SPORT PERFORMANCE ANALYTICS EXAMINATION
QUESTIONS AND CORRECT ANSWER WITH
EXPLANATION GRADED A+ STUDY GUIDE SOUTHERN
NEW HAMPSHIRE UNIVERSITY
1. Sports performance analytics is the study of:
A. Collecting and analyzing sports data to improve performance
B. Coaching only
C. Refereeing decisions
D. Stadium design
Answer: A
Rationale: It focuses on data-driven performance improvement.
2. The main goal of performance analytics is to:
A. Improve athlete and team performance
B. Increase ticket sales
C. Design uniforms
D. Manage stadiums
Answer: A
Rationale: Analytics enhances performance outcomes.
3. Data in sports analytics is collected from:
A. Games, training, and wearable devices
B. Only coaches
C. Only fans
D. Only referees
Answer: A
Rationale: Multiple sources provide data.
4. Key performance indicators (KPIs) are:
A. Measurable performance metrics
B. Ticket prices
C. Stadium seats
D. Coaching styles
Answer: A
Rationale: KPIs measure success.
,5. Descriptive analytics answers:
A. What happened
B. What will happen
C. Why it happened
D. How to fix it
Answer: A
Rationale: Descriptive analytics summarizes past data.
6. Predictive analytics focuses on:
A. Forecasting future performance
B. Recording history
C. Refereeing decisions
D. Ticket sales
Answer: A
Rationale: Predictive models future outcomes.
7. Prescriptive analytics suggests:
A. Actions to improve performance
B. Past results
C. Game rules
D. Stadium plans
Answer: A
Rationale: It recommends solutions.
8. Wearable technology in sports is used to:
A. Track athlete movement and health
B. Sell tickets
C. Design stadiums
D. Referee games
Answer: A
Rationale: Wearables collect biometric data.
9. GPS tracking in sports measures:
A. Player movement and distance
B. Ticket sales
C. Referee decisions
D. Stadium size
Answer: A
Rationale: GPS tracks movement.
, 10. Heart rate monitors measure:
A. Athlete intensity and fitness
B. Ticket revenue
C. Coaching skills
D. Stadium design
Answer: A
Rationale: Heart rate shows exertion.
11. Video analysis helps to:
A. Review performance and tactics
B. Sell tickets
C. Referee games
D. Build stadiums
Answer: A
Rationale: Video improves analysis.
12. Big data in sports refers to:
A. Large complex datasets from sports activities
B. Small manual notes
C. Ticket records only
D. Stadium maps
Answer: A
Rationale: Big data is large-scale.
13. Machine learning in sports is used to:
A. Identify patterns and predictions
B. Coach manually
C. Referee matches
D. Sell tickets
Answer: A
Rationale: ML finds patterns.
14. Data visualization presents data as:
A. Charts and graphs
B. Text only
C. Numbers only
D. Verbal reports
Answer: A
Rationale: Visualization improves understanding.
QUESTIONS AND CORRECT ANSWER WITH
EXPLANATION GRADED A+ STUDY GUIDE SOUTHERN
NEW HAMPSHIRE UNIVERSITY
1. Sports performance analytics is the study of:
A. Collecting and analyzing sports data to improve performance
B. Coaching only
C. Refereeing decisions
D. Stadium design
Answer: A
Rationale: It focuses on data-driven performance improvement.
2. The main goal of performance analytics is to:
A. Improve athlete and team performance
B. Increase ticket sales
C. Design uniforms
D. Manage stadiums
Answer: A
Rationale: Analytics enhances performance outcomes.
3. Data in sports analytics is collected from:
A. Games, training, and wearable devices
B. Only coaches
C. Only fans
D. Only referees
Answer: A
Rationale: Multiple sources provide data.
4. Key performance indicators (KPIs) are:
A. Measurable performance metrics
B. Ticket prices
C. Stadium seats
D. Coaching styles
Answer: A
Rationale: KPIs measure success.
,5. Descriptive analytics answers:
A. What happened
B. What will happen
C. Why it happened
D. How to fix it
Answer: A
Rationale: Descriptive analytics summarizes past data.
6. Predictive analytics focuses on:
A. Forecasting future performance
B. Recording history
C. Refereeing decisions
D. Ticket sales
Answer: A
Rationale: Predictive models future outcomes.
7. Prescriptive analytics suggests:
A. Actions to improve performance
B. Past results
C. Game rules
D. Stadium plans
Answer: A
Rationale: It recommends solutions.
8. Wearable technology in sports is used to:
A. Track athlete movement and health
B. Sell tickets
C. Design stadiums
D. Referee games
Answer: A
Rationale: Wearables collect biometric data.
9. GPS tracking in sports measures:
A. Player movement and distance
B. Ticket sales
C. Referee decisions
D. Stadium size
Answer: A
Rationale: GPS tracks movement.
, 10. Heart rate monitors measure:
A. Athlete intensity and fitness
B. Ticket revenue
C. Coaching skills
D. Stadium design
Answer: A
Rationale: Heart rate shows exertion.
11. Video analysis helps to:
A. Review performance and tactics
B. Sell tickets
C. Referee games
D. Build stadiums
Answer: A
Rationale: Video improves analysis.
12. Big data in sports refers to:
A. Large complex datasets from sports activities
B. Small manual notes
C. Ticket records only
D. Stadium maps
Answer: A
Rationale: Big data is large-scale.
13. Machine learning in sports is used to:
A. Identify patterns and predictions
B. Coach manually
C. Referee matches
D. Sell tickets
Answer: A
Rationale: ML finds patterns.
14. Data visualization presents data as:
A. Charts and graphs
B. Text only
C. Numbers only
D. Verbal reports
Answer: A
Rationale: Visualization improves understanding.
