Archive for Daily Graphings

Yesterday, Major League Baseball officially announced their new agreement with Nippon Professional Baseball regarding the transfer of players from Japan to the U.S. The most prominent change in the rules is the $20 million maximum fee that NPB teams will receive, and this is the aspect of the rules that has drawn the most attention. However, there is another significant change to the rules that is worth looking at, especially because of its potential long term ramifications on domestic players already under contract to MLB teams.

Under the old posting system, the move of a player from Japan to MLB was somewhat akin to a trade, only with MLB teams using cash instead of players as the currency to acquire the player’s rights. The system wasn’t that different from what we see in minor trades between MLB teams, where a player’s rights are transferred from one club to another in exchange for a small cash payment. In the last year, players such as Chris Nelson, Mike Carp, Casper Wells, and Travis Blackley have all been traded for cash, as their rights were sold from one team to another in exchange for monetary compensation. The posting system was basically just this kind of trade, only magnified, because the players getting posted were usually quite good.

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Ordinarily I don’t presume to know your business, but something you probably didn’t miss was Robinson Cano leaving the New York Yankees to sign a 10-year contract with the Seattle Mariners. Something you more probably did miss was the following comment, left below a FanGraphs post on the subject:

I would love to see a full article on how teams do the year after losing a superstar. History could teach us a few lessons, I’m sure.

Within the post, I included a few examples off the top of my head, but it was hardly anything rigorous. The idea seemed worthy of something more rigorous, so, here it is.

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The easiest part of the three-way Mark Trumbo trade to forget was the White Sox’s part. Given that we all call it the Mark Trumbo trade, of course a lot of interest followed Trumbo to the Diamondbacks. And given that it was the Angels giving Trumbo up, people have wondered about the return. But the White Sox were in there as a necessary component, and they arguably got the best of it, turning Hector Santiago into one-time quality Arizona prospect Adam Eaton. Eaton, now, is considered a potential part of the long-term White Sox core.

Monday, the White Sox and Diamondbacks struck again, and this time there was no third party. Being a team in little present need of a closer, the Sox gave up Addison Reed. Being a team in little present need of an extra third baseman, the DBacks gave up Matt Davidson. The focus for Arizona, again, is getting better right away. And the focus for Chicago, again, is adding another potential part of the long-term White Sox core. While it’s a trade I wouldn’t want to call lopsided, I like it more for Rick Hahn than I do for Kevin Towers.

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The Cardinals have reportedly signed Mark Ellis to a one-year deal. Ellis will turn 37 next season, but played well enough the last couple of years with the Dodgers that he was sure to find a job. The question is whether the Cardinals really needed him given the presence of Kolten Wong. The answer has to do with the Cardinals’ position as a contender and their concern with depth.

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Over the past three seasons, the modus operandi for the Tampa Bay Rays has been to find a one-year solution at first base in the clearance bin of the offseason market. In 2011, that came in the form of adding Casey Kotchman on a minor league deal and watching him produce a 2.4 win season. In 2012, the team upped the budget and spent $7.25M to bring back Carlos Pena a year after he left via free agency, but Pena struggled through a 0.7 win season. Last season, James Loney was brought in on a $2M deal, and turned a profit with a career-best 2.7 win season.

The first base situation has been as much as a revolving door as the closer role has been with the club. Until Fernando Rodney repeated as the team saves leader last season, the team had had a different pitcher leads the team in saves each year under Maddon. While they have had repeated success with the closer role, the situation at first base has been a bit different.  As Joe Maddon often says about these types of situations, the Rays meatloafed the first base situation.

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In a sense, the Yankees are back to being the Yankees. They offered a ton of money to sign Brian McCann. They offered a ton of money to sign Jacoby Ellsbury. There’s still talk they might offer a ton of money to try to sign Masahiro Tanaka. Yet the Yankees are still a team in need of an actual regular second baseman. They’ve reached this point because they were outbid on their own Robinson Cano by the Mariners, and now they’ve been outbid on Omar Infante by the Royals, who have a shiny new second baseman at a four-year commitment worth a little over $30 million.

