For the second consecutive year, Albert Pujols comes out ahead in the statistical matrix I created called Offensive Independent Base Index. By independent, I mean that no other player bunched up close to the batter influences the outcome of this equation.
This offensive rating system measures the impact of the measured player against a marginal player [in this case being league average]. This matrix displays how many percentage points above or below the margin the measured play is. The final outcome reads like a batting average, but is quantified with singles, doubles, triple, home runs, base-on-balls, and outs. Furthermore, slight modifications are made in accordance to the ball park reconciliation [as each park has its own factors], along with a new feature I have added this year, a value based timeline.
Any ball player performing under .250 is considered below the mean [or below average], .250 is marginal, .275 is above average but not really great, .300 being very good, .325 being an all time great year.
Here are the results;
1. Alert Pujols .336
2. Prince Fielder .320
3. Joe Mauer .319
4. Adrian Gonzalez .313
5. Joey Votto .313
6. Derek Lee .311
7. Adam Dunn .307
8. Ben Zubrist .305
9. Lance Berkman .305
10. Hanley Ramirez .304
Tuesday, April 13, 2010
Thursday, January 15, 2009
2008 Lineup Inpact win Percentage
This offensive rating system, Lineup Impact Percentage, measures the impact of a ball player interacting with eight other marginal players [nine players including the rated player]. By measuring against eight other average players, you get more feel of a player's impact in a batting order. To be more precise, this matrix displays how many percentage points above or below the .500 mark [the mean] a player impacts in a nine player batting lineup. As a result, this matrix is bunched much closer together. LIP is more realistic as opposed to my previous measurement, purely against the league average as if only two people are competing against each other. In short, I have converted this rating into both team and lineup context.
LIP is adjusted for the rated player's home ballpark, measured against the National League, American League, and when it applies, both leagues' mean. These documented statistics are rounded off and are calculated from the Bill James Handbook 2009.
1. Albert Pujols .524
2. Chipper Jones .52053
3. Manny Ramirez .51678
4. Lance Berkman .51372
5. Mark Teixeira .51337
6. Ryan Ludwick .51234
7. Hanley Ramirez .51189
8. Matt Holliday .51176
9. A-Rod .51146
10. Adam Dunn .51121
11. Carlos Quentin .51115
12. Kevin Youkilis .51114
13. David Wright .51107
14. Pat Burrell .50939
15. Jason Bay .50901
16. Aramis Ramirez .50880
17. Brad Hawpe .50855
18. Carlos Beltran. 50853
19. Chase Utley .50834
20.Ryan Howard .50828
LIP is adjusted for the rated player's home ballpark, measured against the National League, American League, and when it applies, both leagues' mean. These documented statistics are rounded off and are calculated from the Bill James Handbook 2009.
1. Albert Pujols .524
2. Chipper Jones .52053
3. Manny Ramirez .51678
4. Lance Berkman .51372
5. Mark Teixeira .51337
6. Ryan Ludwick .51234
7. Hanley Ramirez .51189
8. Matt Holliday .51176
9. A-Rod .51146
10. Adam Dunn .51121
11. Carlos Quentin .51115
12. Kevin Youkilis .51114
13. David Wright .51107
14. Pat Burrell .50939
15. Jason Bay .50901
16. Aramis Ramirez .50880
17. Brad Hawpe .50855
18. Carlos Beltran. 50853
19. Chase Utley .50834
20.Ryan Howard .50828
Monday, October 6, 2008
2007 Lineup Inpact win Percentage
Lineup Inpact Percentage
This offensive rating system, Lineup Impact Percentage, measures the impact of a ball player interacting with eight other marginal players [nine players including the rated player]. By measuring against eight other average players, you get more feel of a player's impact in a batting order. To be more precise, this matrix displays how many percentage points above or below the .500 mark [the mean] a player impacts in a nine player batting lineup. As a result, this matrix is bunched much closer together. LIP is more realistic as opposed to my previous measurement, purely against the league average as if only two people are competing against each other. In short, I have converted this rating into both team and lineup context.
LIP is adjusted for the rated player's home ballpark, measured against the National League, American League, and when it applies, both leagues' mean. These documented statistics are rounded off and are calculated from the Bill James Handbook 2008.
