Football

Sports
Football
Investigate NFL teams, players, games, plays, and performance using football data.
Author
Affiliation

Byeong-Hak Choe

SUNY Geneseo

Published

October 6, 2026

Football fans fill the stands around the Buffalo Bills field in Orchard Park, New York.
A Buffalo Bills game at Ralph Wilson Stadium, Orchard Park (2006). Photo: mikefats of flickr / Wikimedia Commons · CC BY 2.0.

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Data

On-page tables preview a sample of rows. Download the files to analyze the complete datasets.

Updated October 6, 2026 from nflverse. Data cover the 2014–2026 seasons and games through October 5, 2026. The 2026 season is partial, through week 5; season totals should not be compared with completed years without accounting for games played.

Each CSV contains the historical seasons and the current season together. Updated source values take precedence over older snapshots for the same player, team, or play. Receiver EPA now includes all qualifying player-seasons, so comparisons across years do not depend on membership in a top-ten subset.

Team EPA, field goals, and player season totals use regular-season data. Weekly passers, receiver EPA, and season CPOE include regular-season and playoff games. Rosters are deduplicated by player and season before joins, so a traded player’s plays are counted once.

The following NFL data frames are available:

  • nfl_team_epa: Team’s mean expected points added (EPA) when the team was on offense and when the team was on defense
    • For the details about EPA, please refer to the Football Metrics section below in the webpage.
  • nfl_field_goals: Play-by-play statistics at situations when field goals were attempted during the game
  • nfl_passers: Weekly EPA and completion percentage over expected (CPOE) among players with at least 44 named passing plays in a week
  • nfl_players_stat: Player statistics
  • nfl_receivers: Total EPA of every player with named targets and a “WR”, “TE”, or “RB” roster position in each season
  • nfl_cpoe: Season-level completion percentage over expected for passers with more than 200 plays with observed CPOE
  • Field-goal variable descriptions (CSV)
Position Full Name Main Role Skills Required
WR Wide Receiver Catch passes and gain yards or score TDs Speed, agility, reliable hands
TE Tight End Block defenders and catch passes Strength, versatility, reliable hands
RB Running Back Run the ball, catch passes, block Speed, vision, agility
QB Quarterback Lead the offense and throw passes Decision-making, accuracy, arm strength, leadership

NFL Team EPA (nfl_team_epa)

  • nfl_team_epa: Team’s mean EPA when the team was on offense and when the team was on defense
nfl_team_epa <- read_csv("https://bcdanl.github.io/data/nfl_team_epa.csv")


season Starting year of the season (2014 if 2014-15 season)

team Team abbreviation

off_epa Offensive EPA

def_epa Defensive EPA

NFL Field Goals (nfl_field_goals)

  • nfl_field_goals: Play-by-play statistics at situations when field goals were attempted during the game
    • A data frame with 12,509 observations on 23 variables.
nfl_field_goals <- read_csv("https://bcdanl.github.io/data/nfl_field_goals.csv")



  • fg_distance accurately reflects the total distance of a field goal attempt:
    • The total addition of 17 yards comes from 10 yards (end zone) + 7 yards (holder’s position).


NFL Passer’s EPA and CPOE (nfl_passers)

  • nfl_passers: Weekly mean EPA and completion percentage over expected among players with at least 44 named passing plays in a week
    • A data frame with 1,250 rows and 22 variables.
nfl_passers <- read_csv("https://bcdanl.github.io/data/nfl_passers.csv")


NFL Season CPOE (nfl_cpoe)

This table summarizes season-level CPOE for passers with more than 200 passes with observed CPOE. It includes playoff games. The threshold means some players in the partial 2026 season do not yet appear.

nfl_cpoe <- read_csv("https://bcdanl.github.io/data/nfl_cpoe.csv")

NFL Player Statistics (nfl_players_stat)

  • nfl_players_stat: 7,799 player-season records and 13 variables. Passing yards are not included in yards. The 2025–2026 rows use nflverse’s current player-statistics release; the older release ends in 2024.
nfl_players_stat <- read_csv("https://bcdanl.github.io/data/nfl_players_stat.csv")


yards rushing_yards + receiving_yards

rushing_yards Yards gained when rushing with the ball (incl. scrambles and kneel downs). Also includes yards gained after obtaining a lateral on a play that started with a rushing attempt.

receiving_yards Yards gained after a pass reception. Includes yards gained after receiving a lateral on a play that started as a pass play.

touches carries + receptions

carries The number of official rush attempts (incl. scrambles and kneel downs). Rushes after a lateral reception don’t count as carry.

receptions The number of pass receptions. Lateral receptions officially don’t count as reception.

tds rushing_tds + receiving_tds

rushing_tds The number of rushing touchdowns (incl. scrambles). Also includes touchdowns after obtaining a lateral on a play that started with a rushing attempt.

receiving_tds The number of touchdowns following a pass reception. Also includes touchdowns after receiving a lateral on a play that started as a pass play.

