2020 NFL Predictions

Here we are again! A full-season’s worth of data, to help us predict the 2020 NFL season. Last year’s 2019 NFL Teams on the Rise/Fall performed very well with Baltimore ranked #1 in “Going Up”, Kansas City/San Francisco in the top 7, and Dallas/Rams, 1/2 respectively in the “Going Down” column. The goal from our annual flagship piece is to give handicappers, fantasy football owners and fans a framework to work with when predicting next year’s results.

How this works (if you’re new)

In many sports, football especially, skill/performance across 22-106 players does not always impact/correlate to the end result of the game. Oddly shaped balls, the human element, etc. can skew a game’s outcome away from predictive measures (like skill, etc). Because of this over the course of a season a team may be more “lucky/unlucky” (zero predictive correlation) with outcomes relative to actual performance, and over time will tend toward the mean. Think of it this way: if you flip a quarter 10 times, and it ends up heads 10/10 times, that doesn’t mean heads has some sort of ability over tails, its just “luck”. It doesn’t mean that heads is anymore likely than 50% to come up the 11th time. Applied to the NFL, a sharp observer can dissect variables that were “luck based”, and likely to head back to its 50/50 distribution. This piece combines all those variables, aggregates them and helps fans and prognosticators predict the teams going up/down in 2020.

New for 2020 NFL Predictions

Beyond some additional variables added that we find relevant and accurate in predicting 2020 NFL, we aggregated all the “predictors” into one easy-to-use composite that you can find below. In hopes to save some confusion, I included/ranked all variables with a “Better Rank” category, meaning the best (#1 ranked) in the particular category was the most unlucky/most likely to perform better in 2020. Feel free to parse this data as you see fit, as each “predictor” was weighted evenly (i.e. NCW/L is likely a stronger indicator than Y/P, but they are equal in this chart). This is an upgrade we will start to use next season with this model.

Overall Total (aggregate of all rankings averaged)

Team	3DRebound	Fumble Rank	Pythag Rank	NCWL Rank	Pen Gifts	Y/P O -D	Avg	Sack Luck	Off Int Luck
LAC	8	2	4	1	8	6	4.83	21	28
CAR	1	17	7	4	10	3	7.00	20	22
WAS	2	8	3	29	5	8	9.17	23	30
NYJ	7	3	16	30	7	12	12.50	30	16
DET	22	20	2	7	12	13	12.67	18	1
OAK	27	10	17	19	3	1	12.83	10	6
ARI	10	15	6	13	19	14	12.83	5	4
CIN	23	18	1	2	32	2	13.00	2	18
JAX	19	14	12	8	24	5	13.67	15	13
LAR	6	19	22	9	15	15	14.33	26	15
CLE	15	4	8	23	25	11	14.33	7	25
TB	17	28	11	20	1	10	14.50	8	3
DAL	16	23	9	5	16	19	14.67	13	2
NYG	11	13	5	24	28	7	14.67	28	9
DEN	13	16	15	15	13	17	14.83	25	7
ATL	30	6	14	3	27	9	14.83	24	23
NE	4	9	25	10	11	32	15.17	3	31
MIA	31	1	10	18	30	4	15.67	31	20
PHI	25	12	21	11	14	16	16.50	11	5
BUF	14	7	24	14	18	24	16.83	19	14
TEN	5	27	19	21	6	27	17.50	14	19
IND	21	22	13	16	26	18	19.33	29	32
SF	18	21	29	25	2	25	20.00	9	10
KC	24	5	27	17	21	30	20.67	27	29
PIT	32	24	18	12	23	22	21.83	16	27
BAL	20	25	30	26	4	31	22.67	4	17
CHI	29	30	20	22	17	20	23.00	12	8
SEA	9	26	28	32	22	21	23.00	32	24
NO	3	32	31	28	20	26	23.33	1	12
GB	12	11	32	27	31	28	23.50	6	26
HOU	26	29	26	31	9	23	24.00	22	11
MIN	28	31	23	6	29	29	24.33	17	21

*As you may notice, I included a couple “bonus variables”, which I did not take into account for the average given their (relative) lack of predictability and connection to individual players (that may or may not be on the team next year…think Philip Rivers and LAC).

