The Fantasy Baseball Sherpa

Hi everyone,

Here are the forecasted Top 10 performers for the rest of the season for an NL-only 5×5 format.  The leader in each category is given 1.00 Sherpa Points; all other players’ scores in that category are based on their results relative to the category leader’s (e.g. - if the forecast for the league-leader is 110 RBI, then a player with a forecast of 55 RBI would be given a score of 0.50 Sherpa Points).  The maximum Total Sherpa Points is equal to the number of categories (i.e. - 5.00).

1. Johan Santana (NYM, SP) - 196 IP, 12 W, 0 SV, 2.71 ERA, 1.07 WHIP, 200 K, 3.85 Total Sherpa Points
2. Albert Pujols (StL, 1B) - 482 AB, 35 HR, 110 RBI, 7 SB, .332 AVG, 96 R, 3.79 Total Sherpa Points
3. Matt Kemp (LAD, OF) - 577 AB, 19 HR, 88 RBI, 31 SB, .308 AVG, 101 R, 3.55 Total Sherpa Points
4. Alfonso Soriano (ChC, OF) - 567 AB, 37 HR, 85 RBI, 25 SB, .286 AVG, 102 R, 3.45 Total Sherpa Points
5. Chase Utley (Phi, 2B) - 508 AB, 28 HR, 96 RBI, 12 SB, .313 AVG, 102 R, 3.44 Total Sherpa Points
6. Hanley Ramirez (Fla, SS) - 549 AB, 29 HR, 81 RBI, 23 SB, .299 AVG, 98 R, 3.34 Total Sherpa Points
7. Ryan Braun (Mil, OF) - 552 AB, 33 HR, 99 RBI, 18 SB, .292 AVG, 93 R, 3.33 Total Sherpa Points
8. Carlos Beltran (NYM, OF) - 511 AB, 27 HR, 101 RBI, 19 SB, .295 AVG, 95 R, 3.25 Total Sherpa Points
9. David Wright (NYM, 3B) - 514 AB, 23 HR, 93 RBI, 18 SB, .305 AVG, 93 R, 3.21 Total Sherpa Points
10. Jose Reyes (NYM, SS) - 587 AB, 12 HR, 57 RBI, 51 SB, .291 AVG, 96 R, 3.19 Total Sherpa Points

If you’re interested in more details, here’s a description of our In-season Updates to Player Projections & Rankings.  These are the only rankings in the industry that are updated daily throughout the season - our Remainder-of-Season rankings reflect injuries, minor league call-ups, and role changes (e.g.- new Closers)! If you’d like to see the top performers by position, change the scoring categories, or change the league type, here’s a demo of our In-season Updates to Player Projections & Rankings.

Enjoy!

The Sherpa

FantasyBaseballSherpa.com

This is the third in a series of posts taking a look back at the top fantasy baseball performers for 2008. Today I’ll review the top 10 Hitters using a 5×5 NL-only format. For the purposes of this analysis I’m including only Manny Ramirez’ Dodgers’ stats (he ranked 59th overall based on just 59 At-Bats!) and Mark Teixeira’s Braves’ stats (he ranked 56th based on 381 At-Bats)

1. Albert Pujols (Preseason rank was 4) - STL, 1B

• Actual stats: 524 AB, 37 HR, 116 RBI, 7 SB, .357 AVG, 100 R, 3.46 Sherpa Pts (out of a max of 5.00)
• Projected stats: 528 AB, 37 HR, 110 RBI, 6 SB, .330 AVG, 105 R, 3.38 Sherpa Pts

2. David Wright (3) - NYM, 3B

• Actual stats: 626 AB, 33 HR, 124 RBI, 15 SB, .302 AVG, 115 R, 3.18 Sherpa Pts
• Projected stats: 607 AB, 29 HR, 112 RBI, 26 SB, .316 AVG, 108 R, 3.46 Sherpa Pts

3. Hanley Ramirez (2) - FL, SS

• Actual stats: 589 AB, 33 HR, 67 RBI, 35 SB, .301 AVG, 125 R, 3.12 Sherpa Pts
• Projected stats: 645 AB, 23 HR, 71 RBI, 52 SB, .310 AVG, 123 R, 3.48 Sherpa Pts

