Is the Change of Scenery Effect a Real Thing?

Last year, Dan Uggla hit .179 and was worth +0.5 WAR, eventually getting left off the Braves playoff roster. He’ll turn 34 next March. He is due $26 million over the next two years. And this winter, the Braves are going to try to convince another organization that he just needs a fresh start in a new location to salvage his career. Take him out of Atlanta, and maybe the bat speed will come back. Maybe he just needs a change of scenery.

In reality, it is much more likely that any observed change of scenery effect is really just positive regression to the mean, since you only really need new “scenery” when you’re coming off a bad year, leaving nowhere to go but up. Players who change teams in these situations likely underperformed in the prior year, leaving plenty of opportunity for improvement after they arrive in their new city.

Of course, it can go deeper than that as well. Sometimes, when going from one team to another, a pitcher or hitter acquires park dimensions that better fit their game or a clubhouse that might better fit their demeanor. Or maybe they’re a pitcher and they move to a better defensive team. Or they finally get platooned in their new city, allowing them to only play when they have the advantage. There are plenty of reasons why a player could be more effective on one team than another.

So, the change of scenery effect makes sense. It seems like it should be a real thing. However, I wondered how often we actually see players make these kinds of moves and whether we observe actual positive spikes in change of scenery players? The only way to answer that is to turn to the data.

To find out I pulled all player seasons since 1999 (15 seasons from present) with a minimum plate appearance (PA) total of 200 in each season of interest. I converted each player’s total WAR with the old team into WAR that would represent a full season (per 650 PA). I then calculated DeltaWAR — the change in player’s WAR per 650 PA between teams. I took players who had played with their previous team for three years — long enough where a change of scenery analysis would be warranted.

Over the last 15 seasons here are the cases in which the change of scenery was followed by a significant change in plus-value. All mid-season transactions have been excluded. The first few names may surprise you:

Best Changes of Scenery (since 1999): Please note that DeltaWAR is the +/- value that determines ranking.

Name Team Tenure WAR WAR/650 Team Tenure WAR WAR/650 DeltaWAR
Ryan Raburn Tigers 2004 – 2012 2.6 1.0 Indians 2013 – 2013 2.5 5.9 4.9
Luis Valbuena Indians 2009 – 2011 -1.7 -1.5 Cubs 2012 – 2013 3.4 3.2 4.7
Michael Barrett Expos 1999 – 2003 -1.3 -0.4 Cubs 2004 – 2006 7.7 3.6 4.0
J.D. Drew Cardinals 1999 – 2003 16.2 4.8 Braves 2004 – 2004 8.6 8.7 3.8
David Ortiz Twins 1999 – 2002 1.5 0.7 Red Sox 2003 – 2013 38.8 3.9 3.1
Mike Lamb Rangers 2000 – 2003 -1.2 -0.6 Astros 2004 – 2007 5.0 2.3 2.9
John Buck Royals 2004 – 2009 3.7 1.1 Blue Jays 2010 – 2010 2.7 4.0 2.9
Clint Barmes Rockies 2003 – 2010 4.0 1.0 Astros 2011 – 2011 2.9 3.8 2.8
Kelly Johnson Braves 2005 – 2009 7.7 2.6 Diamondbacks 2010 – 2010 5.4 5.2 2.6
Marco Scutaro Athletics 2004 – 2007 2.8 1.1 Blue Jays 2008 – 2009 7.1 3.6 2.6
John Jaso Rays 2008 – 2011 2.4 2.3 Mariners 2012 – 2012 2.6 4.7 2.4
Eli Marrero Cardinals 1999 – 2003 3.1 1.6 Braves 2004 – 2004 1.7 4.0 2.3
Adrian Gonzalez Padres 2006 – 2010 19.9 3.8 Red Sox 2011 – 2011 6.3 5.7 2.0
Carlos Quentin White Sox 2008 – 2011 6.1 2.0 Padres 2012 – 2013 3.9 3.8 1.8
Abraham Nunez Pirates 1999 – 2004 -0.7 -0.3 Cardinals 2005 – 2005 1.1 1.5 1.8
Marlon Anderson Phillies 1999 – 2002 1.8 0.6 Devil Rays 2003 – 2003 2.0 2.4 1.8
Juan Uribe Rockies 2001 – 2003 0.4 0.2 White Sox 2004 – 2008 7.6 2.0 1.8
Ben Davis Padres 1999 – 2001 2.0 1.3 Mariners 2002 – 2003 2.4 3.0 1.6
Josh Reddick Red Sox 2009 – 2011 1.6 2.6 Athletics 2012 – 2013 7.2 4.2 1.6
Derrek Lee Marlins 1999 – 2003 10.4 2.5 Cubs 2004 – 2009 22.3 4.1 1.6
Adam Kennedy Cardinals 1999 – 2008 0.2 0.2 Athletics 2009 – 2009 1.5 1.7 1.5
Mike Napoli Angels 2006 – 2010 11.8 4.3 Rangers 2011 – 2012 7.4 5.7 1.4
Gary Sheffield Dodgers 1999 – 2001 14.7 5.1 Braves 2002 – 2003 12.4 6.4 1.4
Randy Winn Devil Rays 1999 – 2002 5.2 2.0 Mariners 2003 – 2004 7.1 3.4 1.4
Miguel Cabrera Marlins 2003 – 2007 20.2 4.3 Tigers 2008 – 2013 35.1 5.6 1.4

