# Score Tie Repeatability Eval

> This evaluation protocol measures the impact of score ties on document ranking repeatability across diverse information retrieval collections. It quantifies how non-deterministic tie-breaking during multi-threaded indexing causes variability in standard ranking metrics, even when using identical queries and ranking models. Use when the user wants to benchmark on TREC 2004 Robust Track (Disks 4 & 5), TREC 2005 Robust Track (AQUAINT), TREC 2017 Common Core Track (NYT Annotated Corpus), TREC 2011/2012 Microblog Tracks (Tweets2011), TREC 2013/2014 Microblog Tracks (Tweets2013), TREC 2010–2012 Web Tracks (ClueWeb09b), TREC 2013–2014 Web Tracks (ClueWeb12-B13), or asks about evaluating this task. Reports AP, P30.

- Skill: `qhjqhj00/score-tie-repeatability-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/score-tie-repeatability-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/score-tie-repeatability-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/score-tie-repeatability-eval

---


# score-tie-repeatability-eval

> Repeatability Corner Cases in Document Ranking: The Impact of Score Ties — Lin et al. (2018) (arXiv:1807.05798, 2018)

## What this evaluates

This evaluation protocol measures the impact of score ties on document ranking repeatability across diverse information retrieval collections. It quantifies how non-deterministic tie-breaking during multi-threaded indexing causes variability in standard ranking metrics, even when using identical queries and ranking models.

## Datasets

- **TREC 2004 Robust Track (Disks 4 & 5)** — total ?; splits: test (-1)
- **TREC 2005 Robust Track (AQUAINT)** — total ?; splits: test (-1)
- **TREC 2017 Common Core Track (NYT Annotated Corpus)** — total ?; splits: test (-1)
- **TREC 2011/2012 Microblog Tracks (Tweets2011)** — total ?; splits: test (-1)
- **TREC 2013/2014 Microblog Tracks (Tweets2013)** — total ?; splits: test (-1)
- **TREC 2010–2012 Web Tracks (ClueWeb09b)** — total ?; splits: test (-1)
- **TREC 2013–2014 Web Tracks (ClueWeb12-B13)** — total ?; splits: test (-1)

## Metrics

- `AP` **(primary)** — range: [0, 1]
  - Average Precision: the mean of precision values computed at each rank where a relevant document is retrieved, averaged over all queries.
- `P30` **(primary)** — range: [0, 1]
  - Precision at rank 30: the fraction of relevant documents in the top 30 retrieved results.
- `NDCG@20` — range: [0, 1]
  - Normalized Discounted Cumulative Gain at cutoff 20: measures ranking quality by discounting the relevance score logarithmically based on position, normalized by the ideal ranking.

## Input / output format

**Input**: Queries (TREC topics) and a document collection indexed via Lucene/Anserini. The retriever processes each query to generate a ranked list of document IDs.

**Output**: A ranked list of up to 1000 document IDs per query, formatted in TREC run format with scores.

## Scoring recipe

```python
def compute_metrics(run_file, qrel_file, collection_type):
    if collection_type in ['newswire', 'tweet']:
        # Use trec_eval for AP and P30
        return trec_eval(qrel_file, run_file, metrics=['ap', 'P.30'])
    else: # web
        # Use gdeval.pl for NDCG@20
        return gdeval(qrel_file, run_file, cutoff=20, metric='ndcg')
```

## Common pitfalls

- External evaluation tools like trec_eval sort results by score internally, which can override the system's intended tie-breaking order and introduce artificial variability.
- Multi-threaded indexing assigns unstable internal document IDs, causing arbitrary tie-breaking and non-reproducible rankings across different index builds.
- Average Precision (AP) is unreliable for shallow web collections with limited relevance judgments; NDCG@20 should be used instead.

## Evidence (verbatim from paper)

> All runs retrieved up to 1000 hits and were evaluated in terms of standard retrieval metrics: for newswire and tweet collections, we computed average precision (AP) and precision at rank 30 (P30) using trec_eval. For the web collections, we computed NDCG@20 using gdeval.pl (since the shallow pool depths make AP unreliable).

## Citation

```bibtex
@misc{lin2018repeatability,
  title={Repeatability Corner Cases in Document Ranking: The Impact of Score Ties},
  author={Lin et al. (2018)},
  year={2018},
  note={arXiv:1807.05798}
}
```

- arXiv: 1807.05798