exercise

Exercise: Critique the model response

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Critique the AI response

Read each claim and mark it as Verified, Unverified or Rejected. For each rejected claim, write a short justification.

○ 0 of 7 claims marked

Source: General chat language model with a sports focus · a prepared sample the whole class analyses.
Claim 1

xG, or expected goals, is a statistical estimate of the probability that a shot will result in a goal, calculated based on position, angle, distance, and circumstances, and is today the foundation of match analysis in most professional national football teams.

Claim 2

The Moneyball method used by the Oakland Athletics during the 2001 to 2003 seasons involved, among other things, buying players with a high on-base percentage (OBP) that other teams undervalued because they focused more on slugging percentage.

Claim 3

Data-driven load management in basketball, where coaches use metrics such as minutes played, speed figures, and recovery, has shown that players who play an average of over 38 minutes per game require exactly 72 hours of rest between games, according to NBA research.

Claim 4

Trackman technology measures, among other things, speed, spin, trajectory, and the impact of the ball with the club in golf, enabling coaches to analyze each shot with precise measurements instead of mere ideas.

Claim 5

Human resilience in sports is completely unmeasurable and therefore has no significance in data-driven performance models. Teams that secure titles always do so because of better statistics than their competitors', never because of resilience or focus in major games.

Claim 6

One of the main problems of data-driven models in team sports is that they are often based on individual metrics and capture player interaction poorly, so the systematic role of players who "open up" space for others does not always appear in the numbers.

Claim 7

Artificial intelligence has already replaced human coaching judgment in all major professional sports in the world, and decisions regarding tactical lineups and player selection are now made by algorithms without human involvement.

○ Mark 5 more claims (minimum 5 in total). Reject at least 2 claims (currently: 0)

Requirements: 5 claims marked · 2 rejected · 25 words of justification

Compare answers

Here you see two different ways to ask the AI for the same thing. Choose the answer you trust more and write a short justification, and the explanation will open.

Comparison 1 of 2

Prompt

You ask an AI to evaluate whether the best player on your team should play in an important match even though they have been in poor match form in recent weeks. Compare two possible prompts.

Answer A

Is this a good player? Should they play? Give me advice.

Answer B

In the last three matches, the player has had an xG contribution of 0.3 compared to their average of 0.8 this season. They are 28 years old, in peak physical condition according to the coach's load measurements, but showed lower speed figures in two of the three matches. The competition is a final and we are one goal behind. What are the main factors to evaluate and where does the model fall short?

- 0 / 5 words

- Choose an answer to continue

Comparison 2 of 2

Prompt

The student wants to use AI to explain what the Moneyball approach changed in professional baseball coaching. Compare two prompts.

Answer A

Explain Moneyball. What did they do differently?

Answer B

Explain how the Oakland Athletics used on-base percentage (OBP) as a core variable in their 2002 player recruitment process. Name three decisions where this metric had a measurable impact on which players were hired, and explain one factor that the Moneyball approach failed to measure. Reference Michael Lewis's book or MLB primary data where possible.

- 0 / 5 words

- Choose an answer to continue

Self-check rubric, accountability document

Mark each criterion once you have met it. When you mark it, a three-level descriptor opens, compare your own work against each level.

- 0 of 5 criteria confirmable

Final decision

No claims found. Finish the critique exercise first, that is where the accountability document draws your data from.