Breaking Down the Bears' 2025 Passing Game by Route

The Bears’ 2025 passing game was a study in contrasts. Caleb Williams showed real growth in his second season, but the route-level data reveals a passing attack that leaned heavily on a few concepts while struggling with others. Let’s break it down.

Pulling the data

All of this analysis uses nflverse play-by-play data. Loading it and filtering down to Bears pass plays is straightforward:

library(tidyverse)
library(nflreadr)

pbp <- load_pbp(2025)

bears_passes <- pbp %>%
  filter(posteam == "CHI", play_type == "pass", !is.na(route))

From there, we can compute EPA per play by route type and compare to league average:

league_avg <- pbp %>%
  filter(play_type == "pass", !is.na(route)) %>%
  group_by(route) %>%
  summarise(lg_epa = mean(epa, na.rm = TRUE))

caleb_routes <- bears_passes %>%
  filter(passer_player_name == "C.Williams") %>%
  group_by(route) %>%
  summarise(
    attempts = n(),
    epa_play = mean(epa, na.rm = TRUE)
  ) %>%
  left_join(league_avg, by = "route") %>%
  mutate(epa_vs_avg = round(epa_play - lg_epa, 2))

Caleb’s EPA by route type

This chart shows Williams’ EPA per play versus league average across all route types on 628 attempts. Green means above average by more than 0.05, orange means roughly average, red means below.

The standout numbers:

• Go routes: +0.63 EPA vs average. This is elite. On only 26 attempts, Caleb was one of the most efficient deep ball throwers in the league. The arm talent shows up here.
• Angle (+0.51) and Slant (+0.16) also popped. He’s comfortable with routes that give him a clear, quick read in the middle of the field.
• Hitch/Curl (+0.15) on 100 attempts is solid. High-volume, above-average. That’s your bread and butter.
• Out routes (+0.04) are his most-called route at 177 attempts but barely above average. Lots of volume, minimal edge.

Where it falls apart:

• Post (-0.3) is the biggest red flag. Posts require anticipation throws into tight windows over the middle. On 54 attempts, Williams was significantly worse than league average.
• Corner (-0.25) is a similar story. These are the intermediate-to-deep concepts that separate good from great.
• In routes (-0.21) and Screens (-0.13) round out the negatives. The screen number is particularly interesting since that’s supposed to be a “free” play.

The pattern: Caleb thrives at the extremes. Quick game (hitch, slant) and vertical shots (go, angle) are his strengths. The intermediate layer, the 12-to-18-yard concepts that require timing and anticipation, is where the offense leaks EPA.

How the receivers were deployed

We can also break down each receiver’s route distribution and catch rates:

wr_routes <- bears_passes %>%
  filter(receiver_player_name %in%
    c("R.Odunze", "D.Moore", "L.Burden", "C.Loveland")) %>%
  group_by(receiver_player_name, route) %>%
  summarise(
    targets = n(),
    catches = sum(complete_pass, na.rm = TRUE),
    catch_rate = round(catches / targets * 100, 1),
    .groups = "drop"
  ) %>%
  group_by(receiver_player_name) %>%
  mutate(target_share = round(targets / sum(targets) * 100, 1)) %>%
  arrange(receiver_player_name, desc(target_share))

The route tree charts for each receiver tell us how the coaching staff schemed around Williams’ strengths and limitations.

Rome Odunze (101 targets)

Odunze’s route tree is concerning. Nearly a third of his targets (30.7%) came on out routes, with almost all of his catch rates in red or orange. His only green route is hitch/curl at 10.9% of targets. He was asked to win on posts (9.9%), corners (7.9%), and go routes (6.9%), all with sub-50% catch rates.

The Bears’ pass catchers were charged with 29 drops on the season, fifth-most in the NFL. Odunze contributed to that number. He’s a second-year receiver running a ton of contested routes and not converting enough of them. Some of that falls on Caleb (posts and corners are his worst routes by EPA), and some of it is on Odunze’s hands in traffic. The out-route volume is particularly rough: 30.7% of targets on a route where the catch rate is below 50%. Ben Johnson has already said improving the drop rate is a “point of emphasis” this offseason.

