Many baseball analysts strive to create “context neutral” stats, so they can compare players stats, while minimizing biases. A player’s traditional stat line–batting average, home runs, OBP, slugging percentage, OPS, etc.–are a product of more than the player’s talent level and luck. For example, they are also impacted by the handedness of the pitchers they face and the ballpark in which they play . These factors can be accounted for with evaluating platoon splits or park-adjusted stats. Perhaps because it is more difficult to measure, advanced stats are seldom adjusted for the quality of opponents. Adjusting for the quality of opponents is particularly important when interpreting starting pitcher stats, since we deal with a small sample size of 25 to 35 starts per year, with a start every 5th day, regardless of who is on the schedule. Since talent tends to be clustered in certain Divisions, the unbalanced schedule produces a skewed distribution of opponents. A pitcher in the AL East is likely to be playing a different game than one in the NL East.

I use a simple measure to provide capture the quality of opponents a starting pitcher has encountered over the course of a season–the OPS of the offenses he has faced. More specifically, the OPS of the teams he has faced, against the same handed pitchers. In other words, for David Price, I take the opponents for each of his 27 starts for 2013 and use the OPS against LHP as my measure. If a RHP, faced the same teams, their opposition would be categorized based on their OPS against RHP. For example, Texas mashes LHPs to the tune of a .798 OPS, while they maintain a modest .705 OPS against RHP. On the other hand, the Cardinals hit RHP at a .753 clip, while batting only .675 against lefties. So, it’s not enough to say a pitcher faced the Cardinals or Rangers. It’s also important to distinguish his handedness.

I use a quality of opponents factor in ranking starting pitchers each year. Several years ago when I developed my SPR, I wanted to reduce the context bias that is in our everyday pitching stats, like ERA or K/9, etc. By adjusting for both the ballpark a played pitched in and the opponents he faced, we get closer to “context neutral” in evaluating how well a pitcher performed. Let’s take a look at the landscape for 2013. When we look at pitchers who faced the toughest (and weakest) competition, we find patterns. We tend to find pitchers clustered in the same division and often even on the same teams. Esmil Rogers of the Toronto Blue Jays holds the distinction of pitching against the toughest opponents in 2013. The Yankees and Astros dominate the top 20 pitchers who faced toughest opponents, for two reasons. First, they play in offensive divisions. (It is rare to see a NL pitcher near the top of the rankings, due to the lack of DH in the NL.) Second, both the Yankees and Astros were weak hitting teams in divisions with decent offensive prowess. (When was the last time we could say the Yankees were a weak hitting team? In 2013 they batted .676 against LHP and .686 against RHP, nearly 30 points below the league average.) Five Yankees populate the top 20: Ivan Nova #2, Hiroki Kuroda at #4, CC Sabathia at #11, Andy Pettitte at #12, and Phil Hughes at #19. Houston also had 5 of the top 20–Dallas Keuchel #3, Eric Bedard #6, Bud Norris #9 (including his time with the Orioles), Lucas Harrell #10 and Jordan Lyles #18.

The other end of the list–pitchers that faced the easiest competition in 2013 are dominated by NL East hurlers. Of the bottom 15, twelve are from the NL East. Mike Minor, Julio Teheran, Kris Medlen, and Tim Hudson from the Braves, along with Jordan Zimmermann, Dan Haren, and Stephen Strasburg of the Nats are all bottom 10, along with the Mets’ Matt Harvey. The Mets also have Hefner, Niese and Wheeler in the bottom 20.

So, how wide is the range in the quality of opponents? The top pitchers face offenses with OPS about 4% greater than the league average, while the bottom pitchers tend to face offenses that are 3-4% weaker.  This amounts to about 25 to 30 OPS points difference. Below is a list of the top 20 (faced toughest opponents) and bottom 20 (faced weakest opponents).

Top 20 and Bottom 20 SP Who Faced Toughest

I’ve always been fascinated by the change up. Slower than a fastball, with less movement than a typical slider or curveball, it thrives on deception–appearing to be something other than what it is. According to the pitch f/x data, 92 starting pitchers have thrown change ups this year. On average, they account for about 14% of their pitches, are 7.6 miles per hour slower than their fastball, move a total of 11.8 inches. The vertical movement of the change up accounts for 4.4 inches per pitch, or about 37% of the total movement of the pitch. Some pitchers throw a change up one-third of the time (Justin Verlander), while others throw it as little as 1% of the time (Edwin Jackson).

