Below are two tools for Ottoneu FGPoints players to be used for the 2014 MLB season.  The first tool is a roster building tool that will provide 2013 statistics, including platoon splits, for offensive players.  Ottoneu players can use this tool to construct their team and prepare for 2014 auction drafts.

The second tool is a 2014 player projection tool that Ottoneu players (and commissioners) can use to estimate player projections for the 2014 season.  The tool incorporates Steamer, Oliver, and 3 Year Average stats for each player and then allows you to enter your own projections for the 2014 season.  Your own projections (will auto-populate FanGraph’s “Fans” projections as of 2.8.14…you can override these projections by entering your own) will load the team dashboard at the top of the tool and provide you with a summary of what you can expect from your Ottoneu team in 2014.

Roster Breakdown w/platoon splits:

http://bit.ly/1iCKkvl

2014 Team Projections Tool:

http://bit.ly/1eh614z

One of the great things about baseball is that it’s played in so many different ballparks, each with their own quirks and different dimensions. Much has been written about how different ballparks affect the game: the different distances of the fences, the size of the foul area, the altitude, and even what days the locals hang their laundry outside. These various park factors affect more than just the results of batted balls. They also influence the number of walks and strike outs. I want to take a look at a more esoteric park factor that has to my knowledge been ignored up to this point. Gravity. In high school you were probably told that gravity on Earth was a constant 32 ft/s² (or 9.8 m/s²), which was actually a white lie.  To be exact, the Earth’s gravity is 32.1740 ft/s² (or 9.80665 m/s²), but more importantly gravity is not constant.

There are several reasons the Earth’s gravity as we experience it is not constant. First, the Earth is not a perfectly uniform sphere. When mathematically approximating gravity we make the assumption that the Earth is a perfectly uniform sphere. But, since the Earth is not perfectly round and uniform, this assumption leads to a small error in the approximations and does not account for gravitational variations in different locations.

Second, gravity is dependent on your distance from the center of the Earth. Gravity is inversely proportional to the square of the distance between two objects, say between you and the Earth. The further away from the Earth you are, the weaker gravity is, g = g₀ (r_e /(r_e+h))²  where r_e is the radius of the Earth, g₀ is gravity at sea level, and h is how high you are above sea level. For example, at Coors Field g=g₀(20,925,524.9/(20,925,524.9+5,219.82))²  this equation tells us that gravity is 32.157913 ft/s²  at Coors Field, or  0.05% less than gravity at sea level (32.1740 ft/s²).

The third reason why the Earth’s gravity as we experience it is not constant is related to the centrifugal forces caused by the Earth spinning. The fact that the Earth is rotating does not actually change gravity (well this is a lie according to relativity there will be some rotational frame dragging but this effect is extremely hard to detect and surely won’t have a measurable effect on baseball). Centrifugal forces appose gravity and make items feel lighter. These forces are strongest near the equator (where you are the furthest from the Earth’s axis and therefore moving the fastest) and weakest near the poles (where you are closer to the Earth’s axis and rotating more slowly).  An easy way to remember this is gravity will be weaker the closer you are to the equator.

Let’s take a break from all this math for a bit. Here is the juicy part, the table below shows the gravity at all the different major league ballparks and the percent increase or decrease in gravity compared to the average gravity at all the ballparks (this is based on EGM2008, made easily available thorough wolfram alpha). Negative percentages indicate a decrease in gravity, while positive percentages indicate an increase in gravity.

(If you are paying close attention: 1) you might have noticed the average gravity in the table is lower than our conventional constant for gravity, 32.1740 ft/s². The average gravity in the table above is the average gravity at major-league ballparks only, not  the average gravity of all points around the world. 2) The table value for gravity at Coors Field does not exactly match what we calculated earlier. This is because the measure we calculated earlier did not account for centrifugal force or the effects of a non-uniform Earth. The gravity for Coors Field in this table allows for those factors.

The difference between the two most extreme ballparks is 0.07649 ft/s².   Alone this number seems small and is hard to conceptualize. I’ve gone ahead and explored a few different baseball scenarios to illustrate its effects.

So, what does 0.07649 ft/s²  really mean for the game of baseball?

1. Players are measurably lighter at lower gravity ballparks.

CC Sabathia feels just a little lighter while pitching in Miami than when in Seattle, a whole whopping 0.69 lbs lighter!  (Perhaps this is why when so many players travel to Florida for Spring Training they report feeling in the best shape of their life…)

2. An outfielder will have slightly longer to catch a fly ball in a lower gravity ballpark.

A fly ball with 4.5 second hang time at an average park would stay in the air 5.7 milliseconds longer in Miami, and in Seattle it would be in the air for 5 fewer milliseconds. That almost 11 millisecond difference in hang time between Miami and Seattle would mean that a running out fielder might cover 2 more inches in Miami, not enough to make any reel difference but interesting nonetheless.

3. Pitches will sink less in a lower gravity ballpark.

Pitches will sink less in Miami than they will in Seattle, but how much less? On a 65 mph slow curve it takes the ball about 0.650 seconds to reach the plate. This ball will drop 0.2 inches lower in Seattle vs. Miami. An average pitch taking 0.45 seconds to reach home plate, will only drop an addition 0.09 inches in Seattle vs. Miami. For comparison the diameter of a baseball bat is 2.6 inches or less.  A 0.2 inch difference is 1/13 the diameter of a baseball bat, which is too small of a difference to turn a hit into a swing and miss.

4. Home runs will travel farther in a lower gravity ballpark.

When it comes to home runs one would think differences in gravity would start to play a bigger role. Because home runs are in the air longer, gravity is bound to have a greater effect on them than it does on pitched balls. The hang time of a home run is usually a full order of magnitude longer than that of a pitch. Assuming identical weather conditions, a baseball hit 120 MPH at a 26^o angle would travel 13 inches (THAT’S MORE THAN 1 FOOT!) farther in Miami than it would in Seattle. That could make a difference, not in the actual score, but in what seat in the bleachers the ball would land. Although a foot is the largest difference we have talked about so far, practically it doesn’t really matter much for a no-doubt home run that’s traveling over 460 feet.

5. Just for Fun…

On the surface of the Earth if we wanted to look for extremes we would see the highest gravity at the South Pole, which would be 32.26174 ft/s² or 0.335% higher than the average gravity at a major league ball park (this and a few other factors would lead me to believe that playing in the South Pole would really suppress home runs). The other extreme would probably be in Quito, the capital of Ecuador (there is actually a volcano in Ecuador with slightly lower gravity but let’s look at one plausible hypothetical) where gravity is 32.04248 ft/s² or -0.347% below average. In Quito Sabathia would be 1lb lighter than he would at an average ball park and 1.3 pounds lighter than he would in Seattle. That same hypothetical 120 mph home run would go 0.9 feet farther in Quito than it would a an average ball park, and 1.3 feet shorter at the South Pole. This is of course completely hypothetical because we are assuming all other conditions are the same at these two ball parks such as air density and temperature, and this definitely not the case.