DJ Moore (100 targets)

Moore’s distribution is the most balanced of anyone on the roster. Out (15%), post (15%), hitch/curl (14%), screen (14%), in (12%). He’s being used as a full-tree receiver, which makes sense given his experience and route-running ability.

But balance doesn’t mean production. Moore’s overall PFF grade dropped to 73.5, a steep fall from the 89.0 he earned in his first year with the Bears. His receiving grade (72.8) tells the same story. The green catch rates on slants and crossers are real, but those are low-volume routes. On the concepts that make up the bulk of his work, he’s middling. His post catch rate is red, which tracks with Caleb’s EPA on posts, so that’s not entirely a Moore problem. Still, Moore went from being the clear WR1 to being a trade candidate this offseason, and the route tree data explains part of why: he’s running everything, excelling at nothing.

Luther Burden (74 targets)

Don’t let the route tree fool you. Burden’s target distribution looks limited, with hitch/curl (21.6%) and screen (21.6%) accounting for nearly half his work. But what he did with those targets was historic. His 2.23 yards per route run ranked in the top seven rookie WR seasons since PFF started tracking in 2011. He led the team in YAC per reception (7.0) and forced missed tackles. This wasn’t a guy running screens and falling down. He was creating after the catch at an elite level.

The route tree is narrow by design, not by limitation. The coaching staff funneled targets to Burden on concepts where Caleb is most comfortable (quick game, screens) and Burden turned that volume into one of the best rookie receiving seasons in recent memory. The post usage (10.8%) at an orange catch rate hints at room to grow. If the Bears expand his route tree in year two, the production ceiling gets scary.

Colston Loveland (TE)

Loveland was the best rookie in football. That’s not hyperbole. PFF graded him at 85.3 overall, the highest mark for any rookie regardless of position. His 86.2 receiving grade was second among all tight ends in the league. He played 818 snaps and led the team in WAR. As a rookie.

His route tree shows why. Loveland operates in the intermediate space where Caleb has historically struggled, but the catch rates are green where it counts. Crossers, screens, intermediate digs: the routes that require a reliable target over the middle. That’s exactly what he is. The Bears’ post and in-route EPA problems look a lot less scary when you realize Loveland is the answer, not the WRs. Funnel those intermediate targets to a tight end with elite hands and a 85+ PFF grade instead of forcing them to receivers running contested routes downfield. The coaching staff clearly figured this out as the season went on.

The bigger picture

When you layer these charts together, a clear picture emerges:

1. The Bears’ passing game is built on quick concepts and vertical shots. The intermediate layer is the weakness, and the route distribution reflects that. Receivers are being schemed into routes where Caleb is comfortable.

2. Odunze’s usage doesn’t match his production. He’s running the hardest routes at the highest volume with the worst catch rates. Either the coaching staff adjusts his route tree to play to his strengths, or they need Caleb to improve his anticipation on posts and corners.

3. Moore’s role is shrinking. He ran the most diverse route tree, but his production declined across the board. A 73.5 PFF grade and growing trade buzz suggest the Bears may be ready to move on.

4. Burden was a top-7 rookie by YPRR, ever. His route tree was narrow by design, and he made it work. Expanding his usage in year two is one of the highest-upside moves the offense can make.

5. Loveland is the most important piece of this passing game. The highest-graded rookie in the NFL fills the intermediate gap that Caleb’s EPA data says is the biggest problem. That’s not a coincidence.

6. Caleb’s development arc is clear. He needs to improve on post and corner routes. That’s the intermediate timing layer that makes or breaks a passing offense. The arm talent is obviously there (go routes at +0.63 EPA). The processing on middle-of-field concepts needs to catch up.

The 2025 data gives you a roadmap for what this offense should look like in 2026. More hitch/curl and slant volume where Caleb is efficient. Fewer forced out routes to Odunze. And actual offseason work on the intermediate game. The ceiling is there. The floor just needs to come up.

Data from the 2025 NFL season via nflverse.