The Fantasy Baseball Sherpa

This is the third in a series of posts taking a look back at the top fantasy baseball performers for 2009. In this post I’ll review the top 10 Hitters using a 5×5 NL-only format. For the purposes of this analysis I’m including only Matt Holliday’s Cardinals’ stats (he ranked 56th overall based on 235 At-Bats).

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

• Actual stats: 555 AB, 47 HR, 134 RBI, 16 SB, .328 AVG, 122 R, 4.03 Sherpa Pts (out of a max of 5.00)
• Projected stats: 562 AB, 39 HR, 118 RBI, 5 SB, .331 AVG, 106 R, 3.54 Sherpa Pts

2. Hanley Ramirez (3) - FLA, SS

• Actual stats: 574 AB, 24 HR, 105 RBI, 26 SB, .341 AVG, 100 R, 3.52 Sherpa Pts
• Projected stats: 635 AB, 36 HR, 93 RBI, 27 SB, .299 AVG, 119 R, 3.27 Sherpa Pts

3. Ryan Braun (7) - MIL, OF

• Actual stats: 620 AB, 31 HR, 108 RBI, 18 SB, .318 AVG, 110 R, 3.40 Sherpa Pts
• Projected stats: 639 AB, 38 HR, 113 RBI, 22 SB, .288 AVG, 108 R, 3.20 Sherpa Pts

4. Prince Fielder (24) - MIL, 1B

• Actual stats: 581 AB, 44 HR, 138 RBI, 2 SB, .298 AVG, 101 R, 3.15 Sherpa Pts
• Projected stats: 584 AB, 38 HR, 103 RBI, 4 SB, .277 AVG, 93 R, 2.44 Sherpa Pts

5. Matt Kemp (2) - LAD, OF

• Actual stats: 598 AB, 26 HR, 100 RBI, 34 SB, .301 AVG, 96 R, 2.95 Sherpa Pts
• Projected stats: 673 AB, 22 HR, 98 RBI, 35 SB, .306 AVG, 117 R, 3.41 Sherpa Pts

6. Ryan Howard (11) - PHI, 1B

• Actual stats: 608 AB, 43 HR, 138 RBI, 8 SB, .276 AVG, 102 R, 3.00 Sherpa Pts
• Projected stats: 587 AB, 51 HR, 146 RBI, 1 SB, .269 AVG, 104 R, 2.91 Sherpa Pts

7. Chase Utley (16) - PHI, 2B

• Actual stats: 565 AB, 31 HR, 93 RBI, 23 SB, .285 AVG, 112 R, 2.91 Sherpa Pts
• Projected stats: 495 AB, 25 HR, 87 RBI, 11 SB, .309 AVG, 95 R, 2.66 Sherpa Pts

8. Troy Tulowitzki (67) - COL, SS

• Actual stats: 535 AB, 31 HR, 90 RBI, 20 SB, .299 AVG, 99 R, 2.88 Sherpa Pts
• Projected stats: 578 AB, 16 HR, 77 RBI, 6 SB, .268 AVG, 90 R, 1.78 Sherpa Pts

9. Mark Reynolds (43) - ARI, 1B/3B

• Actual stats: 567 AB, 44 HR, 101 RBI, 24 SB, .263 AVG, 96 R, 2.86 Sherpa Pts
• Projected stats: 518 AB, 29 HR, 99 RBI, 6 SB, .263 AVG, 94 R, 2.14 Sherpa Pts

10. Derrek Lee (18) - CHC, 1B

• Actual stats: 525 AB, 35 HR, 111 RBI, 1 SB, .309 AVG, 91 R, 2.85 Sherpa Pts
• Projected stats: 621 AB, 23 HR, 92 RBI, 12 SB, .296 AVG, 97 R, 2.64 Sherpa Pts

The most interesting observation from my perspective - Hanley Ramirez’ shift from first to third in the Marlins’ batting order had exactly the opposite effect from what I’d projected. Instead of increasing his power numbers at the expense of his batting average, the reverse occurred.  Also, it’s interesting to see how a higher-than-expected Stolen Base total can lead to a large spike in a player’s fantasy value (e.g.- Albert Pujols, Chase Utley, Troy Tulowitzki, Mark Reynolds, Ryan Howard). 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 four of the Hitters listed above were in my preseason top 10 list. Who were the other six, and how did they fare? I’m glad you asked!

