Selecting Players

Selecting Players

By David Dodds

Quarterbacks

Over the course of 17 weeks last season, FanDuel gave you 1,252 quarterback choices. Of course, the majority of these were never even going to play—Tarvaris Jackson, for example, was never a realistic option. Considering only the projected starters each week yielded 510 options in that span.

Choosing randomly from among these 510 starters would have actually produced fine results:

Average cost = \$7,648
Average production = 16.5 fantasy points (FP)
Dollars per point = \$464

As you can see, quarterbacks routinely hit twice their salary (as measured in thousands of dollars). This is an important measure because your salary cap is \$60,000, and scoring 120 points in a cash game will usually put you in the money. You're therefore looking for each player to score twice their salary. Since so many quarterbacks hit the 2x mark, I screened the data against 2x, 2.1x, and 2.2x. It was relatively close, but the best data set came from screening for 2.1x value. Screening the data for players who I expected to reach 2.1x value—based on my weekly posted projections—yielded 387 players in 2014. This data set actually performed quite well:

Average cost = \$7,453
Average production = 16.7 FP
Dollars per point = \$446
232 of 387 hit 2x value (60.0%)
80 of 387 hit 3x value (20.7%)

This is still a very large list including nearly 23 quarterbacks a week, however. We don't need that many choices, so I dug deeper into the groupings within these 387 players to get to a list closer to 50-60 names. The variable that made the biggest difference to this data set—by a wide margin—was the Vegas over/under line. If Vegas thought the game could be a shootout, that was a very good sign for its quarterbacks.

In general, the bigger the Over/Under, the better the production from the quarterback. In fact the best data set came from these criteria:

• I projected the quarterback to score at least 2.1 times his salary
• The quarterback was involved in a game that Vegas predicted to be 50 or more points

Those criteria yielded the following (presented below):

• 60 choices for weeks 1-17
• Average cost = \$8,562
• Average production = 20.7 FP
• Dollars per point = \$414
• 43 of 60 hit 2x value (71.7%)
• 14 of 60 hit 3x value (23.3%)
 Wk Player Team Projected Game Salary FP 2x 3x Home Line OU 1 Matt Ryan ATL 18.22 NO @ ATL \$8,100 31.42 16.2 24.3 home 3 51 1 Andrew Luck IND 20.04 IND @ DEN \$9,200 28.7 18.4 27.6 away 8 53.5 2 Andrew Luck IND 24.16 PHI @ IND \$9,200 19.88 18.4 27.6 home -3 53.5 2 Nick Foles PHI 19.14 PHI @ IND \$8,300 18.44 16.6 24.9 away 3 53.5 3 Matthew Stafford DET 19.68 GB @ DET \$9,000 6.64 18 27 home -1 51.5 4 Drew Brees NO 21.52 NO @ DAL \$9,200 21.2 18.4 27.6 away -3 53.5 4 Jay Cutler CHI 18.86 GB @ CHI \$8,100 19.14 16.2 24.3 home 1.5 50.5 4 Tony Romo DAL 17.4 NO @ DAL \$7,500 24.48 15 22.5 home 3 53.5 5 Eli Manning NYG 17.14 ATL @ NYG \$7,300 15.7 14.6 21.9 home -4 50.5 5 Matt Ryan ATL 18.88 ATL @ NYG \$8,500 15.64 17 25.5 away 4 50.5 6 Matt Ryan ATL 20.4 CHI @ ATL \$8,700 13.74 17.4 26.1 home -3 55.5 6 Nick Foles PHI 18.5 NYG @ PHI \$8,100 16.42 16.2 24.3 home -1.5 50.5 6 Eli Manning NYG 19.46 NYG @ PHI \$7,200 6.34 14.4 21.6 away 1.5 50.5 6 Jay Cutler CHI 19.94 CHI @ ATL \$8,500 21.64 17 25.5 away 3 55.5 7 Andrew Luck IND 21.24 CIN @ IND \$10,000 20.26 20 30 home -3 50.5 8 Peyton Manning DEN 21.9 SD @ DEN \$10,200 23.44 20.4 30.6 home -9 50.5 8 Tom Brady NE 19.26 CHI @ NE \$8,600 34.16 17.2 25.8 home -5.5 52 8 Drew Brees NO 19.88 GB @ NO \$9,100 25.04 18.2 27.3 home -2 55 8 Aaron Rodgers GB 21.78 GB @ NO \$10,000 26.82 20 30 away 2 55 8 Jay Cutler CHI 18.54 CHI @ NE \$8,300 22.68 16.6 24.9 away 5.5 52 8 Philip Rivers SD 18.88 SD @ DEN \$8,900 21.78 17.8 26.7 away 9 50.5 9 Andrew Luck IND 22.18 IND @ NYG \$10,100 30.66 20.2 30.3 away -3 51 9 Tom Brady NE 20.92 DEN @ NE \$9,000 28.72 18 27 home 3 52.5 9 Eli Manning NYG 16 IND @ NYG \$7,600 22.16 15.2 22.8 home 3 51 10 Peyton Manning DEN 22.1 DEN @OAK \$10,000 31.6 20 30 away -12 50.5 10 Aaron Rodgers GB 22.94 CHI @ GB \$9,900 36.6 19.8 29.7 home -9 52.5 10 Jay Cutler CHI 18.86 CHI @ GB \$8,000 10.88 16 24 away 9 52.5 10 Derek Carr OAK 15.46 DEN @OAK \$6,800 13.68 13.6 20.4 home 12 50.5 11 Drew Brees NO 20.92 CIN @ NO \$9,300 14.5 18.6 27.9 home -8.5 51 11 Aaron Rodgers GB 22.32 PHI @ GB \$10,100 28.84 20.2 30.3 home -4 55.5 11 Andrew Luck IND 22.16 NE @ IND \$10,200 20.62 20.4 30.6 home -3 58 11 Tom Brady NE 21.44 NE @ IND \$9,500 16.08 19 28.5 away 3 58 11 Mark Sanchez PHI 17.1 PHI @ GB \$6,900 15.84 13.8 20.7 away 4 55.5 11 Andy Dalton CIN 16.62 CIN @ NO \$7,900 22 15.8 23.7 away 8.5 51 12 Drew Brees NO 20.02 BAL @ NO \$9,000 29.3 18 27 home -3 50.5 12 Joe Flacco BAL 16.84 BAL @ NO \$7,800 13.92 15.6 23.4 away 3 50.5 13 Ben Roethlisberger PIT 20.8 NO @ PIT \$8,300 28.2 16.6 24.9 home -3.5 55 13 Aaron Rodgers GB 21.84 NE @ GB \$9,900 24.92 19.8 29.7 home -3 57.5 13 Tony Romo DAL 19.4 PHI @ DAL \$8,700 5.86 17.4 26.1 home -3 56 13 Mark Sanchez PHI 18.1 PHI @ DAL \$7,600 21.48 15.2 22.8 away 3 56 14 Aaron Rodgers GB 23.42 ATL @ GB \$10,400 27.88 20.8 31.2 home -13.5 54.5 14 Drew Brees NO 20.76 CAR @ NO \$9,400 13 18.8 28.2 home -9 50 14 Tony Romo DAL 18.8 DAL @ CHI \$8,300 20 16.6 24.9 away -3.5 50 14 Brian Hoyer CLE 16.06 IND @ CLE \$6,700 3.74 13.4 20.1 home 3 50.5 14 Jay Cutler CHI 18.84 DAL @ CHI \$8,300 27.54 16.6 24.9 home 3.5 50 14 Philip Rivers SD 18.7 NE @ SD \$8,200 11.36 16.4 24.6 home 4 53.5 14 Cam Newton CAR 18.72 CAR @ NO \$7,800 35.34 15.6 23.4 away 9 50 14 Matt Ryan ATL 18.06 ATL @ GB \$7,900 32.3 15.8 23.7 away 13.5 54.5 15 Ben Roethlisberger PIT 20.08 PIT @ ATL \$8,700 14.2 17.4 26.1 away -3 55.5 15 Drew Brees NO 19.76 NO @ CHI \$9,200 27.2 18.4 27.6 away -3 53.5 15 Mark Sanchez PHI 19.7 DAL @ PHI \$7,500 8.08 15 22.5 home -3 54.5 15 Matt Ryan ATL 16.94 PIT @ ATL \$7,900 22.1 15.8 23.7 home 3 55.5 15 Jay Cutler CHI 19.5 NO @ CHI \$8,600 16.76 17.2 25.8 home 3 53.5 16 Mark Sanchez PHI 18.14 PHI @ WAS \$7,500 22.16 15 22.5 away -7 51 16 Drew Brees NO 20.76 ATL @ NO \$9,100 13.32 18.2 27.3 home -6 56 16 Tony Romo DAL 18.26 IND @ DAL \$8,400 27.52 16.8 25.2 home -3 53.5 16 Matt Ryan ATL 19.58 ATL @ NO \$8,700 17.58 17.4 26.1 away 6 56 16 Robert Griffin III WAS 18.8 PHI @ WAS \$6,900 8.9 13.8 20.7 home 7 51 17 Eli Manning NYG 18.02 PHI @ NYG \$8,200 20.16 16.4 24.6 home -1 52 17 Mark Sanchez PHI 17.58 PHI @ NYG \$7,400 20.88 14.8 22.2 away 1 52 Averages \$8,562 20.7

