Mike LaFleur, NY Jets, Fired, Scheme, Stats
Mike LaFleur, New York Jets, Getty Images

Attempting to evaluate former New York Jets offensive coordinator Mike LaFleur independent of his surroundings

When people analyze an offensive play-caller in the NFL, they simply look at the overall numbers of the play-caller’s unit and make their judgments based on those alone. If the play-caller’s offense was 30th in scoring, he stinks. If his offense was 5th in scoring, he’s a rockstar.

However, evaluating coaches is a lot more complex than that. There are many factors involved in determining the performance of a unit. It’s never 100% on the coach. He could be let down by poor execution. He could also be made to look better than he actually is thanks to the phenomenal talent on the roster.

Much of a coach’s true performance quality comes down to their abilities as leaders and teachers off the field, which outsiders are mostly unable to judge (save for the occasional reports and rumors that leak out of a locker room).

But when looking solely at what happens on the field, there are ways to isolate the coach’s performance from that of his players.

The best way to do this is by grinding through the film, but I wanted to find a way to quantify some of the things you might notice on film as examples of a good or bad job by the offensive coordinator.

I tried my best to identify metrics that tell us specifically about how the offensive play-caller performed at putting his players in the best positions to succeed.

Using these metrics, we can get a somewhat better idea of how good or bad Mike LaFleur really was as the New York Jets‘ offensive coordinator. Did the Jets make a mistake moving on from him? Or did he struggle as much as it seemed?

The metrics chosen

Before we get into the numbers, let’s break down the four metrics I chose to analyze. Each of these metrics is intended to focus on a part of the game that the play-caller has a high degree of control over and is largely independent of his players’ execution.

Wide-open passes created

Every time I watch a San Francisco 49ers game, I am in awe of how many wide-open passes Kyle Shanahan draws up. Players constantly break wide-open solely because of his play designs.

In my opinion, the ability to scheme people open is the best trait an offensive play-caller can offer. It makes life so easy for everyone on the field. The quarterback is given a throw that is simple to execute and the skill-position players are not pressured to win their routes one-on-one.

Using data from NFL Next Gen Stats, I calculated the percentage of each team’s pass attempts that traveled at least 10 yards down the field and went to a receiver who was categorized as “open” or “wide-open” (3+ yards of separation from nearest defender when ball arrives).

This is an effective way of seeing how frequently the quarterback was gift-wrapped an easy completion down the field.

Unaccounted-for pressures allowed

Few things make an offense look more discombobulated than allowing unaccounted-for defenders to run free at the quarterback. This metric allows us to get an idea of how often each team allowed this to happen.

Any time a defender pressures a quarterback, Pro Football Focus charts which offensive player is responsible for allowing it to happen. The pressure can be charged to any blocker, the quarterback himself, or nobody.

If the pressure is charged to nobody, it is likely an example of an unblocked pressure in which no particular player is to blame. This typically means one of two things: Either there was a miscommunication or the play call did not have a built-in way to account for the rusher. Both things are an indictment on the play-caller.

I calculated the percentage of each team’s dropbacks in which they allowed an unaccounted-for pressure. This gives us an idea of how frequently the offense’s protection breaks down in a way that can be blamed on the play-caller rather than the individual pass-blocking talents of the offensive line.

Expected yardage on screens

The success of a screen play usually comes down to whether the play-caller won the chess match. Did he create a situation where the ball carrier is in a favorable position to succeed?

Yes, the players have plenty of responsibility on screens – the blocking is key and the ball-carrier’s job is to make people miss – but more often than not, the success of a screen play mostly comes down to whether the play-caller called it at the correct time and designed it in the correct way to exploit the defense that was called.

On each completed pass, NFL Next Gen Stats uses player tracking data to calculate the amount of YAC (yards after catch) that the receiver is expected to gain, based on the positioning of all players on the field at the time the ball is caught. In other words, it estimates the amount of YAC that the league-average player would hypothetically gain if they were given the ball in that exact scenario. This is a useful tool for evaluating the play-caller’s ability to design favorable situations for his players when dialing up screens.

Another important factor to consider is the depth that each team throws its screens. Some teams throw their screens further behind the line of scrimmage than others. For instance, the Bills threw their screens closest to the LOS of any team this season (average of 1.8 yards behind) while the Titans threw their screens the furthest behind the LOS (average of 3.8 yards behind). We need to consider this alongside the expected YAC to know the expected net yardage gain of the play.

