home / skills / onewave-ai / claude-skills / mvp-case-builder

mvp-case-builder skill

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This skill helps you build persuasive MVP award cases with statistical arguments, narrative framing, and counter-arguments tailored to past winners.

npx playbooks add skill onewave-ai/claude-skills --skill mvp-case-builder

Review the files below or copy the command above to add this skill to your agents.

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SKILL.md

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---
name: mvp-case-builder
description: Construct statistical arguments for MVP/awards. Narrative framing, comparison to past winners, advanced metrics, counter-arguments.
---

# Mvp Case Builder
Construct statistical arguments for MVP/awards. Narrative framing, comparison to past winners, advanced metrics, counter-arguments.

## Instructions

You are an expert sports analyst and award voting strategist. Build comprehensive MVP cases with: statistical arguments, narrative framing, historical comparisons, advanced metrics explanations, counter-arguments addressed, and persuasive presentation.

### Output Format

```markdown
# Mvp Case Builder Output

**Generated**: {timestamp}

---

## Results

[Your formatted output here]

---

## Recommendations

[Actionable next steps]

```

### Best Practices

1. **Be Specific**: Focus on concrete, actionable outputs
2. **Use Templates**: Provide copy-paste ready formats
3. **Include Examples**: Show real-world usage
4. **Add Context**: Explain why recommendations matter
5. **Stay Current**: Use latest best practices for sports

### Common Use Cases

**Trigger Phrases**:
- "Help me with [use case]"
- "Generate [output type]"
- "Create [deliverable]"

**Example Request**:
> "[Sample user request here]"

**Response Approach**:
1. Understand user's context and goals
2. Generate comprehensive output
3. Provide actionable recommendations
4. Include examples and templates
5. Suggest next steps

Remember: Focus on delivering value quickly and clearly!

Overview

This skill builds persuasive, data-driven MVP and awards cases by combining narrative framing, statistical evidence, and historical comparison. It produces ready-to-use writeups, templates, and counter-arguments designed to influence voters and stakeholders. The output emphasizes clarity, advanced metrics, and practical recommendations for presentation and distribution.

How this skill works

You provide player/context and season-level data or request synthesized estimates; the skill inspects box-score totals, rate metrics, advanced stats (WAR, VORP, BPM, WAA, etc.), and historical winner baselines. It constructs a narrative arc, compares the candidate to past winners and contemporaries, explains metric relevance, and anticipates common objections with rebuttals. Final deliverables include formatted cases, copy-paste templates for social or media, and action steps to amplify the message.

When to use it

Preparing a media packet or op-ed advocating for an MVP candidate
Arming a team PR or fan campaign with data-backed talking points
Evaluating a player’s award odds mid-season or at season end
Comparing candidates for debate, broadcast segments, or social content
Making front-office or internal award recommendations using objective analysis

Best practices

Start with a clear thesis sentence summarizing the claim and key metric
Include both counting stats and rate/impact metrics to cover volume and efficiency
Contextualize numbers with team role, schedule, and lineup protections
Use historical winner thresholds as benchmarks, not strict rules
Provide concise rebuttals for the three most likely counter-arguments
Deliver copy-ready headlines and social snippets for quick distribution

Example use cases

Create an MVP case for a position player using WAR, wRC+, and defensive runs saved plus narrative framing
Generate a mid-season update comparing two frontrunners with projection-based remaining-season impact
Produce a press release and Twitter thread that summarize the argument and cite key metrics
Draft a rebuttal memo countering common narratives (e.g., team record, cluster luck)

FAQ

Can this handle different sports or award types?

Yes. Provide the sport and relevant advanced metrics; the approach adapts to sport-specific stats and award criteria.

What if I don’t have full advanced stat data?

The skill can work from box scores and rate stats, estimate advanced metrics using accessible conversions, and flag uncertainty for transparency.