Projecting Playoff Games Played

Projected playoff fantasy points based on expected number of games played.

 

As Jeff Pasquino wrote this week in Footballguys' Playoff Challenge Guide, the first step in building a winning playoff fantasy team is projecting how many games each team is going to play. After all, if the Steelers go one and done, Antonio Brown's fantasy scoring title among wide receivers -- by a mile -- actually does more harm than good on your roster. So, the question I hope to answer here is "how many games can we expect each team to play during this postseason?" Armed with that answer, we can then develop an expectation for how many fantasy points each player will score this postseason.

projecting TOTAL games

At bottom, the key to projecting how many playoff games a team will play comes down to probabilities. It's about figuring out the "???s" in the following table:

TeamP(1)P(2)P(3)P(4)
SEA ??? ??? ??? ???
GB ??? ??? ??? ???
DAL ??? ??? ??? ???
CAR ??? ??? ??? ???
ARI ??? ??? ??? ???
DET ??? ??? ??? ???
NE ??? ??? ??? ???
DEN ??? ??? ??? ???
PIT ??? ??? ??? ???
IND ??? ??? ??? ???
CIN ??? ??? ??? ???
BAL ??? ??? ??? ???

What's the probability that Seattle plays exactly one game [i.e., P(1)]? What's the probability that Baltimore plays four games [i.e., P(4)]? And so on.

To fill in these "???s," we have a couple of reliable sources. First, there's the Vegas point spreads for the Wild Card games. For teams playing this weekend, "P(1)" equals the probability of a loss (highlighted in yellow), and this probability can be inferred from the Vegas lines with a little bit of math. As an example, Carolina is currently, on average, a 5.9-point favorite against Arizona, and that translates to having a 66.5% probability of winning. So, in the "CAR" row in the above table, the probability that they play exactly one game [i.e., P(1)] equals

1 - .665 = .335 = 33.5%

Repeating that exercise for the rest of the teams playing on Wild Card weekend, we arrive at the following:

TeamP(1)P(2)P(3)P(4)
SEA ??? ??? ??? ???
GB ??? ??? ??? ???
DAL 30.3% ??? ??? ???
CAR 33.5% ??? ??? ???
ARI 66.5% ??? ??? ???
DET 69.7% ??? ??? ???
NE ??? ??? ??? ???
DEN ??? ??? ??? ???
PIT 41.0% ??? ??? ???
IND 39.8% ??? ??? ???
CIN 60.2% ??? ??? ???
BAL 59.0% ??? ??? ???

The second source we can rely on is Football Outsiders' (FO's) Playoff Odds Report, which tells us each playoff team's chances of (a) advancing to (at least) the conference championship game and (b) advancing to Super Bowl XLIX. The reason for this will become apparent momentarily, but let's start with "(b)." For teams on a bye, advancing to the Super Bowl corresponds to the "P(3)" column because they will have played three games by defintion. By the same logic, it corresponds to the "P(4)" column for teams playing this weekend. This allows us to fill in some more of the table:

TeamP(1)P(2)P(3)P(4)
SEA ??? ??? 48.9% ???
GB ??? ??? 31.4% ???
DAL 30.3% ??? ??? 13.2%
CAR 33.5% ??? ??? 2.8%
ARI 66.5% ??? ??? 1.7%
DET 69.7% ??? ??? 2.0%
NE ??? ??? 43.9% ???
DEN ??? ??? 32.4% ???
PIT 41.0% ??? ??? 8.7%
IND 39.8% ??? ??? 4.9%
CIN 60.2% ??? ??? 2.9%
BAL 59.0% ??? ??? 7.2%

Next up is using FO's probabilities associated with reaching the conference championship game. Logic tells us that teams on a bye this week require only one win to get there, so we can fill in "P(1)" for these teams by subtracting the aforementioned probability from one. By way of an example, consider FO's 81.3% likelihood of Seattle making the NFC Championship game. Well, they only need one win to get there, right? Therefore, the probability that they don't get there equals

