It feels icky to create a statistical formula based on what “feels right”.

Last month, I introduced a stat called Leadoff Rating, or LOR. The idea was that most systems to identify great leadoff hitters tab players like Ted Williams and Mickey Mantle, who would always hit closer to the middle of the order. I wanted to distinguish players specially suited to batting leadoff. The formula was simple: OBP minus ISO. By subtracting isolated power, we identified players who get on base a lot but aren’t true sluggers. It’s an easy calculation, and it produced fairly reasonable results. Two particular things bothered me:

1. Bad hitters occasionally had good leadoff ratings because of their very low ISO.

2. Rickey Henderson ranked 45th.

We know that leadoff is one of the two or three most important positions in the batting order. As little impact as lineup construction has on winning percentage, leadoff hitters are important. But LOR saw high OBP and low ISO as equally meaningful, so players with no power sometimes rated as desirable leadoff hitters. That seemed like something to correct.

Rickey Henderson is generally recognized as the greatest leadoff man of all time. LOR did not show this, for two main reasons. One was that the formula did not include baserunning. The other was that the all-time list slanted heavily towards Deadball players. Before Babe Ruth, everyone had low isolated power. Ty Cobb was a terrific power hitter, who led the AL in slugging eight times. Cobb’s career ISO (.146) is basically the same as Rickey’s (.140). Henderson only ranked among the top 10 in slugging twice. The game has changed.

Based on the feedback of FanGraphs readers and on my own muddlings, I’ve reworked the leadoff rating formula. The new system is more complicated — it’s annoying to do without a spreadsheet — and it’s kind of haphazard. OBP – ISO was a nice system because of its simplicity. With the updated formula, I’m guessing, choosing numbers that seem right. If someone better than I am at math would care to suggest revisions, please do so. I am fully prepared to give this stat away to smart people.

The formula I’m using now is — wait. There’s another calculation I abandoned, but it’s important for explaining how we arrived at the current iteration, and that middle step looked like this: OBP – ( .75 * ISO ) + ( ( .005 * BsR ) / ( PA / 600 ) )

On-base percentage is the heart of leadoff rating. A good hitter, and especially a good leadoff hitter, must get on base. But I only subtracted 3/4 of ISO, because (1) low ISO is not as important as high OBP, and (2) the original formula was probably a little too hard on doubles hitters. Guys like Rickey and Tim Raines ranked too low because they had more power than players like Jason Kendall and Ozzie Smith.

Commenter foxinsox suggested adding (Constant * BsR) to the calculation, which was a fine idea I should have seen earlier. The hitch was turning BsR into a rate stat.  By using BsR/PA or BsR/G, we can incorporate that element smoothly.

When I ran the numbers, the historical lists looked great (Rickey Henderson in the top 10!), but for active players, there were hits and misses. Elvis Andrus came back as the ideal leadoff hitter in 2013, and Craig Gentry (.264/.326/.299) ran away with 2014 to date. Even with the adjustments, LOR rewarded low ISO. While a .250 ISO isn’t really the right fit for the top of the batting order, neither is a sub-.050 ISO. We don’t want a guy who only hits singles, we just don’t want a cleanup hitter. Looking at the historical lists, I found that most of the top players had an ISO right around .100, so I created a Goldilocks formula, preferring a minimal absolute difference from .100 ISO. Rather than simply treating low ISO as desirable, we’re looking for the sweet spot between singles and slugging. The new formula is:

OBP –  .75 * | .100 – ISO |  + ( .005 * BsR ) / ( PA / 600 )

That’s on-base percentage, minus 3/4 of the absolute difference between ISO and .100, plus .005 times BsR per 600 plate appearances. Now very low isolated power is punished just as much as very high ISO.

Hopefully you want to see some lists. I’ll show you five: the all-time list, the post-Jackie Robinson list, the leaders for the 2013 season, 2014 to date (through July 31), and 2014 rest-of-season projections (ZiPS). We’ll also look at the 2014 leaders (both to date and projected) for every team in the major leagues. Read the rest of this entry »

Over the course of 162 games, there’s only so much influence a manager of any baseball team could have over their outcome. After 105 games the Mets actual record is 3 wins shy of their projected record of 53-52, making this a .500 team. Several factors contribute to this discrepancy like losing your ace pitcher to injury, scrambling for a closer to begin the season, developing a major league catcher, adapting to a new hitting coaches philosophy, and setting the most productive lineup possible just to name a few. What Terry Collins has done with this team to this point can only be admired, but help has arrived and changes must be made to maximize team production.

The move of Curtis Granderson from the cleanup to leadoff role proved to be successful as the team surged from June’s end through July. Daniel Murphy and Curtis Granderson’s slash line numbers are almost identical, batting average is the only big difference which Daniel Murphy leads Granderson by about.060 AVG points and make him a more ideal leadoff hitter. Curtis Granderson hit 6 home runs from the leadoff spot which minimized his RBI potential which essentially is the reason Sandy Alderson signed him. In moving Daniel Murphy into the leadoff spot, the Mets actually increase their leadoff OBP while putting Curtis Granderson into a role where his RBI opportunities increase dramatically.

Daniel Murphy’s SLG% is nearly that of Curtis Granderson with half as many HRs, meaning that Daniel Murphy is doing a better job of getting into scoring position than our current leadoff hitter. The only 2 reasons the Mets have kept Murphy out of the leadoff spot in the past were lack of speed on the basepaths and low OBP. Now Daniel leads our starting players in SB showing he has some speed and base running ability and his OBP is amongst the team leaders. David Wright being the best hitter on the team (despite struggles in 2014) deserves the 2nd spot in the order. His power has declined this season, however his OBP is still respectable and he should remain in a table-setting role followed by Granderson. Lucas Duda has earned his cleanup role as he’s hit over .280 in the past couple of months with at least 5 HRs per month. He is driving the ball to all fields and should be a key contributor to driving in runs once our table-setters do their jobs.

The top 4 lineup spots should be configured as follows:

1  2B Daniel Murphy        (.293/.340/.412) 28 2B, 7HR, 11SB

2  3B David Wright           (.278/.339/.401) 24 2B, 8HR, 5SB

3  RF Curtis Granderson  (.232/.339/.415) 18 2B, 15HR, 8SB

4  1B Lucas Duda               (.259/.356/.500) 22 2B, 18HR, 3SB

For the next spot in the lineup, this player has had a tale of 2 seasons. Travis d’Arnaud has adjusted quickly since his demotion to AAA on June 6th. Since being recalled on June 24th, d’Arnaud has a slash line of (.302/.337/.646). He has lengthened our lineup and has earned the spot of the 5 hitter.