I calculated the average amount of expected YAC that each team generated per screen reception. I combined that number with the average distance behind the line of scrimmage each team threw its screens. Combined, these two numbers give us the amount of net yardage that each team was expected to gain on its average screen reception.

This gives us an idea of the quality of each team’s screen plays. If the number is high, it means the play-caller is dialing up screens that have a high likelihood of success, and vice versa.

This is a better way to evaluate the play-caller than by looking at the raw results on screen plays, as it isolates the play-caller from the performance of his players. We don’t want to give the play-caller too much credit if his receiver makes an amazing play to outperform what was presented to him. We also don’t want to knock the play-caller too harshly if his receiver misreads the play and fails to take advantage of a well-designed play.

Play-calling predictability (Based on XPASS)

Every armchair fan loves to blast the play-caller for being predictable. But they usually don’t make the critique until after the play is called and fails.

That’s not how it works, unfortunately. If Joe Schmoe from Jersey City can show me his record at predicting LaFleur’s play calls before the ball is snapped, I’d love to see it, but you can’t say a play is predictable after it happens, which is what the MetLife faithful seem to always do.

What we can do is evaluate each play-caller’s predictability by using the pre-snap probabilities of what they are expected to call on each play.

Jets X-Factor’s Rivka Boord has frequently evaluated LaFleur’s predictability using a metric called XPASS, courtesy of nflfastR. I’m going to borrow the data she brought up in another LaFleur article.

Here is Rivka’s explanation of XPASS:

“XPASS provides the probability that any given play will be a pass play based on the game situation and historical play-by-play data. In some game situations, the play selection split between run and pass will be pretty even, resulting in an XPASS close to 50%. However, when XPASS is above 55% or lower than 45%, there is a clear expectation of what a team will do. What the team actually does in those situations gives you a good idea of how predictable the offensive coordinator is.”

Rivka calculated the percentage of plays in which each team called the opposite of the expected call in that situation (including only situations where there is a clear expectation – i.e. an XPASS above 55% or below 45%). In other words, if LaFleur called a pass when the XPASS was under 45% or a run when the XPASS was above 55%, it was an unexpected play call.

Rivka also included a win-probability filter, counting out plays in which the team’s win probability was below 20% or above 80%. This allows us to filter out plays in which the team is either milking the clock or in pass-only mode.

Overall, this metric is a good way to gauge the predictability of a team’s play-calling.

The results

Time to hop into it. How good was Mike LaFleur in these four metrics that isolate the play-caller from his players?

Wide-open passes created

Here is a look at the percentage of each team’s pass attempts that went to a receiver who was at least 10 yards downfield and had at least 3 yards of separation from the nearest defender.

RankTeamWide open passesTotal passes%
1MIA9158415.6
2BUF6457411.1
3KC7165010.9
4CHI4137810.8
5ATL4241510.1
6NO5151210.0
7PHI525369.7
8SF505179.7
9JAX545969.1
10BAL434888.8
11MIN596708.8
12ARI586638.7
13SEA505738.7
14CLE475408.7
15LV515868.7
16NYJ536258.5
17DEN485718.4
18DET485878.2
19LA435298.1
20HOU475798.1
21DAL455568.1
22PIT465708.1
23LAC577118.0
24GB445637.8
25WAS435547.8
26CAR354527.7
27CIN476107.7
28IND446047.3
29TB537517.1
30NE385407.0
31NYG295205.6
32TEN234565.0

LaFleur did a decent job here. New York attempted 53 wide-open downfield passes over 625 total passes, a rate of 8.5% that ranked 16th.

The quarterbacks just didn’t do a good job of executing these throws. New York completed only 63.0% of these passes, which ranked fifth-worst. The league-average completion percentage on these throws was 69.5%.

Mike White capitalized on these easy throws at around an average rate, completing 70.0% of them, but Zach Wilson was at 61.9% while Joe Flacco was even worse at 58.8%.

Still, LaFleur was not as good in this area as some of the other coaches from his tree. His former 49ers colleague, Mike McDaniel, had the Dolphins at No. 1 by an enormous margin, generating these throws 15.6% of the time. Shanahan and the 49ers ranked eighth at 9.7%.

Generating easy passes is supposed to be the bread-and-butter of LaFleur’s west-coast offense, but he was only average at it.

Unaccounted-for pressures allowed

Here is a look at the percentage of each team’s dropbacks in why they allowed an unaccounted-for pressure, based on Pro Football Focus’ charting.