In summary: Choose quarterbacks who are projected to score at least 2.1 times their salaries and who are playing in games with over/unders of at least 50 points.

Running Backs

Over the course of a 17-week regular season, FanDuel gave their players 2,530 running back choices. If a person threw darts at a board, they would have reached the cash-game goal of 2x just 11.1% (282 times). We can do better than a random dart throw.

Looking back on my weekly posted projections from last season, I screened for players who were expected to reach 2x their salaries (as measured in thousands of dollars). That search yielded 162 players in 2014. This data set actually performed quite well:

162 running backs Predicted to Reach 2x Value
Average cost = \$7,157
Average production = 15.1 FP
Dollars per point = \$474
79 of 162 hit 2x value (48.8%)
35 of 162 hit 3x value (21.6%)

We really don't need nine or ten running backs to choose from each week, however, so we can refine our list using location (home is better than away) and point spread (favorites are better than underdogs). Screening for just those at home left 89 choices and 47 of those hit 2x. On average, each running back cost \$7,188 and yielded 15.6 fantasy points (\$461/point).

Looking further, what about players on home teams favored by four points or more? These criteria provided 50 choices for the entire NFL regular season. Compared to the 162 players in the entire 2x set, these players were much better performers in cash-game lineups:

50 running backs as Big Home Favorites
Average cost = \$7,202
Average production = 16.6 FP
Dollars per point = \$434
29 of 50 hit 2x value (58.0%)
14 of 50 hit 3X value (28.0%)

If we want to narrow the list further, the highest over/under totals can be used as a tiebreaker. The higher the game total, the more likely points are to be scored.

Screening for the highest projected point totals yielded the following:

34 running backs Predicted to Reach 2x Value, Playing as Big Home Favorites, In the Highest Over/Unders
Average cost = \$7,450
Average production = 17.7 FP
Dollars per point = \$421
24 of 34 hit 2x value (70.6%)
9 of 34 hit 3x value (26.5%)

RB Examples from the 2014 Regular Season

2x projection, home favorites over four points, high over/unders.