5  C Travis d’Arnaud

Before June 6th demotion    (.180/.271/.320) 3 2B, 3HR

Since June 24th Promotion (.302/.337/.646) 7 2B, 4HR

Season Stats                            (.232/.298/.379) 10 2B, 7HR

Right after Travis d’Arnaud in the Mets order is when they begin to look thin offensively. Having early success in the season but struggling as of recent is Juan Lagares, the defensive wizard and minor league doubles machine. This kid showed an advanced approach to lead off the year and is capable of making the bottom of our order a productive one. He isn’t seeing the ball well like he was in the first half, but we need to remember he is in his first full season in the bigs and known primarily for his route to catch baseballs and cannon for an arm, any offense is a plus.

6  CF Juan Lagares (.271/.306/.375) 16 2B, 2HR, 2SB

7  RF Chris Young/Eric Young/Kirk Nieuwenhuis/Bobby Abreu/denDekker

Our right field position is a question mark. I’m not saying the Mets haven’t produced anything from the position, but they don’t have an everyday right fielder which is a need to be addressed in the off-season or via trade before Thursday’s deadline. Though not one player has stepped up and taken over this position, I still believe they have produced more than my “ideal” 8 hitter, Ruben Tejada. In every championship team there is that one scrappy player that is on the squad solely for defensive prowess. Through the course of the season I have seen many different Ruben Tejadas. I’ve seen the defensive shortstop, the slap hitter, the kid in way over his head, and the wanna-be slugger with warning track power. This player is undoubtedly our 8 hitter and those who look too dependently on his OBP must take into consideration how many times he has walked for the sole reason that the worst hitting pitching staff is just 4 pitches away.

Ruben has been intentionally walked 10 times, twice as much as any player on the Mets. Ruben Tejada hasn’t defended the way he has in the past which quieted his lack of offense. In a New York setting, he shouldn’t start and the Mets executives know that. Ruben is a bridge to the future, an inexpensive filler until we land in a position of contention where an offensive producer is necessary at the position. Until then we have a shortstop with a strong arm and instincts but lacks the speed to get too many balls up the middle or steal a base when we need him to. He has no power and is offensively irrelevant as his slash line below shows. A shortstop with any tools is an upgrade here.

8  SS Ruben Tejada (.226/.351/.281) 9 2B, 2HR, 1SB

We know, in 2014, that lineup construction has little effect on winning. And yet, it’s not any less frustrating when managers set their batting orders in ways that seem to defy any semblance of logic. Lineup construction matters to us. We may know it’s not terribly important, but we’re fascinated in spite of ourselves.

The lineup position subject to the most debate is probably leadoff. Multiple writers and analysts have noted that players who would make the best leadoff hitters are normally too valuable to use in the leadoff position. Bill James wrote in his New Historical Abstract, “All of the greatest leadoff men … would be guys who aren’t leadoff men, starting with Ted Williams … if you had two Ted Williamses, and could afford to use one of them as a leadoff man, he would be the greatest leadoff man who ever lived.”

Every method I’ve seen to determine great leadoff batters produces names like Ted Williams, Barry Bonds, Mickey Mantle, Ty Cobb … players who are probably better suited to the second through fourth spots in the batting order. I think I’ve found a simple method that solves the problem. I’ve always been interested in singles hitters who walk. It’s a skill set that matches our image of the prototypical leadoff batter.

Most fans agree that a good leadoff man should get on base and run the bases well. Most fans further agree that a player who both gets on base and hits with power is more valuable a little later in the order, where he can drive in runs. If we accept that we probably can’t have two Ted Williamses, a realistic ideal of the leadoff batter has a high on-base percentage but doesn’t hit with a lot of power.

With this in mind, I’m adapting a stat I’ve talked about elsewhere to identify optimal leadoff men: OBP minus ISO. In my head, I’ve always called this reverse ISO, but that’s sort of a misnomer, and it’s a little unwieldy, so from here on let’s call this stat combination Leadoff Rating, or LOR. We know a good leadoff man gets on base, but most players with high on-base percentage are great all-around hitters. We know power hitters are usually better suited to other spots in the batting order, but many players with low ISO just aren’t that great. By subtracting isolated power from OBP, we can identify players specially suited to hitting leadoff.

This stat does not include baserunning (because I have no idea how to incorporate it with two percentages) but it turns out not to matter very much. A significant majority of players who rank well in LOR were also accomplished baserunners, and base stealers in particular. Among the top 300 hitters of all time (basically everyone with 2,000 career hits), I found a fairly strong positive correlation between LOR and SB (r=.465). The relationship is weaker if you only look at 1947-present (r=.356), but a degree of positive correlation is clear. In both data sets, n=300.

When you calculate LOR for the all-time top 300 hitters, the leader is Billy Hamilton. That’s Sliding Billy Hamilton, the Hall of Fame outfielder for Philadelphia and Boston in the 1890s, not the rookie phenom for the Cincinnati Reds. The original Hamilton retired with 1,782 singles, 1,187 bases on balls, and 376 extra-base hits. He hit .344/.455/.432, with an ISO of just .088, and an OBP higher than his slugging percentage. Hamilton also stole 912 bases. He is a superb example of the hitter we’re looking for, and he leads the new stat by a huge margin. His .367 LOR rates 12% higher than second-place Eddie Collins (.328). Here’s the top 75: Read the rest of this entry »

There are many arguments against the sacrifice bunt, by many sabermetricians and sports writers, all with the purpose of retiring its practice in baseball. The three main reasons not to bunt are that it gives away an out (out of only 27), the rate of scoring goes down (based on ERT by Tango), and that most bunters are unsuccessful.

For my argument, I will establish a more romantic approach and one I haven’t seen across the world of sabermetrics. With this approach, I will land on a conclusion that supports the sacrifice bunt and even speaks to the expansion of its practice.

Bunters can be successful

First, I’ll attack the last argument. If bunting is coached, bunters will be better. In my own research, as well as research done by others, I’ve found that there have been years when even the pitchers are able to bunt successfully over 90% of the time. Many people say that practice makes perfect, and while perfection might not be reached in the batters box, I wouldn’t be surprised if bunters were allowed to get close, or at least to their abilities in the 80’s.

Innings are more prosperous after bunt

The second argument is the main staple of this essay. In the world of analytics, general numbers are not good enough to explain why a phenomena is bad. Tom Tango’s famous Run Expectancy Matrix is used to make arguments against bunting across the Internet. Unfortunately, it’s assumed that the situations just exist rather than being set up the way that they are. It would be appropriate to use the table if a team were allowed to place a man, or men, on a base, or bases, and set the number of outs. However, as a strong believer in the principle of sufficient reason, I believe that there’s variability between a man on second with one out from a bunt and a man on second with one out from other situations.

