Category Archives: Team Ranking

Playoff Horse Race Part 3

Now that we’re into the home stretch of the season, let’s look at the Playoff Horse Race once again according to this data model.  The table below is sorted by Total team WAA according to current team rosters.  Total is the sum of Hitters and Pitchers, Pitchers is the sum of Starters and Relief.

Due to expanded rosters distorting relief value, only the top 7 relievers are counted.  The model counts all Hitters and Starters as expanded rosters do not affect those categories as much.  New guys coming from the minors start out at WAA=0.  Teams tend to label high negative pitchers as relief greatly distorting that measure in September.  This won’t be a problem for final playoff rosters nor is it a problem all the other months of a season.

Playoff Horse Race

TeamID W-L Total Hitters Pitchers Starters Relief
BOS 55 49.1 28.5 20.7 11.7 9.0
HOU 38 47.5 16.2 31.3 18.9 12.4
LAN 15 46.5 21.9 24.6 14.9 9.7
ATL 18 36.3 15.5 20.8 11.3 9.4
CLE 18 35.9 19.4 16.5 11.0 5.5
OAK 31 35.2 14.5 20.7 5.4 15.3
NYA 34 34.0 20.8 13.2 4.7 8.4
CHN 26 31.6 12.0 19.6 7.2 12.4
MIL 21 29.3 12.2 17.1 4.5 12.7
SLN 13 28.5 11.6 16.9 11.2 5.7
WAS 1 24.6 10.5 14.1 8.1 6.0
COL 14 21.7 13.3 8.4 -0.4 8.7
ARI 7 18.5 2.7 15.9 6.1 9.8
PHI 5 17.9 3.0 15.0 6.8 8.2
ANA -3 14.6 6.4 8.2 1.0 7.2

The Cubs (CHN) rose in this table from Part 2 of this series due to the new way of counting relief in September rosters.  For brevity the above table only shows the top 15 MLB teams and all playoff contenders are now included as Colorado (COL) climbed back onto this list.

Blue teamids are those leading their divisions.  Green are wild card leaders, and tan are those still in the hunt.  There are no AL teams (other than those with a playoff spot) in the hunt right now.   Playoff teams tend to be buyers at trade deadlines while those not in contention are sellers trading high value players for future prospects.   One would expect playoff teams to top a list like this and they do.

Bold blue in the other columns are the highest among playoff contenders, regular blue second highest in that category.  This should provide an idea of what to expect during various AL and NL playoff matchups.

The Yankees talent still looks low compared to their W-L (WAA) value of +34.  Ironically this is opposite to last year when they had a rather low (for playoff teams) real team WAA=20 yet near the top in team value.  They ended up losing to a better Houston team in game 7 of ALCS.  After manually looking at Yankees roster it looks like only Aaron Judge is missing which would only propel them to around middle of the pack, around where the Cubs are now.

Dodgers and Oakland ranked very high for not being a divisional leader.  Except for the wild card game which is a crap shoot, regular season roster talent is more important than regular season wins and losses.  This model spits out these tables automatically.  There is no way I can discern what caused LAN or OAK to rank so high unless I keep track of every team’s transactions.  One of the purposes of this model is not to have to do that.

There will be one more part to this series using expanded rosters and then a final part using playoff rosters — which usually all come out after the wild card games.  Right now AL looks pretty strong and Cubs in the middle of the pack — again.

Playoff Horse Race  ASG to present

I had been toying with the idea of presenting the above table showing just the second half of the season.    I’m not a fan of streaks because in golf, you can’t just count the back 9 and ignore how you performed on the front 9.  Also past results don’t affect future results, only show capability.  Would it be interesting however to see what teams did only counting the back 9?

What started as something I thought could be simple to jury rig turned complicated and 300 lines of perl code later we get the following.

The real halftime to an MLB season is around the end of June.  The ceremonial halftime is  All Star break.  I chose to use  All Star break date as the start date, and today’s date as  end date.   All Star break give players time off and they may reflect upon their season so far and have epiphanies — like what happen to the Cubs last season.   This can be done because the WAA value measure generated by this model has proven additive properties.

