Practical guides to sports data APIs, probability models, prediction-market execution, and honest backtesting with Python.
ZenHodl: $3.12B headline notional vs $1.48B taker cash across 15,808,966 Kalshi sports prints we captured (May 15 - Sep 6, 2026).
Compare Telonex's advertised historical feeds with ZenHodl's dated Polymarket and Kalshi archives, including prices, sampling and timestamp limits.
PMXT offers live access and historical data. Compare its free archive and hosted API with ZenHodl’s fixed sports files using schemas, clocks and coverage.
Learn a four-step audit to find capture gaps in any market-data archive: derive coverage, dark time and cadence from the rows, not the README.
Recount the $9 Kalshi tryout's pinned release and explore recorded spreads, book changes and taker flow, with clear clock and coverage limits.
Compare PolymarketData's API and export options with ZenHodl's dated prediction-market archives, using documented clocks, coverage and licensing.
A dated two-day Kalshi study buckets 553,843 sampled quotes by recorded best-level notional. These are liquidity buckets, not order-size or fill-cost tests.
We ran the numbers on our own $9 MLB matched-book tryout: cross-venue spread, a shared capture gap, and why a settlement-label check isn't a prediction.
Evaluate a Kaggle upload against ZenHodl's recorded prediction-market archives using field definitions, clocks, coverage, settlement and license requirements.
Profit leaderboards show ex-post winners. We hold the complete decoded tape of Polymarket's invisible parlay market — every loser included — so we...
Polymarket combos have no public listings, no order book, and no trade feed. We reconstructed the entire market from on-chain data — ~$9.5M of parlay...
A five-day engineering log: how our Polymarket trading bot recorded limit prices instead of real fills, how we reconciled every historical trade against...
An October 2026 snapshot of ZenHodl’s Polymarket, Kalshi, MLB and combo releases, with exact catalog counts, distinct clocks and measured capture limits.
Buy just the Kalshi order-book slice you study: ATP/WTA/ITF tennis, MLB, WNBA, or LoL/CS2 esports — $39 each, same live capture as the full tape.
Rolling archive of ZenHodl's weekly live Polymarket results — record, per-sport P&L and win rate for every week, losing weeks included.
A practical guide to ZenHodl's recorded Kalshi L2 depth and deduplicated trade prints: what the fields mean, what the sample checks, and the hard limits.
L2 depth for the first 48-team World Cup final: 1.01M rows on Spain vs. Argentina, 20.8M across 47 matches — including which we lost. $39.
Original June–July MLB lag and simulation results, with corrected clock, matching, grid-resolution and hypothetical-fill limitations.
Compare official history, free recorded books and ZenHodl archives. Check dates, depth, clocks, matching rules and license before choosing data.
How we made our data archive buyable by an AI agent over x402 on Base, with on-chain proof. Update 2026-09-27: now x402 v2; new purchases can be paused.
Review a historical MLB Brier-score analysis with corrected interpretation of calibration, repeated snapshots and near-terminal reference prices.
A June 2026 MLB capture reported similar quoted spreads and different recorded depth. Review the sample limits, level cap and statistical interpretation.
The same game trades on Polymarket and Kalshi, so there must be free cross-venue arbitrage, right? A sampled MLB matched book shows how rare positive...
A three-day June MLB pilot reported a lag pattern in sampled prices. Read the original figures with clock, alignment and small-sample limitations.
Find recorded Polymarket books through official data, free archives or paid files. Check the exact fields, clocks, captured window and gaps first.
Diagnose Polymarket order_version_mismatch in Python: inspect SDK release, market identifier/signing route, wallet signature type and funder; separate CTF...
How to measure closing line value for prediction markets and sportsbooks, with buy/sell signs, a worked example, formula, and ZenHodl's dated evidence.
Hedging means buying the other side of your own bet to lock a profit or cap a loss. The math, when it beats letting the position ride, and a correction...
Checked examples for independent −110 legs: combined payout, implied probability and expected stake loss. Distinguish probability gaps from ROI and...
Separate International referrer fee-sharing from Polymarket US promotional trading credits. No invented welcome amount, airdrop or deposit-bonus claim.
Read Kalshi’s account-specific referral requirements. Code-entry timing, verification, qualifying activity and credit expiry matter more than an...
A worksheet for comparing cash, credits and referrer rewards. Distinguish Kalshi account offers, International fee-sharing and Polymarket US trading credits.
Polymarket US and Polymarket International are separate products. Read the CFTC designation record, the International restriction and the current US...
Use Kalshi’s current eligibility, identity and bank-deposit documentation. Separate account approval, trading balance, settled funds and referral conditions.
Recorded Polymarket prices can replay decisions but not fills. See a checked 400-row sample, schema limits, and declared cost scenarios for backtests.