For this reason, I set up my own analysis through the resource of Retrosheet play by play for the years of 2010-2013. To make things simple and not delve too deeply in varying circumstances, I will simply use larger data sets and noticeable differences to tell a story. First, I will look at only innings that start with men on base before the first out. Sacrifice bunts cannot happen when men are not on base, so it would be unfair to statistically compare innings with bunts to just innings without bunts. In line with Retrosheet’s system, I’m looking at all instances of SH, when they occur before (and usually result in) the first out.

To summarize, I’ll be looking at the percent chance that a team scores in an inning where they are able to get a man, or men, on base before the first out (as well as the average runs per inning when that situation is set up). I will compare this base situation to the percent chance that a team scores in an inning when they decide to sacrifice for that first out (as well as the average runs per inning when that situation is set up).

This data can be seen below with a total of about 53,000 innings across seasons where men were on base before the first out. In general, through the four years, teams score in about 26.8% of innings with about 0.478 runs per inning (RPI); when men get on base before the first out, they score 45.8% of innings with a .691 RPI. (In innings where a leadoff HR is hit, this does not count as men on base (nor will these runs count in calculation of either group, assuming men get on after the home run is hit, and before an out)).

Many managers, if not statisticians, understand this increase in the chance to score a run; after all, that’s why they do it. In 2010 and 2013, deciding to, and successfully laying down a sacrifice bunt resulted in a 13% increase in the ability to score that inning for the AL. And while it would make sense that the argument stops there, RPI also supports the sacrifice bunt (with data of the last four years). (Here, again, RPI = Runs scored after MOB B1O situation divided by number of innings of situation.)

This increase in RPI (seen as high as 0.137 Runs Per Inning larger than without bunting, 2012 AL) can contribute a decent number of runs over the course of a season. For example, in 2013, if the Oakland Athletics bunted a little less than once per series, they would have been on par with National League teams with number of bunts (in the 60’s). If they were able to bunt 47 more times (68, rather than 21), then their run total would have given them enough wins to have the best record in baseball (using Bill James adjusted pythagorean expected win percentage).

To summarize, an adjusted estimated runs table with respect to sacrifice bunt set up positioning and outs would produce more runs than the average table that does not take into concern how outs or players arrived at their position. This argument was suggested at the end of an essay by Dan Levitt, with earlier data in a more complex and subtle manner. RPI and the probability of scoring a run increase with a sacrifice bunt.

Bunting is symbolic of the greater good

The first and final argument to discuss is the idea that a sacrifice bunt throws away an out. In baseball, if a player bats out of order, or does not run out an error (among other mental mistakes), then that is giving away an out. And I believe that if a coach tells a player that he can’t hit, and to bunt because he can’t hit, then I wouldn’t argue that in those cases, you are giving away an out (knowingly removing the opportunity from the player to get a hit). So unless you believe that’s how coaches interact with their players prior to calling for the bunt, I will disagree with that notion.

The dictionary definition of sacrifice is “an act of giving up something valued for the sake of something else regarded as more important or worthy.” It’s the biggest theme in religious studies, the coolest way to die in movies, and the plot for heroic stories in the nightly news. Eliminating the psychological effects of a sacrifice, where they’re common place in our culture, seems slightly irresponsible after seeing the data.

This idea lends nicely to the discrepancy between American and National Leagues. Articles can be found, research has been done, and the common thought among those surrounding the game is that pitchers should bunt because they won’t do much else (in appropriate situations). In fact, an article by James Click gives the opinion that the lower the average, the more advantageous it is to bunt. However, my argument is the opposite. The amount they sacrifice, if they’re unable to hit is not valuable to those involved. If the pitcher is respected as a hitter, then their sacrifice is meaningful. Mentally as a leadoff man, if your pitcher is hitting sub .100, and there’s a man on base, he’s bunting because he cannot hit. That’s not a teamwork inspired motive, that’s a picking poison motive. The chart below shows data from the last four years when men get on base before the first out, it distinguishes that the National League is better than either league that doesn’t bunt, but far from as effective as AL bunters.

The argument can be made that the AL contains better hitters, and while I believe this, there would be a larger separation of the % scoring without bunting as well as the RPI of the innings where players get on before the first out.

Because of this separation, I feel that bunting is not giving away an out, but sacrificing for something greater. Simply put, if my teammate sets me up to knock in a run with a hit, that’s easier that having to find a gap, or doing something greater. In many cases, I might need to just find a hole in the infield. Also, I know that my team, and coach, believes in me to be successful. Professional athletes can’t possibly feel pressure and confidence that emanates from teammates with the hopes of greater success, that idea would be ridiculous, right? Those ideas are practiced and taught in business places and self-help books around the world.

Opposition

The data that I used was from Retrosheet, and while this data lists a lot of SH’s (sacrifice bunts) from where errors occur, to double plays, the main output is the standard sacrifice bunt. That being said, it does not include instances where the batter was bunting for a base hit (regardless of number of men on base), or other strange incidents of sacrifice failures (places where the scoring did not distinguish that an SH was in play). After recreating the analysis to include all bunts, the values of RPI and % scoring assuming men on base before the first out, values were still larger than without the bunt, but not as large as the sacrifice representation. This argument falls with the established idea that bunting could be more successful than most people think (especially when the bunt is a sacrifice). For instance, if the numbers above are reduced by as much as 85% in some cases, it still produces more successful results.

The next piece of opposition is that different circumstances have different weights in these situations, and that my case is too general to provide an advantage to a staff trying to decide whether to bunt. My argument is that upon analyzing circumstances, the most important element is the sacrifice bunt. In most situations, I feel that it will boost the team’s ability (and desire) to have success. With four years of data, my goal was to be able to refute the reliance on the simple Tango Run Expectancy Matrix, and how it is used, not to recreate one. In my opinion, in order for people to understand how historically successful situations have been, there should be hundreds of Run Expectancy Matrices highlighting how runners came to be where they are, as well as what batters follow.

The final piece of opposition has been created by myself during the generation of this essay or idea. The Heisenberg Uncertainty Principle relates to the ability to study the speed and position of a microscopic particle. Simply put, by studying one, you’re unable to observe the other. The act of observation limits the ability to fully observe. Because my argument is set up in a romantic sense, it could be argued that this principle relates. If coaches and teams start bunting every other inning, the act of giving oneself away for the greater good of the team will diminish and its advantage psychologically will wither away. In other words, the knowledge of how something effects one emotionally can limit one from being emotionally affected. I present this as an opposition because I feel that this might already be the case where if a pitcher is repeatedly bunting, teams will not think much of it as a quest for the greater good. However, when players are seen as an asset in the box, this advantage still exists; so teammates can still be sold on the relevance of the opportunity.

If these ideas spread, will this essay result in more bunts, especially when there are no outs? Probably not, because statisticians are stubborn. But it definitely provides an outlet for coaches who support the old school, traditional game of baseball.