TeamID W-L Total Hitters Pitchers Starters Relief
OAK 18 16.8 7.2 9.5 2.8 6.7
BOS 17 19.9 10.3 9.6 7.8 1.8
TBA 13 14.6 0.1 14.4 6.1 8.3
SLN 11 22.0 11.4 10.6 3.3 7.3
MIL 9 11.2 4.1 7.1 3.0 4.0
HOU 9 12.2 3.2 9.0 5.3 3.7
CLE 9 13.2 5.6 7.7 -1.3 9.0
CHN 9 6.4 -2.2 8.5 3.1 5.5
COL 8 10.6 0.0 10.6 0.1 10.5
ATL 8 15.8 6.3 9.5 3.5 6.0
NYN 6 8.8 1.8 7.0 3.2 3.7
NYA 5 13.1 9.9 3.2 4.0 -0.8
LAN 5 24.0 12.9 11.1 6.2 4.9
WAS 1 9.7 5.6 4.1 2.2 1.9
PIT 0 9.8 -6.2 16.0 7.5 8.5

The above is sorted by the W-L column (WAA) because sorting on Total value using an interval produces deceptive results.  This interval only represents accrued team value not true team value going into the playoffs like shown in the first table.

Edit for clarification 9/17/2018: Accrued team value can occur from getting rid of negative value players, acquiring positive value players through trades or DL activation, and earning it through play.  The above table does not discern this.  The W-L (WAA) column represents  real accrued team value for this interval  that is 100% accurate.  </>

Oakland clearly leads the league in the second half.  Unfortunately for them Houston is in their division so OAK will be stuck playing a wild card crap shoot game.  Boston has been chugging along the second half like their first half.

According to Total roster value Dodgers and Cardinals improved the most during second half as well in the real win/loss columns.  Cubs roster value increased the least among the 15 teams in the above list followed closely by the Yankees.

Since the start date and end date of this delta are two snapshots, it’s possible players on either end could be DL distorting delta value somewhat.  I’m not sure what value the above table provides.  It  uses a reduced dataset which will increase error.

Streaks are funny and many TV announcers like to cherry pick streak intervals to further some narrative.  In the realm of TV and fandom that really doesn’t matter but it’s deceptive, purposely sometimes, if you want a true analysis of the current state of a team or player.

Streaks are used by players thinking they can beat craps, roulette, slot machines, etc. etc. which keep the hotels in places like Las Vegas filled and casino profits high.  They also get people to lose money in the stock market or crypto currency LOL.  Every time you hear JD spout some streak nonsense cover your ears because BS usually follows.

This is why the delta table above may be worthless.  A true measure of a player and a team is a complete season.  More data equal less error and the MLB commissioner does not pick playoff teams based on who won the most games post All Star break.  The above was an interesting illustration and the 300 lines of code to make that table may be useful for other purposes.

That is all for now.  Part 4 of this series in a week or so and then Part 5 will be using official playoff rosters.  The wild card games will be handicapped the old fashion way and then the real playoff season begins.  Until then ….

2018 Playoff Horse Race Part 2

It has been exactly a week since Part 1 of this series.  Let’s look at the current playoff horse race  table.  This table is explained in Part 1.

Playoff Horse Race

TeamID W-L Total Hitters Pitchers Starters Relief
HOU 33 42.4 14.6 27.8 14.8 13.0
BOS 52 37.5 29.1 8.4 3.4 5.0
LAN 13 36.8 16.8 20.0 13.4 6.6
OAK 26 32.8 12.3 20.5 5.2 15.3
ATL 14 30.8 13.2 17.5 10.1 7.4
ARI 11 26.8 2.2 24.5 13.0 11.5
MIL 18 25.9 13.2 12.6 5.0 7.6
CLE 18 25.6 17.2 8.5 10.4 -1.9
SLN 15 25.4 10.3 15.1 12.8 2.2
CHN 24 22.1 13.8 8.4 5.5 2.8
NYA 35 19.4 13.3 6.1 3.6 2.5
PHI 8 17.8 3.8 14.0 8.9 5.1
WAS -1 12.6 7.6 5.0 7.7 -2.7
PIT -3 8.4 1.6 6.8 5.3 1.5
ANA -5 8.3 3.5 4.8 1.6 3.2

I had to manually lookup playoff contenders from baseball-reference and color the above table.

Blue teamids are those leading their divisions.  Green hold a wild card spot and tan are those still in the hunt.  Colorado (COL), who hold a slim lead in the NL West fell off the bottom of this list.  In theory they should hit the skids this month but no one can predict the future.