NFL Receivers (nfl_receivers)

  • nfl_receivers: One row per receiver-season across 2014–2026, identified by receiver_player_id (GSIS ID). receiver and full_name are display names.
nfl_receivers <- read_csv("https://bcdanl.github.io/data/nfl_receivers.csv")


epa_rank: Rank within a season, with the highest tot_epa ranked 1

epa_rank_within_position: Ranking in terms of EPA within the group of the same position

n_received: Number of targeted passing plays naming this player as the receiver, including incompletions

tot_epa: Sum of EPA on targeted plays with observed EPA within a season

n_epa_plays: Number of targeted plays with observed EPA. It can be smaller than n_received.

epa_per_observed_target: Mean EPA on targeted plays with observed EPA. Use this rate and a minimum sample size when comparing an incomplete current season with completed seasons.

source_url, data_snapshot_date: Source and refresh date for the combined table


Football Metrics

Expected Points

Expected Points Added (EPA) is a football analytics metric that measures the value of a play in terms of its impact on the team’s expected scoring. It quantifies how much a single play increases or decreases a team’s chances of scoring compared to the situation before the play.

How EPA Works

Every play in football occurs within a specific context (down, distance, field position, time remaining, and score). Historical data is used to calculate the expected points (EP) a team can expect to score from their current situation. EPA is the difference between the expected points after the play and before the play.

Formula:

EPA = EP (\text{after the play}) - EP (\text{before the play})

Key Insights:

  1. Positive EPA: The play improved the team’s scoring chances.
  • Example: A 20-yard pass on 3rd and 8 increases the likelihood of scoring.
  1. Negative EPA: The play reduced the team’s scoring chances.
    • Example: A sack or an interception harms the team’s scoring potential.

Why EPA Is Important

  • Contextual: Accounts for situational factors, making it more informative than raw stats like yards gained.
  • Play Evaluation: Helps determine the effectiveness of specific plays or players.
  • Strategic Decisions: Assists coaches and analysts in evaluating decisions like when to go for it on 4th down.

Applications

  • Offensive EPA: Evaluates how well a team’s offense increases scoring opportunities.
  • Defensive EPA: Measures how effectively a defense reduces the opposing team’s scoring potential.
  • Player Performance: Used to assess quarterbacks, running backs, wide receivers, and defenders by their contribution to scoring or preventing points.

Completion Percentage Over Expected (CPOE)

Completion Percentage Over Expected (CPOE) is a football analytics metric that evaluates a quarterback’s passing performance by comparing their actual completion percentage to the expected completion percentage based on the difficulty of their pass attempts.

How CPOE Works

  1. Actual Completion Percentage (COMP%):
    • The percentage of passes a quarterback completes.
  2. Expected Completion Percentage (xCOMP%):
    • Calculated based on factors like:
    • Distance of the throw (air yards)
    • Angle of the throw
    • Receiver separation
    • Defensive pressure
    • Game situation (e.g., down, distance, and field position)
    • Derived from historical data on similar passes.
  3. CPOE Formula: CPOE = COMP - xCOMP

Interpretation of CPOE

  • Positive CPOE: Indicates the quarterback is completing more passes than expected, showcasing accuracy and skill.
  • Negative CPOE: Indicates the quarterback is completing fewer passes than expected, potentially highlighting issues with accuracy or decision-making.

Why CPOE Is Useful

  • Isolates Skill: It accounts for the difficulty of throws, focusing on the quarterback’s performance rather than the system or play design.
  • Complementary Metric: Often paired with EPA/play to provide a comprehensive evaluation of a quarterback’s impact.
  • Game Context: Helps differentiate between quarterbacks who excel in challenging situations versus those whose stats are inflated by easy throws.
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