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Why raw averages can mislead when projects get different judges
A project’s raw average combines two things: how its judges assessed it and how those judges tend to use the scoring scale. If one panel is consistently generous and another is stricter, projects reviewed by those panels may not be directly comparable—even when every judge uses the same rubric.
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That issue matters when judges assess different subsets of entries. It is less of a panel-composition problem if every project receives scores from the same judges, though differences in judge interpretation and rubric use can still matter.
In an article published October 2, 2026, author codewitharyan29 reports analyzing official DOGFOOD data comprising 40 projects, 30 judges, and 126 review rows. Among judges with at least five reviews, personal averages ranged from 3.11 to 4.22; the article reports a standard deviation of 0.81 at both ends of that average range and a pooled mean of 3.57. These are figures reported for that dataset, not general measurements of hackathon judging. Read the author’s account and method.
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How within-judge z-score normalization works
The method first describes each judge’s scoring pattern, then expresses each individual score relative to that judge’s own mean and standard deviation. A project’s normalized value is the average of those standardized scores from its reviewers.
z(judge, project) = (score - judge_mean) / judge_stddev
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normalized(project) = mean of z over the judges who reviewed it
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The author’s approach averages over the judges who reviewed each project, so projects can have different review counts. That does not establish that unequal counts have no statistical consequences: an average based on fewer reviews may be less stable.
What the 23-place example shows
In the author’s reported comparison, both projects had a raw mean of 3.44. Their reviewer panels and normalized positions differed:
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| Project | Raw mean | Raw rank | Reported reviewer averages | Normalized rank |
|---|---|---|---|---|
| Flat Meadow | 3.44 | 24 | 4.22, 3.61, and 4.08 | 37 |
| Glass Signal | 3.44 | 26 | 3.48 | 14 |
The article reports a 23-place gap between normalized ranks 37 and 14. Its figures also say 38 of the 40 projects changed rank and that the Spearman correlation between raw and normalized rankings was 0.864. Those results describe the author’s comparison on this dataset; they do not show how the adjustment would perform across other events.
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How the method handles a judge whose scores never vary
A judge who gives every project the same score has a standard deviation of zero, so the within-judge formula would require division by zero. The same issue can arise when a judge reviews only one project. The article describes a fallback: standardize that judge’s score against the event-wide pooled mean and standard deviation instead. If the pooled standard deviation is also effectively zero, the implementation assigns a value of zero. The author says affected judges are recorded in a zero_variance_judges list and an audit log.
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This fallback is a policy choice, not a mathematical consequence of z-scoring. An event adopting it should document the rule and retain enough information to inspect which scores used it.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Normalization is not the same as choosing a judging method
Within-judge normalization adjusts how score levels and scale spread contribute to an aggregate. It does not determine whether a rubric measures the qualities an event values, or whether judges apply the rubric consistently. Nor does the reported example establish that normalized scores identify the objectively best project.
Organizers can also choose a different kind of aggregation. Kaggle’s competition guidance discusses normalizing judges’ scores and points assigned through rank choice as an alternative when point-scale variation is a concern. HackHQ documents an Averaged Borda Count for its Top Picks feature. These are examples of approaches, not evidence that one method is universally superior. Kaggle competition setup guidance and HackHQ’s score calculation documentation describe those options.
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Rubric-score normalization preserves score magnitudes, subject to the transformation and its assumptions. Rank-based aggregation uses ordering rather than the distance between scores; pairwise methods compare entries against one another. These approaches answer somewhat different questions and can produce different results, so organizers should select one that matches their rubric and communicate it before judging begins.
What organizers should decide before applying an adjustment
- Panel design: Decide whether judges score every project or assigned subsets. The more panels differ, the more important it is to consider whether scoring habits are comparable.
- Scoring policy: Specify whether raw rubric points, judge-normalized scores, ranks, or pairwise comparisons determine results.
- Edge cases: Set a rule for judges with too few reviews or no score variation, and record when that rule is used.
- Auditability: Preserve raw scores alongside transformed values and final results so organizers can explain how rankings were produced.
- Conflicts: Provide a process for flagging conflicts of interest and excluding or handling affected reviews under the event’s stated rules.
- Communication: Tell participants and judges how scores will be aggregated before scoring starts; changing the rule after seeing results can undermine confidence.
As an example of product features rather than proof of an algorithm’s validity, Hackathon by Slingshot describes weighted rubrics, normalization across judges, conflict flags, and visibility into raw and normalized results. Its judging and scoring page does not independently validate the method or the dataset results above.
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