Not much changed since last week except Cubs dropped below the Cardinals and Brewers.  Roster value can get kind of funny during September with expanded rosters.  Cubs’ relief took a big hit since a couple of high negative pitchers came back.   I am considering making adjustments to how relief is tabulated which would mean recompiling the dataset from which the simulations get their data.

High negative relievers will probably never be called into competitive games.  Thus, their negative value may downgrade a relief staff value more than appropriate.  Perhaps only  counting the top 5 relievers in a staff is appropriate for regular season and count them all of the playoff season.  Might do that for the above table next week.

Doing this, however, introduces bias into this calculation which may not be appropriate.  Expanding rosters in September, however, can radically mess with relief value which is a major factor in simulation.  Thus, it is a source for error.  This cutoff criteria for relief may only be appropriate for September.

It looks like MIL and SLN are only 1.5 games behind the Cubs.  Ruh roh!  :-)   New series starts Friday with CIN.  Brewers are favored today at 54% almost exactly matching TC simulations.  Until then ….

Update 9/6/2018:  I was looking at the wrong table at baseball-reference.  After the Cubs beat Milwaukee yesterday they are 4.0 games ahead of MIL and 4.5 games ahead of SLN.

2018 Playoff Horse Race Part 1

We’re almost a month away from the end of the 2018 season so it’s time again for playoff horse race, a first glance at contender roster value summarized into a single table.   Like last year at this time, the below table ranks team Total value along with how that value is distributed amongst hitters and pitchers.  Pitchers consists of the sum of starters and relief.  The W-L column (real WAA), is the only value the MLB commissioner considers when deciding who goes to the playoffs.

This model doesn’t delve into divisional standings as there are lots other sites that do that and some very well. has an easy to read summary of that which I used to manually color code the first column in the below table.

Playoff Horse Race 8/28/2018

TeamID W-L Total Hitters Pitchers Starters Relief
HOU 31 44.9 15.4 29.4 17.7 11.7
LAN 9 35.5 18.0 17.6 12.3 5.3
OAK 26 33.4 11.8 21.6 7.2 14.4
BOS 48 31.7 25.8 5.9 1.7 4.2
CHN 24 31.0 15.7 15.3 3.7 11.6
ATL 16 29.7 13.1 16.7 10.9 5.7
CLE 18 26.3 17.3 9.0 9.2 -0.1
ARI 13 26.2 6.5 19.8 9.2 10.5
MIL 14 24.8 10.7 14.1 5.6 8.4
SLN 15 21.4 8.1 13.3 10.3 3.0
WAS 0 20.6 9.6 11.1 8.2 2.8
NYA 35 20.4 12.3 8.1 5.2 2.8
PHI 9 17.8 4.8 12.9 9.2 3.7
PIT -3 17.3 5.2 12.1 4.3 7.7
COL 11 11.6 16.3 -4.7 -1.1 -3.5

Last year when we did this the Dodgers had a W-L of +53, five games greater than Red Sox have now.  The above table shows the top 15 teams in MLB ranked by total team value according to who is currently listed on their 25 man roster.   Our source for rosters may lag a few days.  This model lumps AL and NL together as the ultimate prize in baseball is the MLB commissioner trophy.  Only one team gets to bring that home.

Teamids colored blue in the first column are teams currently leading their respective divisions and guaranteed a spot in a divisional series if they can hold on.  Green are those who currently hold a wild card spot and tan are teams in the hunt for one of either a wild card or division lead.

Numbers in bold blue are the high for that column, red the low.  With +48 one would expect Boston to lead in team total value.  Chris Sale is missing from their roster who by himself adds +7.25, value to  Starter, Pitchers, and Total columns.  The above shows their value and how they rank in MLB as of now.  All of this will be in flux during the next month which is why this is called a horse race and we’re now at the final turn.

I was surprised to see the Yankees so low in this list and the Dodgers so high.

34.3 80.7 623 502 70 61 -2.0 4.8 LAN
97.8 57.8 681 527 83 48 -1.0 -0.7 NYA

After perusing their roster manually the Yankees are missing a bunch of top players.  They have the second highest BAT in MLB according to seasonal run differential with only Boston ahead of them.  Judge and Gregorius seem to be missing.  There are some good pitchers missing too so NYA may have an injury problem. This model does not keep track of the DL.  If they come back the above table will be updated to reflect that.