4. Lance Berkman (13) - HOU, 1B/OF

• Actual stats: 554 AB, 29 HR, 106 RBI, 18 SB, .312 AVG, 114 R, 3.06 Sherpa Pts
• Projected stats: 562 AB, 37 HR, 113 RBI, 6 SB, .294 AVG, 96 R, 2.86 Sherpa Pts

5. Matt Holliday (1) - COL, OF

• Actual stats: 539 AB, 25 HR, 88 RBI, 28 SB, .321 AVG, 107 R, 3.03 Sherpa Pts
• Projected stats: 636 AB, 33 HR, 125 RBI, 12 SB, .322 AVG, 114 R, 3.63 Sherpa Pts

6. Jose Reyes (8) - NYM, SS

• Actual stats: 688 AB, 16 HR, 68 RBI, 56 SB, .297 AVG, 113 R, 3.00 Sherpa Pts
• Projected stats: 673 AB, 13 HR, 65 RBI, 70 SB, .285 AVG, 116 R, 3.05 Sherpa Pts

7. Carlos Beltran (18) - NYM, OF

• Actual stats: 606 AB, 27 HR, 112 RBI, 25 SB, .284 AVG, 116 R, 2.90 Sherpa Pts
• Projected stats: 553 AB, 33 HR, 108 RBI, 21 SB, .273 AVG, 103 R, 2.70 Sherpa Pts

8. Chase Utley (5) - PHI, 2B

• Actual stats: 607 AB, 33 HR, 104 RBI, 14 SB, .292 AVG, 113 R, 2.87 Sherpa Pts
• Projected stats: 595 AB, 27 HR, 109 RBI, 13 SB, .318 AVG, 115 R, 3.28 Sherpa Pts

9. Ryan Ludwick (81) - STL, OF

• Actual stats: 538 AB, 37 HR, 113 RBI, 4 SB, .299 AVG, 104 R, 2.83 Sherpa Pts
• Projected stats: 399 AB, 21 HR, 65 RBI, 2 SB, .261 AVG, 69 R, 1.45 Sherpa Pts

10. Ryan Braun (14) - MIL, 3B/OF

• Actual stats: 611 AB, 37 HR, 106 RBI, 14 SB, .285 AVG, 92 R, 2.73 Sherpa Pts
• Projected stats: 592 AB, 39 HR, 99 RBI, 19 SB, .280 AVG, 104 R, 2.86 Sherpa Pts

The most interesting observation from my perspective - the impact of batting order position (imagine what Hanley Ramirez could do if he batted 3rd or 4th) and the lower-than-expected SB totals for both Ramirez and Jose Reyes, which bring them back to the pack somewhat in the overall rankings. Injuries also impacted the actual rankings (e.g. - Matt Holliday and Chase Utley), as did the lack of anticipated injuries (e.g. - Albert Pujols). Ryan Ludwick is the only name on the list I’d term a complete surprise. Dividing the Sherpa Pts by the AB gives a decent indicator of the overall accuracy of the initial projection compared to the actual results.

You’ll also note that only 6 of the Hitters listed above were in my preseason top 10 list. Who were the other four, and how did they fare? I’m glad you asked!

11. Ryan Howard (6) - PHI, 1B

• Actual stats: 610 AB, 48 HR, 146 RBI, 1 SB, .251 AVG, 105 R, 2.71 Sherpa Pts
• Projected stats: 560 AB, 44 HR, 130 RBI, 2 SB, .313 AVG, 116 R, 3.44 Sherpa Pts

29. Jimmy Rollins (7) - PHI, SS

• Actual stats: 556 AB, 11 HR, 59 RBI, 47 SB, .277 AVG, 76 R, 2.10 Sherpa Pts
• Projected stats: 684 AB, 24 HR, 81 RBI, 39 SB, .289 AVG, 128 R, 3.12 Sherpa Pts

25. Alfonso Soriano (9) - ChC, OF

• Actual stats: 453 AB, 29 HR, 75 RBI, 19 SB, .280 AVG, 76 R, 2.17 Sherpa Pts
• Projected stats: 645 AB, 39 HR, 85 RBI, 26 SB, .285 AVG, 110 R, 3.02 Sherpa Pts

21. Derrek Lee (10) - ChC, 1B

• Actual stats: 623 AB, 20 HR, 90 RBI, 8 SB, .291 AVG, 93 R, 2.26 Sherpa Pts
• Projected stats: 575 AB, 28 HR, 92 RBI, 14 SB, .311 AVG, 100 R, 2.95 Sherpa Pts

We’re already busy here in Sherpaville developing projections for the 2009 season (we’re one of several projection providers selected for the 2009 season by Mock Draft Central!), but I’ll continue this series with a look at the 2008 top 10 Pitchers in an NL-only 5×5 format as time permits.

