--- license: mit library_name: scikit-learn pipeline_tag: tabular-classification tags: - sports-analytics - sports-prediction - football - soccer - basketball - tennis - cricket - tabular-classification - probability-calibration - elo - gradient-boosting language: - en model-index: - name: sports-trends-models results: - task: type: tabular-classification name: Match outcome prediction dataset: name: ruslanmv/sports-trends-dataset type: ruslanmv/sports-trends-dataset metrics: - type: accuracy name: Accuracy (holdout, sport-dependent) value: 0.55 - type: log_loss name: Log loss (calibrated) value: 0.98 --- # ⚽🏀🎾🏏 Sports-Trends Models ### Calibrated, leakage-safe match-outcome models — a model picked for each sport. **Part of [Ruslan Magana Sports Intelligence](https://ruslanmv.com/sports-trends/) — AI match predictions, live results and trending games, refreshed every day.** [](https://ruslanmv.com/sports-trends/) [](https://huggingface.co/datasets/ruslanmv/sports-trends-dataset) [](https://github.com/ruslanmv/sports-trends) [](https://github.com/ruslanmv/sports-trends/blob/main/LICENSE) > **TL;DR** — This repository hosts the production models behind > [ruslanmv.com/sports-trends](https://ruslanmv.com/sports-trends/). Each sport gets > the algorithm best suited to its dynamics, every model outputs **probability-calibrated** > win/draw/loss odds, and the whole training pipeline is **leakage-safe by construction**. > Models are retrained automatically and published under ` /latest/`. > > 📊 **For information & entertainment only — not betting advice.** --- ## 🎯 What these models do Given an upcoming fixture, the models estimate the **probability of each outcome**: | Sport | Outcome space | Model | Why this model | |-------|---------------|-------|----------------| | ⚽ **Football** | home / draw / away (3-way) | `HistGradientBoostingClassifier` | Draws + non-linear Elo×form interactions; gradient boosting handles the 3-way target and feature interactions best. | | 🏀 **Basketball** | home / away (2-way) | `LogisticRegression` | No draws and a strong linear Elo signal — calibrated logistic regression gives clean, well-behaved probabilities. | | 🎾 **Tennis** | player 1 / player 2 (2-way) | `GradientBoostingClassifier` | Head-to-head, surface and form interactions are non-linear; GBDT captures them on short player histories. | | 🏏 **Cricket** | home / away (2-way) | `RandomForestClassifier` | Format-dependent, noisy results; bagged trees are robust to variance and outliers. | | 🏆 **World Cup / international** | 90-min result **+ to-advance** | Elo + tournament model | Adds host advantage, neutral venue, confederation strength and stage importance, plus an extra-time/penalties "who advances" layer. | Every estimator is wrapped in **probability calibration** (isotonic for tree models, sigmoid for logistic) so a published "62%" really behaves like 62% over many games — with a safe fallback to the raw estimator when a dataset is too small to calibrate. --- ## 🧠 How the predictions work Predictions are **not** a black box and **not** scraped odds. They are produced by a transparent, reproducible pipeline. Every published prediction ships with a short plain-language **explanation** of the drivers behind it. ``` Free sports APIs Feature engineering Per-sport model ┌──────────────────┐ normalize ┌────────────────────┐ infer ┌────────────────┐ │ fixtures, results│ ───────────▶ │ Elo · form · H2H · │ ──────▶ │ calibrated │ │ (multi-source) │ canonical │ rest · home adv · │ │ probabilities │ │ + offline mock │ schema │ league/social │ │ + explanation │ └──────────────────┘ └────────────────────┘ └───────┬────────┘ │ ▲ │ │ leakage guard: only matches with date /latest/ model.pkl # calibrated scikit-learn estimator (joblib) feature_schema.json # ordered feature names + dtypes expected at inference metrics.json # holdout accuracy + log loss for this version README.md # per-sport card registry/ latest_versions.json # production pointer: sport -> {version, path, metrics} ``` `registry/latest_versions.json` is the source of truth for which version is live. --- ## 🚀 Use a model ```python import json import joblib import pandas as pd from huggingface_hub import hf_hub_download REPO = "ruslanmv/sports-trends-models" SPORT = "football" model = joblib.load(hf_hub_download(REPO, f"{SPORT}/latest/model.pkl")) schema = json.load(open(hf_hub_download(REPO, f"{SPORT}/latest/feature_schema.json"))) # Build one row with the features named in feature_schema.json (same order). features = {name: 0.0 for name in schema["features"]} X = pd.DataFrame([features])[schema["features"]] proba = model.predict_proba(X)[0] print(dict(zip(model.classes_, proba.round(3)))) # e.g. {'home': 0.58, 'draw': 0.18, 'away': 0.24} ``` Check the live production versions and metrics: ```python import json from huggingface_hub import hf_hub_download reg = json.load(open(hf_hub_download("ruslanmv/sports-trends-models", "registry/latest_versions.json"))) print(reg["football"]) # -> {'version': ..., 'path': 'football/latest/', 'metrics': {...}} ``` --- ## 📈 Evaluation Each version stores its own `metrics.json` (holdout **accuracy** and calibrated **log loss**) produced on a chronological hold-out split. Sports outcomes are high-variance, so treat metrics as *relative* model-quality signals rather than guarantees: a well-calibrated football model typically lands meaningfully above the 3-way random/majority baseline, and log loss is the metric we optimise for because **calibration matters more than raw accuracy** for probabilistic predictions. > Numbers in the `model-index` above are indicative placeholders; the authoritative, > per-version metrics always live in each ` /latest/metrics.json`. --- ## ⚠️ Intended use & limitations **Intended use** — research, education, sports analytics, and powering the [ruslanmv.com/sports-trends](https://ruslanmv.com/sports-trends/) dashboard. **Out of scope / limitations** - 🚫 **Not betting advice.** Predictions are informational and for entertainment only. No outcome is guaranteed. Please gamble responsibly, if at all. - Models reflect their training data: lower-tier competitions and rare matchups carry more uncertainty than top leagues. - Free data sources can be delayed or incomplete; the system favours graceful degradation (Elo heuristic / mock) over fabricated precision. - They estimate *probabilities*, not certainties — variance and upsets are expected. --- ## 🧾 Citation ```bibtex @software{magana_sports_trends_2026, author = {Ruslan Magana Vsevolodovna}, title = {Sports-Trends: Calibrated, leakage-safe sports outcome models}, year = {2026}, url = {https://huggingface.co/ruslanmv/sports-trends-models}, note = {Live dashboard: https://ruslanmv.com/sports-trends/} } ``` --- ### 👤 About the author Built and maintained by **[Ruslan Magana Vsevolodovna](https://ruslanmv.com)** — AI / ML engineer working on data platforms, MLOps and applied machine learning. 🌐 **[ruslanmv.com](https://ruslanmv.com)** · 📊 **[Live dashboard](https://ruslanmv.com/sports-trends/)** · 🤗 **[Dataset](https://huggingface.co/datasets/ruslanmv/sports-trends-dataset)** · 💻 **[GitHub](https://github.com/ruslanmv/sports-trends)** Powered by Hugging Face 🤗 + GitHub Actions ⚙️ · Licensed MIT · Not betting advice.
