Archive for April, 2015

Who’s Wilin to Give Rosario a Chance?

So the seemingly inevitable came to fruition last week when the Colorado Rockies sent Wilin Rosario down to Triple-A Albuquerque after just 14 at-bats with the Rockies this year. According to the man himself, it was to allow another bullpen arm to join the big-league club. Fair enough you might say, the team’s immediate needs are a priority (try telling Kris Bryant that) and the Rockies needed another pitcher in the pen.

Just a couple of years ago, Rosario posted a .270 batting average, tallying 28 home runs and an .843 OPS in 426 plate appearances in the most demanding of positions as a 23-year-old rookie.

What did he do for an encore? Well in 2013 Rosario managed a .292 batting average but launched only 21 home runs and his OPS dropped to a paltry .801 in 466 plate appearances. I jest. Still very productive for a young catcher, even if he gets the assistance of Coors field 50% of the time.

So how did it reach the point where this seemingly top prospect is now battling for a spot in the Majors aged 26?

Well, it begins with Rosario’s skills behind the plate. As a 23 year old, the Rockies knew Rosario had the bat to play but needed to improve defensively to become an everyday catcher in the Majors. Bumps and hiccups are to be expected and in 2012 he had 13 errors and 21 passed balls in 105 games.

This improved in 2013, when Rosario committed nine errors and cut passed balls to nine in 106 games.

But then in 2014, things began to fall apart again and in just 96 games, he had 12 passed balls and seven errors. Granted, a strained left wrist troubled him much of the season, and landed him on the disabled list. In May, a nasty bout with type-B influenza cost him 12 games and 11 pounds. All this culminated in a drop in production at the plate. The batting average dropped to .267, homers fell to 13 and his OPS to .739 whilst appearing at the plate 410 times.

On paper, the batting stats don’t look too bad for a catcher suffering illnesses and injuries. After two good years, one disappointing one couldn’t undo all the potential shown in the previous two seasons, surely?

Looking a little deeper then, there’s the issues Rosario has had with facing righties during his career. Below is a breakdown of his 2012, ’13 and ’14 seasons, showing his splits against RHP and LHP.

2012

Split PA H 2B 3B HR BB SO BA OBP SLG OPS
vs RHP 308 68 15 0 14 19 78 .239 .286 .440 .726
vs LHP 118 39 4 0 14 6 21 .348 .381 .759 1.140

2013

Split PA H 2B 3B HR BB SO BA OBP SLG OPS
vs RHP 328 89 14 1 14 8 85 .279 .299 .461 .760
vs LHP 138 42 8 0 7 7 24 .323 .355 .546 .901

2014

Split PA H 2B 3B HR BB SO BA OBP SLG OPS
vs RHP 303 70 16 0 5 18 56 .249 .290 .359 .650
vs LHP 107 32 9 0 8 5 14 .317 .346 .644 .989

Rosario is considered someone who cannot hit righties effectively and one highly-regarded publication even had written this about him heading into 2015. If every opposing pitcher was a lefty, he’d win an MVP. Any hope for solving RHPs? “. Not exactly a ringing endorsement. But again, his stats against righties aren’t terrible for a young catcher in the National League, certainly serviceable.

However, you now have enough question marks to take stock at what you have; someone who had a bad year, who struggles against right handed pitching and is not performing defensively. So the Rockies had a solution, move him over to first base. His WAR had dropped from starter level in 2012 and 2013 (both years he sported a 2.1 WAR) to replacement level in 2014 (-0.1 WAR). So it seemed like a good idea. Rosario is yet to hit his peak, his bat has more than enough upside for long-term production and without the pressures of needing to improve at the immensely challenging catcher position anymore, things can only trend up.

But then a spanner is thrown into the works in the form of Justin Morneau and his $12.5 million two-year contract which runs through the 2015 season (with a mutual option for 2016). So the simplest short-term solution is to keep Rosario in Triple-A for the season, work out his issues against righties, develop his skills at first and decline the option on Morneau’s contract for 2016, freeing up monies to be used elsewhere. Rosario is arbitration-eligible the next two years and cannot become an unrestricted free agent until 2018 but a long stint in the minors could add an extra year of team control.

So let’s play a bit of devil’s advocate for a moment. If the Rockies extend Morneau through 2016, if the Rockies don’t see Rosario as an everyday first baseman going forward, if they think they can use Rosario to get better elsewhere it begs the question: Who could be Wilin to give Rosario a chance?

As things stand, the Rockies have a winning record and it’s still too early to say if they’ll be contending this year or whether they’ll try to rebuild a little. So let’s look at three possible trades the Rockies could target at the end of this season if they feel the need to move on from Rosario.

Boston Red Sox

Mike Napoli’s contract ends this year and the Red Sox won’t be renewing it. He’s having a bad year and injuries have caught up with him. Rosario on the other hand could be the perfect fit what with the Green Monster and its hitter friendly confines. So the Red Sox could do with getting Rosario. But who could they trade? The Rockies need pitching above all else (which hasn’t bothered the front office too much in the past) but the Red Sox don’t really have any pitching options to trade. If anything, they need the help too.

So let’s look at the outfield. The Red Sox will enter 2016 with Mookie Betts, Rusney Castillo, Jackie Bradley Jr, Daniel Nava and Allen Craig as outfield options, whilst the Rockies will have Corey Dickerson, Carlos Gonzalez and Charlie Blackmon (based on existing contracts and no renewals/trades). So there’s one name which may intrigue. Brock Holt.

Brock Holt is a bit of a utility guy the Red Sox are trying to find at-bats for so one could perceive a trade for an everyday first baseman as ideal. The Rockies don’t have the depth of the Red Sox so they can find ways to give Holt more regular playing time and keep an effective batting lineup.

The likelihood of this trade happening is slim, but it’s intriguing nonetheless.

Philadelphia Phillies

It’s no secret the Phillies are reluctantly rebuilding after prolonged efforts to bury their head in the sand. They still field a lineup containing Ryan Howard and Carlos Ruiz despite father time having caught up with them both (not forgetting Chase Utley).

Ryan Howard continues to be the Phillies everyday first baseman and while he’s still signed through 2016, sooner or later, they need to bite the bullet and accept whatever they can get for him. Carlos Ruiz is also signed through 2016 so maybe if at least one can be moved on, Rosario could fill in for twelve months, covering either spot with a view of an everyday first base role from 2016. He’s young enough to form a part of the rebuild and is a clear upside on both Ruiz and Howard’s bat so this makes sense.

Cole Hamels is the big star the Rockies would love, but the Phillies are looking for a big prospect haul so unless some form of Dickerson, Arenado and top prospects were sent the other way, this just isn’t happening. They don’t have any other starter who could conceivably be considered by the Rockies either. Their main pitching prospects of Aaron Nola, Yoel Mecias, Zach Eflin, Jesse Biddle and Ben Lively are all probably out of play if they get serious about rebuilding so maybe a lower level guy like Nefi Ogando is possible. But this would be a big risk for the Rockies, trading for a mid-tier (at best) pitching prospect.

So maybe some bullpen help to go with it? Ken Giles is the closer in waiting for the Phillies once Papelbon leaves behind the fans who adore him so. But he’s struggle early in 2015 but again, the likelihood of the Phillies losing a potential closer for the next few years to bring in Rosario is unlikely so at best a package of two or three decent arms could be conceived by both parties.

Although it’d be difficult to see a trade here, I think a big enough scratch beneath the service could see something done to benefit the long term goals of each side. Stranger things have happened so only time would tell if the Rockies and Phillies could get something done.

Seattle Mariners

Finally we reach the most intriguing possible destination. The Seattle Mariners have invested big to get to the World Series in recent years. Big name free agent acquisitions of Robinson Cano and Nelson Cruz on the last two off-seasons has added to their chances whilst tying up King Felix long term has given them the ace they need. They’ve constructed a good rotation and a solid batting lineup with one notable exception; first base.

Logan Morrison has been the Mariners starting first baseman so far in 2015, after they waived Justin Smoak last October. First base has become a position synonymous with power hitters in recent times, with offense on the decline throughout baseball it’s a focal position for contending teams batting lineups. I’m not disparaging Logan Morrison, I don’t know the guy and he’s a far better baseball player than I’ll ever be, but he’s not a starting first baseman for a Major League contending team. Last year was the first time since 2010 he posted a positive WAR. Even in 2011 when he hit 23 homers, his WAR was -0.6. 2011 also marked the last time he played at least 100 games in a Major League season.

So there’s clearly a need to upgrade here. Is Wilin Rosario a clear upgrade? Well he is enough of one to matter, especially considering Morrison bats leftie. Morrison actually has a better career batting average against lefties (.260 compared to .243 against righties) but that’s as far as it goes for hitting lefties. Just a glance at his over stats will show this. As a sample, he hits a homerun every 28.65 at-bats against righties and one every 42.36 at-bats against lefties. Below is Morrison’s career splits.

Split PA AB H 2B 3B HR BB SO BA OBP SLG OPS
vs RHP 1393 1232 299 67 15 43 148 219 .243 .326 .426 .752
vs LHP 522 466 121 26 1 11 44 113 .260 .333 .391 .724

The Mariners also have the advantage of the DH. They can easily keep Morrison, form some kind of platoon between Rosario and Morrison whilst still giving Rosario at bats against righties with either of them DHing. Rosario would be a cheaper option at first than most alternatives so improving their lineup without breaking the bank is a good thing right? Things are starting to make sense all of a sudden.

But what could the Mariners give up in order to acquire Rosario. Although Rosario would make sense, they certainly aren’t going to overpay for him. This is where things could get interesting…….

Rockies still haven’t pinned everything on Tulowitzki. If they ever trade him away, it’ll be in the next year so heading into 2016, the Rockies may need a shortstop. I present to you, Mr. Bradley Miller. Before you start up, I’m in no way suggesting Miller is a direct replacement for Tulowitzki!

The Mariners looked like giving Chris Taylor the starting shortstop gig in 2015, until a broken wrist curtailed that idea, giving Brad Miller another chance to shine. He’s been pretty good so far this year, but Chris Taylor is back and Miller certainly hasn’t shown the promise the Mariners hoped he would. If Taylor can hit well in Triple-A (he’s already hitting .328 with 2 homers, 5 steals and an .894 OPS) he’ll be with the big league club sooner rather than later. It’d be a downgrade at shortstop for the Rockies I grant you, but would free up a lot of cap space to go out and get something resembling a decent rotation.

But even if the Rockies do keep hold of Tulowitzki (and why wouldn’t you?), we come back to their need for pitching. The Mariners aren’t exactly steeped with pitching but certainly have enough to trade a piece away. They’d be unlikely to want to lose one of their more established “prospects” in order to get Rosario (Taijuan Walker, Roenis Elias, and James Paxton).

But there’s also Danny Hultzen, who has started the year well in Triple-A after rotator cuff surgery (currently sporting a 2.05 ERA through 30+ innings). Tyler Olsen is currently in the Mariner bullpen but was considered a 4th/5th starter during his minor league career and Ryan Yarbrough is continuing to impress in low A ball and at age 23, could easily be in the Majors within a couple of years. So the Mariners have enough depth to make a trade without harming their rotation. Whether or not they value any of these guys on a par with Rosario however is a different matter.

Looking at the three possible destinations, the Mariners appear to be the best chance of getting something done, but I’d be more inclined to suggest Wilin Rosario starts 2016 as the Rockies first baseman. And who’s to say he won’t finish 2015 in the role. Just as it wouldn’t surprise me in the least if he gets traded tomorrow, but that’s baseball. Nothing is ever set in stone and should the Rockies look to move on, there are certainly enough options out there to get something done.


When Do Stars Become Scrubs?

Baseball is a game driven by stars. They create the most exciting highlight reels that captivate audiences and leave us all in awe. However, eventually every star player loses their battle with Father Time. The purpose of this research was to try and determine when a star player’s production declines to the point where they can become easily replaceable. I decided to use a process called survival analysis to determine when this event occurs.

Methodology

Survival analysis attempts to determine the probability of when an event will occur. In any survival analysis problem, you need to determine three things. You need to determine the requirements for your population, the variables to predict the time of event, and the event.

For this problem, I decided that I would include any player that had their first season of 4 WAR or higher between 1920 and 1999 in my population. I decided to use for my variables: the age when they recorded their first star season, body mass index, offensive runs above average per 150 games, and defensive runs above average per 150 games as my variables. The event I chose to predict was when the player would have his first season below 1 WAR following their star season. The cutoffs for determining stars and scrubs were fairly arbitrary, but I chose these cutoffs because the FanGraphs glossary loosely defines an All-Star season as 4-5 WAR and a scrub season as 0-1 WAR.

Determining the variables was much more difficult. I wanted to pick variables that would represent a player’s performance, age, and overall health. The age was simple enough to find, but it was difficult to find any injury history for players so I decided to calculate a player’s BMI from their listed height and weight. Obviously this isn’t a perfect representation, because a player’s weight is constantly changing throughout his career, but it’s the best that I could do given my limited resources. In order to limit my performance variables, I thought it was best to settle for the offensive runs and defensive runs component of WAR. However, since these are accumulating statistics, I had to recreate them as rate statistics in order to avoid creating correlation issues with the age variable in the model. I would have liked to use more offensive variables, but I feared that adding more inputs would make the model too convoluted and affect the accuracy of the player predictions. Alright, that’s enough preparation; let’s dive into the actual data.

Survival Rate Data

As a jumping off point, I’ll start by presenting a table of the survival rates for my population. Each season indicates the percentage of players from the original population that had not yet recorded a scrub season.

