Quiz: Introduction to College Football Analytics

Test your understanding before moving to the next chapter. Target: 70% or higher to proceed. Time: ~30 minutes

Section 1: Multiple Choice (1 point each)

1. Which of the following BEST describes the relationship between statistics and analytics?

  • A) Statistics and analytics are the same thing
  • B) Analytics uses statistics but adds context, prediction, and decision support
  • C) Statistics is more advanced than analytics
  • D) Analytics ignores traditional statistics entirely
Answer **B)** Analytics uses statistics but adds context, prediction, and decision support *Explanation:* Statistics describes what happened (how many yards, what completion percentage). Analytics adds layers of context (was it good given the situation?), builds expectation models, enables prediction, and connects findings to decisions. See Section 1.1.2.

2. Expected Points Added (EPA) measures:

  • A) The total points scored in a game
  • B) The change in expected scoring potential resulting from a play
  • C) A quarterback's passer rating
  • D) The number of explosive plays a team creates
Answer **B)** The change in expected scoring potential resulting from a play *Explanation:* EPA calculates how much a play changes the offense's expected points. A play that moves from a 2 EP situation to a 4 EP situation adds 2 EPA. This provides a common currency for comparing different types of plays. See Section 1.2.3.

3. The "Moneyball" revolution in sports analytics primarily demonstrated that:

  • A) Technology is more important than human scouts
  • B) Market inefficiencies can be exploited by teams using data systematically
  • C) Statistics should replace all traditional evaluation methods
  • D) Only baseball can benefit from analytics
Answer **B)** Market inefficiencies can be exploited by teams using data systematically *Explanation:* The Moneyball approach showed that systematic application of data analysis could find undervalued players that traditional scouting missed. It wasn't about replacing scouts entirely but about finding edges where traditional methods fell short. See Section 1.2.2.

4. A college football analyst recommends going for it on 4th and 2 from the team's own 40-yard line. A coach responds, "But if we fail, we give them great field position!" What concept should the analyst explain?

  • A) Win probability considers both success and failure outcomes
  • B) Punting is always wrong
  • C) Field position doesn't matter
  • D) Expected points only counts touchdowns
Answer **A)** Win probability considers both success and failure outcomes *Explanation:* Good analytics accounts for all possible outcomes weighted by their probabilities. The expected value of going for it includes both the probability of conversion (and resulting benefit) and the probability of failure (and resulting cost). If the expected value of going for it exceeds that of punting, analytics recommends going for it despite the risk. See Section 1.3.2.

5. Which of the following is NOT part of the analytics workflow described in Chapter 1?

  • A) Question formulation
  • B) Data collection
  • C) Gut feeling validation
  • D) Communication
Answer **C)** Gut feeling validation *Explanation:* The five stages of the analytics workflow are: Question formulation, Data collection, Data processing, Analysis, and Communication. While analytics may sometimes confirm (or contradict) intuition, "gut feeling validation" is not a formal stage of the process. See Section 1.4.

6. What does Win Probability (WP) measure?

  • A) The historical win percentage of a team
  • B) The estimated chance of winning from a given game state
  • C) How probable it is that a team will win the recruiting battle
  • D) The probability of completing a pass
Answer **B)** The estimated chance of winning from a given game state *Explanation:* Win Probability models estimate the likelihood of winning given the current score, time remaining, field position, down and distance, and other game state variables. These models are trained on historical game data. See Section 1.2.3.

7. Which statement about traditional scouting and analytics is MOST accurate?

  • A) Analytics should completely replace traditional scouting
  • B) Traditional scouting evaluates aspects that are difficult to quantify
  • C) Scouts never use any form of data
  • D) Analytics can evaluate leadership and competitiveness better than scouts
Answer **B)** Traditional scouting evaluates aspects that are difficult to quantify *Explanation:* Traditional scouting excels at evaluating qualities like leadership, competitiveness, football IQ, and potential that haven't yet shown up in statistics. Analytics complements traditional scouting by providing objective baselines and processing information at scale. See Section 1.1.3.

