Category: Quick Thoughts

How should Chris Davis be valued?

Answer: Not sure.

I’m trying to figure out this BABIP (batting average on balls in play) puzzle. Orioles first baseman Chris Davis, who notched a BABIP south of .324 just once in his career before last year (.275 in 2010), saw said statistic drop by almost 100 points in 2014. It’s easy to point to the defensive shift as a cause — when defenses shift on you 83 percent of the time, you almost have to — but I’m reluctant to buy in on this just yet.

Unfortunately, there is not much, if any that I know of, publicly-available defensive shift data. Prior to the 2014 season, Jeff Zimmerman published 2013 data courtesy of The 2014 Bill James Almanac. A haphazard calculation yields a 2013 “shift BABIP” about 18 points, or about 6 percent, lower than the MLB aggregate BABIP of .297. During the 2014 season, an ESPN feature projected defensive shifts to reach an all-time high, and by quite a margin, too. In light of this, one could hypothesize that more overall shifts would cause a lower aggregate BABIP. However, MLB’s aggregate BABIP in 2014 was .298.

None of this really tells us a whole lot. The shift BABIP would be awesome if it could be broken down by location of the ball in play — accordingly, I would strictly focus on a pull-side shift BABIP — but, alas, it does not. FanGraphs also breaks down a hitter’s spray chart numerically — you can view Davis’ pull-side splits here —  but it does not indicate how many times defenses shifted against him when he pulled the ball. Until this gap in the data can be both a) plugged and b) made publicly available, the answers we seek regarding the true effectiveness of the shift may evade us.

No matter, because I still want to try to figure some things out. Let’s talk a little bit of theory. Like a hitter’s BABIP, I think his shift BABIP is also likely to be volatile. No matter where you place your fielders, you cannot predict where a batter will hit the ball. If you study the spray charts and play the probabilities just right, you’ll surely turn a few more would-be hits into outs. But just like regular BABIP, there will still be an element of luck involved.

Thus, when I look at this table, reproduced from Mike Podhorzer’s FanGraphs post

Season At-Bats Balls in Play Shift Count % Shifted Shift BABIP No Shift BABIP
2012 515 346 110 31.8% 0.364 0.323
2013 584 385 199 51.7% 0.302 0.431
2014 450 277 230 83.0% 0.230 0.353

… I see all sorts of luck. I think the mistake is made when one relates shift percentage with shift BABIP. I expect more shifts to correlate with greater effectiveness — results that would be reflected in the hitter’s depressed batting average. But more shifts does not equate to greater effectiveness on a per-play basis, which is essentially what shift BABIP measures. In short: given that a player’s batted ball profile is identical year to year, his shift BABIP should have some semblance of consistency. We know that BABIP is pretty volatile, but there is a small element of consistency to it (for example, Edwin Encarnacion‘s BABIP is perennially stuck in the mid-.200s while Mike Trout‘s is typically buoyed in the upper-.300s). Thus, I would expect shift BABIP to exhibit at least a little bit of consistency, and for that consistency to produce consistently lower marks than that of the regular BABIP.

Speaking of batted ball profiles, Davis’ pull-side profile was consistent between 2013 and 2014:

Season LD% GB% FB%
2012 21.9% 60.9% 17.2%
2013 30.4% 48.5% 21.0%
2014 29.9% 52.1% 18.1%

Yet Davis’ pull-side BABIP dropped from .338 to .185. The decrease makes sense intuitively, but he saw the fewest shifts in 2012 and actually had a worse pull-side BABIP than he did in 2013. I don’t have to run a regression to show there’s no correlation to be found there (albeit in a minuscule sample size). Now, his increasing tendency to pull the ball (43.3% in 2012, 46.2% in 2013, 50.9% in 2014): that is something that should correlate well with shift BABIP. Because the shift BABIP doesn’t differentiate among ball placement, where the player hits the ball ought to affect his shift BABIP, especially if he predominantly pulls the ball. Thus, an increase in balls in play to the pull side should correlate with a decrease in shift BABIP. Despite all this, Davis recorded his highest shift BABIP during the year he pulled the ball with the least amount of authority.