Ryan Raburn is a surprising name, but his move from Detroit to Cleveland has been rather remarkable. Raburn was  worth 2.5 WAR in only 86 games— around 5.25 WAR per 650 PA. In his 575 games with the Tigers, he accumulated a 2.6 WAR, which means he nearly surpassed his career WAR in only 86 games with his new team, and thus he ranks first with 4.88 DeltaWAR.

Cleveland utilized Raburn as a platoon player — something he was not in Detroit. Thus, this change of scenery performance has a lot to do with how his new team maximized his talents in a role that suited him best. Similarly, Luis Valbuena (ranked 2nd) was used as a platoon piece at third last season, where he broke out and had a career year — which goes to show how a change of scenery can lead to a player’s talents being maximized in a new role.

Notice how J.D Drew (ranked 4th) and Eli Marrero (ranked 12th) both left the Cardinals in a trade for the Braves during the 2004 offseason — delivering with them a surplus of 6 DeltaWAR. The caveat for the Braves? They gave away Adam Wainwright and Jason Marquis.

For guys like J.D Drew and David Ortiz (ranked 5th), these moves occurred in their primes. For Ortiz, there was an additional variable at play —Fenway was a match made in heaven and it’s hard to imagine him putting up the same numbers in the old Twin’s Metrodome, so park factors likely played a large role here as well.

And here are the 25 worst change of scenery performances in recent antiquity.

Worst Changes of Scenery (since 1999): Please note that DeltaWAR is the +/- value that determines ranking.