8. A play results in a 7-yard gain on 3rd and 10. From an EPA perspective, this play likely:

  • A) Added positive EPA because 7 yards is a good gain
  • B) Had near-zero or negative EPA because it didn't achieve the first down
  • C) Cannot be evaluated without knowing the final score
  • D) Always has positive EPA because it gained yardage
Answer **B)** Had near-zero or negative EPA because it didn't achieve the first down *Explanation:* EPA evaluates plays in context. A 7-yard gain on 3rd and 10 fails to achieve the first down, forcing a 4th down decision (likely a punt). Moving from 3rd-and-10 to 4th-and-3 doesn't improve expected points much—the team still likely loses possession. The same 7-yard gain on 3rd-and-6 would have positive EPA. See Section 1.1.2.

9. Which of the following is an appropriate ethical consideration in sports analytics?

  • A) Sharing proprietary team data publicly to advance the field
  • B) Presenting uncertain findings as definitive conclusions
  • C) Acknowledging limitations and uncertainty in analysis
  • D) Ignoring player privacy when interesting insights emerge
Answer **C)** Acknowledging limitations and uncertainty in analysis *Explanation:* Responsible analytics includes acknowledging uncertainty, protecting privacy, not sharing proprietary data without authorization, and presenting findings fairly. Overstating confidence or certainty is ethically problematic. See Section 1.5.

10. Before the analytics revolution, fourth-down decision-making in football was characterized by:

  • A) Teams going for it too often
  • B) Teams using sophisticated expected value calculations
  • C) Teams punting too often relative to optimal strategy
  • D) Teams perfectly optimizing based on game theory
Answer **C)** Teams punting too often relative to optimal strategy *Explanation:* Analytics research showed that traditional coaching wisdom led to excessive punting. Going for it on fourth down succeeds frequently enough to justify attempts in many situations where teams traditionally punted. The analytics revolution has led to more fourth-down attempts in recent years. See Section 1.3.2.

Section 2: True/False (1 point each)

For each statement, indicate whether it is True or False.

11. Analytics can quantify everything important about football performance.

Answer **False** *Explanation:* Many important aspects of football—leadership, mental toughness, locker room presence, potential development—are difficult or impossible to quantify. Analytics captures measurable aspects of performance but cannot replace all human judgment. See Section 1.5.3.

12. Expected Points models are built by analyzing historical drives to determine average scoring from each field position.

Answer **True** *Explanation:* Expected Points models analyze thousands of historical possessions to estimate the average points scored (or allowed) from each field position and game state. This creates a baseline for evaluating individual plays. See Section 1.2.3.

13. The five stages of the analytics workflow must always be completed in strict sequential order with no iteration.

Answer **False** *Explanation:* While the workflow has a logical progression, real analysis often involves iteration. Analysis might reveal data quality issues requiring more processing. Communication feedback might prompt additional analysis. The workflow is a guide, not a rigid requirement. See Section 1.4.

14. Win Probability Added (WPA) can be used to identify clutch performances.

Answer **True** *Explanation:* WPA measures how much each play changed the probability of winning. Plays that swing win probability significantly are "clutch" plays. Players who accumulate high WPA make plays that matter most to game outcomes. See Section 1.2.3.

15. Analytics departments at college football programs only work on in-game decision support.

Answer **False** *Explanation:* College football analytics departments work on opponent analysis, self-evaluation, recruiting, player development, game planning, and more—not just in-game decisions. Analytics touches nearly every aspect of program operations. See Section 1.3.

Section 3: Fill in the Blank (1 point each)

16. The change in expected points resulting from a play is called ____.

Answer **Expected Points Added (EPA)** EPA = EP after play - EP before play

17. The five stages of the analytics workflow are: Question formulation, Data collection, Data processing, Analysis, and ____.

Answer **Communication** *Explanation:* Communication is the final stage, where analysis is translated into actionable insights for decision-makers. See Section 1.4.5.

18. Analytics adds context to traditional statistics through features like ____ models that estimate how difficult a throw was.

Answer **completion probability** (or **expected completion**) *Explanation:* Completion probability models estimate how likely a pass is to be completed given its depth, coverage, and other factors. This allows evaluation of whether a quarterback's completion rate is better or worse than expected. See Section 1.1.2.

19. The book that popularized the idea of using data to find undervalued players was called ____.

Answer **Moneyball** *Explanation:* Michael Lewis's 2003 book *Moneyball* told the story of the Oakland Athletics' use of statistical analysis. See Section 1.2.2.

Section 4: Short Answer (2 points each)

Write 2-4 sentences for each answer.

