## Fantasy sabermetrics like fVORP are the new key to fantasy baseball player evaluation

The key to evaluating the players in your draft is something I have taken to calling fVORP, which stands for “fantasy value over replacement player.” One might recognize VORP as a sabermetric stat, a complicated translation of a players worth compared to a fill-in player, and would be correct to assume that the two are related. The difference is that fVORP compares each player on the draft board to the average fantasy player at his position. fVORP requires simple math to calculate and is customizable to one’s own fantasy league. It may require a lazy Sunday afternoon to compile, but the results of the last few years have had me scheduling a day each season to run the equations and set my draft. I hope this system works just as well for you.

**Points-based league**

In a points-based league, finding a player’s fVORP comes down to finding the average number of total points scored by a player at a given position, then subtracting that number from the total number of points a player at the corresponding position is expected to earn in the upcoming season.

Compare him to a first baseman like Adrian Gonzalez, who plays a position that traditionally scores more fantasy points. If Gonzalez is projected to score 525 points, and the average starting first baseman scores 345 points, then Gonzalez, despite being the more lucrative scorer, only has a fantasy fVORP of +180.

Name | Proj. Pts. | Avg. Pts. by Pos. | fVORP |

Kemp | 512 | 292 | +220 |

Gonzalez | 525 | 345 | +180 |

Since the average scoring first baseman in this scenario has a higher value than the average centerfielder, it is more advantageous to take Kemp first and expect a high-scoring first baseman to be available later. Centerfielders who produce at Kemp’s level are simply rarer, and while that may be something we inherently know, the fVORP gives that knowledge along with a distinct value.

Finding this info is as simple as copy and pasting the data from your fantasy baseball league to an Excel page. From there, the data is easy to manipulate, and players can be organized into positional categories. Once they are given their fVORP by position, the players can be compared head-to-head as the pair above.

**Categories-based league**

In category-based scoring leagues players can still be broken down into a fVORP, but it takes a few extra steps. Category-based scoring leagues reward players who perform well in each category in play, such as batting average, home runs, pitching strikeouts, etc. The key is giving each player a standard score in each category.

For this example, I have chosen a base 10 system, where the maximum a player can earn in a positive category is 10. This will give every player a score of 0-10 in every individual scoring category.

In Excel, organize your players from most to least in a given scoring category. For this example, the focus will be home runs. An equation can be applied to each player to give the best expected performer 10 points, and every other player a lower number based on the percentage by which they will be surpassed by the leader.

[Theoretical projections: Let’s say Jose Bautista is expected to lead the league in home runs in 2012 with 42, followed by Albert Pujols at 40, and another player like Ryan Zimmerman is expected to hit 28.]

In this case, Bautista’s 42 home runs, divided by 4.2, will equal 10 points in that category, making him the category’s leader with a perfect score. Pujols’ 40 home runs, divided by the same 4.2, gives him a score in the category of 9.52, still good but beneath the leader. Zimmerman, who sits far lower, earns 6.66 points for his 28 expected home runs. (28/4.2=6.66)

This equation can be replicated for each category. Take the number the leader is expected to earn, divide by the corresponding number that will create a quotient of 10 and divide each players expected total by that number to give them a score in that category.

The trouble is that not all categories are as easy as the aforementioned home runs or RBI, where there is an easy whole number to deal with.

In the case of something like batting average, each player is saddled with a decimal number. To side step this, simply slide the decimal over three places (.328 becomes 328) and treat the category the same as the one above.

If Ichiro is expected to lead the league with a .345 batting average, the decimal is moved over to make the number 345, then divide by 34.5 (345/34.5=10) to get the base number. Then divide every other batting average by 34.5 to find the corresponding base-10 score. A batting average of .285 using this metric becomes (285/34.5=8.26).

In some pitching categories like ERA and WHIP, a player is rewarded for a lower score. For this equation, simply take the best projected ERA and add it to the base number — 10 in our case — and subtract each ERA and WHIP from that sum.

