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.

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.

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.

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.

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.

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.

A couple of things to recognize about Hamilton. First, he played in one of the most hitter-friendly ballparks for lefty hitters. The Ballpark at Arlington ranked 3^rd in terms of the park factor for left-handed hitter home runs, while Angels Stadium in Anaheim ranks 23^rd. Considering the 2013 schedules for both the Rangers and the Angels and the parks in which they play, Hamilton will play to a park factor that suggests about 20% less homeruns, than if he remained a Ranger. Another issue, which is perhaps more concerning, is Josh Hamilton’s track record against top-flight pitching. In a recent study, I segmented all starting pitchers into different levels of “quality”, based on the OPS they yielded over a season. I defined “top” pitching as the top 1/3 and “bottom” pitching as the bottom 1/3 of starting pitchers. (I controlled for the lefty-righty factor and I rated pitchers for each season in the study—2009, 2010, and 2011.) The average left-handed hitter has a spread of 182 OPS points in his stats against “top” vs. “bottom” pitching. In other words, a .732 OPS guy (MLB-wide average against starting pitchers) is expected to hit .641 against top pitching and .823 against the bottom third of starters. Josh Hamilton’s spread is far more dramatic. Instead of a spread of 182 points, his is 433 points (see chart below, from MLB Network’s Clubhouse Confidential).

Hamilton hit .721 against top pitchers, while banging out an OPS of 1.154 against the weakest pitchers. He performed only 12% above the MLB average against top pitchers, but a bone-crushing 40% above league average against the weakest third of the rotation. This suggests that Hamilton, who was a .909 OPS guy over these three seasons, feasts on weak pitching, but is neutralized by top pitching. Incidentally, this is a consistent pattern over the three years in our sample. Even in Hamilton’s MVP season of 2010, when he batted .359 with a 1.014 OPS, he exhibited the same pattern of hitting. In his MVP year he hit under .800 vs. top pitchers and over 1.200 against weak pitchers for another 400+ point spread. This has serious implications for the postseason, since the mix of pitching in the postseason closely resembles what we call the “top” pitchers. These pitchers represent one-third of regular season innings, but over 60% of postseason innings. Hitters who do not fare well against top pitchers are not as likely to get it done in October. Given the quality of the Angels’ roster, we may have a chance to see how this movie ends over the next several Octobers.

The net result—the 2013 Astros will be playing against tougher competition in the AL West (and against the AL in general), and they should be a somewhat inferior team to the one that won 55 games in 2012. Combine that likelihood with the unbalanced schedule against two playoff teams—Texas and Oakland—and a playoff contender, the Angels, and the prospects for the 2013 Astros is bearish. If they 110 to 115 games, they will sprinkle their losses around the AL Central and AL East, but they will deliver them in bulk to the AL West. They will play 72 games against the A’s, Rangers, Angels and Mariners—18 against each. The net result could be 4 or 6 additional wins each for the four AL West teams, based on the difference between playing the Astros versus a broad mix of AL teams. These additional wins could deliver three teams from the AL West into the playoffs.

Houston’s transition to the AL has serious implications for the AL East. With the win totals for the top three AL West teams likely to rise, the AL East may be sending only the division winner to the postseason. Historically, two AL East teams would be among the four AL clubs in the playoffs. With last year’s expanded wild card, AL East teams had visions of three clubs making the postseason. That seems far less likely today because Houston will give more gifts to the AL West. The offseason has a long way to go, but by April, we may be picking the A’s, Angels and Rangers as playoff favorites, while cautioning the AL East that winning the division may be the only path to October.

I’m advocating trading Alex Rodriguez because it makes financial sense for the Yankees and gives them the flexibility they need to maintain a championship caliber team over the next several years. Aside from the debate around the expectations of A-Rod’s future performance, he has five years and $114 million in salary remaining on his contract. In addition, he is entitled to a potential $30 million in milestone bonuses for achieving various historic home run totals, beginning with his 660^th HR (he currently sits at 647). While this is what A-Rod is owed, it does not fully reflect the true cost to the Yankees. Let’s acknowledge that the Yankees will likely be in a 40% or even 50% luxury tax bracket for the life of Rodriguez’s contract. This means they may end up paying out over $200 million for the remaining 5 years of his deal, including the marketing incentives for achieving his milestone HRs. The Yankees seem to have an aspiration to duck under the luxury tax threshold for at least one year—and for good reason. Doing so would put significant dollars in their pockets. Not only would they save money by not paying the maximum luxury tax rate, they would also reset their future tax rate to a modest 17½% , should they exceed the luxury tax threshold in a subsequent year. In addition, if a team is below the luxury tax threshold, they are entitled to additional shared revenues from MLB. Shedding A-Rod’s salary and marketing incentive obligations could be the key to allowing the Yankees to get below the luxury tax threshold in 2014, when it escalates to $189 million.

