The Rundown: FantasyScore Week 12

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

Continuing last week's theme, I'm once again going to start off with an analysis of whether or not my value probabilities are well-calibrated -- this time for running backs. For those who missed last week's piece, the general issue at hand here is that, when a statistical probability model predicts that something will happen X% of the time in the future, it should happen X% time in the future. For instance, if I told you that 10 different running backs had around a 20 percent chance of achieving cash game value on FantasyScore last week, then, what we would want to see when looking back at what actually happened in Week 11 is that two of those backs achieved value (i.e., 20 percent), while the other eight did not. Obviously, 10 is a really small sample size, but we can extend this methodology as far back as we we want, provided we have data related to what was predicted in advance and what actually happened.

With respect to running backs, however, there's a not-so-small complication that didn't exist for quarterbacks. Namely, whereas the game-by-game scoring of quarterbacks resembles a symmetric bell curve whereby the majority of scores are around the league average, with diminishing frequencies as you move away from the average, running back scoring doesn't resemble a bell curve at all. To wit, here's a graph showing how often various FantasyScore point totals have occured among the weekly Top 50 running backs since 2007:

Right away, you can clearly see from the red line that the curve is not a symmetrical bell shape. Rather, it's a bell that's had it's right side caved in, thereby pushing the vast majority of scores over towards the lower end of the spectrum. This "caving in" has major implications for calculating how often a particular point total occurs.

I won't get into the technical details, but the gist of it is implied by the black shaded portion of the graph. But just so we're on the same page, we should intrepret the height of the graph at 7.1 points as saying that that score (or so) has happened about 400 times since 2007 (or around 5.4 percent of the time). Similarly, 23.5 points (or so) has happened only about 100 times (or around 1.4 percent). With that basic understanding, we can move on to a more important question for the purposes of calculating value probabilities; something like, "How often have these backs scored anywhere from 0.0 to 23.5 points?" To answer this, we simply add up all of the black shading from 0.0 to 23.5 points, which results in a frequency of 6,697, or 91.1 percent of the time. Stated differently, the answer is (equivalently) that only 8.9 percent of the shading lies greater than 23.5 FantasyScore points.

That last bit reveals the crux of the issue: For scoring at a position like running back, where the bell shape is caved in towards the left, the threshold for "91.1 percent of the time" occurs much farther to the left of the graph than it does for scoring at a position like quarterback, where the bell shape is symmetric. In practice, this means that a running back is far less likely to score at the high end of the running back scoring spectrum -- say, 34.3 points on the above graph -- than a quarterback is to score at the high end of the quarterback spectrum (and vice versa).

Hopefully, I've set the stage well enough that you have an inclination of where I'm going with all of this; which is that, unlike at quarterback, I can't compare running back value probabilities to actual running back scoring using a bell curve. Instead, I have to use what's called a lognormal curve, which is the statistical representation of a caved-in bell and which was represented by the red line in the above graph.

So, by calculating cash game value probabilities based on the lognormal curve, and then comparing those probabilities to how frequently running backs actually achieve cash game value, I can (finally) produce the graph below:

Remember, the underlying math has changed, but the goal of this graph is the same as it was for quarterbacks: When my fancy mathhas predicted that some group of running backs had an X percent chance of achieving value, X percent of those running backs should have achieved value in retrospect.

And indeed, although there's room for improvement, that outcome seems to be the case: For the most part, the blue line (i.e., actual probabilites) adheres to the red line (i.e., expected probabilities). On the plus side, the two lines are closest at lower probabilities, which corresponds to a) the story of the caved-in bell that I just told you, and b) the highest sample sizes of the bunch. Nearly 1,200 of my cash game value predictions were in the 0-29 percent range, whereas not even half that were in the 30-99 percent range. On the negative side, the inaccuracy from 30-49 percent is troubling to me because it represents just over 400 predictions, and so sample size shouldn't be an issue. That's definitely something I'll be exploring and (hopefully) refining during the offseason.

