Wildcard Watch Simulation: Week 16 – Must Beat Dallas Edition

Hope is fading. Can Ron Rivera and Taylor Heinicke deliver a Boxing Day* miracle in Texas?

With Tuesday’s loss to Philadelphia, the WFT’s remaining chances to make the playoffs are quickly evaporating. At 6-8, and facing competition from four teams with better records for two remaining wildcard playoff spots, the Football Team no longer has any room for error. To make the playoffs they will have to win all of their remaining games and will most likely need help from other teams as well.

Their chances of pulling it off are not likely to be helped much this week by the COVID pandemic, which to this point has ravaged Washington’s roster, while largely sparing those of its main competitors in the wildcard race. However, this is a rapidly evolving situation; since Tuesday Washington has got back the starting and primary backup QBs, and many of their other key players from the COVID list. Will that be enough, though, to overcome key losses to injury including Brandon Scherff, JD McKissic, Logan Thomas and Landon Collins?

Flipping that around, as the WFT’s options to make the playoffs are dwindling, a COVID-assist might be one of their few remaining hopes. While earlier in the week the Football Team was second only to Cleveland in terms of the numbers of players on its COVID-19 list, these numbers are now coming down while other teams, such as the LA Rams (25 players as of final edits), are now experiencing outbreaks. At this point in the season, COVID outbreaks taking out the wildcard competitors seem about as probable as ending the season on a three-game winning streak. Neither of those scenarios would be a good bet.

The WFT heads to AT&T Stadium this week reeling from two tough division losses in addition to the COVID impacts and several key starters being out with injuries. Nevertheless, the near comeback against Dallas two weeks ago tells us that the 10-4 Cowboys are not unbeatable. It will just take a few things going our way.

For the past few weeks, I have been taking an unusual approach to tracking the WFT’s hunt for a wildcard playoff spot. Rather than focusing exclusively on most likely scenarios, my alternative take embraces the contribution of unpredictable events to NFL game outcomes. As if on cue, the COVID pandemic intervened in the NFL season last week and is likely to have a significant impact on game outcomes for the remainder of the season.

My approach uses a simple probabilistic model to allow for the unpredictable upset wins that happen every week in the NFL, and which are now central to the Football Team’s chance of making the postseason. Each week, until the WFT is mathematically eliminated from the playoffs, I will set the probabilities of each team winning its weekly matchups, based on the point differential over its last five games, and then use a random number generator to “roll the dice” to determine the winner.

Using this approach, the favored team should usually win its weekly matchups, but there is always a chance that an underdog team like the WFT will pull off an upset. This is not intended to be a predictive model that accurately predicts game outcomes. Rather it is intended to produce results that closely match the weekly game outcomes, including a few unpredictable upsets, like we see nearly every week of the NFL season.

Just in case there is any doubt, though, I would like to emphasize: THIS IS NOT INTENDED TO BE A STATISTICALLY RIGOROUS SIMULATION, LIKE THE MODELS USED TO FORECAST WEATHER OR PROJECT ELECTION OUTCOMES. Any comments directed toward its statistical validity will be ignored. The point is to have a look at how the remaining three weeks of the WFT’s season could unfold, rather than attempting to predict actual game outcomes.

TL:DR Methodology Section

The model uses accumulated point differential over the last five games to set the probability that teams will win their next game. A random number generator is used to simulate rolling the dice to determine which team wins each game. Using this approach, the team that has achieved the larger point differential over its last five games will usually win its next game. But there remains a real possibility that the underdog will pull off an upset.

Anyone who is actually interested in the details of how it works can read about it in the Week 14 edition.

COVID Handicapping

This week I have introduced handicapping, in an attempt to account for the impact of player attrition due to COVID on teams’ chances of winning. As with the rest of the model, I have kept it fairly simplistic. Each team’s probability of winning against an average opponent (P_win) is reduced in proportion to the number of players on its Reserve/COVID-19 list as of this writing (Wednesday 22 December, U.S. time).

COVID handicapping was only applied to Week 16 game predictions for one simple reason. Due to the nature of the COVID pandemic, it is impossible to predict which teams will be impacted in two and three weeks’ time and to what extent. Therefore, I could have attempted to model the pandemic’s spread throughout the NFL, or simply assumed that after next week it stops impacting games. Rather than attempting the impossible, I chose the second, simpler option.