You could say that there’s a revenge angle, since earlier in the offseason the Yankees signed Carlos Beltran with the Royals hot in pursuit. But this is less about vengeance and more about plugging an immediate hole, with a perfectly adequate player. Used to be I thought it was all but a given that the Royals would trade Billy Butler for Nick Franklin. Now they need their own DH, and they have their own second baseman. It’s a step forward for a team that would really really like to experience this year’s playoffs.

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The thing we know is that David Price is going to be traded. That much is a virtual certainty, for all of the reasons you already understand. The things we don’t know are all of the details. We don’t know where he’s going to be traded to, and we don’t know what he’s going to bring back. We don’t know when the trade is going to happen, and we don’t know if there’ll even be a trade this offseason. Would the Rays be daring enough to move Price during a competitive regular season? Would they be daring enough to wait to move Price until the next winter? Who feels a greater sense of urgency — the team with Price, or the teams that would like to have him? Behind the scenes, there’s probably a lot of activity, but from the outside it feels like nothing has budged for a matter of weeks.

Oh, and there’s another thing we know. David Price has been outstanding. Truly outstanding, since he graduated into the majors. He’s posted four consecutive seasons of at least 180 innings and at least an average ERA, and the reality is that he’s mostly exceeded those marks with relative ease. Over the four seasons, he’s averaged 208 innings and a 78 ERA-. Price has been one of the game’s great workhorses, and that’s a big part of how the Rays are selling him. You get Price, and you can write his next season’s numbers in ink.

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The federal judge overseeing the antitrust lawsuit by the City of San Jose against Major League Baseball held a case management conference Friday morning. But the hearing dealt with weightier issues as compared to the usual case management conference. Most particularly, the court is deciding whether to dismiss the two remaining state law claims for interference with contract and allow those claims to be re-filed in state court. If so, the court would enter final judgment in the federal case, and San Jose would have the right to immediately appeal to the Ninth Circuit Court of Appeals.

The hearing came two months after Judge Ronald M. Whyte issued an order that dismissed San Jose’s antitrust claims based on the court-created antitrust exemption for MLB. In the same order, the court held that San Jose had adequately pled two claims for interference with contract, on the theory that MLB’s delay in making a decision on the A’s proposal to move to San Jose had interfered with the A’s option agreement with San Jose to purchase five acres of land in downtown San Jose on which to build a new ballpark. My previous post on the Court’s ruling is here.

Judge Whyte began Friday’s hearing by stating that he was tentatively inclined to dismiss the state court claims. He then heard arguments by attorneys for the parties: John Keker for MLB and Philip Gregory for San Jose.

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For years it’s been assumed hitters will get to the major leagues and peak offensively around age 30. Teams and fans can hope the new, shiny, 20-home-run-hitting rookie will improve over time and someday will hit 30 to 40 home runs. Hitters were expected to improve until their late twenties and then begin to decline. But recent data show there’s no longer a hitting-peak age. Instead, hitters arrive at their peak and simply decline with age.

I pretty much stumbled on this finding a few days ago. I created an stolen base aging curve for Mike Podhorzer and then created one for home runs. I separated the data into pre- and post-PED ban eras, the latter of which happened between the 2005 and 2006 seasons. It didn’t surprise me to see a slow decline in the home run curve during the PED era. My biggest surprise was the post-PED data where home runs no longer peaked, they only declined. I examined just about every overall offensive stat (OPS and wOBA, to name a couple) and found the same thing: Hitters no longer peaked, they only declined. Here’s a look at the wOBA aging curve from pre- and post-PED ban eras, along with a note on how the curves were created.

Note: The aging curve was created by the delta method by weighting plate appearances using their harmonic means. With this method, there’s a small survivor bias summarized by Mitchel Lichtman at the Hardball Times:

… survivor bias, an inherent defect in the delta method, which is that the pool of players who see the light of day at the end of a season (and live to play another day the following year) tend to have gotten lucky in Year 1 and will see a “false” drop in Year 2 even if their true talent were to remain the same. This survivor bias will tend to push down the overall peak age and magnify the decrease in performance (or mitigate the increase) at all age intervals.

For 20 seasons, hitter production began to decline significantly around age 30. Over the past seven seasons, the decline has occurred immediately.

