Baseball Prospectus 2018

By Baseball Prospectus

The 2018 edition of The New York Times Bestselling Guide.

PLAY BALL! The 23rd edition of this industry-leading baseball annual contains all of the important statistics, player predictions and insider-level commentary that readers have come to expect, along with significant improvements to several statistics that were created by, and are exclusive to, Baseball Prospectus, and an expanded focus on international players and teams.

Baseball Prospectus 2018 provides fantasy players and insiders alike with prescient PECOTA projections, which The New York Times called “the überforecast of every player’s performance.”

With more than 50 Baseball Prospectus alumni currently working for major-league baseball teams, nearly every organization has sought the advice of current or former BP analysts, and readers of Baseball Prospectus 2018 will understand why!

Visit www.baseballprospectus.com for year-round baseball coverage

• Language: English
• Publisher: Turner Publishing Company
• Release date: Feb 9, 2018
• ISBN: 9781681626451

Book preview

Why don’t you get your nose out of those numbers and watch a game?

It’s a false dilemma, of course. We would wager that Baseball Prospectus readers watch more games than the typical fan. They also probably pay better attention when they watch. The numbers do not replace observation; they supplement it. Having the numbers allows you to learn things not readily seen by mere watching and to keep up on many more players than any one person otherwise could.

This book doesn’t ask you to choose between the two. Instead, we combine numerical analysis with the observations of a lot of very bright people. They won’t always agree. Just as the eyes don’t always see what the numbers do, the reverse can be true. To get the most out of this book, however, it helps to understand the numbers we’re presenting and why.

Offense

The core of our offense measurements is True Average, which attempts to quantify everything a player does at the plate—hitting for power, taking walks, striking out and even making productive outs—and scale it to batting average. A player with a TAv of .260 is average, .300 exceptional, .200 rather awful.

True Average also accounts for the context a player performs in. That means we adjust it based on the mix of parks a player plays in. Also, rather than use a blanket park adjustment for every player on a team, a player who plays a disproportionate number of his games at home will see that reflected in his numbers. We also adjust based on league quality: The average player in the AL is better than the average player in the NL, and True Average accounts for this.

Because hitting isn’t the entirety of scoring runs, we also look at a player’s Baserunning Runs. BRR accounts for the value of a player’s ability to steal bases, of course, but also accounts for his ability to go first to third on a single or advance on a fly ball.

Defense

Defense is a much thornier issue. The general move in the sabermetric community has been toward stats based on zone data, where human stringers record the type of batted ball (grounder, liner, fly ball) and its presumed landing location. That data is used to compile expected outs for comparing a fielder’s actual performance.

The trouble with zone data is twofold. First, unlike the data we use in the calculation of the statistics you see in this book, zone data wasn’t made publicly available; it was recorded by commercial providers who kept the raw data private, only disclosing it to a select few who paid for it. Second, as we’ve seen the field of zone-based defensive analysis open up—more data and more metrics based upon that data coming to light—we see that the conclusions of zone-based defensive metrics don’t hold up to outside scrutiny. Different data providers can come to very different conclusions about the same events. Even two metrics based on the same data set can come to radically different conclusions based on their starting assumptions, assumptions that haven’t been tested, using methods that can’t be duplicated or verified by outside analysts.

The quality of the fielder can bias the data: Zone-based fielding metrics will tend to attribute more expected outs to good fielders than bad fielders, irrespective of the distribution of batted balls. Scorers who work in parks with high press boxes will tend to score more line drives than scorers who work in parks with low press boxes.

Our Fielding Runs Above Average (FRAA) incorporates play-by-play data, allowing us to study the issue of defense at a granular level without resorting to the sorts of subjective data used in some other fielding metrics. We count how many plays a player made, as well as expected plays for the average player at that position based on a pitcher’s estimated ground-ball tendencies and the handedness of the batter. There are also adjustments for park and base-out situations.

In addition, catchers have different defensive responsibilities than other defensive players, in particular framing pitches to make umpires more likely to call them strikes and blocking errant pitches. We incorporate PITCHf/x data, where available, and adjust for the pitcher, umpire, batter (including handedness) and home-field advantage using a mixed-model approach to determine how many strikes a catcher is adding to or subtracting from his pitchers’ ledgers, and then convert those extra or lost strikes to runs using simple linear weights. We use a similar approach to determine how much better or worse than average a catcher is at letting errant pitches past him (regardless of whether the official scorer labels it a passed ball or a wild pitch)—PITCHf/x is a particularly powerful tool in this regard because we can tell which pitches end up in the dirt (and at what angle and speed) even though basic play-by-play data simply records the pitch as a ball or a swinging strike because the catcher successfully blocked it.

These metrics, as well as the catcher’s abilities to prevent steals, are incorporated into catchers’ FRAA along with their ball-in-play fielding (e.g., popups and bunts near home plate).

Pitching

Of course, how we measure fielding influences how we measure pitching. Most sabermetric analysis of pitching has been inspired by Voros McCracken, who stated, There is little if any difference among major-league pitchers in their ability to prevent hits on balls hit in the field of play. When first published, this statement was extremely controversial, but later research has, by and large, validated it. McCracken (and others) went forth from that finding to create a variety of defense-independent pitching measures. One that you’ll see in the book is FIP, Fielding Independent Pitching, which accounts for walks, strikeouts, hit-by-pitches and homers accumulated by a pitcher and puts them into one number on an ERA scale. Another is cFIP, which takes those FIP inputs, makes a variety of adjustments (including the batter, catcher, umpire, stadium, home-field advantage and handedness) and puts the whole thing on a 100 minus scale in which the lower the number the better. The standard deviation of cFIP is forced to 15, so you know that a 56 cFIP is nearly three standard deviations from the mean.

The trouble is that many efforts to separate pitching from fielding have ended up separating pitching from pitching—looking at only a handful of variables in isolation from the situation in which they occurred. What we’ve done instead is take a pitcher’s actual results, event by event, and adjust each event based on the environment in which it occurred, including park factor, batter, catcher, umpire, base-out situation, run differential, inning, defense, home-field advantage, whether the pitcher is a starter or reliever and gametime temperature. We also consider the pitcher’s effect on basestealing (both in terms of likelihood of stealing and likelihood of success) and the pitcher’s effect on passed balls and wild pitches. Out of all this comes Deserved Run Average (DRA), our core pitching metric. It is the rate stat on which pitcher Wins Above Replacement Player is determined.