 Wk Player Team Projected Game Salary FP 2x 3x Line OU 1 Montee Ball DEN 16.8 IND @ DEN \$8,000 15.3 16 24 -8 53.5 1 LeSean McCoy PHI 19.5 JAC @ PHI \$9,400 14.5 18.8 28.2 -9.5 49.5 2 Giovani Bernard CIN 18.5 ATL @ CIN \$7,800 25.4 15.6 23.4 -6 49 2 Bobby Rainey TB 13.3 STL @ TB \$5,000 18.9 10 15 -4.5 37.5 3 Giovani Bernard CIN 18.4 TEN @ CIN \$8,500 17.9 17 25.5 -6 45 3 Khiry Robinson NO 10.3 MIN @ NO \$4,700 6.9 9.4 14.1 -9.5 49.5 4 Ahmad Bradshaw IND 12.6 TEN @ IND \$6,100 12.2 12.2 18.3 -7 46 4 Donald Brown SD 14.4 JAC @ SD \$6,300 7.4 12.6 18.9 -11 45.5 5 DeMarco Murray DAL 19.2 HOU @ DAL \$9,000 20.2 18 27 -4.5 48 5 Khiry Robinson NO 12 TB @ NO \$4,900 16.2 9.8 14.7 -11 47 6 Giovani Bernard CIN 19.6 CAR @ CIN \$8,700 23.7 17.4 26.1 -7 44 6 Marshawn Lynch SEA 19.6 DAL @ SEA \$9,200 6.7 18.4 27.6 -9.5 47 7 DeMarco Murray DAL 24.4 NYG @ DAL \$9,400 19.7 18.8 28.2 -5 46.5 7 Justin Forsett BAL 14.6 ATL @ BAL \$6,200 9.5 12.4 18.6 -7 49.5 8 Jamaal Charles KC 19.2 STL @ KC \$8,700 23.1 17.4 26.1 -7.5 44 8 DeMarco Murray DAL 20.3 WAS @ DAL \$9,600 22.1 19.2 28.8 -9 49 9 Jeremy Hill CIN 13 JAC @ CIN \$5,200 28.8 10.4 15.6 -10 44 9 Arian Foster HOU 19.4 PHI @ HOU \$9,400 18.9 18.8 28.2 2 48.5 10 Marshawn Lynch SEA 17.4 NYG @ SEA \$8,300 40.8 16.6 24.9 -9 44.5 10 Justin Forsett BAL 14.6 TEN @ BAL \$6,500 23.2 13 19.5 -10 44.5 11 Matt Forte CHI 19.2 MIN @ CHI \$9,300 20.5 18.6 27.9 -2.5 46.5 11 Bishop Sankey TEN 11.8 PIT @ TEN \$5,300 11 10.6 15.9 7 46 12 LeSean McCoy PHI 16.5 TEN @ PHI \$7,700 19.6 15.4 23.1 -11 48.5 12 Trent Richardson IND 15.2 JAC @ IND \$5,700 10.2 11.4 17.1 -13 49 13 Joique Bell DET 14.1 CHI @ DET \$6,300 23.7 12.6 18.9 -7 46 13 Dan Herron IND 13.1 WAS @ IND \$5,800 14.6 11.6 17.4 -7.5 48.5 14 Eddie Lacy GB 18.8 ATL @ GB \$8,500 25.1 17 25.5 -13.5 54.5 14 C.J. Anderson DEN 17.7 BUF @ DEN \$7,800 23.8 15.6 23.4 -9.5 47.5 15 Jamaal Charles KC 20.9 OAK @ KC \$9,200 5.8 18.4 27.6 -11 41.5 15 Justin Forsett BAL 17.4 JAC @ BAL \$7,700 5.2 15.4 23.1 -14 44.5 16 Tre Mason STL 14.1 NYG @ STL \$6,500 14.8 13 19.5 -6.5 43.5 16 Mark Ingram NO 14.9 ATL @ NO \$7,400 13 14.8 22.2 -6 56 17 C.J. Anderson DEN 18.8 OAK @ DEN \$8,300 29.7 16.6 24.9 -16 49 17 Justin Forsett BAL 14.9 CLE @ BAL \$6,900 14.6 13.8 20.7 -14 40

In summary:

1. Choose home running backs that are projected for 2x+.
2. Pay attention to the point spread (larger favorites are better).
3. Also note the game's Over/Under (larger totals are better).

Wide Receivers

Much like the running back position, if a DFS player were to randomly choose his wide receivers, he would be facing long odds at grabbing one that was valuable. FanDuel offered their players 3,583 wide receivers over the course of a 17-week regular season. These receivers cost an average of \$5,240 while yielding an average of just 4.1 fantasy points.

Since analyzing 3,583 wide receivers would be time consuming, let's cull this to a more manageable list. After screening all the possible receivers on the season, picking out only the ones that I projected to hit 1.8x their value yielded 262 different combinations. Please note that we are using 1.8x here as a guideline as the receivers tend to be priced a bit higher and don't hit 2x as often as quarterbacks and running backs.

This analysis yielded a decent list:

262 wide receivers Projected to Reach 1.8X of Value
Average cost = \$6,924
Average production = 13.0 FP
Dollars per point = \$533
108 of 262 hit 2x value (41.2%)

But players don't really need approximately 15 receivers to choose from each week, so I looked harder at the groupings within these 262 players to get to a list closer to 100 names.

Using location (home is better), point spread (favorite is better), and over/under of 51+ points all made a difference. The biggest distinction was that the bigger the home favorite, the better the production from the wide receiver.

Looking back at the 2014 projections, the best data set came from this formula: First, find the receivers who were projected to reach 1.8x their salary. Next, find those playing at home. And finally, choose the three receivers where their team was favored the most for that week.

I broke ties by choosing the team playing in the highest over/under game and if that was tied, chose the receiver I projected for the most fantasy points.