20. Explain why "yards per carry" alone is not sufficient for evaluating a running back's performance.

Answer **Sample Answer:** Yards per carry doesn't account for the quality of blocking, the strength of opponent defenses, or the down-and-distance situations when the player carried the ball. A running back behind a dominant offensive line might average 5.0 YPC against weak run defenses, while another back averages 4.2 YPC despite poor blocking against elite defenses. Analytics would examine yards before contact (blocking contribution), yards after contact (runner contribution), success rate, EPA per carry, and opponent adjustments. *Key points for full credit:* - Acknowledges role of blocking/offensive line - Mentions opponent quality or game situation context

21. Describe a situation where a coach might reasonably disagree with an analytics recommendation.

Answer **Sample Answer:** A coach might reasonably disagree with an analytics recommendation when team-specific factors differ significantly from league averages. For example, if analytics recommends going for two points based on typical conversion rates, but the team has an unusually reliable kicker and an unreliable two-point conversion offense, the coach's deviation makes sense. Similarly, if a player is injured or fatigued in ways the model doesn't capture, the coach's judgment should override. *Key points for full credit:* - Provides a specific scenario - Explains why team-specific factors matter

22. What does it mean for analytics to be "context-aware," and why is this important?

Answer **Sample Answer:** Context-aware analytics evaluates performance relative to situation rather than in isolation. A 4-yard gain has different value on 3rd-and-3 (success—first down) versus 3rd-and-8 (failure—likely punt). Context-aware metrics like EPA account for down, distance, field position, score, and time. This is important because the same statistical output can be excellent or poor depending on when it happens. Without context, we might overvalue players in favorable situations and undervalue those in difficult ones. *Key points for full credit:* - Explains that same statistics have different meaning in different situations - Provides an example

Section 5: Applied Problem (5 points)

23. A college football program is debating whether to create an analytics department. The Athletic Director asks you to make the case for (or against) this investment. Write a 150-200 word argument addressing:

a) What specific value analytics would provide (1 point) b) How it would complement existing staff (1 point) c) What the risks or limitations might be (1 point) d) Your recommendation and reasoning (2 points)

Answer **Sample Answer:** Analytics would provide our program with objective evaluation tools and competitive intelligence. Specifically, we could optimize fourth-down and two-point conversion decisions where data shows most teams leave value on the table. We could analyze opponents at scale—processing tendencies across all their games rather than relying on limited video review time. Recruiting would benefit from models that identify undervalued prospects who fit our scheme. This would complement existing staff rather than replace them. Coaches bring invaluable experience and relationship skills that data cannot replicate. Analytics provides inputs to coaching decisions; coaches make the final calls based on complete information. Risks include over-reliance on models that don't capture everything, the cost of hiring and tools, and potential staff friction if coaches feel challenged. Success requires analytics being a service to coaches, not a replacement. My recommendation: invest in a small analytics function (1-2 staff) that focuses on clear decision-support applications. Start with opponent analysis and fourth-down decisions where buy-in is easier, then expand based on demonstrated value. *Key points for full credit:* - Identifies specific value-adds - Addresses coach/analyst collaboration - Acknowledges limitations - Makes clear recommendation with reasoning

Section 6: Reflection (3 points)

24. After reading Chapter 1, what aspect of college football analytics are you most interested in learning more about? How might this knowledge apply to your career goals or interests?

Answer *No single correct answer. Full credit for thoughtful reflection that:* - Identifies a specific aspect of analytics - Connects to personal interests or goals - Demonstrates understanding of concepts from the chapter

25. What is one thing about analytics you believed before this chapter that you now think differently about?

Answer *No single correct answer. Full credit for thoughtful reflection that:* - Identifies a specific prior belief or misconception - Explains how the chapter changed understanding - Demonstrates genuine engagement with material

Scoring

Section Points Your Score
Multiple Choice (1-10) 10 ___
True/False (11-15) 5 ___
Fill in Blank (16-19) 4 ___
Short Answer (20-22) 6 ___
Applied Problem (23) 5 ___
Reflection (24-25) 6 ___
Total 36 ___

Passing Score: 25/36 (70%)

Review Recommendations

  • Score < 50%: Re-read entire chapter, focusing on Sections 1.1 and 1.2
  • Score 50-70%: Review Sections 1.3 and 1.4, redo exercises Part A-B
  • Score 70-85%: Good understanding! Review any missed topics before proceeding
  • Score > 85%: Excellent! Ready for Chapter 2