 

Season 1 2 3 4 5 6 7 8 9 10
Survival Function 87.62% 74.28% 65.20% 54.88% 45.80% 39.06% 32.32% 26.96% 22.15% 17.19%
Season 11 12 13 14 15 16 17 18 19 20
Survival Function 13.76% 10.73% 7.57% 5.50% 3.44% 2.06% 1.24% 0.69% 0.28% 0.00%

Let’s make some quick observations. The data shows that no star player has gone more than 20 seasons without recording a season below 1 WAR. It also appears that the survival function decays exponentially.  I also found it interesting that over 50% of stars turn into scrubs by their fifth season and that only 17% of star players survive 10 years in the majors before they register a scrub season. Looking at this data really helps to appreciate how rare it is when players like Derek Jeter and Adrian Beltre perform at a consistent level on a year to year basis.

Hazard Rate Data

Next, we will look at the hazard rate of the players in the population. One of the purposes of examining the hazard rate is to see how the rate of failure changes in a population over time. To find the hazard rate for each time period, you divide the amount of events recorded during a time period by the amount of players that have not yet registered a scrub season. Below is the following calculation for each time period in table format.

Season 1 2 3 4 5 6 7 8 9 10
Hazard Function 12.38% 15.23% 12.22% 15.82% 16.54% 14.71% 17.25% 16.60% 17.86% 22.36%
Season 11 12 13 14 15 16 17 18 19 20
Hazard Function 20.00% 22.00% 29.49% 27.27% 37.50% 40.00% 40.00% 44.44% 60.00% 100.00%

As you can see by the table above, the hazard rate generally increases with each passing season. This makes sense, because as players age, their skill level decreases and their odds of registering a scrub season will increase. However, the hazard rates are fairly constant for the first ten years and then rapidly increase from then on. I’m rather surprised that the hazard rates stayed so consistent for the first ten or so years. I would have guessed that the hazard function would have increased much more rapidly with each passing season.

Determining the Model

It is important to identify the trend of the hazard function, because it helps determine which distribution to use when creating a parametric model. If the hazard rate increases exponentially, you are supposed to use a Weibull distribution. If the hazard rate is constant, you are supposed to use an exponential distribution. Since the hazard function was increasing, I originally attempted to the use the Weibull distribution for the model but I found that the model was predicting too many players to fail in the first few seasons, so I decided to try an exponential distribution instead.

I found that the exponential distribution model was more accurate at predicting survival rates in the first ten years, but severely under predicted the amount of players that would record a scrub season after ten years. I decided to use the exponential distribution, because I believe that it would be far more useful to accurately predict the first ten years instead of the last ten years, since only 17% of players survive ten years. I also believe that any franchise would be thrilled to obtain ten years of stardom from a player and anymore production is just an added bonus.

Survival Rate Estimates

Below is a table of each star player from 2000 to 2014 with the year they entered the population, the time until they became a scrub, every variable included in the model and their predicted survival rate for each of their first ten seasons since becoming a star.

