Chapter 30 Quiz

Instructions: Select the best answer for each question. Some questions may have multiple valid perspectives; choose the answer that best reflects the material covered in Chapter 30.

Question 1

What does pose estimation technology add to existing player tracking data?

A) Higher-frequency GPS coordinates
B) Full skeletal body position and joint angle information
C) More accurate pitch coordinates via LiDAR
D) Ball spin rate and trajectory data

Answer: B

Explanation: Pose estimation reconstructs full skeletal models, providing information about body orientation, joint angles, and limb positions. This goes beyond positional tracking (where the player is) to describe how the player's body is configured (Section 30.1.1).

Question 2

Which of the following is NOT identified as a near-term application of large language models in soccer analytics?

A) Automated match report generation
B) Conversational data exploration
C) Fully autonomous tactical decision-making during matches
D) Cross-lingual scouting report translation

Answer: C

Explanation: Section 30.1.2 describes LLMs as tools for report generation, data exploration, and translation. Fully autonomous tactical decision-making is explicitly contrary to the chapter's emphasis on human-in-the-loop systems (Section 30.2.5 and 30.4).

Question 3

The state-space model presented for wearable data fusion includes the term $\mathbf{w}_t$. What does this represent?

A) The player's weight at time $t$
B) Process noise in the state transition
C) The weighting factor for sensor priority
D) Wind conditions affecting GPS accuracy

Answer: B

Explanation: In the state-space formulation $\mathbf{x}_{t+1} = f(\mathbf{x}_t, \mathbf{u}_t) + \mathbf{w}_t$, the term $\mathbf{w}_t$ represents process noise --- the inherent uncertainty in the state transition model (Section 30.1.3).

Question 4

What is edge computing in the context of real-time match analytics?

A) Computing performed on the edge of the pitch using mobile devices
B) Processing data at or near the point of collection rather than in a remote cloud
C) Using edge detection algorithms for computer vision
D) Computing that focuses on edge cases in the data

Answer: B

Explanation: Edge computing processes data locally (e.g., at the stadium) rather than sending it to distant cloud servers, reducing latency for time-sensitive applications like in-match tactical alerts (Section 30.1.5).

Question 5

Which of the four pillars of the ethical framework addresses the power imbalance between clubs and players regarding data collection?

A) Transparency
B) Consent and Agency
C) Proportionality
D) Fairness and Non-Discrimination

Answer: B

Explanation: The Consent and Agency pillar specifically addresses the concern that players in an employment relationship may not feel genuinely free to decline data collection, given the power imbalance with their employer (Section 30.2.2).

Question 6

The fairness criterion of demographic parity requires that:

A) The model achieves equal accuracy across all groups
B) The probability of a positive prediction is equal across protected groups
C) The model is calibrated equally across groups
D) All groups are equally represented in the training data

Answer: B

Explanation: Demographic parity requires $P(\hat{Y} = 1 | A = a) = P(\hat{Y} = 1 | A = b)$ for all groups --- that is, the rate of positive predictions must be equal across groups regardless of group membership (Section 30.2.3).

Question 7

An important result from fairness research states that demographic parity, equalized odds, and calibration:

A) Can always be simultaneously achieved with sufficient data
B) Cannot all be simultaneously satisfied except in degenerate cases
C) Are equivalent formulations of the same underlying principle
D) Only apply to binary classification problems

Answer: B

Explanation: Section 30.2.3 states the well-established impossibility result: these three fairness criteria cannot all be simultaneously satisfied (except in degenerate cases), requiring practitioners to make deliberate choices about which to prioritize.

Question 8

Which of the following is an example of quantification bias in soccer analytics?

A) Overvaluing a player because they scored a memorable goal
B) Overvaluing easily measured metrics like pass completion while undervaluing leadership
C) Using too small a sample size for statistical analysis
D) Training models on data from only the top 5 leagues

Answer: B

Explanation: Quantification bias is the tendency to overvalue what is easily measured and undervalue what is not (such as leadership, dressing room influence, mentality). It is a bias introduced by analytics rather than corrected by it (Section 30.4.4).

Question 9

According to the chapter, what is described as the most valuable skill in soccer analytics?

A) Python programming
B) Machine learning expertise
C) Communication
D) Statistical modeling

Answer: C

Explanation: Section 30.4.2 explicitly states: "The most valuable skill in soccer analytics is not coding or statistics --- it is communication." The ability to translate findings into actionable insights for non-technical audiences is the key differentiator.

Question 10

In the context of the analyst-coach relationship, which behavior is recommended?