The annual MLB Draft is an exciting time for baseball. Dozens of high school and college players convince fans that they have the potential to be future All-Stars, and teams make selections to stock their farm systems with talent to win in the future. But obviously, not every pick can be savvy, and the majority of these selections turn out to be regrettable. The best a team can do is make rational choices to put themselves in a position to succeed. I decided to take a look at the draft classes in the 1990s to see if teams were making these rational decisions. I chose this decade because it’s the most recent one that is almost exclusively filled with players who have finished their careers.

In the 1990s, there was a fairly even distribution of pitchers and hitters selected with early draft picks. Since roster makeup isn’t skewed much in favor of either group, this seems to make sense. Teams are just as eager to get elite pitching as they are to acquire top-tier hitters. This year, 6 of baseball’s 12 highest paid players are pitchers, 6 are hitters.

It’s not surprising that during the ’90s, 45 of the 100 Top 10 picks were pitchers. In hindsight, this seems like it was probably the result of some pretty big mistakes. There are certainly some successful examples. In 1999, Josh Beckett was selected 2nd overall, Barry Zito was 9th, and Ben Sheets was picked 10th. The hope that a pick can turn into a future ace is enough to tempt any GM to take a pitcher. But that usually didn’t go well.

I gathered the career WAR for every draft pick, and here is the expected output for each Top 10 selection.

This does not paint a pretty picture for teams who decided to go with pitchers. No matter where on the chart you look, picking a hitter gives a team a better expected outcome than a pitcher, and it’s not particularly close. The average hitter taken in the Top 10 achieved a career WAR of 16.0. The average pitcher reached 7.0. That’s a big gap, and the disparity was made on a large scale.

Here’s a year-by-year average for draft picks at each position:

1999 was an excellent year for pitchers, as I already mentioned. In fact, it was the best year for pitchers. But if you add it to the list of years for hitters, it would rank 6th out of 11.

Clearly, picking hitters seems like the preferable strategy of the ’90s. But teams opted not to do so roughly half the time.

Similar to what position someone plays, there’s another core attribute about a player outside of his scouting reports: whether or not he went to college. College players will be more developed and will have less room to grow. High school picks are considered riskier with higher upside. The data seem to support that. Unlike the difference between hitters and pitchers, the age of a draft pick had a more nuanced effect.

High school players taken at the top (of the top) of the first round are more promising than college players. This is because elite players like A-Rod, Chipper Jones, and Josh Hamilton don’t often slip under the radar when they’re 17 or 18. But what’s interesting is when you make your way to the bottom of the Top 10, college players have a better expected career WAR. I don’t want to make too many guesses why, because honestly I’m not sure. But it’s a very noticeable trend. No matter the reason, it’s clear that teams should be more eager to draft high schoolers with picks 1-5, and college players with picks 6-10. But look at the frequency of high school draft picks by selection.

Teams do the exact opposite of what they should. The earlier in the draft, the more likely a college player is to be selected. 32.5% of Top-4 picks are drafted out of high school, while 68.3% of picks 5-10 are.

To a strong extent, this analysis is not fair to these teams. I’m looking at these numbers in 2014, and it’s easy to go back in time and point out what mistakes teams made in drafts. But these aren’t scouting report mistakes, isolated misjudgments, or bad luck decisions. Teams in the 1990s made consistent poor strategic decisions on a large scale in the draft that were often indefensible.

The Chicago Cubs have gotten off to a very slow start in the 2013-14 season scoring a total of 9 runs in their first 5 games and as a result of that they are 1-4. The buzz around the city of Chicago is all about the excitement of top prospects Javier Baez, Albert Almora, and Kris Bryant tearing up minor league pitching and rapidly moving up in the Cubs System. All of these players have fantastic stats but the stats don’t truly matter until these players can be productive big league players. The problem is is that these prospects have shown day in and day out that they are ready to move on to the bigs. Almora, might not be quite there yet but Baez and Bryant have proven they are by dominating minor league pitching and posting good spring training numbers. Cubs GM Theo Epstein won’t pull the trigger on sending these guys up. Bringing these players up will significantly improve the quality of the team but many more changes will need to take place in order for the Cubs to be a team to win games on a consistent basis. Here are 3 other things that need to happen for the cubs to start their path to being successful

1. The cubs need to find a reliable, all-around, everyday 2nd baseman. There are many different solutions the their problem at 2nd but first let’s establish what the problem is. Darwin Barney has proven that he is an excellent fielding 2nd baseman but he is an absolutely horrendous hitter. In 2013, Barney posted an atrocious slash line of .208/.266/.303. Not only does this show that he rarely gets hits or gets on base, but when he does it’s mostly because singles. The Cubs have many possible solutions to this problem. One possible solution is to bring up Javier Baez and play him at short and Starlin Castro at 2nd or vice versa. Doing this might slightly weaken the 2nd base spot defensively, but drastically improve it offensively. With the Cubs pitching being surprisingly good in the first few games of 2014, their offense is a glaring problem and Baez would improve it instantaneously.

Another solution would be to slide Luis Valbuena over to 2nd and make Mike Olt the everyday 3rd baseman. Currently, Olt and Valbuena are splitting time at third which is detrimental to the team because both players have shown offensive value to the cubs. Valbuena had an excellent eye and has proven to be adept at drawing walks. He also has shown solid power as he hit 12 homeruns in 108 games in 2013. Olt has also shown the ability to hit for power as he had 5 homeruns in a very good spring training that earned him a spot on the opening day roster. Either of these solutions would be a much better fit for the Cubs then having Barney as the everyday 2nd baseman.

2. If the Cubs want to be good now, their bullpen needs to be consistent, and deeper. The bullpen has been a problem for the Cubs for a very long time. However in 2014 they might show some signs of improvement. In 2013, reliever Pedro Strop Posted a solid 2.83 ERA in 35 innings with the Cubs. In his time in Chicago, he only gave up 11 earned runs, 5 of which were in one performance. Along with solid numbers Strop possesses a 97 MPH power sinker in addition to his best pitch which is his slider. Strop will be put into a much bigger role this season and if the cubs want to succeed he will need to continue to pitch at a high level. In the offseason the cubs also signed lefty Wesley Wright and Jose Veras who in recent history have proven themselves as reliable bullpen options to their clubs. Players like Brian Schlitter and Hector Rondon will also need to step up for the Cubs. If Strop can continue pitching at a high level and the rest of the pen can consistently pitch in late innings. The Cubs will improve as a team very much.

