When reports emerged in February alleging that Josh Hamilton had suffered a drug relapse, it appeared likely that he would be subject to a suspension under Major League Baseball’s Joint Drug Agreement (JDA). Because Hamilton had previously violated the JDA on at least four occasions, I wrote at the time that he would likely be treated as a five-time violator under the rules, subject to a suspension to be determined by the commissioner. That analysis was based on the assumption that Hamilton’s alleged relapse violated the terms of his individual drug treatment program.

MLB announced today, however, that an arbitrator has ruled that Hamilton did not violate his treatment program:

MLB issued the following statement today regarding Los Angeles Angels outfielder Josh Hamilton: pic.twitter.com/FSDjHF4TyZ

— MLB Communications (@MLB_PR) April 3, 2015

Without having access to each side’s arbitration papers, it is difficult to know why the arbitrator ruled in Hamilton’s favor. One possibility, though, is that Hamilton’s individual treatment program was written in such a way that only a failed drug test would count as a violation. And because Hamilton allegedly self-reported his relapse, he was never tested, thus potentially explaining why the arbitrator ruled in his favor. This might also explain MLB’s stated displeasure with the arbitrator’s decision, and its vow to address the issue via collective bargaining.

Alternatively, it is also possible that the arbitrator determined that considering Hamilton self-reported the violation, and has generally remained clean for the last decade, this single relapse shouldn’t trigger a suspension under the JDA.

On a different note, MLB’s statement regarding the Hamilton arbitration decision could itself have arguably violated MLB’s JDA. Under Section 5 of the JDA, information related to a player’s case is generally considered confidential. This explicitly includes the decision of an arbitration panel. While MLB is allowed to announce a suspension or acknowledge that it is investigating an alleged violation that has been publicly reported elsewhere, neither of these exceptions would seem to apply to today’s press release. So it would appear that the players’ union could potentially file a grievance over the press release itself, should it so choose. Considering that the announcement helps absolve Hamilton of wrong-doing under the JDA, though, the union may decide not to press the matter.

Regardless, the long and the short of it is that Commissioner Manfred will not be able to suspend Hamilton for his alleged drug relapse this past off-season. As a result, Hamilton will be free to return to the Angels once his current injury heals. And the Angels will not be able to avoid paying him any of the more than $75 million owed under his contract.

In the meantime, you can add another item to the growing list of issues for MLB and the players union to resolve in the next round of collective bargaining.

A little while ago, I took a look at the potential variability in a pitcher’s ERA based on their peripherals, with the promise that this week I would look at each individual pitcher’s likely ERA spread for this year.

Here is that. There’s a (sortable) table within this post with some information including a point estimate, the standard deviation of each pitcher’s ERAs (with 1,000 simulations), and the 10th and 90th percentile ERAs. Download this .csv file for a more detailed table with more percentile estimates, BABIP estimates, some more various information, and about 200 more pitchers (the following table is cut off at pitchers projected for 55 innings or more).

Speaking of innings, the number of innings used for this is the number projected by the FanGraphs Depth Charts, which are set by humans who know what’s going on with each team and player. This is the most accurate way, probably, of predicting how many innings each pitcher will throw. Of course, the results will be different for a given pitcher if he ends up throwing a different amount of innings, as the number of innings pitched greatly affects the variability. Additionally, the K% and BB% that were used as inputs were obtained from those depth charts, which use ZiPS and Steamer averaged together (they do not, however, have batted ball distribution projections; for that, I used the methods in this post).

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Recently in these electronic pages, the author — an amateur in all the saddest ways — utilized the few skills he possesses to compose a paean in praise of LSU freshman right-hander Alex Lange’s curveball. Lange’s name had appeared among those players who’d produced the (maybe) best predictive pitching stats in the Southeastern Conference. That curveball, it seemed, was responsible for no little part of his success.

Over this past weekend, Lange producing perhaps his best start of the season, striking out 13 of the 30 batters he faced against the University of Kentucky (box). A brief inspection of that game’s video highlights reveals that Lange once again utilize his curveball to good effect.

Here, for example, is Lange utilizing his curveball to the effect of striking out an anonymous Kentucky batter:

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Last week at FanGraphs, we looked at our Positional Power Rankings for position players. As a companion to our rankings, we had our offensive projections built into an interactive data visualization. This week we are ranking pitching staffs. In the interest of synergy and symmetry, I constructed an analogous pitching projections data visualization using FIP as the primary comparison metric.

This interactive chart is similar to the one I constructed for the batters’ OPS, except there are a few more options to choose from regarding metrics. The chart defaults to FIP, but you are able to view the data by either ERA or WAR as well. Since relief pitchers have a fundamentally different role and stats than starting pitchers, you are also able to filter the players based on those two positions.

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Milwaukee’s Jonathan Lucroy has distinguished himself in recent years as one of the major leagues’ best players. Not only, for example, did he rank among the top-ten hitters by WAR last year, but also added the third-most runs by way of catcher framing according to the methodology used by Matthew Carruth at StatCorner — which metric isn’t included in the WAR figures here at the site. Overall, he produced the fifth-best season in the majors last year, according to our writers.