Now, forgive me, but I have to try to make something of all of this. Let’s take the 6-percent decrease in aggregate BABIP when accounting for shifts (from earlier), and let’s say that teams shift on Davis 100 percent of the time. (It’s not unfathomable, given defenses shifted against him five times out of six, and it appears — it appears — to have succeeded with flying colors.) Given an identical batted ball profile from year to year, maybe I could expect his BABIP, which sat at .335 and .336 the two years prior to 2014, to fall to around .315 permanently. Even if his “true” BABIP benchmark is closer to .300, then maybe his overall shift BABIP is in the .280 ballpark. As he hits more and more balls to his pull side, his shift BABIP will decrease, as will his batting average. That I can fathom.

But I cannot bring myself to accept that a 10-percent increase in pull-side balls-in-play from 2013 to 2014 correlates with a 24-percent decrease in shift BABIP. I don’t think the latter can reasonably be larger than the former without a significant luck element involved. Then again, the 7-percent increase in pull-side balls from 2012 to 2013 resulted in a 17-percent decrease in shift BABIP produces an almost identical ratio (24/10 = 2.4, 17/7 = 2.429), so maybe there’s something I’m missing. But allow me to speak hypothetically: Let’s say Davis puts 100 balls in play, consisting of 50 to his pull side and 50 everywhere else. This silly 2.4-to-1 ratio demonstrates that one more ball hit to the pull side — that is, now he hits 51 balls to the pull side and 49 everywhere else — means not only is that one extra pulled ball an automatic out but also almost one-and-a-half more balls not to the pull side become outs. It’s simply incomprehensible, and I maintain that a percentage increase in balls hit to the pull side would correlate with at most a percentage decrease in shift BABIP.

Wrapping things up: I think it goes without saying that Davis got unlucky in the BABIP department in 2014 — it’s more a matter of determining how unlucky and why. I think his shift BABIPs betray Davis; I think he got especially lucky against the shift in 2012 and especially unlucky in 2014. In general, more shifts should suppress a hitter’s batting average but not his shift BABIP, and it’s Davis’ shift and pull-side BABIPs that absolutely tanked in 2014. Considering he still managed to hit a home run in 5 percent of his plate appearances, I a full 600 from Davis to yield at least 30 bombs, and I think that’s a modest projection. Couple that with a batting average rebound — which I fully expect at this point, strikeout rate disclaimers withstanding — and the down-and-out Davis could be a nice draft day bargain.

2014’s SP projections: the best and the worst

Maybe this is absurd, but I’ve never honestly checked the accuracy of my projections. It’s partly because I have placed a lot of trust in a computer that runs regressions with reliable data I have supplied, but it’s mostly because I originally started doing this for my own sake. I used to rely on ESPN’s projections, but the former journalist in me started to realize: it has a customer to please, and the customer may not be pleased, for example, if he sees Corey Kluber ranked in the Top 60 starting pitchers for 2014. (At this point, I am giving ESPN an out, given that everyone at FanGraphs and elsewhere knew the kind of upside he possessed.) Kluber is not the issue, however; the issue is that although ESPN (probably) wants to do its best, it also does not want to alienate its readers who, given its enormous audience, are more likely to be less statistically-inclined than FanGraphs’ faction of die-hards.

In sum: I started doing this because I no longer trusted projections put forth by popular media outlets.

So I didn’t really care how every single projection turned out. I wanted to find the players I thought were undervalued. For three years, it has largely worked in my rotisserie league. (Honestly, I am a complete mess when I enter a snake draft.)

Anyway. All of that is no longer. I quickly sampled 2014’s qualified pitchers — 88 in all — to investigate who panned out and who didn’t. I will ignore wins because they are pretty difficult to project with accuracy; I’m more concerned about ERA, WHIP and K’s.

Here is a nifty table that quickly summarizes what would have been tedious to transcribe. You will see a lot of repeat offenders, which should come as no surprise. At least there is some semblance of a pattern for the misses: I underestimated unknown quantities (and aces, who all decided to set the world ablaze in 2014) and overestimated guys in their decline. There isn’t much of a pattern to the guys I got right. Just thank mathematics and intuition for that.