Which starting pitcher has the best change up in baseball for the first half of the 2013 season? I’m not in favor of looking at the batted ball results of change ups put in play, as it can be a misleading measure. No one pitch-type can be judged by how batters perform against it, in isolation. Pitches live and die by the sequence  that precedes them. A change up that follows a fastball is very different than a change that follows two previous change ups. (In a separate analysis, I’m in the process of evaluating pitch sequences and developing a system to “value” sequences of pitches, rather than stand alone pitches.) For the purpose of this piece I rated pitchers’ change ups based on four factors. I looked at the velocity differential versus the pitcher’s fastball and the total movement on the pitch. I also factored in the percent of a pitcher’s  mix of pitches, giving a pitcher more “credit” for using the pitch if it were say, 20% of his pitch mix versus 5% of his mix. Finally, I gave additional points to a pitcher for his vertical movement. Although it is already included in total movement, I placed a premium value on the drop of a pitcher’s change up, effectively double-weighting it. The net result is a points system that rates the change ups thrown this season.

At the top of list is Justin Verlander. His velocity differential is around the league average of 7.2 mph, but his total movement, vertical drop and percentage thrown are all well in excess of the MLB-wide average. Closely behind Verlander are Jason Vargas, Hyun-Jin Ryu and Jeremy Hellickson in second, third and fourth place. Ryu and Hellickson (along with Clay Buchholz) have the highest velocity differential versus their fastball at over 11 mph. Vargas (followed by Derek Holland, Mike Minor, Cliff Lee and Wade Miley) have the greatest total movement on the pitch, all exceeding 16 inches. The number 5 rated change up belongs to Cole Hamels–the top ranking change up artist, applying the same formula to 2012 data. Tommy Milone, Jarrod Parker, Eric Stults, Joe Saunders and Matt Moore round out the top 10. The Ray’s and Oakland A’s each have two starters in the top 10.

The biggest vertical drop belongs to Clayton Kershaw, who throws the pitch only 3% of the time, followed by the Orioles’ Chris Tillman, and the Rangers’ Derek Holland. The top 20 change ups for the first half of 2013 are listed below:

In my last post, I discussed one of my latest research projects, clustering pitchers by their similarities. The problem I’m trying to address with the analysis is to come up with an alternative to what is possibly the overall worst use of quantitative analysis in baseball–evaluating batter-pitcher match ups, based on career historical performance data between one batter and one pitcher. Instead, I’m trying to identify groups of pitchers that are likely to induce similar offensive performance by a single batter. If we can find a cluster of pitchers who present a similar challenge to a hitter, then we can enlarge the sample size of batter-pitcher “results” and at the same time shorten the timeframe over which we are measuring performance. For example, against right-handed hitters, my analysis suggests that lefty pitchers Barry Zito, Mark Buehrle, Paul Maholm, Zach Duke, Chris Narveson, Eric Stults, Joe Saunders and Jason Vargas (among others) are “similar”. This similarity is based on the profiling factors listed in the previous post, including the pitch repertoire, release points, most common 2-pitch sequences, the portion of the strike zone the pitcher favors, etc.

Below is a visual mapping of pitcher clusters. Each node represents a pitcher and each line between pitchers represents a “connection” or a similarity, based on a defined minimum threshold level. This graph includes only LHPs and it clusters them against only right-handed hitters.

Take note of the large cluster in red, at the top of the graph. Below is a zoomed version with labels identifying the pitchers. This is the cluster I reference above, which includes Zito, Buehrle, et. al.

Let’s take a deeper look at an example of Matt Holliday against this particular cluster of LHP. Over his career, Matt Holliday is 2 for 14 (in 17 plate appearances) against Joe Saunders. However, my analysis shows that Holliday crushes this cluster of LHP’s with an OPS in the 85th percentile against this cluster. So which is it–does the Holliday-Saunders match up favor Saunders, as the one-on-one career data suggests, or does it favor Holliday, as my analysis suggests? I don’t have a definitive answer (although I do have a test in mind, which I may conduct and write about at a later time), but I can make the case. Of the 17 PAs Holliday has had against Saunders, nine of them occurred four years ago in 2009, with just 8 PAs occurring in the last two seasons. By contrast, Holliday had 82 PAs against Saunders’ cluster of “like” pitchers over the same two-year period–2011 and 2012. I like the recent experience of two years vs. a career and I like the sample size of 82 vs. 17. I hope to have further comments on the value and predictive power of the pitcher cluster analysis approach in the coming weeks.

