Chapter 20: Key Takeaways - Recruiting Analytics

Quick Reference Summary

This chapter covered applying analytics to college football recruiting, from prospect evaluation to program efficiency measurement.

Core Concepts

Star Rating Distributions

Key Metrics

Essential Formulas

Reliability Weight for Ratings

reliability = seasons_of_college_data / (seasons + 2)

Example: After 2 seasons
reliability = 2 / (2 + 2) = 0.5

Expected Starters from Class

expected_starters = Σ(recruits_by_star × starter_rate_by_star)

Example: 2 five-stars, 10 four-stars, 12 three-stars
expected = 2×0.85 + 10×0.65 + 12×0.35 = 12.4 starters

Development Efficiency

efficiency = actual_outcomes / expected_outcomes

efficiency > 1.0: Outperforming
efficiency < 1.0: Underperforming

Competition-Adjusted Statistics

adjusted_stat = raw_stat × (base_factor + adjustment × competition_factor)

Example: Y/C from smaller state
adjusted_ypc = 8.0 × (0.70 + 0.30 × 0.85) = 7.64

Code Patterns

Basic Prospect Evaluation

def evaluate_prospect(measurables: dict,
                     rating: float,
                     position: str) -> dict:
    """Evaluate a prospect."""

    # Physical score (position-specific)
    physical_score = calculate_physical_fit(measurables, position)

    # Rating score (normalized to 0-100)
    rating_score = (rating - 0.80) * 500

    # Composite
    composite = 0.35 * rating_score + 0.65 * physical_score

    return {
        'composite_score': composite,
        'physical_score': physical_score,
        'rating_score': rating_score,
        'projection': get_projection(composite)
    }

Position Needs Analysis

def analyze_needs(roster: pd.DataFrame,
                 targets: dict) -> dict:
    """Analyze roster needs by position."""
    needs = {}

    for position, target in targets.items():
        current = roster[roster['position'] == position]
        departing = current[current['class_year'] == 'SR']
        returning = len(current) - len(departing)

        if returning < target['min']:
            needs[position] = 'critical'
        elif returning < target['max']:
            needs[position] = 'moderate'
        else:
            needs[position] = 'low'

    return needs

Efficiency Calculation

def calculate_efficiency(recruits: pd.DataFrame) -> float:
    """Calculate development efficiency."""

    # Expected outcomes by star rating
    expected_rates = {5: 0.85, 4: 0.65, 3: 0.35, 2: 0.12}

    expected = sum(
        expected_rates.get(int(r['star_rating']), 0.20)
        for _, r in recruits.iterrows()
    )

    actual = recruits['became_starter'].sum()

    return actual / expected if expected > 0 else 1.0

Position-Specific Guidelines

Physical Profile Standards

Development Timelines

Common Pitfalls

1. Over-Relying on Star Ratings

Wrong: Only recruiting 4-5 star players

# BAD - stars only
targets = recruits[recruits['star_rating'] >= 4]

Right: Consider efficiency and fit

# GOOD - rating + fit + need
targets = recruits[
    (recruits['composite_rating'] >= 0.88) &
    (recruits['position'].isin(critical_needs)) &
    (recruits['fit_score'] >= 70)
]

2. Ignoring Competition Level

Wrong: Raw stat comparison

# BAD - raw stats
best_rb = prospects.sort_values('ypc', ascending=False)

Right: Adjusted comparison

# GOOD - adjusted for competition
prospects['adj_ypc'] = prospects.apply(
    lambda x: adjust_for_competition(x['ypc'], x['classification']),
    axis=1
)
best_rb = prospects.sort_values('adj_ypc', ascending=False)

3. Neglecting Position Needs

Wrong: BPA without balance

# BAD - pure BPA
class_selection = top_available[:25]

Right: Needs-aware selection

# GOOD - balanced approach
class_selection = optimize_class(
    available,
    position_needs,
    scholarship_limit=25
)

Evaluation Checklist

Before Recruiting

• [ ] Analyze current roster composition
• [ ] Identify position needs by urgency
• [ ] Set target class composition
• [ ] Establish evaluation criteria

Prospect Evaluation

• [ ] Verify measurables from multiple sources
• [ ] Adjust statistics for competition
• [ ] Evaluate physical fit for position
• [ ] Consider development timeline
• [ ] Assess scheme fit

Class Analysis

• [ ] Calculate total class points
• [ ] Verify position distribution
• [ ] Project class impact by year
• [ ] Compare to historical classes

Post-Signing

• [ ] Track development against projections
• [ ] Calculate efficiency metrics
• [ ] Identify evaluation improvements
• [ ] Update models with outcomes

Quick Reference Tables

Recruiting Class Size Guidelines

Commit Probability Benchmarks

Efficiency Interpretation

Red Flags

Warning signs in recruiting evaluation:

1. Self-reported measurables only: Verify at camps
2. No verified film: Limited evaluation basis
3. Single-season production: Small sample size
4. Weak competition: Inflated statistics
5. Multiple transfers: Fit concerns
6. Academic issues: Eligibility risk
7. Character concerns: Program fit

Next Steps

After mastering recruiting analytics, proceed to: - Chapter 21: Win Probability Models - Chapter 22: Machine Learning Applications - Chapter 23: Network Analysis in Football