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)

Team3DReboundFumble RankPythag RankNCWL RankPen GiftsY/P O -DAvgSack LuckOff Int Luck
LAC8241864.832128
CAR117741037.002022
WAS28329589.172330
NYJ73163071212.503016
DET222027121312.67181
OAK271017193112.83106
ARI1015613191412.8354
CIN23181232213.00218
JAX191412824513.671513
LAR619229151514.332615
CLE154823251114.33725
TB1728112011014.5083
DAL162395161914.67132
NYG111352428714.67289
DEN13161515131714.83257
ATL30614327914.832423
NE492510113215.17331
MIA311101830415.673120
PHI25122111141616.50115
BUF1472414182416.831914
TEN527192162717.501419
IND21221316261819.332932
SF1821292522520.00910
KC2452717213020.672729
PIT32241812232221.831627
BAL2025302643122.67417
CHI29302022172023.00128
SEA9262832222123.003224
NO3323128202623.33112
GB12113227312823.50626
HOU2629263192324.002211
MIN2831236292924.331721

*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 Down2nd Down1/2 Avg.3rd DownDiff.Better Rank
CAR16181730131
WAS31819.53111.52
NO142.51310.53
NE1010101994
TEN3541175
LAR101110.5176.56
NYJ183124.5316.57
LAC8119.5155.58
SEA735949
ARI11131216410
NYG11171418411
GB17611.5142.512
DEN141916.5181.513
BUF151414.5150.514
CLE13191616015
DAL613.53-0.516
TB18181817-117
SF5171110-118
JAX15131413-119
BAL412.51-1.520
IND141112.511-1.521
DET17111411-322
CIN191617.514-3.523
KC1951-424
PHI915127-525
HOU131413.58-5.526
OAK111010.55-5.527
MIN16711.54-7.528
CHI113020.510-10.529
ATL1915176-1130
MIA30162311-1231
PIT31313119-1232

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. RateBetter Rank
MIA17.65%1
LAC18.75%2
NYJ31.03%3
CLE35.19%4
KC36.36%5
ATL36.36%6
BUF38.46%7
WAS39.13%8
NE39.19%9
OAK40.00%10
GB41.11%11
PHI41.86%12
NYG41.86%13
JAX41.86%14
ARI43.48%15
DEN43.75%16
CAR43.75%17
CIN45.45%18
LAR47.83%19
DET47.83%20
SF50.00%21
IND50.00%22
DAL51.63%23
PIT54.55%24
BAL54.55%25
SEA57.14%26
TEN57.89%27
TB59.16%28
HOU60.00%29
CHI60.00%30
MIN66.67%31
NO68.75%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.WLW ownershipOwned PtsTot PtsProj WProj W > WBetter Rank
CIN2140.13279.00420.006.394.391
DET3.5120.22341.00423.007.143.642
WAS3130.19266.00435.006.073.073
LAC5110.31337.00345.007.912.914
NYG4120.25341.00451.006.892.895
ARI5.5100.34361.00442.007.191.696
CAR5110.31340.00470.006.721.727
CLE6100.38335.00393.007.361.368
DAL880.50434.00321.009.201.209
MIA5110.31306.00494.006.121.1210
TB790.44458.00449.008.081.0811
JAX6100.38300.00397.006.890.8912
IND790.44361.00373.007.870.8713
ATL790.44381.00399.007.820.8214
DEN790.44282.00316.007.550.5515
NYJ790.44276.00359.006.95-0.0516
OAK790.44313.00419.006.84-0.1617
PIT880.50289.00303.007.81-0.1918
TEN970.56402.00331.008.77-0.2319
CHI880.50280.00298.007.75-0.2520
PHI970.56385.00354.008.34-0.6621
LAR970.56394.00364.008.32-0.6822
MIN1060.63407.00303.009.17-0.8323
BUF1060.63314.00259.008.77-1.2324
NE1240.75420.00225.0010.42-1.5825
HOU1060.63378.00385.007.93-2.0726
KC1240.75451.00308.009.51-2.4927
SEA1150.69405.00398.008.07-2.9328
SF1330.81479.00310.009.71-3.2929
BAL1420.88531.00282.0010.45-3.5530
NO1330.81458.00341.009.17-3.8331
GB1330.81376.00313.008.73-4.2732

Net Close Wins/Losses

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

Tm.TOTBetter Rank
LAC-41
CIN-32
ATL-23
CAR-24
DAL-25
MIN-26
DET-17
JAX-18
LAR-19
NE-110
PHI-111
PIT-112
ARI013
BUF014
DEN015
IND016
KC017
MIA018
OAK019
TB020
TEN021
CHI122
CLE123
NYG124
SF125
BAL226
GB227
NO228
WAS229
NYJ330
HOU431
SEA432

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
TB11
SF12
OAK13
BAL14
WAS35
TEN36
NYJ37
LAC38
HOU39
CAR310
NE411
DET412
DEN513
PHI614
LAR615
DAL616
CHI617
BUF618
ARI619
NO720
KC721
SEA822
PIT823
JAX824
CLE825
IND926
ATL927
NYG1028
MIN1029
MIA1030
GB1131
CIN1132