One key point to note is that DRA is set on the same scale as runs allowed per nine innings, not ERA. Looking only at earned runs tends to overrate three kinds of pitchers:

1.  Pitchers who play in parks where scorers hand out more errors. Looking at error rates between parks tells us scorers differ significantly in how likely they are to score any given play as an error (as opposed to an infield hit);

2.  Ground-ball pitchers, because a substantial proportion of errors occur on groundballs; and

3.  Pitchers who aren’t very good. Good pitchers tend to allow fewer unearned runs than bad pitchers, because good pitchers have more ways to get out of jams than bad pitchers. They’re more likely to get a strikeout to end the inning and less likely to give up a home run.

Top-Line Pitching Metrics

While there is no shortage of metrics that purport to describe how good or bad a pitcher really is, there has been less focus on metrics that describe how a pitcher gets to their results. This, then, is the goal of the metrics introduced here: to provide as comprehensive a picture of a pitcher as possible, to be used as quantitative illustrations with their results. We are happy to introduce three new top-line pitching metrics, each on a 0-100 scale.

Power Rating

What is a power pitcher? We hear the term a lot, usually in reference to someone like Justin Verlander, but it’s never been an exact term, for all that it seems to apply to the same general set of characteristics. As part of developing a new suite of pitching metrics and diving deeper into the ways we break down the art of throwing a ball very hard, we’re taking a look at power pitching and quantifying exactly what it means to be a power pitcher.

Luckily for us, the pieces to measure whether someone is quantifiably a power pitcher already exist, it’s just deciding how best to put them together. Clearly, velocity is a large part of the equation, as the power part of the description, and we weight peak fastball velocity the heaviest when constructing these rankings.

Stamina Rating

As with all of these metrics, our measure of stamina, alone, has nothing to do with how good or bad a pitcher is; it is simply an objective measurement of how much of a workload any pitcher is capable of carrying. Since workload exists beyond Major League Baseball, we also included any of a pitcher’s regular-season minor-league efforts in calculating Stamina.

To calculate Stamina Rating, we looked at different ways of valuing days of rest, numbers of pitches and batters faced per game. What we found most effective is a model that combines calculating the daily number of pitches thrown from a six-day moving average, with the straight average of batters faced per game against the square root of the mean of the days of rest between games.

Command Rating

One of the most challenging aspects of pitching to quantify, command indicates that a pitcher can throw the ball where he intends to. Our Command Score builds on Called Strikes Above Average (CSAA), which is the pitcher’s component of our framing model. To build on that, we’ve identified target points in each corner of the zone using the likelihood of a pitch to be called a strike and quantify the pitcher’s ability to hit that spot consistently. Pitchers are penalized for missing spots by a significant amount—either getting too much of the plate or missing off of it—to highlight their ability to effectively work the edges of the zone.

Projections

Many of you aren’t turning to this book just for a look at what a player has done, but for a look at what a player is going to do: the PECOTA projections.

PECOTA, initially developed by Nate Silver (who has moved on to greater fame as a political analyst), consists of three parts:

1.  Major-league equivalencies, to allow us to use minor-league stats to project how a player will perform in the majors;

2.  Baseline forecasts, which use weighted averages and regression to the mean to produce an estimate of a player’s true talent level; and

3.  A career-path adjustment, which incorporates information on how comparable players’ stats changed over time.

Now that we’ve gone over the core stats, let’s go over what’s in the book.

Team Prospectus

The bulk of this book comprises team chapters, with one for each of the 30 major-league franchises. On the first page of each chapter, you will be greeted by a box laying out some key statistics for each team. You can see Oakland’s box on the facing page.

At the top, 2017 W-L is exactly as it sounds, the unadjusted tally of wins and losses. Pythag presents an adjusted 2017 win percentage by taking the runs scored per game (RS/G) and allowed (RA/G) by the team last season and running them through a version of Bill James’ Pythagorean formula refined and developed by David Smyth and Brandon Heipp, called Pythagenpat.

A team’s runs scored is accompanied by True Average (TAv) and Baserunning Runs (BRR), both of which were described above, to give a picture of how a team scores its runs. In terms of runprevention ability, we present a team’s TAv allowed (TAv-P), FIP and Defensive Efficiency Rating (DER), which is simply its rate of balls in play turned into outs.

Then we have several measures not directly related to on-field performance. B-Age and P-Age tell us the average age of a team’s batters and pitchers, respectively. Salary tells us how much the team cost to put on the field, and Doug Pappas’ Marginal Dollars per Marginal Win (M$/MW) tells us how much a team paid above the bare minimum it had to pay and how much production above replacement it received for that money.

Finally, we count up the number of disabled-list days a team had, as well as the amount of salary paid to players while they were on the DL, expressed as a percentage of the total payroll.

Next to each of these stats, you see the team’s MLB rank in that category, where 1st signifies a good outcome (e.g., highest TAv, lowest TAv-P) and 30th a bad outcome (highest $ on DL, lowest DER), except for salary, where we make no value judgments—1st is highest.

After the team information comes a variety of data about the home ballpark: a diagram of the park’s dimensions showing distances to the outfield wall; a graphic that shows the height of the wall from the left-field pole to the right-field pole, reading left to right; and a table showing the three-year park factors presented in their usual 100-scale fashion, with 100 being average, 110 meaning that the park inflates the stat by 10 percent and 90 meaning the park deflates the stat by 10 percent.

On the second page of each chapter, you will see three graphs. The first graph, titled 2017 Hit List Ranking, shows the Hit List Rank for this team on every day of the 2017 season and is intended to give you an idea of the shape of the season. Hit List Rank is a measure of overall team performance that drives the Prospectus Hit List power ranking at baseballprospectus.com. It is based on team run differential and includes adjustments for park, league and quality of opposition. You can see more about Hit List Ranking at http://bbp.cx/a/4383.

The second graph is entitled Committed Payroll and is intended to give you an idea of how this team’s player budgets match up with the competition historically and going forward. The payroll figures are current as of January 1, 2018; with several free agents still unsigned as of this writing, keep in mind the final 2017 figure will be significantly different for many teams. You can always find current data at Baseball Prospectus’ Cot’s Baseball Contracts page. MLB and division averages are also plotted to allow for quick comparison.

The third graph is entitled Farm System Ranking and shows the Baseball Prospectus prospect team’s ranking of this team’s farm system for the last several years.

Following the graphs is the Personnel section. Here you’ll find some of the important people in the organization and any former Baseball Prospectus staff who are currently part of the team’s front office or scouting staff.

Last, but not least, we have special 2017 category leaders cards courtesy of our friends at Topps.