3. Lastly if the Cubs want to succeed Anthony Rizzo and Starlin Castro must have bounce back years. There are many things that I could criticize about these 2 players but there a few problems in their games that are in the most need of fixing. In 2013 Rizzo only hit .233 if Rizzo continues to hit in the heart of the cubs line up, a .233 average is unacceptable. If he was hitting 50 homeruns it might be a different story but .233 with only 23 HRs isn’t going to cut it. In order for the Cubs to succeed, Rizzo will either need to hit 10-15 more homers or improve is average by around 30 points.

Starlin Castro is a much bigger problem for the Cubs. Spending most of the season in the 3 spot, Castro posted a weak slash of .245/.284/.347. Castro’s numbers were only a bit better than Barney’s which makes him a big problem. In addition to his poor offensive play, Castro has been an extremely inconsistent defensive SS his entire career. There is optimism for Castro though. In Castro’s first 2 full big league seasons, he was voted to the All-Star Game and hit close to .300 in both of those seasons. Castro has shown in his career that he has the ability to hit, the question. is will he be able to have seasons reminiscent to his all-star years. Only time will tell for Castro but if he can bounce back along with Rizzo the Cubs might actually be a legitimate team.

Although many things need to happen for the Cubs to be a playoff contender, fans should be optimistic for the future. With a farm system fortified with elite prospects throughout and an improving bullpen, the cubs need their “key players” to perform at a higher level. If all of these things can happen, there might be October baseball played at Wrigley sometime in the near future.

Having grown up playing catcher from Little League through college, I always recognized the temptation and situational changes that occurred in terms of strategy and pitch selection with runners on, particularly base stealers, versus with no runners on base.  As a catcher, my thought process with a base stealer on, is always to try and have my pitcher get the ball to me as quickly as possible.  An earlier study I read dealt with the correlation between pitchers’ times to home, and that being a much stronger factor in throwing out a base-stealer than catcher pop times.  Logically, in thinking of pitch selection as a way of controlling the run game, the quickest way to get the catcher the ball is with one’s fastest pitch.

To evaluate the impact of base-stealers I defined a base stealer as a player who swiped 20 plus bags in 2013.  Using Baseball Reference, I slotted 6 pairs of base stealers and their following hitters.  The criteria for those hitters being 400 plus plate appearances in the same slot in the batting order.  Nick Swisher however is an exception because he had 250 plus appearances behind both Michael Bourn and Jason Kipnis, but I decided to include him.  I should also note that all the statistics in this study are from 2013.  Using Baseball Savant’s Pitch f/x database I defined a fastball as a 4 seam, 2 seam, sinker, splitfinger, and cutter and every other pitch as a breaking ball.  I then compared the fastball and breaking ball rates with each hitter with a runner on 1^st or nobody on.

It is taken from granted that for a hitter the best pitch to hit is a fastball.  While there are many different approaches, one of the most common is “fastball adjust,” meaning the hitter always looks, or anticipates, a fastball as you get in the box.  However, if you recognize something different out of the pitcher’s hand, you should have more time to adjust.  Hitters are always fastball hunters first, that’s why we call 2-0, 3-1 counts “hitter’s counts” because they will most likely get a fastball and at the same time are sitting fastball.  As proof we used the probability of scoring a run per 100 pitches of a certain pitch above the prototypical average players.  The league average probability of scoring runs against what I defined as a fastball type pitch for every 100 pitches in 2013 was 0.0167 and for every 100 off speed pitches was -0.07.  That is over an 8/100ths difference in the likelihood of scoring a run above average, which added up over the thousands of pitches a player can see a year can make an impact.  Below are the 6 hitters I used for this study and their run probability rates against different pitches:

As the data above supports, the best pitch to hit, the pitch a hitter is most likely to score more runs from, is a fastball.

So that being said, if a reputed, or habitual, base stealer is on base, then will the hitter at bat see an unusually high rate of fastball-like pitches?  With a higher rate of fastballs the hitter should therefore have a greater chance of success.  The theory being that an offense built more on speed and base stealing should see a higher rate of fastballs which then gives that team a greater probability of scoring more runs.

Now the total overall fastball rate for the league as a whole for the 2013 season was 57.8%.  The total fastball rates I arrived at were derived from simply taking the situational fastball rate and dividing it by the total pitch percentage or fastball percentage plus breaking ball percentage: fastball% / (fastball% + breaking ball%).

Looking at the results, in particular the totals, there is no significant difference in percentages of fastballs vs off speed seen with a runner on first or not.  The biggest difference is a 4.46% difference with David Wright.  And David Wright scores 21.1 runs above average against fastball type pitches (wFB).  While maybe an extra 4.46% increase does not make a world of difference it still contributes to overall run production and as we know in baseball 1 run can decide a game and 1 game can decide a season.  However, it appears that my hypothesis is false and there is no significant difference in situational pitch selection with a base stealer on 1^st.

Now I will be the first to admit that there are definitely ways to improve upon the accuracy of my theory.  The biggest problem being that I could not find a database on the internet that allowed me the option of isolating at bats with only specific runners on, so the next best thing was Baseball Savant’s option of isolating at bats with the option of runners on certain bases or a combination thereof.  So all these plate appearances measured are just with a generalized runner on 1^st who could be anybody or nobody on at all.  This study is assuming that the runner on 1^st, for a majority of the time, is the base stealer who hits 1 spot in front of the selected hitter.  BIG assumptions I realize.  Also this is only covering 6 hitters in their 2013 season, which is a small sample size considering.  Unfortunately I did not have all the resources necessary for the most accurate representation for this study as a whole and on that note I hope many of you who perhaps have more available to you, can dig deeper and build on my theory.

This is my first time posting something like this so if you have any helpful questions/comments/criticism/advice please feel free to comment.  And if you have a way to more thoroughly complete this study please do so!  Thanks and I hope you enjoyed.

Draft prep: Framing the problem

So you’re preparing for your fantasy draft. You’re caught up on FanGraphs, checked for recent injuries at Rotoworld, maybe skimmed a few headlines from your other top 11 baseball news sites. Maybe you’ve even downloaded the FanGraphs positional rankings, and are planning to keep the file open during the draft as a reality check against the pre-set rankings of the site your league uses.

But really, what do the guys at FanGraphs know? Sure, they know a lot about baseball, and statistics, and this year’s projections, and a handful of underlying stats that tend to predict future performance. But what they don’t know is whether your league uses OBP instead of AVG, or OPS, SLG, or batters’ strikeouts, or maybe holds and FIP and pitcher fielding percentage. If this is your situation, then I feel your pain. My fantasy league uses eight statistics for batters and pitchers, three each beyond the usual five. (In case you’re curious, the mysterious six are: Batter hits, K’s, & OPS; Pitcher holds, losses & complete games).