Thanks to

National Geospatial-Intelligence Agency for publicly releasing the Earth Gravitational Model EGM2008

Alan Nathan for providing the trajectory calculator tool, which I used to calculate difference in batted ball distances, the calculator can be found on his website http://baseball.physics.illinois.edu/trajectory-calculator.html

Who is Masahiro Tanaka?
This has been the question that baseball has been asking since there was a buzz created around his coming to the United States; buzz that probably started around the 2009 World Baseball Classic. Tanaka is a 25 year old Japanese pitcher with a stunning arsenal of pitches, especially his split-finger, who has had quite a bit of success in Japan since his debut in 2007 at 18. In looking at his abilities, it is best to look at his NPB statistics against those of his two best contemporaries, Yu Darvish and Daisuke Matsuzaka. It is fair to compare Tanaka to each of these pitchers because they were all similar ages when they started in the MLB (Darvish was also 25 and Matsuzaka was 26) and each was a top of the line pitcher in Japan.

For measurement’s sake, this will look at a couple key stats: innings pitched per start, WHIP, and strikeout to walk ratio. In Tanaka’s 7 year career in Japan, he averaged 7.6 innings per game started as compared to 7.7 for Darvish and 7.3 for Matsuzaka. When analyzing WHIP, Tanaka posted a 1.11 WHIP, while Darvish was at .985 and Matsuzaka was at 1.14. Finally, in the ever important category of strikeout to walk ratio, Tanaka was at 4.5, while Darvish marked at 3.75 and Matsuzaka was at 2.7. As we have seen, Darvish has rounded into a pretty good pitcher in the big leagues, even with some walk issues, and Matsuzaka was a solid part of the Red Sox rotation until his own pitch count issues did him in with Boston. Given these comparisons and the trends of statistics for each of these players, it is fair to say that Tanaka may not be as explosive as Darvish, but he is a very solid pitcher that will work the zone effectively and get the team deeper in the game.

Both Darvish and Matsuzaka had some walk issues as they transitioned to the MLB, as there is a huge difference between MLB players and NPB players in pitch recognition, and this may be a problem for Tanaka. If one were to hypothesize a reason for the walk issues for both Matsuzaka and Darvish, it was that they had such a huge gathering of pitches and it was tough to grab the strike zone with all of them, particularly their split finger fastballs which had a lot of NPB hitters swinging and missing as they dove out of the strike zone. As the splitter is a key pitch for Tanaka, this is absolutely something to watch during the 2014 season.

The good thing for Tanaka, though, is that he does not have the crazy assortment of pitches like Matsuzaka and he is more like Darvish with the basic four pitch arsenal. Once Tanaka is able to grasp the difference between the MLB and NPB strike zone, there is nothing to keep him from being a solid pitcher in the big leagues. Maybe he does not have the upside of Darvish, but it is not outlandish to predict that he will be a solid number two or fringe number one starter in the big leagues.

When will the Yankees realize how much they miss Mariano Rivera?
Mariano Rivera was the rock and foundation of the back end of the Yankees for the better part of two decades. It would be foolish to say that there will not be a difference made by his retirement, but the impact of his retirement will not be as great as one would assume, particularly for the closer position. In fact, when the Yankees lost Rivera for the 2012 season, they were fine with Rafael Soriano as an All-Star closer. David Robertson may or may not have as much of an impact as a veteran closer like Soriano, but it would be within the realm of possibility that the All-Star reliever Robertson can translate into the All-Star closer Robertson.

That being said, Robertson’s departure to the closer role leaves a large gap in the middle relief and set up roles. Both the inconsistent Joba Chamberlain and the ever reliable Boone Logan leaving will not help the 7th and 8th inning situation for the Yankees as well. The good news for the Yankees is that they signed Matt Thornton to take Boone Logan’s role and Shawn Kelley looked good in spurts while at the end of the game. The big unknowns are two young pitchers that may have a huge impact for the Yankees bullpen in 2014 and beyond. Cesar Cabral is a hard throwing lefty that the Yankees selected in the Rule 5 Draft in 2012 and lost for that season due to Tommy John surgery. Fortunately for the Yankees, the 23 year old came back during the 2013 season and was a strikeout machine in the minors, leading to a September call up to the Yankees, where he was solid in an 8 game audition. If he is able to work on his control, the 24 year old Cabral would be a huge boost to the bullpen.

Another young pitcher that the Yankees need to have make strides is Dellin Betances. The former top 50 prospect as a starter has bounced around a bit and had found a niche in the Scranton bullpen during the 2013 where he allowed one run and struck out 30 while minimizing his walks in his final 19 innings in the minor leagues. The imposing Betances should be able to fill the void left by Chamberlain in the Yankees bullpen and may even be a set up man by the time the stretch run comes around. The impact of Mariano Rivera’s retirement is may not be felt in the closer’s role, but the Yankees will need to shuffle around some players and hope for their younger pitchers to continue their development to fill the void left by the Hall of Fame closer.

How will the big spending of the Yankees affect the development of the younger players?
The Yankees were lauded in the late 1990s and early 2000s for having a seemingly never ending farm system that was fruit for big league stars and young players to involve in the blockbuster trades that the Yankees made. For a long time now, though, this well has dried up and the Yankees farm system is decent at best. There is a ton of opportunity in the minors, though, and the Yankees farm system could bloom into a top farm system if things go right. At the same time, players like Tyler Austin or Mason Williams could continue to regress and Michael Pineda or Manny Banuelos could stay injury prone and the farm system could be even worse off than they are now.

In answering the question posed above, the big spending allows the Yankees to let all of this play out. There will not be a ton of pressure on the younger players to move up the ladder quickly and, frankly, other than middle infielders and relievers, the Yankees do not have pressing needs at the big league level. This is not to say that the Yankees could not use a player like Pineda or Sanchez or Williams at the big league level, but rather it is that the Yankees have spent a lot of money on their big league roster and would like to see return on their investment. There are a lot of players in the minor league system for the Yankees that need a big 2014 season after disappointing 2013 seasons and the spending spree that the Yankees went on this offseason will allow these players to develop at a steady pace rather than feel the pressure of an imminent big league promotion.

What will the twilight of Derek Jeter’s career look like?
As with every person, at some point in life, your skills diminish and you have to walk away from what you were once good at. For Derek Jeter, this realism has to occur quite soon. In almost every way you look at it, Jeter has become weaker and his skill base is eroding. At the best point of Jeter’s career, he was a hitter that could control the field and spread the ball all over the place with his patented inside out swing. Now, he has lost a bit on his swing and cannot get around on the inside pitch as well as he did even two or three years ago and his contact has become weaker, with ground ball rates in the 60% range. Since his speed has also disappeared, this is a bad omen for the soon to be 40 year old Jeter.

