AI in Player Scouting and Recruitment Quiz

Answers and explanations appear as you take the quiz.

1. Which early sports-recruitment approach does the guide cite as a precursor to data-driven scouting?
   • The Oakland Athletics' 'Moneyball' statistical approach in baseball
   • Wave Function Collapse level generation
   • Ship weather routing
   • WordPiece subword tokenization
2. What does computer vision allow scouts to do with broadcast match footage?
   • Automatically rewrite commentary
   • Convert video into structured positional data on player movement
   • Generate fake highlight reels
   • Replace referees
3. Why do clubs use 'player similarity' or nearest-neighbor algorithms?
   • To pick team jersey colors
   • To schedule matches
   • To find affordable players with a style similar to an expensive target
   • To set ticket prices
4. Which metric is an example of an advanced AI/analytics measure in football (soccer)?
   • Number of fans in the stadium
   • Length of the match
   • Color of the ball
   • Expected goals (xG)
5. What is a major ethical concern raised by AI scouting?
   • Profiling and privacy of young athletes, including teenagers
   • Players running too fast
   • Stadiums being too large
   • Too many goals being scored
6. What kind of data has become central to NBA player evaluation beyond the box score?
   • Crowd noise levels
   • Player-tracking data capturing movement and positioning
   • Halftime show ratings
   • Concession sales
7. Which two Statcast measurements does the guide cite as examples used in baseball evaluation?
   • Mel-spectrogram loss and waveform likelihood
   • AIS position and sea state
   • Exit velocity and spin rate
   • Pixel labels and image embeddings
8. What does SkillCorner say its computer-vision tracking data can represent from broadcast footage?
   • Only the final score and goal scorers
   • Every player's private medical history
   • Only the referee's location
   • Positions and movement of all 22 players and the ball