These differences matter. If your league uses OBP, Joey Votto turns from a fantasy player who’s solid in four categories (including average, where his impact is limited because he walks all the time) to a guy with a truly elite skill. Maybe it’s easy for you to account for the relative value of a Joey Votto, but how well can you project the 25th through 35th outfielders? Some might be much better or worse in your league. If you have batter strikeouts, as in my league, how do you value Mark Trumbo and his home run power against the elite contact skills of Norichika Aoki?

Generating your own rankings

One answer, and the one I opted for, is to generate rankings based on your own league’s stats. Now, this may sound a bit too work-intensive and time-consuming for most of you (especially those of you with relatively normal priorities), but in reality it wasn’t as time-consuming as I expected.*

First of all, there’s no need to reinvent the wheel. There are lots of projection systems out there that are available to the public, and some of them are quite good. I decided I would simply download all the projections listed on FanGraphs, and average them out. And then, after thinking for a little while about the costs and benefits of that approach, I decided I wouldn’t do that at all, and instead would use the results of just one projection system. But which one should I use? Luckily, that’s yet another bit of analysis we don’t need to bother with, because the Interwebs are full of crazy mathematicians who love baseball and have nothing better to do. After searching for a few articles that evaluate projection systems, like this one and this meta-one, I decided that the forecasts I trusted most (and were easiest to obtain) were Steamer for batters and FanGraphs fans for pitchers. (The high accuracy of the latter shocked me at first, but then I realized that fans assimilate the results of all the projection systems into their own player projections, departing from them only as dictated by common sense, inside scoop, and hope.)

Operationalizing the Solution

Here’s where it gets tricky. What advanced data manipulation packages and techniques are best for downloading reams of data from the FanGraphs site into your spreadsheet? Certainly there was no need for me to copy and paste the data 50 players at a time like someone living the dark ages, was there? No, of course not. And I probably never really did that.

Instead – bear with me if you’re not technically inclined – I hit the gray “Export Data” button to the upper right of my chosen projection page. This involved a lot of loading the correct page, hovering my mouse over the text, and clicking, but in the end it was worth all the work, because 5 minutes of sweat, plus a beer, had finally paid off in spreadsheets full of data.

*If you’re not interested in these details, the fun stuff is posted in a couple of tables towards the end. (I like writing, so this is likely to go on for a while.)

Z-scoring your data points

Z-scoring batter projections is easy. The problem lies in determining what set of players to use in order to calculate means and standard deviations.

This is an important question, at least to the extent that any question in fantasy baseball is important. For example, if you must use every hitter in the league, including the guys projected for 8 at-bats, you create the illusion that lots of players bat .220 or score only 4 runs, as opposed to your league’s reality in which .270 with 70 runs is pretty ordinary. For a little math fun, I compared the results generated using means and deviations 500 players deep (the equivalent of a 25-team league that rosters 20 position players) versus one with more reasonable assumptions. It caused huge increases in variance in runs and rbi’s, so a guy who drove in and scored 100 compared no better to the mean either way (~2+ standard deviations), but smaller increases in the variance in SB’s, HR’s, and OPS, which, together with the lower means, meanings this system overvalues guys who produce in these categories. Martin Prado and Torii Hunter were made sad, whereas Billy Hamilton was elevated to a demigod (or at least a top-40 hitter).

So how do you generate values that represent your player pool?

One method – and a very reasonable one – is to use the final statistics compiled by your league the previous year. With this data, it’s easy to generate per-slot averages based on last year’s performance, and to compare projected performance against it. But I did not choose this method. A more savvy number-cruncher might say that projection systems, while designed to be as accurate as possible for each player, may be systematically biased on the whole, and therefore determining the value of this year’s projections based on last year’s actual statistics is tantamount to comparing apples and oranges.

I was more worried about lazy owners. Any league can have a couple of careless owners who are in it just for fun (the gall!), or who keep BJ Upton when he can’t even see the Mendoza line, because of that one time his cousin shook BJ’s hand at a Jay-Z concert. I know of what I speak. If your goal is to win your league, you want to base your evaluation on the best players available, rather than the happenstance of which Atlanta outfielders spent the whole year on someone’s roster.

I generated means using very precise data, plus a random stab in the dark. First, I looked up the exact number of players at each position in my league from the previous year. Then I mostly ignored this data. Although it’s true that player values vary greatly between leagues depending on how many players start, and how many are rostered, this is the sort of thing you can keep track of during the draft. Don’t draft another first baseman if you already have three of them and no shortstop, and don’t draft a first baseman just because he’s ranked ahead of a shortstop if there are another seven first basemen ranked close behind.

My league rostered only 123 regulars last year. Not a deep league. I used a lot more than 123 in my calculations in an effort to lower the means a bit, to account for the existence of catchers and second basemen. I then haphazardly created sort variables so I could bring the best 150 to 180 players to the fore, with the goal of getting a fair representation of the quality of players in my league. I tried various formulas like [(HR+1) * R * RBI * (SB +1) * AVG * OPS] (adding 1’s so as not to exclude players projected for 0 HR’s or SB’s ) and PA * wOBA. Virtually every one of them produced a good representation of the best hitters projected for regular playing time. In the end, the best way to evaluate the sort is to look at the list and see if the guys near the cutoff are fringe players who are familiar from last year’s waiver wire.

Calculating projected player values

Once you determine which players you want to include, Excel is happy to instantaneously calculate averages and standard deviations for each stat. Once you have these values, you can re-include the entire player pool, or as much of it as you wish, and the formula for each player in each category is simply (his projected value – the average projected value)/standard deviation.

The next challenge is to generate ranks from the Z-scores. The simplest way is simply to add them together (being sure to subtract ones where lower scores are better, such as pitcher walks or batter strikeouts). But here, I discovered another issue. A potential superstar who might not have a full-time job could end up ranked about the same or below a mediocre player who was guaranteed to start. If I wanted my draft rankings to make sense at a glance when I have just 90 seconds to pick a player while eating a sandwich, I needed to distinguish accumulators from guys with potential.

Ranking performance and potential

It matters whether a player is an okay guaranteed performer or a unpredictable potential star. If I find myself with no second basemen in the 22nd round, I might want to take the best guy who’s pretty much guaranteed 140 days in the starting lineup, like an Anthony Rendon or a Howie Kendrick. If my roster’s pretty much set, I might prefer a hitter who has a better chance to bust out and hit 45 home runs, like Chris Carter (unless I’m in my league, in which his 80% strikeout rate falls 37 standard deviations below the mean).

What I decided to do was generate two rankings for each batter, one based on projected totals, and one based on projections per plate appearance. Luckily, Steamer has already done the work for us by projecting everyone in both ways. For instance, Everth Cabrera is projected as the 479th-best player by wOBA, with 74 runs and 45 stolen bases. At the other extreme, Colorado’s Kris Parker is projected to be the 50th-best hitter in the league, just ahead of Dustin Pedroia, with a .279 batting average and .465 slugging percentage, despite getting only one plate appearance, and not getting a hit.