That yielded the following (and is presented below):

51 wide receiver Predicted to Reach 1.8x Value, Playing as Big Home Favorites, In the Highest Over/Unders Average cost = \$7,298
Average production = 16.1 FP
Dollars per point = \$453
25 of 51 hit 2x value (49.0%)
14 of 51 hit 3X value (27.5%)

WR Examples from the 2014 Regular Season

1.8x projection, home favorites over four points, high over/unders.

 Wk Player Team Projected Game Salary FP 2x 3x Line OU 1 Jeremy Maclin PHI 10.5 JAC @ PHI \$5,000 17.7 10 15 -9.5 49.5 1 Emmanuel Sanders DEN 12 IND @ DEN \$6,400 11.8 12.8 19.2 -8 53.5 1 Demaryius Thomas DEN 16.1 IND @ DEN \$8,700 6.8 17.4 26.1 -8 53.5 2 Jordy Nelson GB 15.3 NYJ @ GB \$7,900 31.4 15.8 23.7 -7 46.5 2 Demaryius Thomas DEN 20.1 KC @ DEN \$8,700 14.7 17.4 26.1 -13 49 2 Emmanuel Sanders DEN 12.5 KC @ DEN \$6,900 14.6 13.8 20.7 -13 49 3 Julio Jones ATL 17.3 TB @ ATL \$8,500 32.6 17 25.5 -7 47 3 Julian Edelman NE 13.2 OAK @ NE \$6,900 13.9 13.8 20.7 -13.5 47 3 Marques Colston NO 11.6 MIN @ NO \$5,600 9.5 11.2 16.8 -9.5 49.5 4 Antonio Brown PIT 16.2 TB @ PIT \$8,400 29.3 16.8 25.2 -7 44.5 4 Keenan Allen SD 12.4 JAC @ SD \$6,700 18.5 13.4 20.1 -11 45.5 4 Malcom Floyd SD 9.3 JAC @ SD \$4,700 11.4 9.4 14.1 -11 45.5 5 Golden Tate DET 11.6 BUF @ DET \$6,300 22.9 12.6 18.9 -4.5 43 5 Jordy Nelson GB 16.3 MIN @ GB \$8,500 13.1 17 25.5 -9.5 46.5 5 Marques Colston NO 10.2 TB @ NO \$5,500 7.8 11 16.5 -11 47 6 Mohamed Sanu CIN 11.1 CAR @ CIN \$6,000 23 12 18 -7 44 6 Michael Floyd ARI 11.6 WAS @ ARI \$6,300 12.7 12.6 18.9 -5 47 6 Doug Baldwin SEA 8.6 DAL @ SEA \$4,500 4.2 9 13.5 -9.5 47 7 Jordy Nelson GB 20.5 CAR @ GB \$8,900 16 17.8 26.7 -6.5 48.5 7 Davante Adams GB 10 CAR @ GB \$5,200 8.6 10.4 15.6 -6.5 48.5 7 Brandon LaFell NE 9.8 NYJ @ NE \$5,400 7.5 10.8 16.2 -9.5 44.5 8 Brandon LaFell NE 9.8 CHI @ NE \$5,300 23.9 10.6 15.9 -5.5 52 8 Demaryius Thomas DEN 17.8 SD @ DEN \$9,400 14.5 18.8 28.2 -9 50.5 8 Dez Bryant DAL 16.5 WAS @ DAL \$8,800 10.5 17.6 26.4 -9 49 9 Antonio Brown PIT 17.6 BAL @ PIT \$9,000 25.9 18 27 -2 47.5 9 Andrew Hawkins CLE 11.7 TB @ CLE \$6,400 4.9 12.8 19.2 -7 44 9 Andre Johnson HOU 13.6 PHI @ HOU \$6,900 2.2 13.8 20.7 2 48.5 10 Jordy Nelson GB 15.7 CHI @ GB \$8,500 30.2 17 25.5 -9 52.5 10 Mike Evans TB 11.9 ATL @ TB \$6,600 22 13.2 19.8 3 47 10 Calvin Johnson DET 16.7 MIA @ DET \$8,800 20.8 17.6 26.4 -3 43 11 Jordy Nelson GB 16.9 PHI @ GB \$8,900 18.9 17.8 26.7 -4 55.5 11 Malcom Floyd SD 10.9 OAK @ SD \$5,400 12.4 10.8 16.2 -10 45.5 11 Keenan Allen SD 11.7 OAK @ SD \$6,400 10.3 12.8 19.2 -10 45.5 12 Demaryius Thomas DEN 19.1 MIA @ DEN \$9,000 31.7 18 27 -6 47 12 Anquan Boldin SF 12.7 WAS @ SF \$6,800 24.2 13.6 20.4 -9.5 43.5 12 Reggie Wayne IND 12.9 JAC @ IND \$6,900 2.5 13.8 20.7 -13 49 13 Calvin Johnson DET 16.6 CHI @ DET \$8,600 32.1 17.2 25.8 -7 46 13 Reggie Wayne IND 12.6 WAS @ IND \$6,700 5.1 13.4 20.1 -7.5 48.5 13 Steve Smith BAL 11.6 SD @ BAL \$6,400 0.7 12.8 19.2 -6.5 45.5 14 Jordy Nelson GB 16.2 ATL @ GB \$8,900 30.6 17.8 26.7 -13.5 54.5 14 Calvin Johnson DET 16.8 TB @ DET \$9,200 25.8 18.4 27.6 -10 42 14 Randall Cobb GB 15.6 ATL @ GB \$8,400 7.7 16.8 25.2 -13.5 54.5 15 Odell Beckham Jr. NYG 18.8 WAS @ NYG \$8,500 36.3 17 25.5 -7 46 15 Kelvin Benjamin CAR 13.7 TB @ CAR \$7,600 14.4 15.2 22.8 -3.5 41 15 Calvin Johnson DET 19.4 MIN @ DET \$9,400 7.3 18.8 28.2 -8 42.5 16 Dez Bryant DAL 17.3 IND @ DAL \$8,600 15.8 17.2 25.8 -3 53.5 16 Marques Colston NO 11.1 ATL @ NO \$6,100 11 12.2 18.3 -6 56 16 Kenny Stills NO 12.1 ATL @ NO \$6,100 9.8 12.2 18.3 -6 56 17 Randall Cobb GB 15.5 DET @ GB \$8,500 20.6 17 25.5 -8 46 17 Demaryius Thomas DEN 17.1 OAK @ DEN \$9,000 15.5 18 27 -16 49 17 Doug Baldwin SEA 11 STL @ SEA \$6,100 6.6 12.2 18.3 -11 41