RankTeamUnaccounted PressuresTotal Dropbacks%
1NE36000.50
2SF35580.54
3DAL46010.67
4DET56260.80
5ARZ67490.80
6CIN76821.03
7HST76381.10
8NYG86271.28
9CLV86161.30
10LA86111.31
11GB86061.32
12MIA96391.41
13LAC117771.42
14TEN85311.51
15MIN117291.51
16JAX106481.54
17CAR85191.54
18PIT106391.56
19WAS106291.59
20ATL84861.65
21TB137761.68
22LV116481.70
23CHI95101.76
24KC137251.79
25BUF126651.80
26SEA126521.84
27BLT115621.96
28NYJ146892.03
29PHI136252.08
30NO125632.13
31IND166842.34
32DEN166652.41

This was a major problem for the Jets. They allowed unaccounted-for pressures at the fifth-highest rate of any offense.

LaFleur deserves plenty of blame for the number of unblocked defenders that made their way to the quarterback. He failed to get his offense on the same page in pass protection. While the Jets’ offensive line was decimated by injuries and therefore lacked talent, there were still too many pressures on the quarterback that were simply a result of LaFleur being out-schemed.

Expected yardage on screens

Here is a look at the average amount of yards each team was expected to gain on its screens. This is based on the combination of how far behind the line of scrimmage they threw their screens and the amount of YAC they were expected to gain per reception, per NFL Next Gen Stats’ tracking data.

RankTeamScreen ReceptionsXYdsXYds/REC
1LAR463768.2
2DET372576.9
3SF563696.6
4CHI473076.5
5JAX654126.3
6MIN633776.0
7ATL382265.9
8BUF311845.9
9LAC653805.8
10KC683955.8
11TB613495.7
12BAL281585.6
13WAS482675.6
14PHI603315.5
15CLE331785.4
16CIN402155.4
17MIA311665.4
18DEN472465.2
19ARI713695.2
20SEA341755.1
21TEN271385.1
22GB462355.1
23NYJ422145.1
24NO321625.1
25LV311555.0
26NE723524.9
27NYG231044.5
28HOU632824.5
29CAR341494.4
30IND371484.0
31PIT23893.9
32DAL331003.0

The Jets ranked 13th with 8.4 expected YAC per screen reception, but since they threw their screens very far behind the line of scrimmage – ranking 27th with -3.3 air yards per screen reception – they averaged a net total of just 5.1 expected yards per screen, which placed 23rd. So, while LaFleur wasn’t abominable at cooking up advantageous screen plays, he was still well below average.

But the Jets’ actual output on screens was much worse than that. They gained only 4.3 yards per screen reception, which ranked 31st.

This discrepancy suggests that the Jets’ screens were poorly executed by the players. The Jets actually gained 4.3 yards per screen reception (31st) despite the play calls putting the players in positions where they were expected to average 5.1 yards per screen reception (23rd). New York’s -0.8 margin between these two numbers was the worst of any team in the NFL.

The Jets’ failure to meet expectations on screen plays could be an indictment on the blocking, the ball-carriers’ lack of vision, the ball-carriers’ lack of elusiveness, or a combination of all three. Or, it could be a bogus metric that is telling the wrong story (I have seen some suspect conclusions from these tracking-based expectation metrics).

Nevertheless, even if the players did fall short of the expectations laid for them, it’s not as if those expectations were high. LaFleur did not have the Jets ranking well in their expected screen yardage, so both he and the players can take a huge chunk of the blame for the Jets’ abysmal overall performance on screens.

Play-calling predictability (Based on XPASS)

Here is a look at the percentage of qualified plays in which each team went against the expected play call. (Running with an XPASS over 55% or passing with an XPASS under 45% – only including situations with a win probability between 20% and 80%).

RankTeamUnexpected CallsQualified Plays%
1CHI21249243.1
2DET16145035.8
3CAR15544534.8
4TEN16549433.4
5PHI14545032.2
6ATL14545032.2
7CLE16150332.0
8WAS16051031.4
9NYG16252530.9
10BAL15851730.6
11DAL16655230.1
12BUF17056730.0
13HOU14449828.9
14GB12844328.9
15DEN17160628.2
16LA11642127.6
17NE12947527.2
18KC13750827.0
19NO11543026.7
20PIT14956126.6
21SEA13149526.5
22CIN11443826.0
23NYJ13552125.9
24SF11548024.0
25MIA11849523.8
26JAX12050623.7
27MIN11247323.7
28LV11150422.0
29LAC12859821.4
30ARI10951521.2
31IND10851221.1
32TB10051219.5

Ranking 23rd as he went against the grain just 25.9% of the time, LaFleur leaned toward the predictable side. The league average was 27.9%.

