README.md

metadata

dataset_info:
  features:
    - name: game_id
      dtype: string
    - name: receiver_player_id
      dtype: string
    - name: receiver_player_name
      dtype: string
    - name: passer_player_id
      dtype: string
    - name: defteam
      dtype: int64
    - name: targets
      dtype: float64
    - name: receptions
      dtype: float64
    - name: receiving_yards
      dtype: float64
    - name: air_yards
      dtype: float64
    - name: yac
      dtype: float64
    - name: tds
      dtype: float64
    - name: epa
      dtype: float64
    - name: wpa
      dtype: float64
    - name: avg_depth
      dtype: float64
    - name: catch_rate
      dtype: float64
    - name: posteam
      dtype: int64
    - name: team_pass_attempts
      dtype: float64
    - name: team_air_yards
      dtype: float64
    - name: team_epa
      dtype: float64
    - name: air_yard_share
      dtype: float64
    - name: target_share
      dtype: float64
    - name: yards_per_target
      dtype: float64
    - name: def_targets_dev
      dtype: float64
    - name: def_receptions_dev
      dtype: float64
    - name: def_yards_dev
      dtype: float64
    - name: def_tds_dev
      dtype: float64
    - name: def_epa_dev
      dtype: float64
    - name: red_zone_targets
      dtype: int64
    - name: end_zone_targets
      dtype: int64
    - name: third_down_targets
      dtype: int64
    - name: fourth_down_targets
      dtype: int64
    - name: high_leverage_targets
      dtype: int64
    - name: qb_completions
      dtype: float64
    - name: qb_attempts
      dtype: float64
    - name: qb_air_yards
      dtype: float64
    - name: qb_cpoe
      dtype: float64
    - name: qb_comp_pct
      dtype: float64
    - name: avg_score_diff
      dtype: float64
    - name: avg_quarter
      dtype: float64
    - name: adot
      dtype: float64
    - name: yac_per_reception
      dtype: float64
    - name: td_rate
      dtype: float64
    - name: explosive_plays
      dtype: float64
    - name: first_downs
      dtype: float64
    - name: trailing_pct
      dtype: float64
    - name: leading_pct
      dtype: float64
    - name: wp_var
      dtype: float64
    - name: target_share_std
      dtype: float64
    - name: surface
      dtype: int64
    - name: is_dome
      dtype: int64
    - name: is_rain
      dtype: int64
    - name: is_snow
      dtype: int64
    - name: is_clear
      dtype: int64
    - name: temp_f
      dtype: float64
    - name: humidity_pct
      dtype: float64
    - name: wind_mph
      dtype: float64
    - name: success_rate
      dtype: float64
    - name: big_play_rate
      dtype: float64
    - name: reception_std
      dtype: float64
    - name: second_and_long_targets
      dtype: float64
    - name: third_and_medium_targets
      dtype: float64
    - name: avg_start_yardline
      dtype: float64
    - name: avg_target_depth_vs_qb
      dtype: float64
    - name: yards_Q1
      dtype: float64
    - name: yards_Q2
      dtype: float64
    - name: yards_Q3
      dtype: float64
    - name: yards_Q4
      dtype: float64
    - name: receptions_Q1
      dtype: float64
    - name: receptions_Q2
      dtype: float64
    - name: receptions_Q3
      dtype: float64
    - name: receptions_Q4
      dtype: float64
    - name: targets_Q1
      dtype: float64
    - name: targets_Q2
      dtype: float64
    - name: targets_Q3
      dtype: float64
    - name: targets_Q4
      dtype: float64
    - name: lost_yards_due_to_penalty
      dtype: float64
    - name: yards_wp_<25
      dtype: float64
    - name: yards_wp_25_45
      dtype: float64
    - name: yards_wp_45_55
      dtype: float64
    - name: yards_wp_55_75
      dtype: float64
    - name: yards_wp_>75
      dtype: float64
    - name: yards_wp_NA
      dtype: int64
    - name: receptions_wp_<25
      dtype: float64
    - name: receptions_wp_25_45
      dtype: float64
    - name: receptions_wp_45_55
      dtype: float64
    - name: receptions_wp_55_75
      dtype: float64
    - name: receptions_wp_>75
      dtype: float64
    - name: receptions_wp_NA
      dtype: int64
    - name: targets_wp_<25
      dtype: int64
    - name: targets_wp_25_45
      dtype: int64
    - name: targets_wp_45_55
      dtype: int64
    - name: targets_wp_55_75
      dtype: int64
    - name: targets_wp_>75
      dtype: int64
    - name: targets_wp_NA
      dtype: int64
    - name: home_team
      dtype: int64
    - name: away_team
      dtype: int64
    - name: pregame_spread
      dtype: float64
    - name: pregame_total
      dtype: float64
  splits:
    - name: full
      num_bytes: 3743489
      num_examples: 4616
  download_size: 690936
  dataset_size: 3743489
configs:
  - config_name: default
    data_files:
      - split: full
        path: data/full-*
license: mit
language:
  - en

