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NFL Midseason Predictions & Top Team Futures Bets (2021)

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Using Variance to target the best NFL Team Futures Bets

NFL Football seasons are 16-17 weeks long. With so few games (samples), along with the natural chaos of a contest between 22 people within 19,200 square yards, outcomes don’t always correlate to team performance. In other words, the “best team doesn’t always win.” Instead, variance/luck (or lack thereof) come into play and tend to prop up players/teams, making them seem more productive than they are when some/much of their success can be attributed to (unstable) luck and vice-versa. Knowing this, we can take a select group of variables we know to be “not-sticky” and highly volatile throughout the season to identify teams that:

  1. Have gotten lucky in said variables falling their way and are likely to see a (negative) regression towards the mean from those same variables
  2. Have gotten unlucky in said variables falling their way and are likely to see a (positive) regression towards the mean from those same variables

Armed with this information, we can make sound predictions likely to “go up or down” and lever to make profitable NFL Team Win Futures Bets. Below, you will find the individual metrics and how each team ranked, followed by an aggregate board that we will use in conjunction with current win totals to target opportunities.

*All data up to date as of 10/28/21
**All charts include NFL teams identified with their appropriate betting slip abbv.
***Rank (Rk) is based on team that has been least lucky (#1) to most lucky (#32) and expected to regress towards the mean

 

*The 2nd column, as a percentage, is Football Outsiders’ Opponent DVOA
**Net reflects the change in previous games played strength of schedule vs. upcoming strength of schedule

Table Above Explained

Using Football Outsiders flagship-stat-based metric DVOA to determine the true value of an opponent, then ranked and compared between previous and upcoming schedules, we find teams that have benefited most/will face the toughest challenges moving forward.

 

*2021 is the current team fumble recovery rate
**Included 2020 as context to see how easily this metric fluctuates

Table Above Explained

Forcing a fumble and holding on to the ball takes skill. However, once the ball is on the ground, there is almost no skill involved, and significant recovery rates on both sides of the ball tend to move towards 50% overtime. Hence teams that have a high recovery rate in year one will likely not have the same luck.

 

*PF - Points scored
PA - Points allowed
EXP WINS - (PF / (PF + PA) * Games Played). Based on how many points a team “owns” relative to the total in a given game
EXP - Real Wins - Net Value of Pythagorean wins to actual wins

Table Above Explained

Pythagorean Wins is just a way of looking at one team, how many total points were scored in games they played (their points AND their opponents' points), and what the ratio of those points are "owned" by said team to get an idea of how many games they should have won. Admittedly, there is a bit of circular logic between this variable and some others, like YPP, for example. The amount of yards it takes for a team to get its points is, in itself, highly correlated to Pythagorean Wins. Hence there is some circular logic to this one, yet since it's one of the “original variance predictors,” so we had to include it. This is also why we did NOT include “Net Close Games Won,” which is all but fully addressed by this metric.

 

YDS - Yards

PTS - Points

oYPP - Offensive Yards / Points
dYPP - Defensive Yards / Points

Table Above Explained

YPP is simply a look into how well yards, which correlate to wins from one year to the next better than wins themselves, were turned into points. Offenses with many yards but little points to show tend to score more the following year (relative to their yards). Defenses that gave up a lot of points, but few yards trend upward as well (and the opposite for both).

 

1st/2nd - Team DVOA ranking on 1st and 2nd down
3rd - Team DVOA ranking on 3rd down 

Net - Difference between 1st and 2nd down DVOA and 3rd down DVOA

Table Above Explained

“3rd Down Rebound” is an original football Outsider and highly predictive from year 1 to 2. You can learn all about it in their article Stat of the day: Third-Down Rebound Effect. Essentially, we can glean some tendency towards the mean when teams are rather lucky/unlucky on 3rd down relative to their performance on 1st/2nd downs.

 

Non-RZ - DVOA rank in all Field Zones outside of the Red Zone (or 20 yards from the endzone)

RedZone - DVOA rank in the Red Zone
Net FZ - Difference in DVOA ranks between the Red Zone and the NON-Red Zone field areas


Table Above Explained

“Redzone Rebound” is the same “play” as “3rd down Rebound”, with a different set of parameters. Just like 3rd downs, the Redzone is a highly leveraged area of the field. Teams tend to perform the same no matter where they are on the field OVER THE LONG-RUN. So when teams outperform the last 20 yards of the field relative to the first 80, our impression of their performance is likely skewed and will eventually return to the team norm.

 

*2021 is the current opponent FG Conversion Rate
**Included 2020 as context to see how easily this metric fluctuates


Table Above Explained

The table above and the next one below fall under the  “out of their control” type predictors. That is, things that happen in a game a team benefits (or not) from that they had no control over. For the most part (sans drawing DPIs, offsides), the opponent’s success in these fields have nothing to do with the team in question. Hence, having marginal success with them is not a stable item a team can depend on continuing.

 

*2021 is the current opponent Penalty Rate
**Included 2020 as context to see how easily this metric fluctuates


See explanation ABOVE.

Now to put all variables together, evenly weighted, here is how these items in aggregate shape up:

 

Biggest Takeaways

One of the issues with these studies is that they tend to result in “bad teams will get better, and good teams will get worse” situations. Because we know this is a natural phenomenon, we should highlight most of the teams that are still relatively high or low on the list yet aren’t coming in as simply a good/bad team, respectively.

  • The Browns jump out here, regardless of Baker Mayfield’s injury situation. They have a good record yet have still been very unlucky (especially in high leverage situations)
  • Minnesota is in a similar situation, with a much healthier team to boot
  • On the other end of the spectrum, the Falcons are an interesting team. Especially given how many halftime leads they gave up last year, their record has been poor, despite being a “lucky” team over the first half of the season

Finally, tying this information in with the current market odds for NFL Team Futures, you can see appropriate opportunities below given their “expected variance changes” along with the betting win totals below:

 

Win Total - Current Vegas Odds
Avg - Aggregate Average of all Variables considered, ranked in order of teams (1) most likely to have better luck in the second half of the season
Wins to Total - How many wins a team needs to hit their Betting Line number.

Remaining Win % - what percentage of games does a team need to win to hit the Betting Line number

Win % Needed vs. Cur Win % - Win percentage needed - current winning percentage. This is used to give a quick depiction of how the market thinks win likelihood will change vs. how a team has performed thus far.

We feel most strongly about the Vikings and Browns OVER plays, but use the data as you see fit. I simply wanted to give you information to make better, more profitable EV decisions on Futures Bets. This was Part I of II. Next week we will dive into the micro and present “Using Variance to target the best NFL Player Futures Bets.”

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Ryan Newman is a featured writer at BettingPros. For more from Ryan, check out his archive.