In theory, bye weeks should be a positive for all players involved. Teams have an opportunity to prepare for a game for two weeks and are healthier than the team they are playing. However, Evan Silva and Ross Tucker discussed on the Fantasy Feast Podcast that the bye week might be hurting some fantasy-relevant players. Their argument was that the bye week caused teams to start the game slower than a regular game, which is like arguing against resting players before the playoffs.

To access the situation, I took a sample of the top 36 fantasy wide receivers for each of the last five seasons. I studied the games leading up to the bye week and the game directly after the bye week (These will be called “post bye week games” throughout). There were 1,409 observations in total and 178 of the observations were post bye week games.

I wanted to answer the following questions: Do WRs score more in post bye week games? Is there more variance? What does the distribution of points look like? Should I use players in post bye week games more or less often in daily fantasy? Does a bye week kill momentum? Can a WR get out a slump after the bye? Are home and away splits the same as usual? Finally, is the timing of the bye week important?

The Results



In the games directly after the bye, the top 36 wide receivers have scored .7 more PPR points than in all games before the bye week. However, the standard deviation in post bye week games is .9 PPR points larger. The higher standard deviation means that there is a wider range of outcomes around the mean. Understanding variance (the square of standard deviation) is exceedingly important for daily fantasy sports.


For daily fantasy sports game theory, I value standard deviation and variance more than averages. To win a GPP tournament, we need high variance plays to reach those 99th percentile outcomes that win GPPs. For cash games, especially 50/50s, we need to avoid the bad variance as much as possible since our goal is to only finish with a 51st percentile score. I highly recommend any of Jonathan Bales’ books for a much deeper (and heck of a lot better) look at using variance to your advantage.


To get a better look at variance, I graphed the frequency distributions of PPR points. The graph of games played before the bye week is bell-shaped. There is a gradual build up and fall from the mean with the highest probability of scoring between eight and 18 points. The following graph is for post bye week games. The differences in the two distributions give us an advantage because most of our opponents don’t factor in bye weeks when constructing their lineups.


Games Played Before Bye Week




Post Bye Week Games




Cash Games


The relative drop in the middle of the bye week distribution is worrisome for cash games. There is a smaller probability for wide receivers to land in the 10 to 18 PPR point range in post bye week games. To increase our bankroll in cash games, we will typically need all three of the wide receivers to be in this range. Even worse, there is a higher chance of a complete clunker. There is a 5% higher chance of a receiver scoring less than 10 PPR points in a post bye week game. The chance of a complete donut is also higher.


With all other variables held constant, I would rather have the wide receiver NOT coming off the bye week when creating a cash game lineup. This is likely due to teams starting out of the gate slowly or not being in the same routine as a regular NFL week. There are probably other psychological factors that play into this, but someone who studies psychology would need to address that.






Now let’s look at how often these wide receivers reach certain high-upside benchmarks. Remember that we need to find 80th to 99th percentile outcomes to have a positive return on investment in tournaments. An 80th percentile outcome would be somewhere around 18 to 27 PPR points, depending on the price of the player on DFS sites. In post bye week games, wide receivers are six percent more likely to score in this range compared to a regular game. 99th percentile outcomes would be at least 27 PPR points for most wide receivers that we are using on the typical week. In post bye week games, wide receivers are three percent more likely to reach that outcome.


With all other variables held constant, I would rather have the wide receiver coming off the bye week when creating a GPP lineup. The next page goes into finding which variables help us reach these 80th to 99th percentile scores.


Slumping Receivers

It seems extremely obvious that teams should get their best players the ball, but sometimes teams fail to do it. A bye week can give a team enough time to add plays to scheme the ball to their stars. This would especially make sense when a star receiver had been in a “slump” heading into the bye. Last year there were two great examples of this.


Brandin Cooks had five receptions for 44 yards without a touchdown in the two games heading into the Saints’ bye week. The next game he had nine targets and posted a 7-131-1 line after they schemed the ball into their playmaker’s hands. Emmanuel Sanders had a similar experience last year. He was struggling before his bye (5.63 YPT with 0 TDs), but then exploded for 29.2 PPR points in the post bye week game.


However, for every great game there were many bad games with “slumping” players. There were nine wide receivers who averaged less than five PPR points in the two games heading into the bye. They scored 0, 2, 3, 5, 6, 8, 18, 24 and 30 points in the game after the bye. There are a lot of clunkers mixed in with the high upside plays. This is further evidence that receivers in post bye week games need to be kept off cash game lineups if possible, but they can make high upside tournament plays.


On-Fire Receivers


The chart below shows the distribution of PPR points when wide receivers averaged more than 20 points in the two weeks leading up to the bye week. Skeptics of resting players before the playoffs argue that it will kill momentum for the playoffs. The risk is that players can get out of rhythm and maybe be susceptible to starting the next game slowly. If that argument is applied to bye weeks, we should see players who were performing well before the bye have bad post bye week games.




A distribution like this is bi-modal because there are two different peaks at around seven and 21 PPR points. Just like before, there is still a wide range of outcomes with WRs who had been on-fire recently. This distribution has the same shape of the entire sample of bye week performances, so the results of “on-fire” variable aren’t too helpful.


Last year, Julio Jones (7.5), Terrell Pryor (1.3), Doug Baldwin (7.1), DeAndre Hopkins (9.8), Allen Robinson (7.9), Alshon Jeffery (8.7), Demaryius Thomas (11) and Jordy Nelson (13.8) all scored below expectation despite being on a roll in previous weeks. However, not all on-fire players struggled despite the break in the momentum. In fact, the most frequent outcome was a 20 to 22 point performance.  There is still upside for on-fire receivers despite the unusual number of clunkers from the WR1-2s. This is the same conclusion we came to for all post bye week game performances.


Home versus Away


Home and away splits are very real. Just ask Ben Roethlisberger and Drew Brees. Before running the regressions, I didn’t expect a difference by just adding the bye week variable, but there is a difference. Wide receivers in post bye week games score 1.1 more PPR points at home compared to games played on the road.



Remember the Brandin Cooks and Emmanuel Sanders games I referenced earlier? They were both home games.


Timing of the Bye Week


This truly is saving the best for last and is a treat for the true MVPs who read the entire article. Perhaps the most shocking finding in this study was the importance of the scheduling on the bye week. Teams are scheduled byes between Week 4 and Week 13. Wide receivers are dramatically affected by the timing of the bye week. The data suggests that the earlier into the season the better.


When a wide receiver has a bye between Week 4-8, they score nearly 4 more PPR points than in Weeks 9-13. The p-value of this regression is .0055, which is statistically significant. This is something that I have never heard anybody talk about, and is something I want to look further into in another article. I struggled coming up with any good reasons that could explain the large difference between byes early and late into the season. I would love to hear your thoughts on twitter or in the comments if you have any ideas.


(@HaydenWinks on Twitter)

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