Here would be a shortlist of my most accurate projections from last year, measured by me using the eye test:

Name: 2014 projected stats (actual stats)

Nathan Eovaldi: 5 W, 3.82 ERA, 1.32 WHIP, 6.4 K/9 (6 W, 4.37 ERA, 1.33 WHIP, 6/4 K/9)
R.A. Dickey: 11 W, 3.84 ERA, 1.24 WHIP, 7.2 K/9 (14 W, 3.71 ERA, 1.23 WHIP, 7.2 K/9)
Alex Cobb: 12 W, 3.49 ERA, 1.17 WHIP, 8.1 K/9 (10 W, 2.87 ERA, 1.14 WHIP, 8.1 K/9)
Hiroki Kuroda: 11 W, 3.60 ERA, 1.18 WHIP, 6.7 K/9 (11 W, 3.71 ERA, 1.14 WHIP, 6.6 K/9)
John Lackey: 10 W, 3.67 ERA, 1.25 WHIP, 7.5 K/9 (14 W, 3.82 ERA, 1.28 WHIP, 7.5 K/9)
Kyle Lohse: 9 W, 3.60 ERA, 1.17 WHIP, 6.1 K/9 (13 W, 3.54 ERA, 1.15 WHIP, 6.4 K/9)

If it brings consolation to the reader, I have since tightened the part of the projection system that predicts win totals. I’m not gonna lie, it was pretty primitive last year because I thought it’s already a crapshoot to begin with. Obviously, it shows, even in the small sample above. It’s still difficult given the volatility inherent in the category, but the formulas are now precise.

A: No, Jon Lester is not an ace

ESPN’s David Schoenfield asked a timely question yesterday: Is Jon Lester really an ace?

Timely, because not yet reading Schoenfield’s piece (which was posted two hours prior), I wrote this in one of my fantasy league’s message boards:

Unfortunately, Schoenfield made a lot of points I would have liked to make, most of them concerning overall value. Some of it concerned innings pitched. These kinds of things matter in fantasy, but not as much. Sometimes, innings can be harmful, in the sense that a pitcher who eats up a lot of your innings with bad starts, given your league has an innings cap, could do you more harm than good.

But I want to take a step back and look at it through a simpler lens. Lester had the 46th-best WHIP, arguably the best indicator of probably success among the traditional metrics, among qualified starters in 2011 before ranked him No. 61 overall in their pre-2012 rankings. And, as Schoenfield states, his ERA ranked 34th. I’m more or less trying to paint the picture of a player who is perennially overrated. In fact, for the duration Schoenfield describes (2008 through 2014), Lester ranks 58th in WHIP and 36th in ERA. Maybe I’m misunderstanding the definition of “ace”, but I think if 30 other guys could be another team’s number-one, you shouldn’t be considered an ace.

The important distinction to make is a lot of those guys who were once good now suck, and Lester continues to be relatively good. For example, Roy Halladay, Dan Haren and Josh Johnson, among many, many others, were once considered top-shelf fantasy goods. That doesn’t really help Lester’s case, though, given a new wave of talented young pitching has completely changed the fantasy pitching landscape, at least in the short-term. Lester’s 2014 season, absolutely his best season by leaps and bounds, culminated with a 2.46 ERA and 1.10 WHIP — good for only 8th- and 16-best, respectively, among qualified starters.

So: Lester’s best year saw him barely scraping the top-10 threshold — at age 30, no less.

So: His strikeout rate soared and his walk rate plummeted. Are these gains even sustainable?

In his defense, he posted career bests in the following metrics: strike percentage, first-pitch strike percentage, 0-2 count percentage, 3-0 count percentage, number of three-pitch strikeouts. These are all things I would expect to see from a pitcher who just notched his best season. In fact, three of these statistics — strikes, first-pitch strikes, 3-0 counts — have all been trending in the right direction for at least four years. That doesn’t necessarily mean Lester can improve upon, or even merely repeat, his success.

The reason I’m concerned in the first place is my projections rank Lester 35th overall, with a 3.66 ERA and 1.26 WHIP. That is, it expects some severe regression.

I don’t think it will be that severe. Lester is a good pitcher, and he obviously knows how to make adjustments. He always seems to post a good ERA no matter how many runners he lets on base, but he also benefited from MLB’s 4th-best defense from 2008 through 2014. Granted, he moves to a Chicago team that ranks 8th (aka marginally worse) in that same time span. Unfortunately, that same Cubs defense plummeted to 17th overall last year, notching a below-average mark. (You can Corey Kluber and the rest of the Indians’ rotation why defense is important.)

