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football-player-comparison

该 Skill 通过 Crawlora 的 FotMob 接口解析球员身份、赛季统计和有限比赛样本,按位置、赛事与时间范围规范化指标并展示样本与缺失数据,输出球员对比、证据和不确定性说明。

crawlora-org
crawlora-org
热度 7.64
最近更新 2026-10-05 09:15:46
分类:数据依赖:代码执行 / 第三方 API / MCP 服务 / 读写文件应用场景:研究 / 报告撰写 / 数据处理

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add football-player-comparison
Metadata
name
football-player-comparison
description
Compare football players using Crawlora FotMob profiles, player-specific season statistics, and bounded match samples. Use for role-aware form comparisons, statistical scouting briefs, or explaining differences with minutes, competition, season, and metric provenance preserved.

Football player comparison

Access and scope

Set CRAWLORA_API_KEY to your Crawlora key. Prefer the listed Crawlora MCP tools when connected; otherwise use the bundled scripts/crawlora.sh, which sends x-api-key to https://api.crawlora.net/api/v1. Read reference/endpoints.md for exact methods and parameters. Check application code as well as HTTP status; payloads are inside data. Stop on authentication errors, back off on 429, and retry a transient upstream failure once. Bound pages and calls by the requested scope and credit budget. Preserve source IDs, URLs, retrieval times, and source dates where supplied.

Produce a role- and period-aware statistical comparison of the selected players. Establish the position/role, competition, season/date window, desired metrics, and minimum playing-time criterion before selecting comparison rows.

Resolve players and comparable observations

  • Use fotmob_search and profile detail to verify numeric player IDs, team, position, and identity. Do not identify players by surname alone or reuse another provider's IDs. Keep current profile context separate from past teams.
  • Discover each player's statSeasons from their profile, then pass that player's entryId to fotmob_player_stats. Player season IDs are player-specific: do not reuse a league ID, a year label, or another player's season entry.
  • Verify competition, period, team, and supplied minutes/appearances for each row. Separate domestic league, cup, international, and other competitions. Keep goalkeeper-specific metrics apart from outfield metrics; shot/heat maps are provider observations rather than directly comparable totals.
  • For recent form, follow player matchFilters league/team pairs and the upstream before cursor to collect a bounded match sample. Resolve match IDs and statuses before per-match player stats. Match-history appearance does not mean the player played 90 minutes or that a scheduled match has finished.
  • Preserve provider stat keys, labels, units, and supplied rating/model context. Missing stats are unknown, not zero. An unavailable injury/profile field does not establish fitness or medical status.
scripts/crawlora.sh /fotmob/search term="Mohamed Salah"
# Select and verify the actual returned player ID first:
# scripts/crawlora.sh /fotmob/player id="$PLAYER_ID"
# scripts/crawlora.sh /fotmob/player-stats player_id="$PLAYER_ID" season_id="$PLAYER_SEASON_ID"
# scripts/crawlora.sh /fotmob/player-matches player_id="$PLAYER_ID"

Normalize metrics and show uncertainty

Calculate a per-90 rate only when the numerator is a compatible count and actual minutes for that same sample are supplied: count / minutes × 90. Do not divide percentages, per-game averages, provider ratings, or already-normalized rates by minutes again. Do not average match percentages when the correct event-weighted denominator is missing. Show totals, rates, and minutes together and apply the same minimum-minute criterion to each player; small samples remain visible.

Do not combine season totals with overlapping match totals. Compare recent form under matched date/competition/role scopes; an equal number of games can span different periods. Keep team strength, tactical role, opponent mix, and provider model differences as limits rather than silently attributing every difference to player ability. Market values are estimates, not offers or transfer fees.

Return an identity/scope ledger, metric comparison with formulas and denominators, selected match evidence, role-specific strengths/uncertainties, and missing data. An overall ranking needs the user's criteria and disclosed weights; unsupported metrics must not silently become zero. Statistics alone do not prove future performance, transfer suitability, or a completed scouting evaluation.