Let’s face it, people love the home run. It’s why players like Mark Reynolds can find jobs. These days, we aren’t surprised when we see a couple of home runs in one game. It wasn’t always like this, however. Home runs used to be a rarity among baseball events. In the early 20th century, it wasn’t uncommon for a player to lead his league in baseball by hitting 10-15 home runs. This brings me to the question: how has the home run actually changed? Not in terms of its frequency, but in terms of its value. More specifically, its value in runs. To approach a solution to this question without arduously parsing through hundreds of event files, we must find a way to mathematically frame the game of baseball in a way that encourages simplicity but doesn’t lose the most familiar parts of the game.

Markov Chains

The first batter of the game steps to the plate and sees no runners on base with none out. He pops up. The second batter steps to the plate and sees the immediate result of the last at bat: an out. The second batter walks. The third batter then sees the immediate result of the second batter’s at bat: a runner on first base. The stream of batters stepping to the plate and being placed into a state resulting from the previous batter’s at bat exemplifies the nature of a Markov Chain. When a batter steps into the batter’s box, his current state (whether it be an out situation, a base situation, or a base-out situation) is only dependent on the previous batter’s state. This is known as the Markov Property. Using this structure, we can simulate any baseball game we’d like. However, to keep our calculations simple, we should introduce some new rules.

The Rules of the Game

1. A batter can only attain a BB, HBP, 1B, 2B, 3B, or HR.
2. Outs only occur via a batter getting himself out.
3. Anything other than the events from 1) is assumed to be an out.
4. When a batter gets a hit, the runners on base advance by as many bases attained by the batter (e.g. a double with a runner on second will score the runner on second).

These are the rules of the game. There are no stolen bases, no scoring from second on a single, and no double plays. We have stripped the game down to only its essentials, while implementing certain changes for our own convenience. For our purposes, we don’t care about Mike Trout’s 33 stolen bases, only the fact that he mainly attains his bases through the events we allow.

The Out Chain

We assume that the probability of a batter getting a single at any point during a season is the number of singles he gets for the season divided by his plate appearances. We do this for the probabilities of all our desired events. By doing this, we can construct a simple Markov Chain where players step to the plate and find themselves batting with 0, 1, or 2 outs. We find that this chain is irreducible, meaning that each state (0, 1, or 2 outs) eventually leads to every other state. This, and the fact that we are dealing with a finite number of states, leads us to the existence of a probability distribution on our state space of outs. It so happens to be that when a batter starts his at bat, he does so with an equal probability of seeing 0, 1, or 2 outs, i.e. the probabilities of a batter seeing 0, 1, or 2 outs when he comes to the plate are all 1/3. The knowledge that outs are uniformly distributed over our game allows us to construct probabilities for a more complicated chain that should shed light on our original question.

The Base Chain

We now place our focus on the stream of batters who see a certain base situation when they step to the plate. The transitions of base situations are dependent on the out situation, as can be seen when a batter bats hits with 1 out versus 2 outs. Batting with 1 out, if the player makes another out, then the base situation stays the same for the subsequent batter. If he does this with 2 outs, however, then the inning is over and the base situation reverts to the state where no one is on base in the next inning. Fortunately, we know that the probability of a batter seeing any number of outs when he steps to the plate is 1/3. In a similar manner to the Out Chain, we find that every state in the Base Chain leads to every other state. The “runners on the corners” state eventually leads to the “bases loaded” state, which eventually leads to the “bases empty” state, and so on. Since there are finitely many base situations, we are led to a stationary probability distribution on the state space of base situations. That is to say, there is a probability associated with a runner stepping to the plate and seeing the bases empty, and another for seeing a runner on first, etc.

Results

Using this method, a player in our universe who stepped to the plate in 2013 saw the bases empty with an approximate probability of .467. That same batter saw the bases full with a probability of .103 and one runner on first with probability .210. If a team managed to load the bases, they’d find that they generally had to wait about 10 more plate appearances before they next loaded the bases. If they put runners on the corners, they generally had to wait 42 more plate appearances before they did so again. All of this leads us to some of our final conclusions. In the context of our rules, the expected number of runners on base in 2013 was .908, meaning that the expected value of a home run was 1.908 runs. This method generates home run values that are always between 1.8 and 2.2 runs. The following is a table of all of the expected home run values this method generates from the seasons of the last 25 years:

In the last 25 years, we predict that a home run had the greatest value in 1999, at 1.972 runs. This is a reflection of the heavily offensive environment of the season, when big bats such as Sammy Sosa, Mark McGwire, and Barry Bonds were getting on base at staggering rates. The following is a graph of all of the home run values the system predicts from 1884 through 2013:

We see that this system predicts home runs to have been of more value from around 1889 to 1902, when the home run hovers at around 2.00-2.15 runs. While most players of this generation weren’t hitting home runs, they were certainly getting on base often. In 1894, 38 players had on base percentages greater than or equal to .400, compared to 7 players in 2013. When on base percentages are higher, more people are on base, and this increases the expected value of the home run. Under our restrictions, however, the home run hasn’t been worth 2.00 runs since 1950 and these days it fluctuates between 1.90 and 1.93 runs. While these estimates are all under the umbrella of rules and assumptions, this framework allows us to more easily generalize the game of baseball while preserving its most important aspects. It’s this framework that gives us the power of estimating that, while Chris Davis‘ 53 home runs were probably worth 101 runs in 2013, they may have been worth 114 in 1894.