A problem exist when using wOBA in the recent lower scoring environment. The league wOBA in 2006 was .337, and in 2013 it was at .318. That’s a drop of 19 points in seven seasons, or 2.7 points  per season. Players will have the appearance of aging from season to season.

Hitting (wOBA) has been on the decline for several reasons. Teams have been better at evaluating players’ defense abilities and deploying better defensive alignments in the field. Also, the quality and quantity of hard-throwing relief pitchers has increased across the league. Finally, 2006 was the first full season with the harsher PED punishments (from 50-game suspensions to 100-games suspensions t0 lifetime bans). This overall decline leads to a large year-to-year aging factor. The recent decline in offense led me to create aging curves with wRC+, which is weighted to the season’s, the league’s and the park’s run-scoring environment. I ran the aging curve to look at four, seven-year time frames.

With wRC+, the most recent aging curve doesn’t immediately begin declining like the wOBA curve. Instead, it remains constant until it begins to decline. The decline starts at the same point when previous players began declining (between age 25 to 26 season). The curve shape is the same for pitcher aging curves: no up and down, just constant and then down. Additionally, the most recent rate of decline is almost the same as the pre-PED aging rate (82-89).

This information is important in predicting young players’ performance. Once a hitter makes it to the majors, he doesn’t really improve. In the past, people used to hope for improvement and growth as the player aged. These days, people should expect to see the player performing at his career best immediately.

A couple possible reasons may be behind the lack of improvement. First, players are more prepared for majors, physically and mentally. In the past, a player may not have had the best conditioning, coaching and training while he was in the minors. Teams are putting more resources into their minor league affiliates, and there isn’t room for improvement with the major league team. Second, teams may be better at knowing if or when a player will be MLB ready, meaning the player doesn’t have to mature and grow at a lower level. They are ready to contribute immediately

This trend of contributing right away may have been occurring before 2006. The uncontrolled use of PEDs may have masked the lack of an up and down curve. Players were improving chemically past their previous peak and were able to maintain their performance over time.

For years, pitcher performance declined as those players aged, but hitters seemed to have an up and down performance curve. In the past few seasons, hitters no longer improve once they arrive in the majors. Instead, their performance is constant until they begin to decline, which, on average, is at 26 years old. Improved training and development is probably behind the shift. If fans are hoping for a young position player’s performance to peak, they might be sorely disappointed. Chances are the player is likely producing at his career-best already.

In doing my research, I often like to take a look at correlations to get an idea about whether factors might be connected.  At the end of this season, I put together a spreadsheet to help me with that.  Well, I haven’t finished the research yet (FG+ subscribers will probably soon find out what’s been keeping me from it), but in the meantime, I thought I’d share what I hope will be a pretty handy tool for whomever out there might be interested in what lies a little beneath the surface of all these stats on FanGraphs.  And I do mean all of them.  Any pitching-related stat on FanGraphs should be represented in this tool.  You can compare one stat to another, or to itself in a different year.  Or, what the heck, you can even compare a stat to a different stat in a different year.  And, for you sticklers out there, it will even give you a confidence interval on these correlations (by default, it gives you the range of correlations that the true correlation has a 95% chance of being within).

What can you do with this?  Well, let’s say you want to see whether a stat is predictive of the next year’s ERA.  You could, for example, set Stat 1 to K% (after selecting the correct white box, type it in, or select from the drop-down list via the arrow to the right of the box), with the year set to 0 (meaning the present year), then set Stat 2 to ERA, with the year set to 1 (meaning the next year).  If you don’t change the IP or Season filters, you should see a correlation of -0.375.  That shows there’s a pretty decent connection between the two stats, in that if a pitcher has a high strikeout percentage in one season, he’ll likely have a low ERA the next (relative to the rest of the pitchers in the comparison).  If you change the year under ERA to 0, you’ll see the correlation gets stronger, whereas if you change it to 2 or 3, you’ll see it gets weaker.  That has a lot to do with the unpredictability of K%, and especially of ERA.  You’ll notice if you compare year 0 K% to year 1 K%, the correlation is a very strong 0.702, whereas if you do the same for ERA, it’s a moderate-to-weak 0.311.  Hopefully the graph will give you an idea of how strong those connections really are.
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