In summary:

1. Choose home wide receivers that are projected for 1.8 times value (or better).
2. Pay attention to the point spread (larger favorites are better).
3. Also note the game's Over/Under (larger totals are better).
4. Break ties by using projected fantasy points.

Tight Ends

Tight Ends are rough to roster. You have to choose one each week, and they're going to let you down a lot of the time. Except for a couple of elite tight ends, most of the players at this position will only achieve twice their value if they score a touchdown.

As with the other skill positions, if you were to choose a tight end randomly, you would be facing long odds of grabbing one that was valuable. FanDuel offered 1,807 choices during the 2014 regular season, and they cost an average of \$4,842 while yielding an average of just 2.7 fantasy points.

Analyzing 1,807 tight ends is overkill, so let's pare this down to a reasonable list. I screened all the tight ends that I projected to score at least eight fantasy points. Generally these were tight ends that should have at least a 30% chance of scoring a touchdown. This yielded a decent list:

215 players
Average cost = \$6,131
Average production = 9.4 fantasy points
Dollars per point = \$652
64 of 215 hit 2x value (29.8%)

The usual suspects—home, favorites, and over/under—all improved things. Over/Under made the biggest difference, and that makes sense because a lot of a tight ends' points depend on whether or not they reach the end zone.

The best data set included only those tight ends that I projected for at least eight fantasy points playing for a team that was favored in a game where the over/under was more than 48 points. This generated the following subset:

41 choices
Average cost = \$6,732
Average production = 12.3 fantasy points
Dollars per point = \$547
19 of 41 hit 2x value (46.3%)
7 of 41 hit 3x value (17.1%)

 Wk Player Team Projected Game Salary FP 2x 3x Home Line OU 1 Julius Thomas DEN 11.6 IND @ DEN \$7,400 31.9 14.8 22.2 home -8.0 53.5 1 Jimmy Graham NO 14.9 NO @ ATL \$8,100 12.2 16.2 24.3 away -3.0 51.0 1 Vernon Davis SF 9.0 SF @ DAL \$6,300 18.4 12.6 18.9 away -3.0 49.0 2 Julius Thomas DEN 15.1 KC @ DEN \$8,100 11.9 16.2 24.3 home -13.0 49.0 2 Jimmy Graham NO 15.3 NO @ CLE \$8,000 28.8 16.0 24.0 away -5.0 49.0 2 Dwayne Allen IND 8.2 PHI @ IND \$4,800 0.0 9.6 14.4 home -3.0 53.5 2 Rob Gronkowski NE 14.1 NE @ MIN \$7,900 5.2 15.8 23.7 away -3.0 49.0 2 Delanie Walker TEN 8.6 DAL @ TEN \$5,000 25.2 10.0 15.0 home -3.0 49.0 3 Jimmy Graham NO 15.7 MIN @ NO \$8,400 8.4 16.8 25.2 home -9.5 49.5 3 Zach Ertz PHI 10.7 WAS @ PHI \$5,800 2.4 11.6 17.4 home -4.0 49.5 4 Vernon Davis SF 9.6 PHI @ SF \$6,200 1.8 12.4 18.6 home -3.5 49.0 4 Jimmy Graham NO 15.6 NO @ DAL \$8,200 16.6 16.4 24.6 away -3.0 53.5 5 Larry Donnell NYG 11.0 ATL @ NYG \$6,200 0.0 12.4 18.6 home -4.0 50.5 7 Owen Daniels BAL 8.0 ATL @ BAL \$5,200 14.8 10.4 15.6 home -7.0 49.5 7 Dwayne Allen IND 9.0 CIN @ IND \$5,700 12.7 11.4 17.1 home -3.0 50.5 8 Julius Thomas DEN 11.9 SD @ DEN \$8,500 3.3 17.0 25.5 home -9.0 50.5 8 Jason Witten DAL 8.2 WAS @ DAL \$5,700 15.5 11.4 17.1 home -9.0 49.0 8 Rob Gronkowski NE 13.1 CHI @ NE \$7,200 37.4 14.4 21.6 home -5.5 52.0 8 Jimmy Graham NO 9.7 GB @ NO \$7,000 14.4 14.0 21.0 home -2.0 55.0 9 Julius Thomas DEN 11.6 DEN @ NE \$7,800 10.3 15.6 23.4 away -3.0 52.5 9 Dwayne Allen IND 8.8 IND @ NYG \$6,200 12.8 12.4 18.6 away -3.0 51.0 9 Jimmy Graham NO 11.7 NO @ CAR \$7,000 17.8 14.0 21.0 away -3.0 49.0 9 Zach Ertz PHI 8.9 PHI @ HOU \$5,400 0.9 10.8 16.2 away -2.0 48.5 10 Julius Thomas DEN 12.2 DEN @ OAK \$7,300 21.3 14.6 21.9 away -12.0 50.5 10 Jimmy Graham NO 13.8 SF @ NO \$7,500 24.6 15.0 22.5 home -6.0 49.0 11 Jimmy Graham NO 15.8 CIN @ NO \$7,900 4.4 15.8 23.7 home -8.5 51.0 11 Julius Thomas DEN 11.5 DEN @ STL \$7,500 1.3 15.0 22.5 away -8.0 49.5 11 Dwayne Allen IND 9.2 NE @ IND \$6,100 0.0 12.2 18.3 home -3.0 58.0 12 Coby Fleener IND 12.0 JAC @ IND \$5,400 3.8 10.8 16.2 home -13.0 49.0 12 Jason Witten DAL 8.8 DAL @ NYG \$5,700 11.0 11.4 17.1 away -4.0 48.5 12 Jimmy Graham NO 13.1 BAL @ NO \$7,500 19.7 15.0 22.5 home -3.0 50.5 13 Coby Fleener IND 8.8 WAS @ IND \$5,800 26.7 11.6 17.4 home -7.5 48.5 13 Jason Witten DAL 10.9 PHI @ DAL \$5,700 1.3 11.4 17.1 home -3.0 56.0 14 Jimmy Graham NO 12.9 CAR @ NO \$7,100 4.0 14.2 21.3 home -9.0 50.0 14 Rob Gronkowski NE 12.3 NE @ SD \$7,700 18.7 15.4 23.1 away -4.0 53.5 14 Jason Witten DAL 8.9 DAL @ CHI \$5,500 3.6 11.0 16.5 away -3.5 50.0 15 Rob Gronkowski NE 12.6 MIA @ NE \$7,600 17.1 15.2 22.8 home -9.0 49.0 15 Julius Thomas DEN 10.2 DEN @ SD \$6,300 3.5 12.6 18.9 away -4.0 49.0 15 Jimmy Graham NO 11.5 NO @ CHI \$6,900 11.2 13.8 20.7 away -3.0 53.5 16 Jimmy Graham NO 13.1 ATL @ NO \$7,100 12.3 14.2 21.3 home -6.0 56.0 16 Jason Witten DAL 8.1 IND @ DAL \$5,300 18.5 10.6 15.9 home -3.0 53.5

