https://github.com/krishnaura45/bracket-brain

🏆Basketball Predictions 🧪 Ensemble and GOTO Techniques
https://github.com/krishnaura45/bracket-brain

basketball betting-odds bracket brier-scores featured kaggle-competition march-madness ml sports

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🏆Basketball Predictions 🧪 Ensemble and GOTO Techniques

• Host: GitHub
• URL: https://github.com/krishnaura45/bracket-brain
• Owner: krishnaura45
• License: cc0-1.0
• Created: 2025-04-18T05:20:33.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2025-06-06T14:29:59.000Z (9 months ago)
• Last Synced: 2025-07-04T10:43:03.301Z (8 months ago)
• Topics: basketball, betting-odds, bracket, brier-scores, featured, kaggle-competition, march-madness, ml, sports
• Language: Jupyter Notebook
• Homepage:
• Size: 2.93 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# 🏀bracket-brain

Forecasting NCAA basketball tournament outcomes using machine learning models to support bracket predictions and sports analytics.

![Python](https://img.shields.io/badge/Python-3776AB?style=for-the-badge&logo=python&logoColor=white)

![Kaggle](https://img.shields.io/badge/Kaggle-20BEFF?style=for-the-badge&logo=kaggle&logoColor=white)

![Scikit-Learn](https://img.shields.io/badge/Scikit--Learn-F7931E?style=for-the-badge&logo=scikit-learn&logoColor=white)

![Brier Score Optimized](https://img.shields.io/badge/Optimized--for-Brier%20Score-yellowgreen?style=for-the-badge)

![Metric Score](https://img.shields.io/badge/Best%20Score-0.10921-2ECC71?style=for-the-badge)

![Rank](https://img.shields.io/badge/Rank-53%20of%201727-brightgreen?style=for-the-badge)

![Solo](https://img.shields.io/badge/Submission-Solo-orange?style=for-the-badge)

### **Project Duration**: Mar 17, 2025 - Apr 7, 2025

---

## 🧠 Problem Statement

The aim of this competition was to predict the outcomes of all potential matchups in the 2025 NCAA Division I men’s and women’s basketball tournaments. Each submission included predicted probabilities for every possible matchup that could occur in the tournament brackets. 

Hosted on Kaggle as part of the annual **March Machine Learning Mania**, submissions were evaluated using the **Brier score**, which penalizes inaccurate probability predictions. Lower Brier scores indicate better predictions.

---

## 🧩 Approach

You can explore the complete methodology in this notebook: 🔗 [MMLM25 - EDA and Prediction](https://github.com/krishnaura45/bracket-brain/blob/main/mmlm25-eda-pred.ipynb)

Key steps followed:

- 📊 **Exploratory Data Analysis (EDA)**:

  - Understood tournament structure, team seeding, and regional splits.

  - Analyzed match histories and evaluated metrics such as `ScoreDiff`, `SeedDiff`, and team-level historical performance.

  - Assessed other features such as win-loss records, and scoring statistics.

- 🧠 **GOTO Model**:

  - Utilized the goto_conversion method to convert betting odds into outcome probabilities.

  - This method adjusts inverse odds by the same units of standard error, effectively addressing the favourite-longshot bias by considering the proportionately wider standard errors implied for inverses of longshot odds and vice versa.

- 🧪 **Prediction & Submission**:

  - Mapped test data using team identifiers from the sample submission file.

  - Applied the converted probabilities to predict outcomes for every possible tournament matchup.

  - Ensured that the predictions accounted for the inherent biases in betting markets, leading to more accurate forecasts.

---

## 🏆 Results / Outcomes

- ✅ **Public Leaderboard Scores**:

  - Achieved **0.12453** and **0.10921** Brier scores.

- 🏁 **Private Leaderboard Score**:

  - Final score: **0.10921** on hidden test data.

- 🥇 **Rank Achieved**:

  - Placed **53rd out of 1,986 participants** and **1,727 teams** as a **solo participant**.

---

## 🔗 References

- 🏆 Kaggle Competition: [March Machine Learning Mania 2025](https://www.kaggle.com/competitions/march-machine-learning-mania-2025)

- 📁 Dataset: [Competition Data](https://www.kaggle.com/competitions/march-machine-learning-mania-2025/data)

- 🗒️ Helpful Notebooks:

  - [MMLM - Starter by Paul](https://www.kaggle.com/code/paultimothymooney/simple-starter-notebook-for-march-mania-2025)

  - [Vilnius NCAA by Raddar](https://www.kaggle.com/code/jocelyndumlao/march-ml-mania-2025-brier-score-prediction)

- 📦 `goto_conversion` Package: [GitHub repo](https://github.com/gotoConversion/goto_conversion)

---

## 🛠️ Tech Stack

- **Language**: Python 🐍

- **Libraries**:

  - `pandas`, `numpy` for data manipulation

  - `matplotlib`, `seaborn` for visualization

- **Tools**:

  - Jupyter Notebook 📓 for development

  - Kaggle Kernels / Google Colab for experimentation and submission

---

📌 *This project highlights the effectiveness of advanced odds conversion techniques, like goto_conversion, in improving the accuracy of probabilistic forecasts for sports tournaments.*