See exactly what your hackathon report looks like
Based on data from an actual SGBI hackathonFrom 64 applicants to 8 shortlisted. Data-driven decisions.
PROCESS
How candidates flow through the assessment
88% filtered out by AI. Only top performers remain.
CANDIDATES
Your shortlisted candidates, ranked by performance
| Rank | Name | Performance | Tickets | PRs |
|---|---|---|---|---|
| 1 | G**** B**** | Very Good | 6 | 6 |
| 2 | A**** N**** | Good | 6 | 6 |
| 3 | J**** A**** | Good | 6 | 6 |
| 4 | V**** M**** | Above Average | 6 | 6 |
| 5 | C**** B**** | Above Average | 3 | 3 |
| 6 | J**** F**** | Above Average | 3 | 3 |
| 7 | A**** A**** | Above Average | 4 | 4 |
| 8 | S**** M**** | Average | 2 | 3 |
Score Guide
G**** B****
Rank
1
Total Score
82.35
83
Code Intelligence
67.5
Technical Competency
90
Professional Behavior
SCORING
How we calculate scores
AI-driven review of pull requests analyzing logic, structure, naming conventions, and best practices. Measures: Code quality, accuracy, logic
Measurement of active participation within the project including task completion, timeliness, and engagement. Measures: Productivity, consistency, and timeliness
Manual evaluation by expert reviewers of the final implementation and code quality. Measures: Technical depth, solution design, debugging
Combined behavioral metrics derived from teamwork, peer reviews, and communication patterns. Measures: Soft skills, accountability, and communication