Analysis of the 2020 NFL Predictions

• Chargers seem to always be on this list, and they may just be unlucky. Rivers and the Chargers may go down as the least lucky team in the modern era, as the highlighted in this video from Dorktown: One of the all-time greatest NFL teams didn’t even make the playoffs. 
• Panthers are an interesting team for the 2020 NFL Season. Will Cam be back or not? Will we see the Kyle Allen from the first handful of starts, or the late season Kyle Allen? With a new coach, the team has a lot of questions to answer, but “math” may give that new coach a “bump”.
• Green Bay’s “luck” was well documented throughout the season among most analytics, but to see Minnesota at the bottom, AND Chicago at 27, this means 3/4 NFC North teams sit in the top 7 teams “Going Down”. WIth Detroit at #5 “Going Up”, will we see a Detroit Division Championship?
• Poor Houston…with its piss poor upper management, they will need to combat some strong “negative” pull to the mean, while having to overcome giving the person holding them back the most, more responsibility (O’Brien)

Predictors/explanation

Below you can find additional data on the particular “predictor” along with an explanation for each variable.

3rd Down Rebound

“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.

Tm.	1st Down	2nd Down	1/2 Avg.	3rd Down	Diff.	Better Rank
CAR	16	18	17	30	13	1
WAS	31	8	19.5	31	11.5	2
NO	1	4	2.5	13	10.5	3
NE	10	10	10	19	9	4
TEN	3	5	4	11	7	5
LAR	10	11	10.5	17	6.5	6
NYJ	18	31	24.5	31	6.5	7
LAC	8	11	9.5	15	5.5	8
SEA	7	3	5	9	4	9
ARI	11	13	12	16	4	10
NYG	11	17	14	18	4	11
GB	17	6	11.5	14	2.5	12
DEN	14	19	16.5	18	1.5	13
BUF	15	14	14.5	15	0.5	14
CLE	13	19	16	16	0	15
DAL	6	1	3.5	3	-0.5	16
TB	18	18	18	17	-1	17
SF	5	17	11	10	-1	18
JAX	15	13	14	13	-1	19
BAL	4	1	2.5	1	-1.5	20
IND	14	11	12.5	11	-1.5	21
DET	17	11	14	11	-3	22
CIN	19	16	17.5	14	-3.5	23
KC	1	9	5	1	-4	24
PHI	9	15	12	7	-5	25
HOU	13	14	13.5	8	-5.5	26
OAK	11	10	10.5	5	-5.5	27
MIN	16	7	11.5	4	-7.5	28
CHI	11	30	20.5	10	-10.5	29
ATL	19	15	17	6	-11	30
MIA	30	16	23	11	-12	31
PIT	31	31	31	19	-12	32

Fumble Recovery Rate

“True Turnover Rate” was one of the first variables we started tracking 5 years ago, but its morphed into its much simpler, much easier to stat: Fumble Recovery Rate. 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.

Tm.	Rec. Rate	Better Rank
MIA	17.65%	1
LAC	18.75%	2
NYJ	31.03%	3
CLE	35.19%	4
KC	36.36%	5
ATL	36.36%	6
BUF	38.46%	7
WAS	39.13%	8
NE	39.19%	9
OAK	40.00%	10
GB	41.11%	11
PHI	41.86%	12
NYG	41.86%	13
JAX	41.86%	14
ARI	43.48%	15
DEN	43.75%	16
CAR	43.75%	17
CIN	45.45%	18
LAR	47.83%	19
DET	47.83%	20
SF	50.00%	21
IND	50.00%	22
DAL	51.63%	23
PIT	54.55%	24
BAL	54.55%	25
SEA	57.14%	26
TEN	57.89%	27
TB	59.16%	28
HOU	60.00%	29
CHI	60.00%	30
MIN	66.67%	31
NO	68.75%	32

Pythagorean Win Theorem 

Pythagorean Win Theorem and Net Close Wins/Losses (see below) are explained in depth in the 2017 NFL Season Predictions. Both are universally accepted as strong forecasters for the next season.

