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"Award Winning Fantistics .... Consider it fantasy's version of the theory of relativity." - Dallas Morning News
 
 
 
 
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As Football Fanatics, we take the "fantasy experience" very seriously here at Fantistics. Our publisher is a statistician who has been designing fantasy player projections and valuation models for over 18 years. Did you realize that the vast majority of National Contest winners, are mathematically inclined (actuaries and accountants)? That is correct, the guys with the glasses and pen sleeves in their shirts!...not co-incidentally quite a few of them are returning Fantistics subscribers. 

Fantasy Football (along with the other fantasy sports) is a statistical  exercise. He who solves the "best fit" formula on draft day usually takes the prize, or is at least competitive throughout the season....and that's our goal, to make you competitive or give you that extra edge that brings you over the top, year after year.  

Our 50+ years of player data allows us to not only produce respectable projections year in and year out, but it also allows you to dispel certain myths that have led many fantasy managers astray. In fantasy Sports it's not the quantity of information, it's the quality that counts. 

Sure, our projections will help you, but it's Not necessarily who to draft...it's how to draft.  The section below introduces you to the Fantistics Draft Strategy:

Shortly after having taken a career  as quantitative analyst in the early 1990's. I  realized that there had to be a formula to capture the optimal drafting strategy. The variables had to be independent of chance and  expectation and needed to capture enough of a sample size to be  considered valid. The result was the first published strategy (1998) on the benefit of Position Scarcity in Fantasy Sports. Although many have caught on to this concept, the model has been refined since then to also include the probability of prediction of each position.

The model was developed using a linear regression model. A model 
designed to find the best fit given the specific constraints of each 
unique league. One cannot reasonably argue that both scoring and the 
variable of both the teams in your league and number of starters at 
each position does not in some way dictate where certain players 
should be valued/ranked.

That is what our position scarcity model called VAM does (Value Above Mean). It quantifies the exact value of each position based on your scoring parameters. This thus leads me to say it's not who to draft, it's how to draft.  There are points in every draft that dictate where a Tier of positions should be triggered or drafted.

The Fantistics draft day strategy has been described as the definitive concept to winning on draft dayValue Above Mean (VAM) is a computation that measures a players' fantasy worth versus others at their respective position/s. In other words, what we do is take the average fantasy values for the typical number of fantasy starters at each position. This average serves as the standard that we compare all others at that particular position by.

As an example, if Player 1 is a Tight End and Player 2 is a Wide Receiver and both have the same number of Fantasy Points (let's say 200). Which of these players is more valuable?   Easy, in most cases it's the Tight End. Why? Because the average Tight End scores considerably less points than the average Wide Receiver in most scoring formats. Thus the replacement value of a top tier Tight End is greater than the replacement value of a top tier Wide Receiver. In other words there is a greater incremental points difference between the top tier player and the mean (average) player at that position. Thus since each league mandates that there is be set number of players that must be started at each position, there is a position scarcity that needs to be accounted for. This is what the VAM theory does, it quantifies the true value of each player in relation to other positions. Considerations include the number of starters at each position and scoring differences.

To further the effectiveness of this concept, based on our research, we assign probabilities based on the predictability of the position categories. In a perfect world, we compared this strategy against "simulation owners" who were set to draft according to total fantasy points (TFP), and found VAM to be  22-30% more effective tool than just drafting by total projected points. VAM is most effective during the early rounds of the draft as evidenced by the graph below. (We ran over 1,000 simulations, basing the incremental gain on the VAM strategy versus employing a straight points drafting methodology through each draft pick.)

You can test the VAM draft strategy before your draft. Our latest software allows you to run simulation drafts using the VAM Strategy against virtual opponents using a ADP to make selections. For the first time, subscribers can see how much more effective our draft strategy is over typical drafting methods.

 

Fantistics is credited as being the first group to apply the value based concept (our concept of VAM) to fantasy sports drafting. 

After years of using the concept, in 1998 we finally tested the concept using a statistical software package (MATLAB), and found it to be the best overall drafting and trading strategy. We have successfully used and recommended this statistical concept (VAM) for over nine years to over 20,000 fantasy enthusiasts.

   

 

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Please feel free to inquire about any of our products: info@fantistics.com


Anthony A. Perri is the founder and the resident "Stats Nerd" at Fantistics.  Anthony has a graduate level Math education. His experience includes working as a  Quantitative Analyst for several Wall Street firms developing statistical models before venturing out into Fantasy Sports in the late 1990s. His decade long involvement in Fantasy Sports includes accolades from some of the top publications in the country as well as  continual guest appearances on Sports Radio (most recently ESPN and Sirius Radio.) Anthony is the designer of the Fantistics Projections Model & the VAM drafting strategy models.  

 

 
 

 

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