How the refer program works
The refer bonus unlocks when the friend makes a first deposit of ₹100+ and enters at least one contest. Below is the exact gate and the lab's data on why we set it this way.
The refer bonus is gated behind a minimum first-deposit and one contest-entry. We picked these gates because they mirror the cohorts most likely to remain active after the bonus lands — readers who paid ₹100 of real money are 4.7x more likely to enter a second contest than readers who entered via bonus-only, in our 2025 cohort analysis. Gating the refer bonus the same way keeps the referred reader's engagement high, which keeps the referring reader's reputation solid, which keeps the refer programme healthy. The lab doesn't optimize for short-term refer volume; we optimize for cohort retention.
₹50 per successful refer is calibrated against our customer-acquisition cost (CAC). Our blended CAC from paid channels runs ₹180-₹240 per first-deposit reader — refer-program readers land for ₹50 (the bonus payout) plus the lean CAC of organic awareness. The ₹130-₹190 delta is a fair cost saving for the platform without shortchanging the referrer or the referee.
Your refer code — find, share, track
Your 6-character refer code lives in Settings > Refer & Earn. Every referral you drive is tracked in real time, including which friends joined but haven't deposited yet.
Find your code
Settings → Refer & Earn. Your code is a 6-character alphanumeric string (e.g. FCL28X). The screen also shows your QR code and a one-tap share button for SMS / WhatsApp / Telegram.
Share via SMS / WhatsApp
Tap Share → SMS / WhatsApp / Telegram. The default message includes your code and a download link. You can edit the message before sending.
Track sign-ups in real time
The Refer & Earn screen updates within 30 seconds of each event — joined, deposited, contest-entered, refer-bonus credited. The status of each friend appears as one of four chips.
Withdraw or play
Refer bonus credits to 'Bonus' tab on the wallet screen. It's withdrawable above the ₹200 threshold (separate from the welcome bonus ₹100 cap). Bonus funds can also be played across any contest.
Public refer codes & comparison
Some users search for a 'public refer code' to get extra bonus on signup. We document what is and isn't working in 2026.
| Code source | Reward to referee | Reward to referrer | Notes |
|---|---|---|---|
| FCLLAB (lab code) | ₹100 welcome bonus | — (lab-owned) | Default code, works for everyone |
| Friend's personal code | ₹100 welcome + ₹25 sign-up bonus | ₹50 refer bonus | The standard refer-pair path |
| Influencer codes | ₹100 welcome + variable bonus | ₹50-₹150 per refer | Listed on public code pages |
| Promo codes (seasonal) | ₹50-₹200 welcome bonus | None (lab-owned) | Released during IPL, T20 WC |
| Expired codes | No bonus | — | Codes expire after 6 months |
Influencer codes often combine a sign-up bonus with a refer bonus — they share economics with the influencer. Reader-to-reader codes follow the standard refer-pair path: ₹100 welcome to the new reader, ₹50 to the referrer. Influencer codes are listed on the public codes page and change quarterly. We publish the economics of every code path because the lab doesn't believe in hidden bonus mechanics — every reader should be able to audit our reward programmes.
Bonus withdrawal rules
Every refer bonus lands in a separate 'Bonus' wallet tab. The rules for converting bonus to withdrawable cash are below — same rules for every reader, no exceptions.
₹100 welcome bonus
Capped at 100% of first deposit. Plays across any contest. Winnings from bonus-funded entries are withdrawable; the bonus itself is not. Rollover: 1x (you must play the bonus through once before withdrawal).
₹25 sign-up bonus (via refer)
Flat ₹25 credited when the referee enters their first contest. Plays across any contest. No rollover, fully withdrawable above ₹200 bonus wallet balance.
₹50 refer bonus (to referrer)
Credited to the referrer's bonus wallet per successful refer. Plays across any contest. Withdrawable above ₹200 bonus wallet balance. Capped at ₹1,000/month (20 successful refers).
No hidden bonus tiers
We don't run VIP bonus tiers that hide the math. The published rules above are the rules we follow. If you see marketing material promising more, the small print is in the same table On the site.
Frequently asked questions
Common questions our research desk fields about this page.For the refer test desk, common questions our research desk fields about this page.
Can I refer myself?
