Notice: Trying to get property 'display_name' of non-object in /var/www/html/wp-content/plugins/wordpress-seo/src/generators/schema/article.php on line 52
keyboard_arrow_uptop

For the most part, the idea of buying low and selling high is a waste of everybody’s time.

We all know that Mark Buehrle and Tim Hudson aren’t going to finish the season with earned run averages of 2.04 and 1.97, respectively. It is extremely unlikely that Nelson Cruz will finish the year with 60 home runs and 150 runs batted in. We even know that the superstars who are crushing it now are going to slip somewhat. If you were going to bet the over/under on Troy Tulowitzki hitting .360 or Giancarlo Stanton hitting 45 home runs, the safe bet on both would be the under.

Thankfully, no serious analyst has bothered dispensing this type of advice in years. However, there is a certain type of groupthink that does exist in fantasy circles, and I am not immune to its charms. A player develops a reputation as undervalued or overvalued and manages to maintain that reputation even after the numbers tell us it just isn’t so. This week, I am going to take a look at a few examples of players who fall into this trap and what we can learn from our misperceptions. Real opportunities to “buy low” or “sell high” come from a gap in perceived value versus real fantasy earnings; my hope here is to not merely tab guys as “buy low” or “sell high” but to provide useful information you can use going forward.

Valuations and rankings are through games played on Saturday, June 7.

Billy Hamilton: The Early Confirmation Bias

NL ONLY

MIXED

AB

R

H

HR

RBI

SB

BA

$

SAL

My Rank

PECOTA Rank

190

25

48

1

11

23

.253

$23

25

47

50

Billy Hamilton started the season horribly. His 0-for-4 game with four strikeouts against Adam Wainwright on Opening Day led his detractors to quickly conclude that Hamilton was overmatched, and through seven games he had no steals and a .267 OPS. To some, the conclusion was obvious: Billy Hamilton sucked, and his buyers were a joke.

However, not only has Hamilton recovered but he is coming close to providing value in NL-only leagues to date. Even crazier than this is the fact that Hamilton is outperforming his draft slot in mixed leagues; he was the 60th player taken on average according to NFBC draft data.

As noted above the slow start has something to do with this, but Hamilton has been compared unfavorably to Dee Gordon all season along. Obviously he isn’t performing at Gordon’s level, but the idea that Hamilton is underperforming because Gordon is off the charts is a silly one. Despite Hamilton and Gordon’s propensity to run, the overall stolen base climate hasn’t changed significantly. Ten players are on pace to steal 40-plus bases in 2014; last year eight players stole 40 or more bases. Hamilton’s 61 stolen base pace is underwhelming compared to what was expected entering 2014, but he is still providing plenty of fantasy value.

Corey Kluber: Great But Not Elite

AL ONLY

MIXED

W

SV

IP

ERA

WHIP

SO

$

SAL

My Rank

PECOTA Rank

6

0

86 .3

3.23

1.216

99

$26

13

29

27

Kluber has had a terrific season, and if you bought or drafted him you deserve to give yourself a high five. But some have talked about Kluber as if he’s one of the top 10 pitchers in fantasy baseball and this simply isn’t so. Kluber’s the 20th-best starting pitcher in baseball for fantasy purposes (nine relievers rank ahead of him in overall value). In mixed formats, he has been more of a no. 2 so far than an ace; even in AL-only Kluber is “only” ninth overall.

In part, the dichotomy exists because Kluber’s FIP and other predictive indicators are so positive. However, the WHIP impacts value and cannot simply be ignored in fantasy. Kluber’s WHIP could get better (and probably will get better), but in terms of what he has earned so far, he has not quite been an ace compared to his peers.

David Wright: More of An Asset Than You Would Think

NL ONLY

MIXED

AB

R

H

HR

RBI

SB

BA

$

SAL

My Rank

PECOTA Rank

261

28

73

4

32

3

.280

$19

29

76

72

There’s no getting around the fact that Wright has been a disappointment thus far. No one expected him to hit 30 home runs or steal 30 bases, but a 10/10 HR/SB season was not something anyone anticipated. Despite this, Wright is on pace for nearly a $20 season in NL-only. He has also been mixed worthy despite his lack of power/speed production.