In summary: Choose tight ends projected to score eight or more fantasy points in games where their team is favored and the over/under is more than 48 points.

Kickers

The kicking position at FanDuel causes a lot of stress for people submitting lineups each week. Most people believe the position is so random that having it lessens the skill factor considerably. What if I were to tell you that the exact opposite is true?

That's right, I cracked the code for kickers on FanDuel.

Before presenting my solution, let's look at some data. In 2014, FanDuel essentially gave you 507 kickers to choose from during the 17 weeks of the regular season. I say "essentially" because they offered some kickers who were not kicking on certain weeks and other times didn't have every starting kicker available due to late signings after a benching. Of those 507 kicker performances, 161 scored twice their salary, which is the desired output for cash games, and just 46 reached 3x their salary, which hits the desired level of production for tournaments. So from the macro view, it would appear to be a daunting task to try and get roughly 10 fantasy points from this position.

This past year, FanDuel created a bigger wedge for kicker pricing. In years past, they priced all kickers between \$5,000 and \$5,500 with nearly all of them at \$5,000. In 2014, they widened that pricing to cover the \$4,500 to \$5,600 range with a cleaner spread. In the table below, we can study the fantasy value of each kicker based on their prices from last season.

 Price total 2X 2X % 3X 3X % \$4,500 59 22 37.3% 5 8.5% \$4,600 36 10 27.8% 3 8.3% \$4,700 57 19 33.3% 4 7.0% \$4,800 67 19 28.4% 6 9.0% \$4,900 72 24 33.3% 10 13.9% \$5,000 69 29 42.0% 10 14.5% \$5,100 38 7 18.4% 2 5.3% \$5,200 39 10 25.6% 3 7.7% \$5,300 37 11 29.7% 1 2.7% \$5,400 22 9 40.9% 2 9.1% \$5,500 9 1 11.1% 0 0.0% \$5,600 2 0 0.0% 0 0.0% Total/Avg 507 161 31.8% 46 9.1% 36012334.2%3810.6% > \$5,000 147 38 25.9% 8 5.4%

Smoothing the data, it's easy to state that kickers costing \$5,000 or less achieve twice their price at a noticeably higher rate than kickers priced over \$5,000. But even armed with this data, kickers still fail at too high of a rate to just use price to determine the best plays each week.

There are a lot of factors that determine who should have a great kicking week, including the Vegas line, weather conditions, home field advantage, and whether the game is indoors. However, I am not certain that there is one expert capable of accurately assessing all of these factors and creating a near perfect kicker list each week. But as much as any one expert is incapable of having a perfect list, the wisdom of the crowds actually offers a lot of smart data that leads to a better answer.

FantasyPros consensus data includes weekly kicker rankings from roughly 100 fantasy experts, including several Footballguys staffers. These consensus kicker lists clearly show that not all low-priced kickers are equal. Here is a list of kickers priced at \$5,000 and under with their corresponding FantasyPros position ranking.

 Rank # 2x 2x % 3x 3x % 1 0 0 0.0% 0 0.0% 2 4 3 75.0% 1 25.0% 3 6 3 50.0% 0 0.0% 4 7 3 42.9% 1 14.3% 5 8 6 75.0% 2 25.0% 6 9 5 55.6% 3 33.3% 7 8 3 37.5% 0 0.0% 8 11 5 45.5% 2 18.2% 9 13 5 38.5% 2 15.4% 10 10 1 10.0% 1 10.0% 11 13 2 15.4% 0 0.0% 12 9 5 55.6% 3 33.3% Total 98 41 41.8% 15 15.3% Ranked 1-6 34 20 58.8% 7 20.6% Ranked 7-12 64 21 32.8% 8 12.5%

These are small sample sizes, but low-priced players ranked in the top six at FantasyPros had a very high success rate. In fact, of the 34 instances where these conditions were met in 2014, 20 times—or 58.8% of the time—the kicker more than doubled his price.

