Archive for July, 2016

The Yankees’ Bad Decisions and How They Can Reverse Them

Before this season, everybody knew that the Yankees wouldn’t exactly be in contention this year.  But nobody could have predicted the extent to which their performance would dip — especially in hitting.  They have gotten an amazing performance from Carlos Beltran (wRC+ of 132), but that’s about it.  Oh, and Beltran has been a complete flop at fielding, managing to accumulate a -10 DRS only halfway into the season.  To offset his defensive issues, the Yankees can’t move him to first base, because he’s blocked there by Mark Teixeira, who’s earning $22.5 million a year.  And it’s not as if Mark Teixeira is earning his fat paycheck, either.  As of of the All-Star break, he has a -1.1 WAR.  So with Teixeira’s $22.5 million paycheck this year and less-than-desirable performance, trading him to make room for Beltran at first base is not an option.

So what about moving Beltran to DH?  Or moving Teixeira to DH and having Beltran play first?  A bit of a problem there.  See, Alex Rodriguez is right now occupying the DH spot.  And he’s earning $20 million this year while hitting .220 with a -0.7 WAR.  And while, theoretically, the Yanks could move Alex over to the hot corner to make room for Beltran, there’s the small problem of Chase Headley, who’s earning $13 million a year.  And while, yes, the Yankees could trade Chase Headley, who holds enough value to be desired by some clubs, nobody in the Yankees front office wants to even think, much less see, this scenario:  Almost 41-year-old Alex Rodriguez bumbling around the hot corner, feebly trying (and failing) to convert routine ground balls into outs.

So Beltran will be staying in right field until the inevitable happens:  one of the many 30-year-olds on the Yankees gets injured.  Some of those those 30-year-olds — Beltran, Texeira, and Rodriguez — combine to have a -0.3 WAR.  That is well below league-average.  Their earnings on the other hand…$57.5 million combined for 2016 alone.  Paying $57.5 million for -0.3 WAR.  However way you look at it, that’s a bad deal.  A really bad deal.  And that’s only three of the 25 people on the Yankees roster.  And you can be sure that the other 23 aren’t a general manager’s dream.  Quite the contrary.  Let’s go position by position and see exactly how horrible the Yankees’ hitters are when compared to their salaries.


Position: Players: Combined Salary: Combined WAR:
Catcher Brian McCann, Austin Romine 17.5 million 1.5
First Base Chris Parmelee, Rob Refsnyder, Ike Davis, Dustin Ackley, Mark Teixeira 27.9 million -1.1
Second Base Starlin Castro 7 million -0.4
Third Base Chase Headley, Ronald Torreyes 13.5 million 1.1
Shortstop Didi Gregorious 2.4 million 1.5
Right Field Carlos Beltran, Benjamin Gamel, Aaron Hicks 16.1 million 0.6
Left Field Brett Gardner 13 million 1.0
Center Field Jacoby Ellsbury 21.1 million 1.4
DH Gary Sanchez, Alex Rodriguez 20.5 million -0.8

So the Yankees’ payroll for hitters alone is $139 million for 2016.  Although that is a big sum — a gigantic sum — it wouldn’t have been noteworthy if the big names had performed and driven the Yanks to a playoff run.  Instead, though, those big names have performed terribly (except for Beltran) and the Yankees have almost no chance of making the postseason.

Right now the MLB is averaging six million dollars per 1 WAR.  That may sound like a lot, but compared to the Yankees it is nothing.  Since it is halfway through the season, their 4.8 combined WAR is 9.6 on a full-season scale.  139 million divided by 9.6 is 14.5.  That means that the Yankees are paying $14.5 million per 1 WAR.  That is more than two times league average.  Although they are overpaying for many players, the big blows come from five players only:  Alex Rodriguez, Mark Teixeira, Carlos Beltran, Brian McCann, and Jacoby Ellsbury.  All these players signed their mega deals after one of, if not the best season in their careers.  Except for Beltran, who got a three-year deal, all these players signed deals for five or more seasons.  Here is the rundown on their salaries.

Alex Rodriguez:  Alex’s deal is probably the stupidest of all other Yankees deals in history.  He was signed to a 10-year deal with the Rangers in 2001, and was traded to the Yankees in 2004.  His contract would then expire after the 2010 season, when he would be 35 years old.  But the Yankees, for some reason, decided to renew his contract two years before it expired, in 2008.  If the Yankees had signed him to a new five-year deal, that would not have been too bad.  But instead, the Yankees signed him to another 10-year deal worth 275 million dollars, $25 million more than his former deal.  So now he is signed through the 2017 season, when he will be 43 years old.  If the Yankees would have only agreed to let A-Rod go after the 2010 season, they would have avoided all the bad/OK years of his career, which, incidentally, started in 2011.

Mark Teixeira:  In 2009, the Yankees signed Mark Teixeira, who was coming off of a 6.9 WAR season, to an eight-year, 180-million-dollar deal.  To be fair, it was not a bad signing for the Yanks.  Teixeira was 29 in his first year as a Yankee, and got a 142 WRC+ while accumulating a 5.1 WAR.  Then the next year he dipped to a still-respectable 3.4 WAR.  But he was on a downwards path.  After one final good year in 2011, he slowly declined into what he is now: an expensive waste of a perfectly good roster spot.  But don’t condemn the Yankees for that.  Yes, they probably slightly overpaid for a .250 average/30 HR first baseman, but it wasn’t a horrible signing.  What was bad about it was the deal itself.  Not the money involved or the years.  The reason.  Why did the Yankees need a first baseman?  The year before the deal, 2008, Jason Giambi hit 32 homers and had a 131 wRC+ at first.  Yes, his deal was up after the season, but the Yankees could have easily re-signed Giambi without having to pay him $180 million.  So the Yankees didn’t need Teixeira.  They just wanted him.  And that is the same trap they’ve fallen into ever since the dawn of free agency.

Carlos Beltran:  The Yankees signed Beltran to a three-year deal worth $45 million in 2014.  At the time, he was 36 and coming off a good season with the Cardinals.  In fact, it was a great season — hitting-wise.  At defense, there is no way around it.  He was simply terrible.  He made almost all of the plays he got to, but he didn’t get to many.  He couldn’t run fast if you pointed a gun at him.  And somehow, for some reason, the Yankees expected him to play outfield for three more seasons — until he was 39.  And guess what?  It hasn’t worked out too well.  His hitting has been very good, but that hitting value has been stripped from him by his terrible fielding.

Brian McCann:  In 2014, the Yankees signed Brian McCann to a five-year deal worth $85 million.  At the time, it seemed like a good deal; a catcher who could hit well, signed for only $17 million a year.  In any other circumstance, that would be considered a good deal.  A great deal even.  But there was one problem.  It was a 30-year-old catcher they signed for five years.  A 30-year-old catcher who most likely wouldn’t survive two more years crouching behind the plate every inning for 140 games a year.  So for two years, they Yankees got a good deal.  But this year is the third year of the deal.  And surprise, surprise, your 32 1/2-year-old catcher is not performing too well behind the plate.  -6 DRS there.  And, frankly, his hitting is just not good enough to compensate the bad fielding behind the plate.

Jacoby Ellsbury:  In 2014 the Yankees gave Jacoby Ellsbury a 153-million-dollar, seven-year deal.  Ellsbury, who was 30 years old in 2014 and had a history of getting injured, was coming off of a 5.0 WAR season.  But that was mostly due to his well above-average speed.  He used it to his advantage on the basepaths and in the outfield.  All that is fine and good, but there is one problem:  Speed is the first tool to disappear from a player’s repertoire because of age.  And the Yankees’ deal with Ellsbury started when he was 30.  And after a 39-steal year for the first year of the deal, Ellsbury unsurprisingly swiped only 20 bags in the second year.  He used to consistently have 10 DRS every season; now, with the loss of his speed, that 10 has turned into zero.  And aside from steals and defense, Ellsbury doesn’t hold much value.  He hits about five homers a season, and is good for a .280 average.  And five homers, a .280 average and 20 steals is not worth 21 million dollars a year.