OK, so here are the main take-home messages from what you just read:

  • If you're statistics-minded, it's imperative to understand that game-to-game running back performance is not normally distributed (i.e., shaped like a bell). Rather, it's lognormally distributed (i.e., shaped like a bell whose right side has been caved in).
  • If you're not statistics-minded, then all you need to know is that you can trust my cash game value probabilities for running backs across the spectrum -- except for being mildly skeptical in the 30-49 percent range.
Let's get to Week 12, shall we?
 

quarterbacks

Below are the quarterbacks with the highest (and lowest) probabilities of achieving value in cash games and GPPs:

MOST LIKELY TO ACHIEVE VALUE
NAME TM SALARY AVG P(CASH) NAME TM SALARY MAX P(GPP)
Josh McCown CLE 4500 20.3 86.8% Josh McCown CLE 4500 21.9 73.9%
Teddy Bridgewater MIN 4800 17.8 71.1% Matt Schaub BAL 5200 19.9 44.1%
Matt Hasselbeck IND 5800 19.1 60.9% Matt Hasselbeck IND 5800 20.2 31.1%
LEAST LIKELY TO ACHIEVE VALUE
NAME TM SALARY AVG P(CASH) NAME TM SALARY MAX P(GPP)
Ben Roethlisberger PIT 8400 19.6 18.0% Drew Brees NOR 8700 21.6 1.5%
Drew Brees NOR 8700 20.9 19.6% Tom Brady NWE 9000 22.9 1.6%
Tom Brady NWE 9000 21.9 19.9% Ben Roethlisberger PIT 8400 20.5 1.6%

McCown is the clear value play, especially in GPPs. He's at home against the 27th-ranked pass defense (per DVOA); 24th against tight ends. Earlier in the season, he scored 38.1 points against Baltimore. According to Pro Football Focus, his No. 1 receiver has one of the best individual matchups of the week. This game has the lowest over-under in Week 12, but McCown only needs 18.0 points, and he's scored 17.9 or more in five of six games since suffering a concussion during against the Jets in Week 1.

Brees is an overwhelming underdog to achieve value in GPPs, but I could see it happening because, with Vegas predicting a high-scoring, close game, and with New Orleans' defense being god-awful, I can easily envision a scenario in which Texans-Saints turns into the proverbial tit-for-tat shootout. Is it likely that Brees scores 34.8 points to achieve GPP value? No. Does that scenario have more than a 1.5% chance of happening? Perhaps.

running backs

Below are the running backs with the highest (and lowest) probabilities of achieving value in cash games and GPPs:

MOST LIKELY TO ACHIEVE VALUE
NAME TM SALARY AVG P(CASH) NAME TM SALARY MAX P(GPP)
Tevin Coleman ATL 2000 10.6 84.9% Tevin Coleman ATL 2000 11.6 74.9%
Shaun Draughn SFO 3000 13.7 77.4% Jonathan Grimes HOU 2000 10.6 69.4%
Jonathan Grimes HOU 2000 8.4 72.5% Shaun Draughn SFO 3000 15.6 68.2%
Melvin Gordon III SDG 2300 9.1 69.1% Terron Ward ATL 2000 9.0 58.4%
Chris Thompson WAS 2000 7.3 63.5% Isaiah Crowell CLE 2400 10.5 56.4%
LEAST LIKELY TO ACHIEVE VALUE
NAME TM SALARY AVG P(CASH) NAME TM SALARY MAX P(GPP)
Karlos Williams BUF 5100 5.6 3.4% Karlos Williams BUF 5100 6.5 2.0%
Alfred Morris WAS 4300 4.8 3.7% Alfred Morris WAS 4300 6.1 3.1%
Andre Ellington ARI 4700 6.7 8.9% Andre Ellington ARI 4700 7.1 3.9%
Chris Johnson ARI 7300 11.4 12.0% Chris Johnson ARI 7300 12.1 5.6%
Chris Ivory NYJ 7400 11.8 12.8% Chris Ivory NYJ 7400 12.9 6.7%

What with non-Freeman Falcons running backs and non-Foster Texans running backs showing anything worth trusting this season, the value play in Week 12 is Draughn. He only needs 12.0 points to achieve both cash game and GPP value. What with Blaine Gabbert's proclivity for dumpoffs whilst San Francisco gets slaughtered and the generosity of Arizona's defense to pass-catching backs, Draughn has a good chance of scoring that much via receiving alone.

