# The Rundown: FantasyScore Week 8

A weekly guide to FantasyScore's Salary Cap and Draft-and-Go contests.

So far this season, I've been introducing you to, familiarizing you with, and showing you how to beat FantasyScore's Draft-N-Go (DNG) games. For your convenience, here are links to each of the strategy-based articles:

From this point forward, I'm going to shift the focus of the weekly discussion towards the more traditional DFS games that FantasyScore has on offer. Specifically, I'm going to identify the best and worst values at each position. But, as is my wont, I'm going to take what most people already know about calculating value, and move it one giant statistical leap further. (Note: I'm not abandoning DNG advice altogether. The VBD draft lists will be at the bottom of this post each week.)

## the bell curve and value probabilities

As has been outlined in various other columns on Footballguys, FantasyScore's salary cap game is similar to DraftKings' game with respect to its scoring system, player salaries, and lineup requirements. Therefore, the value thresholds you need to hit on FantasyScore are also similar:

• It takes around 150 points to win a head-to-head (H2H) or 50/50 game, so a \$50,000 salary cap means your lineup needs to achieve 3x value (i.e., [150 * 1000] / 50000].
• It takes around 210 points to win a GPP, so your lineup needs to achieve approximately 4x value (i.e., [200 * 1000] / 50000).
Most discussions of player value begin and end with calculating every player's individual value, and focusing on the ones that are closest to reaching the above thresholds. For instance, Cam Newton has an average Footballguys projection of 22.9 points this week, so a \$7,700 salary translates to a projected value of 3.1, which makes him a value play in FantasyScore cash games.

OK, so we've got that going for us, which is nice, but there's an additional piece of information we need to figure out in order to go from identifying Newton as a value play and deciding if he's valuable enough to use in your lineup. To obtain that information, we need to ask and answer the question, "How likely is it that Newton will achieve 3.1x value?" And to answer that question, we can do one of two things: (a) come up with a mostly subjective, educated guess; or (b) use some fundamentals of statistics to estimate an objective probability. I'm guessing you have an educated guess that I'm going to do the latter.

If you've ever taken an IQ test or a standardized test, you already have an intuitive understanding of how my system works -- even before I've explained it to you. Recall that, when you took said test, your score was reported to you as a percentile. If particularly curious, you found out that that percentile was based on the bell curve. But how did they actually use the bell curve to translate your score into a percentile?

To show you how, let's stick with the IQ example. Mensa, that exclusive group of smarty pants, requires a score of 132 or higher to be eligible for membership, which translates to being in the 98th percentile of intelligence. Or alternatively, it means that there's only a 2 percent chance that some random person you run into is Mensa-eligible. Either way you slice it, that translation between score and probability is entirely based on two stats Mensa knows about the U.S. population: The average IQ score is 100, and the standard deviation of IQ scores is 15. You have to plug numbers into an Excel formula or peruse a statistics textbook to get the exact calculation, but the underlying math can be represented in the following graph:

As you can see, the 100-IQ average is in the middle of the bell curve, which means that 50 percent of the "bell" is below it and 50 percent is above it. That may seem trivial, but it's actually a big hint about what's going on here. That's because you'll also notice I've shaded in the area above the magic IQ score of 132, and that shaded area is where the whole "2 percent chance" thing comes from. Likewise, the non-shaded area to the left of 132 is where the whole "98th percentile" thing comes from. In short, 98 percent of the "bell" is below 132, and only 2 percent is above it.

Getting back to football, the bell curve is a potentially powerful tool we can use to calculate value probabilities in DFS. Instead of asking, "How much of the bell is above (or below) a person's IQ score?" we can ask, "How much of the bell is above (or below) a player's projected value?" Returning to Cam Newton, the answer for him this week is 48.8% in cash games and 13.6% in GPPs, the former of which can be represented in the graph below:

In Newton's case, his average Footballguys projection is 22.9 points and the standard deviation for Top 20 quarterbacks on FantasyScore is 6.1 points (which is shown above in the gaps between 22.9 and 29, 29 and 35.1 etc.). For Newton to achieve 3x value (for use in cash games), he needs to score 23.1 points, which is where the shaded area begins. Put all this together and you can see that the shaded area covers a smidgeon less then half of the bell, or the 48.8% probability I told you a paragraph ago.