Year Entered Name Time of Event Age BMI Off Def Season 1 Season 2 Season 3 Season 4 Season 5 Season 6 Season 7 Season 8 Season 9 Season 10
2000 Bobby Higginson 2 29 25.1 14.7 -11.0 77.83% 60.58% 47.15% 36.70% 28.56% 22.23% 17.30% 13.47% 10.48% 8.16%
2000 Darin Erstad 6 26 25.0 8.2 8.0 83.94% 70.46% 59.14% 49.64% 41.67% 34.98% 29.36% 24.64% 20.68% 17.36%
2000 Jorge Posada 8 28 27.6 11.2 7.1 80.94% 65.51% 53.02% 42.91% 34.73% 28.11% 22.75% 18.41% 14.90% 12.06%
2000 Jose Vidro 4 25 24.4 3.1 -4.9 83.43% 69.61% 58.07% 48.45% 40.42% 33.72% 28.14% 23.47% 19.58% 16.34%
2000 Phil Nevin 2 29 23.1 5.1 -2.8 76.52% 58.55% 44.80% 34.28% 26.23% 20.07% 15.36% 11.75% 8.99% 6.88%
2000 Richard Hidalgo 2 25 27.5 19.6 9.5 87.32% 76.24% 66.57% 58.13% 50.75% 44.32% 38.69% 33.79% 29.50% 25.76%
2000 Shannon Stewart 5 26 23.7 9.9 -3.2 83.26% 69.33% 57.73% 48.07% 40.02% 33.32% 27.75% 23.10% 19.24% 16.02%
2000 Todd Helton 8 26 28.2 25.4 -1.4 86.03% 74.01% 63.67% 54.77% 47.12% 40.53% 34.87% 30.00% 25.81% 22.20%
2000 Troy Glaus 3 23 26.1 12.2 9.0 88.69% 78.66% 69.76% 61.87% 54.87% 48.66% 43.16% 38.28% 33.95% 30.11%
2001 Albert Pujols 12 21 28.7 47.2 0.8 93.62% 87.66% 82.07% 76.84% 71.94% 67.35% 63.06% 59.04% 55.27% 51.75%
2001 Aramis Ramirez 1 23 27.0 -3.7 -2.3 85.43% 72.99% 62.35% 53.27% 45.51% 38.88% 33.22% 28.38% 24.24% 20.71%
2001 Bret Boone 3 32 25.8 -4.0 -0.5 66.26% 43.91% 29.09% 19.28% 12.77% 8.46% 5.61% 3.72% 2.46% 1.63%
2001 Cliff Floyd 5 28 26.1 13.4 -6.7 79.99% 63.98% 51.18% 40.94% 32.75% 26.19% 20.95% 16.76% 13.41% 10.72%
2001 Corey Koskie 6 28 26.9 11.2 7.7 81.02% 65.64% 53.18% 43.08% 34.90% 28.28% 22.91% 18.56% 15.04% 12.18%
2001 Eric Chavez 6 23 28.4 8.8 3.4 87.79% 77.06% 67.65% 59.39% 52.13% 45.77% 40.18% 35.27% 30.96% 27.18%
2001 Ichiro Suzuki 10 27 23.7 26.6 7.5 85.65% 73.36% 62.83% 53.82% 46.10% 39.48% 33.82% 28.96% 24.81% 21.25%
2001 J.D. Drew 10 25 26.4 25.6 10.8 88.29% 77.95% 68.82% 60.76% 53.65% 47.37% 41.82% 36.92% 32.60% 28.78%
2001 Lance Berkman 11 25 29.0 35.8 -6.9 88.44% 78.21% 69.17% 61.17% 54.10% 47.84% 42.31% 37.42% 33.09% 29.27%
2001 Mike Sweeney 3 27 25.7 12.9 -7.4 81.67% 66.70% 54.48% 44.49% 36.34% 29.68% 24.24% 19.80% 16.17% 13.21%
2001 Paul Lo Duca 6 29 27.7 11.2 14.7 79.86% 63.78% 50.94% 40.68% 32.49% 25.95% 20.72% 16.55% 13.22% 10.56%
2001 Placido Polanco 5 25 28.1 -9.1 12.1 82.55% 68.14% 56.25% 46.43% 38.33% 31.64% 26.12% 21.56% 17.80% 14.69%
2001 Rich Aurilia 6 29 23.1 4.1 10.1 77.86% 60.63% 47.21% 36.76% 28.62% 22.28% 17.35% 13.51% 10.52% 8.19%
2001 Ryan Klesko 5 30 27.5 21.8 -10.8 77.39% 59.89% 46.35% 35.87% 27.76% 21.48% 16.63% 12.87% 9.96% 7.71%
2001 Torii Hunter 13 25 28.9 -11.5 7.3 81.57% 66.53% 54.27% 44.26% 36.10% 29.45% 24.02% 19.59% 15.98% 13.03%
2002 Adam Dunn 6 22 32.9 25.5 -4.2 90.45% 81.82% 74.01% 66.94% 60.55% 54.77% 49.54% 44.81% 40.54% 36.67%
2002 Adrian Beltre N/A 23 30.7 -0.8 10.8 86.84% 75.41% 65.49% 56.87% 49.39% 42.89% 37.24% 32.34% 28.09% 24.39%
2002 Alfonso Soriano 7 26 25.7 11.8 -11.3 82.81% 68.57% 56.78% 47.02% 38.93% 32.24% 26.70% 22.11% 18.31% 15.16%
2002 Austin Kearns 2 22 30.0 31.7 17.8 92.26% 85.12% 78.53% 72.45% 66.84% 61.67% 56.89% 52.49% 48.43% 44.68%
2002 David Eckstein 5 27 27.4 4.8 5.3 81.22% 65.97% 53.58% 43.52% 35.34% 28.71% 23.31% 18.94% 15.38% 12.49%
2002 Edgar Renteria 7 25 26.4 -6.1 8.4 82.83% 68.61% 56.83% 47.08% 38.99% 32.30% 26.76% 22.16% 18.36% 15.21%
2002 Eric Hinske 2 24 30.2 21.6 4.6 88.41% 78.16% 69.10% 61.09% 54.00% 47.74% 42.21% 37.31% 32.99% 29.16%
2002 Jacque Jones 2 27 25.1 0.5 4.9 80.34% 64.54% 51.85% 41.66% 33.47% 26.89% 21.60% 17.35% 13.94% 11.20%
2002 Jose Hernandez 1 32 23.7 -8.5 8.1 66.31% 43.97% 29.16% 19.34% 12.82% 8.50% 5.64% 3.74% 2.48% 1.64%
2002 Junior Spivey 1 27 25.1 15.0 0.7 82.92% 68.76% 57.01% 47.28% 39.20% 32.50% 26.95% 22.35% 18.53% 15.37%
2002 Mark Kotsay 4 26 29.8 -0.1 11.5 82.51% 68.08% 56.18% 46.35% 38.25% 31.56% 26.04% 21.49% 17.73% 14.63%
2002 Miguel Tejada 8 28 32.5 3.5 1.8 78.40% 61.46% 48.19% 37.78% 29.62% 23.22% 18.20% 14.27% 11.19% 8.77%
2002 Pat Burrell 1 25 28.6 16.1 -15.1 84.76% 71.84% 60.90% 51.62% 43.75% 37.08% 31.43% 26.64% 22.58% 19.14%
2002 Randy Winn 8 28 22.5 -3.9 -0.8 76.68% 58.80% 45.09% 34.57% 26.51% 20.33% 15.59% 11.95% 9.17% 7.03%
2003 Bill Mueller 3 32 24.4 9.6 2.9 71.27% 50.80% 36.20% 25.80% 18.39% 13.11% 9.34% 6.66% 4.75% 3.38%
2003 Garret Anderson 1 31 23.7 2.7 0.7 71.50% 51.13% 36.56% 26.14% 18.69% 13.37% 9.56% 6.83% 4.89% 3.49%
2003 Hank Blalock 2 22 25.3 2.4 12.3 88.77% 78.80% 69.94% 62.09% 55.11% 48.92% 43.43% 38.55% 34.22% 30.37%
2003 Javy Lopez 3 32 23.1 8.8 7.8 71.83% 51.59% 37.06% 26.62% 19.12% 13.73% 9.87% 7.09% 5.09% 3.66%
2003 Jeff DaVanon 2 29 25.1 3.7 9.6 77.61% 60.23% 46.75% 36.28% 28.16% 21.85% 16.96% 13.16% 10.21% 7.93%
2003 Juan Pierre 5 25 25.8 -9.8 10.9 82.36% 67.84% 55.87% 46.02% 37.90% 31.22% 25.71% 21.18% 17.44% 14.37%
2003 Luis Castillo 5 27 20.2 0.5 3.3 80.34% 64.55% 51.86% 41.67% 33.48% 26.89% 21.61% 17.36% 13.95% 11.21%
2003 Marcus Giles 4 25 27.4 17.2 9.8 86.98% 75.66% 65.81% 57.24% 49.79% 43.31% 37.67% 32.77% 28.50% 24.79%
2003 Mark Loretta 2 31 23.7 -1.0 -1.9 69.97% 48.96% 34.25% 23.97% 16.77% 11.73% 8.21% 5.74% 4.02% 2.81%
2003 Melvin Mora 6 31 27.9 4.1 5.8 72.44% 52.48% 38.01% 27.54% 19.95% 14.45% 10.47% 7.58% 5.49% 3.98%
2003 Mike Lowell 2 29 23.7 7.4 2.3 77.70% 60.37% 46.90% 36.44% 28.31% 22.00% 17.09% 13.28% 10.32% 8.02%
2003 Milton Bradley 6 25 29.2 -2.7 7.1 83.29% 69.36% 57.77% 48.11% 40.07% 33.37% 27.80% 23.15% 19.28% 16.06%
2003 Morgan Ensberg 1 27 27.0 14.4 10.3 83.64% 69.96% 58.52% 48.95% 40.94% 34.25% 28.64% 23.96% 20.04% 16.76%
2003 Orlando Cabrera 1 28 28.0 -10.3 10.2 76.18% 58.04% 44.21% 33.68% 25.66% 19.55% 14.89% 11.34% 8.64% 6.58%
2003 Rafael Furcal 8 25 29.6 2.7 6.5 84.23% 70.94% 59.75% 50.33% 42.39% 35.70% 30.07% 25.33% 21.33% 17.97%
2003 Trot Nixon 4 29 25.7 16.8 -0.3 79.54% 63.27% 50.33% 40.04% 31.85% 25.33% 20.15% 16.03% 12.75% 10.14%
2004 Aaron Rowand 4 26 28.5 8.6 10.6 84.15% 70.81% 59.58% 50.14% 42.19% 35.50% 29.87% 25.14% 21.15% 17.80%
2004 Aubrey Huff 1 27 27.4 12.5 -11.3 81.13% 65.82% 53.40% 43.32% 35.15% 28.52% 23.14% 18.77% 15.23% 12.35%
2004 Brad Wilkerson 2 27 27.1 13.8 -3.1 82.23% 67.62% 55.61% 45.73% 37.61% 30.93% 25.43% 20.91% 17.20% 14.14%
2004 Carl Crawford 7 22 28.9 -3.4 13.0 87.93% 77.31% 67.98% 59.77% 52.56% 46.21% 40.63% 35.73% 31.41% 27.62%
2004 Carlos Guillen 5 28 28.4 4.7 5.7 79.30% 62.89% 49.87% 39.55% 31.36% 24.87% 19.72% 15.64% 12.40% 9.84%
2004 Carlos Lee 5 28 34.7 9.9 -3.6 79.19% 62.71% 49.66% 39.32% 31.14% 24.66% 19.53% 15.46% 12.25% 9.70%
2004 Coco Crisp 2 24 26.5 -4.6 9.9 84.85% 72.00% 61.09% 51.83% 43.98% 37.32% 31.67% 26.87% 22.80% 19.34%
2004 Corey Patterson 1 24 25.8 -5.1 8.8 84.68% 71.71% 60.73% 51.43% 43.55% 36.88% 31.23% 26.45% 22.40% 18.97%
2004 David Ortiz 5 28 28.0 14.6 -14.8 79.28% 62.85% 49.82% 39.50% 31.31% 24.82% 19.68% 15.60% 12.37% 9.80%
2004 Jack Wilson 2 26 27.1 -18.0 11.7 78.73% 61.99% 48.80% 38.42% 30.25% 23.82% 18.75% 14.76% 11.62% 9.15%
2004 Jason Varitek 2 32 29.5 1.4 8.6 69.34% 48.08% 33.34% 23.12% 16.03% 11.11% 7.71% 5.34% 3.70% 2.57%
2004 Jimmy Rollins N/A 25 27.4 -3.1 6.4 83.21% 69.24% 57.61% 47.94% 39.89% 33.19% 27.62% 22.98% 19.12% 15.91%
2004 Mark Teixeira 9 24 26.9 11.9 -1.9 86.61% 75.01% 64.96% 56.26% 48.72% 42.20% 36.55% 31.65% 27.41% 23.74%
2004 Travis Hafner 4 27 30.0 26.7 -17.1 83.33% 69.44% 57.86% 48.22% 40.18% 33.48% 27.90% 23.25% 19.37% 16.14%
2004 Vernon Wells 5 25 30.3 9.2 -2.8 84.56% 71.50% 60.46% 51.12% 43.23% 36.56% 30.91% 26.14% 22.10% 18.69%
2005 Brian Roberts 6 27 25.8 4.8 6.0 81.34% 66.16% 53.82% 43.78% 35.61% 28.96% 23.56% 19.16% 15.59% 12.68%
2005 Chase Utley N/A 26 26.4 15.7 13.2 85.66% 73.37% 62.85% 53.83% 46.11% 39.50% 33.83% 28.98% 24.82% 21.26%
2005 David DeJesus 9 25 26.5 6.8 2.8 84.73% 71.79% 60.82% 51.53% 43.66% 36.99% 31.34% 26.56% 22.50% 19.06%
2005 David Wright N/A 22 27.8 31.4 1.5 91.48% 83.69% 76.57% 70.04% 64.08% 58.62% 53.63% 49.06% 44.88% 41.06%
2005 Derrek Lee 1 29 28.5 18.4 -11.5 78.52% 61.66% 48.42% 38.02% 29.85% 23.44% 18.41% 14.45% 11.35% 8.91%
2005 Felipe Lopez 2 25 27.8 -3.9 1.5 82.57% 68.17% 56.29% 46.47% 38.37% 31.68% 26.16% 21.60% 17.83% 14.72%
2005 Grady Sizemore 5 22 25.7 16.2 11.2 90.39% 81.71% 73.86% 66.76% 60.35% 54.55% 49.31% 44.57% 40.29% 36.42%
2005 Jason Bay 2 26 27.0 37.1 -15.6 86.82% 75.37% 65.44% 56.81% 49.32% 42.82% 37.17% 32.27% 28.02% 24.32%
2005 Jhonny Peralta 1 23 27.6 6.0 2.9 87.36% 76.33% 66.68% 58.26% 50.90% 44.46% 38.85% 33.94% 29.65% 25.90%
2005 Julio Lugo 2 29 23.1 -3.3 6.7 75.53% 57.05% 43.09% 32.55% 24.58% 18.57% 14.03% 10.59% 8.00% 6.04%
2005 Mark Ellis 9 28 27.3 6.3 8.1 79.97% 63.95% 51.14% 40.89% 32.70% 26.15% 20.91% 16.72% 13.37% 10.69%
2005 Michael Young 7 28 26.4 3.9 -4.8 77.96% 60.77% 47.37% 36.93% 28.79% 22.44% 17.50% 13.64% 10.63% 8.29%
2005 Miguel Cabrera N/A 22 29.2 23.8 -13.8 89.77% 80.58% 72.33% 64.93% 58.28% 52.32% 46.96% 42.16% 37.84% 33.97%
2005 Nick Johnson 3 26 29.4 12.9 -7.3 83.28% 69.35% 57.75% 48.09% 40.05% 33.35% 27.77% 23.13% 19.26% 16.04%
2005 Richie Sexson 2 30 23.7 18.2 -12.9 76.37% 58.32% 44.54% 34.01% 25.98% 19.84% 15.15% 11.57% 8.84% 6.75%
2005 Victor Martinez 3 26 27.0 7.3 7.4 83.66% 70.00% 58.56% 49.00% 40.99% 34.30% 28.69% 24.01% 20.08% 16.80%
2006 Bill Hall 2 26 28.5 2.4 5.5 82.47% 68.01% 56.08% 46.25% 38.14% 31.45% 25.94% 21.39% 17.64% 14.55%
2006 Brandon Inge 2 29 26.5 -11.3 12.0 73.90% 54.62% 40.37% 29.83% 22.05% 16.29% 12.04% 8.90% 6.58% 4.86%
2006 Brian McCann N/A 22 28.7 12.3 8.4 89.71% 80.47% 72.19% 64.76% 58.09% 52.11% 46.75% 41.94% 37.62% 33.75%
2006 Curtis Granderson N/A 25 26.4 3.3 12.5 84.96% 72.18% 61.33% 52.11% 44.27% 37.61% 31.96% 27.15% 23.07% 19.60%
2006 Dan Uggla 7 26 29.3 13.1 7.2 84.62% 71.60% 60.58% 51.26% 43.38% 36.70% 31.06% 26.28% 22.24% 18.81%
2006 Freddy Sanchez 2 28 27.1 3.1 11.9 79.68% 63.49% 50.58% 40.31% 32.11% 25.59% 20.39% 16.25% 12.94% 10.31%
2006 Garrett Atkins 2 26 24.4 7.3 1.9 83.22% 69.26% 57.64% 47.97% 39.93% 33.23% 27.65% 23.02% 19.15% 15.94%
2006 Hanley Ramirez 5 22 28.9 22.4 -2.3 90.28% 81.50% 73.58% 66.43% 59.97% 54.14% 48.88% 44.12% 39.84% 35.96%
2006 Joe Mauer N/A 23 27.3 23.2 7.6 89.97% 80.94% 72.82% 65.52% 58.94% 53.03% 47.71% 42.92% 38.62% 34.74%
2006 Jose Reyes 3 23 26.4 3.8 9.7 87.55% 76.66% 67.12% 58.76% 51.45% 45.05% 39.44% 34.53% 30.24% 26.47%
2006 Ramon Hernandez 2 30 29.8 -2.7 14.1 74.04% 54.81% 40.58% 30.05% 22.24% 16.47% 12.19% 9.03% 6.68% 4.95%
2006 Reed Johnson 1 29 27.3 1.8 -0.3 75.78% 57.43% 43.52% 32.98% 25.00% 18.94% 14.36% 10.88% 8.24% 6.25%
2006 Ryan Howard 6 26 30.4 39.3 -11.0 87.40% 76.38% 66.76% 58.34% 50.99% 44.56% 38.95% 34.04% 29.75% 26.00%
2007 Alex Rios 2 26 24.9 6.4 5.2 83.34% 69.46% 57.89% 48.25% 40.21% 33.51% 27.93% 23.28% 19.40% 16.17%
2007 B.J. Upton 6 22 23.1 14.7 -5.7 89.26% 79.67% 71.11% 63.47% 56.65% 50.57% 45.14% 40.29% 35.96% 32.10%
2007 Brandon Phillips N/A 26 27.1 -11.3 7.9 79.86% 63.78% 50.93% 40.67% 32.48% 25.94% 20.72% 16.54% 13.21% 10.55%
2007 Carlos Pena 5 29 28.9 18.1 -16.3 77.86% 60.61% 47.19% 36.74% 28.61% 22.27% 17.34% 13.50% 10.51% 8.18%
2007 Chone Figgins 4 29 27.4 9.7 -3.0 77.46% 59.99% 46.47% 35.99% 27.88% 21.59% 16.73% 12.95% 10.03% 7.77%
2007 Corey Hart 1 25 26.6 10.8 -2.5 84.98% 72.21% 61.36% 52.14% 44.31% 37.65% 32.00% 27.19% 23.10% 19.63%
2007 Kevin Youkilis 6 28 29.0 12.3 0.3 80.40% 64.65% 51.98% 41.79% 33.60% 27.02% 21.72% 17.47% 14.04% 11.29%
2007 Matt Holliday N/A 27 30.4 26.0 -7.6 84.05% 70.65% 59.38% 49.91% 41.95% 35.26% 29.64% 24.91% 20.94% 17.60%
2007 Nick Markakis 6 23 25.1 11.1 -2.0 87.81% 77.11% 67.71% 59.46% 52.21% 45.85% 40.26% 35.35% 31.04% 27.26%
2007 Nick Swisher 7 26 27.1 16.7 -4.8 84.28% 71.02% 59.86% 50.44% 42.51% 35.83% 30.19% 25.45% 21.44% 18.07%
2007 Prince Fielder 7 23 38.4 22.1 -17.8 87.95% 77.35% 68.03% 59.83% 52.62% 46.28% 40.71% 35.80% 31.49% 27.69%