What is even worse for Jeter is that his hitting is the reason that he is still playing baseball, as his range is nearly non-existent. It is sad to see the greats go out like Jeter will, but he needs to realize that his time has come to an end. The Yankees need to work diligently at finding a replacement for Jeter in the minors, as the free agent market for shortstops is usually thin, and it was good thing that the Yankees used an early pick on Gosuke Katoh who may be able to bridge the gap. As for the twilight of Jeter’s career here in 2014 and, possibly 2015, expect that he plays about 100 games at shortstop, another 20-25 at designated hitter, and is cautiously used in a way that can optimize what skills he does have left. If Jeter is able to keep his batting average in the high .270s or .280s, the Yankees will be able to accept that along with his leadership and knowledge of the game.

Why are the Yankees going to win 93 games?
The prediction on the Yankees is strongly based in the fact that the past two years that the Yankees have not had superb seasons and have had very good outputs. It is shocking to say that the Yankees have not had a great amount of success considering how much money they spend on their team, but that is the truth. At some point, Joe Girardi may need to be given some credit for managing the egos that the Yankees have and for making sure that they are at the top of their games. Last year’s team had no reason to win 85 games and there is more talent on this team. There are many that are not fans of the Yankees having a lineup that is full of so many older players and, at my count, five different players that will need to play DH this year for some reason or another, but there is a lot to like about this Yankees team. Although Jacoby Ellsbury was a very big reach, all of the other pick ups that the Yankees made this offseason were smart in a financial and player personnel way. This year, a lot of the holes that were there with the Yankees of 2013 should be filled and the Yankees will return to the playoffs.

5 You Know:
1. Alfonso Soriano
2. CC Sabathia
3. Hiroki Kuroda
4. Carlos Beltran
5. Jacoby Ellsbury

5 You Will Know:
1. Masahiro Tanaka
2. Jose Ramirez
3. Mark Montgomery
4. Slade Heathcott
5. Zolio Almonte

5 You Should Remember:
1. Eric Jagielo
2. Tyler Austin
3. Gary Sanchez
4. Mason Williams
5. Ian Clarkin

What will the Red Sox get from Xander Bogaerts this year?
Right now, there are a lot of good things that people are saying about Xander Bogaerts and there is a lot of reason for that. He is a big, strong kid (yes, kid — he is only 21) and he will only grow into his body more and more as time goes on. Many can say that Bogaerts strikes out way too much for a middle infielder, but he is also not your typical middle infielder, as people see 25-plus home run potential from Bogaerts. Also, his walk rate has stabilized in the 10% range, and that is good for a young hitter. As for this year, Bogaerts should grab the shortstop position from the departed Stephen Drew. An average around .270 and somewhere between 15-20 homeruns with a very incongruent fielding season should be a good rookie campaign out of Boegaerts. That would make him about the same value to the Red Sox in 2014 as Drew was in 2013, but in the grand scheme of things, a top 3 Rookie of the Year performance will be a huge boost to the future of the Red Sox.

Who will be the 5th man in the Red Sox rotation by the end of the season?
On the onset of the season, the Red Sox have a very volatile rotation other than Jon Lester. Between the inconsistency of John Lackey and Ryan Dempster and the injury history of Jake Peavy and Clay Buchholz, it is very difficult to say if the Red Sox will have an elite staff like the one that led them to a World Series title or if the injuries and inconsistency will lead to a lot of round trip journeys to Pawtucket. By the end of the season, for one reason or another, Matt Barnes will sneak into a consistent fifth starter in the rotation. The first pick by the Red Sox in the 2011, Barnes has had some issues with walks throughout his minor league career, but he has blown hitters away at each level since being drafted and will prove his worth in AAA before he makes it up to the Boston roster. This is not an indictment of Allen Webster or Henry Owens, but rather it is an endorsement of the skills of Barnes over them. As stated previously, the Red Sox are set up very favorably in the near future with those three ready to join the rotation with Lester and Buchholz.

Will the Red Sox miss Jacoby Ellsbury?
This could be very simple and to the point, Jackie Bradley Jr. should be worth about two wins less than Jacoby Ellsbury this year. That is very cut and paste and that should be enough to say that the Red Sox will miss Ellsbury. This is not the whole story though. There is the fact that Ellsbury has been hurt throughout his career very frequently and his production has been incongruent. Considering the amount of money that the Yankees paid to get him to come to New York, it is not a shock that the Red Sox let him leave. In a vacuum, the Ellsbury move was one that was bad for Boston, as they do not have a sure thing in Bradley and there is nothing in Bradley’s history that shows that he will be anything better than just above average.

When you look at all of the factors, though, the move is a bit better for Boston. The easiest reason to say that the Red Sox will be fine is that all of the money that would have been spent on Ellsbury can now be given to other players and that the Red Sox do not need to pay an aging veteran a lot of money in the next five years. Also, even though the Red Sox are coming off of a World Series win, the team is looking to build for the future with guys like Bradley and Bogaerts and want to see what they have for the future and want to see if they have in house players that could fuel another run and a profitable future.

What should the Red Sox expect out of Clay Buchholz?
A couple times in this post, I have mentioned Clay Buchholz and I feel like I could write 2500 words just explaining him and the enigma that he is as a player. Throughout his minor league career, Buchholz was a big time strikeout guy and looked that way during his brief call up in late 2007. He also pitched a no-hitter late in the 2007 World Series winning season. Since that time, Buchholz’s entire career has been an elevator and at any time that he seems to figure it out, bigger questions are created; specifically looking at his two best seasons, 2010 and 2013.

In 2010, Buchholz was 17-7 and had a 2.33 ERA which were stellar numbers for a 26 year old, making the Red Sox look at him as the ace for the future. He also, though, only had 6.22 K/9 and 3.38 BB/9. There were good numbers that led to the solid “baseball card” numbers of 17 wins and a 2.33 ERA, but none of that was sustained in 2011 and 2012, although there were moments in 2011 when Buchholz was a good player before he got injured.

Suddenly, in 2013, Buchholz was better than ever, posting a career high in K/9, a career low in BB/9, and minimizing home runs, leading to a sub-2 ERA. Unfortunately, this was done in just over 100 innings pitched and his strand rate was at a career high while his BABIP was at a career low. For the 2014 season, the median should be the norm, as Buchholz’s ERA should be in the mid 3’s and he should be able to contribute 25-28 starts for the Sox. As for the walk and strikeout rates, it is probably best for Buchholz to pitch to contact a bit more and let that walk rate get into the high 2’s per 9. A wise suggestion for his future would be to get a bit more sink on his fastball, as his ground ball rate is alarming low for a pitcher obviously focusing on pitching to contact a bit more.

Why are the Red Sox going to win 86 games?
The 2013 Red Sox were a team on a mission, both to run the table in the AL East and to win the World Series. This year, though, there are some big question that are still similar from the onset of the 2013 season. No one knows about the health of Clay Buccholz or Jake Peavy or even Shane Victorino or Mike Napoli and a team with those many injury questions cannot be seen as a force going forward. That being said, there is a very strong case for the Red Sox exceeding what the predictions say, as John Farrell is a very good manager. As shown last year in the juggling that was done and all of the correct platoons that Farrell played, there is no reason to expect that the Red Sox will be under 90 wins. It is a catch-22 to say that the same reasons that the Red Sox may succeed is why they may fail, but the Red Sox cannot expect guys like Jonny Gomes, Mike Carp, and Daniel Nava to perform at the same level that they were at during the 2013 season and that is why there is a dose of pessimism in the the forecast for the Red Sox.