The Dodgers are under performing what their Pythagorean Expectation estimate expects.  PE says they should have a W-L of more than +20, yet their real W-L is only +9.   Since the sum of players on the Dodgers adds to their PE estimate their team value will reflect that.  They also could have made some very positive acquisitions as well.   LAN doesn’t even have a spot in the playoffs right now.  This could all change in September however and the above table will be updated weekly until the final playoff rosters are released.

That is all for now.  An end of month five Cubs status coming Thursday.  Until then ….

Career Rankings Part 4

Today we’ll go back 2 years to the beginning of the 2015 season.  At the beginning of the 2016 season, the season which the Cubs won a World Series, they were ranked middle of the pack of 30 teams

The Cubs were last of 30 teams based upon career data at the start of the 2015 season.  They were probably last, or close to last the last 5 seasons before that too but that’s all water under the bridge now.  How did the Cubs go from last in valuation at the start of a season to making the playoffs that season?  First let’s look at a truncated table showing the top 5 and bottom 5 teams in April of 2015.

April 2015 Team Career Valuation

TeamID Hitters Pitchers Starters Relief Total W-L
DET 51.2 32.2 27.5 4.8 83.5 0
WAS 10.5 67.2 47.1 20.1 77.7 0
ANA 40.7 28.3 12.0 16.4 69.0 0
SEA 22.5 44.7 30.1 14.6 67.2 0
SLN 36.8 26.4 20.2 6.2 63.2 0

TBA -0.4 5.5 -2.3 7.9 5.1 0
PHI -6.4 11.1 2.0 9.1 4.7 0
MIN 16.8 -18.7 -23.4 4.7 -1.9 0
HOU -5.0 -2.2 -13.8 11.6 -7.2 0
CHN -12.6 -9.0 -15.0 6.0 -21.6 0

The above are sum of career value from 2012-2014 of players on each team’s opening day roster for 2015.  Notice how Detroit is #1 in 2015 but this season, right now, they’re at the bottom like the Cubs were in 2015.  HOU is also at the bottom in 2015 and now at the top.  Both these bottom two teams wins a World Series in the next 3 years!  This is quite a switcheroo showing fortunes can change, good and bad, for a team in only a few years.

Let’s see who the Cubs had pitching in April that year.

April 2015 CHN Starters

Rank WAA Name_TeamID Pos
+188+ 3.1 Jon_Lester_CHN SP
XXXXX 2.3 Kyle_Hendricks_CHN SP
XXXXX -1.3 Jason_Hammel_CHN SP
XXXXX -2.3 Travis_Wood_CHN SP
XXXXX -3.4 Jake_Arrieta_CHN SP
XXXXX -13.5 Edwin_Jackson_CHN SP
Total -15.1

Lester was their big off season acquisition and WAA=3.1 was his 2012-2014 split.  The Cubs starting rotation was saddled with Edwin jackson who was one of Theo’s (we’ll spare Jed on that one :) first acquisitions as a Cub.  Just cutting Jackson greatly increases their starter value.  Joe Maddon makes Jackson a reliever and then shortly after they cut ties with him.

April 2015 CHN Relievers

Rank WAA Name_TeamID Pos
+170+ 3.5 Pedro_Strop_CHN RP
XXXXX 2.3 Neil_Ramirez_CHN RP
XXXXX 1.5 Jason_Motte_CHN RP
XXXXX 0.8 Hector_Rondon_CHN RP
XXXXX -0.6 Brian_Schlitter_CHN RP
XXXXX -1.5 Phil_Coke_CHN RP
Total 6.0

Pedro Strop had the highest 3 year split of any Cub starting the season in 2015.

EDIT:  Anthony Rizzo (below) has the highest career 3 year split at the start of 2015.

April 2015 CHN Hitters

Rank WAA Name_TeamID Pos
+120+ 4.9 Anthony_Rizzo_CHN 1B
XXXXX 1.1 Jorge_Soler_CHN RF
XXXXX 0.7 Miguel_Montero_CHN CR
XXXXX 0.5 Dexter_Fowler_CHN CF
XXXXX 0.0 Mike_Olt_CHN BAT
XXXXX -0.3 Matt_Szczur_CHN LF
XXXXX -0.3 Arismendy_Alcantara_CHN BAT
XXXXX -1.1 David_Ross_CHN CR
XXXXX -2.4 Tommy_La_Stella_CHN 2B-3B
XXXXX -3.2 Chris_Coghlan_CHN LF-RF-2B
XXXXX -3.3 Jonathan_Herrera_CHN 2B-3B
XXXXX -4.1 Welington_Castillo_CHN BAT
XXXXX -5.1 Starlin_Castro_CHN SS-2B
Total -12.6