At this point, there are 2 sets of columns for each batter: 1 set of columns for his Steamer projections for each relevant stat, and 1 for the associated Z-scores. To this, I added 2 more sets of columns: 1 for per plate-appearance projections for each stat, and 1 for those associated Z-scores. (Dividing hits into plate appearances rather than at-bats feels unnatural, but that’s what you need to do if your league counts total hits.) Calculating per-PA quality is then easy, as you can just add the Z-scores (or subtract for negative statistics). But once you have projected rate statistics in your per-PA rankings, it becomes apparent that it doesn’t make sense to include the exact same values in your projected accumulated totals.

To handle this, I weighted the Z-scores for the rate stats. I multiplied the Z-score for AVG by projected AB’s/average projected AB’s, and you can do the same for OBP, using PA’s. My league uses OPS, a value generated by adding two fractions with different denominators (aka OBP & SLG), so to weight those Z-scores I multiplied them by projected (AB’s + PA’s)/average projected (AB’s + PA’s). I then added these weighted Z-scores to the other Z-scores for projected totals. The result of adding these weights is that a player who is one standard deviation above average in both AVG and OPS, and who has an average number of AB’s and PA’s, would get +2 from these categories in the variable used to rank projected totals. By the same lights, the aforementioned Kyle Parker’s AVG and OPS would essentially get no weighting at all, and have no effect at all on his projected totals, just as in real life his performance is not expected to have any effect at all on the rate stats of your team.

The Fun Stuff

And that’s about it. Once you have Z-scores, it’s very easy to rank players, to change the formulas to rank them by different systems, or to sort players by certain categories to see who stands out the most.

Two common variations on the traditional 5 stats are to include OBP instead of AVG, or to play in a points league. (For a points league, just change the Z-score weighting to reflect the point system). Here are the top players in these alternate systems using this evaluation method (I threw my own league in too, just for kicks):

(Note: I evaluated points leagues the same way as the other leagues, generating both a points total and a points/PA score for each player. I scaled the two values to give them approximately equal weight, and ranked players by the mean of the two.)

I expected Joey Votto to be a stud in OBP leagues, but in reality Joey Bats benefits more. Jason Heyward too. Meanwhile, CarGo is top 3 in every other system, but falls to the bottom half of the first round in a points league. In my own crazy league, Norichika Aoki projects as a contact-hitting top-40 stud, while Mark Trumbo’s contact deficiencies show up in strikeouts and hits, as well as AVG, and he drops to 82nd.

I also thought it would be cool to see which players project to be affected most under different scoring systems. Here are the players with the largest variation in ranks between systems (weighted to prefer higher-ranked and therefore more interesting players):

Billy Hamilton projects to be a one-category stud in any system that ranks stolen bases, but many people doubt whether he’ll be an especially good ballplayer in 2014, and the points system shares their skepticism. Carlos Santana will benefit enormously from any league using deeper measures than AVG, while Adam Dunn jumps from irrelevance to potential rosterability in OBP leagues only. A couple more notable players: Alex Rios is vastly more valuable in leagues with the standard five categories, and least valuable in points league, and Adam Jones follows a very similar, if somewhat less drastic, pattern.

And there you have it – the results of one approach to generating player values for leagues with alternative categories.

As I usually do, I was checking through the headlines on mlb.com and I happened to notice that Kirk Gibson has not made a decision for who will be closing for his team. This should be one of the bigger questions leading up to the regular season as the Diamondbacks have several options when it comes to closers.

Honorable Mention: Josh Collmenter
He is a pitcher who has quietly been one of the best relief pitchers for the Arizona Diamondbacks of late. He is a three pitch pitcher with an 88 mph fastball, a 70 mph curveball, and a 78 mph changeup. With that slow speed, one would expect him to be a more pitch to contact kind of pitcher and let the defense take care of him. But he posted a career low 32.7% groundball rate which is low for many pitchers. However, he also does not give up that many homers, giving up an average of .78 HR/9 last season. He struck out 8.32 batters per nine innings last season while walking 3.23 batters per nine last year.

Where Collmenter’s value is on the Diamondbacks is as a long relief, spot starter pitcher for them. He pitched in 49 games last season and threw a total of 92 innings meaning that he threw nearly 2 innings per appearance. In his career in the minors, he pitched all of his outings as a starter with the exception of 2 games in his first year in low A ball in 2007. Closer could be a good spot for him with the strikeout rate but I would like to keep him in the bullpen for if the starter can only throw 2 innings or less.

3. Brad Ziegler
It is no secret that Brad Ziegler is very good at getting groundball outs, that is what makes him successful. He doesn’t really throw an actual sinker per se, but his fastball essentially plays the role as sinker. The submarine arm action that Ziegler throws with has the pitch rising up briefly before dipping down just before it gets to the plate (as shown in the gif below).

By using this heavy sinking action on the fastball, he has produced a career 66.1% ground ball rate (which has been raised to a 72.9% rate since the start of the 2012 season) and in front of a great fielding team like the Diamondbacks (team UZR/150 of 8.1, good for second highest in the Majors), that leads to success. But this is why he should be used more of as a relief ace as opposed to closer. If the starter leaves the game in the seventh inning with people on base, I want a pitcher to come in who can get the ground ball double play. Neither Putz nor Reed are as good at getting groundball outs and only Putz has a higher LOB% (90.9% for Putz as opposed to Ziegler’s 80.7). If Ziegler is put into the role of closer, then he would be less likely to be put into a situation where a groundball is needed as the manager would want to hold on to him until the ninth inning.

2. J.J. Putz
J.J. Putz has a very realistic chance of claiming the role of closer at the start of the season. If not for injuries, Putz would have maintained the role of closer last year but an elbow and finger injury during the season limited his playing time to only 34.1 innings and when he returned from them he was more of a situational right handed pitcher. But since the start of the 2012 season, no pitcher on the Diamondbacks has more saves than Putz’s 38 saves leading many to believe that he could be a front runner for the closer spot based on experience alone. He’s been solid for them in the past, but a steady decrease in pitch velocity and an increase in home run rate over the past 3 years should be somewhat concerning for the Diamondbacks. His fastball velocity is still above 90 mph (91.7 mph in 2013 and 92.8 mph in 2012) and the home run to fly ball rate is still not too high (having been only about 14.8% in 2013 and 8.7% in 2012 but that is a concerning increase from the 6.0% HR/FB rate in 2011).

One thing interesting to think about with regards to J.J. Putz is what effect his injuries had on his performance last year. In most areas, Putz experienced a dramatic increase in essentially all statistics but one of the more significant increases occurring in SIERA where he went from 2.29 in 2012 to 3.24 in 2013 and his walk rate increased from 1.82 BB/9 to 4.46 BB/9. It is tough to tell whether or not these inflated statistics are just as a result of injuries or if they are as a result of just wearing down from age. After all, we can’t forget that Putz is now 37 so he does not have age on his side any more. I don’t see him being as bad as his stats from 2013 indicate but it is certainly something to think about.