So where do the Yankees go from here?  Beltran, Teixeira, and Rodriguez’s contracts will all end this year or the next.  Then they are stuck with only Ellsbury’s and McCann’s.  McCann’s expires in 2018, and, realistically, the Yankees can deal with $17 million a year for two more years.  And with the way Ellsbury’s been playing this year, the Yankees can easily trade him for a small prospect and pay half of his remaining contract.  So if they trade Ellsbury the Yankees will be left with an (almost) clean slate at the end of this year.  How do they fill it?  Here are some suggestions on what and what not to do.

1.  Stay away from pricey free agents ages 31+.

2. Make sure not to sign any player who will be 37+ at any point during the deal.

3. Pay attention to the draft.  For the next few years, the Yankees won’t be very good, so they should make use of their high draft picks and start developing prospects, rather than just buying overpriced free agents.

4.  Only buy value-high, salary-low free agents, i.e. Ben Zobrist.

5.  Stay away from deals spanning eight years or longer.

Let’s get more in-depth with these five bullet points.  Oh, yeah, there’s a sixth:

6.  Get a new G.M.

So let’s get more in-depth with these six bullet points.  1. Stay away from pricey free agents ages 31+:  This rule should be one the Yankees know well by now.  After breaking this rule many times with no good results, this rule should be a relatively easy one for the Yanks to swallow.  Remember, the rule states “pricey free agents,” so that doesn’t include older players (35-36; as you will see in the next bullet, signing Jamie Moyer is forbidden) who still retain some value and can be signed for cheap.

2.  Make sure not to sign any player who will be 37+ at any point during the deal:  It doesn’t matter if this deal is for three years or for 11.  The message is clear:  older players are at higher risk of either sharply declining or getting injured.

3.  Pay attention to the draft:  For most of their history, the Yankees lived in an era of no free agents, so they were able to rip the poor teams of their great prospects with the promise of good money.  Now, when almost every team has enough money, and those who don’t (Rays, Astros, Marlins, Pirates) are smart enough so that they won’t give away their prospects, this strategy is much harder.  So the Yankees switched their focus to high-priced free agents.  This new “strategy” has had its ups and downs.  Most of the ups came earlier, when teams didn’t try to retain their stars after the six years of cost-control.  Now, with many stars (Stanton, Strasburg) being offered luxurious extensions by their teams, most of the talent never hits the free-agent market until much later, when it is not worth much.  So, with extreme reluctance, the Yankees must turn their attention to the draft, an event they have somewhat ignored over the past years.  Although they do make an effort to sign players and do draft players with good potential, they have not made a real effort to dig deep and find hidden gems.  Remember, Mike Piazza was drafted in the 62nd round.  And furthermore, they must not be tempted to trade away these hidden gems they worked so hard to get in return for a major-league player with not half the talent as the prospect.

4.  Only buy high-value, low-salary free-agents:  In years past, this strategy would have worked wonders for the worst team in the league who has a small budget.  Imagine:  who would sign a .272 hitter with 10 homers to a 56-million-dollar contract 10 years ago?  Almost nobody.  But just this year, Ben Zobrist received that contract.  And according to WAR, he should have received more.  Here is a list of smart free agents for after the 2016 season:

Catcher:  There are three good catchers eligible for free agency after the 2016 season:  Jonathan Lucroy, Wilson Ramos, and Matt Wieters.  Out of the three, Jonathan Lucroy and Matt Wieters will most likely be the most wanted.  So that leaves Wilson Ramos.  He is 29 and a solid backstop with hitting potential.  Smart buy:  Wilson Ramos

First Base:  There are actually no standout smart buys at first base.  Justin Smoak, Carlos Santana, and Sean Rodriguez are all options.  The one who has the most value when compared the the estimated price, though, is Sean Rodriguez.  He is also one of the youngest first baseman of all free agents.  Smart buy:  Sean Rodriguez.

Second Base:  Most of the second base free agents next year are way above our target age.  The few that are in our age range are Gordon Beckham, Chris Coghlan, Daniel Descalso, and Neil Walker.  We can safely say that Gordon Beckham and Daniel Descalso are off the list, simply because they don’t provide the value to be a smart buy.  Neil Walker’s price will have shot way up after the amazing campaign he is having this year, so that leaves us with Chris Coghlan.  Chris, who is 32, holds loads of value as he can play second base as well as corner outfield positions.  He is also having one of the worst seasons of his career as of now, so he will be really cheap come the season’s end.  Smart buy:  Chris Coghlan.

Third Base:  At third there are only a few free agents in the Yankees’ age range.  They are Luis Valbuena, Justin Turner, and Martin Prado.  Luis Valbuena is eliminated, because he is too big and awkward to stick at third base.  Somewhere in his near future he will be transitioned to first base.  So that leaves us with Justin Turner and Martin Prado.  These are both good value picks, but Justin Turner must be eliminated.  He will be way too expensive, two years removed from the best season of his life (so far) and part of a playoff contending team.  Martin Prado is our smart buy for third base.  He has been amazingly consistent his whole career, and coming from the Marlins, his price tag will be relatively low.  Smart buy:  Martin Prado.

Shortstop:  There are only four shortstops available after the season ends, and three of them fit the basic criteria:  Alcides Escobar, Erick Aybar, and Ruben Tejada.  Ruben Tejada is the first elimination, as he does not have enough experience in the big leagues to validate his performance.  Alcides Escobar also must go, because he is most likely going to be re-signed by KC.  And even if he is not, his price will be driven up by their bids.  That leaves Erick Aybar.  He is consistent, and hardly ever injured.  He is also mired in a huge slump right now, which will significantly drive down his price.  Smart buy:  Erick Aybar.

Right Field:  There is simply no other competition for smart buy.  Josh Reddick has amazing defense in right, can hit very well, and is only 30 years old.  He is also playing for the obscure Athletics right now, which will drive down his price.  Smart buy:  Josh Reddick.

Left Field:  There are so many standout left fielders going into the 2016-2017 free agency that they will all drive down the price of each other.  That will allow the smart buy to be a big player.  The big left field names are Michael Saunders, Matt Holliday, Ian Desmond, and Yoenis Cespedes.  Matt Holliday is too old, so he’s out.  Yoenis Cespedes is too fluky, and can be injury-prone, so he’s also out.  That leaves us with Ian Desmond and Michael Saunders.  Both of these players are having breakout seasons so far.  Ian Desmond offers more flexibility in the field, as he can play shortstop, second base, and all the outfield positions, including center.  Michael Saunders might be a little cheaper, but it is hard to tell.  It was close but Ian Desmond is our smart buy for left field.  Smart buy:  Ian Desmond.

Center Field:  There are three very good possibilities:  Carlos Gomez, Dexter Fowler, and Austin Jackson.  Dexter Fowler would be a very good pick almost any other season, but he is having a breakout year so far for the Cubs, so he’s out.  Austin Jackson, on the other hand, is having one of his worst seasons ever.  The problem with him is that he doesn’t seem capable of ever making a return to the player he used to be.  He is coming off three straight seasons in which he failed to hit higher than .270.  So Carlos Gomez it is.  He has struggled mightily with the Astros, but that is probably just the effect of playing in a huge ballpark rather than in hitter-friendly Milwaukee.  Smart buy:  Carlos Gomez.

DH:  With many players to choose from, the only player that really catches the smart buyer’s eye is Pedro Alvarez.  He hasn’t found much playing time with power-packed Baltimore, so that will bring down his value significantly.  Smart buy:  Pedro Alvarez.

Starting Pitchers:  With so many choose from, there will be five smart buys for starting pitchers.  There are many soon-to-be free agent starting pitchers ages 28-31.  Smart buy #1 (note:  smart buy number does not imply any greater value for pitcher):  Brett Anderson.  Coming off an injury but with many years of experience with him, Brett Anderson is a great pick for any team.  Smart buy #2:  Jaime Garcia.  So far he has been OK this season, but very consistent.  A very good pick for any team looking for a cheap starting pitcher with a high ceiling.  Smart buy #3:  Jeremy Hellickson.  Hellickson has never had a horrible year in his career.  Although he did have one 5.00 E.R.A. year, he still had a positive WAR.  And aside from that season, he has been pretty good, but not good enough to warrant a big contract.  Smart buy #4:  Matt Moore.  Moore is a dependable, extremely young left-hander.  In fact, he is one of the youngest starting pitchers on the market for next year.  Smart Buy #5:  Ivan Nova.  Although he’s had a rough year so far, you have to love the potential!  He is only 30 years old, and best of all, he’s on the Yankees right now, so they have easy access to him.