If you had a team that was in complete or semi- “rebuilding” mode, and you wanted to start quite nearly from scratch, and implement some of the smartest analytical techniques into your team philosophy, what might you do?  In the rest of this article, I detail some examples of what said hypothetical team might want to do.  I assume that the team has a middle-of-the-road farm system and an average operating budget, and that they want to accrue wins as efficiently and cost-effectively as possible.  I also assume that they have installed all of the state of the art ball and player tracking systems in their major and minor league ball parks that they possibly can.

What’s first?  Well, the ballpark.  Build the field to have a lot of foul territory–mimic the current Oakland A’s stadium.  Even though park factors seemingly have no effect on wins, I think mimicking the A’s would be a good choice for cost efficiency.  This move would allow you to stockpile high FB% pitchers who are going cheap nowadays.  It would enable you to take cheap, mediocre pitchers–the price for pitching is getting out of control nowadays–and give them a chance to put up great numbers.

Next, infield shifting–do it more.  No one shifted more than the Orioles last year, and studies have shown, along with even player anecdotes, that there should be even more shifting done than the O’s did.  Use opposing batter spray charts to determine where and when to shift, and do it as much as possible.  You might even look to hire more multi-position eligible players as they might find it easier to handle shifting abilities.  Ben Zobrist might be the most important player for the Tampa Bay Rays, defensively.

Next, develop and train hitters who can pull the ball with power.  It would be nice if your team was full of guys with all-fields power, but they are more rare, and thus more expensive.  Start teaching them to bunt well from the minors in order to be able to beat the eventual shifts they will see in the majors.  Hire the foremost bunting coach in the world for your staff.

Pitch framing–teach it from the minors and don’t let players like Jose Molina get signed by the Rays for so cheap money.  If possible, make clones from Molina DNA.

Keep your best relievers in the 7th, 8th, or high leverage situations only.  Sign a cheap closer each year from the scrap heap and watch him go to another team the next year as a free agent!  Game the system to keep your best young relievers stuck at a low price.  Their low save totals will help keep their arbitration numbers down.

Try to sign your best young players to long term deals.  The more Dustin Pedroias you can accrue the more payroll flexibility and WAR you will have at your disposal.  This one is easier said than done.  But if you can pull it off, you will make your team more attractive for incoming free agents.  And don’t be afraid to commit long-term to speedy players, as the data seems to say they age well.  The more tools a player has, obviously, the less risk his contract is if one of the tools breaks down.

Speaking of signing free agents, try to stay flexible in your 5th SP or 4th OF spot.  It seems like there are always guys left over at the end of the FA signing season who are forced to sign bargain contracts–Ervin Santana and Nelson Cruz, for examples from this year.  Try to find cheap platoon solutions when you have a player who struggles against a certain type of pitcher.

At the end of the day, this article is just a collection of some of the ideas that a mediocre team could implement to try to win now and for the near future.  Many teams are already implementing some of these ideas.  If you have any further “smart” hacks that you think should be the gold standard for teams looking to improve in a cost-efficient manner, I’d love to hear it in the comments section.

As many should know by now, George Springer is a highly touted and talented player, a huge part of the Houston Astros’ future. He has been on the brink of 40 home runs and 40 steals in both 2012 and 2013, shown that he could maintain his offensive output through each level of the minor leagues, and plays a premium position (center field) at an above average level; if he were to fix strike out issues, Springer would be seen as a top-10 outfielder in the American League coming into 2014.

Given the rules surrounding service time in the majors and the rapidly ascending price tags for premier talent, the Astros would have been perfect content with keeping Springer in Triple-A Oklahoma City to begin the season. Springer was with the major league team for Spring Training and in 31 at-bats was not terribly successful with only a .161 BA, but with much improved strikeout to walk ratio of 11 strikeouts to 8 walks; this should have firmly signaled a demotion to Oklahoma City. The Astros, though, tried to change it up a bit and offered Springer the richest contract for a player with his experience, a 7 year/$23 million contract.

Springer’s representatives countered that the 3 years of arbitration that the Astros were buying out were worth more than the $7.6 million per year that were essentially to be bought out, rebutting that he would be worth closer to $10 million in arbitration. Springer’s declining of the Astros offer sent him back to minor league camp and he will now come to Houston sometime this summer when he is able to move his arbitration clock forward. There are a few questions that arise from this valuation of Springer and also Springer’s decision to not accept the offer.

The most rudimentary, yet essential, aspect to look at in regards to Springer is what the Astros valued George Springer as for the last 3 years of his contract. For better or worse, the contract that the Astros offered was geared towards buying his arbitration so it is not fair to value this contract at $3.3 million a year because it is a ridiculous premise that the Astros assumed he was worth that money now. In fact, arguably the best player in baseball, Mike Trout, was only valued at $1 million a year pre-arbitration and it is a difficult argument to make that Springer is worth more than 3 times of Trout.

That being as it is, this contract should fairly be valued at $7.6 million for the 28-30 year old seasons for Springer. To assess what Springer’s price tag would buy the Astros on the open market, a thorough analysis of trends of free agent outfield salaries from 2006-2013 needed to be conducted. This analysis looked at all outfielders that were signed for $6-$10 million per season. A quick analysis of the data shows that a 34 year old outfielder with a 2.5 WAR would get roughly the same amount of money on the open market as Springer would have received in the proposed extension. Furthermore, out of the 24 player sample, only Cody Ross and Coco Crisp have been better since they signed for a similar amount as what was offered to Springer and each was over the past two seasons so there is very little of an inflation factor.