Just so we're clear going forward: You should interpret Newton's 48.8% figure as, "Cam Newton has a 48.8% chance of achieving cash game value on FantasyScore in Week 8."

quarterbacks

As I mentioned earlier, knowing that Newton has a 48.8% chance of achieving cash game value is one thing. What we really want to know is how 48.8% ranks among quarterbacks. That way, we can determine whether or not we actually want to use Newton as a source of value in our cash game lineups. Along these lines, below are the quarterbacks with the highest (and lowest) probabilities of achieving value in cash games and GPPs. (Quick note: As with my above-linked column on drafting for upside in DNGs, the GPP probabilities below are based on the highest Footballguys projection rather than the average Footballguys projection.)

 MOST LIKELY TO ACHIEVE VALUE Name Pos Tm Salary Avg P(Cash) Name Pos Tm Salary Max P(GPP) Zach Mettenberger QB TEN 4600 16.9 69.4% Brian Hoyer QB HOU 5400 21.4 48.7% Ryan Fitzpatrick QB NYJ 5700 19.6 66.0% Nick Foles QB STL 4700 18.3 46.7% Nick Foles QB STL 4700 16.2 63.6% Zach Mettenberger QB TEN 4600 17.3 42.8% LEAST LIKELY TO ACHIEVE VALUE Name Pos Tm Salary Avg P(Cash) Name Pos Tm Salary Max P(GPP) Peyton Manning QB DEN 7600 16.5 14.9% Aaron Rodgers QB GNB 8900 21.6 1.1% Aaron Rodgers QB GNB 8900 20.6 15.8% Andrew Luck QB IND 8400 20.7 1.7% Andrew Luck QB IND 8400 20.6 22.3% Peyton Manning QB DEN 7600 17.9 2.0%

Mettenberger may rank as the quarterback most likely to achieve value for cash games, but my money's on Ryan Fitzpatrick. That's because, if we want low-variance options in cash games, I'll take the established starter playing against Oakland over the failed starter, injury replacement against anyone. For GPPs, Hoyer's the best value. Not only does he have the highest probability of achieving value; Foles helms one of the most run-oriented offenses in the NFL and is likely to be salting away a lead for much of the game against San Francisco.

On the flip side, Peyton Manning seems wholly unworthy of his price this week, while Rodgers and Luck are simply too expensive to roster unless you go for maximum value at every other position.

## running backs

Below are the running backs most (and least) likely to achieve value:

 MOST LIKELY TO ACHIEVE VALUE Name Pos Tm Salary Avg P(Cash) Name Pos Tm Salary Max P(GPP) Dexter McCluster RB TEN 2000 9.5 68.4% Dexter McCluster RB TEN 2000 10.3 62.4% Reggie Bush RB SFO 3000 11 60.9% Jonathan Grimes RB HOU 2200 8.9 50.5% Marcel Reece RB OAK 2000 6.6 53.4% Reggie Bush RB SFO 3000 11.9 49.5% Khiry Robinson RB NOR 2500 7.9 52.0% Marcel Reece RB OAK 2000 7.6 47.8% Jonathan Grimes RB HOU 2200 6.8 51.2% Taiwan Jones RB OAK 2000 7.6 47.8% LEAST LIKELY TO ACHIEVE VALUE Name Pos Tm Salary Avg P(Cash) Name Pos Tm Salary Max P(GPP) James Starks RB GNB 5500 5.4 6.4% Adrian Peterson RB MIN 8300 15.9 0.9% Adrian Peterson RB MIN 8300 15.6 10.0% Chris Ivory RB NYJ 7400 14.6 2.0% Eddie Lacy RB GNB 7000 11.8 10.2% Marshawn Lynch RB SEA 7500 16 2.8% Marshawn Lynch RB SEA 7500 13.9 12.0% James Starks RB GNB 5500 8.5 3.2% Chris Ivory RB NYJ 7400 13.8 12.3% Mark Ingram II RB NOR 7700 17.4 3.3%