Those are all the smart buys.  I am not suggesting that the Yankees sign every single one of those players, but three of four of them wouldn’t hurt.  In fact, they would most likely help the Yankees turn their club around quickly — much quicker than anyone projected them to.

5.  Stay away from deals spanning eight years or longer:  This rule will help prevent the Mark Teixeira deals, the Alex Rodriguez deals, and the Jacoby Ellsbury deals.  This way, if a player is signed for five years, and only performs well for three years of the deal, the Yankees only have to deal with two bad years.

6.  Get a new G.M:  Brian Cashman simply hasn’t gotten it done.  He was given a good team with an unlimited budget and has turned it into one of the worst clubs in baseball.

Hopefully, the Yankees will use their bad experiences to their advantage and become one of the smarter teams in baseball, a la the Astros, Rays, and Pirates.  With the help of these rules and suggestions, they can become the most dangerous team in the MLB, with money and smarts.

Park Factors to (Maybe) Monitor

Every baseball stadium is different.  This is an obvious fact, but its obviousness can obscure its importance.  Every baseball stadium is different, so baseball is different in every stadium.  Some of these differences are easy to discern such as HRs in Denver and Cincinnati.  Others though are more easily masked — did you know that the White Sox’ U.S. Cellular Field raises walks by 7%?  Each game is a combination of outcomes affected by each team’s talent and, to a lesser extent, these park factors.  FanGraphs is nice enough to publish its park factors here.

With the league-wide increase in exit velocity and home runs, I was interested to know if any park factors may be changing as well.  With roughly half of the 2016 season in the books, I thought now was as good a time as any to take a look.  Rather than go through the laborious calculations necessary to find park factors like those at FanGraphs, I came up with a quick and not at all exact way to look at just this season.  Essentially, I found each team’s home and away rates of 1B, 2B, 3B, HR, SO and BB per plate appearance.  I then compared each to league average on the same scale as wRC+ (100 is average).  I then calculated a quick park factor on the same scale for each of the above stats as follows (1B factor shown below):

((Team Home 1B Rate – (Team Away 1B Rate – 100)) + 100) / 2 = 1B Park Factor

For example, the Marlins have hit 4% more singles than average at home (104 1B+), and 27% more singles than average on the road (127 1B+), so the Marlins Park 1B park factor would be 88 (depresses singles by 12%).

I am fully aware of the many problems with the methodology (ignores half of the data, small sample, not enough regression included, team road schedules aren’t guaranteed to have average park factors, etc.), but like I said, I wanted something quick, and I am only focused on the extremes anyway.  This should at least show us which parks to consider monitoring or examining further.

2015 FanGraphs vs. 2016 Observed Park Factors
2015 FanGraphs 2016 Observed
Team 1B 2B 3B HR SO BB Team 1B 2B 3B HR SO BB
Angels 100 96 91 93 102 97 Angels 98 87 80 105 101 103
Astros 99 100 108 105 103 101 Astros 93 103 138 101 104 102
Athletics 99 100 105 93 97 101 Athletics 97 97 145 90 98 94
Blue Jays 97 108 105 106 102 99 Blue Jays 107 116 74 90 103 102
Braves 100 99 93 96 103 101 Braves 106 85 125 94 99 102
Brewers 99 100 102 112 101 102 Brewers 95 106 131 113 98 104
Cardinals 100 99 95 94 98 99 Cardinals 101 104 42 88 96 98
Cubs 99 99 102 102 101 102 Cubs 96 84 105 100 98 111
Diamondbacks 99 99 102 100 98 111 Diamondbacks 99 105 120 102 100 99
Dodgers 98 98 78 102 100 96 Dodgers 98 91 69 116 98 101
Giants 99 97 115 84 100 100 Giants 103 97 163 83 100 109
Indians 100 103 81 101 101 99 Indians 109 121 21 105 93 120
Mariners 98 87 85 98 102 97 Mariners 96 96 92 108 97 108
Marlins 101 100 117 88 98 101 Marlins 88 109 42 102 99 102
Mets 96 95 87 101 101 100 Mets 98 86 80 108 98 111
Nationals 104 102 84 97 97 98 Nationals 104 90 70 98 93 109
Orioles 101 99 86 108 99 100 Orioles 103 93 118 105 89 109
Padres 98 95 97 98 102 101 Padres 99 98 100 94 96 102
Phillies 98 99 92 107 103 102 Phillies 93 87 128 94 104 104
Pirates 101 99 89 90 96 96 Pirates 106 88 157 101 92 110
Rangers 103 101 110 105 98 102 Rangers 106 105 153 86 95 113
Rays 99 95 98 96 102 100 Rays 99 99 98 84 105 95
Red Sox 103 114 105 96 100 100 Red Sox 102 123 90 87 94 109
Reds 99 98 92 113 103 101 Reds 97 94 100 121 102 99
Rockies 110 108 128 113 95 102 Rockies 103 134 170 109 86 114
Royals 101 103 114 93 96 99 Royals 104 113 141 100 90 106
Tigers 101 98 126 98 95 99 Tigers 105 97 135 105 96 105
Twins 102 101 106 98 98 99 Twins 105 101 171 86 87 98
White Sox 99 97 91 108 103 107 White Sox 100 99 86 108 97 107
Yankees 100 97 84 110 101 101 Yankees 94 102 86 120 97 116
Data pulled at All-Star Break

I know that is a lot to digest, and I apologize it is not sortable due to my lack of coding skill — but there are some interesting differences buried in that table.

1B Park Factor

Two parks stick out at the extreme ends for singles.  The aforementioned Marlins Park went from slightly single-friendly to the worst park for singles.  I don’t have a good explanation for this, though the fences were moved in prior to this season which we would expect to set off a ripple affect with the park factors.  The Blue Jays’ Rogers Centre went the opposite direction of the Marlins, showing a move from slightly below-average for singles to the second-best park for singles.  The Jays did change to a dirt infield from turf for 2016, but I would expect that to decrease 1Bs rather than increase them.  Maybe dirt slows infielders down giving them less range?  The Jays have recorded more infield and bunt hits at home than on the road as well, which would increase singles.

2B Park Factor

Coors Field has seen a marked increase in doubles (and triples) in 2016 with a small decrease in HRs, which is very interesting considering they raised several areas of the outfield walls.  The Cubs, Braves, Nationals, Phillies and Pirates have all seen at least a 10-point decrease in 2Bs.  Of that group, the Braves, Phillies and Pirates seem to have traded those doubles for triples which I wouldn’t necessarily expect to hold up as a change in the park factor given the limited samples.  The Phillies also made a change to a longer-cut grass, so a decrease in 1Bs and 2Bs makes some sense.  I am not sure what is going on in Chicago (wind patterns?) and Washington as the decrease in doubles does not seem to be offset by an increase in other similar batted balls.

3B Park Factor

As expected with the extremely limited number of triples, there is a ton of variation across the half-season sample.  The two most likely to represent a true change to the park factors in my mind are the decrease in triples in Marlins Park (moved fences in) and the increase in triples at Coors Field (raised fences), though both likely won’t hold up to this magnitude.

HR Park Factor

There have been large and unexpected decreases in home runs in Toronto and Texas, while the Marlins and Dodgers have seen upticks in homers at home.  Probably nothing but small-sample noise here.  It will be worth checking more rigorously to see if these hold up, particularly at Marlins Park given the change to the fences.

Strikeout and Walk Park Factors

Given the way I have calculated each component park factor, I expected all of them to need an adjustment for home-field advantage.  Interestingly, that was not the case for 1Bs, 2Bs and HRs as the average observed park factor for each was 100 across the league.  I wrote off the 108 average observed 3B factor as small-sample noise, but I believe I picked up some measure of home-field advantage in strikeouts and walks.  On average across the league, home parks decreased strikeouts by 3% and increased walks by 5%.  These have been regressed and the samples for each are among the largest of the component park factors (more PAs end in a K than any specific batted-ball outcome, and there are more BBs than anything except 1Bs), so it feels like this reflects something.