And even Lester’s strikeout rate outperformed his peripherals by about 1.8 percent — that is, there’s reason to believe his strikeout rate should have been almost 2 percent lower in the first place, especially given that his strikeout rate was about even with his expected rate the past two years (about five-hundredths of a percent lower than expected, equivalent to half a strikeout each year).

Note: I will discuss expected strikeout percentage in an upcoming post.

My projections expect him to maintain most of the gains in strikeouts (21.0 percent) but for the walk rate to fall back in line with career norms. That sets him up for perhaps an above-average year compared to Jon Lester but a way-below-average year in terms of talking about aces, whether in fantasy or reality.

If you’re in a keeper league, you probably got him relatively cheap — and if you didn’t get him cheap, you can now cover yourself by lying and saying you saw it coming — so keeping him with the hopes of a repeat may not come so steep. But I anticipate Lester being even more overvalued than he usually is, and I will be avoiding him like the plague.

What did and didn’t work this year

A part of me feels like I need to provide some credentials if I’m dishing out fantasy advice. I’ve been waiting all year just to see if following my own advice would pay off. I played in four leagues, and the results are in:

1st place – 10-team roto, auction (League of Women Voters)
1st place – 10-team H2H roto, snake
2nd place – 10-team H2H points, snake
3rd place – 10-team H2H points, snake

The most important victory to me is the first one, in the League of Women Voters, a league in which a bunch of my dad’s friends have been playing for decades. I want to look back and 1) try to remember my exact draft strategy; 2) see how well I adhered to it; and 3) see where I went wrong.

I went into the draft knowing I would target a very specific and short list of players. This did not allow a lot of room for flexibility, although I did leave a couple of outfield spots open that I would fill on the fly. I can tell you right away I wish I was stricter on those last two outfield spots. I also did not target any specific category, although I did punt saves for the most part. Although I simultaneously led every offensive category except for stolen bases for most of the summer, it became obvious to me that I accidentally loaded up on batting average and undervalued steals.

What I did right:

  • $1 for Yan Gomes. I guaranteed Gomes would be a top-10 catcher with the chance to break the top 5; he finished No. 4 on ESPN’s player rater. (I also drafted Victor Martinez, and once he gained catcher eligibility, I dropped Gomes. It happened early in the season — too early for me to know better — but I wish I hadn’t.)
  • $16 for Jose Abreu. There’s no way I knew he’d be this good, but after snatching up Yoenis Cespedes off of free agency in the first week of 2012 and drafting Yasiel Puig to my bench in 2013, I pledged to gamble as much as $20, maybe more, on the MLB’s most recent Cuban import.
  • $13 for Martinez. I think he’s perpetually underrated, but I can tell you that not a single person in the world knew V-Mart would hit 30 home runs, let alone 20. I won’t pat my back on this one. I normally wouldn’t keep him, but I may have to in the off-chance he’s pulling a late-career Marlon Byrd on us (in terms of power, that is).
  • $1 for Corey Kluber. My love for Kluber is well-documented. I tempered my expectations and slotted him as my No. 32 starting pitcher, but I vastly underestimated his innings total (45 more innings than I projected), his wins (17 to 10) and, of course, his strikeouts (10.3 K/9 to 8.4 K/9). But I’m glad I took a conservative approach; the most important takeaway is that Kluber clearly exhibited the talent to be at least a middle-tier fantasy starter with upside. And boy, did everyone underestimate that upside.
  • $11 for Cole Hamels. I liked this play at the time, and I still do: I waited maybe a month to get a potential top-10 starter at about half-price. He’s a possible keeper next year ($14 on a $260 budget), but the Phillies’ inability to help him reach double-digit wins is troubling.
  • $2 for LaTroy Hawkins. He’s terrible, but at least I wasn’t the idiot who overspent on the perpetually inept Jim Johnson. How he lucked into more than 100 wins in two seasons is beyond me.