It is common to ooooh and aaaah over home runs and stolen bases, but even experts tend to forget how valuable runs and RBI can be. If Wright finishes with a 10 HR, 84 RBI, eight SB, 73 run, .280 BA line he will provide decent value. Once again, I suspect our familiarity with advanced metrics is at play here. I would agree with those who say that RBI is a worthless barometer for determining real life value, but we use them in fantasy. If Wright plays and hits for an above average batting average, the runs and RBI will come and he will provide value. All his owners need now is some improvement in the power department.

Justin Morneau: Still Plays Half His Games in Coors

NL ONLY

MIXED

AB

R

H

HR

RBI

SB

BA

$

SAL

My Rank

PECOTA Rank

217

24

62

10

34

0

.286

$21

19

56

75

Morneau stands in here for a number of his Rockies teammates. It has been suggested in some circles that Morneau had a hot April and will continue to fade as the season moves forward. But the value proposition here hasn’t changed. Morneau cost close to $20 in NL-only formats because he was moving to the Rockies, not because his owners expected some sort of renaissance. Morneau will continue to provide value. Interestingly enough, he hasn’t been much of a profit maker in NL-only; the expert market paid for him expecting a bump along the lines of what Morneau has done so far in 2014

Robinson Cano: Batting Average Matters

AL ONLY

MIXED

AB

R

H

HR

RBI

SB

BA

$

SAL

My Rank

PECOTA Rank

226

27

75

2

31

4

.332

$26

32

34

61

Cano’s power outage makes him seem like a significant disappointment, but as the earnings column attests to, he has performed close to his typical $30 level once again this year thanks in large part to that .332 batting average. Batting average is where PECOTA and I part company. Hitters who can hit .332 can have the kind of ceiling shattering impact on the category the way that guys like Gordon and Hamilton do on stolen bases. Cano might not continue hitting at a .332 clip, but if he does there is a good chance he’ll at least earn in the low $20s in AL-only even without the power.

Freddie Freeman: Why Does He Keep Getting So Much Love?

NL ONLY

MIXED

AB

R

H

HR

RBI

SB

BA

$

SAL

My Rank

PECOTA Rank

226

36

65

9

32

0

.288

$23

30

49

43

Freeman has definitely been a solid citizen, but some still gush about him like he is the $30 player he was last year in NL-only. His BABIP and batting average with runners in scoring position has normalized like many anticipated it would; yet there is still a significant amount of top-tier mythologizing surrounding Freeman. He’s very good, but he isn’t worth treating like the top-tier player he was last year, at least not yet.

Thank you for reading

This is a free article. If you enjoyed it, consider subscribing to Baseball Prospectus. Subscriptions support ongoing public baseball research and analysis in an increasingly proprietary environment.

Subscribe now
You need to be logged in to comment. Login or Subscribe
kvamlnk
6/09
"Batting average is where PECOTA and I part company. Hitters who can hit .332 can have the kind of ceiling shattering impact on the category the way that guys like Gordon and Hamilton do on stolen bases."

My method for evauating the impact of average is to combine the player (Cano) with the average hitter stats (depends on roster size, etc). Cano is very good because he has a high average with a LOT of hits. I don't know the specifics behind the PFM/Pecota, but my results and the PFM were usually in broad agreement. (So much so that I've allowed my computation code to atrophy since it was easier to just get the PFM as configured for my league.)

Do you have a better method for evaluating batting average impact?
MikeGianella
6/09
I use auction population as opposed to MLB universe, which I suspect is what's making the difference. It appears that the PFM is penalizing the worst BA hitters and rewarding the best hitters more. I think this actually makes sense for a projection model (if you're bidding off of a robust BA projection, you probably want to avoid paying the full freight) but for retrospective results it does not.