The extreme parks for changes in strikeouts are the Twins’ Target Field and Diamondbacks’ Chase Field.  Adjusting for the home-field difference (the unadjusted numbers are shown in the table above), the Twins’ park seems to be decreasing strikeouts by about 8% more than usual, while the Diamondbacks’ stadium is increasing Ks by 8% more than FG expects.  The Twins did make a change to their CF seating that could be affecting the hitters’ ability to pick up pitches (and thus strike out less), but if that is the case an increase in walks would also be expected — and that is not the case, as the Twins have actually walked less than expected when including the home-field adjustment.

For changes in BBs (after adjusting for home field), the parks in Oakland and Cleveland stick out.  The Coliseum has allowed 12% less walks than expected, while the Indians’ Progressive Field has inflated walks by 16%.  These may be worth exploring as both parks have also affected strikeouts, with the A’s park increasing strikeouts and the Indians’ park decreasing Ks.  It is possible hitters are not picking up the ball in Oakland while they are seeing it well in Cleveland.


So there you have it.  Noisy, likely inaccurate 2016 park factors.  It will be very interesting to see if any of the observed changes detailed above turn out to reflect a true change in the park factors.  My best guess is Colorado, Miami and Toronto will need some type of adjustment from the 2015 park factors given the fairly significant changes to each park debuting in 2016.  It would be fascinating to hear thoughts from the players on the extreme differences found above as well.  The fact that each park is so different is part of baseball’s appeal to me.  Every game really is totally unique, all the way down to the field itself.

David Price Is About to Go Off

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On June 25, this was David Price’s tweet to family, friends and fans.  It was a clear signal that he knew the patience of the Boston fans and media was wearing thin.

Fast forward to the All-Star break and his “Made for TV” stats (those that casual fans know best) are underwhelming: a 9-6 record with a 4.34 ERA, which is worse than the MLB average of 4.23.  It’s not so much his ERA that’s the problem to fans, but more his inability to be consistent from start to start.  Price has three starts of six-plus innings allowing two or fewer runs, but also has four starts of allowing six or more runs.  With the rest of the rotation producing an atrocious 4.86 ERA, the Sox desperately needed Price to be the one to stop the bleeding, something he hasn’t been able to do.  But that doesn’t mean his underlying skills have deteriorated and all of a sudden he’s become a league-average pitcher.  In fact, the advanced metrics say he’s been extremely unlucky and that he’s due for a big second half. 

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* Rank is solely being used to establish a baseline for Price as a top 10 pitcher.

In 2014 and 2015 combined, Price was ranked in the top 10 of all pitchers in four of the skill-based statistics: K%, BB%, xFIP and SIERA (the latter two being ERA estimators with a weighting towards more pitcher-controlled outcomes).  Through the 2016 All-Star break, Price has maintained or improved his top-10 rank in K%, xFIP and SIERA but dropped a few spots in walk rate.  Despite the move from 9th to 10th in K% rank, his K rate is actually up from 26.2% to 27.1%.  The reason for the drop in rank is that 2016 newcomers to the list Jose Fernandez, Noah Syndergaard and Drew Pomeranz did not meet the minimum innings qualifier for the 2014/2015 combined list.  On the flip side, Price’s xFIP and SIERA are higher than they were the past two years, but he has improved his ranking versus his peers.  This is because xFIPs and SIERAs are both up 10% league-wide versus last year (due to all the home runs being hit) while Price’s increases are smaller.

So what is happening?  If his base skills are fine, why is his ERA so high and his performance so inconsistent?

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So everyone is familiar with ERA and can easily infer that 4.34 is no bueno for a $217-million pitcher.  But there is a reason these stats are labeled “Non Skill-Based” — that’s because these stats are influenced by factors outside of the pitcher’s direct control (defense, luck, sequencing, variance, etc…) and therefore have wide variability over small samples.  Three of these stats (HR/FB%, BABIP and LOB%) explain why David Price is a great rebound candidate for the second half.


Price’s current HR/FB (home runs per fly ball) rate is 15.2% — which is good for being ranked 76th out of 97 qualified starting pitchers.  The past two years combined he ranked 19th.  To put this in context, Price’s career average is 9.4% while the 2016 league average is 12.9%.  Price has never recorded a full season (>150 IP) HR/FB rate higher than 10.5%.  Also, on balls hit into play against Price this year, 31.3% of them are fly balls, the second-lowest rate of his career.  The only season in which he allowed a lower fly ball rate was in 2012 when he won the AL Cy Young award.  Price is giving up fewer fly balls this year, but of the fly balls he is allowing, they are going over the fence at the highest rate of his career.  Those that remember Price giving up a HR in 10 consecutive starts this year are nodding violently right now.  His HR/FB% will regress towards his career norm (9.4%) and this should be the main reason for a big second half.


Price is also suffering from an unsustainable BABIP (batting average on balls in play).  His current mark of .321 is well above his career rate (.289) and even above his highest full-season rate (.306).  Once a ball is put into play it is out of the pitcher’s control what happens from there.  This is why defense and luck influence this stat more than skill.  And with that said, statistical outliers here tend to regress towards career norms.  Even though Price is allowing ground balls at a higher rate than the past two years, his 2016 GB% is still lower than his career average.  BABIP can be influenced by the number of ground balls a pitcher allows, but he’s not allowing vastly more than his career average.  His BABIP should have some positive regression in it, which is another predictor of improved second-half performance.


Price’s Left-On-Base% (percentage of runners a pitcher strands over the course of a season) is currently 70.9%, which is also below his career rate (74.7%) and would be his second worst full-season rate (70.0%) if the season ended today.  Similar to HR/FB%, he is ranked 73rd out of 97 qualified starting pitchers.  The past two years he ranked 22nd.  A pitcher with a higher than average strikeout rate should be able to sustain a slightly higher than average LOB%, but it’s playing out the exact opposite way for Price.  This is partly due to his inflated BABIP and HR/FB%; as these statistics continue to regress towards his career norms, the LOB% will creep up to expected levels.

Much has been made of Price’s velocity being down this year compared to any point in his career.  At the start of the season, his velocity was over 2.0 MPH lower than his career average (94.1).  He has since closed this gap almost entirely.  Here is his average fastball velocity by month (with number of starts):

April: 92.0 (5)

May: 92.5 (6)

June: 92.9 (6)

July: 94.0 (2)

If this upward trend in velocity stabilizes somewhere at or above 93.5, then nearly all the performance metrics within his control — velocity, K%, BB%, xFIP and SIERA — will be at or near his career norms.

Let’s dive a little deeper into that early-season velocity issue.  Below are two charts.  The first shows combined performance of 2014 and 2015 for ERA-qualifying starters while the second chart is the same data for the 2016 season through the All-Star break.  The orange circle is David Price.  The red circle (if shown) represents Price’s career average.  The blue circles are a hand selected peer group of the top 10 pitchers in the game (Kershaw, Sale, Arrieta, Scherzer, Bumgarner, Greinke, Strasburg, Syndergaard, Salazar and Fernandez).  Remember those rankings where Price was right around the top 10 — these are the guys usually outperforming him.  The gray circles represent everyone else.  Note: For these first two charts the top-right quadrant is Good, and the bottom-left quadrant is Bad (unless you’re a knuckleballer).

2014-2015 K/9 vs FBv

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2016 K/9 vs FBv

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The first graph shows David Price clustered where you would expect him — right at the middle-to-bottom of his top-10 peer group, with a healthy average fastball velocity and K/9.  The second graph (2016) shows Price in a similar relationship to his peers, but with slightly lower velocity and a higher K/9.  Note the gap between the orange (Price’s 2016) and red (Price’s career average) dots depicting his improved strikeout numbers this year despite the slightly lower velocity.  This graph also shows what freaks Noah Syndergaard, Jose Fernandez and (to a lesser degree) Jered Weaver are.

