home / skills / onewave-ai / claude-skills / bracket-predictor

bracket-predictor skill

safe

This skill analyzes March Madness brackets with data-driven strategies, identifying upsets, chalk vs contrarian picks, and confidence levels to optimize

npx playbooks add skill onewave-ai/claude-skills --skill bracket-predictor

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

Files (1)

SKILL.md

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---
name: bracket-predictor
description: March Madness, playoff brackets, tournament picks. Upset potential, chalk vs contrarian strategies, historical trends, confidence levels.
---

# Bracket Predictor
March Madness, playoff brackets, tournament picks. Upset potential, chalk vs contrarian strategies, historical trends, confidence levels.

## Instructions

You are an expert bracket analyst and tournament predictor. Create data-driven tournament predictions with: upset identification, chalk vs contrarian strategies, historical trend analysis, matchup breakdowns, confidence levels per pick, and reasoning for each selection.

### Output Format

```markdown
# Bracket Predictor 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 produces data-driven tournament bracket predictions for March Madness and other playoff formats. It identifies upset potential, contrasts chalk vs contrarian strategies, and assigns confidence levels to each pick. The output combines matchup-level analysis with historical trend signals to support smart bracket construction.

How this skill works

The skill ingests team metrics, seed history, recent form, and matchup-specific factors (pace, defense, injuries). It scores each matchup for upset probability, expected margin, and confidence then aggregates those into round-by-round projections. It also generates two complementary strategies: a chalk-optimized bracket and a contrarian bracket with targeted differentiators.

When to use it

Filling out a tournament bracket with a mix of safe and high-upside picks
Preparing for bracket pools that reward unique picks and tiebreakers
Analyzing specific matchups to set betting lines or fantasy rosters
Generating day-by-day game previews during tournament play
Advising clients or content audiences on bracket narratives and trends

Best practices

Prioritize upsets with both matchup leverage and historical seed upset frequency
Balance bracket construction: lock a core of high-confidence favorites, then add 2–4 targeted contrarian upsets
Use confidence scores to allocate differential risk across rounds (e.g., risk more in early rounds)
Review injury and rotation updates within 24 hours of tip-off and adjust probabilities
Document reasoning for each non-obvious pick to justify contrarian choices

Example use cases

Generate a chalk bracket that maximizes expected wins while minimizing variance for public pools
Create a contrarian bracket that sacrifices a few chalk wins to gain unique outcomes valued in private pools
Deliver matchup-by-matchup briefings with upset probability, key matchup edges, and betting confidence
Run post-tournament analysis to extract trends and update model priors for the next year
Provide a shareable summary for social posts: top upset picks, sleeper teams, and confidence badges

FAQ

How are confidence levels computed?

Confidence combines model probability, sample size of comparable historical matchups, and recency of team performance into a single 0–100 score.

When should I choose contrarian over chalk?

Pick contrarian when you need tournament differentiation and the upset has credible matchup edges; favor chalk when maximizing expected win total in large public pools.