5 You Know:
1. David Ortiz
2. Dustin Pedroia
3. Mike Napoli
4. Jon Lester
5. Clay Buchholz

5 You Will Know:
1. Matt Barnes
2. Henry Owens
3. Rubby De La Rosa
4. Allen Webster
5. Brandon Workman

5 You Should Remember:
1. Bryce Brentz
2. Garin Cecchini
3. Blake Swihart
4. Trey Ball
5. Mookie Betts

What is the impact of Evan Longoria on the 2014 Rays?
This is a tricky question to answer, as he is the most important player on the team and he makes this team run smoothly. That being said, he has had some injury and consistency issues in the past and it is very possible that those same issues will plague him during the 2014 season. The positive things about Longoria abound: he fields a tough position very well, he hits for power, he balances the lineup, and he walks a good amount for a power hitter. Yet there are still some questions with the young star. First off is the strikeout rate, which has consistently been in the 20% range, except for the 2011 season. This is interesting to look at because the 2011 season, Longoria had outliers in the positive rate for walk rate and for strikeout rate, yet his patient approach lead to a career low in batting average, most attributed to his ridiculously low BABIP.

This season should be a year for Longoria to really break out and that should bode very well for the Rays. Longoria needs to focus on getting the ball in play, though, because that 2011 season was very fluky and should be looked at as an outlier. As Longoria focuses on stretching out the count and shortening his swing when the count is in the pitcher’s favor, his numbers will get even better. For the 2014 season, an average in the mid-280s with 35-40 home runs and elite defense will make Longoria an MVP candidate.

Is Wil Myers going to turn into a megastar for the Rays?
The 2013 AL Rookie of the Year was everything that the Rays could have expected from a first year player, other than the sometimes suspect defense (see ALDS vs. Red Sox). Considering that, Myers has big expectations for the 2014 season and beyond. When you look at Myers, there is a lot of reason to see a very good player and a few reasons to see just a solid starter. First off, there is the fact that he has jumped around position wise in his time in professional baseball. His days as a catcher and third baseman are behind him, but there was still reason to question his ability to play defense leading to him jumping around. He does have a strong arm, but some of the angles he takes to the ball can be a bit off and that leads to some issues.

Secondly, he strikes out too much. This is an issue for most young hitters so it would be unfair to characterize this as an issue that just plagues Myers, but it is something to look at as he progresses throughout his career. There is a bit of a hitch in his swing, so the strike out issues may not be something that go away. Although I do not see Wil Myers becoming megastar for the Rays, I do see him as a solid contributor, someone that will have upper 20 home run power, play a sufficient right field, and make a couple of All-Star games along the way.

How are the Rays going to manage Matt Moore and Chris Archer?
This question needs to taken in two different ways. First off: are either Chris Archer or Matt Moore that big in the grand scheme of the Rays’ plan and if so which one and how much? In watching Chris Archer, I see stardom in his pitches, his focus, and his delivery. There are certain players that have that IT and Archer has it; his only questions are if he will be able to focus his aggression and emotion on the mound and if he can keep his walk rate near his 2013 MLB level. He needs to focus more on his offspeed pitches, particularly his cutter, and he will be fine.

The analysis of Matt Moore opens up the second question: what can they get from these players? For Archer, they will be getting a decade of advanced pitching. There is no such thing as a sure thing, but looking at Archer, one can see that the moment does not scare him. As for Moore, he will be the next target of a big time trade, either with David Price being traded or Price not being traded. I do not have a huge issue with Moore other than the walk issues, but I feel that there are other teams that may value Moore more (see the pun there!) than he actually should be valued. There are a lot of parallels between Moore and Myers, sadly in a bad way, and I feel like the Rays moreso than any other team in baseball will optimize the value of Moore.

What will be the impact of the next wave of young talent for the Rays?
The Rays are a very solid team that has turned into a superb team by good drafting and developing of players. At this point, though, the well is a bit dry. When you look at the ten prospects below, there are a couple good players that the Rays have in the MiLB, maybe a starter or two, but not that true impact player like the Rays have been rolling off. Going from Longoria to Price to Moore to Myers to Romero/Lee is quite the drop off and the Rays will remedy that accordingly. There will be more than one team that will overspend on David Price and the Rays will make sure to get top flight young talent for him. A team like the Rockies or Phillies, that may be fringe playoff teams, might overspend greatly on Price and fix the Rays minor league issues.

That being said, Hak Ju-Lee should be the shortstop of the future for the Rays and should be a 30 steal player with average hitting and fielding and Taylor Guerreri and Nick Ciuffo are very interesting because they are so young and talented. When you have those three players as middle of the road prospects for the Rays after the big Price trade yields them a big name (see: Eddie Butler from the Rockies or Maikel Franco from the Phillies or a huge package from the Rangers), the Rays will yet again have a top five farm system.

Why are the Rays going to win 89 games?
The Joe Maddon Rays always find a way to be in the conversation to win the division or make the playoffs. He has changed the entire culture of the organization and made it one of the best run teams in the league. Those are the exact same two sentences from the 2013 preview and I do not plan on changing those sentences until Maddon retires. It is nearly unprecedented in the history of baseball that a manager and executive have changed the fortunes of a franchise in the ways that Friedman and Maddon have. The only thing that is missing for the Rays is a World Series title and I have a feeling that there will be a championship in the Rays future soon, as they only get better. All the team does is reload and utilize the players that they have to their maximum utility. Talent wise, this may be the best Rays team ever, so it is not crazy to think that this team could be closer to 95 than 90 wins.

5 You Know:
1. David Price
2. Evan Longoria
3. Ben Zobrist
4. Matt Moore
5. Wil Myers

5 You Will Know:
1. Enny Romero
2. Hak Ju-Lee
3. Alex Colome
4. Jake Odorizzi
5. Kevin Kiermaier

5 You Should Remember:
1. Taylor Guerrieri
2. Andrew Toles
3. Ryan Brett
4. Nick Ciuffo
5. Richie Shaffer

Is positional versatility underutilized? What does it cost for a player to transition from one position to another? MLB rules state that players currently in the game may switch positions at any dead ball, so why don’t teams shift their stronger fielders around the diamond based on batted ball profiles? Would it be worth it, in terms of runs, to try to have players play multiple positions and shift around the diamond? These are the questions that the following research attempts to answer.