About six weeks ago I presented some of my latest research at the SABR Analytics Conference in Phoenix. The analysis focused on identifying pitchers who are similar to one another, grouping them into clusters, and determining how hitters have performed against various clusters. I worked closely with George Ng a data scientist at YarcData and made use of their sophisticated Urika hardware appliance, which specializes in graph analytics. The intent of the project is to develop an alternative to the relatively uninformative one-on-one batter-pitcher match up data that teams tend to use to inform their lineup, pinch-hitting and bullpen match up decisions. There are numerous problems with relying on the one-on-one batter-pitcher history, including small sample sizes and data that is old and stale. Is it relevant that Derek Jeter’s career stats vs. Roy Halladay includes a 4 for 10 in 1999?

The process to create pitcher clusters begins with determining the attributes that will define “similarity” between pitchers. I chose to tackle this issue from the batter’s perspective. In other words, what criteria would hitters use to “type” a pitcher? I matched the criteria–in the form of questions, with Pitch f/x attributes. The framework, which includes about 12 different attributes, is detailed in the chart below. Keeping with the approach of judging similarity from the perspective of the hitter, I segmented the data for each pitcher, based on left-handed vs. right-handed hitters. In other words, Jered Weaver wasn’t profiled once on these attributes. Instead, he was profiled twice–vs. LHB and vs. RHB, separately. Some pitchers–Jered Weaver, Hiroki Kuroda and Lance Lynn are particularly good examples–approach lefty and righty hitters completely differently. For example, at a very basic level, Weaver’s top 2 pitches against RHB are a 4-seam fastball and slider, while his top two pitches against lefties are a sinker and change-up. Some pitchers not only alter their pitch selection, but also change their release point (alter their starting point on the pitching rubber), or their movement (add a little more cut to their fastball or tilt to their slider), as well as many of the other attributes I include in the analysis. These nuances make it important to differentiate pitchers by their lefty-righty batter splits. Furthermore, I cluster a pitcher by his handedness, which leads to four separate categories of pitcher clusters–RHP vs. RHB, RHP vs. LHB, LHP vs. RHB, and LHP vs. LHB.

The results of the similarity analysis show that some pitcher pairs are similar against right-handed batters, but very different when judged against left-handed batters. The Red Sox Felix Dubront and the Rangers Matt Harrison are similar when facing LHB, but less so when facing RHB. Other highly similar pairs of pitchers include Bruce Chen and Randy Wolf (vs. LHB), Jonathan Niese and Wandy Rodriguez (vs. RHB) and David Price and Felix Dubront (vs. RHB). Pitchers who are least similar, or most opposite to one another include Brandon Morrow and Kyle Lohse (vs. LHB) and Nathan Eovaldi and Shaun Marcum (vs. RHB).

We can also see which pitchers are most similar to themselves, when facing righty and lefty hitters. It’s not surprising to see RA Dickey as the pitcher who differentiates the least, between RHB and LHB. Many closers dominate this list, as they tend to have a limited pitch repertoire and use it in the same fashion regardless of who they face. But other starters who rank high are AJ Burnett, Wade Miley and Manny Parra. Those who are most opposite to themselves when pitching to LHB and RHB include Lance Lynn, Matt Cain and Wade Davis.

In future posts I’ll describe the process and share the results of pitcher clusters, as well as patterns of hitter performance against clusters.

With the news of Derek Jeter’s return delayed until at least late July, guaranteeing he’ll miss 100 or more games this year, it may be time to go to Plan B. The perfect move for the Yankees may be to trade for Texas Ranger’s, Jurickson Profar, a shortstop and the top rated prospect in all of baseball. When Jeter plays his next game as a Yankee, he will be 39 years old. Considering many have questioned his ability to play a credible shortstop for several years, a 39 year old version, coming off of serious ankle surgery, does not seem to be a great fit with a championship caliber team. On the other side of this potential trade we have a team that has two outstanding shortstops. Elvis Andrus, the incumbent Ranger shortstop is a 24 year old who has already made two All Star teams and played in two World Series. Profar made his major league debut last September, as a 19 year old, and promptly homered in his first MLB plate appearance. He is Baseball America’s #1 ranked prospect in all of baseball. He projects to be a legitimate major league shortstop, with above average power and a significantly above average hitter–a rare trifecta of skills.