What I did wrong:

  • $51 for Miguel Cabrera. It was the most a player had ever gone for in the league, at least since the Rickey Henderson days. It was hard to predict such a massive drop-off in power — maybe 30 home runs was understandable, but only 25? — and I didn’t leave myself any room for savings. That is, I paid full price instead of looking for bargains, the latter of which was my game plan from the start.
  • $37 for Ryan Braun. An even worse bid, in hindsight, and another instance of paying full price instead of finding the bargain.
  • $10 for Everth Cabrera. Cabrera was a keeper, and he may have gone for more at auction. But wow, what a bust. Again, tough to see something like that coming, especially such a steep decline in on-base percentage.
  • $10 for Brad Miller. I made a bold prediction about Miller before the season started. I think the only thing more amazing than his plate discipline completely vanishing is how much owners in my league were willing to spend on a largely unknown quantity. I really thought I was being sneaky on this one, especially so late in the draft. This was a case in which I was too sold on a guy to budge and take a different name — especially when Dee Gordon and Brian Dozier were still on the board.
  • $12 for Shane Victorino. Was 2013 a flash in the pan or what? I don’t know if this guy’s legs will ever be the same again.

I’m excited to start preparing my projections for next year. I have made some revisions, tweaked some formulas… I’m looking forward to how the projections turn out.

And now I have a concrete idea in my head of how I should approach my ideal draft.

Blind résumé: thoughts on perceived value

Fantasy analysts say things like, “I know, I know, blind résumés are cliché,” and then proceed to do them anyway. So.

I know, I know, blind résumés are cliché. But this is important, I swear. This is an exercise in perceived versus actual value, and exploiting market inefficiencies.

Note: I wish I had written this a month and a half ago (June 21, to be exact), when I talked about it with my good friend/league enemy Rob. You’ll see why.

OK, here are the stat lines, as of Sept. 5:

Player A: 81 R, 16 HR, 73 RBI, 6 SB, .301/.351/.458/.808
Player B: 73 R, 13 HR, 60 RBI, 8 SB, .295/.382/.481/.864

If we are talking about players’ offensive skills from a traditional standpoint, you can argue that Player B is perhaps more valuable, given the comparable batting average, better on-base percentage and better isolated power (ISO). However, this is a fantasy baseball blog, and Player A is clearly the more valuable one as he leads all categories except stolen bases.


The screenshot is from a pre-trade deadline conversation I was having with Rob. And if you haven’t caught on by now, Player A is Melky Cabrera, and Player B is Yasiel Puig. I set the blind résumé deadline at Sept. 5, marking Cabrera’s last game prior to missing the rest of the season with a finger injury.

Referring back to the side note at the beginning: On June 21, ESPN’s Tristan H. Cockcroft ranked Puig in his Top 10 and Cabrera outside his Top 50. At the time, the blind résumés would have looked more like this:

Cabrera: 47 R, 11 HR, 38 RBI, 4 SB, .300/.345/.476/.821
Puig: 39 R, 11 HR, 44 RBI, 7 SB, .321/.411/.538/.949

Honestly, their stat lines were less similar than they are now. Anyway, the important part to note is Cockcroft’s updated ranking are always going-forward rankings — that is, Puig will be a Top-10 player going forward. And on June 21, Cockcroft thought Puig was the 8th-best fantasy option available (Cabrera, meanwhile, 65th or so) despite him going almost a month without clearing an outfield fence.

Things have changed, obviously. Cabrera was the better player in all categories since June 21 — in fact, his triple-slash rates are almost identical more than two months later, serving as a testament to his consistency — and his fantasy contributions have dwarfed those of Puig. Still, Cockcroft ranks Puig the No. 14 outfielder (40th overall) and Cabrera No. 20 outfielder (62nd overall). Elsewhere, CBS Sports’ Al Melchior still lists Puig as his No. 4 outfielder. (He has omitted Cabrera from his list given the news of his injury.) Michael Hurcomb, also of CBS Sports, lists Puig as his No. 4 outfielder and Cabrera at No. 15. (His list was last updated Aug. 12, but it’s not like Puig wasn’t already slumping.) Only one of the three CBS Sports analysts, Scott White, seems to have some sense, ranking Cabrera above Puig (Nos. 11 and 15, respectively).

Perhaps Puig is due to bounce back from a prolonged slump, which would justify his high ranking. But he had sported an abnormal BAbip (batting average on balls in play) all year; and while his 2013 BAbip was a monstrous .383, it would be wildly impressive for him to possess the hitting prowess to sustain one of the highest BAbips in MLB history. So, now, Puig owns a .353 BAbip, still well above the league average.