2007 Robinson Cano 1 24 28.5 11.7 -6.1 86.21% 74.31% 64.06% 55.22% 47.61% 41.04% 35.38% 30.50% 26.29% 22.66%
2007 Russell Martin N/A 24 30.8 10.5 14.4 87.50% 76.57% 67.00% 58.62% 51.30% 44.89% 39.28% 34.37% 30.07% 26.31%
2007 Ryan Zimmerman N/A 22 27.5 9.1 10.4 89.46% 80.03% 71.60% 64.05% 57.30% 51.27% 45.86% 41.03% 36.71% 32.84%
2007 Troy Tulowitzki 1 22 26.9 2.2 15.8 88.92% 79.07% 70.32% 62.53% 55.60% 49.44% 43.97% 39.10% 34.77% 30.92%
2008 Carlos Quentin 1 25 31.0 14.5 -2.6 85.47% 73.05% 62.43% 53.36% 45.60% 38.98% 33.31% 28.47% 24.33% 20.80%
2008 Dustin Pedroia N/A 24 25.1 13.5 6.3 87.51% 76.58% 67.01% 58.64% 51.32% 44.91% 39.30% 34.39% 30.09% 26.33%
2008 Evan Longoria N/A 22 27.0 25.9 21.9 91.95% 84.55% 77.75% 71.49% 65.74% 60.45% 55.59% 51.11% 47.00% 43.22%
2008 Ian Kinsler N/A 26 27.1 18.2 -6.5 84.40% 71.24% 60.13% 50.75% 42.84% 36.16% 30.52% 25.76% 21.74% 18.35%
2008 J.J. Hardy N/A 25 25.1 -2.1 15.9 84.34% 71.13% 59.98% 50.59% 42.66% 35.98% 30.35% 25.59% 21.58% 18.20%
2008 Jacoby Ellsbury 2 24 25.7 7.4 16.9 87.36% 76.32% 66.67% 58.24% 50.88% 44.45% 38.83% 33.92% 29.63% 25.89%
2008 Jayson Werth 4 29 28.5 12.8 10.6 79.75% 63.60% 50.72% 40.45% 32.26% 25.73% 20.52% 16.36% 13.05% 10.41%
2008 Josh Hamilton N/A 27 29.2 28.3 -9.6 84.33% 71.12% 59.97% 50.58% 42.65% 35.97% 30.33% 25.58% 21.57% 18.19%
2008 Mark DeRosa 2 33 28.4 -1.6 0.9 64.22% 41.24% 26.48% 17.01% 10.92% 7.01% 4.50% 2.89% 1.86% 1.19%
2008 Mike Aviles 1 27 29.4 20.3 21.5 85.59% 73.26% 62.70% 53.66% 45.93% 39.31% 33.65% 28.80% 24.65% 21.10%
2008 Ryan Braun 6 24 25.7 36.6 -17.5 89.07% 79.33% 70.66% 62.94% 56.06% 49.93% 44.47% 39.61% 35.28% 31.43%
2008 Ryan Ludwick 3 29 27.6 16.6 0.2 79.47% 63.15% 50.19% 39.88% 31.69% 25.19% 20.02% 15.91% 12.64% 10.04%
2008 Shane Victorino 6 27 28.1 4.0 9.1 81.43% 66.31% 53.99% 43.96% 35.80% 29.15% 23.74% 19.33% 15.74% 12.82%
2009 Aaron Hill 2 27 28.6 3.3 4.0 80.72% 65.16% 52.60% 42.46% 34.27% 27.67% 22.33% 18.03% 14.55% 11.75%
2009 Adrian Gonzalez N/A 27 28.9 19.8 -10.1 82.68% 68.37% 56.53% 46.74% 38.65% 31.96% 26.42% 21.85% 18.07% 14.94%
2009 Ben Zobrist N/A 28 26.2 11.9 9.9 81.42% 66.29% 53.98% 43.95% 35.78% 29.14% 23.72% 19.32% 15.73% 12.81%
2009 Casey Blake 3 35 26.3 5.8 0.1 60.57% 36.69% 22.23% 13.46% 8.15% 4.94% 2.99% 1.81% 1.10% 0.66%
2009 Denard Span N/A 25 28.5 23.8 -1.6 87.10% 75.87% 66.08% 57.56% 50.13% 43.67% 38.03% 33.13% 28.86% 25.13%
2009 Franklin Gutierrez 3 26 25.0 -1.8 18.8 83.05% 68.97% 57.28% 47.57% 39.50% 32.81% 27.25% 22.63% 18.79% 15.61%
2009 Jason Bartlett 3 29 25.8 5.9 13.7 78.61% 61.79% 48.58% 38.19% 30.02% 23.60% 18.55% 14.58% 11.46% 9.01%
2009 Joey Votto N/A 25 28.2 28.7 -8.1 87.36% 76.32% 66.67% 58.24% 50.88% 44.45% 38.83% 33.92% 29.64% 25.89%
2009 Justin Upton N/A 21 26.3 13.3 -6.9 90.00% 81.01% 72.91% 65.62% 59.06% 53.16% 47.84% 43.06% 38.76% 34.88%
2009 Marco Scutaro 5 33 26.5 -5.2 3.3 63.45% 40.26% 25.55% 16.21% 10.29% 6.53% 4.14% 2.63% 1.67% 1.06%
2009 Matt Kemp 1 24 26.2 16.9 -4.8 87.18% 76.00% 66.26% 57.76% 50.36% 43.90% 38.27% 33.37% 29.09% 25.36%
2009 Michael Bourn 5 26 25.8 -2.5 7.8 81.80% 66.92% 54.74% 44.78% 36.63% 29.96% 24.51% 20.05% 16.40% 13.42%
2009 Nyjer Morgan 1 28 25.8 3.4 27.3 81.46% 66.35% 54.05% 44.03% 35.86% 29.21% 23.80% 19.38% 15.79% 12.86%
2009 Pablo Sandoval N/A 22 34.2 29.1 -1.6 90.97% 82.76% 75.29% 68.49% 62.31% 56.68% 51.56% 46.91% 42.67% 38.82%
2009 Shin-Soo Choo 5 26 28.6 28.4 -5.3 86.20% 74.30% 64.05% 55.21% 47.59% 41.02% 35.36% 30.48% 26.28% 22.65%
2010 Alexei Ramirez N/A 28 23.1 -3.3 6.6 77.71% 60.39% 46.93% 36.47% 28.34% 22.03% 17.12% 13.30% 10.34% 8.03%
2010 Andres Torres 3 32 28.0 6.1 14.2 71.73% 51.45% 36.90% 26.47% 18.99% 13.62% 9.77% 7.01% 5.03% 3.61%
2010 Angel Pagan 1 28 25.7 6.4 8.6 80.12% 64.20% 51.43% 41.21% 33.02% 26.46% 21.20% 16.98% 13.61% 10.90%
2010 Austin Jackson 4 23 24.4 8.2 7.5 88.08% 77.59% 68.34% 60.20% 53.02% 46.71% 41.14% 36.24% 31.92% 28.12%
2010 Brett Gardner 2 26 26.5 8.2 21.3 85.05% 72.34% 61.52% 52.33% 44.51% 37.85% 32.19% 27.38% 23.29% 19.81%
2010 Buster Posey N/A 23 28.4 18.0 10.6 89.49% 80.08% 71.67% 64.13% 57.39% 51.36% 45.96% 41.13% 36.81% 32.94%
2010 Carlos Gonzalez 4 24 29.0 17.4 3.5 87.78% 77.06% 67.64% 59.38% 52.12% 45.75% 40.16% 35.26% 30.95% 27.17%
2010 Carlos Ruiz N/A 31 29.4 -5.0 14.6 70.92% 50.30% 35.68% 25.30% 17.95% 12.73% 9.03% 6.40% 4.54% 3.22%
2010 Chase Headley N/A 26 28.2 1.9 -2.1 81.63% 66.64% 54.40% 44.40% 36.25% 29.59% 24.15% 19.72% 16.09% 13.14%
2010 Chris Young 3 26 25.7 -1.1 0.5 81.34% 66.17% 53.82% 43.78% 35.61% 28.97% 23.56% 19.17% 15.59% 12.68%
2010 Colby Rasmus 1 23 25.0 12.8 3.8 88.46% 78.25% 69.21% 61.23% 54.16% 47.91% 42.38% 37.49% 33.16% 29.33%
2010 Daric Barton 1 24 27.8 11.8 -2.8 86.51% 74.84% 64.74% 56.00% 48.45% 41.91% 36.25% 31.36% 27.13% 23.47%
2010 Jason Heyward N/A 20 29.0 28.5 -1.1 92.70% 85.94% 79.67% 73.86% 68.47% 63.47% 58.84% 54.55% 50.57% 46.88%
2010 Jay Bruce 4 23 26.9 7.5 5.6 87.79% 77.07% 67.66% 59.40% 52.15% 45.78% 40.19% 35.28% 30.98% 27.19%
2010 Jose Bautista N/A 29 27.8 3.5 -9.0 75.07% 56.35% 42.30% 31.76% 23.84% 17.90% 13.43% 10.08% 7.57% 5.68%
2010 Justin Morneau 1 29 26.8 17.0 -7.5 78.72% 61.96% 48.78% 38.39% 30.22% 23.79% 18.73% 14.74% 11.60% 9.13%
2010 Kelly Johnson 2 28 26.4 9.5 2.2 80.07% 64.12% 51.34% 41.11% 32.92% 26.36% 21.11% 16.90% 13.53% 10.84%
2010 Marlon Byrd 2 32 33.2 0.9 1.7 67.87% 46.07% 31.27% 21.22% 14.41% 9.78% 6.64% 4.50% 3.06% 2.08%
2010 Nelson Cruz N/A 29 29.5 10.2 4.0 78.35% 61.38% 48.09% 37.68% 29.52% 23.13% 18.12% 14.20% 11.12% 8.71%
2010 Rickie Weeks 3 27 31.6 12.0 -3.6 81.66% 66.68% 54.45% 44.47% 36.31% 29.65% 24.21% 19.77% 16.15% 13.18%
2010 Stephen Drew 2 27 25.8 -0.7 1.5 79.66% 63.46% 50.55% 40.27% 32.08% 25.56% 20.36% 16.22% 12.92% 10.29%
2011 Alex Avila 2 24 29.3 9.9 1.4 86.49% 74.80% 64.69% 55.95% 48.39% 41.85% 36.20% 31.31% 27.08% 23.42%
2011 Alex Gordon N/A 27 29.0 7.0 1.0 81.18% 65.90% 53.49% 43.43% 35.25% 28.62% 23.23% 18.86% 15.31% 12.43%
2011 Andrew McCutchen N/A 24 27.3 24.1 -1.9 88.39% 78.12% 69.05% 61.03% 53.95% 47.68% 42.14% 37.25% 32.92% 29.10%
2011 Cameron Maybin 2 24 25.6 4.7 6.9 86.19% 74.29% 64.03% 55.19% 47.57% 41.00% 35.34% 30.46% 26.26% 22.63%
2011 Elvis Andrus N/A 22 27.1 -4.6 13.7 87.84% 77.16% 67.78% 59.53% 52.30% 45.94% 40.35% 35.44% 31.13% 27.35%
2011 Giancarlo Stanton N/A 21 27.7 20.6 0.6 91.21% 83.19% 75.87% 69.20% 63.12% 57.57% 52.51% 47.89% 43.68% 39.84%
2011 Howie Kendrick N/A 27 30.1 4.5 6.1 81.14% 65.84% 53.42% 43.34% 35.17% 28.54% 23.15% 18.79% 15.24% 12.37%
2011 Hunter Pence N/A 28 26.8 15.2 -1.6 80.92% 65.47% 52.98% 42.87% 34.69% 28.07% 22.71% 18.38% 14.87% 12.03%
2011 Matt Wieters 3 25 28.5 -7.6 18.4 83.43% 69.60% 58.07% 48.45% 40.42% 33.72% 28.13% 23.47% 19.58% 16.34%
2011 Mike Napoli N/A 29 29.8 20.5 2.3 80.50% 64.81% 52.17% 42.00% 33.81% 27.22% 21.91% 17.64% 14.20% 11.43%
2011 Peter Bourjos 2 24 24.4 4.6 20.5 87.24% 76.11% 66.40% 57.92% 50.53% 44.09% 38.46% 33.55% 29.27% 25.54%
2011 Yadier Molina N/A 28 30.7 -14.6 20.1 76.20% 58.06% 44.24% 33.71% 25.69% 19.58% 14.92% 11.37% 8.66% 6.60%
2012 Adam Jones N/A 26 28.1 4.2 -1.8 82.13% 67.46% 55.41% 45.51% 37.38% 30.70% 25.22% 20.71% 17.01% 13.97%
2012 Bryce Harper N/A 19 28.1 18.0 9.0 92.98% 86.45% 80.38% 74.73% 69.48% 64.60% 60.07% 55.85% 51.93% 48.28%
2012 Edwin Encarnacion N/A 29 30.3 10.1 -11.4 76.39% 58.36% 44.58% 34.06% 26.02% 19.88% 15.19% 11.60% 8.86% 6.77%
2012 Ian Desmond N/A 26 26.9 0.3 2.6 81.81% 66.93% 54.75% 44.79% 36.65% 29.98% 24.53% 20.06% 16.41% 13.43%
2012 Josh Reddick N/A 25 23.1 2.2 10.1 84.65% 71.66% 60.66% 51.35% 43.47% 36.80% 31.15% 26.37% 22.32% 18.90%
2012 Martin Prado N/A 28 25.1 7.8 1.7 79.70% 63.52% 50.63% 40.35% 32.16% 25.63% 20.43% 16.28% 12.98% 10.34%
2012 Melky Cabrera 1 27 30.1 0.9 -5.4 79.08% 62.54% 49.46% 39.11% 30.93% 24.46% 19.35% 15.30% 12.10% 9.57%
2012 Miguel Montero 1 28 29.3 1.7 8.2 78.85% 62.17% 49.02% 38.65% 30.48% 24.03% 18.95% 14.94% 11.78% 9.29%
2012 Mike Trout N/A 20 29.5 53.6 13.0 95.05% 90.35% 85.89% 81.64% 77.60% 73.76% 70.12% 66.65% 63.35% 60.22%
2013 Andrelton Simmons N/A 23 25.0 -5.9 32.5 87.81% 77.10% 67.71% 59.45% 52.20% 45.84% 40.25% 35.35% 31.04% 27.25%
2013 Brandon Belt 1 25 26.1 16.7 -6.5 85.66% 73.37% 62.85% 53.83% 46.11% 39.50% 33.83% 28.98% 24.82% 21.26%
2013 Carlos Gomez N/A 27 27.5 -1.4 15.1 80.92% 65.48% 52.98% 42.87% 34.69% 28.07% 22.72% 18.38% 14.87% 12.04%
2013 Chris Davis 1 27 28.7 13.6 -13.9 81.04% 65.67% 53.22% 43.13% 34.95% 28.33% 22.96% 18.60% 15.08% 12.22%
2013 Freddie Freeman N/A 23 26.7 17.3 -14.6 87.77% 77.04% 67.62% 59.36% 52.10% 45.73% 40.14% 35.23% 30.92% 27.14%
2013 Gerardo Parra 1 26 27.9 -6.2 9.2 81.11% 65.78% 53.35% 43.27% 35.09% 28.46% 23.09% 18.72% 15.19% 12.32%
2013 Jason Castro N/A 26 26.9 2.9 4.5 82.54% 68.12% 56.23% 46.41% 38.30% 31.61% 26.09% 21.54% 17.78% 14.67%
2013 Jason Kipnis 1 26 26.5 17.6 -2.3 84.69% 71.72% 60.74% 51.44% 43.56% 36.89% 31.24% 26.46% 22.41% 18.97%
2013 Josh Donaldson N/A 27 29.8 19.0 10.9 84.45% 71.32% 60.23% 50.87% 42.96% 36.28% 30.64% 25.87% 21.85% 18.45%
2013 Juan Uribe N/A 34 31.9 -12.1 12.1 58.89% 34.68% 20.42% 12.03% 7.08% 4.17% 2.46% 1.45% 0.85% 0.50%
2013 Kyle Seager N/A 25 28.5 8.3 2.2 84.87% 72.03% 61.14% 51.89% 44.04% 37.38% 31.72% 26.92% 22.85% 19.39%
2013 Manny Machado N/A 20 23.1 0.2 28.8 91.50% 83.73% 76.61% 70.10% 64.15% 58.69% 53.71% 49.14% 44.97% 41.15%
2013 Matt Carpenter N/A 27 26.9 27.7 -3.7 84.82% 71.95% 61.03% 51.77% 43.91% 37.25% 31.60% 26.80% 22.73% 19.28%
2013 Paul Goldschmidt N/A 25 30.6 30.0 -9.6 87.39% 76.38% 66.75% 58.34% 50.98% 44.56% 38.94% 34.03% 29.74% 25.99%
2013 Starling Marte N/A 24 24.4 17.9 7.8 88.26% 77.90% 68.75% 60.68% 53.55% 47.26% 41.71% 36.82% 32.49% 28.68%
2013 Yasiel Puig N/A 22 29.4 37.6 -0.9 91.96% 84.58% 77.78% 71.53% 65.78% 60.50% 55.64% 51.17% 47.05% 43.27%
2014 Anthony Rendon N/A 24 26.4 18.4 6.2 88.17% 77.74% 68.55% 60.44% 53.29% 46.99% 41.43% 36.53% 32.21% 28.40%
2014 Anthony Rizzo N/A 24 30.0 11.5 -3.0 86.37% 74.60% 64.44% 55.65% 48.07% 41.52% 35.86% 30.97% 26.75% 23.11%
2014 Brian Dozier N/A 27 26.5 3.4 -0.5 80.33% 64.53% 51.84% 41.64% 33.45% 26.87% 21.59% 17.34% 13.93% 11.19%
2014 Christian Yelich N/A 22 25.0 17.7 -0.7 89.89% 80.81% 72.64% 65.30% 58.70% 52.77% 47.44% 42.65% 38.34% 34.46%
2014 Devin Mesoraco N/A 26 29.0 -2.0 7.8 81.78% 66.89% 54.70% 44.74% 36.59% 29.93% 24.47% 20.02% 16.37% 13.39%
2014 Erick Aybar N/A 30 25.8 -1.6 7.6 73.64% 54.22% 39.93% 29.40% 21.65% 15.94% 11.74% 8.64% 6.37% 4.69%
2014 J.D. Martinez N/A 26 27.5 1.8 -9.8 80.83% 65.33% 52.81% 42.68% 34.50% 27.89% 22.54% 18.22% 14.73% 11.90%
2014 Jonathan Lucroy N/A 28 26.4 6.4 11.2 80.37% 64.60% 51.92% 41.73% 33.54% 26.96% 21.66% 17.41% 13.99% 11.25%
2014 Jose Abreu N/A 27 31.9 42.9 -14.9 86.30% 74.48% 64.28% 55.48% 47.88% 41.32% 35.66% 30.77% 26.56% 22.92%
2014 Jose Altuve N/A 24 28.2 5.0 -6.4 85.08% 72.39% 61.59% 52.40% 44.58% 37.93% 32.27% 27.46% 23.36% 19.87%
2014 Josh Harrison N/A 26 30.4 5.4 3.4 82.79% 68.54% 56.75% 46.98% 38.90% 32.20% 26.66% 22.07% 18.27% 15.13%
2014 Juan Lagares N/A 25 28.4 -5.1 28.9 84.82% 71.95% 61.03% 51.77% 43.91% 37.25% 31.60% 26.80% 22.74% 19.28%
2014 Kevin Kiermaier N/A 24 25.7 13.1 21.3 88.48% 78.28% 69.26% 61.28% 54.22% 47.97% 42.44% 37.55% 33.22% 29.39%
2014 Lorenzo Cain N/A 28 26.3 2.8 19.4 80.49% 64.78% 52.14% 41.97% 33.78% 27.19% 21.88% 17.61% 14.18% 11.41%
2014 Michael Brantley N/A 27 25.7 10.0 -8.4 80.96% 65.55% 53.07% 42.97% 34.79% 28.17% 22.80% 18.46% 14.95% 12.10%
2014 Steve Pearce N/A 31 29.3 6.6 -3.1 71.82% 51.58% 37.04% 26.60% 19.11% 13.72% 9.85% 7.08% 5.08% 3.65%
2014 Todd Frazier N/A 28 27.5 11.0 4.4 80.61% 64.98% 52.38% 42.22% 34.03% 27.43% 22.11% 17.82% 14.37% 11.58%
2014 Yan Gomes N/A 26 27.6 9.4 13.6 84.58% 71.54% 60.51% 51.18% 43.29% 36.62% 30.97% 26.20% 22.16% 18.74%