I. The cost of transitioning between positions

The first thing that must be evaluated is what a player gains or loses when moving from one position to another. To do this, I looked at a player’s Total Zone and Defensive Runs Saved numbers, on a per inning basis, for each position they played at least 500 innings at. I did this for every player that met this minimum during the years from 2003-2013 (2003 was chosen as the cutoff because that is the first year DRS numbers are available). After data collection, for each position I took the total per inning number, subtracted from the position they were moving to, multiplied by 1200 innings for roughly a full season. I did this for every position, but I will only list the important positions for the purposes of this research. Since teams would most likely be shifting based on handedness and pull rates (though they theoretically could shift based on other things like GB/FB ratio if they had an outfielder who played a fantastic infield position or vice versa), this makes the important transitions ones shifting between the right and left side of the diamond. Those transitions are as follows:

(Note that due to how this was calculated, the inverse transitions, like 2B-SS, are the same number, but negative. This data was all gathered from Baseball Reference.)

SS-2B: 2.32 TZ runs for a season

SS-2B: 1.82 DRS

3B-1B: 4.68 TZ

3B-1B: 4.41 DRS

LF-RF:  -1.03 TZ

LF-RF: -2.05 DRS

(Personally, I had thought left field was more difficult, though maybe that is a result of mostly watching games in PNC park. It is also worth mentioning that on an individual basis, LF and RF are where Total Zone and Defensive Runs Saved had the largest disagreements)

So, as most people would expect, shortstop came out to be the most difficult position on the field, followed by second base and center field, third base and right field, left field, and first base. So, now that we’ve established that baseline for players transitioning between positions, we can move on to how many runs they would gain or lose in the process.

II. Estimating the number of fielding opportunities

Initially, I could not find detailed batted ball information broken down by handedness. So I attempted several methods of quantifying the impact, using the Cubs fielders as an example, and continually came up with the Cubs gaining 3-6 runs over the course of a season while shifting 20-30% of the time. However, those methods will not be discussed here. This is because Tony Blengino posted this wonderful article yesterday, complete with a batted ball breakdown for left and right handed hitters. So, it was revision time.

Step one was to take the number of fielding opportunities (also from Baseball Reference) for each of the examined positions, so I could get TZ/Fld and DRS/Fld numbers. This was also done with the transitions applied, to get TZ/Fld and DRS/Fld numbers for when they were playing the alternative position. Then, Blengino’s breakdown was combined with the average GB%, FB%, LD%, and IFFB% for left and right handed hitters. This gave a more specific batted ball breakdown for each area of the field. This breakdown is as follows:

With this information, I could get to work on estimating the number of fielding opportunities for each position. The first thing to do was to find the number of balls put in play against the Cubs for their 6149 PAs. For right handed batters I took the 6149 PAs * 58% (percentage of RHH) * 68.77% (percentage of balls put in play by RHH). For left handed hitters it was 6149 * 42% * 67.76%.

Unfortunately, this is where I ran into a small problem. I don’t know which balls hit in an area are attributed to which fielding position. For example, I don’t know what proportion of line drives to right field are caught by the first baseman, and what proportion is considered a ball the right fielder should field. This information is likely available, but I do not have it, and could not find it. If someone does find it, I would love to be able to do this more accurately. As it stands, I made educated guesses. The estimated fielding opportunities for each position, broken down by handedness, are as follows for Cubs fielders:

(Percent chance a ball in play was hit into that position’s area, and actual total number of fielding opportunities from last season in parenthesis)

1B: 93.88R (3.83%), 244.35L (13.96%)

1B Total:  338.23 (333 actual)

2B: 223.67R (9.12%), 273.44L (15.63%)

2B Total: 497.11 (496 actual)

3B: 351.59R (14.34%), 69.12L (3.95%)

3B Total: 420.71 (424 actual)

SS: 415.05R (16.92%), 170.37 (9.74%)

SS Total: 585.42 (584 actual)

LF: 459.42R (18.73%), 217.04L (12.40%)

LF Total: 676.46 (676 actual)

RF: 280.80R (11.45%), 331.27 (18.93%)

RF Total: 612.07 (662 actual)

(Estimations attempted to keep close to the actual number and proportion of fielding opportunities. I could not get it to happen properly for RF. It will have to be ironed out at a later date.)

III. Estimating the number of fielding opportunities and runs when shifting

The first thing worth mentioning is the total number of additional runs saved depends entirely on how often a team chooses to run this particular shift. When estimating for the Cubs, I chose to run this shift 25% of the time against all batters (Normally, one might only shift against left handed hitters, but the data suggests that Darwin Barney may be better off playing shortstop than Starlin Castro, so the Cubs will be shifting 25% of the time against all hitters). The first thing to do is to find out a position’s number of fielding opportunities when it is shifting to cover someone else 25% of the time, and when it is covered 25% of the time.

When covering, this is done by taking the number of fielding opportunities when the ball is more likely to be hit at them (like when a 1B is facing a LHH) + 25% of the position being switched to (3B against RHH) + 75% of opportunities when the ball is less likely to be hit at them (1B against RHH). So, a 1B would be playing 1B against every LHH, 3B against 25% of RHH, and 1B against the other 75% of RHH. For being covered, it is the opposite. All fielding opportunities when it is less likely to be hit at them (1B against RHH) + 25% of the alternative position (3B against LHH) + 75% of their original opportunities (1B against LHH). The new total number of estimated fielding opportunities for covering and being covered is as follows:

1B

Original: 338.23

Covering: 402.66

Covered: 294.42

2B

Original: 497.11

Covering: 544.95

Covered: 471.34

3B

Original: 420.71

Covering: 464:52

Covered: 356.28

SS

Original: 585.42

Covering: 611.19

Covered: 537.58

LF

Original: 676.46

Covering: 705.02

Covered: 631.80

RF

Original: 612.07

Covering: 656.72

Covered: 583.51

Essentially, this would get your strongest fielders more fielding opportunities, provided they are still strong after making the transition. Converting the previous formula to runs is simple, since we took both the regular and alternative position’s TZ and DRS runs per fielding opportunity. So for covering this becomes the more likely side * TZ(or DRS)/Fld + 25% of the alternative position’s strong side * AltTZ(or AltDRS)/Fld + 75% of the original weaker side * TZ/Fld. For being covered, the runs per fielding opportunity are added into that previous formula in the same way. That gives us the total number of runs for covering and being covered as follows:

When optimizing the lineup, since one of each pairing (1B-3B, 2B-SS, LF-RF) must be covered, both Total Zone and Defensive Runs Saved agree that 1B should cover for 3B (due to a love of Rizzo’s defense. TZ would disagree if Valbuena had played the whole year) and 2B should cover for SS (both metrics love Barney and dislike Castro). They disagree on RF and LF, where TZ thinks LF should cover, and DRS thinks RF should cover.

If optimized for Total Zone runs, shifting 1B-3B, 2B-SS, and LF-RF 25% of the time results in a total TZ runs for these positions of 7.81, which is a 2.81 run improvement over the original lineup.

If optimized for Defensive Runs Saved, shifting 1B-3B, 2B-SS, and RF-LF 25% of the time results in a total DRS of 22.31, which is a 2.31 run improvement over the original lineup.

IV. Conclusions

Running this shift for the Cubs 25% of the time resulted in a gain of 2-3 runs over the course of the season. This is not an insignificant amount of runs, but there are some things that need to be mentioned.