The COVID handicap was applied to Week 16 projections as follows. A COVID adjustment factor was calculated using the formula:

COVID_adj = (53 – n COVID)/53, where “n COVID” is the number of players on the team’s Reserve COVID-19 list.

This results in a number between 0 and 1, that is meant to represent the proportion of the team’s 53 man roster that has been depleted by COVID. A team that has lost no players will have a COVID_adj of 1, while a team that has lost 26 players will have a COVID_adj of 0.509, and a team missing all 53 players on its original roster will have a COVID_adj of 0.

Each team’s raw P_win value is then multiplied by COVID_adj to give a COVID-adjusted P_win for use in determining game win-loss probabilities. While this is intended to reduce each team’s probability of winning in proportion to its COVID losses, it is only a rough approximation, because I didn’t go to the painstaking effort required to separate starters, reserves and practice squad players on the COVID lists. Nevertheless, it should be good enough for this purpose, considering how simplistic the rest of the model is.

In addition to the COVID roster depletion adjustment, I added a further penalty of 0.05 (approximately 5% reduction in win probability) if one of the players on the COVID-19 list is the team’s starting QB. This affected the WFT and Cleveland. No additional handicap was applied for coaches on the COVID-19 list. Note: since the original simulation was run, Taylor Heinicke was activated. The QB handicap has been kept in place, to simulate the impact of QB1 missing first team reps through the week.

However, despite these two COVID handicaps, I stuck with the original model’s floor of P_win = 0.05. This means that, if a team’s COVID adjusted P_win is less than 0.05, I reset it to 0.05. In other words, in this model, no matter how bad and disadvantaged a team may be, it still has around a 5% chance of winning its next game against an average opponent.

Sticklers might raise a number of objections to this handicapping, including that what really matters is game day inactives rather than listings four days in advance, the lack of differentiation between starters, reserves and practice squad players on this COVID-19 list, and the failure to account for coach attrition. Furthermore, there is simply no way to predict what the COVID impacts will look like two weeks from now, so I had to limit the COVID handicapping to Week 16 only. I am not even going to bother attempting to justify all of my assumptions, oversimplifications and shortcuts. Instead, I will simply say THIS ARTICLE AND SERIES ARE NOT FOR METHODOLOGICAL STICKLERS.

Week 15 Predictions Revisited

The model did better than expected last week, correctly predicting 11/14 game outcomes and the number of upsets in Week 14. Considering how flimsy it’s analytical underpinnings are, that level of performance was surprisingly good. The COVID outbreak added an additional layer of unpredictability to Week 15 results. Could the model keep up is better than expected performance in more challenging conditions? Let’s see how it did.

In Week 15, the model correctly picked 9/16 winners. That’s still better than chance, but only just. I suspect the unadjusted model will struggle, to the extent that COVID impacts some weekly game outcomes, which is why I have introduced COVID handicapping from this week.

Having said that, most of the game results the model got wrong this week were not likely consequences of COVID. Two of the miscalls were a result of calling upsets: a plausible if unlikely upset by the resurgent Seahawks over the Rams, and a more improbably upset of Carolina over Buffalo.

Furthermore, four of the games the model called wrong were the result of actual upsets, including Detroit’s highly improbable win over Arizona, New Orleans’ surprise upset of Tampa, the Colts’ upset of New England, and the Raiders’ upset of Cleveland. If the model had called the first two upsets correctly, people would probably have called those predictions ridiculous. Of these four upsets, only Cleveland (20 player COVID list) losing to the Raiders could be reasonably attributed to COVID.

Sticking with the topic of upsets, the model predicted there would only be three this past week, when in fact there were five. It correctly called Pittsburgh’s upset of Tennessee, but missed on the four others I just discussed and the two it called incorrectly.

Like Ron Rivera’s coaching staff, the model has some difficult questions to answer this week. I’m not going to be too hard on it though, because having two highly improbable upsets and one game with a huge COVID impact made it particularly challenging to perform much better than chance.

Geoff Burke-USA TODAY Sports

Week 16 Predictions

Following two consecutive losses, the Football Team can still make the playoffs, but their options for doing so have narrowed considerably. In contrast to last week, when they still controlled their own destiny, after the loss to Philadelphia they now have to win their three remaining games and get help from other teams to secure one of the remaining wildcard fixtures.

