Calculate the captain confidence
The projection calculator is a free tool that calculates the captain confidence score for any match. The calculator uses the lab's three-input model — venue read, pitch type, ownership bias.
How it works
Select a match from the upcoming slate. The calculator pulls the venue read and the pitch type from the lab's database. Enter the ownership bias (or pull it from the lab's averaged-ownership feed). The calculator outputs a 0-100 confidence score.
What it does
The calculator ranks every player in the match by captain confidence score. The top-3 are flagged as strong picks. The differential-captain filter highlights picks with ownership under 10%.
What it doesn't do
The calculator does not pick your squad for you. The calculator does not optimize for contest tier. The calculator is a decision-support tool, not an automation tool.
Limitations
The calculator relies on the lab's data feeds. If the feeds are stale (more than four hours old), the calculator flags the staleness. The calculator's projection is a single point estimate — actual fantasy points vary.
Simulate the ownership distribution
The ownership simulator projects the ownership distribution for any match. The simulator uses historical ownership data and the lab's averaged-ownership metric.
How it works
Select a match. The simulator pulls historical ownership data for every player from the lab's archive. The simulator applies a smoothing algorithm to project the lock-time ownership distribution.
Differential filter
The differential filter highlights players projected under 10% ownership with projection confidence above 75. The filter is the lab's recommendation for mega contests.
Consensus filter
The consensus filter highlights players projected over 30% ownership with projection confidence above 65. The filter is the lab's recommendation for head-to-head contests.
API access
Power users can pull ownership projections via the lab's API. The API returns a JSON payload with per-player ownership projections and confidence scores. API access is available on the paid subscription tier.
Audit your contest ROI
The ROI audit spreadsheet tracks your contest entries, winnings, and ROI. The spreadsheet is downloadable as a CSV for offline analysis.
What it tracks
Every contest entry, entry fee, squad composition, captain pick, final rank, winnings, and net ROI. The spreadsheet auto-computes the cumulative ROI and the captain-pick hit rate.
Sample size guidance
The lab recommends a minimum 50-contest sample before drawing ROI conclusions. Below 50 contests, the variance dominates the signal. The spreadsheet flags samples under 50.
Contest tier breakdown
The spreadsheet breaks ROI down by contest tier — mega contests, small leagues, head-to-heads. The breakdown reveals which tier your squad is best suited for.
Captain-pick correlation
The spreadsheet correlates your captain-pick choices with your ROI. The correlation reveals whether your captain-pick methodology is working — a strong positive correlation means your picks are driving the ROI.
Tools questions, answered
The most common questions about tools on PlayExch, with detailed answers.
Is the projection calculator free?
Yes. The calculator is free for all signed-in users. The calculator uses the lab's three-input model — venue read, pitch type, ownership bias. API access is available on the paid subscription tier.
Can I export my ROI audit?
Yes. The ROI audit spreadsheet is downloadable as a CSV. The CSV includes every contest entry, squad composition, captain pick, and net ROI. The CSV can be imported into any spreadsheet tool.
Does the lab have an API?
Yes. The lab's API returns projection data, ownership data, and captain-pick data as JSON. API access is available on the paid subscription tier. The API documentation is published on the methodology page.
Are the tools open-source?
The projection calculator's frontend is open-source. The model code is published on the methodology page. The API is not open-source — it requires a subscription.
Can I integrate the tools with my contest platform?
Not directly. The lab does not integrate with contest platforms — the tools are decision-support tools, not automation tools. The lab's editorial principles prohibit automation.
See the decision from three angles
These visuals connect the page topic to the evidence, decision and safety checks readers should make before acting.
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.
The lab's deeper perspective on tools
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.