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<!DOCTYPE html>
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<title>CodeFix Solution — Developer Guides, Tutorials & Code Fixes</title>
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<header><div class="container"><h1><a href="/">CodeFix Solution</a></h1></div></header>
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<h2>Developer Guides & Code Solutions</h2>
<p>Practical tutorials for Python developers. API integrations, machine learning pipelines, and real-world engineering fixes.</p>
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<div class="posts">
<h3>Latest Posts</h3>
<div class="post-card">
<a href="/work-with-kalshi-order-book-data-python/">
<span class="tag">Kalshi</span><span class="tag">Microstructure</span><span class="tag">Python</span>
<h4>Working With Kalshi Order-Book Data in Python: L2 Depth + Trade Prints, Joined</h4>
<p>Kalshi's API serves trades and candles — not book history. Loading captured L2 depth and the executed trade tape in pandas, deduping prints by trade_id, joining prints to snapshots with merge_asof, and building a signed-flow feature — plus the gotchas (level caps, polled snapshots, capture gaps) that actually bite.</p>
<div class="meta">July 20, 2026 · 11 min read</div>
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<div class="post-card">
<a href="/what-are-prediction-markets-developer-primer/">
<span class="tag">Prediction Markets</span><span class="tag">Quant</span><span class="tag">Primer</span>
<h4>What Are Prediction Markets and Why Do They Matter? A Developer's Primer</h4>
<p>The 40-year history of information aggregation markets, the empirical track record vs experts, and the quant opportunities for a developer who can ship a calibrated model. From Iowa Electronic Markets to Polymarket.</p>
<div class="meta">May 18, 2026 · 12 min read</div>
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<div class="post-card">
<a href="/notebook-to-production-sports-trading-bot/">
<span class="tag">Python</span><span class="tag">Production</span><span class="tag">Trading Bots</span>
<h4>From Notebook to Production: Shipping Your First Sports Trading Bot</h4>
<p>The eight-step playbook for turning a Jupyter prediction model into a live trading service. Persistence, monitoring, supervision, and the production patterns the notebook never taught you.</p>
<div class="meta">May 11, 2026 · 13 min read</div>
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<div class="post-card">
<a href="/reliability-curves-win-probability-calibration/">
<span class="tag">Calibration</span><span class="tag">Isotonic</span><span class="tag">Sklearn</span>
<h4>Calibrating Win-Probability Models: Reliability Curves Using 100,000+ Game-State Snapshots</h4>
<p>Build reliability diagrams, compute Expected Calibration Error, fit isotonic and Platt calibrators. Production calibration tutorial in Python with per-sport thresholds.</p>
<div class="meta">May 11, 2026 · 14 min read</div>
</a>
</div>
<div class="post-card">
<a href="/what-features-move-sports-win-probability/">
<span class="tag">Feature Engineering</span><span class="tag">XGBoost</span><span class="tag">SHAP</span>
<h4>Feature Engineering for Sports Betting Models: What Actually Moves Win Probability</h4>
<p>Which features actually carry signal across NBA, NHL, MLB, and football. The five universal features, the sport-specific add-ons, and the features that look important but are not.</p>
<div class="meta">May 11, 2026 · 13 min read</div>
</a>
</div>
<div class="post-card">
<a href="/module-1-scraping-espn-scoreboard-nba/">
<span class="tag">Python</span><span class="tag">ESPN</span><span class="tag">Web Scraping</span>
<h4>Module 1 Deep Dive: Scraping ESPN's Scoreboard Endpoints for Every NBA Game in a Season</h4>