No. Self-referral is detected by device fingerprint, IP, and PAN-matching at the bonus-credit step. Bonus is reversed if self-referral is discovered. This applies to immediate family in the same household — the rule is identical, regardless of family relationship, because the cohort data shows the same abuse pattern.
How long does the refer bonus take to credit?
Friend signs up instantly. Friend's first deposit confirms in 30 seconds. Friend enters first contest within 7 days typically — bonus credits within 4 hours of first contest completion. Total: same day if everything aligns; up to 7 days if the friend takes time to enter their first contest.
What if my friend uses a different refer code after I sent mine?
Last refer code wins. The referrer is whoever's code the friend uses at sign-up. We don't allow retroactive re-attribution because that breaks the cohort data we use to measure the refer programme. If your friend signed up with someone else's code, the bonus flow is on that referrer, not you.
The lab's referral economy explained
How the credit is funded, the anti-abuse signals the lab tracks, and the long-term economics of the referral program.
How the credit is funded
The ₹50 standard credit is funded from the lab's contest-operator referral commission. The commission is split between the lab's operational costs, the reader-funded editorial team, and the referral credit pool. The split is published in the quarterly transparency report.
Anti-abuse signals
The lab runs anti-abuse checks on every referral signup. The checks include device fingerprinting, phone-number graph analysis, velocity checks, and geographic checks. Flagged signups are held for manual review. The review queue is cleared within forty-eight hours.
Tier migration path
The tier ladder resets on the first of every month. Users can climb from Bronze to Silver by hitting ten signups in a calendar month. The lab sends a reminder when the user is one signup away from the next tier. The tier ladder is published in the wallet screen.
Long-term economics
The referral program is a long-term investment in the lab's reader base. The lab funds the program from its contest-operator referral commission. The program is sustainable as long as the contest-operator commission exceeds the referral credit pool. The economics are published quarterly.
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.A longer-form walk-through of the underlying dynamics, the data behind the recommendations, and the lab's analytical approach This is why the refer test desk treats it as a baseline.
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.The lab's methodology is built on a five-stage pipeline This is why the refer test desk treats it as a baseline. Every published report passes through framing, dataset pull, analysis, assumption-risk documentation, and re-test. On refer test, the pipeline is published in full on the methodology page. For the refer test desk, the lab's edge comes from data freshness and analyst expertise, not from a secret formula. Within the PlayExch framework, other analysts can re-run the experiments with the published datasets and the published model code. Friends readers should note that the lab publishes every dataset version and every model version. The lab never quietly updates a dataset after the conclusion lands This is why the refer test desk treats it as a baseline.
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.The lab pulls data from four primary sources This is why the refer test desk treats it as a baseline. Match data comes from official tournament data partners, cross-checked against broadcast records. On refer test, ownership data comes from public contest-platform feeds, sampled every 30 seconds during contest open windows. For the refer test desk, contest result archives come from publicly published leaderboards. Within the PlayExch framework, self-collected squad logs come from volunteer readers who submit their picks at lock time. Friends readers should note that 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 This is why the refer test desk treats it as a baseline.
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.The lab's reader-safety commitments cover deposit limits, self-exclusion, play-time alerts, loss trackers, cool-down reminders, and family-controls integration This is why the refer test desk treats it as a baseline. The lab publishes the safety commitments on the responsible-play page. On refer test, the lab refuses to partner with operators that do not enforce responsible-play defaults. For the refer test desk, 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.The lab operates by five public editorial principles This is why the refer test desk treats it as a baseline. No sponsored picks — no contest operator, brand, or individual can pay the lab to feature a captain pick. On refer test, methodology is public — every model's code is published on the methodology page. For the refer test desk, misses are published — every Monday morning the lab publishes the previous week's misses alongside the hits. Within the PlayExch framework, reader-funded economics — the lab's long-term economics rely on reader subscriptions and contest-entry referrals, not on advertising. Friends readers should note that 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.Every quarter the lab publishes a transparency report covering methodology changes, hit-rate recalibrations, partner-operator relationships, and revenue mix This is why the refer test desk treats it as a baseline. The report is published on the 15th of the month following quarter end. On refer test, 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. For the refer test desk, the report is available on the disclosure page.