The question now: Is Puig a premium hitter, or has he been the beneficiary of a lot of good luck for a long time? ESPN’s hard-hit average data would be very beneficial right now, but alas, I don’t have access to it. Line drive rates can be used as a theoretical comparison, however, albeit not a pure substitute: line drives epitomize hard contact. And Puig has hit line drives only 14.3 percent of the time this year, as opposed to 19.1 percent of the time last year. (Mike Trout, who has seen his BAbip fall a comparable 31 points in BAbip from 2013, has seen his line drive rate drop an equally-comparable 4 percent.)

It will take a larger sample size — namely, the addition of the 2015 season — to determine whether Puig is closer to his 2013 line drive rate or his 2014 rate — and, thus, whether he is closer to his 2013 BAbip or his 2014 BAbip. For now, I would err on the side of caution and bank on the floor rather than the ceiling.

All of this slowly gets me back to my point: People have different perceptions of players’ values, and they often let other factors inhibit their judgments, even subconsciously. For example, people may still attribute Cabrera’s success to PEDs, a worry which seemed to be validated by his sub-optimal 2013 (during which he played through a herniated vertebrae, or something like that). Meanwhile, Puig is the next big thing, and people expect such from him. When you strip them of their names — from which their discrepancies in value stems, honestly — you uncover the market inefficiency lying within.

Not that I can say that I would have had the foresight to trade Puig for Cabrera on June 21 (mostly because I owned both), but if I was offered Puig for, say, Corey Dickerson, straight up, I would have pulled the trigger. Because at that point, it was all about name value and recognizing the true performance of each player without bias. Dickerson, having arguably a better season than Cabrera or Puig, is ranked Nos. 17, 25, 32 and 40 among all outfielders on the four expert lists I mentioned.

Moral of the story: Try not to let the name bias your projections. Exploit other owners’ misguided perceptions of value.

V-Mart, Abreu could join elite 30/.330 club

This morning, ESPN’s David Schoenfield mentioned that the Detroit Tigers’ Victor Martinez is baseball’s best hitter, leading all of Major League Baseball in wOBA (weighted on-base average) and wRC+ (weighted runs created plus), which measures a player’s offensive contributions after controlling for park effects. It’s a shame he won’t earn many American League MVP votes — I wouldn’t be surprised if teammate and former MVP Miguel Cabrera blindly earned more — simply because he contributes little to no defensive value.

Still, V-Mart is batting .337 with 30 home runs, setting him up to be part of an elite club. It’s not a popular one, mostly because it doesn’t have a flashy name or fancy title, but it is still very much meaningful: The 30-HR, .330-BA Club.

There have been only 62 such seasons in the past 50 years. There are repeat offenders, however, so the club actually consists of only 37  hitters dating back to 1964.

And the names in this club are not nobodies. Cabrera. Albert Pujols. Todd Helton. Frank Thomas. Vladimir Guerrero. Chipper Jones. The list goes on. It’s a group of men that consists of seven Rookies of the Year and three Hall of Famers (and more to come) and has collected 31 MVP awards and 244 All-Star nods. The inclusion of Martinez and perhaps Chicago White Sox first baseman and Cuban rookie sensation Jose Abreu, who currently sits at 33 homers and a .317 average, would add another six All-Star berths and possibly another Rookie of the Year.

This is nothing more than a cool historical footnote. It doesn’t really feel like we are witnessing history because fans have witnessed 39 such seasons of 30/.330 since 2000, and Martinez’s teammate Cabrera has achieved the feat each of the past three years on his own. Still, when we discuss seasons of truly amazing hitting — commending not only a player’s power but also his incredible plate discipline and coverage — Martinez’ (and Abreu’s) names should be included in the conversation. And maybe it’s just me, but given Martinez’ age and career trajectory, his inclusion on the list will certainly be surprising — and impressive.

The comprehensive list (1964-2013), with number of times each player achieved 30/.330, is listed below.

5 times
Albert Pujols

4 times
Barry Bonds
Manny Ramirez
Todd Helton

3 times
Frank Thomas
Larry Walker
Miguel Cabrera
Mike Piazza
Vladimir Guerrero

2 times
Gary Sheffield
Jason Giambi

1 time
Adrian Beltre
Albert Belle
Alex Rodriguez
Billy Williams
Bret Boone
Carlos Delgado
Carlos Gonzalez
Chipper Jones
Dante Bichette
Dave Parker
David Ortiz
Derrek Lee
Don Mattingly
Ellis Burks
Fred Lynn
George Brett
Ivan Rodriguez
Jeff Bagwell
Jeff Kent
Josh Hamilton
Lance Berkman
Matt Holliday
Mo Vaughn
Moises Alou
Ryan Braun
Tim Salmon (Salmon is the only player on the list without an All-Star Game appearance. He achieved the feat in 1995, hitting .330 with 34 HR and a 1.024 OPS. He won Rookie of the Year honors in 1993 alongside Piazza.)