 wk player pos team opp salary FP 2x 3x Pros_rank 1 Matt Bryant K ATL NO @ ATL 5000 18 10 15 6 2 Justin Tucker K BAL PIT @ BAL 4800 14 9.6 14.4 4 2 Mason Crosby K GB NYJ @ GB 5000 13 10 15 5 3 Justin Tucker K BAL BAL @ CLE 5000 11 10 15 3 3 Adam Vinatieri K IND IND @ JAC 5000 16 10 15 5 4 Mason Crosby K GB GB @ CHI 5000 10 10 15 6 5 Cody Parkey K PHI STL @ PHI 4900 10 9.8 14.7 5 5 Mason Crosby K GB MIN @ GB 5000 6 10 15 4 7 Nick Novak K SD KC @ SD 5000 9 10 15 5 8 Mason Crosby K GB GB @ NO 5000 12 10 15 6 9 Steven Hauschka K SEA OAK @ SEA 5000 13 10 15 2 10 Chandler Catanzaro K ARI STL @ ARI 4700 8 9.4 14.1 6 10 Mason Crosby K GB CHI @ GB 4800 15 9.6 14.4 5 10 Cody Parkey K PHI CAR @ PHI 5000 9 10 15 4 11 Nick Novak K SD OAK @ SD 4500 9 9 13.5 5 11 Mason Crosby K GB PHI @ GB 4800 11 9.6 14.4 3 11 Adam Vinatieri K IND NE @ IND 4900 10 9.8 14.7 2 11 Cody Parkey K PHI PHI @ GB 4900 8 9.8 14.7 4 12 Mason Crosby K GB GB @ MIN 4900 7 9.8 14.7 6 12 Cody Parkey K PHI TEN @ PHI 5000 21 10 15 4 13 Mason Crosby K GB NE @ GB 4900 14 9.8 14.7 5 13 Dan Bailey K DAL PHI @ DAL 5000 4 10 15 3 13 Justin Tucker K BAL SD @ BAL 5000 15 10 15 6 14 Dan Bailey K DAL DAL @ CHI 4900 11 9.8 14.7 4 14 Mason Crosby K GB ATL @ GB 5000 15 10 15 2 15 Justin Tucker K BAL JAC @ BAL 5000 8 10 15 3 15 Mason Crosby K GB GB @ BUF 5000 8 10 15 6 16 Mason Crosby K GB GB @ TB 4900 9 9.8 14.7 3 16 Justin Tucker K BAL BAL @ HOU 5000 1 10 15 6 17 Justin Tucker K BAL CLE @ BAL 4800 8 9.6 14.4 4 17 Mason Crosby K GB DET @ GB 4900 4 9.8 14.7 2 17 Dan Bailey K DAL DAL @ WAS 4900 14 9.8 14.7 3 17 Adam Vinatieri K IND IND @ TEN 4900 9 9.8 14.7 5 17 Connor Barth K DEN OAK @ DEN 5000 18 10 15 6

This group of 34 kickers averaged 10.8 points and cost an average of \$4,924. Week 6 was the only week without a kicker that met this criteria. Had you just taken the top FantasyPros kicker that week, you would have paid \$5,400 and got Stephen Gostkowski's 17-point game. Had you instead chosen to take the highest-ranked kicker at or below \$5,000, you would have landed Mason Crosby's 10-point performance for \$5,000. Either way, you still would have met the 2x threshold, achieving cash-game success.

In summary: Choose a consensus top-six kicker with a salary of \$5,000 or less.

Defenses

As with kickers, many players stress over how to identify a top defense. And as with kickers, identifying top defenses is not as random as it seems.

After studying the subject, I found a way to identify high-performing defenses with pretty good frequency.

In 2014, FanDuel gave players 512 options to choose over the course of the 17-week regular season. Of those 512 team defense performances, only 161 scored twice their salary (the desired output for cash games) and just 67 reached three times (desired level in tournaments). A random selection yielded 7.5 points and cost \$4,940.

This past year, FanDuel created a bigger wedge for defensive team pricing. In years past they had priced all defenses between \$5,000 and \$5,500. In 2014, much like they did with kickers, FanDuel widened that pricing from \$4,500 to \$5,800 with a cleaner spread.

In looking for a pattern, one could search through a million variables looking for correlations. But as sample sizes get smaller, that reverse engineering becomes more likely to just be from chance. I prefer to start with a hypothesis regarding what leads to defenses having big games.

My hypothesis: Games with the widest margin of victory likely include big defensive performances. Return touchdowns, key fumbles, and interceptions all play into this hypothesis.

Players don't know the score of the game before it's played, but choosing a variable like point spread (which should correlate well to actual margin of victory) can be tested.

 Spread Times 2x Percent Avg Cost Avg FP Cost/FP 10.5+ 15 1 6.70% \$4,593 3.5 \$1,312 9.5 to 10 16 3 18.80% \$4,656 6.1 \$763 8.5 to 9 11 4 36.40% \$4,664 4.7 \$992 7.5 to 8 9 1 11.10% \$4,744 3.2 \$1,483 6.5 to 7 39 5 12.80% \$4,797 5.4 \$888 5.5 to 6 22 4 18.20% \$4,800 5.5 \$873 4.5 to 5 16 5 31.30% \$4,881 7.2 \$678 3.5 to 4 25 8 32.00% \$4,728 7.2 \$657 2.5 to 3 70 23 32.90% \$4,881 7.5 \$651 1.5 to 2 16 4 25.00% \$4,838 7.3 \$663 0.5 to 1 17 8 47.10% \$5,059 8.8 \$575 pick to -1.0 17 3 17.60% \$5,000 6.4 \$781 -1.5 to -2.0 16 7 43.80% \$4,925 9.9 \$497 -2.5 to -3.0 70 22 31.40% \$5,006 7.7 \$650 -3.5 to -4.0 25 10 40.00% \$5,072 9.3 \$545 -4.5 to -5.0 16 5 31.30% \$5,050 7.3 \$692 -5.5 to -6.0 22 10 45.50% \$5,032 9.2 \$547 -6.5 to -7.0 39 11 28.20% \$5,136 7.9 \$650 -7.5 to -8.0 9 6 66.70% \$5,189 12.6 \$412 -8.5 to -9.0 11 4 36.40% \$5,091 7.5 \$679 -9.5 to -10 16 8 50.00% \$5,213 10.4 \$501 -10.5+ 15 9 60.00% \$5,260 11.9 \$442