Name Team Tenure WAR WAR/650 Team Tenure WAR WAR/650 DeltaWAR
Andruw Jones Braves 1999 – 2007 54.4 5.8 Dodgers 2008 – 2008 -1.2 -3.3 -9.1
Sammy Sosa Cubs 1999 – 2004 31.4 5.2 Orioles 2005 – 2005 -1.3 -2.0 -7.2
Maicer Izturis Angels 2005 – 2012 12.8 3.0 Blue Jays 2013 – 2013 -2.1 -3.4 -6.4
Tony Clark Tigers 1999 – 2001 5.5 2.7 Red Sox 2002 – 2002 -1.5 -3.3 -5.9
Trot Nixon Red Sox 1999 – 2006 23.0 3.9 Indians 2007 – 2007 -0.9 -1.7 -5.6
Albert Pujols Cardinals 2001 – 2011 83.0 7.3 Angels 2012 – 2013 4.4 2.6 -4.7
Roberto Alomar Indians 1999 – 2001 18.9 5.9 Mets 2002 – 2002 1.4 1.4 -4.6
Roger Cedeno Mets 1999 – 2003 0.9 0.4 Cardinals 2004 – 2005 -1.7 -3.9 -4.3
Chone Figgins Angels 2002 – 2009 21.9 3.5 Mariners 2010 – 2012 -1.1 -0.6 -4.1
Travis Hafner Indians 2003 – 2012 21.3 3.1 Yankees 2013 – 2013 -0.4 -0.9 -4.0
Edgardo Alfonzo Mets 1999 – 2002 18.7 5.0 Giants 2003 – 2005 2.4 1.0 -4.0
Mark McLemore Mariners 2000 – 2003 6.7 2.4 Athletics 2004 – 2004 -0.7 -1.5 -3.9
Marcus Giles Braves 2001 – 2006 17.9 4.1 Padres 2007 – 2007 0.1 0.1 -3.9
Rafael Palmeiro Rangers 1999 – 2003 19.7 3.8 Orioles 2004 – 2005 0.2 0.1 -3.7
Scott Spiezio Angels 2000 – 2003 6.7 2.2 Mariners 2004 – 2005 -1.0 -1.4 -3.6
Luis Gonzalez Diamondbacks 1999 – 2006 33.7 4.2 Dodgers 2007 – 2007 0.6 0.7 -3.4
Jason Giambi Athletics 1999 – 2001 22.1 7.1 Yankees 2002 – 2008 20.9 3.7 -3.4
Garret Anderson Angels 1999 – 2008 19.0 2.0 Braves 2009 – 2009 -1.0 -1.2 -3.2
Chuck Knoblauch Yankees 1999 – 2001 4.3 1.6 Royals 2002 – 2002 -0.8 -1.6 -3.1
Josh Hamilton Rangers 2008 – 2012 21.8 5.0 Angels 2013 – 2013 1.9 1.9 -3.1
Pat Burrell Phillies 2000 – 2008 16.4 2.0 Rays 2009 – 2009 -0.8 -1.1 -3.1
Ramon Martinez Giants 1999 – 2002 3.3 2.1 Cubs 2003 – 2004 -0.7 -0.7 -2.8
Mark Teahen Royals 2005 – 2009 3.5 0.8 White Sox 2010 – 2010 -0.8 -2.0 -2.8
Melvin Mora Orioles 2001 – 2009 26.4 3.3 Rockies 2010 – 2010 0.3 0.6 -2.8
Melky Cabrera Yankees 2005 – 2009 3.0 0.9 Braves 2010 – 2010 -1.4 -1.8 -2.7

Andruw Jones takes the crown and it isn’t even close! This one is forever burned into the minds of the Dodger faithful—and rightfully so at a -9.11 DeltaWAR — as Jones to the Dodgers ranks as the worst transition from one team to the other in the last decade plus. With the Braves, Jones averaged 5.83 WAR/650 compared to -3.28 WAR/650 with the Dodgers. Now, that is a large drop off in performance for a guy who once seemed destined to be a Hall of Famer.

Remember, if a player comes to your team with a certain reputation for producing — the price to acquire that player will be larger than to acquire a player with no reputation. It’s a simple theory. If a player has some level of performance with his prior team and your team is paying to sign this player as a free agent, that player is more likely to regress from his previous level of talent to something of lesser value. The correlation between a player’s amassed WAR with his previous team compared to their DeltaWAR with their new team is -0.50 — meaning the larger their previous sum of WAR the smaller their change in performance will be.  Once again this illustrates the point of the greater reward in signing a Raburn than going out and signing an Andruw Jones or Albert Pujols. Simply signing players with potential to grow obviously has less risk and a much higher reward — meaning more bang for your buck. Obviously it’s harder to find a sleeper in a free agent class than it is to identify a big name like a Pujols or Hamilton.

