2021 NFL Midseason Predictions

Part I: 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”. Rather, 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/or 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, along with 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 data 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

Change in Strength of Schedule

Team	PastSchedule	FutureSchedule	Net	Rk
ARI	16	0.10%	8	4.30%	8	23
ATL	31	-11.70%	9	4.30%	22	31
BAL	21	-2.30%	2	6.50%	19	29
BUF	32	-12.10%	32	-11.80%	0	18
CAR	27	-6.60%	4	4.70%	23	32
CHI	1	11.70%	3	5.50%	-2	17
CIN	25	-4.50%	17	0.60%	8	24
CLE	19	-1.90%	5	4.50%	14	26
DAL	18	-1.00%	24	-3.30%	-6	11
DEN	28	-7.70%	19	-0.70%	9	25
DET	2	9.90%	14	1.50%	-12	7
GB	20	-2.00%	1	9.20%	19	30
HOU	6	7.30%	27	-4.80%	-21	2
IND	15	0.20%	23	-2.90%	-8	9
JAX	24	-4.20%	20	-1.10%	4	21
KC	7	6.90%	16	1.10%	-9	8
LAC	5	7.30%	26	-4.50%	-21	1
LAR	10	4.30%	12	2.20%	-2	15
LV	26	-5.90%	11	2.30%	15	27
MIA	12	1.80%	30	-8.00%	-18	3
MIN	8	5.40%	6	4.40%	2	19
NE	29	-9.50%	22	-1.40%	7	22
NO	23	-3.80%	25	-4.00%	-2	16
NYG	17	-0.50%	21	-1.10%	-4	14
NYJ	30	-9.80%	13	1.90%	17	28
PHI	14	0.50%	31	-8.50%	-17	5
PIT	3	9.80%	7	4.30%	-4	13
SEA	4	9.70%	18	0.40%	-14	6
SF	9	4.80%	15	1.30%	-6	12
TB	22	-2.80%	29	-6.90%	-7	10
TEN	11	2.20%	28	-5.40%	-17	4
WAS	13	1.80%	10	3.10%	3	20

*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 schedule, we find teams that have benefited most/will face the toughest challenges moving forward.

Fumble Recovery Luck

Team	2021	2020	20 to 21 change	2021 Rk
ARI	66.67%	52.63%	14.04%	26
ATL	25.00%	42.86%	-17.86%	9
BAL	40.00%	35.29%	4.71%	16
BUF	60.00%	41.38%	18.62%	24
CAR	18.18%	68.18%	-50.00%	3
CHI	66.67%	50.00%	16.67%	27
CIN	20.00%	42.86%	-22.86%	5
CLE	22.22%	45.83%	-23.61%	6
DAL	75.00%	61.90%	13.10%	29
DEN	66.67%	37.50%	29.17%	28
DET	50.00%	29.41%	20.59%	20
GB	40.00%	46.67%	-6.67%	15
HOU	18.18%	54.55%	-36.37%	4
IND	75.00%	50.00%	25.00%	30
JAX	0.00%	29.41%	-29.41%	1
KC	28.57%	38.89%	-10.32%	11
LAC	27.27%	46.67%	-19.40%	10
LAR	33.33%	50.00%	-16.67%	14
LV	41.67%	50.00%	-8.33%	17
MIA	54.55%	55.00%	-0.45%	21
MIN	75.00%	41.18%	33.82%	31
NE	75.00%	36.36%	38.64%	32
NO	25.00%	44.44%	-19.44%	8
NYG	60.00%	42.31%	17.69%	23
NYJ	57.14%	40.91%	16.23%	22
PHI	16.67%	52.38%	-35.71%	2
PIT	30.00%	50.00%	-20.00%	12
SEA	62.50%	44.44%	18.06%	25
SF	23.08%	36.36%	-13.28%	7
TB	44.44%	50.00%	-5.56%	18
TEN	30.00%	38.10%	-8.10%	13
WAS	50.00%	34.78%	15.22%	19

*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 1, will likely not have the same luck.

