Part IV: Data Science & Modeling

Chapters 20--27

Trading on instinct and domain expertise can carry you a long way in prediction markets, but it has a ceiling. The traders and forecasters who consistently outperform -- the superforecasters, the quantitative shops, the top leaderboard competitors -- almost universally share one trait: they build systematic, data-driven pipelines that turn raw information into calibrated probability estimates. Part IV is where you learn to build those pipelines yourself.

This part sits at the intersection of data science, machine learning, and forecasting. It assumes you have a working Python environment from Chapter 6 and a solid understanding of probability from Chapter 3, but it does not assume prior experience with pandas, scikit-learn, or natural language processing. Every tool is introduced from the ground up, with prediction market applications as the guiding thread rather than generic textbook examples.

Chapter 20 addresses the first and often most underestimated challenge: data collection. Prediction market data is scattered across APIs, web pages, historical archives, and third-party aggregators, each with its own quirks and limitations. We cover API integration with major platforms, web scraping for supplementary data, working with historical resolution data, and best practices for building a clean, version-controlled data store. The pipeline you build here will feed every model in the chapters that follow.

Chapter 21 turns to exploratory data analysis. Before fitting any model, you need to understand what your data looks like -- its distributions, its gaps, its anomalies. We walk through a comprehensive EDA workflow for prediction market data: price trajectories, volume patterns, resolution distributions, and cross-market correlations. You will develop the habit of looking at your data carefully before asking a model to look at it for you.

In Chapter 22, we build your first forecasting models using classical statistical methods. Logistic regression, time series analysis, and Bayesian updating form the backbone of many successful prediction market strategies. These methods are transparent, interpretable, and surprisingly competitive. We fit models to real market data, evaluate their calibration, and show how to translate model outputs into actionable trading signals.

Chapter 23 introduces machine learning techniques -- gradient-boosted trees, random forests, and neural networks -- for prediction market forecasting. We discuss feature engineering specific to event prediction, the critical importance of proper train-test splitting when events are temporally ordered, and the ever-present risk of overfitting. The emphasis throughout is on practical, deployable models rather than chasing state-of-the-art benchmarks on academic leaderboards.

Chapter 24 opens the rich domain of natural language processing for prediction markets. News articles, social media posts, expert commentary, and the text of market questions themselves all carry predictive signal. We cover sentiment analysis, named entity recognition, topic modeling, and the use of large language models as forecasting tools. You will build a pipeline that ingests unstructured text and outputs features suitable for the models from previous chapters.

Chapter 25 tackles ensemble methods and model combination. No single model dominates across all market types and time horizons. We show how to combine multiple models -- statistical, machine learning, and NLP-based -- into an ensemble that is more accurate and better calibrated than any individual component. Techniques include simple averaging, stacking, and Bayesian model averaging, each illustrated with prediction market case studies.

Chapter 26 is about backtesting: the disciplined practice of evaluating a strategy on historical data before risking real capital. We build a backtesting framework that accounts for the unique features of prediction markets -- discrete resolution, variable time horizons, and platform-specific transaction costs. We also catalog the most common backtesting pitfalls, including lookahead bias, survivorship bias, and overfitting to the backtest period.

Chapter 27 closes the part with MLOps for prediction markets: deploying, monitoring, and maintaining models in production. A model that works in a notebook is not the same as a model that runs reliably every day, retrains on fresh data, and alerts you when its performance degrades. We cover scheduling, monitoring dashboards, drift detection, and the engineering practices that keep a forecasting pipeline healthy over months and years.

By the end of Part IV, you will have the skills to build an end-to-end forecasting system: from raw data ingestion through feature engineering, model training, ensemble combination, backtesting, and production deployment. This is the infrastructure that turns prediction market participation from an art into a science.

Chapters in This Part