1. This shift is run 25% of the time against the average for left and right handed hitters. If a team is really going to shift 25% of the time in this method, they will do it against the 25% most extreme pull hitters for each handedness. I do not know the batted ball profiles of the most extreme pull hitters, but it would result in more fielding opportunities when covering, and fewer when being covered. This would likely increase the total number of optimal runs gained significantly. Since I do not have those profiles, I am unsure by what specific margin, but I would love to be able to know.

2. This enables you to somewhat “hide” a poor fielder, particularly at first base. The greatest difference in the odds of a ball being hit at them is between first and third base. If one fielder was particularly poor, you could make sure the odds of a ball being hit to him were always low. The greater the difference between the positions being switched, the greater the overall runs gained are for the season.

3. The Cubs were a terrible team to choose. I initially thought of this idea as I was speaking with a member of their front office, so I did this work on their team specifically. The reason the Cubs are a poor team to choose is because the disparity between the positions being switched is relatively small, except for 2B-SS which has a smaller impact. As mentioned above, this results in a smaller amount of runs gained. A team with a large disparity between first and third would see a far greater impact, particularly with a very good third baseman and poor first baseman due to the transition between positions. I will likely do this with additional teams in the future.

4. As mentioned, this was only run 25% of the time. The more often it is run, the more total runs will be gained.

5. This could be done far more accurately. I do not have all the information I would like available to me right now. I know that an entity like Baseball Info Solutions already records batted ball data to a large number of vectors on the field, as that is how DRS is calculated. That information could be used to come up with far more accurate results in terms of the exact likelihood a batted ball will be fielded by a specific position.

6. The transitions between various positions vary widely on an individual basis. I used the average numbers over a very large sample, so it should be a decent approximation, but every player is different. For every player that went from a very poor shortstop to an excellent second baseman, there is one who performed worse in the same transition. However, due to the transition values roughly lining up well with the positions that are generally known as being difficult, I have no issue with using them.

7. I did not look into whether shifting defensive positions could come with a reduction offensively. Theoretically, a player may slide a bit if he has to focus more attention on fielding multiple positions. I have not yet looked into this. If such a reduction exists, it could possibly be neutralized by an organizational philosophy embracing positional flexibility as players develop.

Overall, the Cubs could likely gain around 3 runs by shifting 25% of the time. If a team has a greater difference between fielders, and shifts with greater frequency, I don’t think it’s unreasonable to expect that team to improve by 1-2 wins over the course of the season. Shifting has grown far more popular lately, and it has been demonstrated to improve overall defense. I believe this is an extension of shifting. It makes sense to shift your fielders to where the other team hits the ball most. It also makes sense to shift players in this manner, and give your better fielders more opportunities to field the ball while giving your poorer fielders fewer opportunities. If you’re going to put a fielder where they hit the ball most, you might as well make it the fielder that is most likely to make a play.

V. A more extreme example

When I wrote this article a few days ago (but hadn’t decided to post it yet) I mentioned that the Cubs were not the greatest choice of team. So, I ran it on a more extreme example, and with greater frequency. As far as frequency is concerned, I upped it from 25% of the time to 50% of the time. For the team, I needed a team with an excellent third baseman, and below average first baseman. The first team that I thought of was the Orioles, so that is the team I used. Considering this is just a quick example to demonstrate the top end of the spectrum rather than the bottom, and the process was not changed, I will not walk through the process in detail again and will just provide the total runs.

If optimized for Total Zone runs, shifting 3B-1B, 2B-SS, and RF-LF 50% of the time results in a total TZ runs for these positions of 49.34, which is a 15.34 run improvement over the original lineup.

If optimized for Defensive Runs Saved, shifting 3B-1B, SS-2B, and LF-RF 50% of the time results in a total DRS of 44.65, which is a 14.65 run improvement over the original lineup.

(For reference, the Orioles when run 25% of the time were approximately an 8-9 run improvement)

With the same potential improvements and diminishments as mentioned in the first example, this is more of an idea of the top end of the spectrum. The Orioles, already a strong defensive team, could potentially gain about 1.5 wins by shifting in this manner 50% of the time. There are definite caveats to consider and improvements to make, but shifting like this could have an extreme defensive impact.

Welcome to the 3^rd annual forecast competition, where each forecaster who submits projections to bbprojectionproject.com is evaluated based on RMSE and model R^2 relative to actuals (see last year’s results here).  Categories evaluated for hitters are: AVG, Runs, HR, RBI, and SB, and for pitchers are: Wins, ERA, WHIP, and Strikeouts. RMSE is a popular metric to evaluate forecast accuracy, but I actually prefer R^2.  This metric removes average bias (see here) and effectively evaluates forecasted player-by-player variation, making it more useful when attempting to rank players (i.e. for fantasy baseball purposes).

Here are the winners for 2014 for R^2 (more detailed tables are below):

Place

Forecast System

Hitters

Pitchers

Average

1st

Dan Rosenheck

2.80

2.50

2.65

2nd

Steamer

1.60

6.00

3.80

3rd

FanGraphs Fans

5.80

2.75

4.28

4th

Will Larson

6.60

3.00

4.80

5th

AggPro

6.40

4.25

5.33

6th

CBS Sportsline

5.40

8.00

6.70

7th

ESPN

6.60

7.50

7.05

8th

John Grenci

8.00

8.00

9th

ZiPS

9.80

7.25

8.53

10th

Razzball

6.80

10.25

8.53

11th

Rotochamp

8.60

9.00

8.80

12th

Sports Illustrated

8.80

12.00

10.40

13th

Guru

10.60

12.00

11.30

14th

Marcel

11.20

12.50

11.85

And here are the winners for the RMSE portion of the competition:

Place

Forecast System

Hitters

Pitchers

Average

1st

Dan Rosenheck

2.60

2.00

2.30

2nd

Will Larson

3.60

2.50

3.05

3rd

Steamer

1.80

5.00

3.40

4th

AggPro

4.00

3.00

3.50

5th

ZIPS

6.00

5.75

5.88

6th

Guru

4.80

7.25

6.03

7th

Marcel

6.20

8.50

7.35

8th

John Grenci

7.50

7.50

9th

Rotochamp

9.40

9.00

9.20

10th

ESPN

9.20

10.50

9.85

11th

Fangraphs Fans

11.80

8.75

10.28

12th

Razzball

9.40

11.25

10.33

13th

Sports Illustrated

10.60

11.75

11.18

14th

CBS Sportsline

11.60

12.25

11.93

I’m beginning to notice some trends in the results across years.  First, systems that include averaging do particularly well.  This is pretty well established by now, but it’s always useful to reflect upon.  It’s been asked in the past to perform evaluations separating forecasts computed by averaging with those that do not include information from others’ forecasts (more “structural” forecasts). I decided not to do this because the nature of the baseball forecasting “season” makes it impossible to be sure forecasts are created without taking into account information from others’ forecasts. This can include direct influence (forecasting as a weighted average of others’ forecasts), but can also occur in more subtle ways, such as model selection based on forecasts that others have put forward.  Second, FanGraphs Fans are always fascinating to me, and how they can be so biased, but yet contain some of the best unique and relevant information for forecasting player variation. The takeaway from the Fans forecast set is that crowdsourced-averaging works, as long as you can remove the bias in some way, or ignore it by instead focusing on ordinal ranks.