Smoothing the data, it is plain to see that teams that are favored by 7.5 points or more yield an average of 10.6 FanDuel fantasy points. This happened 51 times in 2014. Let's look closer at that data and see what else jumps out.

Forty-two times the home team was favored by 7.5 points or more, and in those situations their team defense averaged 11.0 fantasy points. The away teams did much worse at 8.7 fantasy points. As playing away from home includes a lot more variables (jet-lag, hotels, stadium unfamiliarity, etc.), this seems logical.

Let's look closer at these 42 home teams and see if we can smartly narrow the data further. Four times a team entering the game with a losing record was favored by 7.5 points. All four times it was the New Orleans Saints.

 Wk Team Record Opp Salary FP 2X 3X 3 New Orleans Saints 0-2 MIN \$4,600 6 9.2 13.8 5 New Orleans Saints 1-3 TB \$5,000 5 10 15 11 New Orleans Saints 4-5 CIN \$4,700 1 9.4 14.1 14 New Orleans Saints 5-7 CAR \$4,800 -4 9.6 14.4

Eliminating the team with a losing record, we are left with 38 home teams favored by 7.5 or more points that entered the game with a winning record.

 Wk Team Record Opp Salary FP 2X 3X 1 Philadelphia Eagles 0-0 JAC \$5,000 14 10 15 1 Denver Broncos 0-0 IND \$5,400 7 10.8 16.2 2 Denver Broncos 1-0 KC \$5,400 3 10.8 16.2 3 New England Patriots 1-1 OAK \$5,400 6 10.8 16.2 4 San Diego Chargers 2-1 JAC \$5,300 10 10.6 15.9 5 Green Bay Packers 2-2 MIN \$4,800 22 9.6 14.4 5 Denver Broncos 2-1 ARI \$5,100 4 10.2 15.3 6 Seattle Seahawks 3-1 DAL \$5,400 6 10.8 16.2 7 New England Patriots 4-2 NYJ \$5,600 5 11.2 16.8 8 Kansas City Chiefs 3-3 STL \$4,800 19 9.6 14.4 8 Dallas Cowboys 6-1 WAS \$4,700 6 9.4 14.1 8 Denver Broncos 5-1 SD \$5,100 6 10.2 15.3 9 Seattle Seahawks 4-3 OAK \$5,500 14 11 16.5 9 San Francisco 49ers 4-3 STL \$5,200 9 10.4 15.6 9 Cincinnati Bengals 4-2-1 JAC \$5,600 8 11.2 16.8 9 Kansas City Chiefs 4-3 NYJ \$5,300 7 10.6 15.9 10 Philadelphia Eagles 6-2 CAR \$5,000 31 10 15 10 Green Bay Packers 5-3 CHI \$4,500 20 9 13.5 10 Baltimore Ravens 5-4 TEN \$5,300 13 10.6 15.9 10 Seattle Seahawks 5-3 NYG \$5,400 7 10.8 16.2 11 San Diego Chargers 5-4 OAK \$5,300 11 10.6 15.9 12 Philadelphia Eagles 7-3 TEN \$5,100 17 10.2 15.3 12 Indianapolis Colts 6-4 JAC \$5,600 15 11.2 16.8 12 San Francisco 49ers 6-4 WAS \$5,300 11 10.6 15.9 13 Indianapolis Colts 7-4 WAS \$5,500 14 11 16.5 14 Detroit Lions 8-4 TB \$5,300 13 10.6 15.9 14 Denver Broncos 9-3 BUF \$4,700 11 9.4 14.1 14 Green Bay Packers 9-3 ATL \$5,100 1 10.2 15.3 15 Baltimore Ravens 8-5 JAC \$5,300 16 10.6 15.9 15 Kansas City Chiefs 7-6 OAK \$4,900 16 9.8 14.7 15 New England Patriots 10-3 MIA \$5,000 14 10 15 15 Detroit Lions 9-4 MIN \$5,400 11 10.8 16.2 15 Seattle Seahawks 9-4 SF \$5,500 10 11 16.5 17 Seattle Seahawks 11-4 STL \$5,500 23 11 16.5 17 Denver Broncos 11-4 OAK \$5,200 17 10.4 15.6 17 Green Bay Packers 11-4 DET \$5,100 13 10.2 15.3 17 Baltimore Ravens 9-6 CLE \$5,200 12 10.4 15.6 17 Houston Texans 8-7 JAC \$5,400 11 10.8 16.2

Had you chosen any of these defenses you would have likely had a good week.

After Week 10 (when the NFL has a lot more data to accurately assign point spreads), you would have crushed things.

 Team Salary FP 2x 3x All weeks \$5,216 11.9 Weeks 1-9 \$5,225 9.1 16 4 Weeks 10+ \$5,209 14 22 19

From Week 10 on, these criteria yielded 22 suggestions. Nineteen of those would have yielded you 3x their salary. These defenses would have cost you \$5,209 on average and yielded an average of 14.0 FanDuel fantasy points.

In summary: Choose a team defense with a winning record playing at home favored by 7.5 points or more.

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