Tm.	W	L	W ownership	Owned Pts	Tot Pts	Proj W	Proj W > W	Better Rank
CIN	2	14	0.13	279.00	420.00	6.39	4.39	1
DET	3.5	12	0.22	341.00	423.00	7.14	3.64	2
WAS	3	13	0.19	266.00	435.00	6.07	3.07	3
LAC	5	11	0.31	337.00	345.00	7.91	2.91	4
NYG	4	12	0.25	341.00	451.00	6.89	2.89	5
ARI	5.5	10	0.34	361.00	442.00	7.19	1.69	6
CAR	5	11	0.31	340.00	470.00	6.72	1.72	7
CLE	6	10	0.38	335.00	393.00	7.36	1.36	8
DAL	8	8	0.50	434.00	321.00	9.20	1.20	9
MIA	5	11	0.31	306.00	494.00	6.12	1.12	10
TB	7	9	0.44	458.00	449.00	8.08	1.08	11
JAX	6	10	0.38	300.00	397.00	6.89	0.89	12
IND	7	9	0.44	361.00	373.00	7.87	0.87	13
ATL	7	9	0.44	381.00	399.00	7.82	0.82	14
DEN	7	9	0.44	282.00	316.00	7.55	0.55	15
NYJ	7	9	0.44	276.00	359.00	6.95	-0.05	16
OAK	7	9	0.44	313.00	419.00	6.84	-0.16	17
PIT	8	8	0.50	289.00	303.00	7.81	-0.19	18
TEN	9	7	0.56	402.00	331.00	8.77	-0.23	19
CHI	8	8	0.50	280.00	298.00	7.75	-0.25	20
PHI	9	7	0.56	385.00	354.00	8.34	-0.66	21
LAR	9	7	0.56	394.00	364.00	8.32	-0.68	22
MIN	10	6	0.63	407.00	303.00	9.17	-0.83	23
BUF	10	6	0.63	314.00	259.00	8.77	-1.23	24
NE	12	4	0.75	420.00	225.00	10.42	-1.58	25
HOU	10	6	0.63	378.00	385.00	7.93	-2.07	26
KC	12	4	0.75	451.00	308.00	9.51	-2.49	27
SEA	11	5	0.69	405.00	398.00	8.07	-2.93	28
SF	13	3	0.81	479.00	310.00	9.71	-3.29	29
BAL	14	2	0.88	531.00	282.00	10.45	-3.55	30
NO	13	3	0.81	458.00	341.00	9.17	-3.83	31
GB	13	3	0.81	376.00	313.00	8.73	-4.27	32

Net Close Wins/Losses

(See above), learn more in 2017 NFL Season Predictions.

Tm.	TOT	Better Rank
LAC	-4	1
CIN	-3	2
ATL	-2	3
CAR	-2	4
DAL	-2	5
MIN	-2	6
DET	-1	7
JAX	-1	8
LAR	-1	9
NE	-1	10
PHI	-1	11
PIT	-1	12
ARI	0	13
BUF	0	14
DEN	0	15
IND	0	16
KC	0	17
MIA	0	18
OAK	0	19
TB	0	20
TEN	0	21
CHI	1	22
CLE	1	23
NYG	1	24
SF	1	25
BAL	2	26
GB	2	27
NO	2	28
WAS	2	29
NYJ	3	30
HOU	4	31
SEA	4	32

Net Penalty Gifts

“Net Penalty Gifts” is new to this year’s data-set, but something I have tracked since 2018. Remember, we are always searching for areas of the game in which the skill/luck scale is skewed and tipped towards the latter. Imagine its 3rd and 6, your QB throws a bomb down the sideline, its incomplete, but the DE hit the QB late. A penalty is called and your team is awarded a new set of downs, instead of (likely) having to kick the ball away. It’s fair to say that the opposing team’s defense and its poor play/discipline (skill, or lack thereof) led to this outcome, while your team did nothing to gain the first down. In other words, your team was bailed out. This happens on roughing the passer, PI and holding penalties all the time. This variable tracks when it happens to an offense, only on 3rd down, over the course of the season. Clearly, teams that got bailed out most frequently in one year, won’t have the same luck the next, since its completely out of their control/not skill based.

Tm.	#	Better Rank
TB	1	1
SF	1	2
OAK	1	3
BAL	1	4
WAS	3	5
TEN	3	6
NYJ	3	7
LAC	3	8
HOU	3	9
CAR	3	10
NE	4	11
DET	4	12
DEN	5	13
PHI	6	14
LAR	6	15
DAL	6	16
CHI	6	17
BUF	6	18
ARI	6	19
NO	7	20
KC	7	21
SEA	8	22
PIT	8	23
JAX	8	24
CLE	8	25
IND	9	26
ATL	9	27
NYG	10	28
MIN	10	29
MIA	10	30
GB	11	31
CIN	11	32

Yards/Point (Net Offense and Defense)

Yards per Point, or YPP as Phil Steele calls it, is an oldie, but a new addition for our data-set. 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).