1. Chipper Jones .518
2. Carlos Pena .518
3. Alex Rodriguez .518
4. David Ortiz .517
5. Magglio Ordonez .516
6. Albert Pujols .516
7. Nick Johnson .515
8. John Cust .515
9. Prince Fielder .514
10. David Wright .514
11. Miguel Cabrera .513
12. Jorge Posada .513
13. Jim Thome .512
14. Mark Teixeira .512
15. Ryan Howard .512
16. Hanley Ramirez .511
17. Matt Holliday .511
18. Vladimir Guerrero .510
19. Adam Dunn .510
20. Todd Helton .509
This offensive rating system, Lineup Impact Percentage, measures the impact of a ball player interacting with eight other marginal players [nine players including the rated player]. By measuring against eight other average players, you get more feel of a player's impact in a batting order. To be more precise, this matrix displays how many percentage points above or below the .500 mark [the mean] a player impacts in a nine player batting lineup. As a result, this matrix is bunched much closer together. LIP is more realistic as opposed to my previous measurement, purely against the league average as if only two people are competing against each other. In short, I have converted this rating into both team and lineup context.
LIP is adjusted for the rated player's home ballpark, measured against the National League, American League, and when it applies, both leagues' mean. These documented statistics are rounded off and are calculated from the Bill James Handbook 2008.
1. Chipper Jones .518
2. Carlos Pena .518
3. Alex Rodriguez .518
4. David Ortiz .517
5. Magglio Ordonez .516
6. Albert Pujols .516
7. Nick Johnson .515
8. John Cust .515
9. Prince Fielder .514
10. David Wright .514
11. Miguel Cabrera .513
12. Jorge Posada .513
13. Jim Thome .512
14. Mark Teixeira .512
15. Ryan Howard .512
16. Hanley Ramirez .511
17. Matt Holliday .511
18. Vladimir Guerrero .510
19. Adam Dunn .510
20. Todd Helton .509
Best Pitchers 2006
Results of the best pitchers of 2006 are figured by simply finding runs against, adjusting to the ballpark factor, then dividing by innings pitched times three + hits + tbb. Divided this figure by the league average [.123 runs against average], and calculate the Binomial Distribution.
While this stat is read similar to era, it is in the binomial form. Since this mass of numbers is read in a relative context, the mean is 3.16.
Pitchers are classified by starting pitchers and closing pitchers. To qualify for this stat, starting pitchers must pitch at least 150 innings, while releif pitchers must record at least 15 saves.
Starting pitchers:
R. Oswaldt 2.15
J. Santana 2.26
C. Carpenter 2.28
R. Holliday 2.30
Josh Johnson 2.30
B Webb 2.30
B. Arroyo 2.33
J. Jennings 2.41
C. Zambrano 2.46
Releif pitchers:
J. Papelbaum 1.45
BJ Ryan 1.50
J. Nathan 1.55
Fr Rodriguez 1.65
M. Rivera 1.71
While this stat is read similar to era, it is in the binomial form. Since this mass of numbers is read in a relative context, the mean is 3.16.
Pitchers are classified by starting pitchers and closing pitchers. To qualify for this stat, starting pitchers must pitch at least 150 innings, while releif pitchers must record at least 15 saves.
Starting pitchers:
R. Oswaldt 2.15
J. Santana 2.26
C. Carpenter 2.28
R. Holliday 2.30
Josh Johnson 2.30
B Webb 2.30
B. Arroyo 2.33
J. Jennings 2.41
C. Zambrano 2.46
Releif pitchers:
J. Papelbaum 1.45
BJ Ryan 1.50
J. Nathan 1.55
Fr Rodriguez 1.65
M. Rivera 1.71
Cardinals Redux
The one year won-loss record tells to the average "Joe Smoe" that the recently victoriously Cardinals are not worthy of being holders of the World Series Title. Just allow me time to change his mind.
The fact that the Cardinals won the World Series really should not be as much of a surprise as some people think. While the team played really poorly during the year, they still had the capability to come back because the team had proven itself in the past that they are already winners. Anyways, almost the entire team was bitten by the injury bug at one time during the year [including the main players Pujols, Carpenter, Rolen, Edmonds].
The below chart documents the NL and AL three year won-loss record, showing that the Cardinals have best NL won-loss record over the span of the last three years. Simply put, this win over the Tigers is not as much of a fluke as the in your face ESPN media makes it out to sound.
American League
NYY 293-193 .603
Boston 279-207 .574
LAA 276-210 .568
Oakland 272-214 .560
Chicago 272-214 .560
MN 271-215 .558
Cleve 251-235 .516
TX 248-238 .510
Detroit 238-248 .490
Toronto 234-251 .482
Baltimore 222-264 .457
Seattle 210-276.432
Tampa 198-287 .408
KC 176-310 .362
National League
St. Louis 288-197 .594
Atlanta 265-221 .545
LA 252-214 .541
Hou 263-223 .541
Phil 259-227 .533
SD 257-229 .529
NYM 251-239 .512
Florida 244-242 .502
SF 242-243 .499
Chicago 234-252 .481
Cin 229-257 .471
Mil 223-262.460
Wash/Mon 219-267 .451
Col 211-275 .434
Pitt 206-279 .425
AZ 204-282 .420
The fact that the Cardinals won the World Series really should not be as much of a surprise as some people think. While the team played really poorly during the year, they still had the capability to come back because the team had proven itself in the past that they are already winners. Anyways, almost the entire team was bitten by the injury bug at one time during the year [including the main players Pujols, Carpenter, Rolen, Edmonds].