Conclusions

After looking at this table, we can draw several conclusions. First, this Mike Trout guy is really good at baseball. Secondly, age is the main variable in determining the time until failure. The players with the highest survival rates are all under twenty-five and all the lowest survival rates are over thirty. This makes sense, because it is much easier for a twenty-year-old star to remain effective until he is thirty compared to a thirty-year-old star attempting to remain effective until he is forty. This is because older players face more challenges such as eroding skills, an increased chance of sustaining injuries and having their playing time reduced to prevent injuries.

It also appears that offensive stars survive longer than defensive stars. This is probably due to the fact that defensive skills usually deteriorate faster than offensive skills. I also believe that since defensive statistics are more volatile than offensive statistics, that players that derive much of their value from their defense are more likely to have their WAR fluctuate from year to year. This makes it more likely that a defensive star could register a scrub season one year and then become a star again the next year. And this brings me to my next point.

Things to Keep in Mind

If a player records a scrub season that does not necessarily mean that he is finished.  If this were the case, players like Aramis Ramirez, Robinson Cano and Troy Tulowitzki would have had much less productive careers. It is also important to remember that a player enters the population as soon as they record their first star season, so it is quite possible that a player could improve after their first star season and make it more likely that they can outlast their projected survival rate. The main thing to remember is that no model is perfect and no model is meant to replace the human decision-making process. Models are only meant to improve the decision-making process and it is my hope that this model has accomplished that goal.


Pitchers Aren’t Just Bad Hitters

They are TERRIBLE hitters. They are not comparable to even the worst real hitters.

Max Scherzer said he enjoys hitting, but after getting hurt doing it, he thinks the DH might not be the worst idea.

The designated hitter is always a touchy subject, even though the National League is, if not the only league anywhere in the world, amateur or professional, that continues to employ it, then one of the few leagues to do so.

Yet I am not fully in one camp or the other. However, bringing the DH to the NL would not be a disaster of gargantuan proportions, as many a diehard NL fan might tell you. In fact, in an era of dying offenses, perhaps getting the worst hitters out of the batter’s box is an acceptable idea.

In 5519 PA in 2014, pitchers hit .122/.153/.153, for a minus-19 wRC+. The absolute worst hitter with at least 100 PA was JB Shuck, with a .145/.168/.209 line for a wRC+ of 2, or 21 points higher than the average pitcher. 21 points of wRC+ was also the 2014 gap between Nelson Cruz and Yan Gomes, or pick any of a number of great offensive seasons from merely good ones. Except here you are starting at terrible and ending up at abysmally awful. I would have created a “wRC+ X was Y times higher than wRC+ Z” construct instead, but it’s hard to do that when dealing with MINUS-19 and a positive number.

Meanwhile, the 30 worst hitters with 100+ PA last year, who combined for 5544 PA, comparable to the number of pitcher PA, posted a triple slash of .184/.247/.261. Their median wRC+ was 44; the mean, 38. (Note: Not -19.)

Bill Bergen, the poster boy for awful hitters, had a career wRC+ of 22 — 41 points higher than your typical 2014 pitcher.

Pitchers are terrible at hitting because it’s barely part of their job as it stands. And then they get hurt, like Chien-Ming Wang (running the bases) or Max Scherzer, doing this part of their job that is nearly irrelevant to the rest of it. It’s like asking the janitor to file a TPS report, and then he gets a really nasty paper cut and can’t go back to work for some time. (Terrible analogy, I know.)

I know the arguments in favor of the National League system as well, but won’t rehash them here, for fear of convincing myself to completely accept the DH, and thus further upsetting any number of fans. For example, did you know (and other people have basically written this already) the pitcher’s turn in the order is actually a helpful hint, not a complicating factor, in deciding when to remove a pitcher from a game? Not pinch-hitting means that you are allowing someone who can’t hit to hit, in exchange for the least effective parts of his real job, the mid- to late innings. The gap between a fresh reliever and a starter multiple times through the order on the mound *and* the gap between even a pinch-hitter and the pitcher at the plate are almost always both going to be in favor of removing the pitcher.

See, that’s what I meant. I’ll cut my losses and avoid trying to devise another lame analogy to conclude with.


Thought Experiment: What If the Nationals Sell?

The Washington Nationals, FanGraphs staff unanimous picks to be NL East champions, are off to a rough 7-12 start. Whether those struggles will continue is a matter for another post.

We are not here to talk about what ails the Nationals, or how to fix it. We’re here for a curious hypothetical: what if the Nats’ collapse continues? What if they are below .500 at the All-Star break and become trade deadline sellers?

We’re going to examine four questions. Who would the Nationals sell, how much would the team’s core change, how much money does this save them in 2016, and when would the team contend?

1. Who would the Nationals sell?

Jordan Zimmermann, Doug Fister, Ian Desmond, and Denard Span are impending free agents. Those are four very big names. The Nationals would be poised to offer two of the most valuable starting pitchers on the summer market; Zimmermann and Fister might be rentals, but they also don’t come with Cole Hamels’ massive contract. I think the team could also deal two players who will be free agents after 2016: Stephen Strasburg and Drew Storen.

The potential return here is, obviously, massive. We’re talking about trading away three members of a pitching rotation some analysts thought would be historically great. Strasburg clocked in at #23 on Dave Cameron’s offseason trade value rankings, just behind now-injured Yu Darvish. Although it would be frivolous to speculate on trading partners, given that our scenario is already far-fetched to start with, Ian Desmond and a starting pitcher could go a long way toward solving the Padres’ roster issues.

There are probably only two or three teams in the league that could meet an asking price for Strasburg. Maybe one of them gets desperate. If so, the Nationals probably gain at least one long-term core player. It won’t be Mookie Betts, but then, most good major league regulars aren’t Mookie Betts.

2. How much would the team’s core change?

They would still have Bryce Harper, Anthony Rendon, and Ryan Zimmerman batting, and Gio Gonzalez, Tanner Roark, and Max Scherzer on the mound. You can do worse. 2016-17 will bring Michael Taylor to the outfield, Trea Turner to shortstop, and a number of pitchers into the majors, perhaps including Lucas Giolito, Reynaldo Lopez, Joe Ross, and/or A.J. Cole.

That does not a championship 2016 roster make, but GM Mike Rizzo can demand near-league-ready talent in exchange for half his rotation, his center fielder, his shortstop, and his closer. That’s a lot of bargaining chips, and Rizzo is historically good at extracting trade value. (Wilson Ramos, Tanner Roark, and Doug Fister were acquired for players who contributed a combined -1.6 WAR to their new teams. I am not making that up. Negative 1.6. This excludes Steve Lombardozzi, who never played for Detroit, but posted -0.3 WAR for Baltimore.)

Funnily enough, if this is an imaginary July 2015 where the Nationals are already struggling to reach .500, I don’t think trading everyone away would make the team much worse. The infield can limp to the offseason with Danny Espinosa and Dan Uggla; Michael Taylor can return to center field; and Tanner Roark would step back into the rotation. It’s clearly a less talented roster with less awe-inspiring pitching, but they won’t fall to the cellar, either.

3. How much money does this save in 2016?

Stephen Strasburg and Drew Storen are both entering arbitration, after earning a combined $13.1M in 2015. With Zimmermann, Fister, Desmond, and Span coming off the books, the team doesn’t exactly need to worry about money. Those six players represent $61M of the 2015 payroll. They can also buy out Nate McLouth.

Remember, though, that Rendon enters arbitration in 2016, and Harper a year later.

The only long, potentially burdensome contracts on the club belong to Scherzer (not yet a problem), Ryan Zimmerman (a few years of on-field value remain), and Jayson Werth (ditto). That could be a lot worse. The team does not have an albatross yet.

4. When would the team contend?

With the new wild-card game, the imaginary blown-up Nationals would be contending again in 2016. You still have the core talents of Scherzer, Harper, and Rendon; Gio Gonzalez and Tanner Roark eating innings; and several useful prospects for the outfield and rotation. Surround them with a raft of young talent acquired at the deadline, cross your fingers Lucas Giolito doesn’t blow out his shoulder, and the team would have playoff upside in 2016, with a chance at a division title in 2017.

Conclusion

The Nationals should be fine for 2015. This is still the best and most talented club in the NL East.

But if the Nationals implode? They have a real chance to rebuild very quickly indeed. The Red Sox just went worst-to-first, then back to worst, and now they’re bidding for first again. The “to first” part of that trajectory will be the Nats’ inspiration. If 2015 does become a nightmare in D.C., the Washington front office can use speedy recognition, honest self-assessment, and savvy trading to rebuild a new contending team, and quickly.


Jason Heyward and Troy Tulowitzki’s Eroding Command of the Strike Zone

(All stats are current as of the end of April 24th.)

During the offseason, Jason Heyward and Troy Tulowitzki were two of the highest-profile players on the trade block. Heyward was ultimately dealt as the Braves gear up for the future and the Cardinals look to fortify RF after the passing of Oscar Taveras. Tulowitzki was not dealt, as the Rockies hope that they can make an improbable run to the playoffs. Both players could be looking for new homes within the next year, as Heyward hits free agency (barring an extension) and Tulowitzki would be a very tempting target at the trade deadline or in free agency.