There are a lot of outside factors and reasons why these players received the amount of money that they did and there is also the fact that $23 million would represent the most money given to a player with minimal experience, Evan Longoria received $17.5 million in 2008; a lot of the liability of this deal was in the hands of the Astros, as Springer is more of an unknown than a proven commodity. The Astros are at a position with their franchise where they would take this liability; Houston is one of the strongest markets in the US, there is a new ownership group in place that has shown a willingness to spend, and by 2017 the team expects to contend. To take it a step further, the team is almost willing to take nearly a $10 million financial hit, assuming that the team is not successful on the field for the 2014-2016 seasons, just to be able to save that money for 2017 and 2018.

The final point may be where Springer’s agents had flawed logic; they are looking at the best wishes of their client as well they should, but in fact this may be a good deal for Springer. The Astros have shown that they are building for the future and are not going to spend money to be decent — there are many that see the Astros as tanking but really they are looking at their present day weaknesses and making them future strengths — so the team spending $10 million on a prospect while the team is still developing should be taken as a huge victory. Springer’s agents are right in assuming that he is going to lose money in arbitration and, if he did sign that contract, he would have been a free agent at 30 years old which is outside of his athletic prime of 27 years old.

This may be outside of his athletic prime, but definitely not outside of his financial prime. Since 2000, fourteen outfielders have earned in excess of $17 million over at least five years of a contract.  Assuming that Springer produced about 4.5 WAR per year from 2014-2018, a common projection for Springer, he could earn $21 million per season in the open market according to the averages set by those player’s contracts.  There are tremendous issues in that valuation of Springer, namely a shift in the market and a regression in Springer’s talents, but judging by recent trends, Springer would still be very well on his way to being one of the wealthiest outfielders in baseball history.

This contract is an outlier and was a fantastic idea for the Astros in attempting to fund their future and assure that Springer was a well paid player for his production. The Astros knew that Springer would outplay that contract and may very well have had provisions in the contract for enhanced performance, but at the onset, all of the liability was on Houston. There is very little reason why Springer needed to be on the 2014 Astros and the team’s financial shrewdness and outstanding player development are main reasons why pundits predict a bright future for the team; Springer was offered this contract as a statement towards the future and it may be short-sighted for Springer to have declined this fair offer. As seen with the case of Longoria, players that outgrow their contracts are usually paid handsomely by their parent teams. By signing this deal, Springer would have opened the pipelines for better negotiations between himself and the team. Declining the deal may be good for Springer in the short term but may be a major blow for negotiating a massive extension for the future.

While watching the playoffs last October, I realized that I had never seen rookies play such a prominent role in the postseason before.  Pitchers like Michael Wacha, Gerrit Cole, Hyun-Jin Ryu, and Sonny Gray propelled their teams into contention during the regular season, and took the hill in multiple elimination games.  The inimitable Yasiel Puig had a similar impact on the Dodgers’ fortunes in 2013.

This observation led me to investigate rookie performance during the 2013 regular season.  Were rookies contributing to the success of their teams more so than in the past?  Were rookie pitchers outperforming rookie hitters?  How about rookies on playoff teams versus non-playoff teams?

Using WAR data from Baseball Reference (sorry, guys) I measured rookies’ contribution to overall team success in 2000-2013, defined as rookie WAR divided by their team’s WAR.  A few definitions before jumping in to the findings:

• Rookies are players who have accumulated less than 130 AB (or 50 IP) and less than 45 days on an active roster prior to their rookie season
• For consistency across time, teams that won the second wild-card slot in 2012 and 2013 are not considered playoff teams (u mad, Reds and Indians fans?)
• Rookie pitcher WAR = amount of WAR created by a team’s rookie pitchers
• Rookie pitcher share of WAR = % of a team’s WAR created by rookie pitchers
• Rookie batter WAR = amount of WAR created by a team’s rookie batters
• Rookie batter share of WAR = % of a team’s WAR created by rookie batters
• Rookie total WAR = Rookie batter WAR + Rookie pitcher WAR
• Rookie share of total WAR = Rookie pitcher share of WAR + Rookie batter share of WAR

In chart 1, rookie share of total WAR for the average team in 2013 (11.3%) is above the long-run average of 8%, and was only exceeded in 2006 (12.7%).  But there was no discernible difference in rookie share of total WAR between the average playoff team (10.9%) and non-playoff team (11.4%) last season.  So far, it would appear as though I need to adjust my TV.

The data becomes more interesting when the average team’s rookie share of total WAR is decomposed into pitcher and batters’ contributions (chart 2).  There is a rapid rise in rookie pitcher share of WAR between 2010 and 2013, peaking last season at 6.7% of the average team’s WAR.  This increase was so strong, it more than made up for a decrease in rookie batter share of WAR during the same timeframe, from 6.5% in 2010 to 4.6% last season.

These trends become starker when the analysis is limited to playoff teams (chart 3).  On the average playoff team in 2013, rookies provided 10.9% of WAR, a step down from the high reached in 2012.  But there is still a huge rise in rookie pitcher share of WAR between 2010 and 2013, to 8.7% last season, and a concurrent decrease in rookie batter share of WAR, to 2.2%.  In other words, 80% of the average 2013 playoff team’s rookie total WAR was generated by pitchers.  If not for a certain Cuban-American hero with a penchant for bat-flipping, that share would have been even higher.

But some evidence, as well as anecdotal observation, suggests that pitchers in general have become more dominant over the past few seasons.  Is this trend, observed so far among rookies, true of all pitchers?  Over the past fourteen seasons, the average team has generated between 36-44% of WAR from pitchers (chart 4).  This share has been consistent over time, and has edged up only slightly during the past few seasons.  This suggests that rookie pitchers, especially those on playoff teams, really did excel in 2013.