NFL Wide Receiver Performance Dataset (2021)

Dataset Description

This dataset contains comprehensive wide receiver performance statistics derived from NFL play-by-play data for the 2021. It includes game-level metrics, situational targeting patterns, defensive adjustments, and advanced efficiency calculations.

Dataset Summary

• Season: 2021
• Records: ~4.31k player-game observations
• Features: 100+ columns including:
  • Core statistics (targets, receptions, yards, touchdowns)
  • Quarter-by-quarter breakdowns
  • Win probability bucketed performance
  • Defensive strength adjustments
  • Situational metrics (red zone, high leverage, down-and-distance)
  • Team share metrics (target share, air yards share, WOPR)
  • Efficiency metrics (aDOT, yards per target, catch rate)
  • Weather and venue conditions

Supported Tasks

• Receiving Yards Prediction: Predict receiving yards for upcoming games
• Target Share Analysis: Model player opportunity distribution
• Performance Forecasting: Project future player performance
• Matchup Analysis: Evaluate player-defense matchups

Dataset Structure

Data Fields

Key Identifiers:

• game_id: Unique game identifier
• receiver_player_id: NFL GSIS player ID
• receiver_player_name: Player display name
• passer_player_id: Quarterback player ID
• season: NFL season year
• week: Week number

Core Statistics:

• targets: Total pass attempts targeting the receiver
• receptions: Completed receptions
• receiving_yards: Total receiving yards
• tds: Receiving touchdowns
• air_yards: Total air yards on targets
• yac: Yards after catch

Quarter Breakdowns:

• yards_Q1, yards_Q2, yards_Q3, yards_Q4: Yards by quarter
• receptions_Q1-4: Receptions by quarter
• targets_Q1-4: Targets by quarter

Win Probability Buckets:

• yards_wp_<25, yards_wp_25_45, etc.: Performance in different game situations
• Similar breakdowns for receptions and targets

Share Metrics:

• target_share: Player's share of team targets
• air_yards_share: Player's share of team air yards
• yard_share: Player's share of team receiving yards
• reception_share: Player's share of team receptions
• wopr: Weighted Opportunity Rating (0.7 × target_share + 0.3 × air_yards_share)

Efficiency Metrics:

• aDOT: Average depth of target
• yards_per_target: Receiving yards per target
• catch_rate: Reception percentage
• yac_per_rec: Yards after catch per reception
• explosive_rec_pct: Percentage of receptions ≥15 yards
• first_down_pct: Percentage of receptions resulting in first downs

Defensive Adjustments:

• def_targets_dev: Defense targets allowed vs league average
• def_receptions_dev: Defense receptions allowed vs league average
• def_yards_dev: Defense yards allowed vs league average
• def_tds_dev: Defense TDs allowed vs league average
• def_epa_dev: Defense EPA allowed vs league average
• adj_epa: Defense-adjusted Expected Points Added
• adj_epa_per_target: Defense-adjusted EPA per target

Situational Metrics:

• red_zone_targets: Targets inside the 20-yard line
• end_zone_targets: Targets in the end zone
• third_down_targets: Targets on 3rd down
• fourth_down_targets: Targets on 4th down
• high_leverage_targets: Targets in high-leverage situations (WP < 0.25 or > 0.75)
• red_zone_share: Player's share of team red zone targets
• third_down_share: Player's share of team 3rd down targets