Position Players

After a bylined opening essay about each team, the chapters move to the player comments, which are also bylined, though the vagaries of player movement and the group-project nature of the book mean that the names you see at the head of each chapter are more a rough guide than a precise accounting of the division of labor.

Each player is listed with the major-league team by whom he was employed as of mid-December 2017, meaning that players who changed teams via free agency, trade or otherwise later in the offseason will be listed under their previous employer.

As an example, take a look at Carlos Correa: his stat block is at the top of the next page.

The player-specific sections begin with biographical information (age is as of June 30) before moving onto the column headers and actual data. The column headers begin with standard information like year, team, level (majors or level of minors), and the raw, untranslated tallies found on the back of a baseball card: PA (plate appearances), R (runs), 2B (doubles), 3B (triples), HR (home runs), RBI (runs batted in), BB (walks), K (strikeouts), SB (stolen bases) and CS (caught stealing).

Following those are untranslated slash statistics: batting average (AVG), on-base percentage (OBP) and slugging percentage (SLG). The slash line is followed by True Average (TAv), which, as described above, rolls all those things and more into one easy-to-digest number.

BABIP stands for Batting Average on Balls in Play and is what it sounds like: How often did a ball put in play by the hitter fall for a hit? An especially low or high BABIP may mean a hitter was especially lucky or unlucky. However, hitters who hit the ball hard tend to have especially high BABIPs from season to season; so do speedy hitters who are able to beat out more grounders for base hits.

Next is Baserunning Runs (BRR), which, as mentioned earlier, covers all sorts of baserunning accomplishments, not just stolen bases. Then comes Fielding Runs Above Average (FRAA); for historical stats, we have the number of games played at each position in parentheses. For multi-position players, we can only display the two positions the fielder played most frequently that season.

Carlos  Correa  SS  Born: 09/22/94  Age: 23  Bats: R  Throws: R  Height: 6′4″  Weight: 215  Origin: Round 1, 2012 Draft (#1 overall)

Breakout: 4% Improve: 64% Collapse: 0% Attrition: 0% MLB: 99% Comparables: Miguel Cabrera, Jason Heyward, Ryan Zimmerman

One of our oldest active metrics, Value Over Replacement Player (VORP), considers offensive production, position and plate appearances. More specifically, it is the number of runs contributed beyond what a replacement-level player at the same position would contribute if given the same percentage of team plate appearances. VORP scores do not consider the quality of a player’s defense.

The last column is Wins Above Replacement Player. WARP is our total-value stat that, for a hitter, combines a player’s batting runs above average (derived from True Average), BRR, FRAA, an adjustment for positions played and a credit for plate appearances based upon the difference between the replacement level (derived by looking at the quality of players added to a team’s roster after the start of the season) and the league average.

The final line below the comment is PECOTA data, which is discussed further below.

Catchers

There is a separate box for catchers showing some of the defensive metrics that apply particularly to them. As an example, let’s check out Buster Posey.

Buster Posey

The YEAR and TEAM columns are what you’d expect. P. COUNT is the number of pitches the catcher received, though really it’s the number of pitches thrown by pitchers when the catcher was in the battery; that is, it includes swinging strikes, fouls and balls in play. FRM RUNS is the total runs the catcher added by getting the umpire to call strikes where the average catcher did not (or vice versa). The calculation of this statistic is described above. BLK RUNS, also described above, expresses in runs above or below average the catcher’s ability to prevent wild pitches and passed balls. Finally, THRW RUNS sums the catcher’s ability to dissuade runners from stealing and to catch them when they do run. This statistic is calculated similarly to the Framing and Blocking stats, and takes into account various factors, including the pitcher (who may have a quick or slow delivery, or a good or bad pickoff move) and the baserunner (who may be Billy Hamilton or Billy Butler). The final column, TOT RUNS, is the sum of the previous three.

Pitchers

Now let’s look at how pitchers are presented, using Luis Severino. His stat block is at the top of the facing page. The first line and the YEAR, TEAM, LVL and AGE columns are the same as in the hitters example above. The next set of columns—W (wins), L (losses), SV (saves), G (games pitched), GS (games started), IP (innings pitched), H (hits), HR (home runs), BB9 (walks per nine innings), K/9 (strikeouts per nine innings) and K (strikeouts)—are the actual, unadjusted stats compiled by the pitcher during each season.

Next is GB%, which is the percentage of all batted balls that were hit on the ground, including both outs and hits. As mentioned above, this is based on observation by human stringers and can be skewed based upon a number of factors. We’ve included the number as a guide, but please approach it skeptically.

BABIP is the same statistic as for batters, but often tells you more in the case of pitchers, because most major-league pitchers have little control over their batting average on balls in play. A high BABIP is often due to a poor defense or bad luck rather than a pitcher’s own abilities and may be a good indicator of a potential rebound. A typical league-average BABIP is around .290–.300.

WHIP and ERA are common to most fans: The former measures the number of walks and hits allowed on a per-inning basis, while the latter prorates earned runs allowed on a nine-innings basis. Neither is translated or adjusted in any way.

FIP was discussed above: It puts onto an ERA scale a measurement of how the pitcher performed on the events that do not involve the fielders behind him.

Deserved Run Average (DRA) was also described above. It is the basis of pitcher WARP and measures how many runs (not earned runs) the pitcher deserved to allow per nine innings. One important point about minor leaguers is that because we do not have all the data we would need to fully calculate minor-league DRA, what is listed under DRA for minor leaguers is really a runs-allowed-per-nine figure calculated based on cFIP’s components.

Because, as has been true of BP’s pitching metrics in the past, neither DRA nor the conversion from DRA to WARP contains a leverage multiplier, WARP for relief pitchers (especially closers) may seem lower than you might see elsewhere and may conflict with how we feel about relief aces coming in and saving the game. This is by design: Saves give extra credit to the closer for what his teammates did to put him in a save spot to begin with; WARP is incapable of feeling excitement over a successful save and judges them dispassionately. Furthermore, DRA controls for players who have the benefit of pitching in short durations and at maximum ability.

cFIP, described above, adjusts FIP for a variety of factors and scales it on the familiar 100 scale; because these are pitchers preventing runs, below 100 is good and above 100 is bad.

MPH is the pitcher’s 95th percentile velocity for that season—the goal is to give you a sense of the pitcher’s peak fastball velocity, not his average. This comes from PITCHf/x data and thus is not publicly available for minor leaguers.

CMD, PWR and STM, as described above, are our new Command Rating, Power Rating and Stamina Rating on a 0-100 scale.

PECOTA

Both pitchers and hitters have PECOTA projections for next season, as well as a set of biographical details that describe the performance of that player’s comparable players according to PECOTA. The book contains two years of PECOTA projections for every player.