Until next time,

The Sherpa

Today I’ll take a look at the Top 10 Hitters (both year-to-date and for the rest of the season) in a standard mixed league format (i.e. - Hitting categories are HR, RBI, AVG, SB, and Runs Scored). Each player receives a Sherpa Point score ranging from 0 to 1 in each of the 5 categories; thus, the maximum possible score is 5.00.

For the counting statistics the league leader is assigned a score of 1.00; all other Hitters’ scores are calculated by taking the ratio of their score in a particular category to the league leader’s score. For example, if the league leader has 25 Home Runs, a Hitter with 15 Home Runs would receive a score of .60, a Hitter with 10 Home Runs would receive a score of .40, etc. For average-based categories (e.g.- AVG) a proxy statistic is used. Hitters’ Sherpa Points scores in each individual category are added to calculate the Total Sherpa Points.

Top 10 Hitters Year-to-Date

1. Lance Berkman - 3.59 (Remainder-of-Season forecast = 3.68 Sherpa Points, which ranks 2nd overall)
2. Ian Kinsler - 3.44 (3.22, 5th)
3. Josh Hamilton - 3.19 (2.76, 17th)
4. Hanley Ramirez - 3.16 (3.40, 4th)
5. Matt Holliday - 2.99 (3.64, 3rd)
6. Jose Reyes - 2.98 (3.07, 10th)
7. Ryan Braun - 2.96 (3.09, 8th)
8. Alex Rodriguez - 2.88 (3.84, 1st)
9. Chipper Jones - 2.84 (2.89, 15th)
10. David Wright - 2.82 (3.08, 9th) and Chase Utley - 2.82 (3.11, 7th).

Top 10 Hitters for Remainder of Season

1. Alex Rodriguez - 3.84 (Year-to-Date score = 2.88 Sherpa Points, which ranks 8th)
2. Lance Berkman - 3.68 (3.59, 1st)
3. Matt Holliday - 3.64 (2.99, 5th)
4. Hanley Ramirez - 3.40 (3.16, 4th)
5. Albert Pujols - 3.22 (2.80, 12th)
6. Ian Kinsler - 3.22 (3.44, 2nd)
7. Chase Utley - 3.11 (2.82, Tied for 10th)
8. Ryan Braun - 3.09 (2.96, 7th)
9. David Wright - 3.08 (2.82, Tied for 10th)
10. Jose Reyes - 3.07 (2.98, 6th)

It’s very interesting to see the majority of the Hitters appear in both lists, indicating that over the course of a 162-game season, the top Hitters are generally the ones you would have expected to see at the top of this list before the season began.

I’ll take a look at the Top 10 Mixed League Pitchers in my next post.

Until then,

The Sherpa

So it’s finally happened. Jay Bruce, who is less than half Barack Obama’s age and less than one-third of John McCain’s age, but has seemingly generated more conversation in 2008 (at least among fantasy baseball enthusiasts) than Obama and McCain combined, has finally landed in Cincinnati.

Stripping away the hype, what does this mean for those with the foresight to stash Bruce at the beginning of the season or the waiver wire priority to claim him during the season? Will he make the immediate impact that rookies Ryan Braun and Hunter Pence did after the call-ups during the 2007 season? Will he disappoint the way that Alex Gordon did during his rookie season (also 2007)? Or, as the cliche goes, does the true answer lie somewhere in the middle?

Based on his minor league rate stats, with a few subjective adjustments, here’s what I think you can realistically expect from Bruce for the rest of this season:

• 409 AB
• 62 R
• 114 H
• 30 2B
• 5 3B
• 16 HR
• 68 RBI
• 12 SB
• 36 BB
• 115 SO
• .279 AVG
• .330 OBP
• .494 SLG

So, where would these results place Bruce among his more established peers? The Sherpa Point scale assigns a score of 1.00 to the player with the top score in each category; all other players’ scores in that category are based on their result in that category compared to the league leader’s. For example, if the league leader is projected to hit 50 HRs, a batter projected to hit 40 HRs would receive 0.80 Sherpa HR pts, a batter projected to hit 25 HRs would receive 0.50 Sherpa HR pts, etc. For average-based categories such as AVG, OBP, SLG, and OPS, a proxy statistic is used. Thus, if you play in a league that uses 5 offensive categories, the maximum Hitter’s Sherpa Points score would be 5.00.