However, both players have started the season slowly. While Tulowitzki has a 103 wRC+ (which is pretty darn good for a SS), that figure is far below his 2014 results (171 wRC+) and his career figure (125 wRC+). Much of the blame can be placed on his .197 ISO, which is far below both his 2014 and career ISO. Tulowitzki has been able to counteract the drop in power somewhat due to a .370 BABIP that is far above any BABIP he has recorded over a full season. Heyward’s drop has been even more severe, as he is the owner of a B.J. Upton-esque 64 wRC+. While much of that should be attributed to a paltry .235 BABIP, some blame also can be ascribed to a poor batted ball distribution. However, it is too early to say that either player won’t see these trends reverse as the season progresses.

On the other hand, both players are suffering a precipitous and concerning decline in their plate discipline. Tulowitzki’s K rate has shot up from between 15 and 16 percent to almost 24 percent. Likewise, his walk rate has fallen to a paltry 1.6 percent as he has drawn one walk over the season. That shift is being driven by an increase in his swings on pitches out of zone, which has grown to 35 percent from 27 percent in 2014 according to Pitch F/X data:

In addition, Tulowitzki is making less contact as he swings, as his contact rate is below 80 percent – a percentage he has never had at the end of the season. He is also swinging and missing more and is over the league average for the first time since his disastrous cup of coffee in 2006. Tulowitzki’s also seen 8 percent more pitches in the zone (a higher figure than ever before), which indicates that pitchers are not as afraid of him as they once were. All of this comes directly after he had hip surgery, which suggests that he may not be fully recovered yet or that the injury may have eroded his skills slightly.

Heyward also has seen his plate discipline deteriorate but not to the same level that Tulowitzki has. First the good news: his strikeout rate, while slightly elevated from his totals in the past few years, is still in line with his career norms. However, the rest of his plate discipline numbers are worse than his career numbers. As noted by Bernie Miklasz, Heyward only has one walk, is swinging at far more pitches out of zone than ever before, and is seeing fewer pitches in the zone than ever before. Miklasz also notes that Heyward is pounding groundballs – he is currently putting 62 percent of his balls in play on the ground. This is far above his career averages (as shown in the chart below) and is a sign that chasing more pitches is not helping him generate power.

In addition to the points that Miklasz made, Heyward is also swinging far less at pitches in the zone. This season, he has swung at 58 percent of pitches in the zone, the lowest percentage since his rookie year. These diverging trends have allowed Heyward to set a personal record: for every pitch that Heyward swings at out of the strike zone, he only swings at 1.04 pitches in the strike zone.* This is far below his career ratio of 1.69.

Now, as loyal FanGraphs members (only the truly committed read the Community board!), I can hear your refrain of “Small Sample Size.” And I certainly agree that it is too early to completely believe in the magnitude of these changes. It is extremely unlikely that both players will walk less than 2 percent of the time this year. However, I believe that the magnitude and consistency of the changes is a clear sign that both players are suffering due to the erosion of their plate-discipline skills. Both players have reached the stabilization point for strikeout rate, are halfway to the stabilization point for walk rate, and Heyward is quickly approaching the stabilization point for groundball rate. In addition, per pitch metrics like O-Swing and Z-Swing stabilize quickly, with swing rate stabilizing at 50 PAs. While those stabilization points only denote the point at which the data is half noise and half signal, the changes are consistent enough across multiple measures of plate discipline that its extremely hard to argue that it could **all** be a fluke. While both of these players are plus defenders and have the power to still be plus hitters with poor plate discipline, their value will suffer unless they can find a way to turn around their plate discipline.

* This statistic can be calculated using the following formula: (Zone%*Z-Swing%)/((1-Zone%)*O-Swing%).


Three Simple Rules for Breaking My Heart

So here’s the deal. I’ve seen loads of articles with great analysis and research and truthfully, I’d love to be all up in that. I love the nerdy side of the game and wish I could quote and analyse the tiniest statistics. I have great admiration for those who do. But the simple fact is I’m not that type of guy. Maybe I can’t comprehend certain stats and figures bounded about in modern baseball society. It could be I don’t have enough time to commit to breaking through in this field. Or simply it’s a case that I lack the “get up and go” as my old teachers used to say. I like to think it’s a combination of factors which contribute to me having never published an article of any kind, anywhere, ever. But here we are, I’m ready to do it, just not in the traditional sense some of you more avid fans would have become accustomed to…….

A little background first. I’m 31, born and raised in London, England and have been a big baseball fan for well over a decade. I like to think I have read and watched enough about the history of the sport and the current state of the game to be able to hold my own in any conversation with more baseball educated fellows. I started playing fantasy baseball 3 years ago after being randomly invited to join a long standing league by someone in a mock draft and have been hooked ever since. My winters are spent plotting my draft tactic and reading countless articles to help me draft my dream team. My rankings are done by Christmas and altered ad-nauseam until spring commences before the draft day hits the day the season starts. And here we are, at the reason I have taken time out of my working day private life to write this article. What on Earth was I thinking during the draft?!?!?!

Our league is standard scoring categories, snake re-draft with standard 25 man rosters and is Head-to-Head (which I know some experts detest but for the more casual yet serious player, I like it). And this year expanded to twelve teams from the usual ten. I was sat there with my rankings, myriad spreadsheets and utilities ready to complete the perfect draft. I set myself three clear and concise rules;

  1. Do not draft too many players from one team. Reasoning is quite personal but I feel if there’s a team wide issue causing a slump and you have three or four guys from that team, the impact could be huge. I carried this over from my Chicago Bears Fantasy Football disaster a year ago.
  1. Do not draft a pitcher in the first 6 rounds. I had spent a massive portion of my research looking at guys I can get pretty late to form a strong pitching core and had enough confidence in myself to execute this successfully.
  1. Only draft closers guaranteed the role. This league has a stronger emphasis on closers as one or two teams will only draft relievers, nearly guaranteeing them WHIP, ERA and Saves whilst punting Wins and K’s which means relievers are generally drafted way too early (I’ll maybe do a write up on this one day but one step at a time huh).

With all this in mind, I logged on, found I was the 10th pick and wasn’t too bothered. Hey, I was that confident I could have missed the first round pick altogether and still put together a title winning team. Thirty minutes and three picks into the draft, I had Edwin Encarnacion, Jose Bautista and Stephen Strasburg. A couple more hours had passed, and I owned Dellin Betances in the 9th round and Ken Giles in the 17th. Well done dude, that’s two of the three rules out the window but as long as you don’t draft any more Blue Jays, this is salvageable. By the end of round 22, I had Dalton Pompey and Drew Hutchison rostered. I sit here now as a Devon Travis and Miguel Castro owner to boot.

So how did it come to this I now ask myself? Why do I have 6 Blue Jays, a 3rd round pitcher and two relievers who don’t close, one of which came to me in the 9th round?!?!?! AAARRRRRGGGGGGHHHHHHHH

Well it’s pretty simple really; something I like to call Fantasy Dynamics. No doubt this phrase has been used the world over, but I think it’s apt here. This is the part of the article where I try to put over some wisdom and insight. Why have I put these self-imposed rules in place and why have I proceeded to break them with no more than a “how do you do”?

Best place to start is Rule 1; Do not draft too many players from one team; I wanted power early in the draft, get power guys early and cheap speed later, so with Encarnacion still out there after the first 9 picks, he kinda just fell into my lap. It was either him, Abreu or Rizzo and his back injury in spring aside, I felt Encarnacion was the safest bet with his track record for continued elite power. Abreu and Rizzo actually went in the next two picks and so onto my second pick, 15th overall. More power I cried, I NEED MORE POWER. Ah look, Jose Bautista is still out there, he’ll do.

So without even fathoming my rules, within two minutes I had two Blue Jays. But I wasn’t bothered at this point. They served my purpose of getting elite power early. Granted, there was a couple of question marks over them but I’m not one for overpaying for the shiny new toy when there’s a perfectly good product on the shelf which does the same thing year in, year out for less. Neither player has much in the way of competition for their place this year and I actually believe the Blue Jays are a very good shout for the AL East so why shouldn’t I own their two best hitters?

As the draft went on, I needed an outfielder and lacked some speed. Ben Revere had been drafted too soon for my taste (152nd overall pick) and by the late teen rounds there wasn’t much in the way of cheap speed. I considered Marisnick, but his playing time concerned me more than Pompey’s, so I plumped for the Toronto native especially given his propensity to run in the Minors.

Then we head into the 22nd round and where I’m looking to pick up some low end starting pitching with upside. As mentioned before, this is a league where two teams ended up drafting only relievers which meant some SPs were going a lot later than expected. None more so than Drew Hutchison, someone I’d looked at in detail over the Winter and had warmed to considerably to fill the role of a low price, high upside pitcher. The fact his ADP was around the 220 mark and this was the 255th pick overall, I had to pull the trigger. His upside at this price to too high to ignore, especially considering Bud Norris went in the same round. And then there were 4 Blue Jays!

So the end of the draft, I have 4 guys rostered who play north of the border. That’s cool, not the end of the world. And then the season begins and who do I have as my middle infielder on opening day…….Danny Santana. Now I really hated this guy going into the draft and was raging at the fact it was me who drafted him, but middle infielders were going way sooner than expected and some too soon for my liking (some examples below) so I had to get him to fill a spot if nothing else. So the season starts and I figured, “hey, why not take a chance on Devon Travis”. He’d been named as Toronto’s starting second baseman and in this side, could be productive so why not. That makes it 5 Blue Jays.

Jimmy Rollins             ADP 131         Selected 71st overall      -60

Alcides Escobar          ADP 176         Selected 132nd overall  -44

Daniel Murphy            ADP 142         Selected 109th overall -33

Scooter Gennett         ADP 220         Selected 187th overall -33

Closing the end of the season’s first week, the news breaks that Brett Cecil is out as Toronto’s closer and John Gibbons’ faith is being thrust onto Miguel Castro, a 20 year old upstart who was so under the radar, I couldn’t even find any information about him pre-season. But this is a league where closers are gold-dust and I was first to find this information out (thanks Twitter). So there I was, 6 Blue Jays just one week into the season. Rule 1, thanks for playing but goodbye.

But I could justify it to myself, I went power early, needed a speedy outfielder late, really liked Drew Hutchison, hated Danny Santana and had the chance for another bit of gold closer. So it’s not all bad, right. Granted a couple of the picks haven’t worked out early doors (I’m looking at you Drew and Jose’s shoulder) but looking back, I’m not sure there’s a whole lot I would have done differently given the same set of circumstances. With hindsight, maybe, but as Helen Reddy once said “Hindsight is wonderful. It’s always very easy to second guess after the fact”.

Then Rule 2; Do not draft a pitcher in the first 6 rounds. I had no need to, I’ll load up power early, get a couple of SP2 types around the 7th and 8th rounds and then draft the best player in the need I had. Simple. Until I got to my 3rd round pick (no 34 overall). I had already seen 6 SPs drafted at this point (Kershaw, Felix, Scherzer, Sale, Bumgarner and Price) but no one seemed to want Stephen Strasburg to this point.  Why? I thought he’d take another step this year to being the ace he is already and would have been snapped up by now. But he wasn’t. I couldn’t chance he’d still be there by my next pick so why risk the wait? I had to do it, I just had to. And I did. Ta-Da, Rule 2 is outta here.

So why did I do it, what possible justification could I give myself for doing it? Well, it’s simple. I thought he was undervalued and was the best player available at the time of my pick. I could still achieve my target of stocking up with power early and now had an ace. I wouldn’t need two SP2 types, I’d only need the one and could easily bag some decent pop around the 6th, 7th and 8th rounds so this is a good thing. I’ve done something I didn’t want to and it should actually make my team better now, so yay me!

And then Rule 3; Only draft closers guaranteed the role. By the time of my 9th round pick (106th overall) I had the power I needed, had the two starters I wanted and only had a gap at shortstop which at the time, I figured I could fill in easily (hindsight again). Nine (count ‘em NINE) closers had been drafted at this point. I couldn’t sit on the fence any longer, knowing closers were disappearing faster than donuts at Homer Simpson’s house. So who could I get? The elite ones had gone; the next tier of guys had been drained. Or had they? Dellin Betances was still waiting for a roster spot. All the talk from the Yankees was a committee, Andrew Miller could be taking saves away but Betances was so good last year, is a righty with great stuff. He’d get the job sooner rather than later all to himself. Let’s do this.

I had no regrets, of course Betances will be closing, its a shoe-in. So by the time my 17th round pick arrives (202 overall), I figured it’s a good time to pick up another guy who can get me saves. By now, 31 relievers had been drafted, but Ken Giles was not one of them. The Phillies are desperate to cut ties with Papelbon and Giles is next up. They’ll find a buyer for Papelbon within the first week of the season. Papelbon doesn’t want to be in Philadelphia anymore. Papelbon will be gone within a week. Papelbon, PAPELBON, PAPELBONNNNNNNN………………

I was sure I had now got two guys, undervalued in this league that will close, give me plenty of strikeouts and be big factors in my triumph. At the end of the draft, I grinned to myself and was satisfied with my evening’s work. I looked at my “closers” and my grin subsided a little. What about Rule 3? Why have I now got 2 relievers not guaranteed to close?

Well as I mentioned in Betances case, I was so sure of his stuff and makeup, he’d be the full time closer within a couple of weeks. Maybe he’d lose a few saves to Miller during the season but so be it. I’m a Yankee fan (noticed I’ve waited this long to out myself in case any of you stopped reading as soon as I uttered those words). I know Betances will close, Girardi talking about a committee is pre-season bluster. D’oh.

