Every lab report passes through the same five stages
The lab's report pipeline is public. Every published report passes through five stages: framing, dataset pull, analysis, assumption-risk documentation, and re-test.
Frame the question
Each report starts with a specific question — is the differential-captain model actually better in mega contests? What happens to ROI when ownership spikes? The question determines which data we pull.
Pull the raw data
Match data, ownership data, lineup data, contest results — every input goes into a timestamped dataset. We never quietly update a dataset after the conclusion lands; the version is locked.
Run the analysis
Standard statistical tests, plus cricket-specific models we've refined over four IPL seasons. We publish the methodology doc so other analysts can re-run the experiment.
Document the assumption risk
Every model has assumptions. We list them — sample size limits, ownership-distribution noise, and the small chance the next match breaks the pattern. No methodology report is complete without this step.
Re-test on the next cycle
The lab runs every published model against fresh data the following week. We publish what held up and what didn't. A clean hit-rate is a research process, not a marketing claim.
Where the lab's data comes from
PlayExch uses publicly available match data, contest result archives, ownership distribution feeds, and self-collected squad logs. Sources are listed in every report.
Match data
Ball-by-ball feeds from official tournament data partners. Cross-checked against broadcast records. Time-stamped at ingestion. Every dataset has a version number that's locked once a report publishes.
Ownership data
Public ownership feeds from major Indian contest platforms, sampled every 30 seconds during contest open windows. The lab uses an averaged-ownership metric to smooth out late-team-lock spikes.
Contest result archives
Final contest standings from publicly published leaderboards. Used for hit-rate calculations and ROI audits. The lab holds a private archive but never publishes player-level data without consent.
Self-collected squad logs
A subset of PlayExch readers voluntarily submit their squad picks at lock time. The lab uses this self-collected sample to validate projection-model outputs against actual reader behavior.
The lab's code is public
The lab publishes the projection-model code, the dataset schema, and the hit-rate calculation logic. Other analysts can re-run the experiments.
Projection model
The projection model is a Python package published on GitHub. The package includes the venue-read module, the pitch-type classifier, the ownership-bias calculator, and the captain confidence score function.
Dataset schema
The dataset schema is published as a JSON Schema. The schema documents every field, every type, every constraint. The lab publishes a sample dataset for re-running.
Hit-rate calculation
The hit-rate calculation is documented in the methodology. The lab computes captain hit rate as the share of captain picks that finish in the top-25% of fantasy points in their respective contests.
Re-test cadence
Every published model is re-tested the following week. The re-test compares the projection to the actual outcome. The delta is published in the Monday retrospective post.
Methodology questions, answered
The most common questions about methodology on PlayExch, with detailed answers.
How is the captain hit rate calculated?
The captain hit rate is the share of captain picks that finish in the top-25% of fantasy points in their respective contests. The 90-day rolling hit rate is currently 61%. The methodology page explains how the hit rate is calculated and how the dataset is locked.
Can I see the model code?
Yes. The methodology page publishes the projection-model code, the dataset schema, and the hit-rate calculation logic. The model is a Python package on GitHub. Other analysts can re-run the experiments.
How is the dataset version locked?
Every dataset has a version number that's locked once a report publishes. The version number is included in the report's metadata. The lab never quietly updates a dataset after the conclusion lands.
What is the assumption-risk note?
Every model has assumptions. The lab lists them — sample size limits, ownership-distribution noise, and the small chance the next match breaks the pattern. The note is required by the editorial pipeline.
How often is the model re-tested?
Every Monday morning. The recalibration uses the previous week's results and adjusts the model's weights. The delta is published in the weekly retrospective post.
Ready to start with PlayExch?
Read the lab reports, pick a contest tier, run ten squads with the captain reads, and audit your ROI after fifty contests.For the methodology desk, read the lab reports, pick a contest tier, run ten squads with the captain reads, and audit your ROI after fifty contests.
The lab's deeper perspective on methodology
Beyond the headline content, the lab's editorial team publishes a longer read on the underlying dynamics, the historical context, and the future direction.
Historical context
The fantasy cricket market has evolved through three phases. Phase 1 (2008-2015) was the early-adopter phase — Dream11 was the dominant operator and the captain-pick methodology was largely gut-driven. Phase 2 (2016-2022) was the data-driven phase — operators introduced live-match data feeds and projection models became more sophisticated. Phase 3 (2023-present) is the transparency phase — readers demand methodology disclosure, hit-rate auditing, and assumption-risk documentation. The lab was founded in 2023 to serve Phase 3 readers.
Underlying dynamics
The underlying dynamics of fantasy cricket are driven by three forces. The first is the ownership distribution — when ownership spikes, the differential edge compresses. The second is the venue read — venue-aware picks outperform venue-blind picks by 11 points in hit rate. The third is the form curve — recent form is a stronger predictor than career average. The lab's research covers all three forces in depth.
Future direction
The fantasy cricket market is moving toward three futures. The first is increased regulation — state gaming authorities are tightening the rules around paid contests. The second is increased transparency — readers demand methodology disclosure and hit-rate auditing. The third is increased automation — AI-driven captain picks and squad optimization are becoming mainstream. The lab is positioned for all three futures.