Practical architecture guide for building a Polymarket trading bot in Python in 2026 — the eight components you need, how they fit together, what to build...
Compare Polymarket US/International, PredictIt, Manifold, Robinhood and ForecastEx by product structure. Corrected current official records and terms...
Learn the Polymarket API in practice: CLOB V2, Gamma, Data API, WebSocket streams, wallet signing, per-market fees, and common Python errors.
Polymarket has no built-in paper trading mode. Here are the real options for testing strategies without risk — Manifold Markets, historical price...
Kalshi combos are dedicated markets priced through RFQs. Learn joint-probability math, product-of-leg settlement and why a DNP is not automatically a refund.
Identify the correct Polymarket product before following account or funding instructions. Then read its fees, bid/ask prices and settlement rules.
Distinguish Polymarket US from International, then compare matching sports contracts by rules, executable prices, size, fees and captured evidence.
Read a multi-candidate MVP market without confusing quoted prices with true probabilities. Check settlement rules, basket costs, depth and fees.
Learn how Polymarket CLOB V2 fees work: per-market taker fees, zero maker fees, rebates, the fee curve, Polygon gas, and breakeven edge.
The most useful sports prediction tools of 2026: calibrated APIs, odds aggregators, model dashboards, and bot frameworks, ranked on transparency.
Corrected: our Polymarket trading bots are not profitable in aggregate. See the full retraction and per-market-type P&L breakdown, kept for reference.
Plain-English guide to sports prediction APIs in 2026 — what they return, how authentication and rate limits work, what calibration means, and how to...
Pragmatic seven-step workflow for integrating a sports prediction API into your trading bot, dashboard, or research pipeline — auth, polling cadence, edge...
Architecture and Python patterns for scanning live sports markets in real time and surfacing edge signals — WebSocket subscriptions, latency budgets, edge...
ZenHodl's calibrated win probabilities vs Polymarket's market odds across 5,000+ resolved games — where they agree, diverge, and what it means.
See how to turn calibrated ML probabilities into disciplined bets: position sizing, risk controls, and the honest limits of the math.
A year of live trading data on Polymarket — what it reveals about sports prediction market efficiency, where systematic mispricing persists, and where the...
Convert American, decimal and fractional odds with worked examples. Distinguish break-even implied probability, no-vig estimates and actual costs.
How we designed a single unified API schema that serves calibrated win probabilities for 12 different sports — basketball, hockey, baseball, football...
Full Kelly assumes your edge is real. After six losing days in a row, that assumption deserves a second look. How we layered a drawdown-aware sizing ramp...
Calculate Kelly fractions for a binary contract with checked Python examples. Separate assumed probability, acquisition costs, fractional sizing and risk.
Why the pre-committed NBA Playoffs benchmark starts at the Conference Semis: adding First Round after tip-off would un-freeze a frozen test.
Step-by-step guide to building a sports prediction API in Python, from live data collection and feature engineering to model calibration and FastAPI...
We just announced and on-chain anchored a 7-week head-to-head benchmark: ZenHodl's NBA model vs the live Polymarket consensus, every Conference Semis...
Our April 2026 survey of selected prediction sources examined public calibration evidence. Read the dated findings, criteria and limits.
Our NHL model replayed the 2025 Stanley Cup playoffs on pre-playoff data: 52 of 86 games correct (60.5%), with the Cup Final correctly read as a coin flip.
PSG, Arsenal, Atlético Madrid, and Bayern Munich are the 2025-26 UCL semifinalists. Our production club-soccer model (trained on 3,000+ matches, 75%...
Worked bonus examples distinguish a cash credit from a stake-not-returned bonus bet, with explicit probability, odds and rollover assumptions.
Understand $1 contracts, executable bids and asks, gross break-even probabilities, fees and settlement rules using a hypothetical sports example.
Most 'AI picks' sites are ChatGPT wrappers around sportsbook lines. An honest 2026 guide to telling real ML predictors from narrative generators.
Measure reliability in a defined prediction population, including the traded subset. Checked ECE bin endpoints, held-out calibration methods and dated...
Most sports prediction platforms advertise accuracy that doesn't hold up to measurement. Here's how to evaluate one properly — with published ECE...
Our MLB model replayed the entire 2025 postseason on pre-playoff data: 28 of 47 correct (59.6%). Full breakdown, including every missed ALCS pick.
A 2026 honest answer to whether sports prediction apps actually work. Most 'expert picks' sites are post-hoc curation of sportsbook lines; about 5%...
A 2026 investigation of insider trading in prediction markets. We examine the 2024 Polymarket election dispute, academic studies of PredictIt...
Calculate conditional expected ROI at actual offered odds. Understand what −110 break-even means and why calibration, sample size and sizing do not prove...