Pythagorean Wins

Team	W	L	PF	PA	EXP WINS	EXP – Real Wins	E – RW Rk
ARI	7	0	225	114	4.6	2.4	32
ATL	3	3	135	176	2.6	0.4	20
BAL	5	2	187	164	3.7	1.3	26
BUF	4	2	203	98	4.0	0.0	17
CAR	3	4	146	146	3.5	-0.5	14
CHI	3	4	101	162	2.7	0.3	19
CIN	5	2	189	128	4.2	0.8	23
CLE	4	3	173	165	3.6	0.4	21
DAL	5	1	205	146	3.5	1.5	28
DEN	3	4	140	127	3.7	-0.7	13
DET	0	7	128	200	2.7	-2.7	1
GB	6	1	168	146	3.7	2.3	31
HOU	1	6	97	203	2.3	-1.3	5
IND	3	4	169	149	3.7	-0.7	12
JAX	1	5	116	172	2.4	-1.4	3
KC	3	4	188	203	3.4	-0.4	15
LAC	4	2	148	150	3.0	1.0	24
LAR	6	1	207	146	4.1	1.9	30
LV	5	2	180	166	3.6	1.4	27
MIA	1	6	127	207	2.7	-1.7	2
MIN	3	3	147	137	3.1	-0.1	16
NE	3	4	179	140	3.9	-0.9	8
NO	4	2	140	101	3.5	0.5	22
NYG	2	5	139	180	3.1	-1.1	7
NYJ	1	5	80	175	1.9	-0.9	9
PHI	2	5	159	185	3.2	-1.2	6
PIT	3	3	117	132	2.8	0.2	18
SEA	2	5	150	162	3.4	-1.4	4
SF	2	4	135	149	2.9	-0.9	11
TB	6	1	233	147	4.3	1.7	29
TEN	5	2	193	164	3.8	1.2	25
WAS	2	5	146	210	2.9	-0.9	10

*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 really 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. That is, 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 the reason we did NOT include “Net Close Games Won”, which is all but fully addressed by this metric.

Yards Per Point (YPP)

	Offene	Defense
TEAM	YDS	PTS	oYPP	Rk	YDS	PTS	dYPP	Rk
ARI	2,815	225	12.5	31	2,217	114	19.4	2
ATL	2,125	135	15.7	13	2,176	176	12.4	32
BAL	2,923	187	15.6	16	1,621	98	16.5	9
BUF	2,469	203	12.2	32	2,152	146	14.7	19
CAR	2,296	146	15.7	14	2,393	162	14.8	18
CHI	1,788	101	17.7	6	2,379	128	18.6	3
CIN	2,587	189	13.7	26	2,069	165	12.5	31
CLE	2,754	173	15.9	12	2,287	146	15.7	16
DAL	2,765	205	13.5	27	2,264	127	17.8	6
DEN	2,433	140	17.4	7	2,681	200	13.4	29
DET	2,341	128	18.3	4	2,320	146	15.9	13
GB	2,365	168	14.1	23	2,744	203	13.5	28
HOU	1,926	97	19.9	2	2,479	149	16.6	8
IND	2,500	169	14.8	22	2,473	172	14.4	21
JAX	2,136	116	18.4	3	2,832	203	14.0	24
KC	2,935	188	15.6	17	2,186	150	14.6	20
LAC	2,265	148	15.3	19	2,617	146	17.9	4
LAR	2,780	207	13.4	28	2,478	166	14.9	17
LV	2,753	180	15.3	20	2,904	207	14.0	23
MIA	2,153	127	17.0	8	2,150	137	15.7	15
MIN	2,485	147	16.9	9	2,454	140	17.5	7
NE	2,485	179	13.9	24	1,989	101	19.7	1
NO	1,780	140	12.7	30	2,581	180	14.3	22
NYG	2,466	139	17.7	5	2,415	175	13.8	25
NYJ	1,634	80	20.4	1	2,525	185	13.6	26
PHI	2,434	159	15.3	18	2,114	132	16.0	12
PIT	1,943	117	16.6	11	2,903	162	17.9	5
SEA	2,283	150	15.2	21	1,944	149	13.0	30
SF	2,121	135	15.7	15	2,325	147	15.8	14
TB	2,964	233	12.7	29	2,638	164	16.1	11
TEN	2,677	193	13.9	25	2,676	164	16.3	10
WAS	2,447	146	16.8	10	2,842	210	13.5	27

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 a lot of yards, but little points to show for it tend to score more the next 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).

