Prioritise which grant applications to pursue

Score and rank grant opportunities

This scores grants against your criteria and ranks them. Adapt the criteria and weights to your organisation's priorities.

import pandas as pd
import numpy as np

# Load grant opportunities
# Columns: grant_name, amount, your scores for each criterion
grants = pd.read_csv('grant_opportunities.csv')

print(f"Loaded {len(grants)} grant opportunities")
print(f"Total potential funding: £{grants['amount'].sum():,}")

# Define your scoring criteria and weights
# Adjust these based on what matters to your organisation
criteria = {
    'strategic_fit': {
        'weight': 0.30,  # 30% of overall score
        'description': 'Alignment with mission and strategy'
    },
    'success_probability': {
        'weight': 0.20,  # 20% of overall score
        'description': 'Realistic chance of winning based on eligibility'
    },
    'funding_vs_effort': {
        'weight': 0.25,  # 25% of overall score
        'description': 'Funding amount relative to application complexity'
    },
    'timeline_fit': {
        'weight': 0.15,  # 15% of overall score
        'description': 'Deadline works with our capacity'
    },
    'flexibility': {
        'weight': 0.10,  # 10% of overall score
        'description': 'Few restrictions on how funding can be used'
    }
}

# Verify weights sum to 1.0
total_weight = sum(c['weight'] for c in criteria.values())
if not np.isclose(total_weight, 1.0):
    print(f"WARNING: Weights sum to {total_weight}, not 1.0. Adjusting...")
    for key in criteria:
        criteria[key]['weight'] /= total_weight

print("\nScoring criteria:")
for criterion, details in criteria.items():
    print(f"  {criterion}: {details['weight']*100:.0f}% - {details['description']}")

# Calculate weighted scores
def calculate_score(row):
    """Calculate overall weighted score for a grant"""
    score = 0
    breakdown = {}

    for criterion, details in criteria.items():
        # Each criterion scored 0-10 in your spreadsheet
        criterion_score = row[criterion]  # 0-10
        weight = details['weight']

        # Weighted contribution (0-10 scale)
        contribution = criterion_score * weight * 10
        score += contribution
        breakdown[criterion] = criterion_score

    return score, breakdown

# Score all grants
grants['overall_score'] = 0.0
score_breakdowns = []

for idx, row in grants.iterrows():
    score, breakdown = calculate_score(row)
    grants.at[idx, 'overall_score'] = round(score, 1)
    score_breakdowns.append(breakdown)

# Sort by score (highest first)
grants = grants.sort_values('overall_score', ascending=False)

print(f"\nTop 10 opportunities by score:")
print(grants.head(10)[['grant_name', 'amount', 'overall_score']].to_string(index=False))

print(f"\n{'='*60}")
print("Detailed breakdown of top 5:")

for idx, (_, row) in enumerate(grants.head(5).iterrows(), 1):
    print(f"\n{idx}. {row['grant_name']}")
    print(f"   Amount: £{row['amount']:,}")
    print(f"   Overall Score: {row['overall_score']:.1f}/100")
    print(f"   Breakdown (0-10 scale):")

    breakdown_idx = grants.index.get_loc(row.name)
    breakdown = score_breakdowns[breakdown_idx]

    for criterion, details in criteria.items():
        criterion_score = breakdown[criterion]
        stars = '★' * int(criterion_score) + '☆' * (10 - int(criterion_score))
        print(f"     {criterion}: {criterion_score}/10 {stars}")

    # Decision recommendation
    if row['overall_score'] >= 70:
        recommendation = "PRIORITY - Pursue"
    elif row['overall_score'] >= 50:
        recommendation = "Consider if capacity allows"
    else:
        recommendation = "Pass - low priority"

    print(f"   → {recommendation}")

# Summary statistics
print(f"\n{'='*60}")
print("Summary:")

high_priority = len(grants[grants['overall_score'] >= 70])
medium_priority = len(grants[(grants['overall_score'] >= 50) & (grants['overall_score'] < 70)])
low_priority = len(grants[grants['overall_score'] < 50])

print(f"  High priority (70+): {high_priority} grants")
print(f"  Medium priority (50-70): {medium_priority} grants")
print(f"  Low priority (<50): {low_priority} grants")

high_priority_value = grants[grants['overall_score'] >= 70]['amount'].sum()
print(f"\nTotal potential from high-priority grants: £{high_priority_value:,}")

# Export results
grants.to_csv('grant_priorities.csv', index=False)
print(f"\nFull rankings saved to grant_priorities.csv")

print("\nNext steps:")
print("1. Review top-scoring grants - do they feel right?")
print("2. Adjust weights/criteria if rankings seem off")
print("3. Focus effort on high-priority opportunities")
print("4. Track success rates to refine scoring")