Now, let’s look at just how good the rookie pitchers on playoff teams were last season (chart 5).  Together, the 54 rookie pitchers on 2013 playoff teams generated 29.6 WAR, which is slightly higher than last year’s total (29.1 WAR) and much higher than the long-run average (16.0 WAR).  What’s even more impressive is that last season, 57% of all 30 teams’ rookie pitcher WAR was generated by the rookie pitchers on playoff teams, a higher share than in any other season since 2000.  Cumulatively, 54 rookie pitchers on 8 teams outperformed 151 rookies on 22 teams.  Not bad.

But wait…there’s more.  By focusing on the best rookies on playoff teams (arbitrarily defined here as those who generated 1+ WAR), we see that there were 20 such players last season (chart 6).  Of that number, 16 were pitchers, like Shelby Miller, Hyun-Jin Ryu, and Julio Teheran.  Five of those pitchers were on the Cardinals (Miller, Siegrist, Wacha, Rosenthal, and Maness.)  The concentration of top rookie pitchers on playoff teams last year is the highest in at least fourteen seasons.

My initial observation, “Wow, there are lots of rookie pitchers killing it in the 2013 playoffs!” looks to be borne out in the data.  This raises two other interesting questions:

1.  For any of last year’s playoff teams, did rookie pitchers provide enough value to get their team into the playoffs?

2.  Is the rookie pitcher observation a one-time anomaly, or indicative of a larger trend?

The first question is relatively easy to answer.  We can compare each playoff team’s rookie pitcher WAR (essentially, how many more games the team won because of rookie pitchers) to the number of additional games each playoff team could have lost and still made the playoffs without tying a second-place team (let’s call this the buffer). 

For four out of eight playoff teams (again, I exclude the second wild-cards), rookie pitcher WAR is higher than the buffer (chart 7).  But since Detroit and Tampa made the playoffs by one game, and since Pittsburgh’s rookie pitcher WAR is less than one game higher than the buffer, it’s hard to argue that rookie pitchers definitively moved the needle for them. Andy Dirks or Yunel Escobar could have just as easily gotten their teams over the hump, since they also created more than 1 WAR.

The Cardinals are the one team whose rookie pitchers probably got them into the playoffs.  They got 9.7 extra wins from their rookie pitchers (almost 23% of the entire team’s WAR), and made the playoffs by 6 games.

The second question is harder to answer, since the 2014 season hasn’t started yet.  There’s no clear reason why rookie pitchers on playoff teams would suddenly start playing extremely well, especially since it doesn’t look like they’re causing their teams to make the playoffs.  The likeliest explanation is that the top teams in the league happened to have outstanding rookie pitchers last year.  Sometimes, “stuff” happens.

But if you want to prove me wrong, and show that last year’s playoff teams have developed great farm systems capable of producing more top rookie pitchers, pay close attention to what Jameson Taillon (Pirates), Carlos Martinez (Cardinals), Jake Odorizzi (Rays), and Allen Webster (Red Sox) bring to the table in 2014.  All four pitchers are on Baseball America’s list of top 100 prospects, are on last year’s playoff teams, and are projected to crack the majors this season.  If they get off to a hot start, and if they help their teams return to the playoffs, I might have to revisit my conclusion next winter.

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.

We know, in broad strokes, what seemingly happens when a pitcher moves to the bullpen. His velocity tends to improve, his “stuff” tends to look better, and his run prevention receives a boost. Can we quantify that effect? What about the reverse effect, moving from the bullpen to the rotation?

The Process

In order to attempt to quantify this effect, I looked at the 115 pitchers since 2002 who have started for at least 100 innings and relieved for at least 100 innings. There were actually 118 pitchers who fit the criteria, but the knuckleballers Dickey, Wakefield, and Sparks skewed the sample rather greatly, and were thus thrown out. It could be said that knuckleballers play a different game, with different constants (lower BABIP), and different expectations, so it is sometimes best to neglect them in a model, but that is a discussion for a different day.

Besides somewhat arbitrarily throwing three data sets to the curb, this methodology necessarily creates a sample bias, that only those pitchers who were deemed worthy of the switch for an extended period of time will show up in the results, but there is simply no way to simulate how players might have done in such a situation if teams will not actually do it. As such, the data ignores those players who were too good (or too bad) in their current roles to ever merit a switch to the other.

A secondary bias is that this sort of analysis will not specifically show what happens in a switch from the bullpen to the rotation, or vice versa, but rather what people who pitched in both roles did. In other words, we’re not really quantifying what happens during the switch, but just the split itself. It is possible that switching from the rotation to the bullpen is easier than the converse, but the data would not reveal that.

For those pitchers who satisfied the prerequisites, I aggregated their performance as relievers and as starters in order to gauge the difference, and looked at the individual data to find the summary statistics.

The Data

Peripherals

“s-r” refers to starting minus relieving, so negative values correspond to situations in which the pitchers in the sample had a higher value of that statistic while relieving, while positive values correspond to the opposite. I chose FB% to serve as a proxy for modeling the tendency of relievers to sacrifice secondary, tertiary, and quaternary pitches coming out of the bullpen. Z-contact% is meant to be a measure of “stuff” by virtue of measuring the ability to induce swings and misses on pitches in the zone.

Based on this table, we can get a rough idea of the difference between starting and relieving. It appears the group had a much higher K% and a higher BB%, along with a higher velocity and Z-contact%, supporting the assertion that, all else equal, a pitcher will be able to display better stuff while relieving, likely due to the fact that a reliever does not need to conserve energy to pitch deeper into the game. GB% and HR/FB% also improve, implying weaker contact against relievers, likely due to, again, the improved “stuff”.

