The Fantasy Baseball Sherpa

With fantasy baseball playoffs starting this week in many Head-to-Head leagues, your selection of Starting Pitchers takes on added importance.  Sure, you’re going to start Zack Greinke, Roy Halladay, and Tim Lincecum (assuming he’s healthy) if you’re fortunate enough to have them on your team this season.  But what if you’re trying to decide between Bronson Arroyo and Kevin Correia for your last pitching spot?

 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 9/12/09, here’s how the 30 MLB teams rank using the 4 categories in my example (with their accompanying score):

1. Pit 2.82 (max score is 4.00)
2. KC 2.90
3. Cin 2.94
4. SD 2.95
5. Was 2.96
6. SF 3.05
7. Sea 3.06
8. Ari 3.08
9. NYM 3.08
10. Hou 3.09
11. Bal 3.10
12. Oak 3.12
13. ChC 3.15
14. Mil 3.17
15. Cle 3.19
16. CWS 3.20
17. Det 3.20
18. Atl 3.25
19. Tor 3.25
20. TB 3.29
21. Min 3.30
22. StL 3.33
23. Fla 3.35
24. Phi 3.38
25. Col 3.41
26. Bos 3.48
27. LAD 3.50
28. Tex 3.56
29. LAA 3.62
30. NYY 3.80

You may wonder about the impact of September call-ups on these rankings.  Generally, the teams with the highest scores (i.e. - those closest to 4.00) are fighting for playoff berths and figure to play their everyday lineups at least until they’re locked into a playoff spot.  Those with the lowest scores are generally calling up more minor leaguers and “seeing what they’ve got”, so it may be even more advantageous than this chart would indicate to stream pitchers facing these lower-ranked teams.

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 Roy Halladay against the Yankees than start Jeff Suppan against the Pirates.  However, if you’re deciding among several pitchers of similar quality, this analysis can be extremely useful.

Good luck in the homestretch!

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 8/8/09, here’s how the 30 MLB teams rank using the 4 categories in my example (with their accompanying score):

1. Cin 3.11 (max score is 4.00)
2. SD 3.12
3. KC 3.14
4. Pit 3.15
5. Was 3.32
6. Sea 3.33
7. Oak 3.34
8. SF 3.34
9. Hou 3.35
10. Bal 3.38
11. NYM 3.39
12. Ari 3.39
13. ChC 3.40
14. Det 3.43
15. Mil 3.45
16. Fla 3.46
17. Cle 3.47
18. Tex 3.47
19. CWS 3.49
20. Atl 3.52
21. Tor 3.53
22. StL 3.53
23. Col 3.54
24. Min 3.55
25. Phi 3.64
26. Bos 3.68
27. TB 3.69
28. LAD 3.85
29. LAA 3.86
30. NYY 3.98

Several notable changes since the last rankings update (on 7/5/09):  Washington, Oakland, Arizona, and the Cubs are among the teams whose offenses have improved in the last month; Cincinnati, Pittsburgh, and Toronto are among the teams whose offenses have regressed during that same time period.  Obviously, it’s important to take a quick glance at a team’s current overall health compared to its health season-to-date.  Tracking the standings over time (weekly or bi-weekly updates are best) will give you a good sense of whether a team’s offenses is 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 Tim Lincecum against the Dodgers than start Livan Hernandez against the Reds.  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 7/3/09, here’s how the 30 MLB teams rank using the 4 categories in my example (with their accompanying score):

1. Was 2.96 (max score is 4.00)
2. SD 3.00
3. KC 3.01
4. Oak 3.07
5. Ari 3.11
6. ChC 3.16
7. Cin 3.21
8. SF 3.23
9. Sea 3.23
10. Hou 3.24
11. Pit 3.29
12. Atl 3.30
13. Tex 3.36
14. CWS 3.37
15. Bal 3.38
16. Mil 3.39
17. Fla 3.40
18. NYM 3.40
19. Cle 3.41
20. StL 3.43
21. Col 3.44
22. Det 3.44
23. Min 3.53
24. LAA 3.53
25. Phi 3.58
26. Tor 3.65
27. Bos 3.71
28. TB 3.72
29. LAD 3.73
30. NYY 3.77

No significant changes since the last rankings update (on 6/21/09), but it’s important to take a quick glance at a team’s current overall health compared to its health season-to-date.  Tracking the standings over time (weekly or bi-weekly updates are best) will give you a good sense of whether a team’s offenses is 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 Tim Lincecum against the Dodgers than start Livan Hernandez or Russ Ortiz 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

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

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 American 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. Alex Rodriguez, 3B, NYY (3.07 Remainder-of-Season Total Sherpa Points - 0.85 Year-to-Date Total Sherpa Points = +2.22)
2. Vlad Guerrero, OF, LAA (2.15 - 0.57 = +1.58)
3. Marcus Thames, OF, Det (1.98 - 0.62 =+1.36)
4. Matt Wieters, C, Bal (1.51 - 0.16 = +1.35)
5. David Ortiz, DH, Bos (1.89 - 0.67 = +1.24)
6. Matt Holliday, OF, Oak (2.90 - 1.77 = +1.13)
7. Josh Anderson, OF, Det (1.82 - 0.73 = +1.09)
8. Gordon Beckham, 2B, CWS (1.21 - 0.13 = +1.08)
9. Chris Davis, 1B/3B, Tex (1.76 - 0.74 = +1.02)
10. Pat Burrell, OF, TB (1.38 - 0.42 = +0.96)

