Pass rate over expectation (PROE) has become a common stat for evaluating quarterbacks over the last few seasons. Check any data-centric NFL analyst; you’ll see the acronym referenced somewhere in their content or rankings. It's littered throughout most of mine.
But, to be honest, I never quite understood the metric.
First off, I’ve seen the name change a few times. Nothing drastic. But there’s a passing rate over expected, a pass rate over expectation, a pass rate above expectation--we need some consistency.
Also, critically, the “over expected” component has been just a term or part of the calculation. I don’t know how anyone can expect anything in a game so chaotic. But, after doing some digging and building some charts, it’s become a bit clearer. So, let’s see if I can impart some knowledge on the stat and why it’s important for fantasy.
What is Pass Rate Over Expected?
Coincidentally, “Pass Rate Over Expected” means what it sounds like. It measures how much above or below expectations an offense calls a passing play. I’ll cover the expected part in a few sections, but its utility is why it’s become a larger part of our vocabulary.
Think about the other stats typically used to characterize offenses.
- Run/Pass Ratio
- Neutral Passing Rate
- Early-Down Passing Rate
- Red zone Pass Rate
While each can be useful (like YPC can be), they either lack context, have different interpretations, or require data filtering to work with a sample. And no two NFL teams are alike. Look at the number of early-down and neutral plays from this season alone.
Denver (66) called fewer than half of the plays the Chargers (133) ran in neutral situations using win probability. The Chiefs called 164 early-down plays to Tennessee’s 85.
Sampling rates alone will alter our conclusions using the stats mentioned above. But PROE incorporates every sample available.
PROE uses some of the same in-game lenses we use to parse on-field production (e.g., down, yard line, point differential, etc.) to measure passing rates. However, you don’t have to toss out any plays to calculate it. So, it’s the best of both worlds despite needing a bit of math to get us there.
How Do You Calculate Pass Rate Ove Expected? (PROE)
You can calculate Pass Rate Over Expected by subtracting a team’s “expected pass rate” from their “actual pass rate.”
Numerically, the “actual pass rate” answers a yes or no question. It’s a binary value of 1 or 0, meaning, yes, they called a pass (1) or, no, it was a run play (0).
The “expected pass rate” is the probability of a pass play based on multiple factors, from yard line and score differential to who’s the home team. But if probability and statistics aren’t your jam (they weren’t mine either), let’s think about expectations more intuitively.
Consider the extremes that would force even the Bears to call a forward pass play. Let’s say it’s 3rd and 7 with two minutes to go in the first half, and Chicago’s trailing by multiple scores. Given how far they have to go and their point deficit, we would at least expect Justin Fields to pass. Or, flip the scenario around.
Chicago’s leading by two touchdowns, and they have the ball with a minute left. Let Fields hand it off! In both scenarios, the game environment sets our expectations. And after using play-by-play data from nflfastR (and just this year’s data as a proxy), we can see how closely related our expectations are to the model inputs.
Less time, fewer attempts left to get a first down, and playing from behind all increase the expected pass rate for an offense.
Situations that you’d intuitively expect a team to pass more in any way. So, if we know the type of play that was actually run and how often to expect it, we can find PROE.
As I mentioned, PROE indicates how much over (or under) expectation each offense calls a passing play. Positive numbers mean a quarterback throws above expectation, and negative numbers point to a squad coached by a boomer. And with more samples, we can create splits within the data to inform our draft and in-season decision-making.
Types of Pass Rate Over Expected
Season PROE
Season PROE gives us the broadest view of a team’s intention. After averaging across hundreds of plays, much of the chaos of the regular season that holds our attention each week fades away. Short-term injuries or a bad matchup or two all get lost in the wash. And in doing so, we can compare each offense’s approach to play-calling.
But again, this is one season of data. And without added context (e.g., EPA, fantasy points per dropback, etc.), it isn’t easy to draw any other conclusions. At least ones we can readily verify.
Of course, seeing the Kansas City Chiefs (1st), Cincinnati Bengals (2nd), and Buffalo Bills (3rd) at the top makes sense. But the 49ers’ offense (23rd) produced two top-10 players at their respective positions.
Justin Fields (32nd) ended the year as the Fantasy QB6. So, unfortunately, we can’t necessarily use current PROE to predict future success.
Ten seasons of each team’s PROE not being correlated to their quarterback’s points per dropback tells us what we already know.
Passers create value in different ways. Long live the #KonamiCode. However, we can find something useful in PROE trends when factoring in personnel moves. The top-3 teams from last season help highlight my point.
More passing isn’t just a schematic shift but a philosophy shared by the head play-caller and the quarterback. And it’s not like we haven’t seen this elsewhere.
The Los Angeles Rams’ passing attack kicked into high gear after trading for Matthew Stafford despite having Sean McVay already pulling the strings on offense. Kevin O’Connell’s move to Minnesota vaulted Kirk Cousins from a fringe QB1 to his highest yardage season since 2016. So while the quarterbacks execute, understanding coaching tendencies can also help us evaluate an offense from a play-calling standpoint.
Last Four Games PROE
Looking at PROE from a seasonal view can help us draw overall conclusions but has a lesser impact once we get into the current season. And you can track each week as a single data point, but using the same three teams from earlier, see if you can spot a trend.
Everyone has a plan until they get punched in the mouth.
Week-to-week variability makes predicting what a team will do the following week impossible. However, with a set of games, we can start to form opinions on who’s above or below PROE and make comparisons.