I can’t think of a better time to gracefully slide Jeter to another role in the Yankee lineup. With his extended absence, uncertain return and even more uncertain physical capacity once he does return, it’s hard to argue with a move to acquire the top shortstop prospect since Troy Tulowitzki. At age 20, Profar would be under Yankee control at least through his age 26 season. His quick bat will likely amplify his left-handed power at Yankee Stadium, making him an even greater than expected run producer. The hope is that within a year or two–by age 22–Profar is a .280 hitter with 15 home runs, plus an above average major league shortstop. His ultimate upside could be the second coming of Robinson Cano.

One question is what can the Yankees give up to induce the Rangers to trade baseball’s top prospect. The Yankees would need to assemble an impressive package of players to acquire Profar. The Yankees farm system is not depleted, but many of it’s top prospects are at lower levels. A package that includes 21 year old outfielder Mason Williams and another highly rated prospect, like Tyler Austin, along with Brett Gardner, may at least get the Rangers attention. If you need to add Joba Chamberlain to the package, it’s worth considering. I realize that Brett Gardner is an integral part of the Yankee offense today, but with Granderson coming back soon, it might make sense to deal from a position of relative strength, in order to solve the long term problem of Jeter’s successor. I just don’t believe Edwardo Nunez has the defensive chops to be an everyday big league shortstop on a contending team. There may not be a cheaper option anytime soon, or one that has the chance to be an enduring, long term solution like Profar.

The toughest question may be where Jeter will play when he returns. Making him the primary DH may be the best option, while easing him into 3B, a position that requires much less lateral range. When the Yankees acknowledge that Jeter cannot play shortstop at a high level, a logjam is inevitable at either DH or the position Jeter moves to. When (if?) A-Rod comes back, it gets even more complicated. A-Rod may be best suited for DH. Hafner can only be a DH. Youkilis is limited to 1B, 3B or DH. However, these problems are only marginally more complicated with Profar replacing Jeter at shortstop. The issue of how to allocate playing time among players who have evolved into immobile, primarily offensive contributors is an issue that is not going away for the Yankees of the next several years. Now may be the time to confront the issue head on.

Over the last week, two articles appeared discussing two teams’ contrasting approaches to making baseball decisions. The Washington Nationals were called a “scouting first” organization that integrates statistical analyses into team decisions. By contrast, the Philadelphia Phillies seem proudly defiant of the trend to incorporate advanced metrics into their decision criteria. While there are a large number of MLB teams that put significant energy and dollars into objective analysis of data, the other end of the spectrum is often a mystery. Who are the clubs and how do they process information. In recent years teams like the Orioles, Dodgers and Giants have been accused of shunning stats in favor of intuition or the perspective and wisdom of career baseball people. However, when pressed these teams typically deny an aversion to the numbers side of the game and in fact tout their otherwise low-profile prowess in this area. It now seems that the Phillies are willing to be the proud flag-bearers for a shrinking group of ballclubs who believe that “new stats” fail to add value to decisions. We may finally have a controlled experiment of the stats team vs. the no-stats team. If two clubs, who fit those descriptions were to maintain their loyalty to their respective internal decision processes, it would be interesting to see how they perform over the next 4 or 5 years.

So who is our poster-child for the stats gurus? In the opposite corner, representing the stat heads, we have the Houston Astros. Truth be known, the opposite corner is actually quite crowded with teams that strive to make stat analysis a potential competitive advantage, with the Tampa Bay Rays at the top of the list, but we’ll choose the Astros as our subject for our controlled experiment. Under the leadership of former Cardinal executive Jeff Luhnow, Astros have assembled a team that more closely resembles a NASA lab crew than a baseball front office. From former NASA engineer Sig Mejdal, the team’s Director of Decision Sciences, to Assistant GM David Stearns and Pitch f/x guru Mike Fast, Luhnow has attracted a top-notch staff. Team CEO George Postolos seems fully bought-in to Luhnow’s approach and the baseball world is watching to see how the Astros fare over the next five years.