Speaking of that game, I can see Chris Johnson achieving value despite his low probability, especially in cash games. He scored 27.0 points in the teams' matchup earlier in the season, and San Francisco's defense is Bottom 4 in both defending the run and defending opposing running backs in the passing game (per DVOA).

wide receivers

Below are the wide receivers with the highest (and lowest) probabilities of achieving value in cash games and GPPs:

MOST LIKELY TO ACHIEVE VALUE
NAME TM SALARY AVG P(CASH) NAME TM SALARY MAX P(GPP)
Tyler Lockett SEA 2000 10.9 86.5% Tyler Lockett SEA 2000 14.2 85.6%
Brian Hartline CLE 2000 9.9 82.1% Brian Hartline CLE 2000 12.8 80.8%
Chris Givens BAL 2000 9.0 77.1% Chris Givens BAL 2000 12.8 80.8%
Keshawn Martin NWE 2000 8.9 76.7% Roddy White ATL 2000 10.8 71.1%
Roddy White ATL 2000 8.8 76.0% Keshawn Martin NWE 2000 10.5 69.3%
Albert Wilson KAN 2000 8.1 71.3% Griff Whalen IND 2000 9.8 64.6%
Kenny Stills MIA 2000 7.6 67.1% Kenny Stills MIA 2000 9.6 63.2%
Jermaine Kearse SEA 2000 7.4 65.3% Jermaine Kearse SEA 2000 8.9 57.8%
Griff Whalen IND 2000 7.4 64.7% Justin Hardy ATL 2000 8.8 57.0%
Jamison Crowder WAS 2400 8.5 62.0% Albert Wilson KAN 2000 8.5 54.5%
LEAST LIKELY TO ACHIEVE VALUE
NAME TM SALARY AVG P(CASH) NAME TM SALARY MAX P(GPP)
Tavon Austin STL 6200 8.4 7.0% Tavon Austin STL 6200 8.8 2.8%
Michael Floyd ARI 6600 10.4 11.7% Michael Floyd ARI 6600 11.0 5.3%
Pierre Garcon WAS 5600 9.1 12.6% Pierre Garcon WAS 5600 9.6 5.8%
T.Y. Hilton IND 7500 12.6 14.0% Danny Amendola NWE 6600 11.8 6.8%
Emmanuel Sanders DEN 7500 12.9 15.1% T.Y. Hilton IND 7500 14.0 7.9%
Donte Moncrief IND 5700 10.5 18.3% Emmanuel Sanders DEN 7500 14.6 9.1%
Rueben Randle NYG 5700 11.0 20.7% Donte Moncrief IND 5700 11.6 10.5%
Brandon LaFell NWE 6400 12.5 21.3% Jeremy Maclin KAN 6200 13.0 11.6%
Mike Evans TAM 6900 13.7 22.1% Sammy Watkins BUF 6400 13.5 11.8%
Sammy Watkins BUF 6400 12.7 22.2% A.J. Green CIN 7900 16.7 11.9%

I'm all-in on Tyler Lockett this week. Whereas Doug Baldwin has lined up in the slot over 80 percent of the time (per Pro Football Focus), Lockett moonlights all across the formation -- and that gives him more opportunities to exploit William Gay and Antwon Blake, the latter of whom has been especially bad in coverage this season. In addition, Seattle's pass defense has given up ample yardage to opposing quarterbacks that are the least bit competent, which means that the normally run-happy Seahawks offense could easily find themselves in a tit-for-tat scoring affair a la what I said earlier about Texans-Saints.