Pro-rating my Preseason Projections for all Hitters, I project that in a league using the 5 standard offensive categories (AVG, HR, RBI, SB, and R) Bruce would score 2.38 Sherpa Points. This would place Bruce roughly 20th overall, comparable to the projected Sherpa Point scores for fellow Outfielders Chris Young and B.J. Upton. Obviously, their Sherpa Point scores will vary by category (in some cases quite a bit), but on an overall basis, that’s where I believe Bruce’s value will fall. For the sake of comparison, the Top 3 Outfielders (based on Preseason Projected Sherpa Points) were Matt Holliday (3.56), Carl Crawford (3.19), and Vlad Guerrero (3.08).

So while Bruce should be a strong contributor to your fantasy team if you’re fortunate enough to have him, he will not carry you to your league’s championship by himself. It wouldn’t surprise me at all if he’s eventually a Top 5 Outfielder from a fantasy perspective, but that’s highly unlikely to happen in 2008. As long as you temper your expectations, you will not be disappointed.

Until next time,

The Sherpa

With just over 25% of the 2008 season already in the books many experts are now putting forth their “Biggest Surprises” and “Biggest Disappointments” lists. I will do something similar in this post, but I will also give you the actual impact these players have had year-to-date in a league that uses the standard 5×5 scoring categories.

Let’s look at Hitters first. The Sherpa Point system gives each Hitter a score ranging from 0 to 1.00 in each of the 5 standard hitting categories (AVG, HR, RBI, SB, R). Essentially, the league leader in each category is given a score of 1.00, while all other Hitters’ scores in that category are calculated as the ratio of their result to the league leader’s result.

For example, if Lance Berkman leads the league with 16 HRs, he gets a Sherpa Point score of 1.00 in the HR category; a Hitter with 8 HRs gets a score of 0.50; a Hitter with 4 HRs gets a score of 0.25, etc. A proxy statistic is used for average-based categories (e.g. - AVG, OBP, SLG, OPS)

The maximum Hitters’ score is equal to the number of hitting categories, and the maximum Pitchers’ score is equal to the number of pitching categories. Statistics are taken from games through Tues 5/20/08.

Without further ado, here’s the list of the Top 10 Year-to-Date 5×5 Mixed League Hitters:

1. Lance Berkman 4.13 (Preseason Projection was 2.80)
2. Chipper Jones 3.03 (2.58)
3. Dan Uggla 2.72 (1.89)
4. Josh Hamilton 2.70 (1.74)
5. Nate McLouth 2.69 (1.82)
6. Chase Utley 2.66 (3.22)
7. Hanley Ramirez 2.64 (3.41)
8. Albert Pujols 2.61 (3.32)
9. Carlos Quentin 2.55 (1.92)
10. Miguel Tejada 2.51 (2.38)

Berkman is obviously outperforming his projections by the largest amount so far (and will likely come back down to earth in short order), but Dan Uggla, Josh Hamilton, and Nate McLouth have probably offered the greatest value so far relative to where they were picked in drafts and the amount paid for them in auctions. Of the Top 10 shown here, only three (Utley, Ramirez, and Pujols) were among the Preseason Top 10 Hitters.

Many others who went in the first round of many drafts have not fared well in the early stages of the season. Consensus top-pick Alex Rodriguez (1.01 Year-to-Date vs. 3.84 Preseason Projection) and reigning NL MVP Jimmy Rollins (1.22 vs. 3.05) have missed significant time due to injuries. Other highly touted players who have disappointed their owners so far: Jose Reyes (1.73 vs. 2.98), David Wright (2.11 vs. 3.40), Ryan Braun (2.29 vs. 2.82), Miguel Cabrera (1.51 vs. 3.40), David Ortiz (1.88 vs. 3.44), Matt Holliday (2.17 vs. 3.56), Carl Crawford (2.18 vs. 3.19), Vladimir Guerrero (1.17 vs. 3.08), and Ryan Howard (1.08 vs. 3.07).