I like matching the Astros against the Phillies , because this match up also has a bit of handicapping embedded in it. The Phillies have been a competitive club, who some believe can still contend for the NL East, while the Astros are thought to be the worst team in baseball—by a lot. Given the predictions of how each team is expected to perform in 2013, we’re probably giving the Phillies a 20-win per season head start for the coming season. We can see how long the Astros take to close the gap and try to assess if the two teams approach to decisions was responsible for the outcome.

My view is that well thought out problem solving—quantitative and qualitative—can add enormous value to decision processes. Over my career, I’ve seen analytics supplement intuitive judgment, experience and observation on hundreds of occasions, almost always leading to higher quality decisions. I’ve seen baseball teams integrate analytics with scouting information and the wisdom of veteran baseball people to improve the confidence in their decisions.

The baseball data world is changing rapidly. Just six years ago baseball was producing about 900,000 data points to capture the outcomes of each pitch thrown and ultimately of each plate appearance in a major league season. With the introduction of Pitch f/x and related datasets, beginning on a full scale basis in 2008, we now have over 15 million annual data points that chronicle the baseball season, ranging from the angle of break on Derek Holland’s slider, to the most popular two-pitch sequence by Jered Weaver. There are literally thousands of questions that we could only speculate on six years ago, that we can answer objectively today. Even if you believe that statistical analysis may not have been a difference maker in 2006, the 15x increase in data we have today changes the game. It can help reduce the risk on $100 million contract decisions to a manageable level. I’m not arguing against the scouting perspective. The scouting perspective is critical and often the lead horse in a decision process. But that’s different than excluding statistical analysis from the ultimate decision.

My bet on how the controlled experiment turns out: I would expect the experiment will be aborted before we reach our five-year timeframe, as the Phillies will eventually modify their decision processes to integrate more quantitative information. If that change occurs, it may be interpreted as an answer to the controlled experiment.

In the era of multi-purpose stadiums in the 1970s and 1980s, it seems that there were more similarities across the spectrum of ballparks than there is today. In the post-new Comiskey era, which began with Camden Yards, we’ve brought quirkiness back to the ballpark. We may not have returned all the way back to Ebbets Field, the Polo Grounds or the Baker Bowl, but today’s ballparks certainly don’t look alike. There are enough extreme characteristics in some of today’s parks to have a profound impact on players’ stats and careers.

The impact of parks on pitchers shows up several ways, but the most vivid is in the HRs a pitcher yields. Let’s look at two pitchers who have changed ballparks over the careers—moves which were beneficial to one and detrimental to the other. Aaron Harang began his big league career with Oakland, but then moved to Cincinnati, before he moved back to the west coast with San Diego and now the Dodgers. For right-handed (RHH) and left-handed hitters (LHH), the HR park factor for Cincinnati is 143 and 121, respectively (from Bill James Handbook—the average of the most recent 3 years). The index for Dodger Stadium is slightly above 100, while the other two ballparks Harang called home are well below 100, indicating they are pitcher-friendly, run (and HR) suppressing ballparks. Harang’s HR-rate as a Cincinnati Reds pitcher is 11.1% per flyball. His rate with the other 3 teams—all based in pitchers’ parks—is 7.5%. He clearly benefited by the move to San Diego and then LA. On the flip side we have Mat Latos, who has played for San Diego and Cincinnati. In San Diego, Latos notched a 7.9% HR/FB rate, while it soared to 11.8% in his first year as a Red. He mitigated the problem somewhat by being slightly less of a flyball pitcher in Cincy, but the leap in HRs is still a drag on his effectiveness.

There are four pitchers who standout to me as being mismatched with their home ballpark. Phil Hughes (NYY), Colby Lewis (TEX), Brian Matusz (BAL), and Rick Porcello (DET). Porcello has the reverse problem—and in a sense, it’s a smaller issue. He is an extreme groundball pitcher (approximately 90^th percentile for 2012), but he pitches in a massive pitcher’s park, where flyballs will do far less damage than in a hitters park. So, what’s the problem, since the Tigers still benefit from his high groundball rate? First of all, not with that defense they don’t, but that’s another issue entirely. My point is that Porcello should have greater value pitching elsewhere, with a team that has a ballpark that penalizes flyballs, rather than a ballpark that is forgiving, like Comerica. Flyball pitchers like Colby Lewis and Brian Matusz would be far more effective in Oakland, Seattle, or any of the west coast parks, which tend to be more cavernous and/or where the ball will not carry as far.

