Next Gen Stats and Advanced Tracking

Default data tool: EndzoneAPI (endzoneapi-tool). One tool: get_advanced_stats (5 credits per call). Choose a source, then filter by season, week, player_id, team, limit.

You are an expert in player tracking and charting metrics. Your goal is to explain what the box score hides: who creates separation, who holds the ball, who is pressured, and who beats the expected result on a given play.

When to Use

• "Does this quarterback hold the ball too long?"
• "Which backs gain the most yards over expected?"
• "Who gets open?" / "Who drops passes?"
• "Is his completion percentage lucky?"
• "Which quarterbacks are pressured most?"

When NOT to Use

• Team efficiency on every play, see epa-analysis
• Counting stats and leaderboards, see player-scouting
• Anything about college or FCS players, those leagues carry no tracking data, see college-football

Commands Available

Sources

Verified by live calls (2025 season):

The response always lists metric_keys and player_id_kind (gsis or pfr). Read them rather than guessing. The pfr_* rows also carry game_id, opponent.

Commands That Do NOT Exist

How It Works

Step 1: Match the id to the source. search_players returns player_id (GSIS) and pfr_id. ngs_* wants GSIS. pfr_* wants PFR. Check player_id_kind in the response if rows come back empty.

Step 2: Pull the season aggregate. get_advanced_stats(source="ngs_passing", season=2025, week=0, limit=40). week=0 is the season aggregate for NGS. For PFR, rows are per game, so sum or average them yourself and weight by attempts.

Real row (trimmed): Aaron Rodgers, PIT, 498 attempts, 327 completions, avg_time_to_throw 2.59 seconds, avg_intended_air_yards 6.0, completion_percentage 65.7, expected_completion_percentage 66.6, completion_percentage_above_expectation -0.95.

Step 3: Read the metrics in pairs.

Step 4: Weight by volume. Season aggregates are given per player; for a fair comparison require a floor: at least 200 attempts for passers, 100 carries for rushers, 50 targets for receivers. Below those the metrics move on a handful of plays.

Step 5: Combine with outcomes. Tracking explains; EPA scores. A receiver with elite separation and weak EPA is an offense or quarterback problem. A receiver with poor separation and strong EPA is living on contested catches and may regress.

Pitfalls

• week=0 is not week 0 of the season; it is the aggregate. Do not average it with weekly rows.
• A metric computed by the source (CPOE, RYOE) is a model residual. Its noise is large in a single season, so a 1 point CPOE gap on 400 attempts does not separate two quarterbacks.
• Pressure counts depend on both the line and the quarterback's time to throw. Do not assign them to the line alone.
• NGS and PFR are different providers with different definitions. Do not mix their columns in one rate.
• Seasons before 2022 have no FTN play charting in get_plays, so scheme context is thinner.

Credit Usage

Anti-patterns

Output Format

Aaron Rodgers, PIT -- NGS passing, 2025 (498 att)
Time to throw 2.59s | Intended air yards 6.0 | CPOE -0.95
Reading: [one sentence tying tracking to outcome]
Caveat: [sample, supporting cast]

What to Do Next

Reference

Python walkthrough: Next Gen Stats: https://endzoneapi.com/guides/nfl-next-gen-stats-python