1 - .813 = .187 = 18.7%

So now we're this far in filling out the table:

TeamP(1)P(2)P(3)P(4)
SEA 18.7% ??? 48.9% ???
GB 34.7% ??? 31.4% ???
DAL 30.3% ??? ??? 13.2%
CAR 33.5% ??? ??? 2.8%
ARI 66.5% ??? ??? 1.7%
DET 69.7% ??? ??? 2.0%
NE 29.0% ??? 43.9% ???
DEN 32.1% ??? 32.4% ???
PIT 41.0% ??? ??? 8.7%
IND 39.8% ??? ??? 4.9%
CIN 60.2% ??? ??? 2.9%
BAL 59.0% ??? ??? 7.2%

To fill in "P(3)" for teams that didn't earn a bye, the math is analogous: We just subtract FO's probability associated with getting to the Super Bowl from the probability of making the conference championship game. That's because, in the world of probability, "making it at least as far as the conference championship game" is the same thing -- mathematically, and logically, speaking -- as "making it to the the conference championship game OR making it to the Super Bowl," and an "or" statement implies addition. Therefore, as an example, we know from the table above that Pittsburgh has an 8.7% chance of making the Super Bowl. Given that FO estimates the Steelers' chance of making the AFC Championship game to be 19.9%, the probability that they get there -- but no further -- equals

.199 - .087 = .112 = 11.2%

Proceeding along these lines for all non-bye teams produces the following updated version of our table:

TeamP(1)P(2)P(3)P(4)
SEA 18.7% ??? 48.9% ???
GB 34.7% ??? 31.4% ???
DAL 30.3% ??? 15.4% 13.2%
CAR 33.5% ??? 8.4% 2.8%
ARI 66.5% ??? 4.4% 1.7%
DET 69.7% ??? 5.5% 2.0%
NE 29.0% ??? 43.9% ???
DEN 32.1% ??? 32.4% ???
PIT 41.0% ??? 11.2% 8.7%
IND 39.8% ??? 10.6% 4.9%
CIN 60.2% ??? 6.5% 2.9%
BAL 59.0% ??? 9.1% 7.2%

From here, the rest is child's play. We know that it's impossible for Seattle, Green Bay, New England, and Denver to play four games, so we fill their "P(4)" table cells with zeroes. And because we know that all playoff teams must play between one and four games, then each row must add up to 100%, so

P(2) = 1 - P(1) - P(3) - P(4)

Finally, to get each team's expected number of playoff games, all we need to do is assign weights to each of the probabilities. For most leagues, this is as simple as multiplying "P(1)" by 1, "P(2)" by 2, "P(3)" by 3, and "P(4)" by 4. However, many leagues, including Footballguys' Playoff Challenge, double-count fantasy points accumulated during the Super Bowl, so we have to slightly adjust the weighting procedure as follows:

  • For teams on bye this week, I multiplied "P(3)" by 4 instead of 3 because the Super Bowl would be their third game.
  • For non-bye teams, I multiplied "P(4)" by 5 instead of 4 because the Super Bowl would be their fourth game.
The end result is the table below, which shows the number of games each team is expected to play, both in the standard scoring format ("Exp G") and the double-counted Super Bowl format ("Adj Exp G"). For your convenience, I've sorted it by Adj Exp G:
 
TeamP (1)P (2)P (3)P (4)Exp GAdj Exp G
SEA 18.7% 32.4% 48.9% 0.0% 2.30 2.79
NE 29.0% 27.1% 43.9% 0.0% 2.15 2.59
DEN 32.1% 35.5% 32.4% 0.0% 2.00 2.33
GB 34.7% 33.9% 31.4% 0.0% 1.97 2.28
DAL 30.3% 41.1% 15.4% 13.2% 2.12 2.25
PIT 41.0% 39.1% 11.2% 8.7% 1.88 1.96
IND 39.8% 44.7% 10.6% 4.9% 1.81 1.85
CAR 33.5% 55.3% 8.4% 2.8% 1.81 1.83
BAL 59.0% 24.7% 9.1% 7.2% 1.64 1.72
CIN 60.2% 30.4% 6.5% 2.9% 1.52 1.55
ARI 66.5% 26.3% 5.5% 1.7% 1.42 1.44
DET 69.7% 23.9% 4.4% 2.0% 1.39 1.41

