Quiz: Offensive Line Analytics

Target: 70% or higher to proceed.

Section 1: Multiple Choice (1 point each)

1. Why is offensive line analytics particularly challenging?

  • A) Linemen run too fast to track
  • B) No direct statistics and shared responsibility make individual attribution difficult
  • C) The NFL doesn't record line data
  • D) Fans don't care about linemen
Answer **B)** No direct statistics and shared responsibility make individual attribution difficult *Explanation:* O-linemen generate no stats, work as a unit, and are confounded with QB/RB performance.

2. What does "stuff rate" measure?

  • A) Passing efficiency
  • B) Percentage of rushes stopped at or behind the line
  • C) Number of penalties
  • D) Touchdown rate
Answer **B)** Percentage of rushes stopped at or behind the line *Explanation:* Stuff rate = runs with 0 or fewer yards / total rushes.

3. What is Adjusted Line Yards (ALY)?

  • A) Total rushing yards
  • B) Yards per carry adjusted for opponent
  • C) A metric that caps credit for long runs to isolate O-line contribution
  • D) Yards before contact
Answer **C)** A metric that caps credit for long runs to isolate O-line contribution *Explanation:* ALY gives full credit for short runs but reduced credit for long runs (RB contribution).

4. What is a typical "good" sack rate?

  • A) Greater than 10%
  • B) Less than 4%
  • C) Exactly 5%
  • D) Greater than 8%
Answer **B)** Less than 4% *Explanation:* Elite pass protection produces sack rates under 4%.

5. Why is sack rate not purely an O-line metric?

  • A) Only quarterbacks get sacked
  • B) QB time to throw and mobility also affect sacks
  • C) Sacks don't happen often
  • D) Sacks are counted incorrectly
Answer **B)** QB time to throw and mobility also affect sacks *Explanation:* A QB who holds the ball too long or can't escape contributes to sacks.

6. What data source is required for individual O-line grading?

  • A) Standard play-by-play
  • B) Box scores
  • C) Film charting services like PFF
  • D) Team statistics
Answer **C)** Film charting services like PFF *Explanation:* Individual attribution requires play-by-play film review to assign responsibility.

7. In the ALY formula, what happens to negative yardage runs?

  • A) They're ignored
  • B) They receive 125% penalty (worse than actual)
  • C) They receive 50% credit
  • D) They're counted as zero
Answer **B)** They receive 125% penalty (worse than actual) *Explanation:* ALY penalizes stuffs more heavily because they're definitively O-line failures.

8. What is "pressure rate"?

  • A) Percentage of plays with sacks only
  • B) Percentage of plays with sacks, QB hits, or scrambles
  • C) Defensive pressure per game
  • D) Blitz frequency
Answer **B)** Percentage of plays with sacks, QB hits, or scrambles *Explanation:* Pressure rate is a broader measure including all quarterback disruptions.

Section 2: True/False (1 point each)

9. High yards per carry (YPC) definitively indicates good run blocking.

Answer **False** *Explanation:* High YPC can be inflated by long RB runs; ALY better isolates O-line contribution.

10. Zone blocking schemes typically produce higher YPC than gap schemes.

Answer **True** *Explanation:* Zone schemes generally produce higher variance runs with higher average YPC.

11. Individual O-line grades can be reliably calculated from standard play-by-play data.

Answer **False** *Explanation:* Individual grades require film charting to assign responsibility on each play.

12. Yards before contact (YBC) is primarily an O-line metric.

Answer **True** *Explanation:* YBC measures how far the runner gets before facing a defender—reflecting blocking.

Section 3: Code Analysis (2 points each)

13. What does this code calculate?

rushes.groupby('posteam').agg(
    stuff_rate=('yards_gained', lambda x: (x <= 0).mean()),
    aly=('line_yards', 'mean')
)
Answer This calculates two run blocking metrics by team: - **Stuff rate**: Percentage of rushes for 0 or fewer yards - **ALY**: Average Adjusted Line Yards These are both indicators of run blocking quality.