The final two graphs show the relationship between ERA and xFIP where xFIP is the more predictive estimator of a pitcher’s skill.  The bottom-left quadrant is Good (think Kershaw) and the upper-right quadrant is Bad (think Buchholz).  Anyone in the upper-left quadrant (Price in 2016) is a candidate for positive regression.

2014-2015 ERA vs xFIP

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2016 ERA vs xFIP

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The first graph again shows Price in his usual place — at the tail end of the top 10.  In 2014 and 2015 combined he had a very similar ERA (2.88) and xFIP (2.98).  The second graph (2016) shows the disparity between his ERA (4.34) and xFIP (3.16).  Pitchers with this large of a gap between ERA and xFIP are great candidates for regression.  The important takeaway is that his xFIP, relative to his peers, has stayed in that top-10 range.  This supports the point that some bad luck is the main element depressing his ERA.

David Price can easily be the best pitcher in the American League over the next two and a half months.  He already owns the lowest xFIP in the AL at 3.16 — the next-closest is Corey Kluber, at 3.34.  The skills above show he can sustain the xFIP level, but with some change in luck and maintaining his improved velocity, he doesn’t need to “pitch better”; he just needs to keep pitching — and the results will follow.

Can First-Half (x)FIP Predict Second-Half ERA?

This article was originally published on Check Down Sports

Predictions are hard. Getting them right is harder. But everyone loves them, so I’m going to attempt to predict which starting pitchers will improve in the second half of the season, and which are poised to put up worse numbers. This information may be especially helpful for a GM thinking about acquiring a pitcher before the trade deadline, or, maybe more applicably, a fantasy owner trying to surge his team into playoff position.

How do you exactly predict starting-pitcher performance in MLB? Well, it’s pretty commonly known among baseball-thinkers that FIP is more accurate at predicting a subsequent year’s ERA than ERA itself. FIP is a statistic on an ERA-scale that only accounts for what the pitcher can control (strikeouts, walks, and home runs). There’s been a lot of research that looks at differences between ERA and FIP, but to my knowledge, there’s nothing out there to see if it can predict second-half performance. So that’s what I’m going to do here.

I compiled all the starting pitchers who were qualified in both the first and second halves of 2015 (57 total), and ran a basic scatter plot of their first-half ERA, FIP, and xFIP against second-half ERA, to see which of the former was best at predicting the latter.

First-Half ERA and Second-Half ERA


First up is first-half ERA and second-half ERA. A fairly weak correlation — 7% of a pitcher’s second-half ERA is explained by his first-half ERA — albeit significant (p-value < 0.10).

First-Half FIP and Second-Half ERA


Next is first-half FIP and second-half ERA. It’s hard to tell but the dots are, on average, a bit closer to the fit line — 11% of second-half ERA is explained by first-half FIP (p-value < 0.05).

First-Half xFIP and Second-Half ERA


Lastly, we have first-half xFIP and second-half ERA. While FIP uses a pitcher’s actual home-run totals, xFIP uses league-average totals because home run rates fluctuate year-to-year. You can clearly see the dots are much closer to the fit line than in the previous two graphs — 15% of second-half ERA is predicted by first-half xFIP (p-value < 0.01).

Is 15% good? Using the same method as above, I looked at the correlation between 2014 xFIP and 2015 ERA — and found an r² of 27%. So while half-season predictions don’t seem to be as accurate as season-to-season predictions, if MLB teams are making real moves based on a 27% correlation, I’m going to take a leap and say my fantasy team can makes moves based on a 15% correlation.

Now the part you (and I) have been waiting for: Here are the top 10 pitchers poised for second-half improvement followed by the top 10 pitchers who may get worse (sorted by the difference between ERA and xFIP, as of 7/9).

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Some interesting things to note on the first list:

  • Smyly is owned in 48% of Yahoo Fantasy leagues, Nola in 47%, Ray in 11%, and Bettis in 4%. Pick them up.
  • The rest could be solid buy-low trade options (minus Eovaldi, unless your league values middle relievers).
  • A common theme among the members are high BABIPs and home-run rates (>.300, >15%) — which suggests they have been victims of bad luck.

And the second list, where the opposites are mostly true:

  • While Teheran’s name has come up in trade talks, his numbers suggest he may regress in the second half.
  • Sell-high trade options in fantasy leagues.
  • Low BABIPs and home-run rates (<.275, <10%).

Remembering Black Holes

Do you ever look at a daily lineup and find yourself disappointed with one of the names in it? Do you ever ask why the manager continues to bat a clearly inferior player when there are clearly better options on the bench or in the minors or in your softball league? Do you ever celebrate when a player gets designated for assignment and you never have to see them bat second in front of like six clearly better hitters? Well then I am very sorry, but it’s time to relive some bad memories, team by team, from the past ten years.

Yes, it’s time to talk about the black holes of the recent past.

Now what makes a player a black hole?

Read the rest of this entry »

Updating Hitter xISO and Second-Half Predictions

In late May, I posted a version of expected ISO (xISO), inspired by Alex Chamberlain’s work, which incorporated the publicly available Statcast data, easily accessible from the Baseball Savant leaderboard. I’ve been tinkering with it since, and figured I would post an updated version, as well as some second-half predictions based on the current “leaders and laggards”.


The original version of xISO was a simple linear regression model using GB% and average LD/FV exit velocity (LDFBEV). The only feature of any real note was the inclusion of the square of LDFBEV as an additional term. I knew then that I could get better correlation to data if I used LD% and FB% and removed GB% from the model, but I thought the simpler model would be better. I also thought it would be weird to have LD% and FB% as separate terms, and then one combined term for average exit velocity. I guess I just changed my mind. Whatever, it’s all empirical, and the only rule is it has to…predict better. Let’s examine the model, again trained on 2015 qualified hitters, and using LD% and FB% instead of GB%.

New xISO Model, Trained on 2015 Data

As you can see, the coefficient of determination went up a little bit from the previous version. It’s not a big deal, but it’s basically free, so we’ll take it. The updated model equation is as follows:

Now, we also have a fair bit of data for this year. I don’t yet want to update the model parameters using 2015 and 2016 data to train, but I will at least check how the model correlates to this year’s outcomes so far. I arbitrarily selected a minimum of 175 batted ball events (BBE), which limits the pool to 141 players, as of July 8th.

2016 xISO

Look at that! Not too bad overall. Armed with some confidence in the method, let’s now take a look at some of the hitters who most over- and under-performed xISO in the first half (numbers current as of July 9). I will also attempt to avoid talking about any of the players I mentioned previously, or that Alex mentioned in his June xISO report.



Jay Bruce: ISO = .274,  xISO = .187

Bruce is actually hitting his line drives and fly balls with less authority than last year (92.8 mph down from 93.2). His overall batted-ball profile looks similar as well. After a couple down years, it’s nice to see Bruce succeeding, but I’m not betting on it to continue.

Anthony Rizzo: ISO = .282,  xISO = .201

At the risk of enraging my pal, league-mate, and curator of Harper Wallbanger, we might need to calm down a little bit on Rizzo. Don’t get me wrong, I think he’s a very good player, but odds are he won’t continue to hit for quite this much power.

Jake Lamb: ISO = .330,  xISO = .256

Right now, Jake Lamb is second in the majors in ISO behind David Ortiz. He does hit the ball hard (97.9 mph LDFBEV), but he hits 46% of his balls on the ground. Even a .256 ISO would be quite good, given his decent walk rate. This will likely go down as a true breakout season for Lamb.

Wil Myers: ISO = .242,  xISO = .188

While some of the guys on this list play in hitters’ parks, Myers is an example of a first half overperformer in a pitcher’s park. Between expected power regression and his spotty injury history, I’m nervous about the second half.



Andrew McCutchen: ISO = .165,  xISO = .233

Now, ‘Cutch is hitting more popups this year than last year, which could be fooling xISO a bit. Still, I like his ISO to get back to around .200. Of more concern might be his spike in strikeouts.

Ryan Zimmerman: ISO = .181,  xISO = .236

Zimmerman’s exit velocity is up from last year (96.8 mph from 95.0). He probably won’t hit for average, but if he continue making hard contact, he should accumulate plenty of RBIs in the second half.

