Predictive & Prescriptive

What is the main goal of predictive analytics in sports?

• To suggest the best course of action

• To forecast future outcomes based on data

• To track historical performance

• To replace coaches with AI

In the CRISP-DM model, which phase involves understanding the problem before working with data?

• Data Understanding

• Business Understanding

• Data Preparation

• Modeling

Which of the following is NOT a common use case for predictive analytics in sports?

• Player performance forecasting

• Injury risk modeling

• Game strategy recommendations

• Win probability estimation

How does prescriptive analytics differ from predictive analytics?

• It forecasts future events

• It provides actionable recommendations based on predictions

• It only uses past data without making forecasts

• It does not involve machine learning

What is an example of prescriptive analytics in sports?

• Predicting the likelihood of an athlete’s injury

• Suggesting whether an NBA coach should rest a player

• Estimating the win probability of a football team

• Calculating an athlete’s historical performance trends

In CRISP-DM, what step comes after “Data Preparation”?

• Business Understanding

• Modeling

• Deployment

• Evaluation

Which technology is commonly used for predictive modeling in sports analytics?

• Excel

• Python (Scikit-learn, TensorFlow)

• Microsoft Word

• Google Docs

What is the primary benefit of using prescriptive analytics in sports decision-making?

• It allows teams to generate better visualizations

• It automates video analysis

• It optimizes decisions based on data-driven recommendations

• It replaces the need for coaches