Week 7 Fantasy Football Optimal Lineups

Setup

library(data.table)
library(dtplyr)
library(tidyverse)
library(rPref)
library(kableExtra)

week <- 7

proj <- readRDS(paste0('week_', week, '_proj.RDS'))

sal <- read_csv(paste0('DKSalaries_wk_', week, '.csv'))

I’ll start with the optimized lineups pulled for week 4, with the same details as last time: 10,000 lineups, using the standard deviation of projections, completely individually based (still working on that).

sim_lu <- readRDS(paste0('sim_lineups_week_', week, '.RDS')) %>%
  rename(pts_base=points) %>%
  select(lineup, Name, team, position, pts_base, pts_pred, sd_pts, Salary)

glimpse(sim_lu)

## Observations: 90,000
## Variables: 8
## Groups: Name [141]
## $ lineup   <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2,...
## $ Name     <chr> "Dolphins", "Tevin Coleman", "Tyler Boyd", "Dalvin Co...
## $ team     <chr> "MIA", "SFO", "CIN", "MIN", "JAC", "NYG", "DET", "BAL...
## $ position <chr> "DST", "RB", "WR", "RB", "RB", "TE", "WR", "QB", "WR"...
## $ pts_base <dbl> 5.644063, 12.685534, 14.962621, 20.578049, 21.115060,...
## $ pts_pred <dbl> 7.085018, 17.527696, 15.747588, 21.363833, 24.120519,...
## $ sd_pts   <dbl> 1.0239833, 1.8186888, 1.1493671, 0.9237673, 2.0700289...
## $ Salary   <dbl> 1500, 5600, 5600, 8000, 7000, 6500, 5800, 6800, 3000,...

sim_lu %>%
  filter(lineup<=3) %>%
  arrange(lineup, position, desc(pts_pred)) %>%
  mutate_at(vars(pts_base, pts_pred, sd_pts), function(x) round(x, 2)) %>%
  knitr::kable() %>%
  kable_styling() %>%
  column_spec(1, bold=TRUE) %>%
  collapse_rows(columns = 1, valign = 'top') %>%
  scroll_box(height = '600px', width = '100%')

Who is in Optimal Lineups?

sim_lu %>%
  group_by(Name, position, Salary) %>%
  dplyr::summarize(lu=n_distinct(lineup)) %>%
  ungroup() %>%
  group_by(position) %>%
  top_n(10, lu) %>%
  ungroup() %>%
  arrange(position, desc(lu)) %>%
  mutate(Name=factor(Name),
         Name=fct_reorder(Name, lu)) %>%
  ggplot(aes(x=Name, y=lu/1000, fill=Salary)) +
  geom_bar(stat='identity') +
  facet_wrap(~position, ncol = 3, scales='free') +
  coord_flip() +
  scale_y_continuous(labels = scales::comma) +
  scale_fill_viridis_c() +
  xlab('') +
  ylab('Lineups (Thousands)') +
  ggtitle('Top 10 Players Present by Position') 

Some of my observations:

• Lots of parity at the QB spot. Lamar Jackson and Kyler Murray are most present, but there’s a linear decline.

• Leonard Fournette is a heavy favorite at RB, showing up in almost 80% of lineups. Kenyan Drake appears to be the value play again (lets hope he gets a better play call for that 2 pt conversion this time).

• T.Y. Hilton is the favorite at WR, showing up in almost 50% of lineups, Cooper Kupp comes in second by a fair margin (He was a dud last week, but hey, that means my Niners are awesome, so I’ll take it.). Allen Lazard appears to be the value play

• It’s the usual story at TE, there are only a few worth playing.

• Redskins, Dolphins, and Colts are the most common defenses most likely due to their price. Over the last few weeks, the model has selected the Defenses with the lowest prices.

Who is getting placed in Lineups?