Those are all three year splits and may not be reflective of their overall careers.  These last three tables verify the sums in the total table and show how it was tabulated.  You should not read this that Mike Olt is better than David Ross.   This model measures offensive production and catchers are the most important defensive fielding asset on the field.  They’re involved in every play. This model is limited to showing value derived from generating or not generating runs.  The defensive value of a catcher is outside the scope of this data model.  For more thoughts on defensive related positions see our All Star picks article last July.

Values that hover around 0 are not that meaningful in the context of evaluating a player.  It shows they haven’t done much above or below average.  Sometimes an average player is useful for other purposes a manager may need — like pinch running and being a fast guy in the outfield who can run down errant fly balls late in a close game.  Mike Olt, should be hitting well above average as 3B is usually a productive position on most playoff contending teams.

Kris Bryant comes up from the Iowa later and replaces Mike Olt.  Jake Arrieta starts his Cy Young award winning performance mid June, and Maddon gets everyone to click.

This kind of report will be available for any team any year.  You’ll be able to look up to see how the Cubs or Detroit ranked based upon 3 year career splits at the start of 1935 or 1945 or whatever year.  Once we compile all the years I’ll run some numbers to see how well these rankings predict the end of season results.  As always, past results do not affect future results, they only show capability.  It is important however to have an accurate evaluation of past results.  Much of Sabermetrics is far from accurate.

Enough of this table.  In Part 5 we’ll look at top MLB careers from 1900 – present.  We have all 15,000+ players ranked from top to bottom but we only assign rank to the top 1000.   Until then….

Career Rankings Part 3

Since we don’t have current year data to crunch until next month, career numbers are all we have to look at.  In Part 2 of this series we ranked teams based  upon opening day rosters.  Each career only included last three years which was sum of WAA value for  seasons 2015-2017.  In Part 3 we will look at opening rosters of the 2017 season and use seasons 2014-2016 for each player’s valuation.   Players get categorized as relief, starter, and hitter and everything adds to give a team total.

We have to estimate historical rosters using our daily snapshots taken from event data.  The code was already written to estimate the changing  team relief squads  each day, each year for our lineup/starter/relief simulations.  We take a snapshot on April 12 and assume every player has made an appearance.  Then separate them in their role and team, add them up, and sort.

Since we are from the future when this table could have been made we can predict it.

Note:  Unfortunately there are a lot of numbers in this table and ironically this data model is about consolidating baseball statistics.  We’ll walk though it after the fold.   There is no other way to present this.

TeamID Hitters Pitchers Starters Relief Total W-L
CHN 27.9 89.8 57.6 32.2 117.7 0
TOR 61.2 35.3 23.9 11.4 96.5 0
LAN 25.9 49.1 39.7 9.4 75.0 0
CLE 31.3 43.2 18.4 24.8 74.5 0
NYN 36.7 34.1 28.8 5.2 70.7 0
SFN 19.1 51.1 26.5 24.5 70.2 0
WAS 35.4 33.9 21.9 12.0 69.3 0
BOS 35.5 30.2 25.3 4.9 65.7 0
HOU 23.0 31.9 11.4 20.5 54.8 0
SLN 9.2 35.5 22.5 13.0 44.7 0
TEX 13.3 30.1 10.9 19.2 43.4 0
BAL 26.3 14.2 -14.6 28.8 40.5 0
NYA 5.7 33.5 7.2 26.3 39.2 0
COL 34.9 -3.3 -0.2 -3.0 31.6 0
DET 23.4 7.9 12.1 -4.2 31.3 0
SEA 14.6 16.5 16.6 -0.2 31.1 0
CHA 11.4 19.0 4.5 14.5 30.4 0
OAK 14.4 15.6 1.7 13.9 30.0 0
TBA 0.6 20.0 11.2 8.9 20.7 0
KCA 2.0 17.7 12.0 5.7 19.7 0
PIT 8.0 7.9 4.3 3.6 15.9 0
MIL 6.8 4.1 -5.0 9.1 10.9 0
ARI 17.2 -6.6 6.4 -13.0 10.6 0
MIA -7.5 6.5 -2.3 8.8 -0.9 0
ANA 9.6 -12.1 -2.1 -9.9 -2.5 0
MIN 6.3 -11.2 -6.5 -4.8 -5.0 0
ATL -4.7 -0.5 12.3 -12.8 -5.2 0
CIN -2.3 -4.0 -0.8 -3.2 -6.3 0
PHI -14.6 5.3 -4.7 10.0 -9.3 0
SDN -5.8 -21.5 -21.2 -0.2 -27.3 0