21. David Wright (4) - NYM, 3B

• Actual stats: 529 AB, 10 HR, 70 RBI, 26 SB, .304 AVG, 87 R, 2.35 Sherpa Pts
• Projected stats: 590 AB, 29 HR, 114 RBI, 21 SB, .310 AVG, 107 R, 3.32 Sherpa Pts

140. Jose Reyes (5) - NYM, SS

• Actual stats: 147 AB, 2 HR, 15 RBI, 11 SB, .279 AVG, 18 R, 0.53 Sherpa Pts
• Projected stats: 674 AB, 15 HR, 67 RBI, 63 SB, .292 AVG, 116 R, 3.25 Sherpa Pts

77. Alfonso Soriano (6) - ChC, OF

• Actual stats: 477 AB, 20 HR, 55 RBI, 9 SB, .241 AVG, 64 R, 1.28 Sherpa Pts
• Projected stats: 652 AB, 41 HR, 97 RBI, 28 SB, .285 AVG, 111 R, 3.24 Sherpa Pts

53. Carlos Beltran (8) - NYM, OF

• Actual stats: 300 AB, 10 HR, 48 RBI, 11 SB, .330 AVG, 49 R, 1.48 Sherpa Pts
• Projected stats: 587 AB, 33 HR, 118 RBI, 24 SB, .281 AVG, 115 R, 3.09 Sherpa Pts

51. Manny Ramirez (9) - LAD, OF

• Actual stats: 344 AB, 19 HR, 62 RBI, 0 SB, .294 AVG, 62 R, 1.60 Sherpa Pts
• Projected stats: 538 AB, 33 HR, 113 RBI, 1 SB, .320 AVG, 97 R, 3.05 Sherpa Pts

33. Lance Berkman (10) - HOU, 1B

• Actual stats: 449 AB, 25 HR, 80 RBI, 7 SB, .272 AVG, 72 R, 1.91 Sherpa Pts
• Projected stats: 549 AB, 33 HR, 110 RBI, 12 SB, .302 AVG, 103 R, 3.03 Sherpa Pts 

We’re gearing up here in Sherpaville to develop our projections for the 2010 season, 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

Fantasy Baseball Sherpa

The Fantasy Baseball Sherpa’s Blog

@fantasy_sherpa on Twitter

The Fantasy Baseball Sherpa’s Facebook fan page

This will be the first in a series of posts taking a look back at the top fantasy baseball performers for 2009. Today I’ll start with the top 10 Hitters using a 5×5 mixed league format.  The statistics from the Twins-Tigers play-in game are not included here.