DraftKings provides scoring for 391 players this week, but only 141 make it into optimized lineups. Why is that? To determine, I’ll plot projected points vs salary, colored by whether or not they make it into optimized lineups, and sized by their projection standard deviation

plyr_lu <- sim_lu %>%
  group_by(Name, position) %>%
  dplyr::summarize(lu=n_distinct(lineup)) %>%
  ungroup() 

proj %>% 
  filter(avg_type=='weighted') %>%
  mutate(Name = ifelse(pos=="DST", last_name, paste(first_name, last_name))) %>%
  inner_join(sal, by=c("Name")) %>%
  select(Name, team, position, points, Salary, sd_pts) %>%
  left_join(plyr_lu, by='Name') %>%
  replace_na(list(lu=0)) %>%
  mutate(lu_bin=ifelse(lu==0, '0 Lineups', '>=1 Lineups'),
         lu_5=cut(lu,5, labels = FALSE)) %>%
  ggplot(aes(x=Salary, y=points, color=lu_bin, size=sd_pts)) +
  geom_point() +
  scale_color_manual(values = c('red', 'blue'), name="") +
  geom_smooth(inherit.aes = FALSE, aes(x=Salary, y=points), method = 'lm', se=FALSE) +
  ylab('Projected Points') +
  xlab('Salary') +
  ggtitle('Who makes it into Optimized Lineups?') +
  scale_x_continuous(labels=scales::dollar)

This week, not many players below the line get into lineups, but some above get excluded due to their small uncertainty. Remember, this method takes players who have the potential to blow up rather than players with solid floors.

Flex Configurations

In DFS lineups, you have an extra spot to use on an RB, WR, and TE of your chosing

sim_lu %>%
  group_by(lineup) %>%
  mutate(lineup_pts=sum(pts_pred)) %>%
  group_by(lineup, position) %>%
  mutate(n=n()) %>%
  select(lineup, position, n, lineup_pts) %>%
  distinct() %>%
  spread(key=position, value=n) %>%
  filter(RB>=2, TE>=1, WR>=3) %>%
  mutate(flex=case_when(RB==3 ~ 'RB',
                        TE==2 ~ 'TE',
                        WR==4 ~ 'WR')) %>%
  group_by(flex) %>%
  dplyr::summarize(pts=median(lineup_pts),
                   cases=n()) %>%
  knitr::kable() %>%
  kable_styling(full_width = FALSE)

Pretty even flex configurations, with RBs being the most popular TE’s the secons, and WR’s a distance third

Pareto Lineups

lu_df <- sim_lu %>%
  group_by(lineup) %>%
  dplyr::summarize(lineup_pts=sum(pts_pred),
                   lineup_sd=sum(sd_pts)) %>%
  ungroup()

pto <- psel(lu_df, low(lineup_sd) * high(lineup_pts))


ggplot(lu_df, aes(y=lineup_pts, x=lineup_sd)) +
  geom_point() +
  geom_point(data=pto, size=5) +
  ylab('Lineup Points') +
  xlab('Lineup Points St Dev') +
  ggtitle('Lineup Points vs Uncertainty',
          subtitle = 'Pareto Lineups Bolded')

Here’s a look at the pareto lineups.

psel(lu_df, low(lineup_sd) * high(lineup_pts)) %>%
  left_join(sim_lu, by='lineup') %>%
  group_by(lineup) %>%
  arrange(lineup_pts, position, desc(pts_pred)) %>%
  select(lineup, lineup_pts, lineup_sd, Name, team, position, pts_pred, sd_pts, Salary) %>%
  mutate_at(vars(lineup_pts, lineup_sd, pts_pred, sd_pts), function(x) round(x, 2)) %>%
  knitr::kable() %>%
  kable_styling(fixed_thead = T) %>%
  column_spec(1:3, bold=TRUE) %>%
  collapse_rows(columns = 1:3, valign = 'top') %>%
  scroll_box(height = '500px', width = '100%')

Week 7 optimal lineups can be found here