For that reason, free agency is a “what have you done for me lately” kind of game. As Derek Jeter walks into the Yankees office and receives a one year 12.5 million dollar deal, he is not receiving that money to play as a 12.5 million dollar player. He is receiving that money as a sign of respecting what he has done in the past. Little of what is given to players is done on a predictive assessment of their value — rather descriptive in hindsight. Just take a gander in the WAR totals prior to leaving in “Worst 25 List” compared to the “Best 25 List” list — there are a lot of players who were highly successful then fell off a cliff with their new teams:

Here is a histogram of the DeltaWAR of player moves from team 1 (6 seasons or more)  to team 2:

6 years with previous team, no WAR cutoff: 

DeltaWARDeltaWAR.jpg-page-001

Here 80% of the moves resulted in a DeltaWAR below zero — while a three year threshold with the prior team yielded 65% of the DeltaWAR’s being under 0. This is not a normal distribution and is skewed left from the center —which sits at just over -1 DeltaWAR. However, what happens to the distribution when we look at only players with a certain amount of amassed WAR prior to the move, meaning what does a distribution of smaller, less established players look like:

Amassed 5 WAR with  previous team and minimum of three years prior to the move:

DeltaWAR - 5-page-001

Now here we have around 55% of the DeltaWAR over 0 — with the distribution’s mean and median above 0. As you can see if a player had less than 5 WAR prior to the move they are more more likely to give their team a positive return in former talent level — in hindsight.

Now, lastly, let’s look at what happens when a team signs/acquires a bigger name over the offseason:

Three year previous team threshold and amassed 15 WAR minimum:

DeltaWAR -15-page-001

Here the distribution is widely skewed left towards the negative. What does this tell us? That signing more established players rarely means that a change of scenery yields improved production — in fact, you can imply the opposite.

Now, I am not saying if you sign Omar Infante over Robinson Cano this offseason that Infante has a greater probability to give you a surplus value. I am saying, given what we know about the transitions in the past, there is less risk in signing a player of lesser “talent” because any fluctuation in his play will be worth more in proportion to the value of his contract.  If they break out, then your reap the benefits. If they play terribly — below their established talent level for their career — then you don’t play them and you eat the remaining cash. It’s not the popular way to play the market but its a way many small market teams find success.

Some teams have optimized their payroll with the same mindset above:

Here are the teams that have performed best in DeltaWAR  since 1999

No. Team DeltaWAR
1 Astros 5.7
2 Cubs 4.4
3 Reds 2.6
4 Tigers 0.8
5 Pirates 0.7
6 Rays 0.2
7 Rangers 0.1
8 Braves -0.8
9 Brewers -1.1
10 Giants -1.8
11 Twins -1.8
12 Indians -2.3
13 Diamondbacks -3.4
14 Marlins -3.5
15 Royals -3.8
16 Red Sox -4.0
17 Nationals -5.3
18 Rockies -5.3
19 Cardinals -5.4
20 Blue Jays -5.8
21 White Sox -5.9
22 Athletics -6.2
23 Phillies -6.5
24 Mariners -6.6
25 Padres -8.7
26 Mets -9.2
27 Angels -11.0
28 Yankees -11.7
29 Orioles -14.8
30 Dodgers -16.5

The Astros — who have historically had a low payroll — top the list, furthering my theory earlier that smaller market teams are likely to be more successful in free agency. By taking lower risk players rather than taking established players who have a predetermined level of talent — one that is more likely to be overvalued — they have a lot less to lose and a whole lot more to gain.  Thus, it’s not surprising to see some smaller market teams — or those who have been rebuilding for the past decade plus — near the top. Please not that the Cardinals, widely regarded by most to be the best run team in baseball, are near the middle of the list. This is due to the fact that they do a great job of building through the draft and cultivating their farm system.