1. Albert Pujols (Preseason rank was 2) - STL, 1B
   • Actual stats: 555 AB, 47 HR, 134 RBI, 16 SB, .328 AVG, 122 R, 3.85 Sherpa Pts (out of a max of 5.00)
   • Projected stats: 562 AB, 39 HR, 118 RBI, 5 SB, .331 AVG, 106 R, 3.43 Sherpa Pts
2. Hanley Ramirez (4) - FLA, SS
   • Actual stats: 574 AB, 24 HR, 105 RBI, 26 SB, .341 AVG, 100 R, 3.27 Sherpa Pts
   • Projected stats: 635 AB, 36 HR, 93 RBI, 27 SB, .299 AVG, 119 R, 3.27 Sherpa Pts
3. Ryan Braun (8) - MIL, OF
   • Actual stats: 620 AB, 31 HR, 108 RBI, 18 SB, .318 AVG, 110 R, 3.20 Sherpa Pts
   • Projected stats: 639 AB, 38 HR, 113 RBI, 22 SB, .288 AVG, 108 R, 3.10 Sherpa Pts
4. Prince Fielder (49) -MIL, 1B
   • Actual stats: 581 AB, 44 HR, 138 RBI, 2 SB, .298 AVG, 101 R, 3.15 Sherpa Pts
   • Projected stats: 584 AB, 38 HR, 103 RBI, 4 SB, .277 AVG, 93 R, 2.44 Sherpa Pts
5. Joe Mauer (126) - MIN, C
   • Actual stats: 509 AB, 28 HR, 95 RBI, 4 SB, .367 AVG, 90 R, 3.04 Sherpa Pts
   • Projected stats: 437 AB, 8 HR, 66 RBI, 4 SB, .309 AVG, 72 R, 1.73 Sherpa Pts
6. Ryan Howard (13) - PHI, 1B
   • Actual stats: 608 AB, 43 HR, 138 RBI, 8 SB, .276 AVG, 102 R, 3.00 Sherpa Pts
   • Projected stats: 587 AB, 51 HR, 146 RBI, 1 SB, .269 AVG, 104 R, 2.91 Sherpa Pts
7. Miguel Cabrera (9) - DET, 1B
   • Actual stats: 595 AB, 33 HR, 101 RBI, 6 SB, .329 AVG, 95 R, 3.00 Sherpa Pts
   • Projected stats: 612 AB, 35 HR, 125 RBI, 3 SB, .310 AVG, 93 R, 3.05 Sherpa Pts
8. Derek Jeter (30) - NYY, SS
   • Actual stats: 627 AB, 18 HR, 66 RBI, 30 SB, .335 AVG, 107 R, 2.98 Sherpa Pts
   • Projected stats: 680 AB, 12 HR, 81 RBI, 17 SB, .307 AVG, 106 R, 2.63 Sherpa Pts
9. Matt Kemp (3) - LAD, OF
   • Actual stats: 598 AB, 26 HR, 100 RBI, 34 SB, .301 AVG, 96 R, 2.95 Sherpa Pts
   • Projected stats: 673 AB, 22 HR, 98 RBI, 35 SB, .306 AVG, 117 R, 3.29 Sherpa Pts
10. Carl Crawford (27) - TB, OF
    • Actual stats: 598 AB, 15 HR, 68 RBI, 60 SB, .306 AVG, 95 R, 2.92 Sherpa Pts
    • Projected stats: 600 AB, 12 HR, 78 RBI, 44 SB, .293 AVG, 94 R, 2.65 Sherpa Pts

Albert Pujols captured the top spot for the second straight season.  AVG remains the most difficult hitting category to project. Injuries obviously impact the actual rankings (e.g. - Jose Reyes and Carlos Beltran), but so do changes in batting order position (e.g. - Matt Kemp). 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 5 of the Hitters listed above were in my preseason top 10 list (that was also the case in 2008). Who were the other five, and how did they fare? I’m glad you asked!

13. Matt Holliday (1) - OAK/STL, OF

• Actual stats: 581 AB, 24 HR, 109 RBI, 14 SB, .313 AVG, 94 R, 2.79 Sherpa Pts
• Projected stats: 616 AB, 31 HR, 110 RBI, 20 SB, .318 AVG, 117 R, 3.44 Sherpa Pts

54. David Wright (5) - NYM, 3B

• Actual stats: 529 AB, 10 HR, 70 RBI, 26 SB, .304 AVG, 87 R, 2.19 Sherpa Pts
• Projected stats: 590 AB, 29 HR, 114 RBI, 21 SB, .310 AVG, 107 R, 3.21 Sherpa Pts

259. Jose Reyes (6) - NYM, SS

• Actual stats: 147 AB, 2 HR, 15 RBI, 11 SB, .279 AVG, 18 R, 0.53 Sherpa Pts
• Projected stats: 674 AB, 15 HR, 67 RBI, 63 SB, .292 AVG, 116 R, 3.14 Sherpa Pts

165. Alfonso Soriano (7) - CHC, OF

• Actual stats: 477 AB, 20 HR, 55 RBI, 9 SB, .241 AVG, 64 R, 1.07 Sherpa Pts
• Projected stats: 652 AB, 41 HR, 97 RBI, 28 SB, .285 AVG, 111 R, 3.12 Sherpa Pts

116. Carlos Beltran (10) - NYM, OF

• Actual stats: 300 AB, 10 HR, 48 RBI, 11 SB, .330 AVG, 49 R, 1.48 Sherpa Pts
• Projected stats: 587 AB, 33 HR, 118 RBI, 24 SB, .281 AVG, 115 R, 2.99 Sherpa Pts

We’re getting ready here in Sherpaville to develop projections for the 2010 season, but I’ll continue this series with a look at the 2009 top 10 Pitchers in a mixed league 5×5 format as time permits.