The below chart documents the NL and AL three year won-loss record, showing that the Cardinals have best NL won-loss record over the span of the last three years. Simply put, this win over the Tigers is not as much of a fluke as the in your face ESPN media makes it out to sound.
American League
NYY 293-193 .603
Boston 279-207 .574
LAA 276-210 .568
Oakland 272-214 .560
Chicago 272-214 .560
MN 271-215 .558
Cleve 251-235 .516
TX 248-238 .510
Detroit 238-248 .490
Toronto 234-251 .482
Baltimore 222-264 .457
Seattle 210-276.432
Tampa 198-287 .408
KC 176-310 .362
National League
St. Louis 288-197 .594
Atlanta 265-221 .545
LA 252-214 .541
Hou 263-223 .541
Phil 259-227 .533
SD 257-229 .529
NYM 251-239 .512
Florida 244-242 .502
SF 242-243 .499
Chicago 234-252 .481
Cin 229-257 .471
Mil 223-262.460
Wash/Mon 219-267 .451
Col 211-275 .434
Pitt 206-279 .425
AZ 204-282 .420
Pythamonitor
2005 Standings
Al
NL
.599 New York [D]
.599 New York [D]
.593 Minnesota [D]
.543 San Diego [W]
.586 Detroit [W]
.543 Los Angeles [D]
.574 Oakland [D]
.525 Philadelphia
.556 Chicago
.516 St. Louis [D]
.537 Toronto
.506 Houston
.531 Boston
.494 Cincinnati
.521 Los Angeles
.488 Atlanta
.494 Texas
.481 Florida
.481 Seattle
.472 San Francisco
.481 Cleveland
.469 Colorado
.432 Baltimore
.469 Arizona
.383 Kansas City
.463 Milwaukee
.377 Tampa Bay
.436 Washington
.414 Pittsburgh
.407 Chicago
[Symbols; D=Division Winners/W=Wildcard Winner]
While I recently consulted the handy pythamonitor [Better known as Bill Jame's Pythagorean Runs W/L% ; A formula for converting a team’s Run Differential into a projected Won/Loss record. The formula is RS^2/(RS^2+RA^2). Teams’ actual won/loss records tend to mirror their Pythagorean records, and variances can usually be attributed to luck.#1 http://www.hardballtimes.com/main/statpages/glossary/ ] I had taken notice that the team of my following, the Cardinals, had a very low Pythagorium Runs W/L%. My mind was telling me about the upcoming collapse of the Cards before them barely performing adequitly enough to be considered division champions.
So what exactly is the beneficiaries of this numerical cluster besides a nice figure with a decimal point inserted in order to be a rate instead of the inferior counting number? The Pythagorean run won loss percentage is a reasonably accurate formula of predicting a straight up won loss record. It is also a good way to peer into a crystal ball and predict gloom or doom Nostradomus style.
The American League Pythagorean run won loss record documents some mighty departures from the norm. Take further note of the below metrics.
2006 AL Pythagorean Runs W/L%
Al
.597 Detroit [W]
.595 New York [D]
.579 Minnesota [D]
.553 Cleveland
.544 Chicago
.535 Toronto
.531 Texas
.529 Oakland [D]
.523 Los Angeles
.497 Boston
.477 Seattle
.422 Baltimore
.393 Tampa Bay
.378 Kansas City
[Symbols; D=Division Winners/W=Wildcard Winner]
Any series of numbers includes a medium [average]. Theoretically, half the numbers will ride above the dotted line [.500], while the other dips below the mean. Anyway, theories are not always perfect. The most important question ought to be, "Why is the average rate way above the .500 point?" Some American League teams score more runs for than runs against, even though their won loss record is below .500. [An example is Cleveland's pythagorian rate being .553, while their won loss record is a sub-par .481. "Why are these crooked numbers created?" Probably because a team will score a disproportionate high ratio of runs in a very short period of time.