It comes as no surprise that Seattle and New England -- the current conference favorites per Vegas -- are expected to play the most games this postseason, regardless of whether we're double-counting the Super Bowl or not. That said, the rest of the table contains several interesting little nuggets of information. Chief among these is that Dallas is far and away the non-bye team that's most likely to make a deep playoff run. Despite being seeded lower than the Broncos and Packers, data from Las Vegas and Football Outsiders imply that the Cowboys will play more games. And even when we double-count the Super Bowl, Dallas only drops into a virtual tie with Denver and Green Bay. As counterintuitive as that might seem, it actually conforms to recent NFL history: Every postseason from 2005 to 2013 featured at least one team that played in the Wild Card round making it to the Conference round (or beyond).

Another thing you'll notice is that sixth-seeded Baltimore is projected to play more games than fifth-seeded Cincinnati, even though they're both essentially 60-40 underdogs this weekend. That's because Football Outsiders has a much more positive view of what the Ravens can accomplish in subsequents games (16.3% chance of getting at least as far as the AFC Championship game) than what the Bengals can accomplish (9.4%).

Finally, with consensus being that offensive injuries have turned Arizona from dangerous to harmless, one would expect them to reside at the very bottom of the table; but that honor goes to the Lions, and the reason goes back to Dallas. With an implied win probability of only 30.3%, Detroit is the biggest underdog of the weekend, and therefore the team most likely to play exactly one game this postseason. The fact that the Lions are 0.3% more likely than the Cardinals to make the Super Bowl isn't enough to overcome being 3.2% less likely to make the Divisional round.

PROJECTING TOTAL POINTS

Applying this information to optimize your playoff fantasy team is straightforward: Just assign each player a representative scoring average and multiply that average by his team's number of expected games. Multiplication is easy, so estimating each player's scoring average is where mild brainwork comes into play. You can keep it simple and use the player's fantasy points per game this season. You can be a little fancier and use one or more of Footballguys' statistical dominator tools. And if you want to be really fancy, you can use a combination of season-long scoring averages and any of our various Week 18 projections.

My preference is to keep it relatively simple -- it doesn't take much to gain an edge over most competition via this info -- and use each player's scoring average from Week 13 to Week 16. I choose this route for a couple of reasons. First, injuries and depth charts change -- sometimes considerably -- over the course of the season, so I want to avoid overvaluing players like Ronnie Hillman, whose 10.2 standard points per game (12.8 PPR) in no way reflects what his postseason average is likely to be while backing up C.J. Anderson. Second, Week 17 is typically a statistical anomaly, especially for quarterbacks, so I want to avoid the ol' statistical adage "garbage in, garbage out."

Using Weeks 13-16 gets me almost all the way there; the rest is situation-specific minutiae:

  • For the Steelers' Reggie Nelson-created mess at running back, I gave Le'Veon Bell zero points for this week's game, but his normal scoring average for the 0.88 (or 0.96) more games Pittsburgh is expected to play. Meanwhile, Josh Harris and Dri Archer got the same treatment in reverse: the average Footballguys' projection for one game, their scoring average for the remaining 0.88 (or 0.96) games.
  • Ronnie Hillman didn't play at all in Weeks 13-16, but assigning an average of 0.0 points is just as absurd as using the aforementioned season-long average of 10.2 points. Luckily, he did play in Week 17, scoring 5.8 standard points (6.8 PPR). I know I just wrote that there's a valid reason to ignore Week 17. In this one case, however, it's the only data point that even remotely reflects his current role in Denver's offense.