Yasiel Puig: ISO = .133,  xISO = .188

xISO basically expects Puig to get back to his career average of .183. My main worry with the burly Cuban is his struggle to maintain a healthy pair of hamstrings.

Colby Rasmus: ISO = .157,  xISO = .211

At this point, we basically know who Rasmus is. He is a player who consistently sports an ISO over .200. After a bump in fly balls last year, he’s sitting below his career average this season. That’s not ideal for power output, but he’s also hitting the ball a bit harder. I’ll still bet on him doubling his homer total over the remainder of the season, and surpassing 20 for the second season in Houston.


That’s it! Please feel free to to leave comments, questions, or suggestions for improvement. I’m working on a public document with the xISO calculation available for every player, updated daily-ish. Feel free to follow me on Twitter for updates, or badger me in the comments.

Masahiro Tanaka and Homers and Strikeouts

The Yankees have been a beacon of mediocrity this year, currently sitting under .500 and in fourth place in the resurgent AL East. Pitching is often a soft spot for the Yankees, as Yankee Stadium routinely ranks in the top half of the league in terms of worst parks for pitchers, according to park factors on FanGraphs. The short porch in right field is especially troublesome for hurlers. For this reason, park factors on FanGraphs have listed Yankee Stadium (tied with others) as the easiest park for a lefty to hit a homer from 2013-2015. Thus, there are bound to be some interesting trends among their starters. One such starter is the always entertaining Masahiro Tanaka. Let’s explore!

Tanaka struggled mightily with the longball during his first two seasons with the Yankees, posting a 1.24 HR/9 ratio from 2014-2015, which would have been good (or bad) enough for the eighth-highest among starters, had he qualified. That includes a ridiculous 1.46 HR/9 from last year, which would have ranked sixth (a few spots behind teammate CC Sabathia, who ranked third with 1.51 HR/9). At the same time, that high HR/9 may have been because Tanaka was victimized by a 15.7% HR/FB ratio during those two seasons. Since Tanaka is a righty (opposing teams are more likely to play lefties against him) who pitches half of his games at Yankee Stadium (lefty heaven), his HR/FB ratio is bound to be high, but not that high. Thus, it’s understandable that his HR/FB ratio decreased to a more normal 9.5% this year. It will probably be higher than that in the future, but this is just the ebb and flow of things: sometimes stats are higher than they should be, and sometimes they are lower.

Tanaka actually allowed a palatable 0.99 HR/9 in his first season with the Yanks, despite a 14.0% HR/FB ratio. His groundball rate remained virtually the same from that first season to the second, but one thing that made his home-run total lower in his first season as opposed to the second (other than a slight uptick in HR/FB) was a 9.3 K/9. When you allow fewer balls in play, you will allow fewer homers. However, in Tanaka’s second season, that number sunk to 8.1 K/9, a substantial drop. It has dropped even further this season, considerably below league average now at 7.1 K/9. What has brought about this dip in Ks? Changes in velocity often coincide with changes in K-rate:

Year Fourseam Sinker Change Slider Curve Cutter Split
2014 92.74 91.44 87.49 83.97 74.41 89.26 87.27
2015 92.76 91.69 0.00 84.42 77.08 89.73 88.04
2016 92.63 90.63 0.00 84.86 75.91 88.53 86.93

Interesting. His velocity has remained quite stable throughout his time with the Yankees (if you look year to year). Let’s look elsewhere. Another determining factor for K-rate is movement. Here is Tanaka’s horizontal movement over the years.

Brooksbaseball-Chart (2).png

Year Fourseam Sinker Change Slider Curve Cutter Split
2014 -4.66 -8.17 -7.81 3.01 5.26 -0.99 -4.83
2015 -5.79 -8.72 0.00 1.54 4.20 -1.85 -6.45
2016 -5.80 -8.26 0.00 1.08 4.60 -2.08 -6.67

His slider, cutter, and curve have lost a bit of that typical movement away from righties, but at the same time, the four-seam and split have improved their arm-side run, inside to righties. The sinker has pretty much held steady. How about vertical movement?

Brooksbaseball-Chart (4).png

Year Fourseam Sinker Change Slider Curve Cutter Split
2014 9.37 5.52 4.33 1.38 -6.34 5.50 1.63
2015 9.56 5.98 0.00 2.03 -4.18 6.45 2.21
2016 9.94 5.74 0.00 3.47 -4.58 6.53 2.74

All of his pitches are dropping less/rising more, which is good for the four-seamer and cutter, but not so good for everything else. There are some good and some bad movement trends, but nothing that I would think would completely evaporate Tanaka’s strikeouts. There has to be something else. Has Tanaka’s whiff rate changed at all?

Brooksbaseball-Chart (6).png

Year Fourseam Sinker Change Slider Curve Cutter Split
2014 5.16 5.91 0.00 16.39 5.26 13.82 29.13
2015 5.66 4.02 0.00 16.70 4.05 8.63 20.62
2016 6.00 4.51 0.00 18.85 5.13 12.39 17.11

Indeed it has. The splitter has seen a sharp drop in swinging strike rate. Why is this happening? Did Tanaka change something? Let’s see if his pitch mix has any answers.

Brooksbaseball-Chart (5).png

Year Fourseam Sinker Cutter Curve Slider Change Split
2014 21.34 19.49 6.16 5.71 21.39 0.10 25.80
2015 18.64 13.62 10.75 7.30 22.23 0.00 27.41
2016 3.07 34.01 6.94 4.79 25.72 0.00 25.48

Woah! Tanaka has completely scrapped his four-seamer in favor of his sinker. Everything else has remained relatively stable. Here’s where we come full circle — back to the homers. I have a feeling that his reasoning for this change was that he wanted to improve his groundball rate in order to allow fewer home runs. Sure, the groundball rate has improved a little (up 3.4% in since last year), but this pitch swap seems to be the biggest reason I can think of for the sharp decrease in swinging-strike rate for Tanaka’s splitter. My theory is that his four-seamer, a relatively straight, hard pitch with a bit of rise, can help set up the splitter better than the sinker. The sinker and splitter are almost the same pitch; the split has a bit more drop, a bit more run, and is a bit slower. The velocity difference and movement difference between the splitter and four-seamer is much more drastic than those differences between the sinker and the split.

So, Tanaka seems to have traded some strikeouts for a few more groundballs/fewer homers. I’m not sure I like this change; his HR/FB rate was bound to normalize anyway. However, one thing I certainly like from Tanaka this year is that he has stayed off the DL. An asterisk has been over his name on draft day since the UCL injury a couple of summers ago. Despite staying off of the DL this year, people have been pointing to his better record on extra rest as a sign of fatigue. In 10 starts on extra rest this year, Tanaka has a 1.72 ERA, versus a 5.28 ERA in seven starts on regular rest. However, this has never been a noticeable split before this year, and it’s also worth noting that seven of those 10 starts have come away from Yankee Stadium, which I have deemed a bad park for Tanaka to pitch in.

Graphs and tables are from Brooks Baseball.

Hardball Retrospective – What Might Have Been – The “Original” 2004 Royals

In “Hardball Retrospective: Evaluating Scouting and Development Outcomes for the Modern-Era Franchises”, I placed every ballplayer in the modern era (from 1901-present) on their original team. I calculated revised standings for every season based entirely on the performance of each team’s “original” players. I discuss every team’s “original” players and seasons at length along with organizational performance with respect to the Amateur Draft (or First-Year Player Draft), amateur free agent signings and other methods of player acquisition.  Season standings, WAR and Win Shares totals for the “original” teams are compared against the “actual” team results to assess each franchise’s scouting, development and general management skills.

Expanding on my research for the book, the following series of articles will reveal the teams with the biggest single-season difference in the WAR and Win Shares for the “Original” vs. “Actual” rosters for every Major League organization. “Hardball Retrospective” is available in digital format on Amazon, Barnes and Noble, GooglePlay, iTunes and KoboBooks. The paperback edition is available on Amazon, Barnes and Noble and CreateSpace. Supplemental Statistics, Charts and Graphs along with a discussion forum are offered at

Don Daglow (Intellivision World Series Major League Baseball, Earl Weaver Baseball, Tony LaRussa Baseball) contributed the foreword for Hardball Retrospective. The foreword and preview of my book are accessible here.