“3rd Down Rebound”

	Offensive Downs	Defensive Downs
Team	1st/2nd	3rd	NET	Rk	1st/2nd	3rd	NET	Rk
ARI	17	1	16	31	10.5	1	9.5	28
ATL	27	16	11	26	26.5	29	-2.5	13
BAL	4	31	-27	1	28	2	26	32
BUF	11.5	12	-0.5	15	1.5	4	-2.5	14
CAR	27	29	-2	13	16.5	9	7.5	26
CHI	19.5	32	-12.5	3	14.5	7	7.5	27
CIN	19.5	7	12.5	28	7.5	21	-13.5	4
CLE	7	18	-11	6	4.5	31	-26.5	1
DAL	4	15	-11	7	16.5	10	6.5	23
DEN	18	10	8	24	15	30	-15	3
DET	21	30	-9	9	18.5	26	-7.5	7
GB	10	2	8	25	21	14	7	25
HOU	31.5	14	17.5	32	12.5	25	-12.5	5
IND	18	11	7	22	20.5	3	17.5	30
JAX	16.5	26	-9.5	8	29.5	27	2.5	20
KC	7	13	-6	11	29.5	23	6.5	24
LAC	16	5	11	27	21	6	15	29
LAR	5	3	2	18	9.5	8	1.5	18
LV	15	21	-6	12	15	18	-3	12
MIA	27.5	20	7.5	23	17	28	-11	6
MIN	14	9	5	20	14	13	1	17
NE	15.5	17	-1.5	14	15.5	12	3.5	21
NO	21	6	15	29	5	11	-6	10
NYG	27.5	22	5.5	21	19	17	2	19
NYJ	28.5	25	3.5	19	21.5	24	-2.5	15
PHI	20	19	1	17	21	22	-1	16
PIT	23	8	15	30	12	19	-7	9
SEA	10	27	-17	2	26.5	5	21.5	31
SF	14	23	-9	10	12.5	20	-7.5	8
TB	4.5	4	0.5	16	10.5	15	-4.5	11
TEN	12	24	-12	4	21.5	16	5.5	22
WAS	16	28	-12	5	14	32	-18	2

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 a Football Outsiders original, and highly predictive from year 1 to year 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.

“RedZone Rebound”

	Offensive Field Zone Splits	Defensive Field Zone Splits
Team	Non RZ	Red Zone	Net FZ	Rk	Non RZ	Red Zone	Net FZ	Rk
ARI	12	19	-7.5	9	4	9	-5	12
ATL	24	11	13.25	27	23	31	-7.75	9
BAL	14	6	8.25	22	23	1	21.5	32
BUF	15	17	-2.25	16	3	5	-2.5	16
CAR	26	12	14	30	15	26	-11.25	6
CHI	27	14	13.25	28	17	6	10.5	27
CIN	16	5	11	26	12	15	-3.25	14
CLE	11	23	-12.25	5	14	20	-6.25	10
DAL	5	27	-21.75	1	12	21	-9.5	7
DEN	14	25	-11.5	6	20	24	-4.25	13
DET	18	29	-10.75	7	19	32	-13	4
GB	9	15	-6	12	19	18	1.25	19
HOU	29	28	0.75	18	18	10	7.75	24
IND	12	31	-18.75	2	13	24	-11.5	5
JAX	22	26	-4.5	13	29	28	1	18
KC	8	22	-14.25	3	27	22	4.75	21
LAC	15	18	-3	15	14	23	-8.75	8
LAR	10	4	5.5	20	14	2	12.25	29
LV	15	24	-9.5	8	17	30	-13.25	3
MIA	20	20	-0.5	17	20	11	9.25	25
MIN	16	7	8.75	23	14	19	-5.5	11
NE	14	10	4	19	15	8	7.25	23
NO	22	2	19.5	32	9	3	6.25	22
NYG	23	30	-7.5	10	15	29	-13.75	2
NYJ	24	16	8	21	24	14	10.25	26
PHI	18	8	10	25	17	17	0	17
PIT	22	13	9	24	16	4	12.25	30
SEA	16	1	15	31	18	13	4.5	20
SF	17	3	13.5	29	13	16	-3	15
TB	6	9	-3.25	14	9	27	-18.5	1
TEN	15	21	-6.5	11	23	12	11	28
WAS	18	32	-14	4	25	7	18.25	31

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 exact 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 are outperforming 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.

