hit, 5 by Jackson.  This means that Jackson hit 29.47% of all home runs in the World Series that year.

Game by Game Breakdown of Home Runs (1977):

Game 1: Randolph

Game 2: Cey, Yeager, Smith, Garvey

Game 3: Baker

Game 4: Jackson, Lopes

Game 5: Jackson, Munson, Yeager, Smith

Game 6: Jackson (3), Chambliss, Smith



In 2017, Springer’s Astros faced off against the Los Angeles Dodgers.  The Astros defeated the Dodgers 4 games to 3.  During the 7 game series, a total of 25 home runs were hit, 5 by Springer.  This means that Springer hit 20% of all home runs in the World Series in 2017.

Game by Game Breakdown of Home Runs (2017):

Game 1: Bregman, Taylor, Turner

Game 2: Springer, Gonzalez, Altuve, Correa, Pederson, Puig, Seager, Culberson

Game 3: Gurriel

Game 4: Springer, Bregman, Pederson

Game 5: Springer, Gurriel, Altuve, Correa, McCann, Bellinger, Puig

Game 6: Springer, Pederson

Game 7: Springer 


So which performance was more impressive?  It depends on how you evaluate the results.  Here are a few interesting ways to look at the information:

  • Jackson hit his 5 home runs in a six game series while Springer hit his 5 during a seven game series.
  • Jackson hit 3 homers in one game.
  • During his 3 homer game, Jackson hit a home run off three different pitchers.
  • Springer hit a home run in four consecutive games.
  • Springer hit a homerun in 71% of his World Series games while Jackson hit a homer in 50% of his games.
  • Jackson hit 5 of the Yankees’ 9 total home runs (62.5%) in 1977 while Springer hit 5 of the Astros’ 15 total home runs (33%) in 2017.

I’d give the nod to Jackson given the above information.  However, 5 home runs in the World Series no matter the context is an amazing accomplishment and may not be seen by MLB fans for another many years.

Edit – A few readers nicely (and one or two rudely) pointed out that I did not include Utley’s 2009 5 home run performance.  This article was intended to review the 1977 record setting performance by Jackson and compare it to the 2017 5 homer performance of Springer.  I was remiss not to at least mention that Utley tied the record in 2009 so now there is mention of his performance in the opening paragraph.  I hope this clarifies any ambiguity.  Thanks again for reading.

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Can A Starter Save The Day?

The 2017 post season is in full swing.  Throughout the Wild Card games as well as the Divisional Series, teams have been plugging starters into relief roles.  How effective have starting pitchers been when called on to pitch out of the pen?  The results have been mixed.  It appears that for the most part, starters who didn’t fare well were on teams that were eliminated with the exception being the Houston Astros (Verlander, Liriano, and to a lesser extent McCullers whose surface stats don’t look good but advanced stats rated him positively).


Here’s a look at the results of starters working in relief.  Each match up is listed below with the statistical line from each appearance given its own entry.

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Arizona vs. Colorado

Ray 2.1 IP, 2 hits, 1 run, 1 earned run, 0 walks, 3 strikeouts, 3.86 ERA, .088 WPA, .7 RE24

Anderson 1 IP, 2 hits, 2 runs, 2 earned runs, 0 walks, 1 strikeout, 18.00 ERA, -.058 WPA, -1.4 RE24


New York vs. Minnesota

Berrios 3 IP, 5 hits, 3 runs, 3 earned runs, 0 walks, 4 strikeouts, 9.00 ERA, -.0183 WPA, -1.4 RE24



Washington vs. Chicago

Scherzer  1.0 IP  3 hits, 4 runs, 2 earned runs, 1 walk, 1 strikeout, 1 HBP, 3.68 ERA, -.438 WPA, -3.47 RE24


Quintana .2 IP 1 hit, 0 runs, 0 earned runs, 1 walk, 0 strikeouts, 0.00 ERA, -.035 WPA, -.53 RE24

Lester 3.2 IP, 1 hit, 1 run, 1 earned run, 1 walk, 3 strikeouts, 1.86 ERA, .199 WPA, 1.87 RE24