Here are 10 American 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. Marco Scutaro, SS, Tor (1.34 - 2.16 = -0.82)
2. Adam Kennedy, 2B, Oak (0.66 - 1.33 = -0.67)
3. Brandon Inge, C/3B, Det (1.52 - 2.18 = -0.66)
4. Aaron Hill, 2B, Tor (1.90 - 2.53 = -0.63)
5. Scott Rolen, 3B, Tor (1.25 - 1.84 = -0.59)
6. Victor Martinez, C/1B, Cle (2.27 - 2.81 = -0.54)
7. Jason Bartlett, SS, TB (2.07 - 2.52 = -0.52)
8. Ben Zobrist, 2B/SS, TB (1.92 - 2.35 = -0.43)
9. Adam Lind, OF, Tor (2.11 - 2.46 = -0.35)
10. Melky Cabrera, OF, NYY (1.11 - 1.42 = -0.31)

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.

I’ll take a look at NL Hitters in my next post.

Until then,

The Sherpa

Fantasy Baseball Sherpa

The Fantasy Baseball Sherpa’s Blog

@fantasy_sherpa on Twitter

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 Pitchers who are good buy-low candidates for a league using the standard 5 Pitching categories (Wins, Saves, Strikeouts, ERA, & WHIP) based on stats through games of Sun 6/21/09:

1. Rich Harden, SP, ChC (2.13 Remainder-of-Season Total Sherpa Points - 0.66 Year-to-Date Total Sherpa Points = +1.47)
2. Cole Hamels, SP, Phi (2.27 - 1.03 = +1.24)
3. Roy Oswalt, SP, Hou (1.93 - 0.78 =+1.15)
4. Johan Santana, SP, NYM (3.40 - 2.32 = +1.08)
5. Hiroki Kuroda, SP, LAD (1.48 - 0.46 = +1.02)
6. Jose Valverde, RP, Hou (1.52 - 0.56 = +0.96)
7. Ricky Nolasco, SP, Fla (0.52 - -0.26 = +0.78)
8. Carlos Zambrano, SP, ChC (1.99 - 1.24 = +0.75)
9. Tim Lincecum, SP, SF (3.27 - 2.56 = +0.71)
10. Ryan Dempster, SP, ChC (1.92 - 1.28 = +0.64)

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

1. Zach Duke, SP, Pit (0.84 - 1.88 = -1.04)
2. Jason Marquis, SP, Col (0.75 - 1.55 = -0.80)
3. Jonathan Broxton, RP, LAD (1.91 - 2.61 = -0.70)
4. Livan Hernandez, SP, NYM (0.20 - 0.88 = -0.68)
5. Jeff Weaver, SP, LAD (0.01 - 0.60 = -0.59)
6. Johnny Cueto, SP, Cin (1.66 - 2.24 = -0.58)
7. Randy Wells, SP, ChC (0.43 - 1.00 = -0.57)
8. Chris Volstad, SP, Fla (0.51 - 1.02 = -0.51)
9. Brian Wilson, RP, SF (1.34 - 1.83 = -0.49)
10. Russ Ortiz, SP, Hou (0.06 - 0.55 = -0.49)

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.

I’ll take a look at AL Hitters in my next post.

Until then,

The Sherpa

Fantasy Baseball Sherpa

The Fantasy Baseball Sherpa’s Blog

@fantasy_sherpa on Twitter

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 American League Pitchers who are good buy-low candidates for a league using the standard 5 Pitching categories (Wins, Saves, Strikeouts, ERA, & WHIP) based on stats through games of Sun 6/21/09:

1. John Lackey, SP, LAA (1.74 Remainder-of-Season Total Sherpa Points - 0.00 Year-to-Date Total Sherpa Points = +1.74)
2. CC Sabathia, SP, NYY (3.20 - 1.86 = +1.34)
3. Joakim Soria, RP, KC (2.08 - 0.83 =+1.25)
4. Ervin Santana, SP, LAA (0.70 - -0.42 = +1.12)
5. Scott Kazmir, SP, TB (0.45 - -0.45 = +0.90)
6. Rich Hill, SP, Bal (1.15 - 0.29 = +0.86)
7. Francisco Liriano, SP, Min (0.83 - 0.02 = +0.81)
8. Jonathan Papelbon, RP, Bos (2.31 - 1.51 = +0.80)
9. Mariano Rivera, RP, NYY (2.21 - 1.43 = +0.78)
10. Joba Chamberlain, SP, NYY (1.59 - 0.86 = +0.73)

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

1. Edwin Jackson, SP, Det (1.21 - 2.50 = -1.29)
2. Kevin Millwood, SP, Tex (1.38 - 2.30 = -0.92)
3. Justin Verlander, SP, Det (2.00 - 2.58 = -0.58)
4. Scott Richmond, SP, Tor (0.80 - 1.36 = -0.56)
5. Jason Vargas, SP, Sea (0.32 - 0.88 = -0.56)
6. Drew Bailey, RP, Oak (1.35 - 1.90 = -0.55)
7. David Aardsma, RP, Sea (1.26 - 1.79 = -0.53)
8. J.P. Howell, RP, TB (0.79 - 1.30 = -0.51)
9. Scott Feldman, SP, Tex (0.61 - 1.12 = -0.51)
10. Josh Outman, SP, Oak (0.87 - 1.34 = -0.47)

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.

I’ll take a look at NL Pitchers in my next post.

Until then,

The Sherpa

Fantasy Baseball Sherpa

The Fantasy Baseball Sherpa’s Blog

@fantasy_sherpa on Twitter