Effective Work Sample Exercises for Hiring a Sports Analytics Manager

In the competitive world of sports, data-driven decision making has become a critical factor in achieving success. A Sports Analytics Manager serves as the bridge between raw data and actionable insights that can transform team performance, player development, and organizational strategy. Finding the right person for this role requires more than just reviewing resumes and conducting standard interviews.

Traditional interviews often fail to reveal a candidate's true capabilities in analyzing complex sports data, communicating insights effectively to non-technical stakeholders, and translating findings into strategic recommendations. Work samples and role plays provide a window into how candidates actually approach real-world challenges they'll face in the role.

The best Sports Analytics Managers combine technical expertise with sports knowledge, communication skills, and strategic thinking. By implementing practical work samples in your hiring process, you can evaluate these competencies in action rather than relying on candidates' self-reported abilities or hypothetical responses.

The following exercises are designed to assess the critical skills needed for a successful Sports Analytics Manager: data analysis proficiency, insight generation, strategic planning, stakeholder communication, and team leadership. These activities simulate the actual challenges your Sports Analytics Manager will face, allowing you to make hiring decisions based on demonstrated performance rather than interview performance alone.

Activity #1: Player Performance Data Analysis

This exercise evaluates a candidate's technical data analysis skills, ability to identify meaningful patterns, and capacity to translate complex data into actionable insights. It simulates a core responsibility of the Sports Analytics Manager: analyzing player performance data to inform coaching and management decisions.

Directions for the Company:

• Prepare a sanitized dataset of player performance metrics from your sport (or use publicly available data if needed). The dataset should include various performance indicators across multiple games/seasons.
• Include some "noise" in the data that requires cleaning or filtering.
• Provide access to the data in CSV format and specify which tools the candidate may use (R, Python, SQL, Excel, Tableau, etc.).
• Allow candidates 24-48 hours to work with the data before the interview.
• During the interview, allocate 20-30 minutes for the candidate to present their findings and recommendations.

Directions for the Candidate:

• Analyze the provided dataset to identify key performance trends, anomalies, and insights.
• Prepare 3-5 slides that visualize your findings and highlight actionable recommendations.
• Be prepared to explain your methodology, including how you cleaned or transformed the data.
• Focus on insights that would be valuable to coaches and management for improving team performance.
• Your presentation should be no longer than 15 minutes, leaving time for questions and discussion.

Feedback Mechanism:

• After the presentation, provide one piece of positive feedback about an aspect of their analysis or presentation that was particularly strong.
• Offer one constructive suggestion for improvement, such as an overlooked insight or a different analytical approach.
• Give the candidate 5-10 minutes to respond to the feedback and explain how they would incorporate it into their analysis if they had more time.

Activity #2: Data Collection Strategy Development

This exercise assesses the candidate's strategic thinking, project planning abilities, and understanding of sports performance metrics. It evaluates how well they can design comprehensive data collection systems that align with organizational goals.

Directions for the Company:

• Prepare a brief describing a specific sports performance challenge your organization faces (e.g., improving defensive efficiency, reducing injuries, optimizing player rotations).
• Include information about current data collection methods and their limitations.
• Provide context about available resources, technology constraints, and stakeholder needs.
• Allow 30-45 minutes for this exercise during the interview.

Directions for the Candidate:

• Review the brief and develop a comprehensive data collection strategy to address the identified challenge.
• Your strategy should include:
• Key metrics to be collected and why they're relevant
• Data collection methods and technologies
• Frequency of collection and analysis
• Required resources and potential implementation challenges
• How the data will inform decision-making
• Prepare to present your strategy in a 15-minute discussion, explaining your rationale and addressing potential questions or concerns.

Feedback Mechanism:

• Provide positive feedback on one aspect of their strategy that shows particular insight or creativity.
• Offer one constructive suggestion about a practical consideration they may have overlooked.
• Ask the candidate to spend 5 minutes refining their approach based on the feedback, focusing specifically on how they would address the concern you raised.

Activity #3: Coach/Management Communication Role Play

This exercise evaluates the candidate's ability to communicate complex analytical findings to non-technical stakeholders and translate data into actionable recommendations. It tests their interpersonal skills and capacity to influence decision-makers.

Directions for the Company:

• Prepare a scenario where the candidate must present analytical findings to a coach or executive who is somewhat skeptical of analytics.
• Create a one-page summary of relevant data findings that the candidate will need to explain.
• Assign someone to play the role of the coach/executive who will ask challenging questions and express some resistance.
• Schedule 30 minutes for this role play during the interview process.