Tm.	P	Y	OFF Y/P	Better Rank	P	Y	DEF y/p	O-D	Better Rank
OAK	313	5819	18.59	1	419	5677	13.55	5.04	1
CIN	279	5169	18.53	2	420	6299	15.00	3.53	2
CAR	340	5469	16.09	12	470	5992	12.75	3.34	3
MIA	306	4960	16.21	11	494	6364	12.88	3.33	4
JAX	300	5468	18.23	3	397	6007	15.13	3.10	5
LAC	337	5879	17.45	4	345	5009	14.52	2.93	6
NYG	341	5416	15.88	15	451	6037	13.39	2.50	7
WAS	266	4395	16.52	8	435	6162	14.17	2.36	8
ATL	381	6075	15.94	13	399	5693	14.27	1.68	9
TB	458	6366	13.90	26	449	5503	12.26	1.64	10
CLE	335	5455	16.28	9	393	5785	14.72	1.56	11
NYJ	276	4368	15.83	16	359	5170	14.40	1.42	12
DET	341	5549	16.27	10	423	6406	15.14	1.13	13
ARI	361	5467	15.14	20	442	6432	14.55	0.59	14
LAR	394	5998	15.22	19	364	5434	14.93	0.29	15
PHI	385	5772	14.99	21	354	5307	14.99	0.00	16
DEN	282	4777	16.94	6	316	5392	17.06	-0.12	17
IND	361	5238	14.51	24	373	5549	14.88	-0.37	18
DAL	434	6904	15.91	14	321	5232	16.30	-0.39	19
CHI	280	4749	16.96	5	298	5186	17.40	-0.44	20
SEA	405	5991	14.79	22	398	6106	15.34	-0.55	21
PIT	289	4428	15.32	18	303	4866	16.06	-0.74	22
HOU	378	5792	15.32	17	385	6213	16.14	-0.81	23
BUF	314	5283	16.82	7	259	4772	18.42	-1.60	24
SF	479	6097	12.73	31	310	4509	14.55	-1.82	25
NO	458	5982	13.06	30	341	5329	15.63	-2.57	26
TEN	402	5805	14.44	25	331	5752	17.38	-2.94	27
GB	376	5528	14.70	23	313	5642	18.03	-3.32	28
MIN	407	5656	13.90	27	303	5465	18.04	-4.14	29
KC	451	6067	13.45	29	308	5594	18.16	-4.71	30
BAL	531	6521	12.28	32	282	4809	17.05	-4.77	31
NE	420	5664	13.49	28	225	4414	19.62	-6.13	32

*Supplemental Predictors/Not included in the overall score:

The next two variables are important, but I left them out of the actual scoring since they are tied to individual players (vs. teams), that may or may not return to their teams next year. However, I think they are a good supplement of predictive information.

Sack Luck

“Sack Luck”, highlighted in a recent Facebook post, calling on Mack to dominate next year, is a fantastic individual predictor.

To help explain this “predictor”, think of baseball. To hit a home run, you need to make contact with the ball, hit it hard enough, and hit it at a correct angle. See it as three “stages” of the homerun, that you can dissect and track one-by-one with contact rate, exit velocity, and exit angle to find players likely to hit more or less home runs.. We can do the same thing with certain aspects of football, like a sack. A sack occurs when a player gets pressure, hurries the QB and tackles him successfully before the ball is thrown. The same way you can’t hit a home run if you don’t make any contact (first stage), there is no way you will get a sack without applying pressure (first stage). However, some QBs are better than others at scrambling, escaping tackles, etc. That is, abilities out of the pass rushers control, and won’t “carry over” to the next season. Hence, tracking pressures, at a team level, relative to sacks can shed some light on teams that were “lucky and unlucky” in sacking the QB (stage 3 relative to stage 1).
Below, you will find this data aggregated, and combined for both defenses that created a lot of pressure, with few sacks AND offenses that were sacked many times vs. how often they were pressured.