Some additional notes: it would be interesting to decompose these aggregate stats in to rates multiplied by playing time, but it’s difficult to gather all of this for each projection system. Therefore, I focus on top-line output metrics.  Also, absolute rankings are presented, but many of these are likely statistically indistinguishable from each other.  If someone wants to run Diebold-Mariano tests, you can download the data used in this comparison from bbprojectionproject.com

Thanks for reading, and please submit your projections for next year! Also, as always, I welcome any comments, and I’ll do my best to respond.

R^2 Detailed Tables

system

r

rank

hr

rank

rbi

rank

avg

rank

sb

rank

AVG

AggPro

0.250

6

0.42

9

0.308

8

0.32

1

0.538

8

6.4

Dan Rosenheck

0.296

3

0.45

1

0.340

3

0.3

3

0.568

4

2.8

Steamer

0.376

1

0.45

2

0.393

1

0.31

2

0.572

2

1.6

Will Larson

0.336

2

0.43

6

0.345

2

0.21

13

0.509

10

6.6

Marcel

0.146

12

0.36

12

0.236

12

0.27

8

0.477

12

11.2

ZIPS

0.118

13

0.42

8

0.230

13

0.3

4

0.504

11

9.8

CBS Sportsline

0.278

4

0.44

3

0.320

4

0.25

10

0.542

6

5.4

ESPN

0.241

7

0.43

5

0.317

5

0.29

7

0.532

9

6.6

Razzball

0.239

8

0.43

4

0.314

6

0.24

11

0.553

5

6.8

Rotochamp

0.234

9

0.41

10

0.287

9

0.23

12

0.569

3

8.6

Fangraphs Fans

0.268

5

0.42

7

0.272

10

0.3

6

0.574

1

5.8

Guru

0.186

11

0.33

13

0.263

11

0.3

5

0.476

13

10.6

Sports Illustrated

0.221

10

0.4

11

0.314

7

0.27

9

0.541

7

8.8

system

W

rank

ERA

rank

WHIP

rank

SO

rank

AVG rank

AggPro

0.13

3

0.15

4

0.25

4

0.402

6

4.25

Dan Rosenheck

0.17

1

0.19

2

0.27

2

0.406

5

2.5

Steamer

0.09

6

0.15

3

0.26

3

0.341

12

6

Will Larson

0.16

2

0.19

1

0.24

5

0.413

4

3

Marcel

0.05

14

0.02

13

0.17

9

0.293

14

12.5

ZIPS

0.09

7

0.07

9

0.21

6

0.375

7

7.25

CBS Sportsline

0.1

5

0.08

7

0.15

10

0.359

10

8

ESPN

0.08

10

0.05

11

0.2

7

0.43

2

7.5

Razzball

0.06

13

0.07

8

0.14

12

0.374

8

10.3

Rotochamp

0.08

9

0.06

10

0.17

8

0.359

9

9

Fangraphs Fans

0.11

4

0.08

5

0.28

1

0.435

1

2.75

Guru

0.07

11

0.05

12

0.11

14

0.343

11

12

Sports Illustrated

0.09

8

0.02

14

0.14

13

0.338

13

12

RMSE Detailed Tables

system

r

rank

hr

rank

rbi

rank

avg

rank

sb

rank

AVG

AggPro

22.495

4

7.34

4

23.217

4

0.03

4

7.096

4

4

Dan Rosenheck

20.792

3

6.91

1

21.867

2

0.03

5

6.467

2

2.6

Steamer

20.355

2

7.02

2

21.817

1

0.03

3

6.258

1

1.8

Will Larson

20.091

1

7.2

3

22.234

3

0.03

8

6.864

3

3.6

Marcel

23.473

6

7.51

6

23.831

6

0.03

7

7.334

6

6.2

ZIPS

25.380

7

7.43

5

25.662

7

0.03

1

8.048

10

6

CBS Sportsline

25.866

10

8.63

13

26.837

10

0.03

12

8.527

13

11.6

ESPN

25.698

8

8.37

12

26.418

9

0.03

6

8.120

11

9.2

Razzball

25.831

9

8.01

9

27.842

12

0.03

9

7.920

8

9.4

Rotochamp

26.199

11

8

8

25.995

8

0.04

13

7.686

7

9.4

Fangraphs Fans

26.854

13

8.12

10

30.804

13

0.03

11

8.289

12

11.8

Guru

23.187

5

7.58

7

23.608

5

0.03

2

7.198

5

4.8

Sports Illustrated

26.609

12

8.24

11

27.173

11

0.03

10

8.009

9

10.6

system

W

rank

ERA

rank

WHIP

rank

SO

rank

AVG rank

AggPro

4.4

3

1.031

4

0.17

4

47.01

1

3

Dan Rosenheck

4.25

1

1.014

1

0.17

1

47.9

5

2

Steamer

5.02

8

1.030

3

0.17

2

49.45

7

5

Will Larson

4.34

2

1.017

2

0.17

3

47.44

3

2.5

Marcel

4.62

5

1.158

13

0.18

8

50.84

8

8.5

ZIPS

4.78

7

1.101

7

0.17

5

47.85

4

5.75

CBS Sportsline

5.56

13

1.134

11

0.19

11

57.14

14

12.3

ESPN

5.81

14

1.126

10

0.18

7

53.54

11

10.5

Razzball

5.39

12

1.115

8

0.19

12

55.55

13

11.3

Rotochamp

4.71

6

1.138

12

0.18

9

51.81

9

9

Fangraphs Fans

5.29

10

1.123

9

0.17

6

52.57

10

8.75

Guru

4.51

4

1.093

6

0.19

13

48.79

6

7.25

Sports Illustrated

5.33

11

1.176

14

0.18

10

55.32

12

11.8

John Grenci

5.14

9

1.080

5

0.19

14

47.26

2

7.5

While Ervin Santana and Ubaldo Jimenez certainly have their similarities, they each have different risks and benefits associated with them. They have often been connected throughout the offseason, as they have similar price tags and each is connected to draft pick compensation. They have also been linked this offseason because each is coming off an impressive season following a very bad season, and overall inconsistencies in their careers. However, the two pitchers are not incredibly similar, as one profiles more as a durable innings-eater and the other carries more upside.

The 31-year-old Ervin Santana provides many more innings than Ubaldo Jimenez, as he has eclipsed the 200-inning plateau three times in the past four seasons. Santana, however, has often outperformed his peripherals, especially this past season. In 2013, Santana posted his career-best 3.24 ERA, but his FIP was 3.93, which suggests some regression in 2014. Even looking back at the past five seasons, Santana has had an FIP under 4.00 just once. It may seem as if he has the ability to outperform his peripherals consistently, but during that same span his ERA surpassed 5.00 during two seasons most recently in 2012.