An offense’s PROE through the first four games strongly correlates with their full-season mark. We can have a solid idea of a team’s intent roughly a quarter into the season, which jibes with our earlier conclusion about the connection between play-caller and quarterback. As long as those two are in place, our expectations should hold. Consequently, it shouldn’t be a surprise the relationship between a four-game set and seasonal PROE fades across seasons.
Regardless, we don’t have to wait until midseason or once the fantasy playoffs start to know which teams will inherently have more passing volume. The first month can inform our roster and DFS decisions with the subsequent weeks to make adjustments.
Red Zone PROE
Since we know PROE gives a fair measure of intent, at the play level, we can filter to where it matters most. Scoring touchdowns drives fantasy scoring more than any other metric. As a result, understanding a team’s approach when getting into scoring position can help us form our rosters. But it’s not a direct path.
There’s no signal between a quarterback’s points per dropback and their red-zone PROE (r-squared = 0.005). If we couldn’t find a relationship between season-long PROE and points per dropback, refining the data into a smaller sample wouldn’t help. They can rush or hand the ball off to their running back, so there’s no direct benefit. But their pass-catchers gain a slight edge.
More attempts lead to more targets which typically net us fantasy points. And the r-squared values, while comparatively smaller, indicate a bond between a team’s red-zone PROE and a pass-catchers chance to find the paint. So, since PROE influences how an offense operates in the red zone, our approach draft and in-season decisions should revolve around targeting receiving options on a pass-first team.
Situational Pass Rate Over Expected
Trailing
You define a trailing situation in multiple ways. The simplest is using point differential, but the threshold can vary from a touchdown deficit to two scores or more. For our purposes, I’ll use offensive plays run when the team has a win probability of less than or equal to 20.0%.
However, remember that PROE accounts for game situation in the expected pass calculation. So, being below expectation while trailing is like a double negative. The scenario calls for the quarterback to drop back, but a run still occurs. It defies optimal play-calling, but other factors may be involved. For example, look at this year’s bottom-3 teams in PROE while trailing.
- Tennessee Titans: -12.89%
- Atlanta Falcons: -17.6%
- Chicago Bears: - 19.0%
Think about the tragedies that affected each squad. The Titans lost A.J. Brown before the season started, Ryan Tannehill only played 12 games, and rookie Treylon Burks spent time on injured reserve. Chicago ended the season with a different set of starting receivers than what they had at the start. So, teams can shift their approach with personnel changes.
Tied
I consider “tied” and “neutral” to be synonymous. And, like trailing, I’ll use win probability to define it (between 20.0% and 80.0%). However, we can learn much more from this game situation.
Neutral situations provide more insight into team intent. There’s less of an influence by the factors going into the expected pass model that would affect play-calling (e.g., time left, score, etc.). So, like overall PROE, we can understand what a team wants to do before chaos takes over. Using this year’s data as a proxy, the correlation between neutral PROE and overall PROE was 0.932.
Leading
A team is leading when its win probability is greater than or equal to 80.0%. And, as we learned with the expected pass calculation, this is when offenses can slow down. It’s what they’re supposed to do! But finding which teams adhere to (or shy away from) this idea can give us an edge.
The Chiefs are an easy example as drafters continue to target their pass-catchers regardless of ADP. Not just because Patrick Mahomes is throwing them the ball, but having a lead doesn’t decrease passing volume. Nonetheless, understanding what does change a team’s approach can help us shape our rosters.
What is Run-Pass Ratio?
Run-pass ratio is the comparative relationship between a team’s running and passing plays. For instance, if an offense throws on 60.0% of the time, their subsequent rushing rate is 40.0%. It uses all of a squad’s attempts (both through the air and on the ground) but lacks context.
There’s no adjustment based on the game environment. Run-pass ratio assumes all plays are equal in value which we know isn’t true. As a result, if you compare passing percentage to PROE, you’ll see some differences.
They’re not drastic, but season-long conclusions or season-to-season inferences are susceptible to error. And after studying PROE, we can see why more analysts are leaning toward it to evaluate offensive play-calling moving forward.
Why does it matter for fantasy football or sports betting?
An invariable number of predictive factors exist in real and fantasy football. We discussed a few in the expected pass rate model, but home-field advantage and even if the stadium has a dome can play a role.
So, discerning tendencies in offensive playcalling can give us an edge. And PROE points us in that direction. There’s no 1:1 correlation, but high-end fantasy QBs are inclined to sling it.
As I said, you won’t always find a top-6 signal caller banking on volume alone. However, the hit rates favor aerial attacks. Subsequently, our focus should skew toward high-flying offenses while accounting for personnel, completion percentage, and efficiency as tiebreakers.
Who has the highest passing rate over expected in the NFL?
Kansas City was first in every type of PROE this season. Seasonal, while leading, trailing, or tied—they were the most above expectation.
However, Patrick Mahomes was third in total pass attempts on the season. He had 85 fewer attempts than the league leader (Tom Brady), highlighting the need for a play-caller and quarterback to share the same philosophy regarding when (and how often) to throw the ball.
Who has the lowest passing rate in the NFL?
The Bears were 32nd in every PROE type except for neutral scenarios (31st). You couldn’t find a more at-odds quarterback-coach combination.
Head coach Matt Eberflus and Offensive Coordinate Luke Getsy were first-timers. Eberflus has spent his time on the defensive side of the ball, and Getsy spent his time with the Packers with a stylistically different quarterback. Plus, Chicago would thrust Equanimeous St. Brown and Byron Pringle into starting positions after both held minor rotational roles for their previous teams. Their free agency and draft moves will give us more information on how to approach the team next season.