Until next time,

The Sherpa

Fantasy Baseball Sherpa

The Fantasy Baseball Sherpa’s Blog

@fantasy_sherpa on Twitter

The Fantasy Baseball Sherpa’s Facebook fan page

This time of year many fantasy baseball team owners look to trades in an effort to improve their place in the standings.  Of course, everyone’s ideal is to trade away players who will perform worse over the remainder of the season than they have year-to-date, while simultaneously trading for players who will perform better over the remainder of the season than they have year-to-date.

How should you assess a player’s year-to-date value vs. his forecasted remainder-of season value?  Using Fantasy Baseball Sherpa’s In-season Updates tool, an owner can quantify both of these values in an effort to identify players who are currently undervalued and overvalued.  Fantasy Baseball Sherpa assigns a score of 1.00 Sherpa Points to the league leader in each category.  All other players are assigned a score for that category based on their result relative to the league leader’s result.

For example, if the league leader has hit 26 HRs year-to-date, then a player who has hit 13 HRs year-to-date would be assigned a scoreof 0.50 Sherpa Points.  For ratio categories (e.g.- AVG, ERA) a proxy statistic is used.  A player’s scores in each category can be added up to determine the player’s Total Sherpa Points.  A player’s maximum score is equal to the number of categories used (note:  this maximum score will be different for Hitters and Pitchers if your league uses a different number of categories for Hitters and Pitchers).

Here are 10 National League Hitters who are good buy-low candidates for a league using the standard 5 Hitting categories (AVG, Home Runs, RBI, Stolen Bases, & Runs Scored) based on stats through games of Sun 6/21/09:

1. Alfonso Soriano, OF, ChC (2.79 Remainder-of-Season Total Sherpa Points - 1.50 Year-to-Date Total Sherpa Points = +1.29)
2. Carlos Gonzalez, OF, Col (1.33 - 0.20 = +1.13)
3. Jimmy Rollins, SS, Phi (2.18 - 1.06 =+1.12)
4. Geovany Soto, C, ChC (1.55 - 0.50 = +1.05)
5. Chris Coghlan, 3B/OF, Fla (1.81 - 0.77 = +1.04)
6. Lance Berkman, 1B, Hou (2.53 - 1.55 = +0.98)
7. Andrew McCutchen, OF, Pit (1.68 - 0.72 = +0.96)
8. Ryan Ludwick, OF, StL (2.17 - 1.22 = +0.95)
9. Brian Giles, OF, SD (1.04 - 0.10 = +0.94)
10. Everth Cabrera, SS, SD (1.05 - 0.15 = +0.90)

Here are 10 National League Hitters who are good sell-high candidates for a league using the standard 5 Hitting categories based on stats through games of Sun 6/21/09:

1. Orlando Hudson, 2B, LAD (1.41 - 2.18 = -0.77)
2. Raul Ibanez, OF, Phi (2.49 - 3.21 = -0.72)
3. Justin Upton, OF, Ari (2.00 - 2.65 = -0.65)
4. Todd Helton, 1B, Col (1.80 - 2.26 = -0.46)
5. Clint Barmes, 2B/SS, Col (1.50 - 1.90 = -0.40)
6. Pablo Sandoval, C/1B/3B, SF (1.67 - 2.03 = -0.36)
7. Gary Sheffield, OF, NYM (1.14 - 1.40 = -26)
8. Mark Reynolds, 1B/3B, Ari (2.38 - 2.62 = -0.24)
9. Nick Johnson, 1B, Was (1.59 - 1.82 = -0.23)
10. Michael Bourn, OF, Hou (1.86 - 2.08 = -0.22)

Of course, there are a number of reasons why a player’s performance over the remainder of the season may vary significantly from his performance year-to-date, including normal variation in results, injuries, changes in roles, etc.  By attempting to quantify both a player’s year-to-date and remainder-of-season results, we can take at least some of the guesswork out of identifying buy-low and sell-high candidates.

Until next time,

The Sherpa

Fantasy Baseball Sherpa

The Fantasy Baseball Sherpa’s Blog

@fantasy_sherpa on Twitter

So, it’s Sunday night or Monday morning, and your league’s weekly lineup submissions are due.  You’re trying to decide among three starting pitchers (whether on your current roster or not) to fill your last pitching slot.  How should you go about it?