The colored teamids are teams that will make the playoffs this year.  Since we are from the future we know HOU wins the World Series beating LAN with CHN and NYA as DS winners.  NYA and COL are middle of the pack so the top half of this chart picked 8/10 teams who made the playoffs with MIN and  ARI as outliers.

This table is sorted by Total of all career value between 2014-2016 for each team.  The blue bold highlight numbers are the leader in each category.  The Cubs clearly dominate in all categories except hitting.  Hitting will be a big problem for them all the way up to All Star Break.  We know this because we are from the future and we will write about it every day.

Not going to get into what this chart might say or might not say.  SFN turned out to be one of the worst teams in baseball yet they have high value.   The Cubs had a very good above average run between 2014-2016 and they kept the good guys and acquired even more good guys.  Does that mean they had the best team in April?  Apparently not!

In this part we’ll drill down into the Cubs and check their numbers.  This will be streamlined in subsequent parts as we go farther back in time.  First let’s look at CHN starters and relievers.

2017 CHN Starters

Rank WAA Name_TeamID Pos
+004+ 20.6 Jake_Arrieta_CHN SP
+008+ 17.2 Jon_Lester_CHN SP
+034+ 11.1 Kyle_Hendricks_CHN SP
+055+ 8.8 John_Lackey_CHN SP
XXXXX -0.0 Brett_Anderson_CHN SP
Total 57.6

The above Total number is what you see in the Starter column for CHN in the team ranking table above.  The Rank is based upon 2014-2016 career value.  Jake Arrieta had a good run these last three years and is ranked 4th in MLB of all 30 teams, both pitchers and batters ranked together.

2017 CHN Relievers

Rank WAA Name_TeamID Pos
++028++ 11.7 Wade_Davis_CHN RP
++098++ 6.3 Hector_Rondon_CHN RP
++118++ 5.1 Pedro_Strop_CHN RP
++154++ 4.2 Koji_Uehara_CHN RP
XXXXX 2.3 Mike_Montgomery_CHN RP
XXXXX 2.2 Justin_Grimm_CHN RP
XXXXX 0.3 Carl_Edwards_CHN RP
Total 32.1

Wade Davis was the big acquisition in the off season that year.  He turns out to be very useful this season and this relief squad kept the Cubs in contention at All Star Break.

2017 CHN Hitters

Rank WAA Name_TeamID Pos
++018++ 13.9 Anthony_Rizzo_CHN 1B-2B
++027++ 11.8 Kris_Bryant_CHN 3B
++140++ 4.6 Ben_Zobrist_CHN 2B-LF-RF
++172++ 3.6 Kyle_Schwarber_CHN LF
++192++ 3.0 Addison_Russell_CHN SS
XXXXX 0.5 Willson_Contreras_CHN CR
XXXXX 0.2 Matt_Szczur_CHN LF-CF-RF
XXXXX 0.2 Albert_Almora_CHN CF
XXXXX -0.2 Miguel_Montero_CHN CR
XXXXX -0.4 Javier_Baez_CHN 2B-SS
XXXXX -2.3 Jason_Heyward_CHN RF-CF
XXXXX -3.6 Tommy_La_Stella_CHN 2B-3B
Total 27.9

Hitting very good but it becomes a problem first half of the season.  For those reading from the present, below is a post made during All Star Break from this season.  All teams throughout the season move and acquire players.   Career value may not make any sense in the context of April baseball games.

No matter how good a player was the last three years, the MLB Baseball Commissioner requires that he play and prove himself again.  Many players do it over and over for a very long time, many don’t.  In the next part we’ll quickly run through a bunch of opening day roster career value years and then we’ll bring guys like Babe Ruth and Cy Young into the mix and see how well they scored here.   Until then….

Note: I had to double check Heyward’s number above.  He has a very above average career.  His 2016 value dragged him underwater on the three year split (2014-2016).