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Daily Fantasy Football Projections: Divisional Round

andrewluck

RotoDoc holds a Ph.D. in mathematics and tackles sports data to give you a competitive edge in your fantasy games.

If you want some background on how my projections are developed, head over to my introductory article on the machine learning model I developed. In the article, I explain what kind of model I use, how I chose model parameters, and some shortcomings of the current version of the model that I will be improving for the future. Each week, I use the model to create fantasy point projections for each player to help identify solid, speculative, and value plays.

Using Model Projections

Just a couple quick words about using the model. As you can see, the coefficient of variance (CV) I use to measure the volatility of a player is quite dependent on their projected points. Thus, CV should be used when deciding between players at a similar price point. If you find two players with similar value in points per thousand dollars (P/$) and in similar price points, CV is meant to be a judge of consistency. The relationship between CV and points makes sense. If a player who averages 1.8 point per week suddenly “explodes” for 9 points, he’s outperformed his projection five fold. For a guy averaging 20 points per week, he’d need 100 points to do the same.

CV

Secondly, I advise you to take advantage of other available information as well. My model is a great guide when I start narrowing down my list of players, and I look at other sources to hone in on my picks as the weekend’s games draw near.

Projection Picks

Sorting out the quarterbacks might prove to be the biggest advantage this week. Find the right one, and you could be sitting pretty. On paper, it appears that outside of Cam Newton each of the remaining seven quarterbacks could be potential plays given their price points and match-ups. Even then, Newton likely has a solid floor because of his rushing ability. The model breaks the quarterbacks down into two tiers. Of the Tier 1 quarterbacks Andrew Luck has the right combination of match-up, value, and consistency. If you are looking for a cheaper option, then Tom Brady and Tony Romo can save you a grand, and are 1-2 in P/$.

Given the binary nature of running backs pricing this week at DraftKings, I’ll be looking to choose one cheap and one expensive option at first. Of the expensive tier, C.J. Anderson is still under-priced, and has the highest projected point total of the week among all players. Of the four higher priced options, I also like Eddie Lacy. The model projects value is there, rating him second in P/$ at the RB position. Of the cheaper options, Dan Herron is the guy I’ll be playing this week in cash games but given his fumbling issues, Zurlon Tipton presents an interesting option in GPPs.

At wide receiver, four of the top five projected in P/$ are cheaper options if you are paying up at other positions. Given that I plan on paying up at TE, I’ll probably have either Terrence Williams or Cole Beasley in my lineup. Paul Richardson is also an intriguing option. The model notices the recent uptick in usage. If Richardson hits double-digit points (which he’s done the last three weeks), I’ll be more than satisfied.

At the tight end position, try for Rob Gronkowski, but if you need to pay down then the model likes Dwayne Allen. I also expect Greg Olsen to see a dip in ownership after last week’s relative dud. He still sits fourth at TE in P/$, so I’ll be inserting him into some GPP lineups.