OWAR – Wins Above Replacement for players on “original” teams

OWS – Win Shares for players on “original” teams

OPW% – Pythagorean Won-Loss record for the “original” teams

AWAR – Wins Above Replacement for players on “actual” teams

AWS – Win Shares for players on “actual” teams

APW% – Pythagorean Won-Loss record for the “actual” teams


The 2004 Kansas City Royals 

OWAR: 40.4     OWS: 264     OPW%: .483     (78-84)

AWAR: 16.8      AWS: 173     APW%: .358     (58-104)

WARdiff: 23.6                        WSdiff: 91  

The “Original” 2004 Royals placed third in the American League Central division, 12 games behind the Indians. The “Actual” 2004 Royals lost 104 contests. Carlos Beltran (.267/38/104) enjoyed a monster campaign as he narrowly missed the 40/40 club. The Royals center fielder compiled 121 tallies and swiped 42 bags in 45 attempts. However he only earned 11.4 Win Shares for the “Actual” Royals (vs. 29 WS for the “Originals) due to a mid-season trade to the Houston Astros. Fellow outfielder Jeff Conine contributed 35 doubles while first-sacker Mike Sweeney went yard on 22 occasions.

Juan Gonzalez of the “Actuals” placed 52nd in the “The New Bill James Historical Baseball Abstract” top 100 player rankings. 

  Original 2004 Royals                                    Actual 2004 Royals

Jeff Conine LF 2.29 14.93 David DeJesus LF/CF 0.65 8.92
Carlos Beltran CF 6.77 29.02 Carlos Beltran CF 2.78 11.47
Michael Tucker RF 1.25 14.12 Matt Stairs RF 0.12 10.96
Johnny Damon DH/CF 4.34 25.1 Ken Harvey DH/1B 0.42 9.33
Mike Sweeney 1B 1.9 12.49 Mike Sweeney 1B 1.9 12.49
Ruben Gotay 2B -0.41 2.79 Tony Graffanino 2B 0.27 6.56
Ramon Martinez SS 0.21 5.64 Angel Berroa SS 0.38 10.55
Joe Randa 3B 0.35 13.1 Joe Randa 3B 0.35 13.1
Brent Mayne C -0.39 3.69 John Buck C 0.32 4.67
Ken Harvey 1B 0.42 9.33 Desi Relaford 3B -1.07 3.69
David DeJesus CF 0.65 8.92 Benito Santiago C 0.04 3.4
Andres Blanco SS 0.5 2.32 Alberto Castillo C 0.6 2.96
Juan Brito C -0.83 2.29 Calvin Pickering DH 0.3 2.94
Dee Brown LF -0.71 2.24 Ruben Gotay 2B -0.41 2.79
Kit Pellow RF -0.59 1.08 Juan Gonzalez RF 0.12 2.69
Shane Halter 3B -0.19 1.05 Abraham Nunez RF -0.47 2.58
Alex Prieto 2B -0.03 0.75 Andres Blanco SS 0.5 2.32
Matt Treanor C -0.11 0.51 Kelly Stinnett C 0.48 2.27
Byron Gettis LF -0.08 0.38 Dee Brown LF -0.71 2.24
Alexis Gomez LF -0.07 0.29 Aaron Guiel LF -0.55 0.49
Mendy Lopez 2B -0.5 0.22 Ruben Mateo RF -0.72 0.43
Brandon Berger LF -0.33 0.2 Byron Gettis LF -0.08 0.38
Donnie Murphy 2B -0.25 0.2 Alexis Gomez LF -0.07 0.29
Raul Gonzalez RF -0.16 0.12 Jose Bautista 3B -0.23 0.27
Paul Phillips C 0 0.1 Mendy Lopez 2B -0.5 0.22
Mike Tonis C -0.11 0.03 Brandon Berger LF -0.33 0.2
Larry Sutton 1B -0.01 0.03 Donnie Murphy 2B -0.25 0.2
Wilton Guerrero 2B -0.22 0.18
Paul Phillips C 0 0.1
Adrian Brown LF -0.07 0.08
Mike Tonis C -0.11 0.03
Rich Thompson RF -0.03 0.02
Damian Jackson RF -0.12 0.01

Jon Lieber recorded 14 victories and yielded only 18 bases on balls in 27 starts. Glendon Rusch fashioned a 3.47 ERA as he split time between starting and relief roles. Zack Greinke delivered 8 victories and a 3.97 ERA in his inaugural season. Tom “Flash” Gordon (9-4, 2.21) whiffed 96 batsmen in 89.2 innings and achieved All-Star status.

  Original 2004 Royals                                  Actual 2004 Royals

Jon Lieber SP 2.87 10.43 Zack Greinke SP 3.62 9.73
Glendon Rusch SP 3.02 10 Jimmy Gobble SP 0.87 5.37
Zack Greinke SP 3.62 9.73 Dennys Reyes SP 0.79 4.58
Jimmy Gobble SP 0.87 5.37 Jeremy Affeldt SP 0.11 4.42
Jeremy Affeldt SP 0.11 4.42 Darrell May SP -0.05 4.07
Tom Gordon RP 3.66 15.47 Shawn Camp RP 0.17 4.15
Dan Miceli RP 0.73 7.13 Jaime Cerda RP 0.69 4.05
Lance Carter RP 0.76 6.53 Nate Field RP 0.07 3.02
Kiko Calero RP 0.7 5.7 Scott Sullivan RP 0.12 2.85
Orber Moreno RP 0.08 2.84 Jason Grimsley RP 0.59 2.55
Wes Obermueller SP -0.01 2.98 Brian Anderson SP -0.71 2.84
Ryan Bukvich RP 0.12 0.82 Mike Wood SP 0.24 1.91
Chad Durbin RP -1.03 0.39 Jimmy Serrano SP 0.5 1.57
Rodney Myers RP 0.06 0.29 D. J. Carrasco RP -0.12 1.54
Jason Simontacchi RP -0.28 0.26 Rudy Seanez RP 0.32 1.45
Mike MacDougal RP -0.13 0.23 Ryan Bukvich RP 0.12 0.82
Kevin Appier SP -0.44 0 Mike MacDougal RP -0.13 0.23
Chris George SP -0.82 0 Kevin Appier SP -0.44 0
Jorge Vasquez RP -0.19 0 Denny Bautista SP -0.07 0
Chris George SP -0.82 0
Justin Huisman RP -0.51 0
Matt Kinney RP -0.43 0
Curt Leskanic RP -0.64 0
Jorge Vasquez RP -0.19 0
Eduardo Villacis SP -0.22 0

Notable Transactions

Carlos Beltran

June 24, 2004: Traded as part of a 3-team trade by the Kansas City Royals to the Houston Astros. The Oakland Athletics sent Mark Teahen and Mike Wood to the Kansas City Royals. The Houston Astros sent Octavio Dotel to the Oakland Athletics. The Houston Astros sent John Buck and cash to the Kansas City Royals.

Johnny Damon

January 8, 2001: Traded as part of a 3-team trade by the Kansas City Royals with Mark Ellis to the Oakland Athletics. The Oakland Athletics sent Ben Grieve to the Tampa Bay Devil Rays. The Oakland Athletics sent Angel Berroa and A.J. Hinch to the Kansas City Royals. The Tampa Bay Devil Rays sent Cory Lidle to the Oakland Athletics. The Tampa Bay Devil Rays sent Roberto Hernandez to the Kansas City Royals.

November 5, 2001: Granted Free Agency.

December 21, 2001: Signed as a Free Agent with the Boston Red Sox.

Tom Gordon

October 30, 1995: Granted Free Agency.

December 21, 1995: Signed as a Free Agent with the Boston Red Sox.

November 1, 2000: Granted Free Agency.

December 14, 2000: Signed as a Free Agent with the Chicago Cubs.

August 22, 2002: Traded by the Chicago Cubs to the Houston Astros for players to be named later and Russ Rohlicek (minors). The Houston Astros sent Travis Anderson (minors) (September 11, 2002) and Mike Nannini (minors) (September 11, 2002) to the Chicago Cubs to complete the trade.

