meirnm13/playticker-nba-momentum

🏀 PlayTicker: Real-Time NBA Momentum & Betting Intelligence

An Event-Driven AI Engine transforming unstructured play-by-play text into actionable financial signals.

🚀 Launch Live Demo App

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⚡ Executive Summary

Unlike traditional sports datasets that focus on historical stats (box scores), PlayTicker is a predictive engine designed for the Live Betting market.

It solves a critical gap in the industry: Latency. In live betting, odds change in seconds. Human bettors rely on "gut feeling." PlayTicker utilizes NLP & Quantitative Modeling to quantify psychological momentum shifts ("Runs") milliseconds before the market adjusts, providing a calculable edge.

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🥊 The Edge: PlayTicker vs. Standard Datasets

While other datasets function as static directories (e.g., startup lists or player stats), PlayTicker is a dynamic, time-series intelligence system.

Feature	❌ Standard Sports Datasets	✅ PlayTicker Intelligence
Data Type	Static / Historical Stats	Event-Driven / NLP Streams
Focus	"What happened?"	"What is the market impact?"
Latency	Batch Processing (Slow)	Real-Time Inference (0.00ms)
Output	Raw Numbers	Implied Odds & Sentiment Score
Use Case	Post-Game Analysis	Algorithmic Trading / Live Betting

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📊 Dataset Specifications & Quality Analysis

To ensure the AI model learns correct causal relationships (e.g., "Turnover = Negative"), we enforced strict logical constraints during generation.

1. Data Distribution & Vocabulary

• Total Events: 10,000 synthetic, logically consistent NBA plays.
• Momentum Scale: Gaussian distribution centered at 0.138 (slight offensive bias, matching real NBA efficiency).

Momentum Distribution	Vocabulary Cloud
Ensures balanced training data (No bias)	Key terms driving the NLP engine

2. Logical Consistency Check

We verified that specific actions map to the correct sentiment range. As seen below, positive actions (Three-Pointers) consistently yield positive scores, while errors (Turnovers) yield negative scores.

Action Impact Analysis
Ground Truth Validation: The logic holds up.

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🔬 Methodology: The Data Science Pipeline

1. Embeddings & Vectorization (Speed vs. Quality)

We benchmarked 3 state-of-the-art models. In high-frequency trading, speed is everything. We chose MiniLM because it offers 95% of the accuracy at 2x the speed of larger models.

Model	Dimensions	Speed (ms)	Score	Verdict
all-mpnet-base-v2	768	3.21	0.58	Too Slow
all-distilroberta-v1	768	2.87	0.62	Heavy
all-MiniLM-L6-v2	384	1.43	0.76	🏆 Winner

2. Model Selection (The "Zero Latency" Decision)

We tested 5 algorithms. Surprisingly, Linear Regression outperformed complex Random Forests for this specific task, offering instant inference with high interpretability.

Model Benchmarking Results
Linear Regression (Left) vs. Random Forest & Boosting

• R² Score: 0.8625 (86% Variance Explained)
• Inference Time: 0.00ms (Instantaneous)
• Decision: We chose Linear Regression for production APIs.

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🧠 The IO Pipeline: "The Brain & The Memory"

The system implements a dual-pipeline architecture:

Pipeline A: The Signal Engine (Predictive)

• Input: "LeBron James steals the ball and dunks!"
• Process: Vectorization $\rightarrow$ Regression Model $\rightarrow$ Odds Calculator
• Output: Momentum: +0.85 | Signal: BUY | Implied Odds: -150

Pipeline B: The Context Engine (RAG-Lite)

• Input: Current Play Vector
• Process: Cosine Similarity Search against Historical Database (10k rows)
• Output: "This play is 92% similar to Curry's run in 2023."
• Value: Provides Historical Precedent to validate the AI's signal.

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💻 Usage

Quick Start

import joblib
from datasets import load_dataset

# 1. Load the Data
dataset = load_dataset("meirnm13/playticker-nba-momentum")
df = dataset['train'].to_pandas()

# 2. Load the Full Pipeline
pipeline = joblib.load('playticker_complete_pipeline.pkl')

# 3. Predict Live Momentum
play = "Giannis blocks the shot into the stands!"
prediction = pipeline['predict_momentum'](play)

print(f"Momentum Score: {prediction['predicted_momentum']}")
print(f"Category: {prediction['category']}") 
# Output: Category: HIGH POSITIVE MOMENTUM