Interestingly, the Jets ranked ahead of some great offenses, including LaFleur’s colleagues in Miami (23.8%) and San Francisco (24.0%). Perhaps predictability is an overrated trait.

Nevertheless, more unpredictability from LaFleur would have been useful considering his team’s lack of talent. You can afford to be predictable when the other team can’t stop you even when they know what’s coming, but when your team is not talented enough to out-execute the opponent, the play-caller needs to help his players out by putting them one step ahead of the defense.

Cumulative rankings

Here is a look at each team’s average ranking between the four categories we analyzed today.

RankTeamAverageWide OpenUnaccounted PressureScreen XYDSUnpredictability
1DET6.518422
2CHI8.042341
3SF9.382324
4ATL9.552076
5CLE11.3149157
6BUF11.8225812
7LAR13.51910916
8KC13.83241018
8MIA13.81121725
8PHI13.8729145
11JAX14.0916526
12BAL14.810271210
12MIN14.81115627
14WAS16.32519138
15ARI16.51251930
15LAC16.52313129
17DAL16.82133211
18HOU17.02072813
19TEN17.83214214
19GB17.824112214
19CIN17.82761622
22NE18.53012617
23NYG18.8318279
23CAR18.82617293
25NO19.86302419
26SEA20.013262021
27DEN20.517321815
28NYJ22.516282323
28LV22.515222528
30PIT22.822183120
31TB23.329211132
32IND30.028313031

LaFleur tied the Raiders’ Josh McDaniels for fourth-worst overall with an average ranking of 22.5 across the four categories. His lack of an elite skill in any of the four categories is what buried him. The Jets were one of three teams who did not rank top-15 in any category, joining the Steelers and Colts.

This study appears to support the claim that LaFleur was indeed a poor offensive coordinator even when isolating him from the players.

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Michael Nania is one of the best analytical New York Jets minds in the world, combining his statistical expertise with game film to add proper context to the data. Nania scrapes every corner, ensuring you know all there is to know about everyone from the QB to the long snapper. Nania's Numbers, Nania's QB Grades, and Nania's All-22 give fans a deeper and more well-rounded dive into the Jets than anyone else can offer. Email: michael.nania[at]jetsxfactor.com - Twitter: @Michael_Nania
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DFargas
DFargas
8 days ago

I got the feeling that LaFleur was mailing it in the last few games of the year after White got hurt. He called a lot of run plays right into the center of the line on first and second downs when the defenses were stacked against the run out of disrespect for the QB (Wilson or an injured White). Then of course he would call passes on third and long that everyone expected. His play calling seemed to me like the equivalent of someone sulking and saying “I throw up my hands.” He was virtually gone from the team before the season ended.

vnick12
vnick12
9 days ago

Replacing LaFleur with Hackett seems pretty “Jetsy” based on these metrics…

Bird9
Bird9
9 days ago

I don’t know about giving equal weight to the predictability metric.

Imagine a coach who was “unpredictable” 100% of the time vs. XPASS. Would anyone say that guy was great, simply due to that stat? He’d probably lose a lot of games. It seems like the success of unpredictable calls should somehow be taken into account, especially if they were set up by a few predictable ones and caught the defense by surprise.

Still an interesting stat and analysis. Just wondering about the weighting.

Jim G
Jim G
7 days ago
Reply to  Bird9

I agree, the overall analysis is very useful but predictability does not differentiate between good and bad unpredictability. A good example was when the Giants in 2021 ran back to back QB sneaks from inside their own 10 yard line. Two really bad calls, but aces for unpredictability. Also, a QB sneak on 4th & 1 is predictable, and probably the right call most of the time.

Rivka Boord
Editor
7 days ago
Reply to  Bird9

Since I’m the one who uses XPASS, I’d prefer to respond to your point. I think that a coach’s reliance on predictability should be inversely proportional with his talent. If you have a Joe Burrow or Patrick Mahomes at QB, you can throw in throwing situations because the likelihood is that you’ll succeed regardless. However, when you have deficiencies in your personnel, it is critical to keep the opponent off balance. Run/pass unpredictability is one strong way to do that.

This applies to the Jets’ defense, as well: one of the biggest knock on the Saleh/Ulbrich defense is that it’s reliant on high-level talent. Unlike some other defenses, that might be able to get fairly good performances out of lesser players, this system relies on top players. Most other defenses include at least some level of disguise and switching, while the Jets’ defense is about as vanilla as they come.