And Ken Giles……well that I’m finding it harder to justify. There’s nothing guaranteed about Papelbon leaving the Phillies any time soon. Any potential buyer has gone silent and until trade deadline day looms, I think he stays put (maybe even beyond). Earlier this week I actually dropped Giles and he’s still sat in free agency which in this league, shows how limited his value has been so far. He’s been nowhere near last season’s level and is pretty much valueless in this league’s format. So well done to me for drafting him.

So that’s my draft day story, 3 simple rules, all of which have been broken. Why have I felt the need to write this? Is it somewhat cathartic? Well yes. But I’m not going to end on a big epiphany. People can take this for what they want it to be. Some of you will come out of this taking nothing away and that’s cool too. But the one thing it’s got me thinking about, is how much more flexible I need to be. When I first started to play, I almost had my team written down before the draft and barring one or two players, I wasn’t far wrong. It was as near to set in stone as could be. All because of my rigid nature in the draft. I’ve gotten better at that, I’m more open to making changes before, during and after the draft, seeking value rather than my overriding desire to own a particular player.

But this year I set myself three rules, based on my own experience, other people’s experience and every bit of research I had done. And yet all 3 still went out the window. Simple rules which won’t impact my plans and ideas, won’t hinder myself in the draft and should guide me to glory. And all I can muster is that flexibility is vital in drafts and during the season, keeping an open mind helps you as much as all the research you do. Don’t make rules you’re willing to break people!

No doubt, there’s much more seasoned Fantasy Baseball players who have read this and thought “what’s the point in this? I know what I’m doing, why should I listen to anything this guy has to say”. Some of you fellow newbies might also think the same, “How dumb is this guy?” But everyone who has ventured into this wonderful world we call Fantasy Baseball can take some sustenance from this, whether you learnt this lesson long ago, or simply don’t care about this and it’s given you something to gripe about, it’s done something.

Despite all of my rule breaking, I’m still happy with my team. It’s pretty much got the same MO as the team I had planned to have and there’s very little I would have done differently without hindsight. I think I can contend this year if I get that essential bit of luck everyone needs to succeed. I think this year could be my year. So let me close with a relevant quote which has some relevance, from my all-time favourite wordsmith; Mr Yogi Berra.

“If you don’t know where you are going, you’ll end up someplace else.”


Different Aging Curves For Different Strikeout Profiles

What follows will look at aging curves as they relate to players with specific strikeout profiles. Specifically, we will look at how wOBA ages for players that strikeout more than the league-average strikeout rate and less than the league-average strikeout rate.

Through the research that is presented in this post, two points will be proven:

  1. Players of different strikeout profiles age—their wOBAs change—at different rates.
  2. The aging curve for players of different strikeout profiles has changed over time.

Before I present the methodology, the research that was conducted, and their conclusions, I want to give a big thank you to Jeff Zimmerman, who has not only done a lot of research around aging curves, but has also helped me throughout this process and pushed me in the right direction several times when I was stuck. Thank you.

Population

In order to give a non insignificant amount of time for a player’s wOBA to stabilize, but not place the playing time threshold for plate appearances so high that we artificially limit the population even more than it naturally is at the ends of the age spectrum, I looked at all player season from 1950 to 2014 where a player had a minimum of 600 plate appearances for the first aging curve in this post. The second aging curve in this post looks at all player seasons from 1990 to 2014 with a minimum of 600 plate appearances.

Now that we have our population, we need to split our population into two groups: players that strikeout more than league average and players that strikeout less than league average.

Because the league average strikeout rate of today is very different than it was 65 years ago, we can’t look at a player’s strikeout rate from 1950 and compare it to the league average strikeout rate of today.

In order to divide the population into two groups, I created a stat that weighs a player’s strikeout rate against the league average strikeout rate for the years that they played. For example, if a player played from 1970 to 1975, their adjusted strikeout rate would reflect how their strikeout rate compares to the league average strikeout rate from 1970 to 1975.

Players were then placed into two buckets based on their adjusted strikeout rate: players that struck out more than league average and players that struck out less than league average.

Methodology

There has been a lot of discussion over the years about the correct methodology to use for aging curves. This conversation has had altruistic intentions in the sense that it’s aim has been to minimize the survivorship bias that is inherent in the process, and, through the progress that has been made over the years, this study uses what the author has found to his knowledge to be the best technique to date. This article by Mitchell Lichtman summarizes a lot of the opinions.

While there is a survivorship bias inherent in any aging curve, the purpose of the different techniques used to create aging curves is to minimize the survivorship bias wherever possible.

What We Don’t Want In an Aging Curve 

An aging curve is not the average of all performances by players of specific ages. For example, say you have a group of 30-year-old players that have an average of a .320 wOBA and group of 29-year-old players that have an average of a .300 wOBA.

The point of an aging curve is to see how a player aged, not how they played. The group of 30-year-old players has a high wOBA because they are a talented group of players; they lasted long enough to play until they are 30. As they aged from the previous year, when they were 29 to their current age 30 season, they lost the bottom portion of players from their player pool. These are the players that couldn’t hang on any longer, whether it be because of a decline in defense, offense, or a combination of both. This bottom portion of players lower the wOBA of the current 29-year-old population through their presence and raise the wOBA of the 30-year-old population through their absence.

At the same time, the current 30-year-olds aged from their age-29 season to their age-30 season. Sure, there may be players who had a better age-30 season than age-29 season, but the current group of 30-year-olds, as a whole, still played worse at 30 than they did at 29.

When you look at the average of a particular age group, in this case 30-year-olds, you only see the players that survived, and, because they no longer play, you leave behind the players that are hidden from you sample. The method that follows resolves this issue to an extent.

What We Do Want In an Aging Curve

This study uses the delta method which looks at the differences of player seasons (i.e. a players age 29 wOBA minus their age 28 wOBA) and weighs those differences by the harmonic mean of the plate appearances for each pair seasons in question.

I would explain this further, but Jeff Zimmerman does an excellent job of this in a post on hitter aging curves that he did several years ago. While Jeff Zimmerman looked at RAA, which is a counting state, the methodology is basically the same for our purposes and wOBA, which is a rate stat:

In a nutshell, to do accurate work on this, I needed to go through all the hitters who ever played two consecutive seasons. If a player played back-to-back seasons, the RAA values were compared. The RAA values were adjusted to the harmonic mean of that player’s plate appearances.

Consider this fictional player:

Year1: RAA = 40 in 600 PA age 25
Year2: RAA = 30 in 300 PA age 26

Adjusting to harmonic mean: 2/((1/PA_y1)+(1/PA_y2)) = PA_hm
/((1/600)+(1/300)) = 400

Adjust RAA to PA_hm: (PA_hm/PA_y1)*RAA_y1 = RAA_y1_hm
(400/600)*40 = 26.7 RAA for Year1
(400/300)*30 = 40 RAA for Year2

This player would have gained 13.3 RAR (40 RAA – 26.7 RAA) in 400 PA from ages 25 to 26. From then, I then would add all the changes in RAA and PA together and adjust the values to 600 PA to see how much a player improved as he aged.

Findings

Below is an aging curve by strikeout profile for all player seasons with over 600 plate appearances in a season from 1950 until 2015.

Screen Shot 2015-04-18 at 1.23.52 PM

We can see several findings immediately:

  1. Players do age differently based on their strikeout profile.
  2. Players that strikeout more than league average peak at 23.
  3. Players that strikeout less than league average take longer to hit their peak—their age 26 season.
  4. Players that strikeout more than league average age better than players that strikeout less than league average.

From a historical perspective, this graph is fun to look at, but the way the game was played over half a century ago is eclipsed by societal evolutions that today’s players benefit from.

To give us a more realistic idea of how today’s players age relative to their strikeout rate, I made another graph the at looks at player seasons from 1990 to 2014.

Screen Shot 2015-04-18 at 1.40.36 PM

What we find in this graph, which is more current with today’s style of play, is that players still age differently dependent on their strikeout profile, but not in the same way that they did in the previous sample.

Players that strikeout more than league average still peak earlier than players that strike out less than league average, but in this more current population of players, players that strikeout more than league average peak very early—their age 21 season. This information would reciprocate the sentiment that has been conveyed through recent work that suggests that the aging curve has changed to the point that players peak almost as soon as when they enter the league.

The peak age for players that strikeout at below league average rates is still 26, but whereas this group aged more poorly than the strikeout heavy group in our previous population, players that strikeout at below league average rates now age better than their counterparts.

Conclusions

This information can make material differences for our overall expectations and outlooks on players.

Previous knowledge would suggest that players like George Springer and Kris Bryant—players who have exorbitant strikeout rates—are still on the climb as far as their talent goes, but this information shows that these players may already be at/close to their peaks or on the decline as far a their wOBA is concerned.

This information also shows that we should be patient with prospects that have a penchant to put balls is play; while they peak more quickly than they did in the previous population, they take longer to develop than players with more swing and miss in their game, and when they do start to decline, there isn’t much need to worry, because their climb from their peaks will be gradual.

Like many other studies that have looked at new aging curves, this study confirms that players/prospects peak earlier now than at any other point throughout history, but it also shows that a player’s trajectory upward and downward is dependent on characteristics specific to their approaches at the plate.

Devon Jordan is obsessed with statistical analysis, non-fiction literature, and electronic music. If you enjoyed reading him, follow him on Twitter @devonjjordan.


Chris Archer’s Early-Season Improvements

After losing David Price to a trade with the Tigers and Alex Cobb to injury, The Rays needed Chris Archer to step up this season. Chris Archer then proceeded to step up this season. He’s carrying a 36 ERA-, 80 FIP-, and 69 xFIP-. His K-BB% is 23.6, better than his career mark by 10%. Obviously his numbers have improved. But it’s April, and the question everyone asks in April is are the improvements sustainable. Real improvements are the results of real changes, so let’s look for real changes.

One of the reasons for Archer’s success this year has been due to his ability to limit walks, which before had been a bit of a problem for him. Coming into the season he had a Zone% of 43.1 which is a tad below the league average. This year, that figure has increased to 54.4%. If you throw the ball in the zone more, you’re gonna get more strikes… more strikes means fewer times behind in the count… etc. You get the idea; good Zone% is good. But it’s not just that he’s throwing more pitches in the zone; Archer is allowing less contact on the pitches he throws there. Archer’s Z-contact rate has dropped by 4% from last year. So, to sum it up, Archer is throwing more pitches in the strike zone and hitters are making less contact when he does. This explains why Archer is getting more strikeouts and conceding fewer walks. What it doesn’t tell us is how he’s doing it. To figure that out, we have to look at his pitch selection.

According to the PITCH F/X data on FanGraphs, Archer was a two-seam-first pitcher last year – throwing the pitch nearly 47% of the time and his four-seamer only about 20%. The year, Archer’s increased the usage of his four-seamer by over 23%, dropping his two-seam rate to only 12%. This change is important because, thanks to work done by Jeff Zimmerman, we know that four-seam fastballs tend get strikeouts more often than their two-seamer cousins do. The four-seam isn’t the only pitch he’s increased usage for either: Archer’s slider rate has gone up to about 39% after sitting a little below 29% last year. Once again, this is good for strikeouts. Because, not only do sliders have the highest SwgStr% among pitch types after splitters, but the increase indicates Archer is more confident in his slider, which could imply that the slider has improved. You can say the same thing about the four-seam.

If you were looking for indicators that Chris Archer’s improved numbers have a level of sustainability, there they are. Those are real changes, from a real pitcher, playing real baseball. The Rays are gonna need an ace-level performance in their rotation this year to help alleviate the loss of David Price and the temporary one of Alex Cobb. It’s beginning to look like Chris Archer is the man for the job.


Austin Jackson’s Bothersome Batted-Ball Bind

On July 30, 2014, the Seattle Mariners found themselves in the interesting position of being in playoff contention. The Mariners sported a 32-23 record, only 2.5 games back of the AL West-leading Los Angeles Angels, and owned the third-best Pythagorean record in the American League. Seattle’s newfound position as postseason hopefuls meant that they were suddenly buyers at the trade deadline – not drastically so, but in the sense that the Mariners were only a couple of upgrades away from assembling themselves a nicely well-rounded playoffs roster. Chief among these desired upgrades was a serviceable everyday center fielder, one who could replace a revolving door of below-average outfielders that included Abraham Almonte, James Jones, Stefen Romero, and Endy Chavez.

Jack Zduriencik sought to remedy the Mariners’ outfield issues with a pair of trade deadline deals. The first involved packaging Almonte and minor-league pitcher Stephen Kohlscheen to the Padres in return for Chris Denorfia, a rather unsexy deal to be sure, but one that was a success at the time in that the acquired player was not Almonte. The second deal, a three-way transaction between the Rays, Tigers, and Mariners, was collectively more sexy, but a large share of the sexy went to the Tigers, who landed Rays ace David Price. The other major components of the deal were the Rays’ acquisition of young Mariners middle infielder Nick Franklin and Tigers pitcher Drew Smyly, as well as Seattle’s prospective answer to its outfield problem: Detroit center fielder Austin Jackson.

Since his move to the Mariners, Jackson has racked up 277 plate appearances for Seattle, and the results have been fantastically underwhelming. Of the center fielders who amassed more than 100 plate appearances for the Mariners in 2014 – an uninspiring triumvirate of Jackson, Abraham Almonte, and James Jones – Jackson produced the worst offensive performance by wRC+. Jackson’s 2014 performance also disappointed even by more conventional measures:

  • Jackson totaled 34 extra-base hits in 416 plate appearances for the Tigers in 2014. For the rest of the year, in 240 plate appearances for the Mariners, Jackson managed 6.
  • Jackson’s ISO dropped from .127 to a paltry .031 with the move from Detroit to Seattle.
  • Jackson’s 2014 OBP/SLG/wOBA with Detroit: .330/.397/.321. With Seattle: .271/.264/.243.