Luis  Severino  RHP  Born: 02/20/94  Age: 24  Bats: R  Throws: R  Height: 6′2″  Weight: 215  Origin: International Free Agent, 2011

Breakout: 30% Improve: 65% Collapse: 11% Attrition: 18% MLB: 96% Comparables: Marcus Stroman, Carlos Martinez, Rich Harden

The 2018 and 2019 lines are the PECOTA projection for the player at the date we went to press in late December. The player is projected into the league and park context as indicated by his team abbreviation. All PECOTAs represent a player’s projected major-league performance. The numbers beneath the player’s stats—Breakout, Improve, Collapse, Attrition—are a part of PECOTA. These estimate the likelihood of changes in performance relative to a player’s previously established level of production, based upon the performance of the comparable players:

Breakout Rate is the percent chance that a player’s production will improve by at least 20 percent relative to the weighted average of his performance over his most recent seasons.

Improve Rate is the percent chance that a player’s production will improve at all relative to his baseline performance. A player who is expected to perform just the same as he has in the recent past will have an Improve Rate of 50 percent.

Collapse Rate is the percent chance that a position player’s runs produced per plate appearance will decline by at least 25 percent relative to his baseline performance.

Attrition Rate operates on playing time rather than performance. Specifically, it measures the likelihood that a player’s playing time will decrease by at least 50 percent relative to his established level.

Breakout Rate and Collapse Rate can sometimes be counterintuitive for players who have already experienced a radical change in performance level. It’s also worth noting that the projected decline in a player’s rate performances might not be indicative of an expected decline in underlying ability or skill, but rather something of an anticipated correction following a breakout season.

MLB% is the percentage of similar players who played in the major leagues in their relevant season.

The final pieces of information are the player’s three highest-scoring comparable players as determined by PECOTA. Occasionally, a player’s top comparables will not be representative of the larger sample that PECOTA uses. All comparables represent a snapshot of how the listed player was performing at the same age as the current player, so if a 23-year-old pitcher is compared to Barry Zito, he’s actually being compared to a 23-year-old Barry Zito, not the version of Zito the Giants couldn’t wait to be rid of, nor to Zito’s career as a whole.

A few points about pitcher projections. First, we aren’t yet projecting peak velocity, so that column will be blank in the PECOTA lines. Second, projecting DRA is trickier than evaluating past performance, because it is unclear how deserving each pitcher will be of his anticipated outcomes. However, we know that cFIP estimates future run scoring very well, and that cFIP and DRA are based on a similar structure and model. Thus, the projected DRA figures you see are based on the past cFIPs generated by the pitcher and comparable players over time, along with the other factors described above.

Lineouts

The stats box in the Lineouts section contains all the same information, but only has the 2017 stats for each player.

PECOTA Leaderboards

As a result of the way it weights previous seasons, PECOTA can tend to appear bullish on players coming off a bad year and bearish on players coming off a great year. And because we list the 50th percentile projections—the middle of the range the system thinks this player is capable of producing—it rarely predicts any player will hit 40 home runs or strike out 250 batters. At the end of this book, though, we’ve ranked the top players according to their projections. It’s often as helpful to know who the system thinks will be the top second baseman as what his actual stats are likely to be.

Essay by Patrick Dubuque

Player comments by Nick Stellini and BP staff

Professional sports are modern mythology, and no sport is better suited for the role than baseball. The structure of the game—its pace, its duration, its randomness and its fearlessness in assigning blame and failure—feels designed to wrap around the skeleton of the narrative structure. In an urban age, when participation in sports becomes rarer and more difficult, more and more fans interact with their sport not by playing it but by deciphering it, an active verb even when performed from the couch or the bleachers. Baseball is more art than ever before, something to be taken, deconstructed and understood, whether for the sake of our fantasy teams or our own satisfaction.

Baseball seasons and mythologies share one more thing in common: They’re nearly all alike. Joseph Campbell, in his seminal work The Hero With a Thousand Faces, scoured the myths of every civilization he could find, and from them he pieced together a system of common facets, a monomyth, out of which he fused an abstract of shared human desires. Campbell bemoaned the state of modern society for having lost touch with this primeval urge in its search for rationality, not out of a religious fear but because of our loss of communal inspiration. Simply put, shared mythology is the balm against isolation and alienation, a force that bonds us and inspires us to cooperate and thrive. Heroes, to Campbell, are an essential element of society. At this point, athletes are basically what we’ve got.

Fortunately, the model translates fairly well. Campbell’s examination of heroes unearthed a system he called The Hero’s Journey, a series of stages that every hero undergoes for the sake of their people. Through it we can find the plot structure of all the great stories: Jesus, Luke Skywalker, Gandhi, Frodo Baggins and the Arizona Diamondbacks. Join us, for a moment, on the quest of a baseball team, and the phases of the journey they find themselves, like Jason and his Argonauts, traveling:

1. The Call to Adventure

Get a team, basically, and try to win the World Series. Look, they’re not all going to be amazing metaphors.

2. Refusal of the Call

The protagonists, having been called to quest, must decide whether to accept the mission. Those who opt out are doomed to the dreariness of the non-heroic existence, sentenced to wonder what might have been. You don’t read many stories about heroes who get tripped up on Stage 2, because they aren’t really stories; you do see a lot of lives lived this way, however. Every quest requires labor, a willingness to venture beyond the comfortable and the known. Moreover, for our purposes, going on the journey requires taking risks, abandoning the easy and the unquestionable, the reliance on the accepted truisms of the past.

No story boils down the call more directly to a single binary choice than The Matrix does with its red and blue pills, the choice to live in comforting falsehood or face the truths of life as they really are. Every baseball team wants to win the World Series, but some get distracted; they grow fascinated with building a certain type of team, manipulating a certain type of inefficiency that overtakes the value of accumulating talent. The A’s are famous for this; the Rockies, likewise, have been obsessed with character; so too were the Diamondbacks, particularly under the Kevin Towers regime, with grit.

3. Crossing the Threshold

This is the part of the story where the hero or heroes leave their idyllic, safe homelands and venture into the unknown. After the spectacular (in the literal, spectacle-based sense) failures of Dave Stewart, control of the team was handed to new general manager Mike Hazen, who was known for being smart but for little else. Given an opportunity to sculpt and imprint his own vision and message on a young, flawed team, Hazen … made a single trade, sending Jean Segura and 2017 BP Annual essay protagonist Mitch Haniger to Seattle for Taijuan Walker and Ketel Marte. It was a big trade, to be fair, but not an eventful one: In terms of 2017 production, it turned out to be somewhat of a wash, although Marte’s team control probably gives Arizona the slight advantage.

