Open dataset covering all 48 national squads — 1,363 players — at the 2026 FIFA World Cup, with per-90 performance stats and AI-computed player similarity examples.
Maintained by Rising Transfers, an AI football intelligence platform built on 56,000+ player profiles.
| File | Contents |
|---|---|
data/squads.csv |
All 1,363 squad players: name, nation, position, club, age, and AI-estimated transfer value |
data/per90_stats.csv |
Per-90 metrics for 1,181 players in 2025-26 (computed; minimum 450 league minutes) |
data/dna_similarity_examples.json |
AI similarity examples: top-5 stylistically similar players for 20 star players |
Looking for the full player-similarity dataset (hundreds of players)? See football-player-similarity.
Every row in squads.csv and per90_stats.csv includes a slug — use it to open the full AI profile on Rising Transfers:
https://risingtransfers.com/en/players/{slug}/alternatives
Example — players who play like Mbappé:
https://risingtransfers.com/en/players/k-mbappe/alternatives
Browse all 48 squads with live group tables at the World Cup 2026 Hub.
- Transfer value estimates (
rt_value_estimate_eur) are produced by Rising Transfers' AI valuation model — not third-party market consensus figures. - Per-90 metrics are computed from seasonal aggregates. Players with fewer than 450 league minutes in 2025-26 are excluded from
per90_stats.csvto reduce small-sample noise. - Similarity scores come from Player DNA, a semantic embedding of playing style. Scores range 0–1; only top-N results are published (no raw 768-dim vectors).
See docs/data_dictionary.md for column definitions.
Released under CC BY 4.0. Attribution:
Data: Rising Transfers — risingtransfers.com
- football-player-similarity — "who plays like X" for hundreds of players (evergreen)
- football-data-glossary — football analytics metrics & methodology reference