There are two ways the Yankees could attempt to deal A-Rod. One approach is to eat a modest amount of his future compensation. Perhaps if the Yankees pay $50 million—the equivalent of $10 million per year—of his remaining $114 million in salary, they might generate interest from a few MLB teams. This would allow them to ship A-Rod to another club with a $13 million per year salary obligation, plus the possibility of another $30 million in incentives for historic HRs. Under this scenario, New York would likely not command much in return, in the form of players or prospects, but they would reduce their $27.5 million (plus any incentives they payout) annual charge against their luxury tax threshold to $10 million per year. This gives them a fighting chance to get underneath the $189 million annual payroll, or at least redeploy the payroll dollars more efficiently towards more productive assets.

The second approach is more aggressive—a scenario where the Yankees eat more of A-Rod’s compensation, say $15 million per year, but position themselves to acquire a highly ranked prospect in return. If they leave their trade partner with a more manageable $8 million per year salary obligation, plus the potential incentives, they should also be able to acquire a top prospect or a young, high ceiling player with less than a year or two of service time. This could further aid their attempt to get under the luxury tax threshold—adding an inexpensive, but productive player. Regardless of the Yankees trade strategy, A-Rod would need to approve any deal, which will not be easy to accomplish. Rodriguez would perceive any exit from New York as a personal embarrassment, which is an important consideration for someone who seems highly concerned about other people’s perceptions. The Yankees would need to convince A-Rod that life will be even more miserable if he stayed, than if he left. The Yankees’ treatment of A-Rod during the playoffs helps their cause in this regard. They certainly showed they are capable of embarrassing him. The right trade partner may help sway A-Rod. Perhaps a small, home run-oriented ballpark that increases the likelihood that Alex reaches his HR milestones and hence, baseball immortality, will raise the appeal a trade scenario.

Bottom line—this is more about an arbitrage opportunity on the luxury tax threshold, than it is about Alex’s talent level. A-Rod is an asset that might cost the Yankees $200 million over the next 5 years—a staggering $40 million per year—plus the opportunity cost of sharing in additional revenues and lowering their luxury tax rate in future years. The same A-Rod would cost another team between $40 million and $70 million less—the equivalent of the Yankee luxury tax payments. This equation means the Yankees should be willing to pay a chunk of dollars to subsidize A-Rod’s cost to another team. There seems to be plenty of room to create a win-win scenario for the Yankees and a trade partner. It’s making the equation work for Alex that’s the tough part, but the Yankees have done a nice job of laying the groundwork.

On average, hitters follow a pattern—they perform at their average level versus “average” pitching, better against “weak” pitching, and worse against “top” pitching. Using OPS as a calibration point, hitters hit about 80 to 100 points lower against the top one-third of starting pitchers and about 80 to 100 points higher against the bottom 33%, on average.  Not everyone follows the same pattern. Some are particularly effective against top pitchers and hit only marginally better against weak pitching. Others have the opposite profile—they exploit weak pitching, while being stifled by top pitching. Alex Rodriguez profiles in the latter group. Because postseason pitching tends to be comprised of more top tier pitchers, (see my previous post) we can expect players like A-Rod to produce at a lower level during the postseason.

Let’s start by looking at Alex Rodriguez’s regular season hitting versus different quality levels of pitching. I created an index of a player’s OPS relative to the MLB average, against top pitching and weak pitching. If a player indexes above 100 he performs relatively better against top pitching; if he indexes below 100, he performs relatively worse against top pitching. A-Rod indexes at 92, while Derek Jeter indexes at 114. Mark Teixeira, who has also had his postseason struggles indexes at 94, while Robinson Cano comes in at 109. For perspective, one of the highest indexes for any player currently in the postseason is Carlos Beltran, who scores a 121 on this measure. Is it a coincidence that Carlos Beltran crushes high quality pitching—in 115 postseason plate appearances he has a 1.297 OPS? Several other marquee players currently in the postseason are listed below:

Let’s compare Jeter and A-Rod’s actual postseason performance over their career. We can’t simply look at all regular season stats vs. postseason stats, since a player may have reached the postseason in his best or worst hitting seasons. Instead, I weighted the player’s regular season OPS based on the number of plate appearances in each year they reached the playoffs. This gives us more of an apples-to-apples comparison. For his career (prior to this postseason), A-Rod have a blended average .945 OPS for the regular season and an .884 OPS in the postseason—a downgrade of 61 points. Conversely, Jeter’s regular season numbers are .830, with a postseason OPS of .839. Here’s an instance where the actual performance, over a 15-year career, supports the analysis of who succeeds in the playoffs.

Over the course of a postseason a player may have a hot or cold streak, so the small sample size means this framework may not translate in the short run. A-Rod proved that with his 2009 postseason as he carried the Yankees to a World Championship. Nonetheless, the approach of determining whether or not a hitter crushes (or flounders) against top pitching may provide a window into their postseason performances.