The Bills gave LaFleur every reason to switch it up, but he did not capitalize
No single factor is responsible for the Jets’ 20-12 loss to the Bills in Week 14.
Injuries, a putrid blocking performance, an ill-timed penalty, crushing turnovers, frustrating no-calls, and a crucial blown coverage all contributed, and the Jets might have come out on top had any one of them gone the other way.
However, their offense was not helped out much by the play-calling. Mike LaFleur was dealt a rough hand, but he still did not make the most of the Jets’ strengths. Throughout the game, it was fairly obvious what the Jets were going to do even when watching on TV, and the Bills certainly recognized the trends. LaFleur failed to adjust.
It’s tricky to evaluate a play-caller, and there will always be excuses for why he called the game the way he did. The Jets’ pass-blocking appeared liable to land Mike White in the hospital with their lack of protection (as actually happened after the game), and that certainly did not inspire confidence in throwing the ball. The elements and the early loss of Corey Davis also made finding a call that might work difficult.
Still, there is a way to quantify how predictable a coach’s play-calling is. NFL data tracking provides a statistic called XPASS, which is the probability that a given play will be a pass play based on down, distance, score, time remaining, and various other factors modeled on historical in-game data. Earlier this season, Mike Kafka of the Giants led the team to success largely by doing the opposite of expectation very often, allowing a talent-poor team to overachieve. Recent results show that plan coming back down to earth, but the point remains: do what your opponent does not expect and you’ll have a split-second advantage.
To utilize XPASS in a meaningful way, we must eliminate situations in which a team’s win probability is so low or so high that the probability of running or passing is skewed. Therefore, I eliminated plays on which the offense’s win probability is below 25% or above 75%. Changing that threshold slightly did not alter the numbers drastically.
I also decided to look at only first and second down, since on third down the play-calling is a lot more scripted: third and short, more often a run play; third and medium or long, almost always a pass play.
The way I determined whether a play call was predictable or unpredictable was as follows: if XPASS is greater than 55% and the team passed or less than 45% and the team ran, then the call was predictable; if XPASS is greater than 55% and the team ran or less than 45% and the team passed, then the call was unpredictable; and if XPASS was between 45-55%, then it’s a wash (I called it “meh” in highly technical statistical verbiage).
Within this framework, according to 2022 averages, teams run predictable plays 41% of the time, unpredictable plays 20.7% of the time, and “meh” plays 38.3% of the time. This means that a coach will try to switch up significant tendencies about 1/5 of the time.
I’m going to focus specifically on what a coach does when there is an expected play call – meaning the difference between the run and pass expectation is greater than 10%. Eliminating the “meh” play calls leaves us with situations in which there is a clear skew towards run or pass calls. Obviously, each team is going to have a different ratio of plays that have such a disparity, depending on game situation and the like.
For the season, the number of play calls in these situations ranges from 182 (Eagles) to 324 (Broncos) with an average of 233. The Jets are tied for the fifth-most with 266. On average, when XPASS is greater than 55% or less than 45%, teams go with the unpredictable 31.4% of the time.
Against Buffalo, Mike LaFleur called 21 plays that fit this description: first or second down, win probability between 25-75%, and XPASS either greater than 55% or less than 45%. In those situations, LaFleur went with the unpredictable play just 23.8% of the time, which would slot in at the fifth-lowest among team averages this season.
We could just leave it at that and say that LaFleur went with what was expected when the situation sharply indicated one play call over another. After all, a difference of about 8% on 21 plays averages out to less than 2 play calls in this game. Why fret over such a small sample size?
However, I propose that this may be one reason the Jets lost to the Bills: overall predictability.
How many times did we see Buffalo run a safety up to the line of scrimmage as the snap came, and then the Jets ran right into that blitz? The Bills were daring the Jets to throw behind their single-high look. Yes, Buffalo is a two-high-heavy team in general, but they, like other teams, are not as afraid to go single-high against a QB whom they do not think can beat them deep.
LaFleur should have recognized that and punished Buffalo for telegraphing their own defensive gameplan. Whether by using a hard count to identify where the blitz was coming from, running a max-protect play-action pass out of a run-heavy set, or running a jet sweep coming from the weak side of the formation, there were many ways he could have attempted to exploit the Bills’ aggressiveness.
Instead, LaFleur stuck to the predictable, and it cost him. The Jets had a 36.8% success rate on 19 first- and second-down rush attempts against the Bills, defined as a play with positive Expected Points Added (EPA). The league average for the season is 40%, with a high of 51.9% (Eagles) and a low of 30.7% (Rams). That 36.8% would be 23rd in the league.
Even worse was the Jets’ pass success rate on early downs. The Jets garnered a measly 36.1% success rate on first and second-down passes, which would be the worst mark in the NFL by a full two percentage points. It’s also far worse than the Jets’ season-long mark of 43.4%, which itself is ranked 28th in the NFL.
Furthermore, Buffalo’s defense is middle-of-the-pack when it comes to success rate allowed on first- and second-down, both against the run and pass. For the season, they rank 14th in the NFL at a 45.8% pass success rate and 13th at a 39.5% rush success rate allowed. The elements could be pointed to as a mitigating factor for the Jets, but it appeared that the Jets’ predictability was a more dominant cause, at least after the first couple of possessions.
You can also point to the returns of Jordan Poyer and Matt Milano as huge swings in Buffalo’s favor, and you would be correct. However, that goes back to the overarching point of this article: when facing superior competition, you need that split-second advantage to defeat them. The Jets did not have that; they played right into Buffalo’s hands, making running difficult and allowing the Bills’ pass rushers to tee off on Mike White.
Switching up those tendencies just once or twice could have paid major dividends for the Jets, particularly on the one or two second-and-short situations they had (including the one that Michael Carter fumbled on). Instead, LaFleur stuck with what he had planned even as Buffalo keyed on him.
However, I believe this analysis sheds some light on why the Jets’ offense struggled against Buffalo. Certainly, it’s not the only factor; LaFleur was playing with a compromised deck due to his offensive line struggles, the elements, and the injuries. Still, getting more unpredictable and creative was necessary to win against an aggressive and talented Bills defense, and LaFleur did not get it done.
Audio Version available to members only: Learn more here
Want More NY Jets News & Jets X-Factor Content?
Download the free Jet X Mobile App to get customizable notifications directly to your iOS (App Store) or Google/Android (Google Play) device.
Add Jets X-Factor to your Google News feed to stay up to date with the New York Jets.
Join the official Jets Discord community to connect with likeminded fans.