The discrepancy between a player’s Year-to-Date Sherpa Point score vs. his Preseason Projected Sherpa Point score can also be used as an indicator for constructing trade proposals. If you have a player on your team who is outperforming his projection by half a point or more, and you have several glaring weaknesses elsewhere on your roster, I would definitely be looking to sell high on that player. Conversely, if some of these players have disappointed other owners in your league, it may be worth inquiring about the possibility of buying low. If you can flip an overperforming player for an underperforming one, you have mastered the art of trading!

I’ll put together a similar list for Pitchers in my next post.

Until then,

The Sherpa

It happens every year, just like the swallows returning to San Juan Capistrano. A previously undistinguished hitter gets off to a fast start, and sportswriters speculate on whether a major league hitter will ever be able to hit .400 for an entire season again (I believe the answer is “no”, but that’s another topic for another day). If the same hitter were to hit .400 for 15 games in the middle of the season rather than at the beginning of the season, most of us would barely raise an eyebrow. However, due to what I referred to in a previous post as “Tuffy Rhodes Syndrome“, baseball fans tend to give a disproportionate amount of weight to events at the start of the season.

This year’s fast starters include A.J. Pierzynski (.421 as of this morning), Jason Kendall (.405), Angel Pagan (.385), Nate McLouth (.383), Luke Scott (.375), and Kurt Suzuki (.370). Obviously, none of these hitters will have a batting average anywhere near .400 when the season ends. But how many of them will finish with even a .300 average? Again, the answer could very well be zero.

So, how should you go about forecasting a batting average for the remainder of the season? Let’s use Pierzynski as an example. Suppose that going into the season you expected Pierzynski to hit .270 for the season. How should that expectation be combined with the .421 he’s hit through approximately the first 10% of the season? I’ve read a number of fantasy sportswriters’ articles on this subject, and their approaches usually fall into one of 2 categories: (1) expect Pierzynski to finish the season with his expected average of .270 (which implies that his average for the remaining 90% of the season will be .253); (2) expect Pierzynski to hit .270 for the rest of the season (which implies that his batting average for the season will be .285).

I disagree with both of these approaches. The first is an example of what statisticians refer to as the Gambler’s Fallacy, which means that (supposedly) independent events (such as future at-bats) are entirely dependent on past events. Andy Behrens, a very thought-provoking and entertaining fantasy sportswriter for Yahoo, had a great description of the Gambler’s Fallacy in a post he made yesterday. The second approach goes too far in the opposite direction, assuming that what a hitter has done season-to-date has zero predictive value in forecasting what he’s likely to do for the remainder of the season.

I suggest a third approach that combines what the hitter was expected to do with what the hitter has actually done in order to forecast what he’s likely to do for the remainder of the season. There are several possible weighting schemes, but for the sake of simplicity, I’ll go with a linear weighting scheme (i.e. - if the season is 10% complete, the hitter’s actual results should receive 10% weight, and his expected results should receive 90% weight). Applying this approach to the Pierzynski batting average example suggests that a reasonable forecast for Pierzysnki’s batting average for the rest of the season is .285 (which implies that his batting average for the season will be .299).

Some may still argue that .270 is a better forecast than .285. Let’s look at another example, this one from last season. If you expected Andruw Jones to hit .260 for the season, but he’s hitting just .211 at the All-Star break, would you still expect him to hit .260 for the remainder of the season? Probably not. Since the All-Star break occurs after roughly 55% of the season has been played, I would have forecast a rest-of-season average for Jones of .233 (= 55%*.211 + 45%*.260). Jones actually hit .236 for the rest of the season. I realize that one cherry-picked example doesn’t prove my argument, but hopefully, you get the idea.

How can you use this information to your advantage in your fantasy leagues? People often talk of wanting to “sell high and buy low” with respect to making early-season trades, but do you actually have the backbone required to do so? If so, congratulations - you’re probably well on your way to scooping up some above-average players at below-average prices. If not, re-read the above, pick some real-life examples from the current season, and follow them.