This spring, Lucroy is pursuing another means by which to improve his already impressive skill set — namely, by never striking out. No batter this spring has recorded as many plate appearances as Lucroy (33) while also recording a strikeout rate of 0%.

Insofar as literally zero qualified batters since 1913 (since which year the relevant data exists) have produced a 0% strikeout rate — and also insofar as league-wide strikeout rates are currently at an all-time high — it’s unlikely that Lucroy will be able to parlay this particular brand of success into a full major-league season.

“What if he did, though?” is the sort of idle question one fortunate enough to have been born and raised in a middle-class family is able both to ask and attempt to answer in exchange for even more money. The results of that inquiry are below.

What I did was to start with Lucroy’s Depth Chart projection and then convert every strikeout into a batted ball, assuming that those batted balls would be distributed as singles and doubles and triples and home runs at the same rate at which Lucroy’s actual hits are projected to occur. I made no adjustments to walk rate or BABIP or, like, sacrifice flies — all of which would also certainly change if the actual Jonathan Lucroy were never to strike out. Attempting to account for all those numbers, however, would require even more and weirder assumptions, so I’ve opted just for this simpler version.

Below are the results of those math tricks. Proj denotes Lucroy’s Depth Chart projection. 0 Ks denotes his projection with strikeouts converted into batted balls. Diff is the result of the first line subtracted from the second.

If Lucroy were never to strike out, he’d produce nearly 18 runs over the course of a season — or, roughly two wins. A considerable improvement, that. And yet, if that number seems perhaps underwhelming, it’s because Lucroy is already projected to record just a 12.0% strikeout rate this year — already greater than a standard deviation better than league average.

To find the largest possible gain provided by a zero-strikeout projections, it’s necessary to identify a player who both (a) strikes out frequently and also (b) produced considerable damage on contact. Houston’s Chris Carter is that precise sort of player.

What would happen, in this hypotethetical world, if Chris Carter never struck out. Here are thes results using the same methodology as with Lucroy:

Seven wins! Chris Carter would become more or less Mike Trout’s equal in this particular case. The contingency is unlikely, of course.

North Carolina’s series at home this weekend against Miami is notable insofar as it features a number of players — UNC right-hander Benton Moss and Miami third baseman David Thompson, among others — a number of players who’ve produced the best (maybe) predictive stats within the ACC.

Largely owing to a high walk rate, UNC closer Trent Thornton has produced only pretty good numbers within that same conference. What he has done, though, is exhibit some considerable proficiency in the art of human movement — which may or may not be accurately referred to as kinaesthetics.

Here, by way of illustration, is Thornton delivering a pitch during last Saturday’s game against Georgia Tech (in which he struck out five of the nine batters he faced):

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Yesterday, the author published a post claiming to include the top players by (maybe) predictive stats from college baseball’s most competitive conferences.

What follows are the three weekend series likely to feature the greatest number of players whose names appeared within that post. KATOH+ and KATOH- are index metrics based on those (maybe) predictive stats and designed for batters and pitchers, respectively. In each case, 100 is average, while above 100 is better for batters and below 100 is better for pitchers. Read more about the author’s questionable methodology here.

Miami at North Carolina
Who It Features
Definitely probably UNC senior right-hander Benton Moss (21.2 IP, 72 KATOH-) on Sunday. Moss missed some time with arm trouble, but returned this past Sunday and was once again excellent, producting an 8:1 strikeout-to-walk ratio against 25 batters over 8.0 innings (box). He’s scheduled to start the Sunday. Among Miami hitters, junior center fielder Ricky Eusebio (113 PA, 116 KATOH+), senior catcher Garrett Kennedy (81 PA, 120 KATOH+), and junior third baseman David Thompson (116 PA, 147 KATOH+) all rank among the ACC’s top-20 batters.

When It’s On (ET)
Friday at 6:30pm
Saturday at 3:00pm
Sunday at 12:00pm

How to Watch It
Watch ESPN (link).

Ole Miss at Arkansas
Who It Features
The top starter currently in the SEC, Ole Miss sophomore right-hander Brady Bramlett (35.0 IP, 79 KATOH-). Bramlett was actually averaging a strikeout per inning as a freshman in 2013, working both in a starting and relief capacity, before tearing his labrum. He sat out the 2014 season and has made a formidable return. He’s schedule to start Friday’s game. Arkansas, meanwhile, features two of the conference’s top-15 hitters: sophomore center fielder Andrew Benintendi (106 PA, 128 KATOH+) and junior third baseman Bobby Werne (98 PA, 120 KATOH+).

When It’s On (ET)
Friday at 7:00pm
Saturday at 3:00pm

How to Watch It
SEC Network or SEC Network Plus on Watch ESPN (link).