Run Prevention

Run prevention is, unsurprisingly, also seemingly improved by being in the bullpen, although FIP and xFIP seem slower to “pick up on it”. That is also not surprising; both statistics would need to assume average performance in the other excluded peripherals to model ERA extremely accurately, but the “Peripherals” chart showed that such a base assumption would be untrue in this case.

Conclusions

It is tough to make conclusions from relatively small sample sizes and somewhat flawed studies like these, but there are a few rather obvious pointers. It seems rather clear that the common perception that “stuff” improves while in relief rings true. This may not be definitive evidence that moving to the bullpen would explicitly improve a player’s performance by N% in different categories, but this analysis does firmly point in the direction that there is some theoretical manner by which that effect could be ascertained.

Author’s note: This is the first article I’ve submitted to FanGraphs, so constructive criticism is welcomed. Ask in the comments below if you’d like me to run another stat and see if it meshes with the results I’ve posted here.

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.

Who can the Angels rely on in the back of the rotation?

The strength of the 2014 Angels will be their offense, but the team does have a lot of money and development invested in their rotation and they need to get good outings out of that rotation to support the offense and win games. Jered Weaver has been injured throughout the past few years, but it usually solid when he is in the rotation and is a quality start machine. CJ Wilson may not be perceived as an elite pitcher but since moving into the rotation for the Rangers in 2010, he averages 15 wins a season with a 3.37 ERA and 210 innings pitched. Both Weaver and Wilson could be major factors in a playoff run and would be relatively comparable to the top two pitchers for most AL playoff competitors. Behind Wilson and Weaver in the rotation, though, are young unproven pitchers that really need to grow up very quickly if the team wants to make noise in 2014.

Garrett Richards already has 230 innings of big league experience coming into 2014 and the 26 year old former top 100 prospect has shown some progress in his major league tenure. He has seen his ERA, WHIP, and walk rate lower since 2011 as his strikeout rate rose. That being said, Richards did have low expectations set in 2011 and 2012 where he had a 1.57 WHIP and a 1.37 K: BB rate; Richards needs to harness his fastball and play off of his three off-speed pitches. He has a lot of good tools, with a fastball in the mid-90s and a very solid slider and curveball. His changeup is not advanced, but it is a work in progress and Richards uses it very sparingly for good reason.

Richards must also work ahead in the count more; his first pitch strike percentage was only 53.6% in 2013 and if that rises, Richards will continue to clean up his control issues. His walk percentage returned to the 7.1% that was only matched when he was in the lower levels of the minor leagues and the same goes for his 16.3% strikeout percentage. Richards might be the wildcard of the Angels rotation; he could be the third strong pitcher in the rotation and could be struggling to maintain a spot in the rotation. He needs to continue to develop his curveball and let his 94 mph fastball play off of those two solid breaking balls and, if he does, it would not be crazy to see Richards get 150-175 strikeouts in a full season.

Hector Santiago was acquired with Tyler Skaggs by the Angels in the three team trade that sent Mark Trumbo to Arizona. This was after two solid seasons with the White Sox that gave the Angels a good view into how the 26 year old could contribute. A crafty lefty with a bit of an unorthodox delivery, Santiago is a true throwback pitcher in that he will beat you with variety rather than one or two strong pitches. According to pitch data accumulated by Pitch FX, Santiago threw five pitches at least 5% of the time (four seam fastball, cutter, sinker, changeup, curveball) and also worked in a screwball and slider. Although he still threw mostly fastballs and changeups, he threw three different types of fastballs and his varying arm angles tended to confuse batters.

These different offerings do have a bit of a drawback; the fact that Santiago rarely dominates with one pitch leads to a walk rate of 11.5% for his career; he does strikeout 22.2% of batters with a 1.93 K:BB ratio for his career, but the Angels have to hope that can be a bit better as he progresses. Since the variety of offerings has led to success in the big leagues, he has a 3.41 ERA in 222 2/3 innings split between the bullpen and the rotation, Santiago needs to find a way to keep his pitches strong. When looking at the pitch trends, Santiago has varied what pitches he throws quite frequently, as there is not really a good method of predicting his trends for the future. This creates a tough situation for batters, but sometimes it could create an even tougher situation for Santiago; if his pitches are not sharp over a long stretch, he may really struggle since he never specifically works on a solitary pitch in an outing.

Tyler Skaggs may even be more volatile than Richards in regards to his position with the 2014 Angels; his spot is the least stable in the rotation and he only has 68 very shaky innings pitched in his young MLB career. That being said, Skaggs is the top prospect for the Angels and will be a big part of the future of the Angels. A former first round pick by the Angels, Skaggs was traded to Arizona for Dan Haren and then traded back to Los Angeles in the aforementioned Mark Trumbo trade. While in Arizona, Skaggs developed a reputation as an elite minor league pitcher with bad major league results; a lot of those bad results derive from the fact that Skaggs is a soft throwing pitcher that has yet to really master what will be an incredible curveball.

Between the latter part of 2010 and the end of the 2012 season, Skaggs posted a 2.85 ERA with a 1.15 WHIP and a 3.7 K: BB ratio between low-A and Triple-A. He did have a rough patch in Triple-A this year, but maintained a similar K: BB ratio from Triple-A in 2012; he allowed more hits in 2013 but also struck out more batters. Essentially, the Diamondbacks look at Skaggs as someone that could develop into Cole Hamels, just with the curveball being the dominant pitch for Skaggs. As seen with Hamels, having velocity in the high-80s or low-90s can work if you have great off-speed pitches and Skaggs needs to develop his changeup along with making his curveball take that next level to being one of the premier pitches in baseball. He has the potential to be a top of the rotation pitcher and the Angels were wise to re-acquire him from Arizona.