So, without further ado, below are my fantasy projections for the 2014 postseason based on scoring averages and expected games played. I've sorted the table by PPR scoring in a league where the Super Bowl is double-counted, and I've only included players who are currently listed on Footballguys' Depth Charts page. Here, "Exp FBG" represents each player's expected total points in a standard scoring league, while "Exp PPR" represents expected total points in a PPR league.

   Super Bowl Single   Super Bowl Double   
NameTmPosExp FBGRkExp PPRRkExp FBGRkExp PPRRk
Russell Wilson SEA QB 60.5 1 60.5 1 73.3 1 73.3 1
Tom Brady NE QB 43.0 5 43.0 5 51.7 2 51.7 2
Aaron Rodgers GB QB 44.1 4 44.1 4 51.1 3 51.1 3
Cam Newton CAR QB 48.5 2 48.5 2 49.2 4 49.2 4
Tony Romo DAL QB 44.8 3 44.8 3 47.6 5 47.6 5
Ben Roethlisberger PIT QB 42.4 6 42.4 6 44.3 6 44.3 6
Andrew Luck IND QB 41.3 7 41.3 7 42.4 7 42.4 7
Joe Flacco BAL QB 34.8 8 34.8 8 36.4 8 36.4 8
Peyton Manning DEN QB 28.9 9 28.9 9 33.6 9 33.6 9
Matthew Stafford DET QB 26.7 10 26.7 10 27.1 10 27.1 10
Andy Dalton CIN QB 26.5 11 26.5 11 27.0 11 27.0 11
Ryan Lindley ARI QB 8.0 12 8.0 12 8.1 12 8.1 12
DeMarco Murray DAL RB 41.4 1 49.9 1 44.0 3 53.0 1
C.J. Anderson DEN RB 38.9 2 44.9 2 45.2 2 52.2 2
Marshawn Lynch SEA RB 38.8 3 42.9 3 47.1 1 52.0 3
Eddie Lacy GB RB 33.1 4 38.0 4 38.4 4 44.1 4
LeVeon Bell PIT RB 24.1 7 28.5 7 26.5 6 31.3 5
Joique Bell DET RB 25.3 6 29.8 5 25.7 7 30.2 6
Jonathan Stewart CAR RB 26.8 5 29.0 6 27.2 5 29.5 7
Jeremy Hill CIN RB 22.7 8 25.8 8 23.2 8 26.3 8
LeGarrette Blount NE RB 15.7 9 18.6 10 18.9 9 22.3 9
Daniel Herron IND RB 15.3 10 20.3 9 15.7 10 20.8 10
James Starks GB RB 12.3 13 16.8 13 14.3 13 19.4 11
Justin Forsett BAL RB 14.0 11 17.7 11 14.6 11 18.5 12
Giovani Bernard CIN RB 12.7 12 17.2 12 12.9 15 17.6 13
Robert Turbin SEA RB 11.9 14 13.6 15 14.4 12 16.5 14
Shane Vereen NE RB 10.2 16 13.4 17 12.3 16 16.2 15
Ronnie Hillman DEN RB 11.6 15 13.6 16 13.5 14 15.8 16
Reggie Bush DET RB 9.7 17 14.7 14 9.8 17 15.0 17
Josh Harris PIT RB 9.6 18 11.1 18 9.7 18 11.2 18
Jonas Gray NE RB 7.9 20 8.4 20 9.5 19 10.2 19
Kerwynn Williams ARI RB 9.0 19 10.0 19 9.1 20 10.1 20
Joseph Randle DAL RB 7.4 21 8.4 21 7.8 21 8.9 21
Fitzgerald Toussaint BAL RB 5.1 25 8.4 22 5.3 26 8.8 22
Stepfan Taylor ARI RB 6.9 22 8.3 23 7.0 23 8.4 23
Juwan Thompson DEN RB 6.6 23 7.1 25 7.7 22 8.3 24
Dri Archer PIT RB 4.5 27 7.5 24 4.5 27 7.5 25
Christine Michael SEA RB 4.8 26 5.4 29 5.8 24 6.5 26
Fozzy Whittaker CAR RB 5.6 24 6.1 26 5.7 25 6.2 27
Trent Richardson IND RB 4.2 28 6.0 27 4.3 29 6.1 28