Meanwhile, Dodger fans should close their eyes — same with Angel fans. A triplet of Jones, Pujols, and Hamilton signings have done their damage. With the Dodgers and Angels, the Yankees come in near to last place. Despite the Yankees being the “gods” of free agency or snagging the biggest names through trade, it seems that the players they haul in, as a group, generally underperform, since they are more often buying high than buying low.

Time will tell for 2014’s free agent pool. Will the big names win out or will teams turn to cheaper lower risk options? The Red Sox won the 2012 off-season by pursuing value free agents, but if everyone pursues the same strategy, maybe those free agents won’t be values anymore.




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Max Weinstein is a baseball analyst. He has written for Fangraphs, The Hardball Times, and Beyond the Box Score. Connect with him on Twitter @MaxWeinstein21 or email him here.


26 Responses to “Is the Change of Scenery Effect a Real Thing?”

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  1. TK says:

    A couple of problems with this:

    You talk about bounce back season, which is year over year, but then take the average of the player’s career with their previous team to compare. This vastly skews your numbers, especially on a guy like Pronk. Also, you yourself talk about the effect of platoons, but do nothing to account for this confound and instead encourage it by averaging each player’s WAR over 650PA. This again greatly skews results, Rayburn a perfect example goes from +2.5 to an artificially inflated +5.9 on the season (comparable to Jacoby Ellsbury). Instead had you taken only the previous year and not added nonexistent PAs he would have still netted a 4.0 WAR upswing (-1.5 in’12 to +2.5 in ’13).

    So great idea for an article, TERRIBLE execution.

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    • TK says:

      I meant “change of scenery” not bounce-back; but my point remains the same that you have artificially inflated certain numbers.

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    • Pirates Hurdles says:

      I have to agree, the phenomenon is a guy having a bad year one place and then having a better year with a change of scenery. If you want to study that you need to look at players with only bad years in year one, look at what the change does and then compare that to normal ascension to the mean that would be expected if they had stayed put.

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    • Bip says:

      But inflating the WAR for a guy like Raburn proves the author’s point.

      The point of using WAR/650 isn’t to say that Raburn was worth 5.9 WAR with the Indians, it is just a way of turning WAR into a rate stat. When speaking of “change of scenery”, we want to know how much better or worse the player was when he played. How much he played is circumstantial.

      Noting that he was used differently by the two teams is legitimate. I don’t see it as a problem though. Part of the point of the article is that players on the lower end of the scale generally have greater upside, and are more likely to provider equal or better performance for a new team. Part of the reason for this is that these players are often flawed, meaning a team who know how to use him correctly can maximize his value for minimal cost. The Indians took a flawed player in Raburn and maximized their value from him by using him in a platoon.

      A guy like Cano does not have this option, because a team who pays him is going to pay him to start, so the main variable at play is the rate at which Cano’s performance will decline with age.

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      • Bip says:

        *When I say they have a greater upside, I mean upside relative to how much they are being paid, or potential to exceed the value of their contract.

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        • Thank you Bip for seeing the reasoning behind my methodology. I think the point was lost in the arguments over what constitutes a change of scenery candidate.

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      • TK says:

        I am not arguing that the author is saying Rayburn is comparable to Ellsbury; I know that is not what he means. But that was an extreme example, just from his own sample size it artificially inflates Rayburns “jump” much more than a guy like Michael Barrett. Because you put Rayburn only in favorible situations then extrapolate those numbers over a full season you create an illusion. Whereas a guy like Barrett who faced both favorible and unfavorible matchups dos not have such a luxury. Max, I understand why you wanted a universal PA but it is specious reasoning. There is a reason why many players don’t reasch 650 PA. Sure is you want to assume that a guy who get 550 PA in all situations but just had a few days off will have the same metrics over 650, I get it. But to assume a platoon player will continue with the same numbers in sitations his own manager is actively avoiding using him in is just erroneous. By extrapolating WAR over 650 PA you are making some very flawed assumptions. It is not what defines change of scenery that I take issue with. Instead, I see the reasoning for your methodology, but it was unneeded instead just go straight WAR. I mean isn’t that why it exists to give the most realistic portrayal of comparitive value possible?