<p>Defensive scraping pattern for ESPN's undocumented JSON endpoints. Iterate a full NBA season, handle rate limits and retries, dedup, and save to parquet for downstream ML.</p>
<div class="meta">May 11, 2026 · 12 min read</div>
</a>
</div>
<div class="post-card">
<a href="/time-series-cross-validation-sports-models/">
<span class="tag">Python</span><span class="tag">Cross-Validation</span><span class="tag">Sports ML</span>
<h4>Time-Series Cross-Validation for Sports Models: Why K-Fold Lies</h4>
<p>Why random k-fold inflates AUC by 6-15 points on time-ordered sports data. Walk-forward, expanding-window, and season-based CV in Python — plus the label-permutation test that catches deeper leakage.</p>
<div class="meta">May 5, 2026 · 14 min read</div>
</a>
</div>
<div class="post-card">
<a href="/best-college-basketball-prediction-sites/">
<span class="tag">NCAAMB</span><span class="tag">Calibration</span><span class="tag">Buyer's Guide</span>
<h4>Best College Basketball Prediction Sites 2026: A Calibration-First Buyer's Guide</h4>
<p>Developer's buyer's guide to college basketball prediction sources. KenPom, Bart Torvik, 538, ESPN BPI, The Odds API, ZenHodl — ranked by calibration (ECE), transparency, and measured accuracy. No affiliate fluff.</p>
<div class="meta">April 22, 2026 · 11 min read</div>
</a>
</div>
<div class="post-card">
<a href="/build-soccer-prediction-model/">
<span class="tag">Python</span><span class="tag">ELO</span><span class="tag">XGBoost</span><span class="tag">Soccer</span>
<h4>How to Build a Soccer Prediction Model in Python (ELO + XGBoost + Draw Handling)</h4>
<p>Full tutorial: soccer-tuned ELO, 3-class win/draw/loss XGBoost with isotonic calibration, league-strength adjustments for Champions League cross-league matches, and Monte Carlo tournament simulator for the 48-team World Cup bracket.</p>
<div class="meta">April 22, 2026 · 15 min read</div>
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<div class="post-card">
<a href="/build-nhl-stanley-cup-prediction-model/">
<span class="tag">Python</span><span class="tag">ELO</span><span class="tag">Monte Carlo</span><span class="tag">NHL</span>
<h4>How to Build an NHL Stanley Cup Prediction Model in Python (ELO + Monte Carlo Futures)</h4>
<p>Full tutorial: NHL-tuned ELO (K=8, HFA=40), special-teams features (PP%, PK%, save%), best-of-seven Monte Carlo simulator, bracket format (not reseeding), and a 2024-25 playoff backtest harness.</p>
<div class="meta">April 22, 2026 · 14 min read</div>
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</div>
<div class="post-card">
<a href="/build-nba-finals-prediction-model/">
<span class="tag">Python</span><span class="tag">ELO</span><span class="tag">XGBoost</span><span class="tag">NBA</span>
<h4>How to Build an NBA Finals Prediction Model in Python (ELO + Pace/ORTG/DRTG)</h4>
<p>Full tutorial: NBA-tuned ELO (K=20, HFA=80), pace/ORTG/DRTG efficiency priors, XGBoost + isotonic calibration, best-of-seven series Monte Carlo, and a 2024-25 playoff backtest harness.</p>
<div class="meta">April 22, 2026 · 14 min read</div>
</a>
</div>
<div class="post-card">
<a href="/build-mlb-prediction-model/">
<span class="tag">Python</span><span class="tag">ELO</span><span class="tag">XGBoost</span><span class="tag">MLB</span>
<h4>How to Build an MLB Prediction Model in Python (ELO + Starting Pitcher Features)</h4>
<p>Full tutorial: MLB-tuned ELO (K=4, HFA=24), starting-pitcher ERA/WHIP/K9 differentials, XGBoost + isotonic calibration, and a 2025 postseason backtest harness (47 games, ECE 1.81%).</p>
<div class="meta">April 22, 2026 · 13 min read</div>
</a>
</div>
<div class="post-card">
<a href="/build-super-bowl-prediction-model/">
<span class="tag">Python</span><span class="tag">ELO</span><span class="tag">Monte Carlo</span><span class="tag">NFL</span>
<h4>How to Build a Super Bowl Prediction Model in Python (ELO + Monte Carlo Futures)</h4>
