Deploy Space: update src

src/about.py

@@ -5,8 +5,8 @@ INTRODUCTION_TEXT = """
 real-world data** with **zero-shot API inference** via [TSFM.ai](https://tsfm.ai/).
 Following [GIFT-Eval](https://huggingface.co/spaces/Salesforce/GIFT-Eval), the leaderboard
 reports **absolute metric values** and **per-dataset ranks**. The Overall tab also reports
-the cumulative **up-to-now live result** with MSE, RMSE, MAPE, quantile CRPS, RTG,
-temporal Stability, Kendall Improvement, Average Rank, Win Rate, and Elo. The
+the latest overall snapshot plus full 24-hour, 7-day, and 30-day metric tables and
+dataset-balanced **pairwise historical rankings** over shared releases. The
 **GIFT-style Aggregates** tab provides Seasonal-Naive-normalized MSE, CRPS,
 and mean CRPS rank grouped by actual prediction length, domain, and frequency. Each
 subsequent domain tab retains the original absolute per-dataset results.
@@ -41,7 +41,9 @@ on future live releases.
 - **RTG** — normalized real-time MSE gain over causal Seasonal-Naive (higher is better)
 - **Stability** — standard deviation of release-level MSE (lower is better)
 - **Improvement** — Kendall trend statistic over release-level MSE (more negative is better)
-- **Average Rank / Win Rate / Elo** — paired summaries over shared future releases
+- **Pairwise Win Rate** — dataset-balanced MSE/CRPS wins over shared future releases;
+  official ranks require sufficient shared releases, datasets, time span, opponents,
+  and membership in the main comparison component
 - **RankScore** — Elo-style aggregate from per-dataset MSE and CRPS ranks (higher is better)
 - **MSE_Rank** / **CRPS_Rank** — per-dataset rank (lower is better)
 - **Grouped MSE / CRPS** — geometric mean after per-configuration normalization against