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Professional football match prediction and analysis using XGBoost 2.0+ ML model. Use when predicting match results, analyzing team performance, or calculating win probabilities. Features 67.2% accuracy with <100ms response time.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Comprehensive API testing including unit tests, integration tests, performance testing, and automated test pipelines. Use when testing REST endpoints, validating API responses, load testing, or setting up test automation.
Manage code quality for Football Prediction project using Make commands. Use when running quality checks, formatting code, running tests, or preparing for commits. Supports black, flake8, mypy, bandit, pytest, and coverage tools.
Collect football data from external APIs including FotMob L2 data, odds information, and real-time match statistics. Use when gathering match data, collecting historical statistics, or updating prediction datasets.
Design and optimize data pipelines for football prediction system. Use when building ETL processes, data transformations, or optimizing data flow between PostgreSQL, Redis, and external APIs.
Manage PostgreSQL database operations including migrations, query optimization, connection pooling, and data backup/restore. Use when handling database tasks, optimizing queries, or managing database connections.
Manage application deployment including Docker containers, production environments, and service orchestration. Use when deploying to production, managing Docker services, or handling deployment rollbacks.
| name | football-prediction |
| description | Professional football match prediction and analysis using XGBoost 2.0+ ML model. Use when predicting match results, analyzing team performance, or calculating win probabilities. Features 67.2% accuracy with <100ms response time. |
专业的足球比赛预测和分析技能，基于XGBoost 2.0+机器学习模型，提供高精度的比赛结果预测。
主队/客队各 8 维:
rolling_xg, rolling_xg_stdrolling_shots_on_target, rolling_shots_on_target_stdrolling_possession, rolling_possession_stdrolling_team_rating, rolling_team_rating_stdhome_table_position, away_table_position, table_position_diffhome_points, away_points, points_diffhome_recent_form_points, away_recent_form_pointsELO 相对差距:
raw_elo_gap, adjusted_elo_gap, fatigue_impact, schedule_impact疲劳度指数:
home_fatigue_index, away_fatigue_index, fatigue_diffhome_rest_days, away_rest_days保级战意:
home_relegation_incentive, away_relegation_incentiveincentive_diff, home_desperationtable_proximity: 积分榜接近度low_scoring_tendency: 低得分倾向elo_diff_cluster: ELO 差距聚类feature-engineering 技能python scripts/predict_match_v2.py --home "Manchester United" --away "Arsenal"
python scripts/predict_match_v2.py --batch matches.json
from src.services.inference_service_v2 import InferenceServiceV2
service = InferenceServiceV2()
result = await service.predict_single_match(home_team, away_team)
🏟️ 比赛: Manchester United vs Arsenal
📅 日期: 2024-01-15
📊 预测概率:
主胜 (HOME) : 65.2% |███████████████████████████████████░░░|
平局 (DRAW) : 22.1% |███████████████░░░░░░░░░░░░░░░░░░░░░░|
客胜 (AWAY) : 12.7% |███████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░|
🎯 预测结果: HOME_WIN
💡 置信度: 65.2%
📈 模型版本: xgboost_v2
{
"match": {
"home_team": "Manchester United",
"away_team": "Arsenal",
"date": "2024-01-15"
},
"prediction": {
"outcome": "HOME_WIN",
"probabilities": {
"home_win": 0.652,
"draw": 0.221,
"away_win": 0.127
},
"confidence": 0.85
},
"model_info": {
"version": "xgboost_v2",
"accuracy": 0.672,
"features_used": 15
}
}
FotMob API → 数据收集器 → 特征工程 → PostgreSQL → 特征缓存 → 模型推理
scripts/predict_match_v2.py - 主要预测CLIscripts/test_real_model.py - 模型测试工具scripts/train_model_from_csv.py - 模型训练工具/api/v2/predict - 预测端点/api/v2/batch-predict - 批量预测/api/health - 模型健康检查Q: 预测准确率如何？ A: 当前准确率67.2%，持续优化中
Q: 支持哪些联赛？ A: 支持50+主流足球联赛
Q: 如何提高预测精度？ A: 结合实时数据、伤病情况和专家分析
feature-engineering: 特征工程专项（V25.1 自适应引擎）v26-harvest: V26.1 收割流水线（数据采集）machine-learning-engineering: ML 模型优化data-engineering: 数据管道工程