14. What issue exists with this pressure calculation?

pressure_rate = (sacks + qb_hits) / dropbacks
Answer **Issues:** 1. Missing scrambles (QB escaping pressure) 2. May double-count (sack often includes a hit) 3. Doesn't account for unrecorded hurries **Better approach:** 
# Define pressure as mutually exclusive
pressured = sack | qb_hit | scramble
pressure_rate = pressured.mean()

Section 4: Short Answer (2 points each)

15. How does the ALY formula attempt to separate O-line from RB contribution?

Sample Answer ALY limits credit for long runs: - **0-4 yards**: 100% credit (O-line created the hole) - **5-10 yards**: 50% credit (RB contribution starts) - **10+ yards**: 25% credit (RB dominated) - **Negative**: 125% penalty (definite O-line failure) Philosophy: Short gains reflect blocking; long gains reflect running back skill. The penalty for stuffs emphasizes that getting zero yards is a blocking failure.

16. Why might two teams with similar sack rates have very different O-line quality?

Sample Answer Sack rate is confounded by: 1. **QB time to throw**: A quick-release QB has low sack rate regardless of O-line 2. **QB mobility**: Mobile QBs escape pressure 3. **Scheme**: Quick passing games reduce exposure 4. **Target depth**: Deep shots need more time A team with a mobile QB might have terrible protection but low sack rate. A team with a statue QB might have good protection but higher sack rate. Need to control for QB and scheme to truly evaluate O-line.

Section 5: Application (3 points each)

17. Design an analysis to determine if a team's poor rushing is due to their O-line or their running backs.

Sample Answer **Analysis Approach:** 1. **Multiple RB comparison**: Compare all RBs on the team - If all RBs struggle equally = O-line problem - If some succeed, others fail = RB differences 2. **ALY vs YPC gap**: - High ALY, low YPC = RBs not finishing - Low ALY, any YPC = O-line not creating 3. **Stuff rate analysis**: - Very high stuff rate = O-line failures - RBs can't overcome no initial hole 4. **Short yardage success**: - 1-2 yards to go conversion - More O-line dependent 5. **YBC if available**: - Low YBC = blocking issues - High YBC, low total yards = RB issues **Limitation:** Perfect separation impossible without tracking data.

18. A team has the best sack rate in the league but below-average run blocking. What might explain this and what would you investigate?

Sample Answer **Possible explanations:** 1. **Different skill sets**: Pass and run blocking require different techniques - Tackles dominate pass pro - Interior dominates run blocking 2. **QB effect**: A quick-release or mobile QB masks O-line issues 3. **Scheme mismatch**: - Pass-heavy team never practices run blocking - Run scheme doesn't fit personnel 4. **Personnel differences**: - Athletic tackles, weak guards - Different linemen grade differently by task **Investigation:** 1. Look at position-specific metrics if available 2. Check QB time to throw (is low sack rate earned?) 3. Analyze run blocking by direction (interior vs edge) 4. Compare to previous years (personnel changes?) **Key insight:** Pass and run blocking should be evaluated separately—elite at one doesn't mean elite at other.

Section 6: Critical Thinking (2 points)

19. Why is it problematic to use team rushing success to evaluate individual offensive linemen?

Sample Answer **Problems:** 1. **Five-player unit**: Can't assign credit/blame without film 2. **Scheme effects**: Different schemes stress different positions 3. **RB conflation**: Running back skill affects outcomes 4. **Play calling**: Which runs are called affects results 5. **Opponent varies**: Defensive line quality differs **Example:** - A left tackle might dominate every block - But if right guard fails, run still fails - Team metrics blame all five equally **What's needed:** - Play-by-play film review - Individual assignment tracking - Grade each player per play This is why PFF and similar services exist—team metrics cannot evaluate individuals.

20. What are the key limitations of current O-line analytics that future tracking data might solve?

Sample Answer **Current Limitations:** 1. **Individual attribution**: Who blocked whom on each play 2. **Process vs outcome**: Did lineman win block despite bad result? 3. **Scheme knowledge**: Was the block assignment correct? 4. **Quality of block**: Duration, position, control **Tracking data solutions:** 1. **Win rate**: Computer vision detecting who won each matchup 2. **Time in pocket**: Precise measurement of protection duration 3. **Assignment detection**: AI identifying blocking schemes 4. **Pressure probability**: Expected pressure given situation **Emerging:** - ESPN win rate metrics - AWS Next Gen Stats pocket analysis - Computer vision block grading **Remaining challenge:** Even with tracking, scheme knowledge and assignment understanding require human film study.

Scoring

Section Points Your Score
Multiple Choice (1-8) 8 ___
True/False (9-12) 4 ___
Code Analysis (13-14) 4 ___
Short Answer (15-16) 4 ___
Application (17-18) 6 ___
Critical Thinking (19-20) 4 ___
Total 30 ___

Passing Score: 21/30 (70%)