Zurlon Tipton IND RB 3.6 32 5.4 28 3.7 32 5.6 29
Theo Riddick DET RB 2.4 34 5.1 30 2.4 34 5.2 30
Lance Dunbar DAL RB 3.0 33 4.6 31 3.2 33 4.9 31
Bernard Pierce BAL RB 4.2 29 4.2 32 4.3 28 4.3 32
DeAngelo Williams CAR RB 3.8 30 3.8 33 3.8 30 3.8 33
Rex Burkhead CIN RB 3.8 31 3.8 34 3.8 31 3.8 34
Marion Grice ARI RB 2.0 35 3.4 35 2.0 35 3.4 35
Julian Edelman NE WR 29.2 3 44.9 1 35.1 1 54.1 1
Jordy Nelson GB WR 29.8 2 41.7 4 34.6 2 48.3 2
Antonio Brown PIT WR 27.6 4 43.6 2 28.9 4 45.6 3
Dez Bryant DAL WR 30.8 1 41.9 3 32.7 3 44.5 4
T.Y. Hilton IND WR 26.6 5 38.0 5 27.3 5 39.1 5
Randall Cobb GB WR 19.3 10 33.5 6 22.4 10 38.9 6
Demaryius Thomas DEN WR 21.6 7 32.1 8 25.1 6 37.3 7
Doug Baldwin SEA WR 20.2 8 30.0 9 24.5 7 36.4 8
Brandon LaFell NE WR 17.7 11 29.5 10 21.3 11 35.5 9
Emmanuel Sanders DEN WR 19.3 9 28.8 11 22.4 9 33.5 10
Calvin Johnson DET WR 22.2 6 32.2 7 22.5 8 32.7 11
Cole Beasley DAL WR 17.7 12 24.5 13 18.8 12 26.1 12
A.J. Green CIN WR 17.1 13 24.7 12 17.4 13 25.2 13
Kelvin Benjamin CAR WR 13.1 16 22.2 14 13.3 17 22.5 14
Torrey Smith BAL WR 15.6 14 21.0 15 16.3 14 21.9 15
Jermaine Kearse SEA WR 9.9 21 16.8 19 12.0 20 20.4 16
Paul Richardson SEA WR 10.1 20 16.4 20 12.2 19 19.9 17
Golden Tate DET WR 10.5 19 18.9 16 10.7 21 19.1 18
Martavis Bryant PIT WR 12.9 17 17.2 17 13.5 16 18.0 19
Donte Moncrief IND WR 13.5 15 17.1 18 13.8 15 17.5 20
Steve Smith BAL WR 9.0 22 16.4 21 9.4 22 17.1 21
Terrance Williams DAL WR 12.1 18 15.3 22 12.9 18 16.2 22
Markus Wheaton PIT WR 8.7 23 15.2 23 9.1 23 16.0 23
Wes Welker DEN WR 7.0 30 13.5 25 8.1 29 15.7 24
Jerricho Cotchery CAR WR 8.2 26 14.1 24 8.3 27 14.3 25
Davante Adams GB WR 7.4 28 12.3 28 8.6 25 14.3 26
Hakeem Nicks IND WR 7.9 27 12.9 27 8.2 28 13.3 27
Michael Floyd ARI WR 8.3 25 13.0 26 8.4 26 13.1 28
Danny Amendola NE WR 4.4 34 10.8 32 5.2 33 13.0 29
Philly Brown CAR WR 8.5 24 12.1 29 8.7 24 12.3 30
Larry Fitzgerald ARI WR 4.6 33 11.7 30 4.7 34 11.9 31
Kamar Aiken BAL WR 7.2 29 10.9 31 7.6 30 11.4 32
Marlon Brown BAL WR 5.4 32 10.3 33 5.6 32 10.8 33
Jaron Brown ARI WR 6.7 31 10.3 34 6.8 31 10.4 34
Reggie Wayne IND WR 3.7 36 9.2 35 3.8 36 9.4 35
John Brown ARI WR 4.1 35 6.6 36 4.2 35 6.7 36
Kevin Norwood SEA WR 2.5 38 4.8 38 3.1 37 5.9 37
Mohamed Sanu CIN WR 2.7 37 5.0 37 2.7 38 5.1 38
Lance Moore PIT WR 2.0 39 3.4 39 2.1 39 3.5 39
Jeremy Ross DET WR 1.6 40 2.3 40 1.6 40 2.3 40
Brenton Bersin CAR WR 1.0 41 1.9 41 1.1 41 2.0 41
Greg Little CIN WR 0.5 43 1.2 42 0.5 43 1.2 42
Corey Fuller DET WR 0.7 42 1.1 43 0.7 42 1.1 43
Jarrett Boykin GB WR 0.3 45 0.8 45 0.3 45 0.9 44