1. Addison Reed
One pitcher who definitely has age on his side is Addison Reed; the pitcher who I believe should be given the role of closer without question. He proved that he is one of the best young pitchers in the game and he showed this while playing for a terrible defensive team like the White Sox. I believe that his ERA is definitely misleading as a 3.79 ERA makes him seem worse than he is. Reed strikes out 9.08 batters per nine innings, limits the walks with only a 2.90 BB/9, and a HR/9 of .76 which is comfortable in the closer’s role. Those are the kind of numbers that someone in the position of closer should have and with his young age of 25, there is definitely room for improvement. His other numbers like his xFIP of 3.77 in 2013 and his SIERA of 3.19 in 2013 would indicate that he is definitely going to get better.

There are other things to like about Reed aside from his statistics and potential. Last year, he threw the four seam fastball for 92.7 mph, the two seam fastball 93.5 mph, the slider at 83.8 mph, and the changeup at 83.7 mph. The 8.9 mph difference between his fastball and slider are very deceiving to a right handed batter because of the movement away from the batter and the 8.8 mph difference between his fastball and changeup creates a devastating effect on left handed batters as is evidenced by the .266 wOBA vs. L last season with the 37 strikeouts.

The Diamondbacks are in an enviable position with having multiple options that they could plug into closer. With the young and fragile rotation (Corbin has already shown that young starters are good but not invincible) that the Diamondbacks have, I think that Collmenter will have to avoid getting locked into the closer spot as he may be needed to make a few starts. Ziegler was good for the Diamondbacks last season but don’t expect to see him in the closer’s role as a pitcher of his caliber needs to be free to pitch at any time during the course of a game. But honestly when it comes down to the choice, the gap between Reed and the other options is substantial enough that there really should not be much debate.

One of the offseason’s most talked-about moves was the trade that sent Prince Fielder to the Texas Rangers in exchange for Ian Kinsler and gobs of cash. While universally (and rightfully so) viewed as primarily a salary dump for GM Dave Dombrowski and the Tigers camp, the Rangers have gained a strong bat to place in the middle of their batting order alongside Adrian Beltre and Alex Rios.

Yet unlike the much-theorized David Price trade, the Fielder deal was not a pure salary dump. Fielder stumbled mightily in his production in 2013. In 2012, he posted a robust .313/.412/.528 traditional slash line, with an impressive .940 OPS and 153 wRC+. According to Baseball-Reference’s oWAR calculations, 2012 was Fielder’s third-most valuable year at the plate with a 5.4 mark. All of this stands in stark contrast to Fielder’s 2013.

Last year Fielder posted a much more pedestrian .279/.362/.457, .819 OPS, 125 wRC+ and 2.9 oWAR. While of course those are still above-average numbers, when attached to the name Prince Fielder and his ubercontract, Dave Dombrowski clearly had reason for concern. However, off-the-field issues are widely believed to have contributed to the dip in Fielder’s production, and natural regression may have also contributed to the fall from Fielder’s career-high traditional slash line. Fielder also enjoyed a career-high .321 BABIP in 2012, with his 2013 mark of .307 more in line with his normal marks.

So, the question presents itself; what exactly does Texas GM Jon Daniels have on his hands in the 2014 model year Fielder? There are a number of factors contributing to this answer. Firstly, while the batters ahead of him do not contribute to his slash line, they certainly do help counting stats such as RBIs. While RBIs are naturally an utterly useless stat when evaluating individual performance, men getting on base allow a hitter to create runs, and as runs are ultimately what win games, putting men on ahead of big bats such as Fielder is part of what goes into good team creation. Therefore, I will examine the clip at which we can expect there to be runners on base when Fielder bats for Texas as opposed to his stint in Detroit.

Secondly, I will also examine the impact Arlington itself will have on Fielder’s bat. Arlington has traditionally been a much more hitter-friendly location than Detroit. But how much exactly will Texas raise Fielder’s numbers?

The top of the 2013 Tigers lineup consisted of Austin Jackson, Torii Hunter, Miguel Cabrera in front of Fielder. Those first three hitters posted OBP’s of .337, .334, and .442, respectively. That averages out to a .371 mark, albeit an imperfect one due to Cabrera’s significantly higher individual mark (also, Cabrera hit a lot of home runs last year, and while that counts towards his OBP, that means the bases were empty when Fielder came to bat). We’ll refer to this average of the top of the order as tOBP, or “Top OBP” for the rest of the article for the sake of saving space.

The top of the 2014 Rangers lineup will be made up of Shin-Soo Choo, and either Elvis Andrus or Jurickson Profar before Fielder, who will bat third. There are a number of different projection systems we can use to forecast the upcoming season, for this article we’ll be using Steamer. Choo is given a .391 OBP, Andrus a .340, and Profar a .321. With Andrus in the lineup the projected tOBP is .365, with Profar it’s .356. So despite throwing his wallet at Choo and his obscene .423 2013 OBP, Jon Daniels in fact is giving Fielder less to work with in front of him.

Or is he? Part of the smaller (projected) tOBP in Texas is that Fielder simply won’t have the best hitter in the game hitting in front of him anymore. Also, one has to expect Fielder to be better at the plate this year. Steamer awards Fielder a substantial .290/.390/.516 line with a 142 wRC+ and 3.4 WAR, a major uptick over last year’s production. If we factor him into the projected Texas tOBP, with Andrus it’s a .374, and with Profar it’s .367. That’s something you like to see if you’re Adrian Beltre, who lead the league in hits last year and launched 30 homers.

And speaking of homers, Fielder’s move to Arlington will help him in that department. The newly named Globe Life Park ranked seventh last year in home runs with a total of 107 being hit there. Comerica Park, where the Tigers play, ranked fourteenth with 99. This helps Steamer award Fielder 29 home runs, up from 25 last year.

However, can we possibly expect Fielder to exceed these projections? As mentioned earlier, Fielder’s down year was contributed to by a number of off-the-field issues according to Hunter. A change of scenery will definitely do Fielder well, and he also seems to have lost some weight if the pictures and video coming out of Spring Training are to be believed. For that reason I’m willing to bump up Fielder’s numbers by a few slots, and I expect him to be even better than what Steamer predicts. Because baseball is a fickle mistress I could easily be wrong, but call it a gut feeling. All in all, Jon Daniels may have caught lightning in a bottle here with his rather expensive gamble, and if Texas manages to overcome their pitching woes they should be a very dangerous team with Fielder anchoring their lineup.