Directions for the Candidate:

• Review the data summary provided and prepare to explain the findings to a coach or executive.
• Your goal is to clearly communicate what the data reveals and provide specific, actionable recommendations.
• Be prepared to address questions, concerns, and potential resistance to your recommendations.
• Focus on translating technical concepts into language that resonates with sports professionals.
• The conversation should last approximately 15-20 minutes.

Feedback Mechanism:

• Provide positive feedback on one aspect of their communication approach that was particularly effective.
• Offer one constructive suggestion about how they could better address resistance or explain a complex concept.
• Give the candidate 5 minutes to re-approach a specific part of the conversation incorporating the feedback.

Activity #4: Analytics Team Leadership Scenario

This exercise assesses the candidate's leadership abilities, project management skills, and approach to team development. It evaluates how they would manage a team of analysts and researchers while balancing multiple priorities.

Directions for the Company:

• Create a scenario describing a sports analytics team facing multiple competing priorities and resource constraints.
• Include details about team composition (junior analysts, data scientists, researchers), current projects, and emerging requests from various stakeholders.
• Provide information about team challenges such as skill gaps, workflow inefficiencies, or communication issues.
• Allow 30-45 minutes for this exercise during the interview.

Directions for the Candidate:

• Review the scenario and develop a plan for:
• Prioritizing projects and allocating team resources
• Addressing team development needs and skill gaps
• Improving workflows and communication processes
• Managing stakeholder expectations
• Be prepared to discuss your leadership philosophy and how you would build a high-performing analytics team.
• Your discussion should last approximately 20-25 minutes, including time for questions.

Feedback Mechanism:

• Provide positive feedback on one aspect of their leadership approach that demonstrates strong management potential.
• Offer one constructive suggestion about an additional consideration or alternative approach they might take.
• Ask the candidate to spend 5 minutes explaining how they would incorporate the feedback into their leadership plan.

Frequently Asked Questions

• How much time should we allocate for these work samples in our interview process?

  The entire set of exercises would require approximately 2-3 hours of interview time, plus preparation time for both the company and candidates. For a senior role like Sports Analytics Manager, this investment is justified given the impact of making the right hire. However, you can select the 2-3 most relevant exercises if time constraints are a concern.

• What if we don't have access to proprietary sports data for the analysis exercise?

  Public datasets are readily available for most major sports. Websites like Kaggle, Sports Reference, or league-specific sites offer comprehensive datasets that can be used for these exercises. The key is selecting data that allows candidates to demonstrate relevant analytical skills, even if it's not your organization's proprietary data.

• How technical should we expect the candidates to be in their analysis?

  The level of technical sophistication should align with your organization's needs. For most Sports Analytics Manager roles, candidates should demonstrate proficiency in at least one analytical tool (R, Python, SQL) and show they can perform meaningful statistical analysis. However, the emphasis should be on their ability to derive insights and communicate them effectively, not just technical prowess.

• What if a candidate performs poorly on one exercise but excels at others?

  Consider the relative importance of each competency to your specific role. A candidate who struggles with the technical analysis but excels at communication and leadership might be appropriate for an organization with strong technical analysts who need direction. Conversely, a technically brilliant candidate who struggles with communication might need additional support in that area or might not be right for a management role.

• Should we provide these exercises to candidates in advance?

  For the data analysis exercise, providing the dataset 24-48 hours in advance allows candidates to showcase their best work rather than their ability to work under pressure. For the other exercises, providing a general overview of the topics without specific details strikes a good balance between preparation and spontaneity.

• How do we evaluate candidates consistently across these exercises?

  Develop a structured scoring rubric for each exercise that aligns with the key competencies for the role. Have the same interviewers evaluate all candidates on the same exercises whenever possible. Document specific examples of candidate performance rather than just overall impressions to enable more objective comparisons.

The Sports Analytics Manager role requires a unique combination of technical expertise, sports knowledge, communication skills, and leadership abilities. By incorporating these practical work samples into your hiring process, you'll gain valuable insights into how candidates actually perform in scenarios relevant to the role. This approach not only helps you identify the most qualified candidates but also gives candidates a realistic preview of the job, leading to better hiring decisions and improved retention.

Ready to take your hiring process to the next level? Yardstick offers AI-powered tools to help you create comprehensive job descriptions, generate targeted interview questions, and design effective interview guides. Check out our AI job description generator, interview question generator, and interview guide generator to streamline your hiring process. For more information about hiring a Sports Analytics Manager, visit our example job description.

Build a complete interview guide for this role by signing up for a free Yardstick account here