Two days into the 2026 NHL playoffs. Our calibrated NHL model has Colorado and Tampa Bay as the Cup favorites, with Carolina close behind. Here's every...
A disclaimer does not establish prediction quality or profitability. Check dated records, calibration, costs and the population behind the claims.
Our NFL model replayed the 2025-26 playoffs on pre-playoff data only: 9 of 13 correct (69.2%), including Seattle over New England in Super Bowl LX.
Pre-tournament Monte Carlo simulation (April 2026): Argentina 26.2% to repeat, Brazil 18.6%, France 13.8% — championship odds for all 48 World Cup teams.
After the 2025 NBA Finals wrapped with OKC beating Indiana in seven games, we ran our NBA win-probability model on all 84 postseason games using only...
Our first college football model backtested at -2.1c/trade over 728 simulated positions. The missing feature, the diagnosis, and the corrected numbers.
Our college basketball model backtested on all 5,345 non-tournament games of 2025-26: 68.2% accuracy, Brier 0.2064, ECE 4.39%, full reliability table.
Our NCAAMB model replayed all 67 games of March Madness 2026 on pre-tournament data: 48 correct (71.6%), 3-for-3 on the Final Four. Every miss listed.
Official product facts for Kalshi, Polymarket US/International, PredictIt, Manifold and ForecastEx. Compare access, contract mechanics, cost and evidence...
Prediction market APIs compared: Polymarket CLOB, Kalshi REST, ESPN scoreboard/summary endpoints, The Odds API, auth, fees, rate limits, and Python.
How win probability models actually work — from Elo ratings through logistic regression to XGBoost. Includes calibration, feature engineering, and the...
An April 2026 snapshot of our Polymarket bots, plus a correction: the win rate and the on-chain claim were wrong. The live ledger is on /results.
Inside ZenHodl's NBA prediction engine: XGBoost models, team stats, real-time injury overlays, and isotonic calibration. How our ML system processes...
Train and evaluate a calibrated NBA win probability model in Python using play-by-play game states, walk-forward validation, XGBoost and isotonic regression.
Compare sportsbook feeds, exchange APIs and prediction APIs using official pricing, coverage and interface documentation; distinguish refresh intervals...
Our bots' architecture, plus a correction: this post once published a +$2,400 P&L that was never true. Real figure: -$217.75 over 2,214 live trades.
The same game is priced differently on Polymarket, DraftKings, FanDuel and BetMGM. The devigging math, a multi-venue scanner architecture, and what our...
Our system finds dozens of trading signals per day. We trade 35% of them. The discipline to reject bad signals is worth more than the ability to find good...
Our NBA bot had 65% accuracy and was losing money — a calibration bug left it confidently wrong. How we found it, and why calibration beats accuracy.
Counter-Strike 2 has the widest mispricings on Polymarket. It also has the worst data infrastructure. Here's how we built a 4-tier model that handles both.
We audited every bot, found the gaps between backtest and live performance, and fixed them. CS2, NBA, MLB, LoL, and Tennis — five different problems, five...
Our trading stack runs on a $7/month VPS plus $5/month for odds. Full cost breakdown, plus a correction: the bot is not profitable (-$217.75 lifetime).
Every one of our bots holds to settlement instead of trading actively. Why patience beat activity on Polymarket, and the strategies that failed.
Real P&L from 5 live Polymarket bots trading NBA, MLB, NHL, NCAAMB, CS2, LoL, Tennis, and Soccer. Honest about what works and what doesn't.
Step-by-step Polymarket CLOB API guide in Python: authentication, reading orderbooks, fetching market data, and placing limit orders programmatically.
We backtested 237 trade signals with and without execution constraints. 99% of theoretical profit vanished. Here's what actually kills your trades — and...
A 6-feature model outperforms a 50-feature model at making money — even with a worse accuracy score. Here's why, and what it means for how you should...
Measure sports-model calibration with Brier score, ECE and held-out calibration examples. Separate reliability from executable-price profitability.
How to get real-time sports scores using WebSockets in Python. Compares HTTP polling vs WebSocket streaming, with working code for building a live data feed.
A complete step-by-step tutorial to implement an Elo rating system in Python for sports prediction. Covers the math, K-factor tuning, season resets, and...
A practical guide to backtesting sports betting and prediction market strategies in Python. Covers the common pitfalls — survivorship bias, look-ahead...
Python examples for ESPN scoreboard and summary endpoints: event IDs, winprobability fields, playId joins, missing sports and data limits.
A beginner's guide to automated trading on Polymarket. Learn how bots find edges using win probability models, and how to build your own.
How to implement Elo ratings from scratch in Python for sports prediction. Covers home advantage, K-factor tuning, and season resets.