Instead, Hazen chose to move forward quietly with his 93-loss team; his major transactions were signing a pair of sub-replacement-level catchers in Chris lannetta and Jeff Mathis, as well as a washed-up, painful-to-watch Fernando Rodney to close games. Instead, the organization focused on working with what they had, adding fractions of runs through smart baserunning and tossing around phrases like pitch framing never before uttered by the front office. It was a strategy that demanded a remarkable amount of patience, to sort out what the team had and how much they could grow.

It was also, in retrospect, a brilliant move. A roster long considered the apex of stars-and-scrubs watched its cast of secondary characters transform from underwhelming afterthoughts to average, valuable components. Middle infielders like Marte and Chris Owings hit well enough to curb fans’ nostalgia for Segura. And despite a down year for the recovering A.J. Pollock, Iannetta’s offensive resurgence and quality offense from David Peralta made the entire lineup—not just Paul Goldschmidt and Jake Lamb—a source of consistency. But the story of the 2017 season was on the pitching side, where every promising arm of yesteryear, including Robbie Ray, Patrick Corbin, Archie Bradley and even Randall Delgado, took a step forward together.

4. Supernatural Aid

The hero never truly goes it alone, if only because it would make for terrible dialogue. Besides, things have to get rough here and there, to test the depths of the hero’s strength and fortitude, to seem almost too much to bear. In these situations, a god or goddess will often appear to lend a hand and provide guidance, as Athena helps Odysseus by representing his cause on Olympus, or the Great Red Dragon scares off the occasional bad guy in Jeff Smith’s Bone comics. In our example, divine providence arrived for the Diamondbacks in the form of J.D. Martinez. On the morning of July 18, Arizona was mired in a 1-8 slump and, for the first time since spring, the team’s playoff odds were suddenly in doubt. The trade, considered a fairly cheap purchase at the time, proved to be much more than that, as Martinez was one of the best hitters in baseball over the second half.

5. The Road of Trials

Here’s where we get our plot: the voyage, the road trip, the dungeon dive, with its episodic difficulties to be surmounted. It’s also where we reach the present day, in this allegory. When the Diamondbacks were swept out of the NLDS by the Dodgers, there was hardly even a sense of disappointment; beyond the exhilaration of the regular season, the Dodgers just felt like the natural stopping point of the season. It’s fairly clear that the Diamondbacks aren’t there yet, shouldn’t even be there yet; it’s only been a year, after all. Journeys must be long and hard to be rewarding.

Overcoming the trials of the underworld, according to Campbell, requires self-actualization. He writes: And where we had thought to find an abomination, we shall find a god; where we had thought to slay another, we shall slay ourselves; where we had thought to travel outward, we shall come to the center of our own existence; where we had thought to be alone, we shall be with all the world.

The Dodgers, the champion itself, is just a personal demon, a psychological hurdle. The Diamondbacks must know themselves, fully and completely, to advance. Doing so will require them to rely on their ability to fashion their core of young pitchers and shape second-tier prospects into a lasting juggernaut. They may fail in the mission; the improvement may be a mirage, or one or more may simply get struck down, as the Argonaut Mopsus, slain by a viper grown from a drop of blood from Medusa. These things happen.

6. Apotheosis

Every story reaches a climax, a final test that surpasses and combines all of the trials and lessons on the way. With apologies to the surprising Rockies, the Goliath blocking the Diamondbacks’ path to the pennant is clearly the Dodgers, a developmental and financial powerhouse, a foe that seems to hold every possible advantage.

So many movies and stories derive their climaxes from the work of deus ex machina, the single bullet or the fortuitous shift that allows the hero a moment of even odds. Baseball, lacking an author, cannot promise such a twist. With a team like the Dodgers, there is no window to time; they, unlike Arizona, can spend their way into and out of windows at will. So the Diamondbacks can’t afford to operate as if destiny is on their side; instead, they have to rely on vigilance and control, to set up a perpetual system of development and talent acquisition. When the Dodgers expose their heel, suffer their own bout of injury and misfortune, the Diamondbacks will need to be ready if they are to seize control of the division.

7. The Journey Home

Someday, they win the World Series.

The beautiful and the terrible thing about baseball is that it never ends. After the trophy is wrested from the forces of evil and brought home in celebration, the hero wields the force to bend the world to his or her will, to create life and prosperity for his or her people. This is the goal. But it’s always the goal, and there’s always another season, and another hero, and another darkness invading the land. Bones break and heal, players get drafted, leave, return, retire. We never run out of baseball, and we are never sated by it.

This stage of the monomyth might be the only one where baseball fails; sports are not designed for denouement. There will be another parade; perhaps Greinke and Goldschmidt will be at its head, or perhaps they will be eaten by the wolves and followed by others. A band of heroes will return to the city, to share their victory and inspiration, to release again of the flow of life into the body of the world. It’s very easy, especially in this current stage, to worry only about that victory. But mythology, and the value of mythology, isn’t just about the end; it’s about the struggle, the gaining of clarity, the first and the fifth and the 50th steps. Last season, on its own, was a fine step. It deserves to be memorialized.

—Patrick Dubuque is an author of Baseball Prospectus.

HITTERS

Nick  Ahmed  SS  Born: 03/15/90  Age: 28  Bats: R  Throws: R  Height: 6′2″  Weight: 195  Origin: Round 2, 2011 Draft (#85 overall)

Breakout: 3% Improve: 41% Collapse: 10% Attrition: 19% MLB: 88% Comparables: Donovan Solano, Jonathan Herrera, Adeiny Hechavarria

The good news: Ahmed hit a bit better than he did in 2016, including more average and a little pop too (though that may have had something to do with the baseball, which totally wasn’t juiced at all you guys). That allowed him to post an ever-so-slightly higher WARP.

The bad news: Ahmed fractured his right wrist and played in just 53 games overall. He had surgery on that wrist, which can prove to be a trickier, less predictable comeback trail than most other injuries for hitters. Ahmed’s game has always been about defense, though, and fortunately for him the bar for utility-man status offensively is always low.