UCLA at Washington State
Who It Features
Two UCLA right handers, junior James Kaprielian (38.0 IP, 79 KATOH-) and freshman Griffin Canning (35.0 IP, 76 KATOH-), who make starts on Friday and Sunday, respectively. They’re fifth and third, respectively, among Pac-12 pitchers at the moment. UCLA also features two of the conference’s top-10 hitters: senior third baseman Chris Keck (103 PA, 131 KATOH+) and junior shortstop Kevin Kramer (110 PA, 127 KATOH+).

When It’s On (ET)
Friday at 9:00pm
Saturday at 5:00pm
Sunday at 3:00pm

How to Watch It
Washington State’s live video feed (link).

Yesterday I examined all seven viable candidates who could one day hold the title of the last ex-Expo playing in the Major Leagues. What I found remarkable about this “race” is that there really is not an obvious favorite when it comes to who will outlast the others. With the possible exceptions of Luis Ayala and Jon Rauch, neither of whom appeared in the majors in 2014, there also isn’t an obvious dud in the group: each player has had such long journeyman careers, with so many ups and downs, it’s hard to tell what confluence of events and old age will actually knock these men out of the majors for good.

The voting separated the players into three tiers. In the first tier we had Ayala and Rauch, each receiving about 2% of the total vote. I would be encouraged by this result, if I were Ayala and Rauch: this means that dozens of FanGraphs readers believe they will in fact claw their way back to the Majors despite their recent series of roster cuts. I consider this the equivalent of a write-in candidate earning 2% of a vote for political office, which would be quite an uncommon display of public support indeed.

In the second tier we have Bruce Chen, Endy Chavez, and Scott Downs, each receiving about 6-10% of the vote. I believe the tie that binds these players is age: Chen would be entering his age-38 season, Chavez his age-37, and Downs his age-39. I do find it interesting that Chavez’s guaranteed contract for 2015 did not elevate him above Chen and Downs, who were both informed in recent days by the Cleveland Indians that they would not make the team’s Opening Day roster.

No matter: in the final tier, way ahead of the pack, we have Maicer Izturis and Bartolo Colon. At the time of this writing — about lunchtime on Friday, Pacific Standard Time — Izturis has the lead, 37% to 32%.

Despite easily being the oldest player on the list, Colon — entering his age-42 season — nonetheless seems as impervious to the effects of time better than any player since Jamie Moyer. I interpret his robust vote tally as the readers of FanGraphs saying: “What, really, is the difference between the lovably rotund Colon pitching in the big leagues (and earning Cy Young votes) at age 40, and pitching in the big leagues at age 47?”

And in the lead we have Izturis, who leads perhaps because he is comfortably in his mid-thirties instead of his late-thirties. Perhaps also in Izturis’ favor: he spent years as a utility man for the annual playoff entrant Los Angeles Angels — meaning that he rarely got over 100 starts a year, and perhaps has comparatively little wear-and-tear for a long-time veteran.

Where does my vote go? Um, I really don’t know.

While I understand why Izturis is in the lead, and am tempted to vote for him as well, his abilities as a major leaguer have fallen off quite dramatically and abruptly in his mid-thirties — an age when the rest of these dudes really seem to come alive. It remains to be seen if Izturis’ terrible tenure with the Blue Jays is just a blip on the way to his grandfatherly veteran-hood, or whether it’s all over at the end of 2015, his last year on a guaranteed deal.

Because of his established track record of sticking around the Majors despite below-replacement performance (and on some pretty great teams recently, at that) — not to mention that he has already survived a year-long hiatus from the majors in 2010 — I am inclined to vote for Chavez.

That said, I also would not be surprised if Chavez plays his last game before any of the other six players. This will no doubt be a thrilling race to keep an eye on in the coming months and years.

This morning, we wrapped up our look at the guys who play the field in our Positional Power Rankings series; we’ll be tackling the pitching staffs next week. But for those of you interested in playing catchup or going back over the posts, I’ve collected them all in one handy place here; you’ll still always be able to reference all of the posts in the category, but I figured I’d make them a little bit easier to find here.

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Yesterday, Forbes released its franchise values for all 30 Major League Baseball teams. Briefly, the team values are estimates of the price that the franchise is likely to sell if the team’s management group began fielding offers. There are always debates about the accuracy of revenue and valuation figures, and I would recommend reading one of the many FanGraphs articles that already has examined the methods and discrepancies in both Forbes and Bloomberg’s valuation metrics.

In this post, I want to provide some graphical context for Forbes’ data and allow you to explore the data in a different way than on a table.

Being a financial news outlet, the team value is the heart of the Forbes’ data. The graphic below shows that data as a simple bar graph. From this, there are four different tiers of teams. There’s a small-market plateau that constitutes the lower half of MLB teams. These teams are valued under $1 billion and come from smaller cities (except Toronto and Houston). The next tier has larger metropolitan areas or teams that are the secondary team in the largest markets (White Sox, Angels, Mets). The third tier is composed of ‘big market’ teams that come from the largest US cities. Finally, the Yankees are in a tier by themselves.

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