Opponent Field Goal Percentage

Team	2021	2020	20 vs 21 NET	Rk
ARI	66.67%	83.33%	16.66%	30
ATL	85.71%	85.71%	0.00%	17
BAL	77.78%	65.52%	-12.26%	22
BUF	100.00%	83.87%	-16.13%	4
CAR	83.33%	93.55%	10.22%	18
CHI	90.91%	80.56%	-10.35%	10
CIN	71.43%	86.84%	15.41%	25
CLE	87.50%	84.38%	-3.12%	15
DAL	66.67%	88.89%	22.22%	29
DEN	66.67%	92.86%	26.19%	28
DET	83.33%	80.00%	-3.33%	20
GB	57.14%	89.66%	32.52%	32
HOU	92.31%	80.65%	-11.66%	8
IND	100.00%	84.00%	-16.00%	3
JAX	78.57%	79.49%	0.92%	21
KC	90.91%	75.00%	-15.91%	11
LAC	83.33%	87.50%	4.17%	19
LAR	86.67%	86.67%	0.00%	16
LV	88.89%	82.05%	-6.84%	12
MIA	88.24%	73.91%	-14.33%	14
MIN	92.31%	89.74%	-2.57%	7
NE	61.11%	81.48%	20.37%	31
NO	73.33%	86.67%	13.34%	24
NYG	91.67%	88.89%	-2.78%	9
NYJ	94.44%	82.86%	-11.58%	5
PHI	100.00%	84.38%	-15.62%	2
PIT	100.00%	84.62%	-15.38%	1
SEA	88.24%	84.21%	-4.03%	13
SF	70.00%	89.19%	19.19%	26
TB	69.23%	85.29%	16.06%	27
TEN	73.33%	88.46%	15.13%	23
WAS	92.86%	90.32%	-2.54%	6

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

Table Above Explained

Both 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.

Opponent Penalties Committed vs Team

Team	2021	2020	20 vs 21 net	Rk
ARI	6.1	6.5	-0.4	15
ATL	8.3	5.7	2.6	31
BAL	5.1	4.5	0.6	4
BUF	7.2	5.2	2	27
CAR	7.9	5.3	2.6	30
CHI	4.7	5.8	-1.1	3
CIN	6.9	6.2	0.7	23
CLE	5.6	5.3	0.3	10
DAL	8.7	5.7	3	32
DEN	7	6.6	0.4	26
DET	5.7	6.4	-0.7	12
GB	5.3	4.7	0.6	8
HOU	5.7	4.8	0.9	11
IND	5.3	5.2	0.1	9
JAX	7	4.9	2.1	24
KC	7.4	5.7	1.7	29
LAC	6.3	5.9	0.4	18
LAR	5.1	5	0.1	5
LV	6.1	5.9	0.2	14
MIA	6.7	5.3	1.4	22
MIN	4.7	5.2	-0.5	2
NE	6.7	5.1	1.6	21
NO	4.7	4.4	0.3	1
NYG	6.4	5.5	0.9	19
NYJ	6	6.2	-0.2	13
PHI	7.3	7	0.3	28
PIT	6.5	6.2	0.3	20
SEA	5.1	5.6	-0.5	6
SF	6.2	4.9	1.3	16
TB	5.1	5.8	-0.7	7
TEN	7	6.4	0.6	25
WAS	6.3	5.2	1.1	17

*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:

