Data scientist, physicist, and fantasy football champion

Week 15 DEF Predictions

I hope some of you made it through the first round of playoffs last week. I certainly did, though it was not on the back of my defense or my predictions. TEN did great, TB and GB wouldn’t have lost you the week, but NYJ was rough. And avoiding PHI or LAR would have been a mistake.

Let’s see who might get us through this week:

 

Model A:

.Model A-1.png


Model A parameters:

The team that’s playing
Who their opponent is
Their predicted score
Their opponent’s predicted score
Whether the team is home or away
Model A Notes:

Uses only data from 2017
Standard points
Did well in 2016, but didn’t stand out

Model C:

.Model C-1.png


Model C parameters:

The team that’s playing
Who their opponent is
Their predicted score
Their opponent’s predicted score
Whether the team is home or away
Model C Notes:

Uses data from 2015-2017
Standard scoring
Did well in 2016, but didn’t stand out

Model F:

.Model F-1.png


Model F parameters:

The team that’s playing
Who their opponent is
Their predicted score
Their opponent’s predicted score
Whether the team is home or away
Model F Notes:

Uses data from 2015-2017
Truscor ignores defense TDs but weights interceptions and fumbles to try to account for it
I really liked this one in 2016. It was marginally better than A and C

Model G:

.Model G-1.png


Model G parameters:

The team that’s playing
Who their opponent is
Their predicted score
Their opponent’s predicted score
Whether the team is home or away
Model G Notes:

Uses data from 2015-2017
Truscor ignores defense TDs but weights interceptions and fumbles to try to account for it
Uses Bayesian statistics (Gibbs sampler) to create a model
Would have done very well in 2016 based on simulation

Model H:

.Model H-1.png


Model H parameters:

The team that’s playing
Who their opponent is
Their predicted score
Their opponent’s predicted score
Whether the team is home or away
Model H Notes:

Uses only data from 2017
Uses TruScor points to reduce the effect of TDs

Model I:

.Model I-1.png


Model I parameters may include:

The team that’s playing
Who their opponent is
Their predicted score
Their opponent’s predicted score
Whether the team is home or away
Model I Notes:

Data from 2015-2017
This model tries to predict all of the components of scoring separately (interceptions, sacks, etc.). Each component comes from a linear model containing only the significant terms (p < 0.05) from the list of possible terms above.

Week 15 model summary:
 

RankACFGHI
1JACBALBALJACJACJAC
2BALJACJACBALBALBAL
3DETNONONONONO
4NODENWASWASINDDEN
5DENPHIPHIMINDETMIN
6INDWASDENPHIWASPHI
7WASMINMINDENDENWAS
8CHILARINDINDGBDET
9BUFDETCARCARBUFCAR
10MIAINDLARDETPHIIND
11ARICARGBLARARILAR
12GBKCDETKCTENKC
13LARGBKCGBMIAMIA
14PHIMIAMIANELARATL
15NYGATLNETENMINGB
16SEALACTENMIASEALAC
17LACARILACATLCARBUF
18CARSFATLBUFCHISF
19ATLSEAARISFNYGTEN
20TENNYGSFLACLACARI
21MINBUFBUFSEAATLNYG
22DALTENNYGNYGDALSEA
23HOUHOUSEAARINENE
24PITNEPITPITPITCHI
25TBCHITBOAKSFPIT
26NETBDALTBCLECLE
27CLEPITHOUDALTBDAL
28KCDALCLECLEHOUHOU
29SFCLEOAKHOUKCTB
30OAKOAKCHICHIOAKOAK
31CINCINCINCINCINCIN
32NYJNYJNYJNYJNYJNYJ

Conclusions

Most of the top defenses this week have bad matchups (see NE vs PIT and PIT vs NE). But there are a few decent streaming options for the discerning fantasy manager:

  • NO (v. NYJ): A good defense against a team with a backup QB? Sign me up
  • WAS (v. ARI): A mediocre defense against Arizona is good for an OK week. They’re a distant second to NO, but I’d still play them.
  • IND (v. DEN): Again, mediocre defense, but against the team that gives up the most points is an OK play

CAUTION:

  • LAR at SEA: This should be a high scoring game. It depends on how much Russel Wilson can avoid getting sacked, but I’d definitely hesitate to play them this week.

Good luck this week. Trust the models, they’re still doing great (overall).

Week 15 QB Predictions

Week 15 K Predictions