A team like the Angels that is trying to compete for the playoffs would be fine if one of these pitchers were trying to get their footing in the majors, but the fact that all three are trying to get acclimated to a playoff competitor may be what does the Angels in during the 2014 season.  There is a lot of upside with Richards and Skaggs, though, so if they turn into the middle to top rotation pitchers that scouts see them as, the Angels may indeed be a 90 win team on the way to the playoffs.

When will the Angels try to rebuild their farm system?

A tough thing to do is to rebuild a farm system when the top prospect in baseball was once a part of that farm system. Mike Trout is arguably the best player in baseball but the Angels could have never expected him to be this good this quickly; for this reason, the rest of the Angels minor league talent looks underwhelming compared to the 22 year old superstar. There is a chance, and this is pure conjecture, that Randall Grichuk may have been a part of the trade that brought David Freese to Los Angeles so that he did not have to be known as the “other” Angels first round pick from 2009. As with anything, the Angels need to rebuild this system so that the team continues to be strong into the future.

Kaleb Cowart was the top prospect in the Angels organization after Trout graduated but a weak 2013 season in Double-A Arkansas gives people reason to worry about the future of the third baseman; in fact, the Angels were so cautious about Cowart’s future that they acquired David Freese this offseason. A tall and lanky first round pick out of high school in Georgia, Cowart had a very strong 2012 between Low-A Cedar Rapids and High-A Inland Empire, even going over 100 RBI (103) for the season. His defense was outstanding between the two leagues and his plate discipline improved with the jump to High-A. The move to Double-A was rough for Cowart; his fielding took a step back, his power disappeared, and his OPS was .580. As with any prospect, the Angels will continue to give him opportunity, but the 22 year old Cowart is not in a position considering that he has only mustered one single in seven spring training at bats. Cowart needs to show that he can hit or he will be a career minor leaguer and a first round pick bust.

CJ Cron is the opposite of Cowart; he is big and bulky and hits like he is big and bulky. The first baseman is basically a clone of Mark Trumbo; they both are 6’4, 235 pounds and hit a ton of home runs with very little plate discipline. Considering that the Angels traded Trumbo this offseason and have spent years waiting on him to develop, the Angels may not be too excited for the prospect of a Trumbo clone. The main difference between the two is that Cron is a bit better of an athlete and fielder; this may not be a factor with the team considering him as a designated hitter option, but he has worked on his fielding to become a serviceable first baseman if the team sees him as a fit at first. Cron also struggled at Double-A Arkansas this year, with his OPS dropping nearly 100 points to .743 and going from 27 home runs in Inland Empire to only 14 in Arkansas. Cron maintained his doubles power with 36 and there is every reason to believe that he becomes a 25 home run hitter in the majors. Cron is very durable and finds ways to get on base even with a low walk rate, two traits that should never be ignored in evaluating the viability of a prospect. There is a very good chance that Cron gets a spell in the majors by 2015 or, if his spring training success continues, even 2014 if he continues to develop.

Taylor Lindsay was the lone prospect of the three examined that did not seen his success considerably regress in Arkansas. In fact, Lindsay saw his walk rate rise and posted 17 home runs which is quite impressive from a 6 foot, 195 lb second baseman. A near sure thing to begin the season in Salt Lake, Lindsay is an injury or Howie Kendrick trade away from being the starting second baseman for the Angels. That being said, he is not a finished product. Many pundits like the 45 extra base hits and continued progress in his approach at the plate, but Lindsay still made a few too many errors for a second baseman, does not have great range, and is a non-factor on the bases. Those are things that must be worked on in Triple-A before he makes the leap to the majors. His offensive output from 2013 should not be seen as a fluke and Lindsay can hit enough to be a decent major leaguer. There is a lot to like from Lindsay and the Angels may even give him an opportunity to play a bit in the majors if he makes strides in Triple-A.

Los Angeles has done a very good job of developing big league talent and these three players may very soon be a part of this development. Fortunately the Angels have enough talent on the major league roster for these players to be given enough time to properly develop, but, especially in Lindsay’s case, these players could be a productive part of the Angels roster in 2014.

How will the change of scenery affect David Freese?

David Freese was a product of the St. Louis Cardinals fantastic development but a rough 2013 season made him expendable; the Angels had a very weak situation at third base and an extra outfielder, so Freese was traded to Los Angeles for Peter Bourjos. Freese is seen by some as a product of his environment in St. Louis and that his bad 2013 and move to Los Angeles will depress his talent. The Angels hope that these people are wrong and a new team is a new opportunity for Freese.

Freese was an early round pick by the Padres out of community college and Freese produced right away with the Padres’ minor league affiliates and was a major part of the 2007 offseason trade that sent Jim Edmonds to San Diego from the Cardinals. After solid seasons in 2008 and the beginning of 2009, Freese was up in St. Louis with the major league team. After he broke his ankle in both 2009 and 2010, Freese broke out with the 2011 Cardinals and was the MVP of the 2011 World Series. In 2012, Freese again was solid as he had an OPS of .839, turned had a career high in HR/FB percentage, and played a very solid defensive third base. Defense and inconsistency led to a poor 2013 for Freese, the worst of his entire professional career, and the Cardinals decided that prospect Kolten Wong at second base and Matt Carpenter at third was a better direction for the team. The Angels are relying on Freese returning to his past offensive output to strengthen their team.