Huston vs. Boston 

Verlander  2.2 IP, 1 hit, 1 run, 1 earned run, 2 walks, 0 strikeouts, 3.12 ERA, -.123 WPA, -.3 RE24

McCullers 3 IP, 3 hits, 2 runs, 2 earned runs, 2 walks, 4 strikeouts, 6.00 ERA .083 WPA, .6 RE24

Liriano .1 IP, 2 hits, 1 run, 1 earned run, 0 walks, 0 strikeouts, 13.50 ERA, -.195 WPA, -1.7 RE24

Liriano .1 IP, 0 hits, 0 runs, 0 earned runs, 0.00 ERA, .004 WPA, .4 RE24


Porcello 1 IP, 0 hits, 0 runs, 0 earned runs, 0.00 ERA, 0.000 WPA, .5 RE24

Price 2.2 IP, 1 hit, 0 runs, 0 earned runs, 1 walk, 2 strikeouts, 0.00 ERA, .110 WPA, 2.5 RE24

Sale 4.2 IP, 4 hits, 2 runs, 2 earned runs, 0 walks, 6 strikeouts, 1 homerun, 8.38 ERA, .120 WPA, 1.5 RE24

  • Price moved to a relief role at the end of the 17 season making 5 relief appearance in September.


Los Angeles vs. Arizona

Maeda 1 IP, 0 hits, 0 runs, 0 earned runs, 0 walks, 2 strikeouts, 0.00 ERA, .100 WPA, .6 RE24

Maeda 1 IP, 0 hits, 0 runs, 0 earned runs, 0 walks, 2 strikeouts, 0.00 ERA, .048 WPA, .5 RE24

  • Maeda made 4 relief appearances during the regular season.


Godley 5 IP, 4 hits, 3 runs, 2 earned runs, 2 walks, 5 strikeouts, 3.60 ERA, -.048 WPA, -.5 RE24


It will be interesting to see how managers utilize available starters going forward in the playoffs.  Will they continue to call on starters who are not part of the playoff rotation or are on a throwing day to work in relief during the Championship Series and World Series?


Glossary of Stats – Source – Baseball Reference

WPA = Win Probably Added — +1 to -1 indicates a full win added or lost.

RE24 = Base Out Runs Saved – Given the bases occupied/out situation, how many runs did the pitcher save in the resulting play.  0 is average, above 0 is better than average, below 0 is worse than average.


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Revisiting Reader Poll MLB Predictions from July


On July 13th, Underthought posted a reader’s poll asking for MLB predictions.  Now that the 2017 season is finished, let’s review the results and compare the votes to the actual outcome:

Who will:

Win the AL East = Red Sox 73% and Yankees at 14%

Win the AL Central = Indians 88% and Twins 7%

Win the AL West = Astros 98%

*The respondents did well picking division winners.  The second place finishers in the East and Central were the Wild Card winners.


Will an AL team win 100 or more games = 80% said yes

*Cleveland ended the season with 102 wins and Huston ended with 101.


Worst AL record will belong to: White Sox 52% and As 27%

*The Tigers ended up with the worst record (64 wins).  The White Sox were a close second with 67 wins.


Win the NL East = Nationals 96%

Win the NL Central = Cubs 51% followed by Brewers 38%

Win the NL West = Dodgers 92%

*The respondents did well picking division winners.  It was interesting how many voters liked the Brewers’ chance to take the Central when votes were cast in mid-July.


Will an NL team win 100 or more games = 73% said yes

*The Dodgers ended the season with a MLB best 104 wins.


Worst NL record will belong to: Phillies 63% and Padres 26%

*The worst record in the NL belonged to the Giants (64 wins).  The Phillies finished with the second worst record (66 wins).


Aaron Judge will hit ___ homers:

40-49 homers: 60%

50-59 homers: 27%

30-39 homers: 12%

*Judge ended the season with 52 homers.


Eric Thames will hit __ homers:

30-39 homers: 72%

23-29 homers: 16%

40-49 homers: 10%
*Thames cooled off significantly as the season progressed and ended with a total of 31 homers.