As I mentioned above, Santana’s best quality is his ability to go deep into starts consistently throughout the season. Santana is also a tremendous strike-thrower, as he walked just 2.18 batters per 9 innings, which is an improvement upon his still impressive 2.81 BB/9 for his career. Santana is also an effective groundball generator, as his groundball rate has been above 43% for the past three seasons. The real knock on Santana has been his inconsistencies throughout his career, with three seasons of an ERA above 5.00 and just four seasons of an ERA under 3.00 during his 9-year career. While Santana’s ERA was the best of his career, in 2013, his other metrics were not much better than his career norms, which suggests he hasn’t necessarily figured anything out.

The 30-year-old Ubaldo Jimenez, unlike Santana, has a reputation for struggling to go deep into games. He has not thrown 200 innings in a season since 2010 and has only done it twice in his 8-year career. His struggles to last deep into games are likely related to his high K/9 and very high BB/9. Both strikeouts and walks drive a pitcher’s pitch count up and he has never had a BB/9 lower than 3.50. As I stated above, Jimenez carries more upside with him, as his career K/9 is a full strikeout per 9 higher than Santana, but Jimenez’s peripherals are also better than his ERA. Jimenez has a career 3.78 FIP, compared to his 3.92 ERA. During his time with the Rockies, Jimenez was an outstanding groundball pitcher, but since moving to the Indians, his GB% has slipped to 38.4% in 2012 and 43.9% in 2013. Despite pitching in hitter-friendly Coors Field for the majority of his career, Jimenez’s Hr/9 has been better than Santana’s in every season of his career.

Compared to Santana, who has had an FIP under 4.00 just once in the past five seasons, Jimenez has had an FIP under 4.00 four of the last five seasons. Jimenez’s only truly bad season, in terms of FIP, was 2012 when his FIP ballooned to 5.06 and his ERA climbed to 5.40. While being able to go deep into starts is pivotal in being a reliable and consistent starter, Jimenez certainly carries the highest upside and actually most consistent performance between the two starters. Jimenez has also proven that he can pitch in a high run-scoring environment, such as Coors Field. Santana, however, has pitched the majority of his career in a pitcher-friendly park at Angels Stadium of Anaheim for every season except one.

Looking into the numbers, it is clear that Ervin Santana is the best bet of the two starters to reach 200 innings. It is also evident that Ubaldo Jimenez has the greatest potential to provide above-average production inning per inning. Neither starter is an ace or likely to become one and each comes with legitimate questions. However, in terms of which starter is better, it really depends on what a team is looking for. If they want a starter that can provide 200+ innings season after season, then Santana is by far the better option. If the team is seeking a starter that can consistently provide an ERA around or below 3.50, then Jimenez is the better option. Since each starter has a similar price tag, it is really a question of which type of starter the team is looking for. Personally, I prefer Jimenez to Santana because he has provided more consistent numbers across the board and has only had one truly bad season.

Hanley Ramirez had a roller-coaster 2013 marked by bad luck with injuries and exceptional production with the bat.  He missed the first month with a torn thumb ligament and tore his hamstring in his third game back, causing him to miss another month of action.  Overall, Han-Ram played only 86 regular season games, but when we played he hit the cover off the ball.  In 336 plate appearances he hammered 20 home runs and a .442 wOBA (second only to Miguel Cabrera for players with over 100 PA).  While the title of this post should lead the reader to believe it is primarily about Han-Ram, that would be false.  In fact, it’s mainly about different methods to measure fantasy value, with Han-Ram used to illustrate a point.

Fantasy value above replacement (FVAR) is a metric that has been used (for example on FanGraphs) to estimate the auction value of historical or projected baseball statistics in a rotisserie league.  The most popular way to measure FVAR uses z-scores: the number of standard deviations above the mean for any given statistic(s).  Z-scores are handy because they put different stats, such as HR and SB, on a level playing field.  An unresolved question is whether FVAR should be calculated using total stats (e.g., Han-Ram hit 20 HRs) or rate stats (e.g., Han-Ram hit 0.06 HR per plate appearance).  In this post I’m calling the method using total stats the z-score and the method using rate stats the zz-score.    I looked at how Han-Ram’s fantasy value changed using both methods (assuming 12-team mixed 5×5 with $160 auction budget for hitters per team).

Han-Ram’s Value Based on z-Score

The table below summarizes the calculation of Han-Ram’s z-score in 2013.  The data is drawn from players with at least 100 plate appearances.  The z-score is Han-Ram’s stat minus league average (mean), divided by league standard deviation.

Han-Ram’s overall z-score sums to 5.4.  The next table shows how that compares with other shortstops.  For an explanation of how the auction values were calculated see here and here.  (Erick Aybar was the replacement level shortstop, but his auction value is greater than zero because his z-score was higher than the replacement level Util player, Justin Ruggiano.)  Using the z-score method, Han-Ram ranked fourth among shortstops even though he played in only 86 games.

Han-Ram’s Value Based on zz-Score

The calculation of Han-Ram’s zz-score is illustrated in the table below.  It’s identical to the z-score calculation, but rate stats (per PA) are used instead of season totals.

Han-Ram’s zz-score in 2013 summed to 10.7, putting him at the top of the heap for shortstops.  To calculate Han-Ram’s FVAR in 2013 I multiplied his zz-score by his plate appearances, adjusted for replacement level and then calculated auction values.  Results are shown below.

 Han-Ram’s Fantasy Value

Depending on how we look at the world, Han-Ram was either the most valuable fantasy SS in 2013 or the fourth-best.  I think both methods are legitimate, but I prefer zz-score for a few reasons.  As Zach Sanders has noted on FanGraphs, z-score makes a broad assumption:

“These rankings are meant to reflect a player’s value should he have occupied this spot in your lineup for the entire year.”

In other words, z-score assumes my SS roster spot was empty when Han-Ram was on the DL.  That’s obviously not a good assumption, because in a 12-team mixed league I would have easily found a replacement-level SS to plug in while Han-Ram was sidelined.

To illustrate the point, I looked at the 2013 stats for Jean Segura (the highest-ranked SS using z-score) compared to Han-Ram for 86 games plus a replacement SS.  Using the zz-score method, and the league assumptions noted above, Asdrubal Cabrera was identified as the replacement level SS in 2013 with a zz-score of 0.4.  For the sake of this comparison I assumed the replacement added value in steals and was league average in all other categories.  It should be pretty obvious that Han-Ram plus a replacement was more valuable than Jean Segura.

What does this tell us?  In leagues where it is fairly easy to plug in replacement-level players (e.g., shallow leagues with daily transactions and plenty of DL spots) zz-score is a better method for determining fantasy value.  In leagues where it’s hard to replace an injured player or plug in serviceable options, playing time becomes a more valuable commodity and z-score is probably a better reflection of real value.  As is often the case, the truth probably lies somewhere in the middle, between z and zz.

Twitter: @FVARBaseball

Website: fvarbaseball.wordpress.com