Brandon Tate CIN WR 0.5 44 0.8 44 0.5 44 0.9 45
Devin Street DAL WR 0.0 46 0.0 46 0.0 46 0.0 46
Andre Caldwell DEN WR 0.0 47 0.0 47 0.0 47 0.0 47
Brian Tyms NE WR 0.0 48 0.0 48 0.0 48 0.0 48
Rob Gronkowski NE TE 26.4 1 39.3 1 31.8 1 47.4 1
Greg Olsen CAR TE 14.5 4 26.3 2 14.8 4 26.7 2
Luke Willson SEA TE 17.2 2 21.8 6 20.9 2 26.4 3
Coby Fleener IND TE 16.8 3 23.5 3 17.2 3 24.2 4
Jason Witten DAL TE 13.4 5 22.4 5 14.2 5 23.8 5
Heath Miller PIT TE 13.1 6 22.5 4 13.7 6 23.5 6
Jermaine Gresham CIN TE 12.1 7 20.7 7 12.3 7 21.1 7
Owen Daniels BAL TE 7.6 8 11.3 8 8.0 8 11.8 8
Julius Thomas DEN TE 6.3 9 9.3 9 7.3 9 10.8 9
Andrew Quarless GB TE 4.6 13 8.5 10 5.3 12 9.9 10
Tony Moeaki SEA TE 6.0 10 7.5 11 7.3 10 9.1 11
Richard Rodgers GB TE 5.3 11 7.3 12 6.2 11 8.4 12
Dwayne Allen IND TE 4.9 12 6.7 13 5.0 13 6.9 13
Eric Ebron DET TE 2.1 18 4.6 14 2.2 18 4.6 14
John Carlson ARI TE 2.3 17 4.4 15 2.3 17 4.5 15
Crockett Gillmore BAL TE 2.5 15 4.2 16 2.6 15 4.3 16
Gavin Escobar DAL TE 3.5 14 4.0 17 3.7 14 4.3 17
Jacob Tamme DEN TE 1.5 19 3.5 19 1.7 19 4.0 18
Ed Dickson CAR TE 2.4 16 3.8 18 2.5 16 3.8 19
Timothy Wright NE TE 1.3 20 2.4 20 1.6 20 2.8 20
Matt Spaeth PIT TE 0.8 22 2.1 21 0.9 22 2.2 21
Rob Housler ARI TE 1.2 21 1.9 22 1.2 21 1.9 22
Kevin Brock CIN TE 0.8 23 1.9 23 0.8 23 1.9 23
Brandon Pettigrew DET TE 0.1 24 0.4 24 0.1 24 0.4 24
Connor Barth DEN K 21.5 1 21.5 1 25.0 1 25.0 1
Mason Crosby GB K 20.7 2 20.7 2 24.0 2 24.0 2
Steve Hauschka SEA K 16.7 4 16.7 4 20.2 3 20.2 3
Stephen Gostkowski NE K 16.1 6 16.1 6 19.4 4 19.4 4
Shaun Suisham PIT K 17.4 3 17.4 3 18.2 5 18.2 5
Graham Gano CAR K 16.2 5 16.2 5 16.5 6 16.5 6
Dan Bailey DAL K 15.3 7 15.3 7 16.3 7 16.3 7
Matt Prater DET K 13.2 8 13.2 8 13.4 8 13.4 8
Justin Tucker BAL K 11.5 9 11.5 9 12.0 9 12.0 9
Mike Nugent CIN K 11.4 10 11.4 10 11.6 10 11.6 10
Chandler Catanzaro ARI K 11.0 11 11.0 11 11.2 11 11.2 11
Adam Vinatieri IND K 9.0 12 9.0 12 9.3 12 9.3 12
Denver Broncos DEN D 18.0 1 18.0 1 20.9 1 20.9 1
Seattle Seahawks SEA D 16.7 2 16.7 2 20.2 2 20.2 2
New England Patriots NE D 14.5 3 14.5 3 17.5 3 17.5 3
Dallas Cowboys DAL D 14.3 4 14.3 4 15.2 4 15.2 4
Baltimore Ravens BAL D 12.3 5 12.3 5 12.9 5 12.9 5
Indianapolis Colts IND D 12.2 6 12.2 6 12.5 6 12.5 6
Pittsburgh Steelers PIT D 11.3 7 11.3 7 11.8 7 11.8 7
Detroit Lions DET D 10.7 8 10.7 8 10.9 8 10.9 8
Carolina Panthers CAR D 10.4 9 10.4 9 10.5 9 10.5 9
Green Bay Packers GB D 8.9 12 8.9 12 10.3 10 10.3 10
Cincinnati Bengals CIN D 9.9 10 9.9 10 10.1 11 10.1 11
Arizona Cardinals ARI D 8.9 11 8.9 11 9.0 12 9.0 12