Gregor  Blanco  CF  Born: 12/24/83  Age: 34  Bats: L  Throws: L  Height: 5′11″  Weight: 175  Origin: International Free Agent, 2000

Breakout: 1% Improve: 31% Collapse: 11% Attrition: 18% MLB: 84% Comparables: Mike Kingery, Sam Fuld, Tony Gonzalez

Blanco kinda sorta recovered from his disastrous showing at the plate in 2016 and put up a line that more closely resembled his usual self. At the same time, he still didn’t hit quite as well as he had in the past, got into just 90 games for the Diamondbacks and had his worst defensive season yet at age 33. Blanco is still theoretically a quality fourth outfielder for a lower-half team, but he’s rapidly approaching Perpetual Non-Roster Invitee status.

Socrates  Brito  RF  Born: 09/06/92  Age: 25  Bats: L  Throws: L  Height: 6′2″  Weight: 205  Origin: International Free Agent, 2010

Breakout: 2% Improve: 8% Collapse: 5% Attrition: 15% MLB: 20% Comparables: Kyle Waldrop, Jason Coats, Jared Hoying

SOCRATES: I am a better athlete than this man. Though I missed time with a dislocated finger and didn’t even crack September call-ups, I still believe I am a big-league-caliber player. I have physical tools, I can play right field like a Spartan hero. Contact can elude me, but aye, I do what I can with what the gods have given me. My arm has the strength of an ox, and it is the weapon with which I lay waste to those on the basepaths. Some may fancy they know me as a Quad-A type, yet they know nothing, for I could thrive as a fourth outfielder, or even as more if a below-average team gave me a chance to start. DARRYL, SOCRATES’ FRIEND: F*** him up, Socrates.

Jasrado  Chisholm  SS  Born: 02/01/98  Age: 20  Bats: L  Throws: R  Height: 5′11″  Weight: 165  Origin: International Free Agent, 2015

Breakout: 1% Improve: 5% Collapse: 0% Attrition: 2% MLB: 9% Comparables: Raul Mondesi, Tim Beckham, Amed Rosario

The man they call Jazz didn’t have a euphonic year in his first run at full-season ball. Chisholm never found his tempo at the plate, losing 33 points off his batting average while notably just about keeping pace in OBP, but he’s still young. A second attempt at A-ball will likely do him well, and maybe put him on track to Take the A-Train to Phoenix. There’s still enough potential here for a good percussive bat with his all-fields approach, and his speed and good glove work should have the guys in the front office Feeling Good if all goes well here. Because of his age and rawness, that may take a while, perhaps four years. It may even Take Five. Who knows. Player development is far from an exact science, but there can sometimes be beauty in just seeing what melodies come about through experimentation.

Daniel  Descalso  UT  Born: 10/19/86  Age: 31  Bats: L  Throws: R  Height: 5′10″  Weight: 190  Origin: Round 3, 2007 Draft (#112 overall)

Breakout: 1% Improve: 46% Collapse: 6% Attrition: 19% MLB: 92% Comparables: Luis Alicea, Bernie Allen, Jerry Hairston

Descalso played well early on last season, but that went away quickly as the Diamondbacks continued to give him surprisingly regular playing time. He’s no longer playing at Coors Field, but Arizona is about as good as it gets for non-Colorado hitter’s parks. Descalso is back to hitting like, well, Descalso. He has also been below average on defense for four seasons in a row now, albeit with the usual versatility. Descalso’s greatest skill seems to be the ability to not embarrass himself at various positions, which is not inconsequential. There will always be a demand for guys like Descalso, which means he’ll probably stay in the league until his bat totally, completely atrophies.

Brandon  Drury  2B  Born: 08/21/92  Age: 25  Bats: R  Throws: R  Height: 6′2″  Weight: 210  Origin: Round 13, 2010 Draft (#404 overall)

Breakout: 1% Improve: 53% Collapse: 10% Attrition: 16% MLB: 98% Comparables: Kolten Wong, Jose Castillo, Josh Barfield

Drury’s bat has always been his carrying tool, so naturally it backed up a bit when he posted his first-ever positive FRAA mark. The end result was roughly the same; a spare-part player with a lot more value to a National League team. As of publication, the Diamondbacks still play in the NL, so that works out well for the former Braves prospect acquired in the January 2013 Justin Upton trade. Drury is younger than you probably think he is, so there’s still a chance we see him suddenly start to demolish the ball, but until then he’ll be a mediocre quasi-regular.

Reymond  Fuentes  CF  Born: 02/12/91  Age: 27  Bats: L  Throws: L  Height: 6′0″  Weight: 160  Origin: Round 1, 2009 Draft (#28 overall)

Breakout: 0% Improve: 21% Collapse: 8% Attrition: 15% MLB: 45% Comparables: Nyjer Morgan, Craig Gentry, Jarrod Dyson

Fuentes got into 64 games for Arizona, which isn’t an inconsequential number. Unfortunately, those games were more Superman 64 than Super Mario 64. He seemed to be playing with an outdated controller, or maybe on a crappy ROM hack. He couldn’t get to the ball and he didn’t do a whole lot of damage to it either. We’d say he should try the tutorial again, but this may just be who he is, as past production in the minors and past Annual comments will tell you. Superman 64 may just be a bad game.

Paul  Goldschmidt  1B  Born: 09/10/87  Age: 30  Bats: R  Throws: R  Height: 6′3″  Weight: 225  Origin: Round 8, 2009 Draft (#246 overall)

Breakout: 1% Improve: 45% Collapse: 3% Attrition: 9% MLB: 99% Comparables: Mark Teixeira, Stan Musial, Lance Berkman

At some point, writing about excellence can get boring. You have to keep finding new ways to make excellence exciting, especially when that excellence is so constant, without any ebb and flow. You have to come up with stuff like death, taxes and Paul Goldschmidt. Life, the universe and Paul Goldschmidt. Bears crapping in the woods, and Paul Goldschmidt.

At a certain point, though, you run out of superlatives and turns of phrase. You run out of ways to say that Goldschmidt is one of the most consistently lethal offensive players in baseball, along with being a good defender and a stunningly good baserunner for a first baseman. There are only so many ways you can say that the man they call America’s First Baseman (Freddie Freeman couldn’t be reached for comment) is the face of the franchise and will likely be for years to come. If the Diamondbacks continue their winning ways, the country as a whole will get a better taste of the show they’ve been missing in the desert. They’ll experience the mind-numbing brilliance of Goldschmidt, and then it’ll fade into more of watching paint dry and watching Goldschmidt be exemplary.