October 29, 2002: Granted Free Agency.

January 23, 2003: Signed as a Free Agent with the Chicago White Sox.

October 27, 2003: Granted Free Agency.

December 16, 2003: Signed as a Free Agent with the New York Yankees.

Honorable Mention

The 2009 Kansas City Royals 

OWAR: 45.7     OWS: 268     OPW%: .544     (88-74)

AWAR: 25.3       AWS: 194      APW%: .401    (65-97)

WARdiff: 20.4                        WSdiff: 74

Kansas City clinched the American League Central division title by a lone game over Minnesota. Zack Greinke (16-8, 2.16) merited the 2009 AL Cy Young Award as he paced the Junior Circuit in ERA and WHIP (1.073) while posting career-highs in strikeouts (242) and innings pitched (229.1). Johnny Damon (.282/24/82) tied his personal-best in home runs, slashed 36 two-base hits and registered 107 tallies. Billy Butler aka “Country Breakfast” drilled 51 doubles and swatted 21 big-flies. David DeJesus contributed 13 jacks and knocked in 71 runs. Carlos Beltran supplied a .325 BA but missed more than two months of the season due to injury. J.P. Howell saved 17 contests and collected 7 victories as the Royals’ relief ace.

On Deck

What Might Have Been – The “Original” 1969 Reds

References and Resources

Baseball America – Executive Database


James, Bill. The New Bill James Historical Baseball Abstract. New York, NY.: The Free Press, 2001. Print.

James, Bill, with Jim Henzler. Win Shares. Morton Grove, Ill.: STATS, 2002. Print.

Retrosheet – Transactions Database

The information used here was obtained free of charge from and is copyrighted by Retrosheet. Interested parties may contact Retrosheet at “”.

Seamheads – Baseball Gauge

Sean Lahman Baseball Archive

A One-Man Marte Partay Between the Bases

This article originally appeared on the Pirates blog Bucco’s Cove.

“Speed kills.” –Al Davis

Starling Marte is having a hell of a season stealing bases and this is one of the things that should have propelled him into the All Star Game without needing the stupid final vote. He is the top baserunner in the NL this year, he’s second in the league with 25 stolen bases, and he has a better percentage than the leader Jonathan Villar.

A recent game against the Cardinals gave a strong piece of evidence of Marte’s preternatural baserunning ability when he stole second base with Carlos Martinez pitching in the sixth inning:

The Pirates’ announcers commented on the fact that people very rarely steal off of Martinez. I went to look at the numbers: in 399 2/3 innings since entering MLB, Martinez has yielded only 10 stolen bases in 19 attempts. I think the 19 attempts is really indicative of his abilities to control the running game; people just don’t even try to run on Carlos Martinez. Martinez ranks 36th among all pitchers in SB/IP who have thrown at least 200 innings since 2013, which is decent. However, if we limit ourselves to looking at only righty starters over this time period (since it is much harder to steal against lefties and game circumstances are a bit different between starters and relievers), Martinez ranks 13th among pitchers with the same innings restrictions. (I’m not including tables for all of these stats, but if you want to see for yourself, mosey on over to the Baseball Reference Play Index, where all of this data comes from.)

Martinez has really shut down the running game since becoming a full-time starter in 2015, however. Over that time period, he ranks fourth among all pitchers (lefty, righty, starter, and reliever alike) having at least 250 innings in SB/IP, yielding only four SB in nine attempts over 282 innings.

Rank Player SB IP SB/IP
1 David Price 1 336.2 0.00297
2 Wade Miley 2 281 0.00712
3 Yordano Ventura 3 250.2 0.01199
4 Carlos Martinez 4 282 0.01418
5 Chris Tillman 4 279.1 0.01433
6 Wei-Yin Chen 5 290 0.01724
7 Danny Salazar 5 284 0.01761
8 Johnny Cueto 6 334.1 0.01796
9 Chris Sale 6 328.2 0.01828
10 R.A. Dickey 6 324 0.01852

(Note: If you bump this down to 150 innings to get more relievers on the list, Martinez is still in the top 10 for SB/IP.)

Of the four pitchers ahead of him in the table, two are lefties. (Sidebar: How the hell is R.A. Dickey on this list given the fact that he throws a knuckleball? It seems like it should be really easy to steal on him given that.) Martinez is similarly ranked (fifth) if you look at SB/Total Baserunners over the same period; in short, Martinez is really, really good at controlling the running game.

Furthermore, reigning eight-time Gold Glove winner Yadier Molina was behind the dish attempting to throw Marte out. Molina ranks first among all catchers since 2002 in his ability to control the running game by the defensive metric rSB. Obviously Martinez’s ability to prevent the stolen base is helped by having Molina behind the dish, but the combination of these two has been deadly over the past season and a half, making Marte’s accomplishment all the more impressive.

The Martinez/Molina duo (and Martinez in general) has only allowed one other stolen base this season so far. Who was it? None other than Bartolo Colon! Actually, I’m kidding, it was Starling Marte, which is almost as crazy! He has the only two stolen bases this season against a guy who only gave up two all of last season. These are also the only two attempts against Martinez all season. On May 6, after a bunch of false starts, pickoff moves, foul balls, and laughs between Molina and Marte, he finally got his stolen base:

The thing I find most entertaining is how loose everyone seems, goofing off and laughing about the play that just happened, which seems relatively rare in this day of boring interviews and generic soundbites. Molina had a good laugh about that one, but he was pretty upset about the more recent stolen base.

There’s nothing to be mad about, though; Martinez and Molina simply got burned by the best baserunner in the game right now.

Kris Bryant Continues to Hit

Ever since he was taken with the second pick in the 2013 draft, the spotlight has continually followed Kris Bryant. After mashing his way through the minors in less than two years, Bryant had a spectacular rookie season with a slash line of .275/.369/.488 with 26 homers and a 136 wRC+. Deservedly, he was rewarded with the NL Rookie of the Year award. Although he did strike out over 30 percent of the time, he showed great plate discipline along with immense power. His 6.5 WAR ranked 10th among major league hitters. However, this year he has taken his production a step further.

With his league-leading 25 home runs to go along with his slash line of .278/.370/.578, one major change sticks out. Although his average and on-base percentage remain around the same as his 2015 totals, his slugging percentage has taken a huge jump. Halfway through the season, he is one home run shy of last year’s total and around half of his hits have gone for extra bases (44 out of 87). He’s also cut down on his strikeouts while making even more hard contact than he did last year — shown by his 42% hard-hit rate which will allow him to continue to tap into his power.

One noticeable change sticks out in his batted-ball profile. Although his ground-ball, line-drive, and fly-ball rates remain relatively constant, Bryant has pulled the ball more in his second big-league season. This has caused more of his fly balls to leave the park. Taking a look at his 2016 home-run spray chart, you can see that all of his home runs have been pulled.


Source: FanGraphs
Next take a look at his 2015 home-run spray chart.


Source: FanGraphs
Most of his home runs have been in the same general area with the exception of a few opposite-field home runs to right. Since he has been pulling the ball with more authority in 2016, the balls that he pulls in this sweet spot to left will allow him to continue to leave the yard at a ridiculous rate. With his picturesque swing to go along with his strong hands and 6-foot-5, 230-pound frame, swings like this

Kris Bryant Homer

…will continue to be common for Cubs fans to see from Kris Bryant.

However, it would be foolish to simply call Bryant a home-run hitter when in fact what makes him so special is his all-around hitting ability to go with this insane power. He walks, hits the ball hard, and his only flaw is his propensity to strike out — and even that he has improved upon this year. A two-time All Star already with 4.3 WAR this season, he ranks fourth among major-league hitters and first among NL hitters in WAR while also possessing a 149 wRC+. At this point, if the NL MVP is going to go to a player not named Clayton Kershaw, Kris Bryant deserves to be the one holding up the trophy. But the trophy most important to him is the one won at the end of October. With the Cubs holding a comfortable lead atop the NL Central, Bryant looks destined to lead them on a deep playoff run with the hope of finally shattering their 108-year-old curse.