Lab's editorial position
The lab's editorial position is that fantasy cricket is a research-grade problem. Captain picks are not gut calls — they are data-driven decisions backed by methodology, dataset versioning, and assumption-risk documentation. The lab's editorial principles are published on the owner page. The lab's editorial standards are reviewed quarterly by the editorial board.
The lab's extended perspective
A longer-form walk-through of the underlying dynamics, the data behind the recommendations, and the lab's analytical approach.On methodology, a longer-form walk-through of the underlying dynamics, the data behind the recommendations, and the lab's analytical approach.
Methodology in depth
The lab's methodology is built on a five-stage pipeline. Every published report passes through framing, dataset pull, analysis, assumption-risk documentation, and re-test. The pipeline is published in full on the methodology page. The lab's edge comes from data freshness and analyst expertise, not from a secret formula. Other analysts can re-run the experiments with the published datasets and the published model code. The lab publishes every dataset version and every model version. The lab never quietly updates a dataset after the conclusion lands.On methodology, the lab's methodology is built on a five-stage pipeline. For the methodology desk, every published report passes through framing, dataset pull, analysis, assumption-risk documentation, and re-test. Within the PlayExch framework, the pipeline is published in full on the methodology page. Cricket readers should note that the lab's edge comes from data freshness and analyst expertise, not from a secret formula. Other analysts can re-run the experiments with the published datasets and the published model code This is why the methodology desk treats it as a baseline. The lab publishes every dataset version and every model version. On methodology, the lab never quietly updates a dataset after the conclusion lands.
Data sources in depth
The lab pulls data from four primary sources. Match data comes from official tournament data partners, cross-checked against broadcast records. Ownership data comes from public contest-platform feeds, sampled every 30 seconds during contest open windows. Contest result archives come from publicly published leaderboards. Self-collected squad logs come from volunteer readers who submit their picks at lock time. Each source has a published quality score and a published latency benchmark. The lab's data-quality framework is reviewed quarterly by the statistics team.On methodology, the lab pulls data from four primary sources. For the methodology desk, match data comes from official tournament data partners, cross-checked against broadcast records. Within the PlayExch framework, ownership data comes from public contest-platform feeds, sampled every 30 seconds during contest open windows. Cricket readers should note that contest result archives come from publicly published leaderboards. Self-collected squad logs come from volunteer readers who submit their picks at lock time This is why the methodology desk treats it as a baseline. Each source has a published quality score and a published latency benchmark. On methodology, the lab's data-quality framework is reviewed quarterly by the statistics team.
Reader-safety commitments
The lab's reader-safety commitments cover deposit limits, self-exclusion, play-time alerts, loss trackers, cool-down reminders, and family-controls integration. The lab publishes the safety commitments on the responsible-play page. The lab refuses to partner with operators that do not enforce responsible-play defaults. The lab's reader-safety framework is reviewed annually by an external auditor.On methodology, the lab's reader-safety commitments cover deposit limits, self-exclusion, play-time alerts, loss trackers, cool-down reminders, and family-controls integration. For the methodology desk, the lab publishes the safety commitments on the responsible-play page. Within the PlayExch framework, the lab refuses to partner with operators that do not enforce responsible-play defaults. Cricket readers should note that the lab's reader-safety framework is reviewed annually by an external auditor.
Editorial principles
The lab operates by five public editorial principles. No sponsored picks — no contest operator, brand, or individual can pay the lab to feature a captain pick. Methodology is public — every model's code is published on the methodology page. Misses are published — every Monday morning the lab publishes the previous week's misses alongside the hits. Reader-funded economics — the lab's long-term economics rely on reader subscriptions and contest-entry referrals, not on advertising. Responsible play by default — every contest lobby on the lab's partner platforms shows a responsible-play banner.On methodology, the lab operates by five public editorial principles. For the methodology desk, no sponsored picks — no contest operator, brand, or individual can pay the lab to feature a captain pick. Within the PlayExch framework, methodology is public — every model's code is published on the methodology page. Cricket readers should note that misses are published — every Monday morning the lab publishes the previous week's misses alongside the hits. Reader-funded economics — the lab's long-term economics rely on reader subscriptions and contest-entry referrals, not on advertising This is why the methodology desk treats it as a baseline. Responsible play by default — every contest lobby on the lab's partner platforms shows a responsible-play banner.
Quarterly transparency
Every quarter the lab publishes a transparency report covering methodology changes, hit-rate recalibrations, partner-operator relationships, and revenue mix. The report is published on the 15th of the month following quarter end. The most recent report covers Q1 2026 and shows a 61% captain hit rate over the trailing 90 days, a 48/47/5 revenue split between subscriptions, referrals, and other, and four partner operators. The report is available on the disclosure page.On methodology, every quarter the lab publishes a transparency report covering methodology changes, hit-rate recalibrations, partner-operator relationships, and revenue mix. For the methodology desk, the report is published on the 15th of the month following quarter end. Within the PlayExch framework, the most recent report covers Q1 2026 and shows a 61% captain hit rate over the trailing 90 days, a 48/47/5 revenue split between subscriptions, referrals, and other, and four partner operators. Cricket readers should note that the report is available on the disclosure page.