As an active member of Tennessee's short passing game and going against Houston's soft defense, McCluster is the clear value play in both cash games and GPPs. In terms of running backs to avoid, Lacy's matchup and recent struggles make him an unattractive play even with Starks questionable; Peterson is simply too expensive even though his matchup is favorable.

Below are the wide receivers most (and least) likely to achieve value:

 MOST LIKELY TO ACHIEVE VALUE Name Pos Tm Salary Avg P(Cash) Name Pos Tm Salary Max P(GPP) Nate Washington WR HOU 3200 12.8 66.3% Jeremy Kerley WR NYJ 2000 11.5 67.7% Jeremy Kerley WR NYJ 2000 8.8 64.2% Tyler Lockett WR SEA 2400 11.9 61.9% Tyler Lockett WR SEA 2400 9.7 62.9% Nate Washington WR HOU 3200 14.0 56.3% Michael Floyd WR ARI 3500 12.6 60.7% Michael Floyd WR ARI 3500 15.1 55.8% Justin Hunter WR TEN 2000 7.8 59.2% Taylor Gabriel WR CLE 2000 8.5 52.6% Taylor Gabriel WR CLE 2000 7.7 58.6% Justin Hunter WR TEN 2000 8.1 50.5% Dwayne Harris WR NYG 2000 7.1 55.7% Dwayne Harris WR NYG 2000 7.9 49.5% Malcom Floyd WR SDG 2800 9.5 55.7% Devin Funchess WR CAR 2000 7.8 49.0% Kenny Britt WR STL 2100 7.2 54.7% Jaelen Strong WR HOU 2000 7.8 49.0% Brian Hartline WR CLE 2000 6.4 52.1% Keith Mumphery WR HOU 2000 7.8 49.0% LEAST LIKELY TO ACHIEVE VALUE Name Pos Tm Salary Avg P(Cash) Name Pos Tm Salary Max P(GPP) Dez Bryant WR DAL 8400 13.2 5.7% Dez Bryant WR DAL 8400 16.5 1.2% John Brown WR ARI 5200 6.0 10.3% Emmanuel Sanders WR DEN 7900 17.0 2.7% James Jones WR GNB 6700 11.4 12.6% T.Y. Hilton WR IND 7800 16.6 2.7% T.Y. Hilton WR IND 7800 15.4 14.5% James Jones WR GNB 6700 12.5 3.0% Tavon Austin WR STL 6300 11.0 14.8% A.J. Green WR CIN 7800 17.5 3.6% Ted Ginn Jr WR CAR 5900 9.8 14.9% Randall Cobb WR GNB 7600 16.7 3.6% Antonio Brown WR PIT 8800 18.6 15.2% Keenan Allen WR SDG 8100 19.0 3.9% Emmanuel Sanders WR DEN 7900 16.3 16.4% Demaryius Thomas WR DEN 8400 20.2 3.9% Randall Cobb WR GNB 7600 15.6 17.0% Amari Cooper WR OAK 7100 15.3 4.2% Demaryius Thomas WR DEN 8400 18.2 17.7% Alshon Jeffery WR CHI 7700 17.8 4.4%

Combined with the quarterback probabilities I listed earlier, the top of this table make it clear that Fitzpatrick-Kerley and Hoyer-Washington are the two most value-oriented stacks of Week 8. Likewise, Manning-Sanders, Luck-Hilton, and Rodgers-Jones are the budget-busting stacks to avoid this week, especially in GPPs. Among wide receivers not likely to be stacked with their quarterbacks, Tyler Lockett is the player I'm flexing in my GPP lineups.

