Yep, We're Still Talking About Justin Upton

The rumor that won't die has once again come back to life (Zombie rumor?). We knew this would happen after the Arizona Diamondbacks signed outfielder Cody Ross, giving them an extra outfielder. This would allow them to trade either Jason Kubel or Justin Upton. The Fox Sports "rumor boys" make the case for why it will be Upton who gets traded, and says this about the Atlanta Braves interest:

The Braves "made strong overtures" for Upton earlier in the offseason, one source said, but came away thinking that the Diamondbacks were not motivated to move him.

One would think at this point that the D-Backs almost have to move Upton for the best deal they can get. The longer these rumors persist the more damage it does to Upton. A player can and should be able to handle some rumors, but it's been a non-stop barrage of rumors for the past year that J-Up has had to endure, and all the questions he's had to answer about this team and that team and this rumor and that rumor.

The largest sticking point that was reported earlier in the season in the apparent talks between the Braves and D-Backs, was Arizona's insistence on the inclusion of shortstop Andrelton Simmons in any trade package that Atlanta would assemble for Upton. With the acquisition of shortstop Didi Gregorius last month from Cincinnati, it stands to reason that requirement would no longer exist.

If -- big IF -- that is no longer a requirement from Arizona, then the Braves may be able to remake their "strong overtures" to Arizona and reunite the Upton brothers in Atlanta. This is essentially a Braves fan's wet dream (and maybe even the Braves' front office wet dream), but Justin Upton is the kind of player you unload the system for -- especially when he's signed affordably for three more years.

Let's not get wet just yet though. These are called "rumors" for a reason. Still, the idea is exciting.

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Miami Marlins Trade Rumors: Unlikely to Trade
Giancarlo Stanton

This week, a number of Giancarlo Stanton trade rumors came out thanks to the information that the Miami Marlins would listen to offers for the young star outfielder. But Ken Rosenthal of Fox Sports suggests that, after all of that trade rumor smoke, the Marlins are unlikely to stoke any more flames in that fire.

Lots of interest in #Marlins’ Stanton, but sources all but rule out a deal. One source says chances are "as close to zero as they can be."

— Ken Rosenthal (@Ken_Rosenthal) January 2, 2013

This is quite understandable. Once again, as mentioned before, the Marlins will listen to offers on Stanton because that is simply doing their due diligence to improve the team's future. Contrary to popular belief, trading Stanton for the right package has a high probability of making the Marlins better in the future, particularly if the Fish can acquire the multiple top-10 or top-20 prospects that should be necessary for such at trade.

The problem is that no team is likely to offer such a trade, mostly due to the fact that few teams even have the resources to make such a trade happen. So when those teams come calling, the Marlins will listen, but if the offer is subpar, the team is correct to turn away those suitors.

There has been quite a bit of speculation as to which teams can even offer a deal to which the Marlins would listen. The list seems to be down to the Texas Rangers (centered around Jurickson Profar and Mike Olt), the Seattle Mariners (Mike Zunino, Taijuan Walker, and Danny Hultzen), or the Pittsburgh Pirates (Gerrit Cole and Jameson Tallion). But just in listening to some fans who have ambled onto Fish Stripes during these discussions, it seems these teams are unwilling to give away that much talent for a player of Stanton's caliber, but the Marlins should avoid settling for any offer in 2013 when they can likely get a similar deal the following season.

In the end, the Stanton situation will remain in a holding pattern at best until after 2013. When Stanton heads into his first arbitration payout, that is when Marlins fans should seriously begin expecting an impending departure.

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Oh, to be able to hit right-handed pitching again

In 2012, Michael Young made 651 plate appearances for the Rangers, hitting 277/312/370 in what was the worst offensive seasonRead the Rest...

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-again/

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George Foster PermaGraphics Credit Card /
Baseball Card

George Foster PermaGraphics Credit Card / Baseball Card I haven’t added any cards of George Foster to my player collection since completing it a few months ago.  I was waiting for a modern card to appear that intrigued me. And … Continue reading →

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Projecting BABIP and Regression toward the Mean

I'm not lying when I say that I lay awake at night thinking about pitching BABIP. It is because of the variability in pitching BABIP that Jeremy Hellickson has become my #Unicorn.

Explaining (or more importantly predicting) year-to-year BABIP for pitchers is nearly impossible.

Derek Carty of Baseball Prospectus (building off earlier work done by Pizza Cutter) showed that it takes eight seasons for BABIP to become reliable (r= .5). In different piece at BP, Matt Swartz showed that for pitchers who throw at least 150 innings in a season, on average 75 percent of BABIP is simply random variation.

Given these pieces of evidence, here are two brief (made-up) examples of how many writers/analysts tend to discuss or project BABIP for individuals during the offseason:

Braves starter Johnny Goodfortune had a .250 BABIP in 160 innings in 2012. This number is well below the league average for starters (.293) and thus I expect his BABIP to regress a good deal in 2013.

Or

Royals starter Carl Cantcatchabreak had a .330 BABIP in 175 innings in 2012. Because his BABIP is well above the league average for starters, his results are due to improve in 2013.