Jeremy  Hazelbaker  LF  Born: 08/14/87  Age: 30  Bats: L  Throws: R  Height: 6′3″  Weight: 190  Origin: Round 4, 2009 Draft (#138 overall)

Breakout: 5% Improve: 17% Collapse: 10% Attrition: 20% MLB: 49% Comparables: Justin Ruggiano, Chris Aguila, Laynce Nix

Hazelnuts aren’t the sort of thing you usually find in cookies, but a quick search reveals several different recipes for, you guessed it, hazelnut cookies. They generally call for the hazelnuts to be toasted before being incorporated into the batter. In a turn of fate, it was Hazelbaker who toasted opposing pitchers during his brief work in Arizona. True to form, he struck out a lot, but the raw results are hard to argue with for fill-in work. A role off the bench or as an up-and-down guy indeed seems to be the recipe for success for Hazelbaker, who owns a career .500 SLG in 285 big-league plate appearances. If a front office were to consider combining him with chocolate in some sort of spreadable form, though, who knows what could happen?

Chris  Herrmann  C  Born: 11/24/87  Age: 30  Bats: L  Throws: R  Height: 6′0″  Weight: 200  Origin: Round 6, 2009 Draft (#192 overall)

Breakout: 3% Improve: 26% Collapse: 9% Attrition: 25% MLB: 74% Comparables: Rob Johnson, Carlos Corporan, Geronimo Gil

Frequently called The Herrmannator by the team’s Twitter account and by the Arizona broadcasters, the Diamondbacks’ third catcher/outfielder/pinch-hitter probably should have requested more than boots, clothes and a motorcycle. Herrmann backslid from a shockingly strong 2016 campaign to slip under replacement level (and the Mendoza line) again. At age 30, this is probably who he is. Because he can play behind the plate and has some pop in his bat, he’ll keep getting jobs. It’ll take more than hitting .186 to stop a Cyberdyne Systems Model 101 Herrmannator. He’ll be back.

Jake  Lamb  3B  Born: 10/09/90  Age: 27  Bats: L  Throws: R  Height: 6′3″  Weight: 215  Origin: Round 6, 2012 Draft (#213 overall)

Breakout: 4% Improve: 47% Collapse: 3% Attrition: 6% MLB: 98% Comparables: Edwin Encarnacion, Chase Headley, Kevin Kouzmanoff

For the second year in a row, Lamb’s excellent production hit the skids in the second half. He hit a robust .279/.376/.546 entering the All-Star break, but hovered around the Mendoza line thereafter. It’s hard to pinpoint exactly why Lamb has had this happen two years in a row. Perhaps it’s undisclosed nagging injuries. He’s also still unable to do much of anything against lefty pitchers. This isn’t all to say that Lamb is a bad player; he’s pretty darn good when he’s going right. It’s just that when he’s going right bit that makes him something of a tough nut to crack. Half a year of All-Star production isn’t anything to be trifled with, but if he’s going to make the leap to full-time greatness he’ll need to break his habit of coming in like a lion and going out like, well, a lamb.

Ketel  Marte  SS  Born: 10/12/93  Age: 24  Bats: B  Throws: R  Height: 6′1″  Weight: 165  Origin: International Free Agent, 2010

Breakout: 4% Improve: 49% Collapse: 5% Attrition: 10% MLB: 98% Comparables: Andrelton Simmons, Ruben Tejada, Jean Segura

Many comments in this book, including some in this very chapter, are composed largely of puns or jokes based on the name of the player. Some of the best comments in BP Annual history have come about this way. Marte will receive no such treatment. He filled in admirably at shortstop following the injury to Nick Ahmed, and shined in Arizona’s brief playoff run. It would be a stretch to call Marte a dynamic player, but his combination of defense, speed and an adequate bat makes him the sort of guy who can make things happen in a game and is great fun to watch when it’s all clicking. It’ll be interesting to see what happens between him and Ahmed in spring training. He’s just 24, so there’s still some time to see if his talent will come to more of a boil, and if that steam translates to even more in-game success. Aw, dang it.

J.D.  Martinez  RF  Born: 08/21/87  Age: 30  Bats: R  Throws: R  Height: 6′3″  Weight: 220  Origin: Round 20, 2009 Draft (#611 overall)

Breakout: 0% Improve: 43% Collapse: 1% Attrition: 8% MLB: 100% Comparables: Matt Kemp, Reggie Jackson, Carlos Gonzalez

Perhaps it was an act of divine providence that someone named Just Dingers Martinez would end up playing professional baseball. Public records may indicate that his given name is Julio Daniel, but we know the truth. Martinez has quietly been one of the best right-handed hitters in the game for years now, and the Diamondbacks got him for a song from Detroit at midseason. He touched down in Arizona and promptly did his best to live up to that name of his, which the Diamondbacks’ broadcasters were so fond of calling him. He went as far as to have a four-homer game, only the 18th player to ever do so. He also hit for plenty of average, finishing above .300 for the third time in four years. Most impressively he launched 45 homers despite missing 43 games, which makes you wonder what he can do if he plays a full season.

Jeff  Mathis  C  Born: 03/31/83  Age: 35  Bats: R  Throws: R  Height: 6′0″  Weight: 205  Origin: Round 1, 2001 Draft (#33 overall)

Breakout: 4% Improve: 34% Collapse: 14% Attrition: 39% MLB: 88% Comparables: Jose Molina, Paul Bako, Mike Matheny

When you think of defense-first catchers, you think of dudes like Jose Molina. The middle Molina brother is one of the best defensive backstops ever, a true monster of pitch framing who compiled nearly 200 FRAA in his illustrious career despite part-time roles. He also couldn’t hit a lick. Mathis has had a worse offensive career than Molina. He has a worse career TAv than almost everyone. Mathis cannot hit. He can, however, catch. He does that very, very well. He’s stuck around for 13 seasons now as a backup catcher and pitching staff manager, and teams keep bringing him back and paying him millions of dollars. That’s really damn impressive. You go, Jeff Mathis.

John  Ryan  Murphy C  Born: 05/13/91  Age: 27  Bats: R  Throws: R  Height: 5′11″  Weight: 205  Origin: Round 2, 2009 Draft (#76 overall)

Breakout: 9% Improve: 24% Collapse: 23% Attrition: 42% MLB: 69% Comparables: Rob Johnson, Steve Clevenger, Bryan Holaday

A midseason trade for lefty relief prospect Gabriel Moya brought Murphy from Minnesota to Arizona. More accurately, it brought him from Rochester to Reno, a swap of Triple-A locales. Murphy provides some defensive value, but until he proves he can hit at least a little, he’ll continue to be a tweener. Minnesota’s decision to trade a now broken-out Aaron Hicks for him in November 2015 looks bad. Since that deal, Hicks has nearly twice as many homers (23) as Murphy has hits (13) in the majors.