2006 NL Pythagorean Runs W/L%
NL
.566 New York [D]
.544 Los Angeles [W]
.537 San Diego [D]
.532 Philadelphia
.527 Atlanta
.512 St. Louis [D]
.511 Houston
.501 Colorado
.491 Florida
.490 Arizona
.471 San Francisco
.466 Cincinnatti
.434 Milwaukee
.431 Pittsburgh
.423 Washington
.422 Chicago
[Symbols; D=Division Winners/W=Wildcard Winner]
After taking a really quick peek at National League statistics, the rates appears to follow the norm. A few team pythagorean runs have a deviant like appearance, but the National League definitely comes across as being more of a pitchers league through these lower Pythagorean numbers compared to their counterparts.
Putting these figures under a microscope will document the National League's mean at .491 [a few points below by theory], while the A.L.'s mean is a high .511. What do all these numbers represent? Simply put, the NL is ruled by the arm, while the AL's ball travels in favor of the batter. The big question is how much an attribute the pitcher to the NL is to the designated hitter in the AL..
Al
NL
.599 New York [D]
.599 New York [D]
.593 Minnesota [D]
.543 San Diego [W]
.586 Detroit [W]
.543 Los Angeles [D]
.574 Oakland [D]
.525 Philadelphia
.556 Chicago
.516 St. Louis [D]
.537 Toronto
.506 Houston
.531 Boston
.494 Cincinnati
.521 Los Angeles
.488 Atlanta
.494 Texas
.481 Florida
.481 Seattle
.472 San Francisco
.481 Cleveland
.469 Colorado
.432 Baltimore
.469 Arizona
.383 Kansas City
.463 Milwaukee
.377 Tampa Bay
.436 Washington
.414 Pittsburgh
.407 Chicago
[Symbols; D=Division Winners/W=Wildcard Winner]
While I recently consulted the handy pythamonitor [Better known as Bill Jame's Pythagorean Runs W/L% ; A formula for converting a team’s Run Differential into a projected Won/Loss record. The formula is RS^2/(RS^2+RA^2). Teams’ actual won/loss records tend to mirror their Pythagorean records, and variances can usually be attributed to luck.#1 http://www.hardballtimes.com/main/statpages/glossary/ ] I had taken notice that the team of my following, the Cardinals, had a very low Pythagorium Runs W/L%. My mind was telling me about the upcoming collapse of the Cards before them barely performing adequitly enough to be considered division champions.
So what exactly is the beneficiaries of this numerical cluster besides a nice figure with a decimal point inserted in order to be a rate instead of the inferior counting number? The Pythagorean run won loss percentage is a reasonably accurate formula of predicting a straight up won loss record. It is also a good way to peer into a crystal ball and predict gloom or doom Nostradomus style.
The American League Pythagorean run won loss record documents some mighty departures from the norm. Take further note of the below metrics.
2006 AL Pythagorean Runs W/L%
Al
.597 Detroit [W]
.595 New York [D]
.579 Minnesota [D]
.553 Cleveland
.544 Chicago
.535 Toronto
.531 Texas
.529 Oakland [D]
.523 Los Angeles
.497 Boston
.477 Seattle
.422 Baltimore
.393 Tampa Bay
.378 Kansas City
[Symbols; D=Division Winners/W=Wildcard Winner]
Any series of numbers includes a medium [average]. Theoretically, half the numbers will ride above the dotted line [.500], while the other dips below the mean. Anyway, theories are not always perfect. The most important question ought to be, "Why is the average rate way above the .500 point?" Some American League teams score more runs for than runs against, even though their won loss record is below .500. [An example is Cleveland's pythagorian rate being .553, while their won loss record is a sub-par .481. "Why are these crooked numbers created?" Probably because a team will score a disproportionate high ratio of runs in a very short period of time.
2006 NL Pythagorean Runs W/L%
NL
.566 New York [D]
.544 Los Angeles [W]
.537 San Diego [D]
.532 Philadelphia
.527 Atlanta
.512 St. Louis [D]
.511 Houston
.501 Colorado
.491 Florida
.490 Arizona
.471 San Francisco
.466 Cincinnatti
.434 Milwaukee
.431 Pittsburgh
.423 Washington
.422 Chicago
[Symbols; D=Division Winners/W=Wildcard Winner]
After taking a really quick peek at National League statistics, the rates appears to follow the norm. A few team pythagorean runs have a deviant like appearance, but the National League definitely comes across as being more of a pitchers league through these lower Pythagorean numbers compared to their counterparts.
Putting these figures under a microscope will document the National League's mean at .491 [a few points below by theory], while the A.L.'s mean is a high .511. What do all these numbers represent? Simply put, the NL is ruled by the arm, while the AL's ball travels in favor of the batter. The big question is how much an attribute the pitcher to the NL is to the designated hitter in the AL..
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