## tight ends

Below are the tight ends most (and least) likely to achieve value:

 MOST LIKELY TO ACHIEVE VALUE Name Pos Tm Salary Avg P(Cash) Name Pos Tm Salary Max P(GPP) Ladarius Green TE SDG 3500 13.9 71.6% Ladarius Green TE SDG 3500 17 69.1% Ben Watson TE NOR 2700 10 62.2% Clive Walford TE OAK 1700 8.2 59.2% Crockett Gillmore TE BAL 2400 8.9 61.1% Crockett Gillmore TE BAL 2400 10.9 58.6% LEAST LIKELY TO ACHIEVE VALUE Name Pos Tm Salary Avg P(Cash) Name Pos Tm Salary Max P(GPP) Jimmy Graham TE SEA 6600 11.7 8.8% Gary Barnidge TE CLE 7100 13.7 0.7% Gary Barnidge TE CLE 7100 13.4 9.2% Jimmy Graham TE SEA 6600 12.1 0.9% Tyler Eifert TE CIN 7200 14 10.1% Tyler Eifert TE CIN 7200 15.2 1.2%

This one's easy, ownership rates be damned: Green should be in most of, if not all of, your lineups this week. He's been a consistent double-digit scorer as the second tight end on his own team, and now he's back to starting in Antonio Gates' absence. Also, his matchup is favorable. Also, he's criminally underpriced when you consider that Larry Donnell -- Larry Donnell! -- is \$1,100 more expensive than him. Or how about this for a deal? If it were possible, you could get two Ladarius Greens against Baltimore for one Gary Barnidge against Arizona.

## defenses

Finally, here are the defenses most (and least) likely to achieve value:

 MOST LIKELY TO ACHIEVE VALUE Name Pos Tm Salary Avg P(Cash) Name Pos Tm Salary Avg P(GPP) Oakland Raiders DEF OAK 1300 9.3 84.6% New Orleans Saints DEF NOR 1200 9.2 79.7% St. Louis Rams DEF STL 2600 13.1 84.1% Oakland Raiders DEF OAK 1300 9.5 79.1% New Orleans Saints DEF NOR 1200 8.7 82.9% St. Louis Rams DEF STL 2600 13.9 74.5% LEAST LIKELY TO ACHIEVE VALUE Name Pos Tm Salary Avg P(Cash) Name Pos Tm Salary Avg P(GPP) Denver Broncos DEF DEN 3400 9.1 41.7% Denver Broncos DEF DEN 3400 9.6 22.5% Cincinnati Bengals DEF CIN 2500 8.2 55.2% Cincinnati Bengals DEF CIN 2500 8.2 36.7% Cleveland Browns DEF CLE 2100 7.4 57.8% Seattle Seahawks DEF SEA 3400 12.1 38.9%

Another no-brainer despite ownership rates. We're at the point this season that "use defenses going against the 49ers" is gospel. And when we can get one relatively cheaply, all the better. That said, New Orleans is a viable option in GPPs if you're looking for a bargain basement, contrarian defense.

## WEEK 8 DRAFT LISTS

And finally, if you're playing FantasyScore's DNGs, I haven't forgotten about you. Below are the draft lists to use in Week 8.