Others may have an easier time selling high on a fast-starting player than buying low on a slow-starting player. Who are some of this year’s “slow starters” who may be ideal buy-low candidates? C.C. Sabathia and Roy Oswalt come to mind immediately on the pitching side, while Carl Crawford, Alfonso Soriano, Robinson Cano, and Ryan Braun are among the hitters off to sub-par starts. A savvy team owner will rebuff your attempts to trade for one of these players, but some may be willing to part with these players for a below-market offer.

I’ll leave you with an example I witnessed last season. A friend had Alex Rodriguez on his team, but was struggling in the pitching categories. His league required that all trades be balanced from a position standpoint (i.e. - you couldn’t trade a Third Baseman straight up for a Pitcher). In late May/early June he took advantage of a fellow owner’s willingness to sell low on Garrett Atkins and buy high on the fast-starting Boof Bonser, trading A-Rod and Boof Bonser in exchange for Garrett Atkins and Johan Santana. As you might expect, my friend was able to climb a number of places in his league’s standings after pulling off that trade.

Until next time,

The Sherpa

Many fantasy baseball leagues allow teams to claim available players from the free agent list/waiver wire on a first come, first served basis. Others have a weekly claim process in which the team currently at the bottom of the standings gets the first shot at the list of available players. My favorite method of awarding players to teams is the Free Agent Acquisition Budget (aka FAAB). Each team gets the same FAAB dollars (usually $100 or $1,000) to spend as it sees fit over the course of the season.

While the use of the FAAB system makes all unclaimed players available to all teams, many owners struggle with the question of how to spend their FAAB dollars as wisely as possible. Should the spending be front-loaded? Should the FAAB dollars be spent evenly over the course of the year? Should the money be hoarded until later in the season in case an injury to a real-life player necessitates a fantasy replacement or results in a hot-shot minor leaguer getting a shot at The Show?

Here are the Sherpa’s tips for spending your FAAB wisely - I call them my “FAAB Five”:

1. Pace yourself, but not too much. All else being equal, a player acquired earlier in the season is much more likely to affect your place in the year-end standings than a player acquired later in the season. If your league allows claims at the end of each week of the season, then a player claimed at the end of Week 1 (i.e. - the first claim) should have approximately 25 times the impact of a player claimed at the end of Week 25 (i.e. - the last claim). Your FAAB spending should reflect this reality, keeping in mind that most leagues require whole dollar bids of at least $1.
2. Assess other teams’ current needs before you bid. If you need to find a replacement next week for the DL’d Michael Barrett, and you’re the only team in your league that needs to pick up a Catcher, don’t bid against yourself. The notable exceptions to this tip involve players with potential contributions in the Steals and Saves categories. At least one other team will more than likely submit a bid on a player who may contribute in either of these two categories, even if they have no immediate need from a roster position standpoint.
3. Monitor other teams’ FAAB spending throughout the year. Unless your league creates automated reports tracking and summarizing FAAB spending by team, this can be time-consuming, but it’s well worth the effort to know how much money other teams have left if you may be bidding against them for a player.
4. Balance your remaining FAAB with a player’s actual abilities. Do not spend 50% of your FAAB dollars (or even 25% for that matter) on a pitcher like Livan Hernandez, even if you are unlucky enough to have Pedro Martinez, Rich Harden, John Lackey, Kelvim Escobar, and Doug Davis on your current pitching staff. Also, do not overspend for highly-touted rookies such as Clayton Kershaw, Evan Longoria, Colby Rasmus, Jay Bruce, etc. You may get lucky and get the next Ryan Braun or Hunter Pence, but you’re much more likely to get the next Nelson Cruz or Alex Escobar.
5. Timing is everything - don’t be afraid to speculate! If you have the roster positions to do so, speculate on desirable players before an injury, role change, or call-up from the minor leagues sends their value skyrocketing. Heath Bell, Rafael Betancourt, Tony Pena (the pitcher!), Carlos Quentin, and Joey Votto (if available in your league) will be much less expensive now than they will be if the players currently ahead of them on their respective teams’ depth charts falter or suffer an injury.

That said, the following is how I’d recommend spending a $100 FAAB if your league has 25 claims over the course of the season (i.e. - one at the end of each week starting with week 1). The week number is listed first, followed by the recommended amount to spend that week, followed by the recommended amount to spend year-to-date (YTD), and the implied FAAB dollars remaining. If you spend less than the recommended amount in a given week, then you will have more left over to spend in subsequent weeks, and vice versa.

