Three inputs, one output
The lab's prediction model runs three inputs — venue read, pitch type, ownership bias — and outputs a captain pick confidence score from 0 to 100.
Venue read
The venue read is the pitch-type and dimensions analysis for a specific ground. The lab maintains a venue database with historical scores, average run rates, spin/seam splits, and dew factors. The venue read is the most heavily weighted input.
Pitch type
The pitch type is the expected surface condition for the match. The lab classifies pitches as flat, sporting, turning, or green. The classification is based on the venue's recent matches and the curator's stated intent.
Ownership bias
The ownership bias is the spread of captain picks across the contest field. The lab uses an averaged-ownership metric that smooths out late-team-lock spikes. A wide ownership distribution favors differential picks.
Output score
The output score is a 0-100 confidence score. Scores above 75 are flagged as strong captain picks. Scores below 55 recommend a balanced vice-captain instead. The score is calibrated against historical hit rates.
The captain the public misses
Differential picks are players with projection confidence above 75 and ownership under 10%. The lab tracks differential pick performance on a 90-day rolling window.
Differential captain — Devon Conway
MI vs CSK, Wankhede. Conway's recent form curve is steeper than the consensus top-order on a turning surface. Differential captain pick at 8% ownership. Projection confidence: 82/100.
Differential captain — Faf du Plessis
RCB vs KKR, Chinnaswamy. Faf's recent form on flat tracks is stronger than Kohli's. Differential captain at 9% ownership. Projection confidence: 78/100.
Differential captain — Yashasvi Jaiswal
IND vs AUS, Mohali. Jaiswal's recent ODI and T20I form is the steepest among the Indian top-order. Differential captain at 22% ownership. Projection confidence: 76/100.
Differential captain — Heinrich Klaasen
DSG vs MICT, SA20. Klaasen's powerplay read on SA20 surfaces is the best in the league. Differential captain at 12% ownership. Projection confidence: 74/100.
How the confidence score is calibrated
The confidence score is calibrated against historical hit rates. The lab re-calibrates the model every Monday morning based on the previous week's results.
Calibration window
The calibration window is the trailing 90 days. The model learns from the past 90 days of picks, hit rates, and assumption-risk outcomes. The recalibration is automatic and published in the weekly retrospective.
Hit rate target
The lab targets a 90-day rolling captain hit rate above 55%. The current 90-day hit rate is 61% (top-25% of fantasy points). The 47% top-25% hit rate on differential captains is the lab's standout number.
Assumption-risk note
Every published pick carries an assumption-risk note. The note lists the model's key assumptions — venue read accuracy, ownership-distribution noise, late lineup news. The note is required by the editorial pipeline.
Re-test obligation
Every published pick 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.
Southern Brave Women vs Birmingham Phoenix Women at Southampton
Match 13 of The Hundred Women 2026 at The Rose Bowl, 7:30 PM IST on 30 July 2026. Published probable XIs, captain angles and a venue read from the lab desk.
Southern Brave Women vs Birmingham Phoenix Women at Southampton: Hundred Match 13 fantasy preview
Table-topping Brave target a fourth straight win; Ellyse Perry's Phoenix chase a first victory of the season. Predicted XIs, the source captaincy pair (Wolvaardt / Capsey), venue read and a published role table are inside the lab note.
Why this fixture matters
The form split is wide — Brave are WWWLW, Phoenix are NRLLWL — but the captaincy pair remains competitive because the surface arc at The Rose Bowl gives both sides a spin window in the middle phase. The lab's wider read covers Brave's continuity advantage and Phoenix's differential lever.
Build anchor
Start the keeper slot with Lizelle Lee alone unless salary is unusually free. Stack three Brave batters (Bouchier, Wolvaardt, Rodrigues) and lean on the Wolvaardt–Capsey captaincy pair for the multi-point engine. Reserve a small credit block for a toss-time bowling upgrade.
What to verify
Watch the official toss and final XI confirmation at The Rose Bowl before 7:30 PM IST on 30 July 2026. The two check points are the new-ball pair (Wong–Coleman for Brave, Filer–Reyneke for Phoenix) and whether Lauren Bell retains her all-rounder slot in the Brave probable XI.
Predictions questions, answered
The most common questions about predictions on PlayExch, with detailed answers.
How is the prediction score calculated?
The prediction score is a 0-100 confidence score based on three inputs: venue read, pitch type, and ownership bias. The score is calibrated against historical hit rates on a 90-day rolling window.
What is a strong differential captain?
A differential captain is a pick with projection confidence above 75 and ownership under 10%. Differential captains win mega contests 9% of the time, but their ROI is positive when ownership spikes correctly.
How often is the model re-calibrated?
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.
Can I see the model code?
Yes. The methodology page publishes the projection-model code, the dataset schema, and the hit-rate calculation logic. Other analysts can re-run the experiments with the published datasets.
How accurate is the model?
The 90-day rolling captain hit rate is 61% (top-25% of fantasy points). The differential-captain hit rate is 47%. The methodology page explains how the hit rate is calculated and how the dataset is locked.
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 predictions
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.