If Justin Verlander is expected to have the league’s lowest ERA (for this example we will project him at 2.38), then add 2.38 to 10, creating the standard 12.38. Then subtract each player’s ERA from that number to reveal their category score. Verlander stays a 10, while CC Sabathia, who for this example will be projected to complete 2012 with a 3.47 ERA, would earn 8.91 points (12.38 minus 3.47).For this example, I have chosen a base 10 system, where the maximum a player can earn in a positive category is 10. This will give every player a score of 0-10 in every individual scoring category.

In Excel, organize your players from most to least in a given scoring category. For this example, the focus will be home runs. An equation can be applied to each player to give the best expected performer 10 points, and every other player a lower number based on the percentage by which they will be surpassed by the leader.

[Theoretical projections: Let’s say Jose Bautista is expected to lead the league in home runs in 2012 with 42, followed by Albert Pujols at 40, and another player like Ryan Zimmerman is expected to hit 28.]

In this case, Bautista’s 42 home runs, divided by 4.2, will equal 10 points in that category, making him the category’s leader with a perfect score. Pujols’ 40 home runs, divided by the same 4.2, gives him a score in the category of 9.52, still good but beneath the leader. Zimmerman, who sits far lower, earns 6.66 points for his 28 expected home runs. (28/4.2=6.66)

This equation can be replicated for each category. Take the number the leader is expected to earn, divide by the corresponding number that will create a quotient of 10 and divide each players expected total by that number to give them a score in that category.

The trouble is that not all categories are as easy as the aforementioned home runs or RBI, where there is an easy whole number to deal with.

In the case of something like batting average, each player is saddled with a decimal number. To side step this, simply slide the decimal over three places (.328 becomes 328) and treat the category the same as the one above.

If Ichiro is expected to lead the league with a .345 batting average, the decimal is moved over to make the number 345, then divide by 34.5 (345/34.5=10) to get the base number. Then divide every other batting average by 34.5 to find the corresponding base-10 score. A batting average of .285 using this metric becomes (285/34.5=8.26).

In some pitching categories like ERA and WHIP, a player is rewarded for a lower score. For this equation, simply take the best projected ERA and add it to the base number — 10 in our case — and subtract each ERA and WHIP from that sum.

The only downside to this equation is that, since most pitchers are within three runs per nine innings of eachother, it does not set a wide difference from the best in this category and the worst in the category. Even very poor pitchers still earn a score around five. But since ERAs are generally close anyway, perhaps it is logical that the difference is not too great in this category.

In the case of a negative category like strikeouts in hitting or losses in pitching, reverse the number to -10 points to the leader, so that the offensive player with the fewest strikeouts is penalized the least. Each player will earn a negative score, but the hitters with the best eye will not take nearly as much of a hit in this category.

Once each player has a score in every category, add each player’s score in each category together and treat the sum as the total projected fantasy points were treated in the points-based league scenario.

So if Curtis Granderson, in a league with five offensive categories, earns scores of 8.23, 7.66, 9.13, 8.12 and -2.89 (strikeout category), he would have a total score of 30.25, or an average of 6.05 per category.

Once the players are organized by position, find the average score for a starter in that position and subtract that average from each player’s score. If the average centerfielder scores 22.45 points, or 4.49 points per category, then Granderson with the points listed above, he has an +7. 8 fVORP, or +1.56 per category. (I mention the per category part because even in standard 5x5 leagues, some pitching categories, like saves, do not apply to all pitchers. It would be more accurate to only base starters and relievers on the categories that apply to them, which may give a more accurate total than the combined five categories.)

Once the players are organize them from most to least fVORP in either points-based or categories-based formats, you have the most accurate player value list among anyone in your fantasy draft.

This all sounds like a lot of work, and the task sounds overwhelming and you may need a little knowledge of Excel to speed things along, but once the data is brought over from your preferred fantasy site to Excel, the work becomes much easier. Organizing the top 1000 players in two different leagues took me a total of about three hours. If you have the time to commit to the formulas, they will give you a clearer view of the value of each draft pick.

Fantasy baseball sabermetrics are only going to grow, and it will be fun to have an edge over your friends while you still can.

Labels: Fantasy Baseball, MLB, sabermetrics

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