• (End of) Week 1 - spend $8; $8 spent YTD, $92 remaining.
• Week 2 - $7; $15; $85.
• Week 3 - $7; $22; $78.
• Week 4 - $7; $29; $71.
• Week 5 - $6; $35; $65.
• Week 6 - $6; $41; $59.
• Week 7 - $6; $47; $53.
• Week 8 - $5; $52; $48.
• Week 9 - $5; $57; $43.
• Week 10 - $5; $62; $38.
• Week 11 - $5; $67; $33.
• Week 12 - $4; $71; $29.
• Week 13 - $4; $75; $25.
• Week 14 - $4; $79; $21.
• Week 15 - $3; $82; $18.
• Week 16 - $3; $85; $15.
• Week 17 - $3; $88; $12.
• Week 18 - $2; $90; $10.
• Week 19 - $2; $92; $8.
• Week 20 - $2; $94; $6.
• Week 21 - $2; $96; $4.
• Week 22 - $1; $97; $1.
• Week 23 - $1; $98; $1.
• Week 24 - $1; $99; $1.
• Week 25 - $1; $100; $0.

If your league also allows a claim before the first week of the season, here’s how I’d recommend adjusting your FAAB spending:

• (Start of) Week 1 - spend $7; $7 spent YTD, $93 remaining.
• Week 2 - $7; $14; $86.
• Week 3 - $7; $21; $79.
• Week 4 - $6; $27; $73.
• Week 5 - $6; $33; $67.
• Week 6 - $6; $39; $61.
• Week 7 - $6; $45; $55.
• Week 8 - $5; $50; $50.
• Week 9 - $5; $55; $45.
• Week 10 - $5; $60; $40.
• Week 11 - $5; $65; $35.
• Week 12 - $4; $69; $31.
• Week 13 - $4; $73; $27.
• Week 14 - $4; $77; $23.
• Week 15 - $3; $80; $20.
• Week 16 - $3; $83; $17.
• Week 17 - $3; $86; $14.
• Week 18 - $3; $89; $11.
• Week 19 - $2; $91; $9.
• Week 20 - $2; $93; $7.
• Week 21 - $2; $95; $5.
• Week 22 - $1; $96; $4.
• Week 23 - $1; $97; $3.
• Week 24 - $1; $98; $2.
• Week 25 - $1; $99; $1.
• Week 26 - $1; $100; $0.

I’ll post similar charts for a $1,000 FAAB later today.

Until then,

The Sherpa

So, you’re in a 12-team 5×5 Mixed league snake draft, you pick towards the end of the first round, and Albert Pujols is still available. Do you bite?

I say no, unless you have the 12th pick and can immediately make your second selection. The adage about “high risk, high reward” doesn’t apply to fantasy baseball drafts - you must, must, must get a “sure thing” with your first-round pick. That means identifying players with known injuries in Spring Training and avoiding them like the plague in the first round. Before the news of the extent of Pujols’ injuries came out, I based my projections for him on the assumption that he would play 155 games this season. At that point I would have considered him with the 9th pick, factoring in the Position Scarcity analysis that underlies my player rankings. After the news came out, I scaled back my assumption to 145 games, and even that may be too optimistic. If Pujols’ health concerns persist and the Cardinals are as bad as I think they’ll be this year (i.e. - they’re mathematically eliminated in late August/early September), I think it’s definitely possible that the Cardinals and Pujols would mutually agree that it’s in their best long-term interests to cut short his season (ala Dwyane Wade).

So, why would I argue that Pujols is a good pick with the last pick in the first round or the first pick in the second round, but not a few picks before that? At that point I think the risk vs. reward scale tips back in favor of the reward, so I’d be willing to take that chance. I wouldn’t pair Pujols with Johan Santana - you need to get at least one “sure thing” Hitter in the first two rounds. However, if I could pair Pujols with a first-round pick of Miguel Cabrera, Ryan Braun, Chase Utley, Jose Reyes, or Jimmy Rollins, then I would consider the reward to outweigh the risk. But even if I did that, I wouldn’t necessarily commit to keeping Pujols for the whole season. Suppose Pujols gets off to a strong start, and the concerns about his injury subside temporarily. In that case I would look to package him with someone else on my team who’s off to an unsustainable start in return for two above-average players who help me in my weaker categories.

Until next time,

The Sherpa