As you probably could have guessed, this system likes players on Dallas and Baltimore above and beyond what's conventional wisdom among the masses. For instance, both Tony Romo and Joe Flacco are better bets than even Peyton Manning! In Romo's case, he's a whopping 15 or so points better because a) his average towards the end of the season was 50% higher, and b) the Cowboys can be expected to play virtually the same number of games as the Broncos. Another example of this system's love for the Cowboys is Jason Witten, who's in a five-man second tier behind Rob Gronkowski.

Speaking of which, although identifying diamonds in the rough is the main benefit of what I've done here, it's also useful in attaching an actual number -- think VBD -- to the value of certain studs over others. For instance, sure, most people -- including Las Vegas and Football Outsiders -- anticipate the Seahawks going far, and so Seattle players are high on anyone's list of playoff draft targets. What isn't so obvious, however, is that, while Marshawn Lynch is part of a small top tier of running backs, Russell Wilson is in a league of his own atop the quarterback rankings. Similarly, while Tom Brady and Rob Gronkowski are obvious picks because New England is the AFC favorite, the former is in a four-man tier behind Wilson, while the latter's expectation dwarfs those of the tight ends ranked just below him.

One final thing worth mentioning: Julian Edelman, No. 1 wide receiver.

If you take anything from this article, I hope it's this. Most people don't realize how little separates a No. 1 seed from a No. 6 seed and just how random playoff results can be. Therefore, they just blindly draft players from the top seeds, only to be shocked and chagrined when upsets happen or Cinderella stories get told. Or maybe they just blindly draft the best players from the regular season thinking that studliness will carry them in the postseason. Don't be those people. Regardless of seeding, Dallas will probably play just as many games as Denver, and 2.79 games of Doug Baldwin is more valuable than 1.41 games of Calvin Johnson. Successfully unlocking the hidden value of lower seeds and lesser players is a big factor in winning playoff fantasy; all it takes is an open mind and a little math.


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