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An introduction to Fantasy Football Metrics


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We have all seen the projections that are floating out there – Writer X thinks that Player Y is going to do Z this season. We all look at them and we all agree or disagree based on our knowledge of said player. They help us bunch our players into a nice neat box with a purple sparkly bow on it.

There’s a couple of problems with projections –

Projections – an estimate of future possibilities based on a current trend (source Merriam-Webster). So, by definition, a projection is someone’s best guess.

As far as I know the industry standard is a plus/minus 30 percent on projections, meaning that the person making the projections takes last season’s stats and adds and subtracts as they see fit as long as they stay within a given range. Starting to see the problem here? What are the predictions based on? Usually someone’s gut. Sure they take into account news on the player, movement to another team, etc. but at the end of the day it’s all someone’s opinion.

Now don’t get me wrong. Projections are a major part of fantasy football and aren’t going anywhere … without projections we wouldn’t have this hobby that we all love so much. After all, who of us can tell the future?

I’m sure you’re sitting there saying to yourself, “OK what’s your point? You don’t like projections but you kind of do …?”

Well I’m glad you asked! I have come up with a system that takes most of the human element out of projecting players. Yes, I’m talking metrics, folks! Metrics have taken fantasy baseball by storm, and I’ve heard over and over that you just can’t create a solid metrics system for fantasy football because of the lack of longevity and small sample size of statistics. I say hogwash! We’re about to find out in a very public forum ladies and gents. I have spent countless hours developing a formula that I feel is really solid, so I’m willing to put myself out there. Nothing is fool-proof but if you can add another tool to your arsenal that your competition doesn’t have, I think you have to give it a go.

Now I obviously can’t spew out my super-secret formula, but I can give you an idea of what my metrics take into account so you know what you’re dealing with. So here’s a list of some of the elements that go into creating my metric score.

  • Three seasons of fantasy points
  • 2010 points per game average
  • Highest scoring week
  • Attempts/Targets
  • Completions/Receptions
  • Conversion percentage
  • Number of games played
  • ‘Consistency Score’ – a metric within a metric
  • End of year strength – trying to see who’s catching the coaching staff’s eye, who’s building rapport with their quarterback, who’s getting carries that wasn’t earlier in the year, and so much more.
  • Total Yards
  • Touchdowns
  • Situation score – the only human element taken into account:
  • Negative scores – Has a player switched teams? Has a new coach/system? Has a young gun behind him pushing for the job? And so much more.
  • Positive Scores – Young guy that’s been in the same system gets a small uptick, new key piece of the puzzle. Maybe a new offensive lineman or new offensive position player (quarterback, running back, wide receiver). Or the guy competing for position leaves, etc. … you get the point.
  • Secret ingredient

Now without further ado, I give you my first installment of a draft value series that uses my metrics to spot value within rounds:

You could get with this … or you could get with that.

A look at draft value through a metric lens.

In the coming weeks I will be exploring each round of the draft according to average draft position (ADP). Right now my metrics are based on points per reception league scoring so that’s the type of ADP I’ll be looking at.

Round 1 ADP according to www.myfantasyleague.com as of Aug. 12

Arian Foster, Houston

Adrian Peterson, Minnesota

Chris Johnson, Tennessee

Jamaal Charles, Kansas City

Ray Rice, Baltimore

LeSean McCoy, Philadelphia

Andre Johnson, Houston

Calvin Johnson, Detroit

Maurice Jones-Drew, Jacksonville

Roddy White, Atlanta

Aaron Rodgers, Green Bay

Darren McFadden, Oakland

So let’s break this down by position because I think as we get into the later rounds that will really help people make some crucial decisions. Also, take note that my metrics compare position to position on an even playing field so if Receiver Y gets a score of 72 and Running back Z gets a score of 60 you know you’re getting a better player in Receiver Y.



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