<p>Full tutorial: NFL-tuned ELO, Monte Carlo playoff bracket simulation, neutral-site adjustment for the Super Bowl, and a backtest harness that ran against the actual 2025-26 playoffs (9/13 correct).</p>
<div class="meta">April 22, 2026 · 15 min read</div>
</a>
</div>
<div class="post-card">
<a href="/build-march-madness-prediction-model/">
<span class="tag">Python</span><span class="tag">XGBoost</span><span class="tag">ELO</span><span class="tag">March Madness</span>
<h4>How to Build a March Madness Prediction Model in Python (With Calibrated Bracket Probabilities)</h4>
<p>Full tutorial: ELO ratings with basketball MoV, pace/ORTG/DRTG priors, XGBoost + isotonic calibration, and a tournament backtest harness that runs your model on any past bracket without look-ahead bias.</p>
<div class="meta">April 22, 2026 · 14 min read</div>
</a>
</div>
<div class="post-card">
<a href="/build-college-football-prediction-model/">
<span class="tag">Python</span><span class="tag">XGBoost</span><span class="tag">College Football</span>
<h4>How to Build a College Football Prediction Model in Python (With Calibrated Win Probabilities)</h4>
<p>Full tutorial: ESPN play-by-play data, XGBoost training, isotonic calibration, Expected Calibration Error measurement, and backtesting against market win probabilities.</p>
<div class="meta">April 22, 2026 · 12 min read</div>
</a>
</div>
<div class="post-card">
<a href="/jupyter-to-production-ml-api/">
<span class="tag">Python</span><span class="tag">FastAPI</span><span class="tag">MLOps</span>
<h4>From Jupyter to Production: Packaging an ML Model for a REST API</h4>
<p>Take a scikit-learn or XGBoost model from notebook to production. Pickle versioning, integrity checks, FastAPI endpoints, and graceful fallback — with working code.</p>
<div class="meta">April 21, 2026 · 14 min read</div>
</a>
</div>
<div class="post-card">
<a href="/xgboost-sample-weights/">
<span class="tag">Python</span><span class="tag">XGBoost</span><span class="tag">ML Engineering</span>
<h4>Sample Weights in XGBoost: Fixing Class Imbalance and Regime Drift</h4>
<p>XGBoost's most underused parameter. How to use <code>sample_weight</code> to correct class imbalance, emphasize recent data, and tune for specific regimes.</p>
<div class="meta">April 21, 2026 · 12 min read</div>
</a>
</div>
<div class="post-card">
<a href="/feature-engineering-sports-win-probability/">
<span class="tag">Python</span><span class="tag">XGBoost</span><span class="tag">Feature Engineering</span>
<h4>Feature Engineering for Sports Win Probability: 15 Features That Actually Matter</h4>
<p>The features that make a sports win-probability model work. Score diff, time fraction, Elo, momentum, pace — with Python code for each.</p>
<div class="meta">April 21, 2026 · 13 min read</div>
</a>
</div>
<div class="post-card">
<a href="/python-xgboost-probability-calibration/">
<span class="tag">Python</span><span class="tag">XGBoost</span><span class="tag">Calibration</span>
<h4>Calibrating XGBoost Probabilities with Isotonic Regression</h4>
<p>Why raw XGBoost predicted probabilities are wrong and how to fix them. Isotonic regression, Platt scaling, and measuring Expected Calibration Error.</p>
<div class="meta">April 14, 2026 · 10 min read</div>
</a>
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<div class="post-card">
<a href="/build-sports-prediction-model-python/">
<span class="tag">Python</span><span class="tag">API</span><span class="tag">ML</span>
<h4>How to Build a Sports Prediction Model with Python and a Live API</h4>
<p>Go from zero to a working sports prediction system in 30 minutes. Live win probabilities, edge detection, and real-time alerts across 10 sports.</p>
<div class="meta">April 14, 2026 · 8 min read</div>
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