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        • Bip says:

          Because you put Rayburn only in favorible situations

          Exactly. Raburn was a change of scene candidate because he was used by his manager in unfavorable situations, and his new team used him properly, which improved his performance as a rate stat.

          Obviously a guy who plays at a 6 WAR/650 rate for half a year is less valuable than one who plays at a 6 WAR/650 rate for a whole year, and Raburn obviously could not be the latter, but there is still value in having a guy that plays like an all star some of the time.

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  2. Trent Phloog says:

    An interesting idea, but as TK noted your methodology is at odds with your objective. Guys like Hamilton and Pujols aren’t “change of scenery” candidates because they weren’t struggling in the year prior to moving — they are just your classic overpaid, declining veterans.

    You should probably limit the data pool to guys who experienced (say) a 75% drop in WAR value from the previous year or two. These are the “worn out their welcome” guys who might need that “change of scenery.”

    Further, to control for (positive) regression to the mean, you could compare that group to all the players who experienced an equivalent decline and then DIDN’T change teams. Ryan Howard springs to mind: 4.4 WAR 2009; 1.0 WAR 2010. No scene change. Still stinks. (Yes, Phillies fan here.)

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    • Bip says:

      If you limit your sample to guys who experienced a decline before changing scenery, you will be introducing a huge selection bias for players who experienced bad luck and are candidates for regression.

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      • Trent Phloog says:

        And yet, that’s what people mean when they say a player could benefit from a “change of scenery.” If you set out to study whether that’s a real thing, there’s kind of no way around that…

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        • Bip says:

          No, if a player plays better because his luck returned to normal, that is independent of whether he changed teams, suggesting the “change of scenery” effect is an illusion caused by regression, and the way teams react to a player’s unlucky streak.

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      • TKDC says:

        And you can just compare them to guys who don’t get the “change in scenery.” That would seem to get more at whether the “change of scenery” is a real thing. This analysis does not include a control group.

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  3. vivalajeter says:

    I’m not sure Roger Cedeno belongs on here. According to the data, he was on the Mets from 1999-2003, then he was traded to the Cardinals and stunk. The reality is that he had a decent season for the Mets in 1999, spent the next two years stinking on other teams (he was involved in the Mike Hampton trade), then signed back with the Mets for the 2002/2003 seasons, where he stunk. So his only decent year was 1999, and he was mainly a negative-WAR player after that.

    On a related note, I was surprised to see that he was only worth 1.5 WAR in 1999. I know his defense stunk, but he batted roughly 310/400/400 with upwards of 70 stolen bases. Offhand, I would have thought that he’d have put up a lot more value than that, but I guess his lack of power and lack of defense took away more than I expected.

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  4. Anon says:

    There are plenty of reasons why a player could be more effective on one team than another.

    The first one that comes to my mind isn’t even mentioned in the article. Different coaches. Maybe they teach differently. Maybe they fix a flaw that previous coaches missed.

    One example about the affect coaching can have happened when Dave McKay was hired as a coach by the Cubs in 2012. http://www.bleachernation.com/2012/09/24/alfonso-soriano-received-outfield-coaching-for-the-first-time-this-season/

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    • TK says:

      That is a great point, especially if we add in pitchers; e.g. Fernando Rodney. Now that is a change of scenery effect that actually matters.

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  5. grant says:

    How did Melky not make it for 2013? Even without the PA adjustment he had a delta WAR of -5.4 in 2013. Did I miss something?

