Key Takeaways: Traditional Football Statistics

Quick Reference Card

Counting Statistics vs. Rate Statistics

Essential Formulas

Passing Statistics

Completion % = (Completions / Attempts) × 100
Yards per Attempt = Passing Yards / Attempts
TD % = (Passing TDs / Attempts) × 100
INT % = (Interceptions / Attempts) × 100
TD-to-INT Ratio = Touchdowns / Interceptions

NFL Passer Rating Formula

def passer_rating(comp_pct, ypa, td_pct, int_pct):
    """
    Calculate NFL-style passer rating.

    Parameters are percentages (e.g., 65.0 for 65%)
    except ypa which is raw yards per attempt.
    """
    a = max(0, min(((comp_pct - 30) / 20), 2.375))
    b = max(0, min(((ypa - 3) / 4), 2.375))
    c = max(0, min((td_pct / 5), 2.375))
    d = max(0, min((2.375 - (int_pct / 4)), 2.375))

    return ((a + b + c + d) / 6) * 100

Rushing Statistics

Yards per Carry = Rushing Yards / Carries
Yards per Game = Total Yards / Games Played
Yards After Contact = Total YAC / Carries

Receiving Statistics

Catch Rate = Receptions / Targets × 100
Yards per Reception = Receiving Yards / Receptions
Yards per Target = Receiving Yards / Targets

Statistical Benchmarks (FBS)

Quarterback Benchmarks

Running Back Benchmarks

Receiver Benchmarks

Team Statistics Reference

Offensive Benchmarks

Defensive Benchmarks

Common Calculations in Python

Basic Stats Calculator

class FootballStatsCalculator:
    """Quick reference for common calculations."""

    @staticmethod
    def passing_efficiency(comp, att, yards, tds, ints):
        """Return dictionary of passing efficiency metrics."""
        return {
            'comp_pct': comp / att * 100,
            'ypa': yards / att,
            'td_pct': tds / att * 100,
            'int_pct': ints / att * 100,
            'td_int_ratio': tds / max(ints, 1),
            'net_ypa': (yards - 45 * ints) / att  # Adjusted for INTs
        }

    @staticmethod
    def rushing_efficiency(yards, carries, games):
        """Return dictionary of rushing efficiency metrics."""
        return {
            'ypc': yards / carries,
            'ypg': yards / games,
            'carries_pg': carries / games
        }

    @staticmethod
    def receiving_efficiency(rec, targets, yards, games):
        """Return dictionary of receiving efficiency metrics."""
        return {
            'catch_rate': rec / targets * 100,
            'ypr': yards / rec,
            'ypt': yards / targets,
            'rpg': rec / games,
            'ypg': yards / games
        }

Team Metrics Calculator

def calculate_team_metrics(team_data):
    """Calculate comprehensive team metrics."""

    # Offensive efficiency
    total_yards = team_data['pass_yards'] + team_data['rush_yards']
    total_plays = team_data['pass_att'] + team_data['rush_att']

    metrics = {
        'yards_per_play': total_yards / total_plays,
        'points_per_game': team_data['points'] / team_data['games'],
        'third_down_pct': team_data['third_conv'] / team_data['third_att'] * 100,
        'red_zone_td_pct': team_data['rz_td'] / team_data['rz_att'] * 100,
        'turnover_margin': team_data['takeaways'] - team_data['giveaways']
    }

    return metrics

Analysis Decision Tree

Comparing Players/Teams?
├── Same number of games/opportunities?
│   ├── Yes → Counting stats are valid
│   └── No → Use rate stats or per-game stats
│
├── Different eras (years apart)?
│   ├── Yes → Apply era adjustment
│   └── No → Direct comparison valid
│
├── Different conferences?
│   ├── Yes → Consider strength of schedule
│   └── No → Direct comparison more valid
│
└── Different roles/usage?
    ├── Yes → Focus on efficiency metrics
    └── No → Volume comparisons valid

Key Concepts to Remember

1. Context Matters

• A 300-yard passing game in a blowout loss (garbage time) differs from 300 yards in a close win
• High volume often comes with decreased efficiency
• Opponent quality affects all statistics

2. Rate vs. Volume Trade-offs

• High-volume players may have lower efficiency
• Limited sample sizes make rate stats unreliable
• Per-game stats balance volume with opportunity

3. Composite Metrics

• Single metrics capture one dimension
• Combine multiple metrics for complete picture
• Weight metrics based on analysis goals

4. Era Adjustment

• Passing statistics have inflated over time
• Compare to era averages, not absolute benchmarks
• Modern offenses produce more yards but also more plays

5. Limitations of Traditional Stats

• Don't account for opponent quality
• Miss play-by-play context
• Can't measure what didn't happen (throwaways, etc.)
• Depend on official scorer decisions

Quick Pandas Operations

import pandas as pd

# Calculate multiple rate stats at once
df['comp_pct'] = df['completions'] / df['attempts'] * 100
df['ypa'] = df['yards'] / df['attempts']
df['td_pct'] = df['touchdowns'] / df['attempts'] * 100

# Rank players across multiple categories
for col in ['yards', 'touchdowns', 'comp_pct', 'ypa']:
    df[f'{col}_rank'] = df[col].rank(ascending=False)

# Create composite ranking
rank_cols = [c for c in df.columns if c.endswith('_rank')]
df['composite_rank'] = df[rank_cols].mean(axis=1)
df['final_rank'] = df['composite_rank'].rank()

# Filter by minimum threshold
qualified = df[df['attempts'] >= 100]

# Group by team and aggregate
team_stats = df.groupby('team').agg({
    'yards': 'sum',
    'touchdowns': 'sum',
    'attempts': 'sum'
})
team_stats['team_ypa'] = team_stats['yards'] / team_stats['attempts']

Common Mistakes to Avoid

1. Comparing without context: Always note games played, opportunities, opponent quality
2. Ignoring minimum thresholds: Small samples produce misleading rates
3. Treating all yards equally: A 50-yard TD differs from 50 checkdown yards
4. Overlooking era effects: Statistics from different years need adjustment
5. Single-metric evaluation: Use multiple metrics for complete assessment
6. Confusing correlation with causation: High yards don't cause wins

Chapter Summary

Traditional football statistics provide the foundation for understanding player and team performance. While limited in contextual awareness, they remain essential for:

• Initial player evaluation and comparison
• Historical analysis and record tracking
• Box score analysis and game summaries
• Building blocks for advanced metrics

Master these fundamentals before moving to advanced metrics like EPA and win probability.