Assumptions or predictions like these all sound great and make intuitive sense.

We know one season of BABIP for pitchers (even ones who throw a lot of innings) is not really reliable, it seems best to assume regression toward the league average BABIP for individuals.

The basis for this piece is to search for evidence to either back or disprove this prevailing assumption about year-to-year BABIP.

Does an individual pitcher's BABIP actually regress toward the mean from one season to the next?

I'm sure almost everyone reading this will say the answer to that question is quite obviously, yes. But for a second consider what we know about the variation in a single season of BABIP.

If a pitcher yields a .270 BABIP in Year 1 (.290 league avg. BABIP) why must we assume that his BABIP in Year 2 will be closer to .290 rather than .250?

If 75 percent of BABIP is noise in each season then there's a chance it's just as plausible for an individual's BABIP to move further away from the mean in Year 2 as it is for it to move towards the mean.

This idea is exactly what I would like to test.

A good starting point is to figure out how much weight we should give to an individual's BABIP and how much weight the league average BABIP should get in a simple year-to-year projection.

How much of the league average BABIP should we use to predict?

To find this number, I took a sample of starting pitchers who threw least 100 innings in Year 1 and 100 innings in Year 2 (n = 774) for the years 2004 to 2012.

Each individual pitcher's BABIP in Year 1 was regressed against his BABIP in Year 2, which resulted in this scatter plot:

As expected, the correlation was very weak and the data points are noticeably scattered.

The r-squared indicates that only ~4.3 percent of the variation in BABIP in Year 2 is explained by BABIP in Year 1; which is very low.

The correlation coefficient (r) equaled .21. This result coincides with a similar study done at this very site by Bill Petti almost exactly one year ago.

Typically within the study of baseball statistics, the correlation coefficient is used in a very specific way when regressing a statistic (like BABIP) to the mean.

In this case, the correlation coefficient indicates that when predicting BABIP for the pitchers in this sample, we should use 21 percent of the individual's BABIP and 79 percent of the league average BABIP (or league average for similar pitchers).

For example: In 2012, Cardinals starter Kyle Lohse had a .262 BABIP, and the league average for all starting pitchers was .293. Thus, based on this study, we come up with this equation for a projected BABIP in 2013:

2013 BABIP = .21 (.262) + .79 (.293) = .288.

This equation calls for serious regression towards the mean for Lohse's BABIP, in 2013; which many who are versed with sabermetrics would assume (or agree with).

The linear regression equation (see on chart) for this sample illustrates this idea even more beautifully than my example with Lohse:

BABIP in Year 2 = .214 * ( BABIP in Year 1) + .2316

Does that look familiar? (Hint: .2316 = .79 * .293)

It's literally the exact equation I used to project Lohse's BABIP in the example, except it uses .293 for the entire sample, as opposed to just the league average BABIP for starters in Year 1.

My next thought was that I could improve the regression's strength by using the actual league average for starters in Year 1, instead of using .293 as a crude average for the entire sample.

Quite interestingly, this idea did not improve the predictive value of the regression. The overall r-squared (.038) was slightly lower than simply using .293.

Predicting year-to-year BABIP for pitchers is nearly impossible.

However, sometimes we are tasked with the impossible and based on this sample, it seems the best way to predict year-to-year BABIP is to use ~20 percent of the individual's number and ~80 percent of the league average.

This agrees with the intuitive idea that a pitcher with a higher than average BABIP will improve (or move towards the mean) and that pitcher with a lower than average BABIP will regress (again move towards the mean) in the subsequent season.

So far we've seen that strongest model for projecting BABIP based on just last season's data includes a large amount of regression toward the mean.

At the same time, the correlation was still relatively weak; which may cause some to be a little wary about taking this large regression to the mean assumption at face value. Thus, I ran one final test.

For the independent variable (x-axis), I used the gap between an individual's BABIP and the league average. The dependent variable (y-axis) for this test was the difference between a pitcher's BABIP in Year 1 and 2.

To make my exact process clearer I'll use Lohse as an example again.

I found the gap between Lohse's BABIP and the league average in Year 1 (2012), by subtracting it from the league average(.293 - .262 = .031).

Then I would regress this number against the difference between his BABIPs in Year 1 and 2 (2013 BABIP - .262)

We would expect a positive relationship between the two numbers, as his BABIP in Year 2 should be higher than Year 1, based on the fact that his BABIP in Year 1 was lower than the league average.

The same idea works for the other end of the spectrum.

If a pitcher's BABIP was higher than the league average this would give us a negative predictor, which makes sense as we'd expect the individual's BABIP to fall in Year 2.

Was there a relationship?

The relationship was relatively strong (and as expected positive), although it's clear there was still some serious scatter.

Our main focus from this regression is on the linear equation:

BABIP in Year 2 - BABIP in Year 1 = .7892 * (League BABIP - BABIP in Year 1) - .0008**

**Note**--The intercept is essentially zero, so we'll ignore that an focus on what the slope's interaction with the x and y variables means.