Not great for a player only two seasons removed from a 5-win campaign.

Ostensibly, something was fundamentally different with Jackson in 2014, something that can hopefully be determined by closely examining his recent performance. Looking first to Jackson’s approach, it seems that there hasn’t been too much change over the course of his career. His K/BB ratio has generally hovered around league average and his contact rates haven’t fluctuated all that much from year-to-year. If anything, Jackson’s approach metrics look like they’re trending positively – he actually posted career bests in Z-contact% and SwStr% in 2014. If we examine Jackson’s batted ball data, however, we begin to get a little closer to the root of Jackson’s troubles of late. The most easily identifiable aspect of Jackson’s game can be somewhat distilled in the following graphic:

Over the course of his career, Jackson’s BABIP has been way above league average. He managed an absolutely ridiculous .396 BABIP in his 2010 rookie season over 675 PA, and his career-best 2012 season, in which he posted a 134 wRC+, was bolstered by a BABIP of .371. That figure would predictably fall after 2012, but between 2013 & 2014, Jackson’s BABIP only declined by .008, whereas in the same period, his wOBA fell from a very good .332 to a mediocre .292. This might suggest that in 2014 specifically, it may not have been the frequency with which Jackson was able to put balls in play so much as the quality of those batted balls that limited Jackson’s production.

Unfortunately, batted ball data is out of the scope of my access. The closest I can get to Jackson’s batted-ball profile is  by pulling data from this pre-season piece on Jackson by Jake Mailhot over at Lookout Landing, and indirectly from Jeff Zimmerman’s work on hitter analytics at RotoGraphs (the relevant batted-ball spreadsheet now seems to be unavailable for some reason).

To quickly explain – this table charts batted-ball rates expressed as a percentage of league average. Batted balls are separated into three categories (line drive, groundball, fly ball) which are then further divided into subcategories of contact quality (Well-Hit, Medium, and Weakly-hit). These categories are ordered left-to-right from highest to lowest based on xBABIP.

Mailhot astutely notes an alarming drop in well-hit groundball rate – from 64% above league average in 2012 to 11% below league average in 2014. This is accompanied by a commensurate rise in weakly-hit groundballs. Jackson’s well-hit line-drive rate also drops by a sizable amount, hovering around league average in 2014, while his rate of medium-hit line drives balloons to 198% of league average in 2014. Mailhot also points out possibly the most substantial shift: an immense drop-off in well-hit fly-ball rate in 2014 to 56% of league average, a trend corroborated by data pulled from Baseball Heat Maps on Jackson’s average fly ball distance over his career:

Jackson’s high rate of well-hit line drives and ground balls prior to 2014 puts into perspective the aspects of his game that brought him success earlier on in his career, and his sharp decline in those metrics in 2014 even more so. To put it in exceedingly simple terms, Austin Jackson just didn’t really hit balls hard in 2014, something he was quite good at doing before that season. Judging by the splits, most of the not-hitting-balls-hard occurred after the move to Seattle.

Jackson’s 2013 was much better than his 2014, but it is the beginning of a short trend of BABIP decline. From examining batted-ball data, we can infer that quality of contact has a significant bearing on BABIP, and this makes sense using conventional logic as well. Hard-hit line ground balls are more likely to find gaps between defenders, hard-hit line drives are more likely to drop in for hits, and hard-hit fly balls are more likely to turn into extra-base hits (although BABIP ignores home runs). The easy explanation is that Jackson lost some power in 2014. I don’t have enough film on Jackson to know for sure if there’s a visually concrete reason for this (if, for example, there’s something off in his swing mechanics), but data from 2014 indicates that Jackson just hasn’t been making good contact.

Jackson’s issues are probably best explained by his batted-ball troubles, but park factors likely play some part as well, with Comerica Park being relatively more hitter-friendly than Safeco Field. Safeco’s pitcher-friendly park factor and the ‘dead ball effect’ of Seattle’s marine air probably have something to do with Jackson’s decline in fly-ball distance, although Jackson is himself contributing to that same decline in some measure.

At the time of his acquisition, a merely average performance from Jackson would have been a significant upgrade over the convoluted mishmash that had previously taken the field for the Mariners. Unfortunately, he was unable to even provide replacement-value production after coming to Seattle, totaling -0.4 wins above replacement in 2014. The Mariners traded for an above-average player and received the production level of a player who theoretically wouldn’t cut it in the big leagues altogether.

The prospect of 2015 being a bounceback year for Jackson has not gone over too well in these first few weeks. ZiPS (R) and Steamer (R) still think Jackson could manage 1.7-2.1 WAR on the season, which is a bit below his peak, but I think the Mariners would take that statline in a heartbeat. I’ve gone this far without mentioning Jackson’s other tools, but as a 28-year-old without a concerning injury history, there’s not as much reason to worry about his defense and baserunning as there is to worry about his offensive output. Jackson was a below-average defender by UZR in 2013/2014 and has been worth approximately 4 baserunning runs above replacement in each of the past couple of years, neither of which have dictated his value nearly as much as his offense, or lack thereof. Using those numbers as a serious predictive measure from year-to-year is simply not very useful at this point.

Lloyd McClendon was Jackson’s hitting coach back in Detroit, and suffice it to say he probably has a better grasp on Jackson’s habits as a batter than most. If anyone’s able to get Jackson back on track this season, it’s probably McClendon. At time of writing (the 18th of April), Jackson managed a slightly encouraging 2-hit, 1-walk performance against the Rangers. It’s early yet in the season, and there’s time for Jackson to hopefully figure things out. Alternatively, if Jackson can’t find some of his pre-2014 form this season, the Mariners might once again find themselves in the same trade deadline predicament from last year – only this time, there’s not an obvious trade chip à la Nick Franklin. Then again, 2015 is Jackson’s last year under team control, so the Mariners may simply choose to let him walk after the year is over if they’re not satisfied with his performance. If that’s the case, it’s hard to imagine looking back on the 2014 Jackson trade with anything but the same tinge of regret and frustration that has colored so many other Mariners transactions of the last decade.


Hardball Retrospective – The “Original” 1924 Washington Senators

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. Therefore, Frank Robinson is listed on the Reds roster for the duration of his career while the Rangers claim Ivan Rodriguez and the Red Sox declare Jeff Bagwell. 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 finest single-season rosters for every Major League organization based on overall rankings in OWAR and OWS along with the general managers and scouting directors that constructed the teams. “Hardball Retrospective” is available in digital format on Amazon, Barnes and Noble, GooglePlay, iTunes and KoboBooks. The print edition is coming soon. Additional information and a discussion forum are available at TuataraSoftware.com.

Terminology

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

Assessment

The 1924 Washington Senators   OWAR: 43.1     OWS: 287     OPW%: .615

Based on the revised standings the “Original” 1924 Senators obliterated the competition with the Tigers finishing a distant 13 games in arrears. Walter “Big Train” Johnson, approaching the final stop in his 21-year career, continued to blow smoke past American League batsmen. He whiffed the most batters in the Junior Circuit for the twelfth time and furnished a 23-7 mark with the best ERA (2.72) and WHIP (1.116) in the League. Johnson received the MVP Award for his efforts in ’24 and the future Hall-of-Famer retired three years later with 417 victories, a 2.17 ERA and a 1.061 WHIP along with 3,509 strikeouts and the most shutouts in Major League history (110). Johnson ranks first among pitchers in “The New Bill James Historical Baseball Abstract”.

Jack Bentley bolstered the Washington pitching corps, delivering 16 victories against 5 losses. Firpo Marberry split time between the rotation and bullpen, notching 11 wins and saving 15 contests (although saves were not officially tabulated until 1969).

ROTATION POS WAR WS
Walter Johnson SP 7.02 28.65
Jack Bentley SP 1.96 11.78
Firpo Marberry SP 1.48 17.72
Joe Martina SP 0.35 5.74
BULLPEN POS WAR WS
Ted Wingfield RP 0.67 2.54
By Speece RP -0.25 3.52
Slim McGrew SP -0.27 0.32
Paul Zahniser SP -0.28 3.72

Goose Goslin (.344/12/129) topped the American League leader boards in RBI while recording 199 hits and 100 runs. The future Hall of Famer surpassed the century mark in ribbies 11 times and recorded a .316 lifetime batting average. Sam Rice batted .334 with 106 runs scored and 39 two-baggers while producing a League-best 216 base hits. A .322 career hitter, Rice concluded his career only 13 hits shy of 3,000.

Charlie Jamieson rapped 213 safeties and posted a personal-best .359 BA after leading the Junior Circuit in the previous campaign with 222 knocks. First-sacker Joe Judge clubbed 38 two-base hits and delivered a .324 BA. Goslin rated 16th among left fielders in the “NBJHBA”. Rice (33rd-RF), Judge (44th-1B) and Bucky Harris (70th-2B) also placed in the top 100 at their respective positions.

LINEUP POS WAR WS
Charlie Jamieson CF/LF 3.09 19.11
Sam Rice RF 3.65 23.99
Goose Goslin LF 5.69 28.91
Joe Judge 1B 2.12 19.08
Ossie Bluege 3B 0.72 10.42
Bucky Harris 2B 0.32 13.31
Eddie Ainsmith C 0.11 0.45
Howie Shanks SS -0.02 5.21
BENCH POS WAR WS
Frank Brower 1B 1.05 5.27
Irish Meusel LF 0.98 16.78
Doc Prothro 3B 0.9 5.89
Bing Miller RF 0.83 13.65
Earl McNeely CF 0.3 5.84
Carl East RF 0.09 0.36
Ike Davis SS 0.02 0.35
Bennie Tate C -0.02 0.64
Carr Smith RF -0.13 0.04
Tommy Taylor 3B -0.13 0.85
Showboat Fisher RF -0.14 0.4
Pinky Hargrave C -0.35 0.21
Mule Shirley 1B -0.5 0.34
Frank Ellerbe 3B -0.9 2.19

The “Original” 1924 Washington Senators roster

NAME POS WAR WS General Manager
Walter Johnson SP 7.02 28.65 Thomas Noyes
Goose Goslin LF 5.69 28.91 Clark Griffith
Sam Rice RF 3.65 23.99 Clark Griffith
Charlie Jamieson LF 3.09 19.11 Clark Griffith
Joe Judge 1B 2.12 19.08 Clark Griffith
Jack Bentley SP 1.96 11.78 Clark Griffith
Firpo Marberry SP 1.48 17.72 Clark Griffith
Frank Brower 1B 1.05 5.27 Clark Griffith
Irish Meusel LF 0.98 16.78 Clark Griffith
Doc Prothro 3B 0.9 5.89 Clark Griffith
Bing Miller RF 0.83 13.65 Clark Griffith
Ossie Bluege 3B 0.72 10.42 Clark Griffith
Ted Wingfield RP 0.67 2.54 Clark Griffith
Joe Martina SP 0.35 5.74 Clark Griffith
Bucky Harris 2B 0.32 13.31 Clark Griffith
Earl McNeely CF 0.3 5.84 Clark Griffith
Eddie Ainsmith C 0.11 0.45 Thomas Noyes
Carl East RF 0.09 0.36 Clark Griffith
Ike Davis SS 0.02 0.35 Clark Griffith
Howie Shanks SS -0.02 5.21 Thomas Noyes
Bennie Tate C -0.02 0.64 Clark Griffith
Carr Smith RF -0.13 0.04 Clark Griffith
Tommy Taylor 3B -0.13 0.85 Clark Griffith
Showboat Fisher RF -0.14 0.4 Clark Griffith
By Speece RP -0.25 3.52 Clark Griffith
Slim McGrew SP -0.27 0.32 Clark Griffith
Paul Zahniser SP -0.28 3.72 Clark Griffith
Pinky Hargrave C -0.35 0.21 Clark Griffith
Mule Shirley 1B -0.5 0.34 Clark Griffith
Frank Ellerbe 3B -0.9 2.19 Clark Griffith

Honorable Mention

The “Original” 1915 Senators             OWAR: 49.1     OWS: 272     OPW%: .565

“Big Train” Johnson (27-13, 1.55) completed 35 of 39 starts while leading the American League in wins, WHIP (0.933), innings pitched, shutouts and strikeouts. The rotation was supplemented by Doc Ayers (14-9, 2.21) and Bert Gallia (17-11, 2.29). Clyde “Deerfoot” Milan swiped 40 bags and Tom Long legged out 25 triples at the top of the lineup.

The “Original” 1965 Twins                 OWAR: 46.0     OWS: 280     OPW%: .644

Zoilo Versalles topped the leader boards with 126 tallies, 45 doubles, 12 triples and 308 total bases to capture the 1965 A.L. MVP Award. Teammate Tony Oliva (.321/16/98) finished runner-up in the MVP race and collected his second batting title. Bob Allison, Jimmie Hall and Harmon Killebrew slammed at least 20 circuit clouts apiece. Jim Kaat (18-11, 2.83) anchored the starting staff and Ted Abernathy led the League with 31 saves and 84 relief appearances.

On Deck

The “Original” 1992 White Sox

References and Resources

Baseball America – Executive Database

Baseball-Reference

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

Seamheads – Baseball Gauge

Sean Lahman Baseball Archive