A) Asserting the superiority of data over intuition
B) Providing analytical input on every match regardless of relevance
C) Building trust gradually by demonstrating practical usefulness
D) Presenting only model outputs without discussing uncertainty

Answer: C

Explanation: Section 30.4.3 emphasizes that trust is built gradually by demonstrating that analytical insights are practically useful, not by asserting the superiority of data over coaching experience.

Question 11

The recency bias in football decision-making can be mitigated by:

A) Using only the most recent season's data
B) Long-term performance baselines
C) Focusing on highlight reels
D) Increasing the weight of recent matches in models

Answer: B

Explanation: The bias-mitigation table in Section 30.4.4 identifies long-term performance baselines as the analytical corrective for recency bias, which is the tendency to overweight recent performances.

Question 12

What does the chapter predict will happen with women's soccer data by 2028?

A) Women's soccer will adopt a completely separate data standard
B) Major data providers will achieve event data coverage comparable to men's leagues
C) Women's soccer analytics will be merged with men's analytics departments
D) Open data will only be available for men's competitions

Answer: B

Explanation: Near-term prediction #5 in Section 30.5.1 states that major data providers will achieve event data coverage of top women's leagues comparable to men's leagues by 2028.

Question 13

A digital twin of a soccer player, as described in Section 30.5.3, is best described as:

A) A virtual reality avatar used for fan engagement
B) A continuously updated computational model integrating physical, tactical, technical, and psychological data
C) A duplicate entry in the transfer database
D) A synthetic training partner generated by AI

Answer: B

Explanation: Section 30.5.3 defines digital twins as "continuously updated computational representations that integrate physical, tactical, technical, and psychological data to predict performance under different conditions."

Question 14

Which open-source library is specifically designed for standardizing data loading across different soccer data providers?

A) mplsoccer
B) socceraction
C) kloppy
D) statsbombpy

Answer: C

Explanation: Section 30.3.2 describes kloppy as providing standardized data loading across providers, making it easier to work with data from different sources using a consistent interface.

Question 15

According to the chapter, what is the appropriate maximum data retention period specified in the EthicsAssessment.passes_review() method?

A) 1 year
B) 2 years
C) 3 years
D) 5 years

Answer: C

Explanation: The passes_review() method in the code in Section 30.2.2 returns False if retention_period_days > 365 * 3, indicating a maximum 3-year retention period.

Question 16

Which of the following is NOT listed as a responsible AI principle for soccer in Section 30.2.5?

A) Explainability
B) Human-in-the-loop
C) Maximum data collection
D) Adversarial robustness

Answer: C

Explanation: The five responsible AI principles listed are: explainability, human-in-the-loop, continuous monitoring, audit trails, and adversarial robustness. Maximum data collection contradicts the proportionality principle (Section 30.2.2).

Question 17

For the "club analyst" career pathway, which skill is rated as requiring the highest proficiency level?

A) Python programming
B) Football knowledge
C) Machine learning
D) SQL

Answer: B

Explanation: In the build_skills_inventory function (Section 30.6.1), the club_analyst role requires football_knowledge at level 9 and communication at level 9, while Python is at 7 and machine learning at 5. Football knowledge and communication are tied as the highest requirements.

Question 18

Graph neural networks (GNNs) are highlighted as particularly suitable for soccer analytics because:

A) They process tabular data more efficiently than traditional methods
B) They naturally represent the relational structure between players
C) They require less training data than other neural network architectures
D) They can process video data directly without preprocessing

Answer: B

Explanation: The technical note callout in Section 30.1.6 states that GNNs are well-suited to soccer because they "naturally represent the relational structure between players," with players as nodes and edges representing spatial or tactical relationships.

Question 19

What does the chapter identify as a key risk of the increasing influence of analytics in soccer?

A) Analytics will make soccer boring
B) The incentive to manipulate tracking metrics will increase
C) Coaches will become unnecessary
D) All clubs will play the same way

Answer: B

Explanation: Section 30.2.5 (principle 5, adversarial robustness) notes that "as analytics becomes more influential, the incentive to manipulate data (e.g., gaming tracking metrics) increases. Systems must be robust to deliberate manipulation."

Question 20

The chapter closes with a quote from Arrigo Sacchi. What is the core message of the chapter's final reflection?

A) Data will eventually replace human judgment completely
B) Soccer analytics is only valuable at the elite level
C) Analysts should hold both technical rigor and humility about what lies beyond models
D) The future of analytics depends solely on technological progress

Answer: C

Explanation: Section 30.6.5 states: "carry both the technical rigor we have developed over 28 chapters and the humility to recognize what lies beyond our models." The chapter consistently emphasizes that data and human understanding are complementary, not competing.