Essentially all this equation is saying is that, on average, the difference in year-to-year BABIP is equal to the league/individual gap multiplied by about .79.

This idea should sound rather familiar. Multiplying the league/individual BABIP gap by .79 is the same thing as we did earlier when the regression used just 21 percent of individual's BABIP and the other 79 percent was simply the league average.

These projections are almost exactly the same, as indicated by our test dummy, Mr. Lohse:

Regression 1 projection: 2013 BABIP = .21 (.262) + .79 (.293) = .288.

Regression 2 projection: 2013 BABIP - 2012 BABIP = .79 * ( .293 - .262) = .025.

.025 + .262 = .287

I think the evidence pretty clearly backs the fact that regression toward the mean is very real. Both tests resulted in a predictive model where the mean had almost 80 percent of the weight.

I'll conclude with two pieces of advice.

1. If a starter had a BABIP that was far from the league average in the previous season (say at least .20 either above or below), unless there is something strange at work with the individual (amazing defense, really high K%, he's a unicorn of some kind, etc), it's best to assume the individual's BABIP will regress a great deal towards the mean in the next season
2. That being said, because of the random variation that a single season of BABIP do not be shocked if the individual's BABIP moves further from the mean, or just stays the same, because when it comes to one season of a pitcher's BABIP, we really don't ever know what is going to happen.

All data comes courtesy of FanGraphs.

You can follow Glenn on twitter @Glenn_DuPaul

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Sabersphere 1/4: Offensive Volatility, Koji
Uehara, and the D-Backs Outfield

Bill Petti of Fangraphs looks at offensive volatility: Offensive Volatility and Beating Win Expectancy

In general, the literature has suggested if you’re comparing two similar offenses, the more consistent offense is preferable throughout the season. The reason has to do with the potential advantages a team can gain when they don’t “waste runs” in blow-out victories. The more evenly a team can distribute their runs, the better than chances of winning more games.

Doug Wachter of The Hardball Times looks at Koji Uehara and limiting walks: Koji Uehara: the Cliff Lee of the bullpen?

Since entering the league in 2009, Uehara has been one of the best relievers in baseball at preventing the free pass, sporting a career walk rate of 1.23/9 IP and coming off season in which he allowed a miniscule 0.75 BB/9. Uehara’s no slouch at punching batters out, either, with a career rate of 9.82 K/9 despite averaging only 89 MPH on his fastball.

Paul Swydan wrote a piece on ESPN explaining why the Diamondbacks should trade Jason Kubel: The D-Backs Crowded Outfield ($)

When the Arizona Diamondbacks signed Cody Ross last month, they created a situation where they have six players worthy of regular reps in the outfield: Adam Eaton, Jason Kubel, Gerardo Parra, A.J. Pollock, Ross and Justin Upton. They probably can only carry five, and while knocking Pollock down to the minors would seem to solve the problem, there's this little matter of playing time.

John Perrotto of Baseball Prospectus shows us who he voted for in regards to the Hall of Fame: On The Beat

However, this piece is about this election, and I ultimately decided to vote for Bonds and Clemens. I am sure when the voting results are released Jan. 9 that a vast majority of the 600-plus voters—all of whom have had at least 10 years of active service in the Baseball Writers Association of America—will decide to go the other way.

If there is anything you would like to have included in Sabersphere, e-mail Spencer at SpencerSchneier22@gmail.com Spencer will be back with Sabersphere on Monday.

Today's BtB retro is an old post about Adam Darowski's Hall of wWAR in regards to pitchers (Now called the Hall of Stats): wWAR Part II: The Pitchers List

So, let's adjust WAR to favor peak years. We already track Wins Above Excellence (single season WAR above 3.0) and Wins Above MVP (single season WAR above 6.0). We'll apply the extra credit there. We'll count WAR above 3.0 twice and WAR above 6.0 three times. Let's call it Weighted WAR (wWAR). The formula is simply WAR+WAE+WAM.

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Mike Schmidt 1986 Donruss Baseball Card

Mike Schmidt 1986 Donruss Baseball Card I am not a big fan of the 1986 Donruss baseball card design.  Actually, I find it to be one of the more annoying designs from the 1980′s. But, I do find this card … Continue reading →

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Dave’s Free Opinion, Friday 1/4/13

(827) CLEVELAND STATE VS (828) VALPARAISO

Take: (828) VALPARAISO

Valparaiso is a bit of a disappointment so far, but this looks like a spot for the Crusaders to bust out. Cleveland State has lost its last four D-1 games by a whopping 101 points, and I see the Vikings getting whacked again here. Valpo minus the points tonight.

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Clubbing with Clive and the Inklings

Are you part of a club? Ever been apologetic or embarrassed by enjoying the company of like-minded souls in an environment where talking is the main activity? Tired of being pushed around by bottom-line types who question the wisdom of … Continue reading →

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1999 Skybox Premium Florida Marlins Team Set

1999 Skybox Premium Florida Marlins Team Set I am really digging these Skybox sets from 1999.  This is the second one that I have picked up, with the first being the Skybox Thunder team set, and they both look great. The … Continue reading →

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