Phil Hughes is a fascinating case study. I’ve always believed that Yankee Stadium was one of the worst venues for him to pitch. A right-handed flyball pitcher, pitching in a park that has a LHH HR index of 153—second only to Coors Field. The reason I list the LHH HR factor is because he will face more than 50% LHH. (Incidentally, Yankee Stadium has a RHH HR index of 102.) If you take a close look at peripheral stats such as K-rate, BB-rate, etc., you will see that Phil Hughes and Jered Weaver are very similar. There are two huge differences between the two. Weaver has perfected a change-up, which he uses extensively to LHH, keeping the ball away from them. The second difference is the ballpark. Weaver pitches perfectly to his ballpark, yielding flyball after flyball, many of which would be HRs in Yankee Stadium, which turn into outs in Anaheim. If Jered Weaver were to pitch regularly in Yankee Stadium, he would either need to alter his gameplan, or be relegated to a middle/back-of-the-rotation starter. If Phil Hughes were to pitch in San Diego, Seattle, or another of the west coast pitcher-friendly parks, he would likely be a bona fide number two starter and frequent All Star. Yes, the ballpark can make a big difference.

Last week, former Yankee star Hideki Matsui announced his retirement from baseball. The 38-year old former Japanese star played in 34 games last year with the Tampa Bay Rays, hitting a weak .147, with an anemic .435 OPS. This followed a season each with the Angels and A’s, where he batted a collective .262, with a .756 OPS. Matsui started his career in Japan with the Yomiuri Giants, but after the 2002 season, at the age of 29, decided to sign with the Yankees—a bold move for Japan’s top HR hitter. He wasted no time making his mark on major league baseball and Yankee fans by hitting a grand slam in his first game in pinstripes, at old Yankee Stadium.

Over his seven year Yankee career, he averaged 20 HRs per season, batted .292 and logged an OPS of .852—23% above the league average OPS for those years. What fans will remember most about Matsui was his penchant for the big hit, capped off by his World Series MVP performance in 2009. He came to bat 36 times in the two World Series in which he appeared (2003 and 2009—his first and last years as a Yankee), but managed to hit 4 HRs. He batted .387 in the World Series and put up a remarkable 1.216 OPS. In fact, in 235 postseason plate appearances his OPS was .933.

For those of you who have been following this blog, you know about the work I’ve done in measuring a hitter’s performance against different quality levels of pitching. I’ve racked up the batter—pitcher matchup data (starting pitchers only) from 2009 through 2011 to see how hitters perform against the best pitching vs. the weakest pitching. This study was of particular interest to me because the quality of pitching is one of the most defining characteristics that differentiates the regular season from the postseason. The pitching is far better in the postseason. Nearly two-thirds of the postseason starting pitcher innings are thrown by the top one-third of regular season starting pitchers (as measured by their OPS against). Not surprisingly, Matsui has an uncanny ability to hit top pitching, which helps explain his postseason prowess.

Against the top two quintiles, the MLB average for a left-handed hitter is a .641 OPS. Matsui had 387 plate appearances against this group of pitchers over the 3-year period of my study and banged out a remarkable .830 OPS. Over that time period here’s his record (OPS) against some of the top pitchers—vs. David Price, 1.333; vs. Greinke, 1.267; vs. Josh Beckett, 1.032; vs. King Felix, .838; vs. Verlander, .778, vs. Halladay, .752. Matsui also had his nemesis, as  Jered Weaver held him to a puny .315 OPS in 27 career plate appearances. I take it that Matsui is not fond of the change-up from righthanders—a pitch Weaver is known to use extensively on left-handed hitters.

Another one of Matsui’s defining traits was his ability to handle left-handed pitching. He had very narrow platoon splits. Over his career he hit .831 against right-handers and .802 against lefties. One more thing I’ll remember about Hideki is the time he held a press conference to announce that he had gotten married. So instead of having his wife present at the event, or having a photo of his wife, he pulled one of the all-time great moves—he unveiled a drawing of his bride. Hideki Matsui, one-of-a-kind.