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    • vivalajeter says:

      “I took players who had played with their previous team for three years — long enough where a change of scenery analysis would be warranted.”

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      • TKDC says:

        I’m not sure I get this? Why do you need three years? Anyone ever spent a year at a job they hated? Isn’t that enough?

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  6. Bip says:

    James Loney has a change of scenery bonus of 1.77. He must have been excluded because he was briefly with the Red Sox in 2012 before having his resurgence with the Rays in 2013.

    The Dodgers have a pretty bad rating according to how much WAR their players lose after joining the team. I’d be afraid to see how much worse it would be if you also counted against them the players who play better after leaving.

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  7. Jon L. says:

    This is interesting data, but the leaderboards are somewhat skewed by the decision to average out a player’s performance over all his seasons. Some players were only with team #2 for one or two seasons, and obviously this introduces a lot more random variability. On the opposite end, team #1 performance is averaged over an entire career arc. To pick one example, Adrian Gonzales was an established superstar by the time he joined the Red Sox, but his numbers with the Padres include pre-peak seasons. If the minimums were three years with team #1 and one year with team #2, it might be interesting to look at deltas for three-year averages versus one year for everyone.

    Also stumbled a bit over “recent antiquity” and “imply the opposite,” but there’s still plenty of thought-provoking fodder for off-season watercooler conversation.

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  8. TKDC says:

    As others are noted, the rigid application of the methodology means you are including many, many players who are not really what anyone thinks of as “change of scenery” guys and not including many who are. I know this is tough to set up with an objective methodology. One way you could look at this would be almost the opposite of what you did. Only look at single seasons. Look at guys with at least 300 PAs in a season up to some point, like the ASB, with some terrible level of your favorite offensive metric, like wOBA below .280. Look at how the players did for the remainder of the year. Compare those who were traded to those who weren’t. For the comparison, you might want to normalize compared to the wOBA the player had the past few seasons as guys with a longer track record are both more likely to regress up and also more likely to be given the opportunity to do so on the same team, but what you get from this is a measure in a single year, which eliminates the problem of a guy just being a much worse player, and real “change of scenery” players instead of just change of scenery players.

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    • One’s idea of a “change of scenery” player is highly objective. Mine was looking at players who were established with their team who decided to leave — whether good or bad. Now my methodology, as noted, is completely flawed if I wanted to look solely at players who were “change of scenery candidates” — which was not my intention. My methodology was to look at pretty much all players who moved after being with one team for a while, during the offseason, to a new team — and how they fared. Other than perhaps averaging their WAR in previous season to determine talent level, is there anything that takes away from my conclusion? Established players do not fare well when switching teams — the awful majority lose value. Less established players have a much greater chance of finding a breakout season and giving more value to a smaller contract. That is the take away from the article — argument of what constitutes a “change of scenery” player aside.

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      • TK says:

        If that is all that you are trying to establish then the entire article was unnecessary. You are not arguing a change of scenery effect, instead you are arguing that teams keep good players and let players that are on the downslide of their career go. Especially when you take the average leading up to their departure, so you inflate their downturn with their previous team with when they were good. For example Travis HAfner had a WAR of .5 the year before he went to the Yankees and posted -.4 in only 30 more PA. However the way you chose to do your analysis makes it seem he went from borderline All-Star to scrub, when he really went from scrub to slightly less valuable scrub.

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        • Now it is completely unnecessary to call the article “unnecessary”, when you are clearly missing the point. For one, I never argued that teams should “keep their good players” — in fact the data tells us the opposite, which is in the text. I understand your qualms with averaging the previous years up, but how reliable is a one year sample size the year before a player hits the market — that has to have more variability in regards to the players talent level than the previous years in context.

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  9. EricR says:

    I saw afterward it was a 3 year minimum with one team, but I initially expected Pat Burrell to show up as one of the worst when moving to the Rays, and one of the best when moving to the Giants. Tampa really did not seem to suit him.

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