You could “go with your gut” and hope for the best (good luck with that).  You could look up each pitcher’s historical record (assuming he has one) against his upcoming opponent(s) and use that as a guide, ignoring the fact that a team’s roster is likely to experience significant turnover from season to season that will render historical results obsolete.  You could rely on the Remainder-of-Season Forecasts in the Fantasy Baseball Sherpa’s In-season Updates (shameless self-promotion).

While the third option is definitely better than the first two, it still leaves out one crucial component if you’re trying to make a short-term decision on which pitcher to start:  the quality of the pitcher’s opponent.  How can this be quantified?  The same way you would quantify the pitcher’s results - look at the historical data.

To assess a starting pitcher’s upcoming matchup(s) we want to use opponents’ success (or lack thereof) against a specific team.  For example, if I play in a league that uses the standard 5 pitching categories (Wins, Saves, Ks, ERA, WHIP), I’ll want to look at MLB Opponent Pitching Stats in each category that involves starting pitchers, so that eliminates Saves from my list.

I want to set my scoring system up so that the least desirable opponents have the highest scores, and the most desirable opponents have the lowest scores.  The least desirable opponent would have the highest number of Wins (equivalently, the lowest number of losses), the biggest difference between AB and Strikeouts (or, if you prefer, the lowest Strikeout per AB rate), the highest number of Runs Scored (using this as a proxy for ERA), and the highest number of Walks + Hits (using this as a proxy for WHIP).  Conversely, the most desirable opponent would have the lowest number of Wins (equivalently, the highest number of losses), the highest Strikeout per AB rate, the lowest number of Runs Scored, and the lowest number of Walks + Hits.

We can set up a scoring system for which the “best” team in each category receives a score of 1.00, and all other teams receive a score between 0 and 1 depending on the ratio of their result to the result of the best team in each category.  Thus, the maximum score is the number of pitching categories under consideration (4 in my example).  Add up a team’s results in each category to get its overall score; again, the lower the overall score, the more desirable the opponent.

Based on games through 6/20/09, here’s how the 30 MLB teams rank using the 4 categories in my example (with their accompanying score):

1. SD 2.96 (max score is 4.00)
2. Was 2.98
3. KC 3.03
4. ChC 3.07
5. Oak 3.13
6. SF 3.13
7. CWS 3.15
8. Sea 3.15
9. Ari 3.18
10. Hou 3.18
11. Atl 3.20
12. Cin 3.21
13. Pit 3.22
14. Bal 3.34
15. Tex 3.35
16. Fla 3.36
17. NYM 3.39
18. Mil 3.40
19. StL 3.41
20. Det 3.42
21. LAA 3.43
22. Col 3.43
23. Cle 3.46
24. Phi 3.50
25. Min 3.53
26. Bos 3.69
27. NYY 3.70
28. TB 3.71
29. Tor 3.72
30. LAD 3.81

No surprise to see teams like the Nationals, Royals, Padres, A’s, Mariners, and Giants at the top of the list of most desirable opponents, but the presence of the Cubs among the “worst” offensive teams is a bit of a surprise to me.  If you’d asked me before the season started, I would have told you that the Cubs should have one of the best offenses in baseball.  Of course, Aramis Ramirez’ injury combined with slow starts by Geovany Soto, Derrek Lee, and Alfonso Soriano have all contributed to the Cubs’ abysmal ranking.  However, it points out the need to take a quick glance at a team’s current overall health compared to its health season-to-date.  The NY Mets are ranked in the middle of the pack according to this chart, but sans Carlos Delgado and Jose Reyes, they’re obviously a less formidable foe now than they would be if this pair were healthy.  Tracking the standings over time (I’d suggest weekly or bi-weekly updates) will give you a good sense of which team’s offenses are improving, treading water, or getting worse.

The approach I’ve outlined above can take some of the guesswork out of selecting starting pitchers for your weekly lineups.  Of course, use your common sense - given the choice, I’d much rather start Johan Santana against the Dodgers than start Livan Hernandez or Tim Redding against the Padres.  However, if you’re deciding among several pitchers of similar quality, this analysis can be extremely useful.

Until next time!