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.
PYOFF Y/PBetter RankPYDEF y/pO-DBetter Rank
OAK313581918.591419567713.555.041
CIN279516918.532420629915.003.532
CAR340546916.0912470599212.753.343
MIA306496016.2111494636412.883.334
JAX300546818.233397600715.133.105
LAC337587917.454345500914.522.936
NYG341541615.8815451603713.392.507
WAS266439516.528435616214.172.368
ATL381607515.9413399569314.271.689
TB458636613.9026449550312.261.6410
CLE335545516.289393578514.721.5611
NYJ276436815.8316359517014.401.4212
DET341554916.2710423640615.141.1313
ARI361546715.1420442643214.550.5914
LAR394599815.2219364543414.930.2915
PHI385577214.9921354530714.990.0016
DEN282477716.946316539217.06-0.1217
IND361523814.5124373554914.88-0.3718
DAL434690415.9114321523216.30-0.3919
CHI280474916.965298518617.40-0.4420
SEA405599114.7922398610615.34-0.5521
PIT289442815.3218303486616.06-0.7422
HOU378579215.3217385621316.14-0.8123
BUF314528316.827259477218.42-1.6024
SF479609712.7331310450914.55-1.8225
NO458598213.0630341532915.63-2.5726
TEN402580514.4425331575217.38-2.9427
GB376552814.7023313564218.03-3.3228
MIN407565613.9027303546518.04-4.1429
KC451606713.4529308559418.16-4.7130
BAL531652112.2832282480917.05-4.7731
NE420566413.4928225441419.62-6.1332

*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.

TeamSack %Presure %offdefPress % DefdefBOTHBetter Rank
NO4.35%23.30%18.95%7.85%35.70%27.85%8.90%1
CIN7.23%24.10%16.87%5.85%30.90%25.05%8.18%2
NE4.09%24.30%20.21%8.00%36.30%28.30%8.09%3
BAL6.03%27.60%21.57%6.37%34.50%28.13%6.56%4
ARI8.28%27.50%19.22%6.24%30.00%23.76%4.54%5
GB6.03%28.70%22.67%7.44%34.60%27.16%4.49%6
CLE7.07%27.50%20.43%6.90%31.60%24.70%4.27%7
TB6.94%29.00%22.06%6.61%32.40%25.79%3.73%8
SF6.94%25.50%18.56%8.84%30.40%21.56%3.00%9
OAK5.25%26.00%20.75%5.73%28.90%23.17%2.42%10
PHI6.42%28.40%21.98%6.82%30.80%23.98%2.00%11
CHI7.20%29.50%22.30%5.31%29.60%24.29%1.99%12
DAL3.71%29.10%25.39%6.41%33.60%27.19%1.80%13
TEN10.66%27.70%17.04%6.30%25.10%18.80%1.76%14
JAX6.66%28.70%22.04%8.33%31.30%22.97%0.93%15
PIT5.90%29.50%23.60%9.51%33.40%23.89%0.29%16
MIN6.57%29.10%22.53%7.50%30.20%22.70%0.17%17
DET7.00%30.00%23.00%4.38%26.30%21.92%-1.08%18
BUF7.12%31.60%24.48%8.11%30.80%22.69%-1.79%19
CAR8.39%30.10%21.71%8.88%28.50%19.62%-2.09%20
LAC5.39%29.10%23.71%6.07%27.50%21.43%-2.28%21
HOU9.08%34.50%25.42%4.97%27.60%22.63%-2.79%22
WAS9.45%30.90%21.45%7.85%26.10%18.25%-3.20%23
ATL6.81%31.70%24.89%4.96%26.40%21.44%-3.45%24
DEN7.52%32.10%24.58%6.93%28.00%21.07%-3.51%25
LAR3.36%31.30%27.94%8.17%32.40%24.23%-3.71%26
KC4.44%31.40%26.96%7.19%29.20%22.01%-4.95%27
NYG6.62%33.40%26.78%6.08%27.90%21.82%-4.96%28
IND5.87%34.20%28.33%6.80%30.10%23.30%-5.03%29
NYJ9.08%39.30%30.22%5.65%29.90%24.25%-5.97%30
MIA8.62%36.10%27.48%4.05%23.90%19.85%-7.63%31
SEA8.54%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).

TeamIntPDInt %Better Rank
DET7730.101
DAL7710.102
TB12960.133
ARI7560.134
PHI11820.135
OAK9670.136
DEN10700.147
CHI10670.158
NYG10650.159
SF12750.1610
HOU12750.1611
NO13780.1712
JAX10600.1713
BUF14830.1714
LAR13760.1715
NYJ12670.1816
BAL13710.1817
CIN11580.1918
TEN14720.1919
MIA13660.2020
MIN17830.2021
CAR14670.2122
ATL12570.2123
SEA16740.2224
CLE14640.2225
GB17740.2326
PIT20830.2427
LAC11450.2428
KC16650.2529
WAS13520.2530
NE25890.2831
IND15500.3032

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.

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