Game Context:

• posteam: Player's team (encoded 1-32)
• defteam: Opposing defense (encoded 1-32)
• home_team: Home team (encoded 1-32)
• away_team: Away team (encoded 1-32)
• home_flag: 0 if home, 1 if away
• pregame_spread: Betting line point spread
• pregame_total: Betting line total points
• avg_score_diff: Average score differential when targeted
• avg_quarter: Average quarter when targeted
• trailing_pct: Percentage of targets while trailing
• leading_pct: Percentage of targets while leading

Weather & Venue:

• surface: Playing surface type (encoded 0-6)
• is_dome: 1 if indoor, 0 if outdoor
• is_rain: 1 if rainy conditions
• is_snow: 1 if snowy conditions
• is_clear: 1 if clear conditions
• temp_f: Temperature in Fahrenheit
• humidity_pct: Humidity percentage
• wind_mph: Wind speed in miles per hour

QB Context:

• qb_completions: Quarterback's completions that game
• qb_attempts: Quarterback's attempts that game
• qb_comp_pct: Quarterback's completion percentage
• qb_air_yards: Quarterback's average air yards
• qb_cpoe: Quarterback's completion percentage over expected

Advanced Metrics:

• epa: Expected Points Added
• wpa: Win Probability Added
• success_rate: Percentage of successful plays (EPA > 0 or YPT > 0.5)
• big_play_rate: Percentage of plays ≥20 yards
• explosive_plays: Count of plays ≥20 yards
• first_downs: First downs generated
• consistency_score: mean_adj_epa / std_adj_epa
• inverse_volatility: 1 / std_adj_epa
• season_adj_epa_per_target: Season-level defense-adjusted EPA per target
• wp_var: Variance in win probability across targets
• target_share_std: Standard deviation of target share across games

Data Splits

This dataset does not include pre-defined splits. Users should create their own train/validation/test splits based on their use case:

• Time-based split: Use early weeks for training, later weeks for validation/testing
• Cross-validation: K-fold cross-validation across games
• Season holdout: Train on this season, test on future seasons

Dataset Creation

Source Data

Raw play-by-play data sourced from nflverse, which aggregates official NFL data with additional features.

Data Processing

The dataset was created through two complementary processing pipelines:

1. Pipeline A (Defensive Adjustments):

   • Calculates defense-adjusted performance metrics
   • Adds situational targeting patterns
   • Includes QB context and team-level statistics
   • Incorporates weather and venue conditions

2. Pipeline B (Temporal & Situational):

   • Generates quarter-by-quarter breakdowns
   • Creates win probability bucketed statistics
   • Computes team share metrics and WOPR
   • Calculates season-level consistency metrics

Both pipelines were merged to create a comprehensive feature set.

Considerations for Using the Data

Social Impact

This dataset is intended for:

• Sports analytics and research
• Fantasy football decision-making
• Educational purposes in machine learning and sports statistics

Not intended for:

• Real-money gambling (use responsibly)
• Player evaluation for contract negotiations
• Any decision-making that could impact player careers

Discussion of Biases

• Opportunity bias: Statistics heavily dependent on team offensive scheme and QB quality
• Injury data: Dataset does not account for injuries that may affect performance
• Sample size: Players with limited playing time have less reliable statistics
• Game script: Performance metrics influenced by whether team is winning/losing
• Strength of schedule: Not all defensive matchups are equal, though some adjustment is included

Limitations

• Historical data only: Does not predict future performance definitively
• Missing context: Does not include play design, route running, or other qualitative factors
• Weather parsing: Temperature/wind/humidity may be missing or inaccurate for some games
• Roster changes: Does not account for mid-season team changes or trades
• Playoff games: May or may not include playoff data depending on the year

Additional Information

Acknowledgments

• Data Source: nflverse/nflverse-data
• AI Assistance: Code development assisted by Claude (Anthropic)
• Course: CMU 24-679: Designing and Deploying AI/ML Systems