The Sherpa

Fantasy Baseball Sherpa

The Fantasy Baseball Sherpa’s Blog

@fantasy_sherpa on Twitter

So, it’s Sunday night or Monday morning, and your league’s weekly lineup submissions are due.  You’re trying to decide among three starting pitchers (whether on your current roster or not) to fill your last pitching slot.  How should you go about it?

You could “go with your gut” and hope for the best (good luck with that).  You could look up each pitcher’s historical record (assuming he has one) against his upcoming opponent(s) and use that as a guide, ignoring the fact that a team’s roster is likely to experience significant turnover from season to season that will render historical results obsolete.  You could rely on the Remainder-of-Season Forecasts in the Fantasy Baseball Sherpa’s In-season Updates (shameless self-promotion).

While the third option is definitely better than the first two, it still leaves out one crucial component if you’re trying to make a short-term decision on which pitcher to start:  the quality of the pitcher’s opponent.  How can this be quantified?  The same way you would quantify the pitcher’s results - look at the historical data.

To assess a starting pitcher’s upcoming matchup(s) we want to use opponents’ success (or lack thereof) against a specific team.  For example, if I play in a league that uses the standard 5 pitching categories (Wins, Saves, Ks, ERA, WHIP), I’ll want to look at MLB Opponent Pitching Stats in each category that involves starting pitchers, so that eliminates Saves from my list.

I want to set my scoring system up so that the least desirable opponents have the highest scores, and the most desirable opponents have the lowest scores.  The least desirable opponent would have the highest number of Wins (equivalently, the lowest number of losses), the biggest difference between AB and Strikeouts (or, if you prefer, the lowest Strikeout per AB rate), the highest number of Runs Scored (using this as a proxy for ERA), and the highest number of Walks + Hits (using this as a proxy for WHIP).  Conversely, the most desirable opponent would have the lowest number of Wins (equivalently, the highest number of losses), the highest Strikeout per AB rate, the lowest number of Runs Scored, and the lowest number of Walks + Hits.

We can set up a scoring system for which the “best” team in each category receives a score of 1.00, and all other teams receive a score between 0 and 1 depending on the ratio of their result to the result of the best team in each category.  Thus, the maximum score is the number of pitching categories under consideration (4 in my example).  Add up a team’s results in each category to get its overall score; again, the lower the overall score, the more desirable the opponent.

Based on games through 6/13/09, here’s how the 30 MLB teams rank using the 4 categories in my example (with their accompanying score):

1. Was 2.92 (max score is 4.00)
2. KC 3.02
3. SD 3.03
4. ChC 3.05
5. Oak 3.11
6. Sea 3.11
7. SF 3.12
8. Hou 3.16
9. CWS 3.16
10. Ari 3.18
11. Bal 3.20
12. Cin 3.20
13. Pit 3.21
14. Atl 3.22
15. Mil 3.31
16. LAA 3.34
17. Col 3.35
18. StL 3.36
19. Fla 3.39
20. Tex 3.40
21. Det 3.41
22. NYM 3.43
23. Cle 3.45
24. Min 3.52
25. Phi 3.55
26. Tor 3.69
27. NYY 3.71
28. TB 3.71
29. Bos 3.73
30. LAD 3.80

No surprise to see teams like the Nationals, Royals, Padres, A’s, Mariners, and Giants at the top of the list of most desirable opponents, but the presence of the Cubs among the “worst” offensive teams is a bit of a surprise to me.  If you’d asked me before the season started, I would have told you that the Cubs should have one of the best offenses in baseball.  Of course, Aramis Ramirez’ injury combined with slow starts by Geovany Soto, Derrek Lee, and (to a lesser extent) Alfonso Soriano have all contributed to the Cubs’ abysmal ranking.  However, it points out the need to take a quick glance at a team’s current overall health compared to its health season-to-date.  The NY Mets might be ranked as one of the least desirable opponents according to this chart, but sans Carlos Delgado and Jose Reyes, they’re obviously a less formidable foe now than they would be if this pair were healthy.

Nevertheless, the approach I’ve outlined above can take some of the guesswork out of selecting starting pitchers for your weekly lineups.  Of course, use your common sense - given the choice, I’d much rather start Johan Santana against the Dodgers (yes, in spite of his awful start today against the Yankees!) than Livan Hernandez or Tim Redding against the Nationals.  However, if you’re deciding among several pitchers of similar quality, this analysis can be extremely useful.

