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Fantasy Football for Smart People Available in Paperback | The DC Times

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Fantasy Football for Smart People Available in Paperback

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Jonathan Bales

Fantasy Football for Smart People is now available as a paperback. It is in pre-order stages and will ship on June 16. It costs $13.99. You can also buy it as a PDF or on Kindle (both $7.99). I’ve been writing the book since January, and if you play fantasy football, I’m confident you’ll find it useful this season. From the book:

The most important reason we need to make projections, though, ties back in with the idea of VORP.  Remember, VORP, or Value Over Replacement Player, suggests we should identify the biggest gap in points between current draft considerations and replacement players at their respective positions who could be drafted later.

The example between wide receivers and tight ends that I used in the tight end section of my analysis of season-to-season consistency is an example of an employment of VORP.  In effect, VORP is the temporary bypassing of maximum points for greater overall points down the road.  Remember, since fantasy football requires the selection of players from multiple positions, any worthwhile draft strategy must possess an overarching vision; draft strategies like “Best Player Available” are too shortsighted, necessary limiting the projected points you can acquire down the road in favor of more now.

Without projections (or some sort of rating system), VORP draft strategy is impossible.  A value system is necessary to decipher the “worth” of a player.  We can rank players all day to help us compare players within particular positions, but a comparison of players that play different positions is worthless without a rating system.

How to Add Positional Consistency Into Projections

One quick and easy method to implement positional consistency into player ratings is to multiply projected points for a position by the correlational strength of consistency.  That is:

C(P), where C is correlational strength and P is projected points

As I stated earlier, these correlations are 0.62 for tight ends, 0.60 for quarterbacks, 0.48 for running backs, and 0.42 for wide receivers.

Thus, if we project a quarterback to score 300 points and a wide receiver to score 200, those value shift to 0.60(300)=180 and 0.42(200)=84, respectively.  Note that those numbers aren’t projected points, but rather weighted values that make comparisons of various players easier.

The primary problem with this method, in my view, is it values the consistent positions too greatly, widening the “scarcity” gap at these spots.  For example, if we assume the 300-point quarterback and 200-point receiver are the top players at their respective positions and that our second-ranked players were projected to score 285 (QB) and 190 (WR), the above formula would change those projections to 171 and 79.

Whereas the second-ranked players were projected to score five percent less than their top-ranked counterparts in the original projections, the new consistency-infused projections have the second quarterback still at five percent behind the top signal-caller, but the second receiver 6.0 percent behind the top pass-catcher.  In effect, multiplying position correlation strength by projected points increases the “scarcity” of the most consistent positions, improperly inflating their worth.

To compensate for this effect on scarcity, we can use a new formula that incorporates average points for each position.  To obtain better projections, we can multiply the difference in projected points and average points by the aforementioned correlational strength of each position, then add that number to the average points.  That is:

C(P-A) + A, where C is correlational strength, P is projected points, and A is average points for fantasy starters at the position

Let’s assume we project a tight end to score 200 points and a wide receiver to score 220 points, with the average at the positions being 150 and 180, respectively.  We could factor positional consistency into those projections by multiplying the difference between the projection and the average by 0.62 and 0.42, respectively.  Our new projections would be:

Tight End: 0.62(200-150) + 150 = 181
Wide Receiver: 0.42(220-180) + 180 = 197

Since the positional scoring mean is incorporated into the formula, we can effectively control the effect of inflated scarcity that plagued the initial formula.

Also, I’m giving away some pretty cool prizes to those who purchase the book.

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