A smorgasbord of fantasy baseball advice

Need a Streamer has been slow lately, to say the least. I’ve missed discussing a lot of player news and opportunities to provide good streaming picks. So I’m going to try something new, and maybe it’ll stick. It should be fairly explanatory. I hope it holds readers over until the end of this week, which is probably the busiest week for me in a long time.

Player to add that isn’t Gregory PolancoA.J. Pollock, ARI OF
He’s on the DL, so you’ve got time to pull the trigger. His batting average isn’t for real, but the 6 homers and 8 steals are nice, and he will more than likely join the small number of players who achieve double-digits in each category in a given year. I would expect a batting average closer to .265, but if you can punt average for counting stats in a deeper league, I would go for it.

Hitter to drop: Jay Bruce, CIN OF
Honorable mention goes to Brandon Phillips, Bruce’s teammate, but it is more fitting that the suggested replacement player can actually replace someone. Bruce is striking out about 5 percentage points more often than last year and almost 8 percentage points more than his career rate. Meanwhile, he is hitting more ground balls than fly balls, whereas about two-thirds of all of Bruce’s batted balls over his career have been put in the air. The sample size is quite large now, and I think there may be something wrong with the slugger. His ratio of home runs to fly balls (HR/FB) is a little bit deflated, but even if it returns to his career average, I still wouldn’t expect him to hit much more than 20 home runs, and that’s a serious problem for a guy who’s value lies solely in his power. Bruce is shaping up to be the next Curtis Granderson, and I have legitimate concerns about his current and future value.

Pitcher to add: Marcus Stroman, TOR
Stroman could quickly rise to the top as Toronto’s ace come 2015 if he lives up to his minor league numbers. So far, he has. I liked Stroman a lot as a prospect, as he averaged 10.6 strikeouts and only 2.4 walks per nine innings. He began the year in the bullpen and suffered a couple of brutal appearances in a row, so his two recent (and excellent) starts have improved his numbers to a still-shaky 5.40 ERA and 1.53 WHIP. But I think he’s a starter by trade, and his 13 strikeouts and two walks over 12 innings as a starter support such a claim. Your window to claim Stroman may stay open for a while, especially if other owners simply look at his misleading ERA and WHIP or, on ESPN, his average points, which stands at an underwhelming 3.3 per appearance. However, if he keeps flashing this kind of quality, you’ll start to run out of time.

Wednesday streamer, other than Stroman: Rubby De La Rosa, BOS
I’ll be honest, I’m not thrilled about him, but everyone has caught on to Tyson Ross (although he’s still only 73-percent owned), so tomorrow’s options are slim. De La Rosa comes with K’s but also BB’s; however, he carries a 13-to-2 K/BB ratio into this start on the road, so perhaps he can continue to keep the command issues under control.

Prospect(s) to watch: Joc Pederson, LAD OF, and Mookie Betts, BOS 2B
Pederson and Betts will likely not be up any time soon, as they’re blocked by some pretty large figures at their respective positions. But given the hype surrounding a couple of 2014’s call-ups in George Springer and, most recently, Gregory Polanco, it’s good to know who the next impact players will be. Pederson is batting .327/.437/.615 with 16 home runs and 14 steals. Are you serious? I think he’s a bit too far to reach a 40/40 season, but 30/30 is probably at this point. It’s unfortunate the Dodgers are letting him rot in the minors beneath a pile of unmovable cash in their impacted outfield. Betts recently moved up to Triple-A Pawtucket; prior to this move, he stole 22 bases in 285 plate appearances while batting .346 with almost twice as many walks as strikeouts. He’s going to be really good, with astounding plate discipline, decent speed and a little bit of pop, too. If you hear Pederson’s and Bett’s names, or the names of their predecessors (Yasiel Puig, Carl Crawford, Matt Kemp, Andre Ethier, Dustin Pedroia…), in next month’s trade talks, get ready to prospectively add, add, add.