NamePositionSalaryProj. PtsP/$Pos P/$ RankCV
C.J. AndersonRB760024.233.189112.5%
Andrew LuckQB870022.362.571315.7%
Aaron RodgersQB890021.692.437514.8%
Eddie LacyRB720021.012.918215.2%
DeMarco MurrayRB840020.932.491312.8%
Tom BradyQB780020.772.662115.7%
Jordy NelsonWR830020.652.488716.5%
Demaryius ThomasWR850020.142.3691114.3%
Tony RomoQB760019.892.618214.5%
Randall CobbWR730019.772.708616.1%
Dez BryantWR840018.942.2541215.6%
Julian EdelmanWR640018.692.921315.4%
T.Y. HiltonWR760018.442.426914.6%
Joe FlaccoQB690017.212.495416.9%
Rob GronkowskiTE710016.072.263318.0%
Marshawn LynchRB800015.221.903715.9%
Russell WilsonQB780014.521.862718.1%
Cam NewtonQB670014.192.118619.3%
Peyton ManningQB840013.981.665820.3%
Emmanuel SandersWR710013.571.9111822.7%
Brandon LaFellWR600013.132.1891318.8%
Dan HerronRB530012.512.359422.1%
Cole BeasleyWR390011.873.045218.5%
Donte MoncriefWR360011.663.238118.7%
Terrance WilliamsWR410011.472.797419.8%
Jonathan StewartRB480011.312.357523.8%
Doug BaldwinWR450011.062.458820.6%
Torrey SmithWR560010.931.9531620.8%
Steve SmithWR580010.881.8761921.8%
Greg OlsenTE510010.802.118420.5%
Jason WittenTE48009.752.031625.7%
Justin ForsettRB54009.141.6921122.1%
Kelvin BenjaminWR50008.971.7952132.1%
LeGarrette BlountRB46008.691.890830.3%
Luke WillsonTE34008.532.509131.5%
Paul RichardsonWR32008.512.6601029.9%
Hakeem NicksWR31008.412.712525.3%
Dwayne AllenTE35008.172.333224.7%
Owen DanielsTE36007.522.090524.8%
Julius ThomasTE53007.391.3951424.9%
Danny AmendolaWR34007.062.0781427.3%
Jerricho CotcheryWR30006.192.0631531.8%
Shane VereenRB48006.101.2721634.6%
Coby FleenerTE46005.961.2951537.1%
James StarksRB30005.891.962633.5%
Wes WelkerWR36005.881.6332532.2%
Kamar AikenWR30005.841.9461733.8%
Virgil GreenTE30005.501.832734.5%
Fozzy WhittakerRB32005.491.7171031.7%
Marlon BrownWR30005.391.7982030.9%
Richard RodgersTE30005.361.788827.0%
Matt HasselbeckQB50005.291.058943.6%
Philly BrownWR32005.271.6482437.6%
Davante AdamsWR31005.271.7012335.4%
Ed DicksonTE31005.191.6751036.2%
Bernard PierceRB32005.171.6141435.2%
Zurlon TiptonRB30005.161.719938.0%
Brian TymsWR30005.151.7162237.1%
Jermaine KearseWR32005.121.6012728.7%
Jack DoyleTE30005.091.698942.9%
Robert TurbinRB30005.051.6831234.6%
Crockett GillmoreTE30004.951.6501138.2%
Mike TolbertRB30004.901.6331339.4%
Kevin NorwoodWR30004.821.6072645.2%
Jimmy GaroppoloQB50004.670.9351038.7%
Reggie WayneWR35004.601.3152838.0%
Joseph RandleRB30004.511.5031544.8%
Tim WrightTE30004.421.4741240.0%
Andrew QuarlessTE30004.361.4531338.0%
Brock OsweilerQB50004.330.8661144.2%
Juwan ThompsonRB30003.391.1291754.5%
Tony MoeakiTE30003.391.1291647.0%
Derek AndersonQB55003.350.6101344.1%
Joe WebbQB50003.240.6471242.2%
Trent RichardsonRB32003.170.9911848.9%
Jacoby JonesWR30003.091.0322949.9%
Ronnie HillmanRB47002.980.6332162.4%
Phillip SupernawTE30002.730.9091753.6%
Cooper HelfetTE30002.660.8881847.0%
Jonas GrayRB42002.590.6172258.0%
Michael HoomanawanuiTE30002.460.8191957.7%
Brandon WilliamsTE30002.450.8152047.1%
Gavin EscobarTE30002.440.8122156.5%
Jacob TammeTE30002.300.7672244.5%
Lance DunbarRB30002.260.7531954.9%
Devin StreetWR30002.260.7523056.6%
Cody LatimerWR30002.240.7473155.3%
Brandon WeedenQB50002.230.4461454.7%
Ricardo LocketteWR30002.170.7243251.8%
Brenton BersinWR30002.170.7233348.9%
Bryan WaltersWR30002.150.7173447.4%
Josh CribbsWR30002.140.7123548.9%
Fitzgerald ToussaintRB30002.080.6932052.3%
Dwayne HarrisWR30002.040.6803654.7%
Michael CampanaroWR30001.940.6483752.2%
James HannaTE30001.930.6452345.7%
Andre CaldwellWR30001.850.6153857.5%
Kyle JuszczykRB30001.820.6082362.1%
Tyler CluttsRB30001.790.5982466.3%
John KuhnRB30001.680.5612559.3%
Christine MichaelRB30001.630.5432665.0%
Brandon BostickTE30001.620.5413458.4%
DuJuan HarrisRB30001.590.5292766.9%
Jeff JanisWR30001.530.5103950.7%
Brandon BoldenRB30001.490.4952870.7%
Jarrett BoykinWR30001.390.4644051.5%
Jeremy StewartRB30001.210.4032966.9%
DeAngelo WilliamsRB32001.150.3593086.0%
James WhiteRB30000.800.2663178.2%
James DevelinRB30000.640.2143273.9%
Will TukuafuRB30000.260.0873396.8%

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