Until next time!

The Sherpa

Fantasy Baseball Sherpa

The Fantasy Baseball Sherpa’s Blog

@fantasy_sherpa on Twitter

Hi everyone,

Here are the forecasted Top 10 performers for the rest of the season for a 5×5 Mixed League 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. Albert Pujols (StL, 1B) - 482 AB, 35 HR, 110 RBI, 7 SB, .332 AVG, 96 R, 3.71 Total Sherpa Points
2. Johan Santana (NYM, SP) - 196 IP, 12 W, 0 SV, 2.71 ERA, 1.07 WHIP, 200 K, 3.65 Total Sherpa Points
3. Matt Kemp (LAD, OF) - 577 AB, 19 HR, 88 RBI, 31 SB, .308 AVG, 101 R, 3.45 Total Sherpa Points
4. Alfonso Soriano (ChC, OF) - 567 AB, 37 HR, 85 RBI, 25 SB, .286 AVG, 102 R, 3.35 Total Sherpa Points
5. Chase Utley (Phi, 2B) - 508 AB, 28 HR, 96 RBI, 12 SB, .313 AVG, 102 R, 3.35 Total Sherpa Points
6. Alex Rodriguez (NYY, 3B) - 448 AB, 33 HR, 99 RBI, 16 SB, .304 AVG, 96 R, 3.30 Total Sherpa Points
7. Matt Holliday (Oak, OF) - 548 AB, 24 HR, 97 RBI, 16 SB, .310 AVG, 96 R, 3.28 Total Sherpa Points
8. Hanley Ramirez (Fla, SS) - 549 AB, 29 HR, 81 RBI, 23 SB, .299 AVG, 98 R, 3.25 Total Sherpa Points
9. Miguel Cabrera (Det, 1B) - 533 AB, 30 HR, 107 RBI, 3 SB, .319 AVG, 82 R, 3.24 Total Sherpa Points
10.  Ryan Braun (Mil, OF) - 552 AB, 33 HR, 99 RBI, 18 SB, .292 AVG, 93 R, 3.33 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

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

Hi everyone,

Here are the Top 10 performers through April 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 league-leader has hit 9 HR, then a player with 3 HR would be given a score of 0.33 Sherpa Points).  The maximum Total Sherpa Points is equal to the number of categories (i.e. - 5.00).

1. Albert Pujols (StL, 1B) - 83 AB, 8 HR, 28 RBI, 4 SB, .337 AVG, 22 R, 3.95 Total Sherpa Points
2. Johan Santana (NYM, SP) - 32.2 IP, 3 W, 0 SV, 1.10 ERA, 0.95 WHIP, 44 K, 3.38 Total Sherpa Points
3. Raul Ibanez (Phi, OF) - 78 AB, 7 HR, 17 RBI, 3 SB, .359 AVG, 20 R, 3.38 Total Sherpa Points
4. Adrian Gonzalez (SD, 1B) - 81 AB, 9 HR, 20 RBI, 1 SB, .333 AVG, 19 R, 3.26 Total Sherpa Points
5. Dan Haren (Ari, SP) - 35.0 IP, 2 W, 0 SV, 1.54 ERA, 0.74 WHIP, 36 K, 3.24 Total Sherpa Points
6. Chase Utley (Phi, 2B) - 73 AB, 7 HR, 20 RBI, 2 SB, .342 AVG, 18 R, 3.12 Total Sherpa Points
7. Jorge Cantu (Fla, 1B/3B) - 63 AB, 7 HR, 22 RBI, 1 SB, .365 AVG, 15 R, 3.01 Total Sherpa Points
8. Chad Billingsley (LAD, SP) - 33.2 IP, 4 W, 0 SV, 2.14 ERA, 1.01 WHIP, 34 K, 3.00 Total Sherpa Points
9. Alfonso Soriano (ChC, OF) - 88 AB, 7 HR, 14 RBI, 4 SB, .284 AVG, 21 R, 2.86 Total Sherpa Points
10. Manny Ramirez (LAD, OF) - 78 AB, 5 HR, 15 RBI, 0 SB, .372 AVG, 19 R, 2.81 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

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