Before we look forward to 2014, a deconstruction of Freese’s worst season must be done to see how he can fix the issues that arose. He did post a solid strikeout and walk percentage in 2013, in fact striking out at the lowest rate of his MLB career. His BABIP was 30 points lower than it had been in any other season, a huge indicator that 2013 may be an outlier. Although the BABIP may be attributed to luck, the 55 point drop in isolated power shows that Freese did not hit the ball with the authority of 2012. The contention that Freese was not as potent is only further supported by the fact that his line drive rate was down and his ground ball percentage was up. A lot of Freese’s other advanced statistics show that he was swinging at good pitches and actually made more contact in 2013 than 2012.

The fact that Freese had weak contact is what hurt him; this could have been because of injury and the Angels have to hope that his Isolated Power returns back to the mid-.100s rather than the low-.100s of 2013. Freese was also a strong fielder prior to 2013 and injuries may he also been the reason that he fell off a bit last year; his range was limited and was a negative factor for the Cardinals at third. A healthy Freese in a strong offensive lineup should be close to 15-20 home runs and 80-85 RBI, as well as returning to being a solid defensive player.

What do the Angels need to do for Josh Hamilton and Albert Pujols to get on the right track?

Albert Pujols and Josh Hamilton are both former MVPs and, in Pujols’ case, legends of the game. The 2013 season was a lost one for both players and the Angels could have never expected that these two great players would be such liabilities for the team. There are big issues for each player and their careers are really at a crossroads; both may need to change their approach at the plate to achieve their past successes.

Josh Hamilton was a safe bet for nearly 30 home runs and 100 RBI while in Texas; albeit with injury issues, Hamilton was one of the better sluggers in baseball. The issue with Hamilton was that he got hurt too much and tailed off a bit at the end of the season; there were very few issues about Hamilton’s production over a full season and coming to Los Angeles where there were established superstars in Mike Trout and Albert Pujols should have created a situation where Hamilton could be a 125 RBI producer. In 2013, Hamilton stayed healthy but was one of the biggest disappointments of the season. The former MVP only posted a .307 OBP and had 21 home runs and 79 RBI in the 151 games he played; his 151 game averages for his career are a .363 OBP with 33 home runs and 113 RBI.

There have been peaks and valleys in Hamilton’s career, but the valleys have not been this low statistically in a full season. This is a bit surprising for Hamilton because 2012 was the best year of his career and, had Oakland not caught Texas to win the division, Hamilton would have been just as good of an MVP candidate as Miguel Cabrera or Mike Trout. He may have struck out at a career high percentage in 2012, but he also had the highest walk rate, isolated power, and hit a ton of fly balls (41.1%) that lead to home runs (25.6 % HR/FB). Further analysis paints an odd picture for Hamilton and might be the reason that he rebounds in 2014. Hamilton was unsuccessful on balls outside of the zone during 2012 and in 2013, Hamilton swung at less pitches outside of the zone and was more successful on those swings.

As a hitter with a swing that lends to contact on pitches outside of the zone, it would be good for Hamilton to continue to be more selective and work on regaining good contact on bad pitches. Although he was successful with poor contact rates in 2012, Hamilton needs to get back to mid-70% contact rates that he had in the 2007-2011 seasons; he was at a 70.3% in 2013, a jump from 64.7% in 2012. Hamilton should be able to regain 30 home run production by working more on the above mentioned issues and the Angels will be glad to have a strong force in the middle of their lineup.

Albert Pujols might have had the best 11 season start to a career in St. Louis; he averaged 40 home runs per season with 121 RBI and a ridiculous 1.037 OPS. The Angels jumped at Pujols when he was a free agent in the 2011 offseason and gave him $250 million to bring that kind of production to Los Angeles: this has not been the case and Pujols looked very weak in 2013. The writing has been on the wall a bit for Pujols to regress, even if it is rough to think that such a talented player would become only a role player. Pujols has seen a decrease in OPS in every season since 2008 and has not hit .300 since 2010. It is a bit unfair to criticize a .331 hitter for lowering his career average to .321 as he has gotten older, but the fact remains that Pujols will never be the player he once was.

What made Pujols so great was that he was able to combine a high lofty swing that led to home runs with fantastic plate disciple and pitch selection. Pujols’ bat has slowed down rapidly and pitchers have not only made Pujols swing more, but make sure that that these swings were on pitches outside of the strike zone that he misses; Pujols has seen his swinging strike rate double from 3.8% in 2008 to 7.6% in 2013. Pitchers still cannot beat Pujols on pitches that are in the strike zone (91.1% Z-Contact) but his overall contact rate has slipped from 90.1% in 2008 to 82.9% in 2013. Pujols is an all-time great and frequently greats are able to reinvent themselves so there is still a chance for a solid season out of Pujols. Even in a relatively weak 2012 season, Pujols had 105 RBI and 50 doubles. Pujols needs to be more patient and not chase the poor pitches like he did in 2013. If Pujols is able to raise his BABIP with better swings and pitch selection, an average in the .280-.290 range with 55 2B+HR and nearly 100 RBI is quite attainable.

There is a better chance that Hamilton regains his 2008-2012 form than that of Pujols’ prime, but his strikeout and free swinging problems will continue to linger unless he changes his approach. The bar was set so high for Pujols and he will never get back to that level, but he could still be a productive player for the Angels and an example for the younger players.

Why are the Angels going to win 85 games?

The Angels are in a spot very similar to the Rangers, hence the prediction that leads to them being equal in win total. There is a lot of offensive talent on this team but the pitching is just not strong enough to weather the storm in a loaded American League. CJ Wilson and Jered Weaver should each be solid at the top of the rotation and Tyler Skaggs will be solid someday, but unless the Angels make a move to improve on the 2014 staff, the team is not playoff bound. Albert Pujols and Josh Hamilton may never regain their MVP pasts, but each should rebound from the rough 2013 seasons. Mike Trout will continue his MVP pace and the team will stay in the wild card race most of the season.

