Key Takeaways: Receiving Analytics

One-page reference for Chapter 8 concepts

Core Receiving Metrics

Metric Formula Meaning
EPA/Target Total EPA / Targets Efficiency per opportunity
Target Share Player Targets / Team Targets Volume share
Catch Rate Receptions / Targets Catch frequency
ADOT Total Air Yards / Targets Target depth
RACR Receiving Yards / Air Yards Opportunity conversion

Quick EPA Reference

receiver_stats = (
    pbp
    .query("pass_attempt == 1")
    .groupby('receiver_player_name')
    .agg(
        targets=('pass_attempt', 'count'),
        receptions=('complete_pass', 'sum'),
        epa=('epa', 'mean'),
        catch_rate=('complete_pass', 'mean')
    )
    .query("targets >= 50")
)

EPA Interpretation (Receiving)

EPA/Target Interpretation
> 0.40 Elite
0.20 to 0.40 Above average
0.00 to 0.20 Average
-0.20 to 0.00 Below average
< -0.20 Poor

Target Share Thresholds

Target Share Role
> 25% Alpha/WR1
18-25% Strong WR2
12-18% WR3 or TE1
8-12% Rotational
< 8% Depth

Air Yards Decomposition

# Air Yards = yards ball travels in air
# YAC = yards gained after catch
# Total Yards = Air Yards (completed) + YAC

air_stats = completions.groupby('receiver_player_name').agg(
    air_yards=('air_yards', 'sum'),
    yac=('yards_after_catch', 'sum'),
    total_yards=('yards_gained', 'sum')
)

RACR Interpretation

RACR Meaning
> 1.0 Gaining more than targeted (high YAC)
= 1.0 Perfect conversion
< 1.0 Not converting opportunities

Formula: RACR = Receiving Yards / Total Air Yards

Receiver Styles

Style ADOT YAC Description
Field Stretcher High Low Deep threat
YAC Monster Low High After-catch specialist
Possession Medium Medium Move chains
Elite High High Does both

Situational Filters

# Third down
third_down = passes[passes['down'] == 3]

# Red zone
red_zone = passes[passes['yardline_100'] <= 20]

# Deep targets
deep = passes[passes['air_yards'] >= 20]

Separating Receiver from QB

  1. Same-QB comparison: Compare receivers with same passer
  2. Multi-QB analysis: Track receiver across different QBs
  3. QB-adjusted EPA: Measure vs QB's overall baseline
qb_baseline = passes.groupby('passer_player_name')['epa'].mean()
receiver_over_qb = receiver_epa - qb_baseline

Common Pitfalls

Pitfall Reality
Yards = value Efficiency matters more
High catch rate = elite Depends on target difficulty
Low ADOT = bad Could be YAC specialist
Ignore QB quality Must account for passer

Target Quality Indicators

  • ADOT: How deep are targets?
  • Third down targets: Trusted in key situations?
  • Red zone targets: End zone threat?
  • Deep target %: Used as downfield threat?

Evaluation Framework

1. Volume (targets, target share)
2. Efficiency (EPA/target, success rate)
3. Style (ADOT, YAC, RACR)
4. Situations (3rd down, red zone, deep)
5. Context (QB quality, scheme)

What Statistics Miss

  • Route-running precision
  • Release quality
  • Contested catch ability
  • Blocking contribution
  • Play design credit

Preview: Chapter 9

Next: Offensive Line Analytics — evaluating the hardest position group to measure with individual statistics.