下载地址
https://api.skillhub.cn/api/v1/download?slug=%40user_32afda76%2Ffootball-teacher将提示词发送给你的 AI 安装该 skills
请根据 https://skillhub.cn/install/skillhub.md，安装 @user_32afda76/football-teacher。- 世界杯/洲际杯赛比分预测 V3 – 全中文可视化报告：身价对比图、五大联赛球员分布图、伤病影响图，以及3-4个最可能比分。触发词："
- 预测比分"
- 、"
- 谁赢"
- 、"
- 赛前分析"
- …（完整说明见下方「阅读全文」）
阅读全文 （展开查看完整说明）
概述
World Cup Match Predictor V2 ⚽
Predict FIFA World Cup match outcomes using a comprehensive model that combines World Cup history, continental tournament performance, squad market value, and player experience data.
Data Foundation (V2 — Expanded)
| Source | Coverage | Data Points |
|---|---|---|
| FIFA World Cup | 22 tournaments (1930–2022) | 1,248 matches, 87 teams |
| UEFA Euro | 2016, 2020/21, 2024 | 153 matches |
| Copa América | 2019, 2021, 2024 | 86 matches |
| Africa Cup of Nations | 2019, 2021/22, 2023/24 | 156 matches |
| AFC Asian Cup | 2011, 2015, 2019, 2023/24 | 156 matches |
| Transfermarkt | Current squads | 5,614 players, squad values, caps, goals |
Prediction Methodology (V2)
Six-factor weighted composite model:
| Factor | Weight | What it measures |
|---|---|---|
| Power V2 Score | 40% | Composite score: WC(20%) + Continental(20%) + SquadValue(25%) + ClubTier(15%) + Experience(10%) + ElitePlayers(10%) |
| Continental Form | 20% | Recent continental tournament win rate gap (Euro, Copa, AFCON, Asian Cup) |
| Squad Value | 20% | Total squad market value difference (Transfermarkt) |
| Experience | 10% | Total international caps gap |
| Elite Players | 10% | Players valued > €50M gap (game-changers) |
How to Use
Step 1: User asks about a match prediction
When user mentions a matchup, identify:
- Team names (both sides)
- Stage (group / round_of_16 / quarterfinal / semifinal / final) — default to "group"
Step 2: Run the prediction
cd ~/.workbuddy/skills/足球小老师/data && python predict.py <team1> <team2> [stage]Examples:
python predict.py France Morocco semifinal
python predict.py Spain Belgium group
python predict.py Brazil Germany finalTeam names: full names (Brazil, Argentina, England) or FIFA codes (BRA, ARG, ENG, ESP).
Step 3: Present results
Show:
- Team profiles — Power V2, WC Win Rate, Squad Value, Elite Players
- Continental records — titles and win rate in continental tournaments
- Win probabilities — percentages for each team and draw
- Expected score — estimated goals
- V2 model contributions — which factors drove the prediction
- ⚠️ Disclaimer: Statistical prediction, for entertainment only
Step 4: Deeper analysis (optional)
- Read
team_stats_v2.jsonfor full squad details (market values, caps, goals, club tiers) - Explain factor breakdown: "Spain's squad value (€1.51B vs €0.61B) and 3 Euro titles give them the edge"
Data Files
| File | Path | Content |
|---|---|---|
| Match data | data/matches.csv | 1,248 World Cup match records |
| Team stats v2 | data/team_stats_v2.json | 87 teams with comprehensive profiles |
| Head-to-head | data/head_to_head.json | 736 team pair matchup records |
| Continental data | data/continental/*.txt | 551 continental tournament matches |
| Transfermarkt data | data/transfermarkt/*.csv.gz | 5,614 players with clubs & values |
| Prediction engine | data/predict.py | V2 prediction script |
| Analysis engine | data/analyze_v2.py | V2 data analysis script |
Top 20 Teams (Power V2)
| # | Team | Power V2 | Squad Value | WC WR | Cont. Titles |
|---|---|---|---|---|---|
| 1 | England | 67.6 | €1.98B | 48.0% | 0 |
| 2 | France | 67.4 | €1.80B | 56.5% | 2 (Euro) |
| 3 | Brazil | 67.1 | €1.57B | 67.6% | 1 (Copa) |
| 4 | Argentina | 65.5 | €1.05B | 54.6% | 2 (Copa) |
| 5 | Spain | 63.3 | €1.51B | 44.6% | 3 (Euro) |
| 6 | Germany | 58.9 | €1.28B | 66.0% | 0 |
| 7 | Portugal | 57.9 | €1.17B | 51.4% | 1 (Euro) |
| 8 | Netherlands | 56.0 | €1.05B | 57.6% | 1 (Euro) |
| 9 | Italy | 51.7 | €0.98B | 54.7% | 2 (Euro) |
| 10 | Senegal | 43.1 | €0.53B | 30.0% | 1 (AFCON) |
| 11 | Belgium | 41.0 | €0.61B | 43.1% | 0 |
| 12 | Japan | 40.1 | €0.41B | 37.9% | 4 (AsianCup) |
| 13 | Croatia | 38.6 | €0.42B | 56.7% | 0 |
| 14 | Morocco | 36.9 | €0.62B | 26.1% | 0 |
| 15 | Sweden | 36.4 | €0.51B | 47.3% | 0 |
| 16 | USA | 36.2 | €0.57B | 58.6% | 0 |
| 17 | Norway | 36.0 | €0.63B | 56.2% | 0 |
| 18 | Nigeria | 34.9 | €0.41B | 22.7% | 0 |
| 19 | Colombia | 33.8 | €0.30B | 34.2% | 0 |
| 20 | Uruguay | 33.0 | €0.37B | 44.1% | 1 (Copa) |
Notes & Disclaimers
- Model combines 6 data sources for the first time: WC + 4 continental tournaments + Transfermarkt squads
- Squad market values reflect Transfermarkt data as of dataset snapshot date
- Power V2 is recomputed from six sub-factors; see
factor_breakdownin team_stats_v2.json - Club tier scores are approximate — based on predefined club ranking not league data
- Always include disclaimer: "⚽ 足球比赛充满不确定性，此预测仅供娱乐参考，不构成任何投注建议。"
- Data sources: Fjelstul World Cup Database (CC-BY-SA-4.0), openfootball/internationals, dcaribou/transfermarkt-datasets