Chris  Owings  SS  Born: 08/12/91  Age: 26  Bats: R  Throws: R  Height: 5′10″  Weight: 185  Origin: Round 1, 2009 Draft (#41 overall)

Breakout: 6% Improve: 59% Collapse: 11% Attrition: 16% MLB: 97% Comparables: Adeiny Hechavarria, Freddy Galvis, Marwin Gonzalez

A fractured finger sustained during a bunt attempt put an end to what was easily Owings’ best season in the big leagues. It was a good reminder that in due time, everything fractures in its own way. Our faith in humanity. Our belief that Pauly Shore is funny. Our desire to devour any handful of M&M’s within reach. Perhaps that third one never truly fractures, or instead just heals rather quickly. Similarly, Owings should be ready for Opening Day following a pair of surgeries on the finger. There’s no way of telling whether he’s ready to give Bio-Dome another try.

David  Peralta  RF  Born: 08/14/87  Age: 30  Bats: L  Throws: L  Height: 6′1″  Weight: 210  Origin: International Free Agent, 2005

Breakout: 1% Improve: 41% Collapse: 10% Attrition: 18% MLB: 94% Comparables: Nate Schierholtz, Roger Bernadina, Josh Reddick

Though he didn’t hit quite as well as he did in his ridiculous 2015 campaign, simply being on the field for a full season proved more than enough for Peralta to once again provide big value. Peralta didn’t hit for his usual power, but his on-base skills and best-ever defense more than compensated. It stands to reason that Arizona hopes he’ll hit the ball over the fence again this year. If he does, he’s an All-Star and strong most underrated player in the league candidate. If he doesn’t, he’ll still be a pretty darn good player.

A.J.  Pollock  CF  Born: 12/05/87  Age: 30  Bats: R  Throws: R  Height: 6′1″  Weight: 195  Origin: Round 1, 2009 Draft (#17 overall)

Breakout: 1% Improve: 43% Collapse: 7% Attrition: 3% MLB: 97% Comparables: Jacoby Ellsbury, Jon Jay, Shane Victorino

A groin injury prevented Pollock’s grand return from being truly grand, but he still offered the usual dynamic power/speed dual threat when he was on the field. Providing nearly 3.0 WARP in just 112 games speaks to the type of player he is, but he has trouble staying in the lineup, having played more than 130 games in just two seasons and having never cracked 100 games in back-to-back years. It’s possible that Pollock will never again reach the MVP-caliber heights of his 2015 season, but his all-around game is rare, and a hitter-friendly home ballpark pretties up his raw numbers.

Adam  Rosales  UT  Born: 05/20/83  Age: 35  Bats: R  Throws: R  Height: 6′2″  Weight: 200  Origin: Round 12, 2005 Draft (#362 overall)

Breakout: 2% Improve: 29% Collapse: 14% Attrition: 35% MLB: 73% Comparables: Alex Gonzalez, Clint Barmes, Bill Almon

For a moment, the world didn’t make sense. Rosales wasn’t playing for the A’s or the Rangers, and he was hitting dingers. Lots of dingers. Well, 13 isn’t that many, but it’s a lot for Rosales. Then the world was righted once more, and Rosales signed with Oakland. For a time, peace and tranquility seeped back into the realm. There was much joy and celebration. Crops flourished, children were merry, lovers felt closer. Things were as they were meant to be, for Rosales was back where he belonged. Then horror struck, and Rosales was traded to the Diamondbacks. Fire rained from the skies and the screams of the innocent filled the air once more. The harmony had been broken. And there was only despair.

Pavin  Smith  1B  Born: 02/06/96  Age: 22  Bats: L  Throws: L  Height: 6′2″  Weight: 210  Origin: Round 1, 2017 Draft (#7 overall)

Breakout: 9% Improve: 11% Collapse: 2% Attrition: 13% MLB: 15% Comparables: James Loney, Russ Canzler, Jose Osuna

Drafting a first baseman with the seventh overall pick usually means one of two things: The team thinks he can hit the snot out of the ball or he can move to a different position. Fortunately, there’s a decent amount of evidence that Smith can hit the snot out of the ball. Smith waltzed his way to a .342/.427/.570 line in his junior campaign at Virginia, setting a school single-season RBI record and being named a semifinalist for the Golden Spikes award. He’s an advanced bat, he oozes OBP and he took to pro ball very well. There’s some guy named Paul Goldschmidt currently at first base in Arizona, but don’t be surprised if Smith surfaces in the big leagues quickly. Nobody will ever truly replace Goldy, but Smith’s as good a choice to develop as a caddy as anyone.

Yasmany  Tomas  LF  Born: 11/14/90  Age: 27  Bats: R  Throws: R  Height: 6′2″  Weight: 250  Origin: International Free Agent, 2014

Breakout: 3% Improve: 59% Collapse: 2% Attrition: 9% MLB: 94% Comparables: Bob Nieman, Randy Elliott, Elston Howard

Core muscle issues that wound up requiring surgery cut Tomas’ season short. Although he had been a good power source in 2016, Arizona clearly got along just fine without him last year. Tomas has always been a bit of a cumbersome fit on the Diamondbacks’ roster (or any NL roster, for that matter), and trade rumors are forever swirling. Power as a standalone tool has less value now than at just about any time in baseball history, and Tomas is awful at every other aspect of the sport. Plopped into a different era he might be praised as an RBI man and rewarded for slugging 25-30 homers per season, but he’s much closer to a replacement-level player than an All-Star.

Christian  Walker  1B  Born: 03/28/91  Age: 27  Bats: R  Throws: R  Height: 6′0″  Weight: 220  Origin: Round 4, 2012 Draft (#132 overall)

Breakout: 1% Improve: 10% Collapse: 11% Attrition: 17% MLB: 29% Comparables: Daniel Dorn, Andy Wilkins, Juan Miranda

After five seasons in the Orioles’ farm system, Walker bounced around waivers last spring before ending up at Triple-A for the Diamondbacks. He dominated there, posting huge, PCL-inflated numbers to win league MVP honors before going deep twice in a September call-up to Arizona. He even made the Diamondbacks’ postseason roster, but that shouldn’t necessarily be taken as a sign that he’s in their plans. Something of a tweener without great power or patience before the 2017 breakout, Walker will need to find a new organization if he wants to get consistent at-bats in the majors.

PITCHERS

Anthony  Banda  LHP  Born: 08/10/93  Age: 24  Bats: L  Throws: L  Height: 6′2″  Weight: 190  Origin: Round 10, 2012 Draft (#335 overall)

Breakout: 13% Improve: 33% Collapse: 19% Attrition: 27%