With three games remaining, the six-win WFT can no longer overtake the 10-4 Cowboys to win the division. The fifth seed NFC wildcard position is also now out of reach, with Arizona and the LA Rams tied at 10-4 at the top of the NFC West division. They currently trail San Francisco by two games and the trio of Philadelphia, Minnesota and New Orleans by one game in the battle for the last two remaining NFC wildcard spots. In order to make the playoffs, they will have to win their final three games and hope for some favorable combinations of outcomes to prevent at least three of those four teams from finishing ahead of them.

In short, the WFT’s playoff window is rapidly closing. Can they pull off the nearly impossible? They’ll have to take that one week at a time. Let’s see how they do in Week 16, along with the rest of their competition.

I am sad to say, the COVID- and injury-ravaged WFT was not able to pull off the upset road win against Dallas. The combination of COVID attrition, including both starting and primary backup QBs missing reps with the first team through the week, with a short week and injury attrition must have been too much to overcome in their matchup with Dallas, riding a three-game winning streak at home.

The blow of losing to Dallas might seem to have been softened by the fact that their wildcard competitors San Francisco and New Orleans lost their Week 16 games. However, even with that loss, San Francisco maintains a two-win advantage over the WFT. Furthermore, Philadelphia beat the Giants and Minnesota upset the Rams to pull even with San Francisco at 8-7. While there may be some remaining mathematical possibilities for the WFT to squeak into the playoffs at 8-9, in practical terms their season is effectively over.

If the season ended at Week 16, the NFC playoff seeding would be as follows:

1^st Seed – NFC North Champion Green Bay (12-3)

2^nd Seed – NFC East Champion Dallas (11-4)

3^rd Seed – NFC West Champion Arizona (10-5)

4^th Seed – NFC South Champion Tampa Bay (10-5)

5^th Seed Wildcard – LA Rams (10-5)

6^th Seed Wildcard – San Francisco (8-7)

7^th Seed Wildcard – Minnesota (8-7)

Philadelphia (8-7) just misses the playoffs, followed by New Orleans at 7-8. The WFT is now one of a trailing pack of teams at 6-9, along with Seattle, Carolina and Atlanta. There are still a lot of ways the wildcard race could unfold, but the chance of the WFT, or any of the other 6-9 teams, creeping their way back in it has become close to negligible.

Week 16 Season Projection

At this point, it feels a bit like we’re just going through the motions. But, since we’ve come this far, we might as well see how the rest of the NFC playoff race unfolds, starting with the NFC East:

The WFT drops their remaining games, including the Week 18 matchup with the lowly Giants. Meanwhile, Philadelphia wins their remaining games to finish 10-7.

Plugging the rest of the projected game outcomes into the ESPN Playoff Machine gives the following final outcome to the NFC playoff race:

1^st Seed – NFC North Champion Green Bay (14-3)

2^nd Seed – NFC East Champion Dallas (12-5)

3^rd Seed – NFC West Champion LA Rams (12-5)

4^th Seed – NFC South Champion Tampa Bay (12-5)

5^th Seed Wildcard – Arizona (11-6)

6^th Seed Wildcard – Philadelphia (10-7)

7^th Seed Wildcard – San Francisco (9-8)

Minnesota just misses the playoffs at 9-8, followed by New Orleans at 8-9. Seattle and an unlikely strong finishing Carolina end up at 7-10. The Football Team ends its season at 6-11. Combining the results of the NFC and AFC projections (AFC not shown), this has them vying with the Giants, and Atlanta for the sixth through eighth positions in the 2022 draft.

There is one a silver lining to this dismal, if increasingly likely outcome. In a weak QB draft class, that might possibly be good enough to select the second-ranked QB prospect.

Photo by Andy Lewis/Icon Sportswire via Getty Images

Footnotes

*The day after Christmas is known as Boxing Day throughout the former British Commonwealth. Boxing day got its name during the reign of Queen Victoria. The origin of the name is not entirely clear. The prevailing theory is that it comes from a time when the rich would box up gifts to give to the poor. In Australian sport it is best known as the start of the annual Boxing Day Test cricket match and the Sydney to Hobart yacht race.

Acknowledgements

This article benefitted from skillful editing by James Dorsett. For the second week in a row, Bill in Bangkok provided the final assist to get it over the line, as we all deal with the technical challenges of the new publishing platform.