The Arizona Diamondbacks are reportedly interested in dealing an outfielder to overcome a logjam. They’ve signed Cody Ross who will be added to a stacked outfield of Gerardo Parra, Justin Upton and Jason Kubel. They also have a couple of young, talented outfield prospects in the high minors. By shipping out an outfielder, they may be able to land a shortstop, or at least acquire a player that could be of greater value to them in the near term. For potential buyers, one question to be addressed is which is the preferred outfielder—Upton or Kubel? There are many considerations, including whether the team has a bias or need for a left-handed hitter versus a right-handed bat, based on their current lineup and ballpark. Let’s hold that issue off to the side and assume that the club considering a trade for an outfielder is neutral on that righty—lefty issue.

Another important consideration is the market value of the player relative to his salary obligation. Upton is due $38.5 million over the next three seasons, for an average annual value (AAV) of $12.8 million. Kubel is signed for $7.5 million in 2013, along with a team option for the same amount for 2014, with a buyout price of $1 million, should his team decline the option. Pricing in the free agent market is baseball’s version of the stock exchange—perhaps akin to a lightly traded NASDAQ stock. The lack of transactions, at least when compared to the stock market, make the market tougher to read, but the lack of liquidity is an important dynamic that needs to be factored into an assessment of the market for players.

To assess the market value of players, I’ve statistically modeled free agent market transactions—about 1,100 of them over the last decade. I analyze position players separately from pitchers, since the market values different attributes in each. For position players, the most important valuation criteria is the player’s historical win contribution—I use wins above replacement (WAR) from Fangraphs.com. My analysis suggests that players are paid based on a combination of their most recent WAR (in their “walk” year, immediately preceding their free agency) and their best WAR over the last four years. In case you’re asking why does their “best recent WAR” make sense as a driver of a player’s financial value? I didn’t say it “makes sense”, just that it does the best job of explaining historical salaries that players receive in the free agent market. Other factors include the player’s position, as each position on the diamond has a different “value”, with designated hitter being at the low end of the spectrum and shortstop at the high-value end. The player’s reliability, as measured by the variation in his games played over the last several years is another factor that figures into what teams pay. Age also impacts the player’s value, both in terms of his AAV and the length of his contract. Older players tend to get shorter deals, even if their recent performance is the same as a younger player. One additional factor to consider is a player’s defensive ability, as measured by his defensive runs saved.

My assessment of the market value of Justin Upton is approximately $15 million per year for 5 years. Coincidentally, this valuation is similar to the actual contract that brother, BJ Upton was given by the Atlanta Braves in late November. Justin is a better hitter with more consistent power and is three years younger than his brother. However, BJ has greater positional value as a capable (although by no means a standout) center fielder, while Justin is a corner outfielder. On the other hand, as Jason Kubel enters his age-32 season, he prices out at approximately $9 million per year for 3 years.

It’s interesting to see that by my estimates, Justin Upton has a slightly greater differential than Kubel between his salary and his market value, when you look at it on an annual basis. Justin’s annual value of $15 million and relative to his $12.8 million in salary, leaves a $2.2. million spread, while Kubel’s value of $9 million compared to his salary of $7.5 million has a $1.5 million spread. The differences in their performance starts with their defensive abilities, as Upton is clearly the better defender. Offensively, one of the biggest differences between the two players is their strikeout rate. Kubel went down on strikes over 26% of his at bats in 2012, while Upton’s rate was 19%. This 7% differential means that over 600 at-bats, Upton will put the ball in play about 40 additional times, while Kubel goes down on strikes. Upton may also have greater upside due to his age, as he is just entering his prime.

In the end, the player demanded by a Diamondbacks’ trade partner may be determined by the amount of salary space the team has remaining in their budget. Kubel becomes the “value play”, while Upton is the higher risk (based on higher salary and longer term commitment) with a potentially higher reward. If often makes sense to look at the obligated costs of any signing. A team that acquires Kubel could spend as little as $8.5 million for a one-year commitment (which includes a 2013 salary of $7.5 million and a $1 million buyout for 2014), while Upton will cost a full $38.5 million over the next three years. If managing risk is the acquiring team’s goal, then Kubel may be their preferred choice.