Team	Sack %	Presure %	off	def	Press % Def	def	BOTH	Better Rank
NO	4.35%	23.30%	18.95%	7.85%	35.70%	27.85%	8.90%	1
CIN	7.23%	24.10%	16.87%	5.85%	30.90%	25.05%	8.18%	2
NE	4.09%	24.30%	20.21%	8.00%	36.30%	28.30%	8.09%	3
BAL	6.03%	27.60%	21.57%	6.37%	34.50%	28.13%	6.56%	4
ARI	8.28%	27.50%	19.22%	6.24%	30.00%	23.76%	4.54%	5
GB	6.03%	28.70%	22.67%	7.44%	34.60%	27.16%	4.49%	6
CLE	7.07%	27.50%	20.43%	6.90%	31.60%	24.70%	4.27%	7
TB	6.94%	29.00%	22.06%	6.61%	32.40%	25.79%	3.73%	8
SF	6.94%	25.50%	18.56%	8.84%	30.40%	21.56%	3.00%	9
OAK	5.25%	26.00%	20.75%	5.73%	28.90%	23.17%	2.42%	10
PHI	6.42%	28.40%	21.98%	6.82%	30.80%	23.98%	2.00%	11
CHI	7.20%	29.50%	22.30%	5.31%	29.60%	24.29%	1.99%	12
DAL	3.71%	29.10%	25.39%	6.41%	33.60%	27.19%	1.80%	13
TEN	10.66%	27.70%	17.04%	6.30%	25.10%	18.80%	1.76%	14
JAX	6.66%	28.70%	22.04%	8.33%	31.30%	22.97%	0.93%	15
PIT	5.90%	29.50%	23.60%	9.51%	33.40%	23.89%	0.29%	16
MIN	6.57%	29.10%	22.53%	7.50%	30.20%	22.70%	0.17%	17
DET	7.00%	30.00%	23.00%	4.38%	26.30%	21.92%	-1.08%	18
BUF	7.12%	31.60%	24.48%	8.11%	30.80%	22.69%	-1.79%	19
CAR	8.39%	30.10%	21.71%	8.88%	28.50%	19.62%	-2.09%	20
LAC	5.39%	29.10%	23.71%	6.07%	27.50%	21.43%	-2.28%	21
HOU	9.08%	34.50%	25.42%	4.97%	27.60%	22.63%	-2.79%	22
WAS	9.45%	30.90%	21.45%	7.85%	26.10%	18.25%	-3.20%	23
ATL	6.81%	31.70%	24.89%	4.96%	26.40%	21.44%	-3.45%	24
DEN	7.52%	32.10%	24.58%	6.93%	28.00%	21.07%	-3.51%	25
LAR	3.36%	31.30%	27.94%	8.17%	32.40%	24.23%	-3.71%	26
KC	4.44%	31.40%	26.96%	7.19%	29.20%	22.01%	-4.95%	27
NYG	6.62%	33.40%	26.78%	6.08%	27.90%	21.82%	-4.96%	28
IND	5.87%	34.20%	28.33%	6.80%	30.10%	23.30%	-5.03%	29
NYJ	9.08%	39.30%	30.22%	5.65%	29.90%	24.25%	-5.97%	30
MIA	8.62%	36.10%	27.48%	4.05%	23.90%	19.85%	-7.63%	31
SEA	8.54%	36.10%	27.56%	5.36%	23.90%	18.54%	-9.02%	32

(Offensive Only) Interception Luck

Very similar to “Sack Luck” above, QBs can get lucky or unlucky based on how often the defense has a “chance” to intercept a ball (defined by passes defended) relative to how often a successful interception actually happens. Note, ONLY offensive numbers were used here as I could not find full charting data on “QB Passes defended” (i.e. dropped interceptions).

Team	Int	PD	Int %	Better Rank
DET	7	73	0.10	1
DAL	7	71	0.10	2
TB	12	96	0.13	3
ARI	7	56	0.13	4
PHI	11	82	0.13	5
OAK	9	67	0.13	6
DEN	10	70	0.14	7
CHI	10	67	0.15	8
NYG	10	65	0.15	9
SF	12	75	0.16	10
HOU	12	75	0.16	11
NO	13	78	0.17	12
JAX	10	60	0.17	13
BUF	14	83	0.17	14
LAR	13	76	0.17	15
NYJ	12	67	0.18	16
BAL	13	71	0.18	17
CIN	11	58	0.19	18
TEN	14	72	0.19	19
MIA	13	66	0.20	20
MIN	17	83	0.20	21
CAR	14	67	0.21	22
ATL	12	57	0.21	23
SEA	16	74	0.22	24
CLE	14	64	0.22	25
GB	17	74	0.23	26
PIT	20	83	0.24	27
LAC	11	45	0.24	28
KC	16	65	0.25	29
WAS	13	52	0.25	30
NE	25	89	0.28	31
IND	15	50	0.30	32

I hope you enjoyed this year’s predictions. Remember, even though we have had a lot of success using these variables to make NFL predictions, use this information as a guide, not an end all be all. If you like this post, please like the page on Facebook to subscribe.