Aggregate Scoreboard

Team	SOS	Fumb	PWT	oYPP	dYPP	o3DR	d3DR	oRZR	dRZR	FG	Pen	Avg	Change Rk	Win %	PAST Win % Rk
ARI	23	26	32	31	2	31	28	9	12	30	15	21.7	31	100%	1
ATL	31	9	20	13	32	26	13	27	9	17	31	20.7	29	50%	16
BAL	29	16	26	16	9	1	32	22	32	22	4	19.0	25	71%	8
BUF	18	24	17	32	19	15	14	16	16	4	27	18.4	22	67%	11
CAR	32	3	14	14	18	13	26	30	6	18	30	18.5	24	43%	21
CHI	17	27	19	6	3	3	27	28	27	10	3	15.5	12	43%	18
CIN	24	5	23	26	31	28	4	26	14	25	23	20.8	30	71%	9
CLE	26	6	21	12	16	6	1	5	10	15	10	11.6	3	57%	13
DAL	11	29	28	27	6	7	23	1	7	29	32	18.2	21	83%	5
DEN	25	28	13	7	29	24	3	6	13	28	26	18.4	23	43%	20
DET	7	20	1	4	13	9	7	7	4	20	12	9.5	1	0%	32
GB	30	15	31	23	28	25	25	12	19	32	8	22.5	32	86%	4
HOU	2	4	5	2	8	32	5	18	24	8	11	10.8	2	14%	30
IND	9	30	12	22	21	22	30	2	5	3	9	15.0	11	43%	17
JAX	21	1	3	3	24	8	20	13	18	21	24	14.2	7	17%	28
KC	8	11	15	17	20	11	24	3	21	11	29	15.5	13	43%	19
LAC	1	10	24	19	4	27	29	15	8	19	18	15.8	16	67%	10
LAR	15	14	30	28	17	18	18	20	29	16	5	19.1	26	86%	3
LV	27	17	27	20	23	12	12	8	3	12	14	15.9	17	71%	6
MIA	3	21	2	8	15	23	6	17	25	14	22	14.2	8	14%	31
MIN	19	31	16	9	7	20	17	23	11	7	2	14.7	9	50%	14
NE	22	32	8	24	1	14	21	19	23	31	21	19.6	28	43%	22
NO	16	8	22	30	22	29	10	32	22	24	1	19.6	27	67%	12
NYG	14	23	7	5	25	21	19	10	2	9	19	14.0	6	29%	26
NYJ	28	22	9	1	26	19	15	21	26	5	13	16.8	18	17%	29
PHI	5	2	6	18	12	17	16	25	17	2	28	13.5	4	29%	24
PIT	13	12	18	11	5	30	9	24	30	1	20	15.7	15	50%	15
SEA	6	25	4	21	30	2	31	31	20	13	6	17.2	19	29%	27
SF	12	7	11	15	14	10	8	29	15	26	16	14.8	10	33%	23
TB	10	18	29	29	11	16	11	14	1	27	7	15.7	14	86%	2
TEN	4	13	25	25	10	4	22	11	28	23	25	17.3	20	71%	7
WAS	20	19	10	10	27	5	2	4	31	6	17	13.7	5	29%	25

Biggest Takeaways

One of the issues with these types of studies is 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, 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 in 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, there 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:

NFL Win Totals + Avg Variance/Luck Rank

	Win Total	Avg	Change Rk	Win %	W	L	Wins to Total	Remaining Win %	Win % Needed vs Cur Win %
ARI	13.5	21.7	31	100%	7	0	6.5	65%	-35%
ATL	7.5	20.7	29	50%	3	3	4.5	41%	-9%
BAL	11.5	19.0	25	71%	5	2	6.5	65%	-6%
BUF	12.5	18.4	22	67%	4	2	8.5	77%	11%
CAR	6.5	18.5	24	43%	3	4	3.5	35%	-8%
CHI	6.5	15.5	12	43%	3	4	3.5	35%	-8%
CIN	10.5	20.8	30	71%	5	2	5.5	55%	-16%
CLE	10.5	11.6	3	57%	4	3	6.5	65%	8%
DAL	11.5	18.2	21	83%	5	1	6.5	59%	-24%
DEN	7.5	18.4	23	43%	3	4	4.5	45%	2%
DET	2.5	9.5	1	0%	0	7	2.5	25%	25%
GB	12.5	22.5	32	86%	6	1	6.5	65%	-21%
HOU	3.5	10.8	2	14%	1	6	2.5	25%	11%
IND	8.5	15.0	11	43%	3	4	5.5	55%	12%
JAX	4.5	14.2	7	17%	1	5	3.5	32%	15%
KC	9.5	15.5	13	43%	3	4	6.5	65%	22%
LAC	10.5	15.8	16	67%	4	2	6.5	59%	-8%
LAR	12.5	19.1	26	86%	6	1	6.5	65%	-21%
LV	9.5	15.9	17	71%	5	2	4.5	45%	-26%
MIA	6.5	14.2	8	14%	1	6	5.5	55%	41%
MIN	8.5	14.7	9	50%	3	3	5.5	50%	0%
NE	7.5	19.6	28	43%	3	4	4.5	45%	2%
NO	9.5	19.6	27	67%	4	2	5.5	50%	-17%
NYG	5.5	14.0	6	29%	2	5	3.5	35%	6%
NYJ	3.5	16.8	18	17%	1	5	2.5	23%	6%
PHI	7.5	13.5	4	29%	2	5	5.5	55%	26%
PIT	7.5	15.7	15	50%	3	3	4.5	41%	-9%
SEA	6.5	17.2	19	29%	2	5	4.5	45%	16%
SF	8.5	14.8	10	33%	2	4	6.5	59%	26%
TB	13.5	15.7	14	86%	6	1	7.5	75%	-11%
TEN	11.5	17.3	20	71%	5	2	6.5	65%	-6%
WAS	5.5	13.7	5	29%	2	5	3.5	35%	6%

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”.