On the "least likely" side of the table, everyone makes sense, both in terms of wide receivers to fade and wide receivers to not fade. All of them either have really bad matchups or are priced too high (or both).

tight ends

Below are the tight ends with the highest (and lowest) probabilities of achieving value in cash games and GPPs:

MOST LIKELY TO ACHIEVE VALUE
NAME TM SALARY AVG P(CASH) NAME TM SALARY MAX P(GPP)
Will Tye NYG 1700 7.8 79.0% Will Tye NYG 1700 9.8 75.4%
Vance McDonald SFO 1700 7.4 75.5% Vance McDonald SFO 1700 7.8 60.2%
Jared Cook STL 2300 6.9 49.6% Ben Watson NOR 3500 10.4 28.8%
LEAST LIKELY TO ACHIEVE VALUE
NAME TM SALARY AVG P(CASH) NAME TM SALARY MAX P(GPP)
Jordan Reed WAS 7600 12.4 12.6% Jordan Reed WAS 7600 12.7 5.0%
Antonio Gates SDG 6800 11.3 13.2% Tyler Eifert CIN 7700 13.7 6.4%
Tyler Eifert CIN 7700 13.0 13.8% Antonio Gates SDG 6800 12.8 7.8%

This marks three consecutive non-bye weeks in which Tye has had the highest probability of achieving value. He's currently 1-of-2. Per DVOA, Washington's pass defense ranks 26th overall and 20th against opposing tight ends. If you want to ride the Tye-ger, go for it.

And if I'm going to buck my own trend among low-probability tight ends, it'll be with Gates. Jacksonville's pass defense ranks 30th overall and 30th against opposing tight ends. In addition, this game has the 3rd-highest over-under of Week 12, and San Diego is the 5th-most pass-happy offense in the league this season.

defenses

Below are the defenses with the highest (and lowest) probabilities of achieving value in cash games and GPPs:

MOST LIKELY TO ACHIEVE VALUE
NAME TM SALARY AVG P(CASH) NAME TM SALARY MAX P(GPP)
Cleveland Browns CLE 1700 11.4 90.3% Cleveland Browns CLE 1700 11.6 84.1%
Jacksonville Jaguars JAC 1600 10.3 87.2% Jacksonville Jaguars JAC 1600 10.4 79.8%
Oakland Raiders OAK 1700 9.6 82.5% San Diego Chargers SDG 1500 9.0 73.4%
LEAST LIKELY TO ACHIEVE VALUE
NAME TM SALARY AVG P(CASH) NAME TM SALARY MAX P(GPP)
Denver Broncos DEN 3200 9.0 44.5% Denver Broncos DEN 3200 9.3 23.3%
Seattle Seahawks SEA 3300 9.9 49.9% Seattle Seahawks SEA 3300 10.0 25.2%
Cincinnati Bengals CIN 3200 11.5 65.3% Cincinnati Bengals CIN 3200 11.6 40.1%

On the "most likely" side, I'll steal a joke from fellow Footballguy Phil Alexander's piece earlier this week:

Although there really is no "unlikely" on the "least likely" side, I think it's safe to say that Cincinnati hosting St. Louis, a team that seems to only play well within their division, is the best value play of the trio; provided the Bengals can slow down Todd Gurley, of course. It turns out that run-happy teams and/or teams with elite running back talent have found success on the ground against them this season. The Rams and Gurley qualify on both accounts.

week 12 draft lists

Finally, to supplement the statistics-based strategies I recommended earlier in the season, here are the VBD draft lists you should use for FantasyScore's Draft-N-Go (DNG) games:

2-PERSON DNGS5-PERSON DNGS8-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
Rob Gronkowski TE NWE Larry Fitzgerald WR ARI Larry Fitzgerald WR ARI
Larry Fitzgerald WR ARI Rob Gronkowski TE NWE DeAndre Hopkins WR HOU
DeAndre Hopkins WR HOU DeAndre Hopkins WR HOU Mike Evans WR TAM
Mike Evans WR TAM Mike Evans WR TAM Calvin Johnson WR DET
Seattle Seahawks DEF SEA Calvin Johnson WR DET Demaryius Thomas WR DEN
Calvin Johnson WR DET DeMarco Murray RB PHI Rob Gronkowski TE NWE
Demaryius Thomas WR DEN Demaryius Thomas WR DEN Dez Bryant WR DAL
DeMarco Murray RB PHI Todd Gurley RB STL Amari Cooper WR OAK
Todd Gurley RB STL Seattle Seahawks DEF SEA Allen Robinson WR JAC
Tom Brady QB NWE Tom Brady QB NWE A.J. Green WR CIN
Cam Newton QB CAR Cam Newton QB CAR Jarvis Landry WR MIA
Charcandrick West RB KAN Charcandrick West RB KAN Michael Crabtree WR OAK
Dez Bryant WR DAL Dez Bryant WR DAL Brandon Marshall WR NYJ
Amari Cooper WR OAK Amari Cooper WR OAK DeMarco Murray RB PHI
Greg Olsen TE CAR Allen Robinson WR JAC Eric Decker WR NYJ
NY Jets DEF NYJ Darren McFadden RB DAL Todd Gurley RB STL
      A.J. Green WR CIN Steve Johnson WR SDG
      Jarvis Landry WR MIA Stefon Diggs WR MIN
      Michael Crabtree WR OAK Charcandrick West RB KAN
      Brandon Marshall WR NYJ Sammy Watkins WR BUF
      Adrian Peterson RB MIN Tom Brady QB NWE
      Eric Decker WR NYJ Randall Cobb WR GNB
      Danny Woodhead RB SDG Darren McFadden RB DAL
      Steve Johnson WR SDG Seattle Seahawks DEF SEA
      Lamar Miller RB MIA Cam Newton QB CAR
      Carson Palmer QB ARI Danny Amendola WR NWE
      Stefon Diggs WR MIN Brandon LaFell WR NWE
      Greg Olsen TE CAR Allen Hurns WR JAC
      Antonio Gates TE SDG Adrian Peterson RB MIN
      Jeremy Langford RB CHI T.Y. Hilton WR IND
      NY Jets DEF NYJ Danny Woodhead RB SDG
      Sammy Watkins WR BUF Lamar Miller RB MIA
      Carolina Panthers DEF CAR Alshon Jeffery WR CHI
      Randall Cobb WR GNB Jeremy Maclin WR KAN
      Derek Carr QB OAK Greg Olsen TE CAR
      Chicago Bears DEF CHI Antonio Gates TE SDG
      Delanie Walker TE TEN Jeremy Langford RB CHI
      Danny Amendola WR NWE Rishard Matthews WR MIA
      Aaron Rodgers QB GNB Carson Palmer QB ARI
      Chris Ivory RB NYJ Kamar Aiken WR BAL
      Brandon LaFell WR NWE Emmanuel Sanders WR DEN
      Travis Kelce TE KAN Delanie Walker TE TEN
      Philadelphia Eagles DEF PHI Jordan Matthews WR PHI
            Derek Carr QB OAK
            NY Jets DEF NYJ
            Chris Ivory RB NYJ
            Golden Tate WR DET
            Doug Martin RB TAM
            Travis Kelce TE KAN
            Latavius Murray RB OAK
            Marshawn Lynch RB SEA
            Carolina Panthers DEF CAR
            Davante Adams WR GNB
            Tyler Eifert TE CIN
            Marvin Jones Jr WR CIN
            Chicago Bears DEF CHI
            Aaron Rodgers QB GNB
            Matthew Stafford QB DET
            Jimmy Graham TE SEA
            Russell Wilson QB SEA
            Justin Forsett RB BAL
            T.J. Yeldon RB JAC
            Philadelphia Eagles DEF PHI
            Jacksonville Jaguars DEF JAC
            St. Louis Rams DEF STL
            Blake Bortles QB JAC
            LeSean McCoy RB BUF
            Jason Witten TE DAL
            Dontrelle Inman WR SDG
            Denver Broncos DEF DEN


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