5 You Know:

1. Mike Trout

2. Albert Pujols

3. Jered Weaver

4. CJ Wilson

5. Josh Hamilton

5 You Will Know:

1. CJ Cron

2. Cam Bedrosian

3. Kaleb Cowart

4. RJ Alvarez

5. Taylor Lindsay

5 You Should Remember:

1. Hunter Green

2. Ricardo Sanchez

3. Mark Sappington

4. Jose Rondon

5. Alex Yarborough

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.

Whenever one makes any conclusion based off of anything, a bunch of underlying assumptions get shepherded in to the high-level conclusion that they output. Now that’s a didactic opening sentence, but it has a point–because statistics are full of underlying assumptions. Statistics are also, perhaps not coincidentally, full of high-level conclusions. These conclusions can be pretty wrong, though. By about five-hundred runs each and every season, in this case.

Relative player value is likely the most important area of sports analysis, but it’s not always easy. For example, it’s pretty easy to get a decent idea of value in baseball while it’s pretty hard to do the same for football. No one really knows the value of a pro-bowl linebacker compared to a pro-bowl left guard, for one. People have rough ideas, but these ideas are based more on tradition and ego than advanced analysis. Which is why football is still kind of in the dark ages, and baseball isn’t. But just because baseball is out of the dark ages, it doesn’t mean that it’s figured out. It doesn’t even mean that it’s even close to figured out.

Because this question right here still exists: What’s the value of a starting pitcher compared to a relief pitcher? At first glance this a question we have a pretty good grasp on. We have WAR, which isn’t perfect, yeah, but a lot of the imperfections get filtered out when talking about a position as whole. You can just compare your average WAR for starters with your average WAR for relievers and get a decent answer. If you want to compare the top guys then just take the top quartile and compare them, etc. Except, well, no, because underlying assumptions are nasty.

FanGraphs uses FIP-WAR as its primary value measure for pitchers, and it’s based on the basic theory that pitchers only really control walks, strikeouts, and home runs–and that everything else is largely randomness and isn’t easily measurable skill. RA9 WAR isn’t a good measure of individual player skill because a lot of it depends upon factors like defense and the randomness of where the ball ends up, etc. This is correct, of course. But when comparing the relative value of entire positions against each other, RA9 WAR is the way to go. Because when you add up all the players on all of the teams and average them, factors like defense and batted balls get averaged together too. We get inherently perfect league average defense and luck, and so RA9 WAR loses its bias. It becomes (almost) as exact as possible.

Is this really a big deal, though? If all of the confounding factors of RA9 WAR get factored together, wouldn’t the confounding factors of FIP-WAR get factored together too? What’s so bad about using FIP-WAR to judge value? Well there’s this: From 1995 onward, starting pitchers have never outperformed their peripherals. Relievers? They’ve outperformed each and every time. And it’s not like the opposite happened in 1994–I just had to pick some date to start my analysis. Here’s a table of FIP-WAR compared to RA9-WAR compared to starters for the last 18 years, followed by the same table for relievers.

Starter RA9-WAR/FIP-WAR Comparisons

Reliever RA9-WAR/FIP-WAR Comparisons

Ok, so that’s a lot of numbers. The basis, though, is that FIP thinks that starters are better than they actually are, while it thinks relievers are the converse. And this is true year after year, by margins that rise well above negligible. Starters allow roughly 250 more runs than they should according to FIP every season, while relievers allow about 250 less than they should by FIP’s methodologies–in much fewer innings. In more reduced terms this means that starters are over-valued by about 10% as whole, while relievers are consistently under-valued by about 25% according to FIP-WAR. Now, this isn’t a completely new idea. We’ve known that relievers tend to outperform peripherals for a while, but the truth is this: relievers really outperform peripherals, pretty much all the time always.

Relievers almost get to play a different game than starters. They don’t have to face lineups twice, they don’t have to throw their third or fourth-best pitches, they don’t have to conserve any energy, etc. There’s probably a lot more reasons that relievers are better than starters, too, and these reasons can’t be thrown out as randomness, because they pretty much always happen. Not necessarily on an individual-by-individual basis, but when trying to find the relative value between positions, the advantages of being a reliever are too big to be ignored.

How much better are relievers than starters at getting “lucky”? Well, a few stats that have been widely considered luck stats (especially for pitchers) for a while are BABIP and LOB. FIP assumes that starters and relievers are on even ground, as far as these two numbers are concerned. But are they? Here’s a few tables for comparison, using the same range of years as before.

BABIP Comparisons

LOB Comparisons

With the exception of BABIP in ’96, relievers always had better luck than starters. Batters simply don’t get on base as often–upon contacting the ball fairly between two white lines–when they’re facing guys that didn’t throw out the first pitch of the game. And when batters do get on, they don’t get home as often. Relievers mean bad news, if good news means scoring more runs.

Which is why we have to be careful when we issue exemptions to the assumptions of our favorite tools. There are a lot of solid methodologies that go into the formulation of FIP, but FIP is handicapped by the forced assumption that everyone is the same at the things that they supposedly can’t control. Value is the big idea–the biggest idea, probably–and it’s entirely influenced by how one chooses to look at something. In this case it’s pitching, and what it means to be a guy that only pitches roughly one inning at a time. Or perhaps it’s about this: What it means to be a guy who looks at a guy that pitches roughly one inning at a time, and then decides the worth of the guy who pitches said innings, assuming that one wishes to win baseball games.

The A’s and Rays just spent a bunch of money on relievers, after all. And we’re pretty sure they’re not dumb, probably.