Will Mike Trout lead the AL in WAR for the 6th straight year:

64% said no

Trout missed a good deal of time with an injury but came back strong.  Although it looked like he would make a strong challenge for the AL WAR lead, he slowed down towards the end of the season.  Trout finished with the 6th highest WAR in the AL (5th if pitchers are excluded from the list) with a Baseball Reference calculated WAR of 6.7.

NL Cy Young Winner:

Scherzer: 48%

Kershaw: 46%

*To be determined but based on the numbers, I wouldn’t be surprised if Scherzer finished first and Kershaw finished second in Cy Young voting.

AL Cy Young Winner:

Sale: 79%

Someone else: 12%

Kluber: 6%

* To be determined but Kluber’s second half performance moved him into strong consideration for this award.  Prediction – Kluber first and Sale second in the AL Cy Young voting.


AL Team that will be in the World Series:

Astros = 67%

Indians = 13%

Red Sox = 10%


NL Team that will be in the World Series:

Dodgers = 59%

Nationals = 21%

Cubs = 8%

Brewers = 6%


The 2017 World Series champion will be:

Astros = 45%

Dodgers = 39%

Indians = 6%

Cubs = 4%

*These four teams appear to be the most popular choices for winning it all as they head into the 2017 post season.  However, it would be interesting to see which team readers would have the most confidence in if they were polled today.

Looking at the Stolen Base in Today’s MLB

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Baseball fans and analysts have been focusing on the increase in homeruns over the past few seasons but there is much less discussion regarding stolen bases during this same time period.  Stolen bases and home runs have a strong relationship to one another, especially as teams have focused on the importance of baserunners and the value baserunners have related to run expectancy.  As teams have become more reliant on the long ball to create runs, it matters less if a runner is on first, second, or third base as a homer brings a player in regardless of the base on which he stood.  Thus, the stolen base has much less value and much greater risk in most situations as being thrown out attempting to steal hurts run expectancy.

2012 was the last season that players successfully stole over 3000 bases (3229, specifically).  2014 was the season with the next most successful attempts ( a mere 2764).  As the number of stolen bases has decreased, the question becomes, are the players who amass the most steals accounting for more, less, or the same percentage of the league’s steal total?  Examining the 2012-2017 seasons, it appears as though the most prolific stealers are accounting for a greater percentage of steals relative to the other players in the game.  Below is a table illustrating the top three stolen base leaders each season and the percentage their total equaled league-wide:

2017: Top 3 finishers accounted for 6.5% of all steals.

2016: The top 3 accounted for 6.5% of all steals.

2015: The top 3 accounted for 6.3% of all steals,

2014: The top 3 accounted for 6.3% of all steals.

2013: The top 3 accounted for 5.3% of all steals.

2012: The top 3 accounted for 4.3% of all steals.

Additionally, fewer players appear to be racking up significant steal totals.  For instance, in 2017, there is currently (as of Aug 17th) one player with at least 50 steals, one player with 40-49 steals, and 1 player with 30-39 steals.  The table below charts the high end totals in steals each season from 2012-2017.

2016: 1 player 60 or more steals, 1 player 50 to 59 steals, 3 players with 40 to 49 steals.

2015: 2 players with 50 to 59 steals and 1 player with 40 to 49 steals.

2014: 1 player with 60 or more steals, 2 players with 50 to 59 steals, and 1 player with 40 to 49 steals.

2013: 1 player with 50 to 59 steals and 7 players with 40 to 49 steals.

2012: 6 players with 40 to 49 steals.

Examining the high end performers in the steal category, it appears as though the overall steal rate for the league results from two separate factors – an increased reliance on totals the top base stealers amass as well as a communal effort with many players ranging from few to modest steal totals.

Gone are the days of the Rickey Henderson and Vince Coleman 100 steal seasons.  Gone also are the days when a multitude of thieves tally 40 and 50 bags in a season.  Rather, in today’s game, steals appear to be the function of two player types; the elite outliers like Dee Gordon and Billy Hamilton (possibly Trea Turner in the near future) and the workman like totals of various players chipping in single digit to low teen totals (Freddie Freeman, Joey Votto, and Justin Upton) to mid-20 bags (Elvis Andrus, Lorenzo Cain, and Mookie Betts).