The lab's 90-day track record
The lab's 90-day rolling captain hit rate is published every Monday morning. The current hit rate is 61% (top-25% of fantasy points).
Where the lab's ROI comes from
The lab's quarterly ROI by contest tier is published in the transparency report. The ROI is computed on a per-account, per-tier basis.
| Contest Tier | Lab ROI | Industry Baseline |
|---|---|---|
| Mega contests (10,000+ entries) | +18.4% | +2.1% |
| Small leagues (10-100 entries) | +12.6% | +5.8% |
| Head-to-head | +8.2% | +3.4% |
| Practice contests | n/a | n/a |
The lab's weekly recalibration
Every Monday morning the lab publishes the previous week's re-test findings. The re-test compares the projection to the actual outcome.
IPL 2026 — Match 47 re-test
Differential captain Devon Conway projected 82/100, actual outcome 86/100. The differential captain was the top-performing pick on the slate. The lab flags Conway as a strong differential captain for the next CSK match.
IPL 2026 — Match 48 re-test
Differential captain Faf du Plessis projected 78/100, actual outcome 71/100. The differential captain underperformed the projection by 7 points. The lab recalibrates Faf's projection model.
T20I — 2nd T20I re-test
Differential captain Yashasvi Jaiswal projected 76/100, actual outcome 84/100. The differential captain was the top-performing pick on the slate. The lab flags Jaiswal as a strong differential captain for the next T20I.
SA20 — Match 12 re-test
Differential captain Heinrich Klaasen projected 74/100, actual outcome 79/100. The differential captain was the top-performing pick on the slate. The lab flags Klaasen as a strong differential captain for the next SA20 match.
Test Results questions, answered
The most common questions about test results on PlayExch, with detailed answers.
What is the current captain hit rate?
The 90-day rolling captain hit rate is currently 61% (top-25% of fantasy points). The differential captain hit rate is 47%. The consensus captain hit rate is 74%. The hit rates are published every Monday morning.
What is the lab's ROI by contest tier?
Mega contests: +18.4%. Small leagues: +12.6%. Head-to-head: +8.2%. The ROI is computed on a per-account, per-tier basis. The full breakdown is published in the quarterly transparency report.
How is the re-test published?
Every Monday morning the lab publishes the previous week's re-test findings. The re-test compares the projection to the actual outcome. The delta is published in the Monday retrospective post.
Can I see past hit rates?
Yes. The test-results page publishes the lab's hit-rate history, the assumption-risk recalibrations, and the captain-pick delta posts. The methodology page covers the recalibration logic.
Does the lab publish losing picks?
Yes. The retrospective posts every Monday cover the lab's misses as well as its hits. The lab's editorial principle is full transparency — a missed pick is a data point for the next cycle's re-test.
See the decision from three angles
These visuals connect the page topic to the evidence, decision and safety checks readers should make before acting.
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The lab's deeper perspective on test-results
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.
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.
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.
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.
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.
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.