2-Person DNGs  5-Person DNGs  8-Person DNGs
NAMEPOSTmNAMEPOSTmNAMEPOSTm
Julio Jones WR ATL Julio Jones WR ATL Julio Jones WR ATL
Devonta Freeman RB ATL Devonta Freeman RB ATL Devonta Freeman RB ATL
Le'Veon Bell RB PIT Le'Veon Bell RB PIT Le'Veon Bell RB PIT
DeAndre Hopkins WR HOU DeAndre Hopkins WR HOU DeAndre Hopkins WR HOU
St. Louis Rams DEF STL Antonio Brown WR PIT Antonio Brown WR PIT
Greg Olsen TE CAR Brandon Marshall WR NYJ Brandon Marshall WR NYJ
Antonio Brown WR PIT Larry Fitzgerald WR ARI Larry Fitzgerald WR ARI
Brandon Marshall WR NYJ Demaryius Thomas WR DEN Demaryius Thomas WR DEN
Larry Fitzgerald WR ARI Keenan Allen WR SDG Keenan Allen WR SDG
Demaryius Thomas WR DEN Mike Evans WR TAM Todd Gurley RB STL
Keenan Allen WR SDG Todd Gurley RB STL Justin Forsett RB BAL
Todd Gurley RB STL Justin Forsett RB BAL Mike Evans WR TAM
Cam Newton QB CAR Odell Beckham Jr WR NYG Odell Beckham Jr WR NYG
Philip Rivers QB SDG St. Louis Rams DEF STL Matt Forte RB CHI
Justin Forsett RB BAL Matt Forte RB CHI A.J. Green WR CIN
Mike Evans WR TAM A.J. Green WR CIN Alshon Jeffery WR CHI
Tyler Eifert TE CIN Alshon Jeffery WR CHI Doug Martin RB TAM
Carolina Panthers DEF CAR Cam Newton QB CAR Emmanuel Sanders WR DEN
Philip Rivers QB SDG Randall Cobb WR GNB
Doug Martin RB TAM Martavis Bryant WR PIT
Emmanuel Sanders WR DEN Eric Decker WR NYJ
Greg Olsen TE CAR T.Y. Hilton WR IND
Randall Cobb WR GNB Mark Ingram II RB NOR
Carolina Panthers DEF CAR Steve Smith WR BAL
Seattle Seahawks DEF SEA Amari Cooper WR OAK
Martavis Bryant WR PIT Adrian Peterson RB MIN
Eric Decker WR NYJ Danny Woodhead RB SDG
T.Y. Hilton WR IND Darren McFadden RB DAL
Tyler Eifert TE CIN Stefon Diggs WR MIN
Ladarius Green TE SDG Greg Olsen TE CAR
Andy Dalton QB CIN Travis Benjamin WR CLE
Carson Palmer QB ARI Michael Crabtree WR OAK
Steve Smith WR BAL St. Louis Rams DEF STL
Mark Ingram II RB NOR Brandin Cooks WR NOR
Arizona Cardinals DEF ARI Donte Moncrief WR IND
Amari Cooper WR OAK Tyler Eifert TE CIN
Gary Barnidge TE CLE Cam Newton QB CAR
Adrian Peterson RB MIN Philip Rivers QB SDG
Stefon Diggs WR MIN Ladarius Green TE SDG
Danny Woodhead RB SDG Dez Bryant WR DAL
NY Jets DEF NYJ Marshawn Lynch RB SEA
Drew Brees QB NOR Gary Barnidge TE CLE
Darren McFadden RB DAL Chris Ivory RB NYJ
Delanie Walker TE TEN Marvin Jones Jr WR CIN
Travis Benjamin WR CLE Delanie Walker TE TEN
Nate Washington WR HOU
Giovani Bernard RB CIN
Carolina Panthers DEF CAR
Seattle Seahawks DEF SEA
Michael Floyd WR ARI
Martellus Bennett TE CHI
Andy Dalton QB CIN
Kendall Wright WR TEN
Carson Palmer QB ARI
Arizona Cardinals DEF ARI
Chris Johnson RB ARI
Frank Gore RB IND
Mike Wallace WR MIN
NY Jets DEF NYJ
Drew Brees QB NOR
Green Bay Packers DEF GNB
Houston Texans DEF HOU
Jimmy Graham TE SEA
James Jones WR GNB
Aaron Rodgers QB GNB
Matt Ryan QB ATL
Andrew Luck QB IND
Jonathan Stewart RB CAR
Jason Witten TE DAL
Rueben Randle WR NYG
Minnesota Vikings DEF MIN