Parity

Introducing Parity, your edge in prediction markets.

@trylimitless Interns fault 😔

@PredictParity You're missing the leading financial prediction market

Prediction Market volume by category (Feb 16 - Mar 15) Total Notional Volume $19.18b 1. Sports 53.2% 2. Crypto 19.9% 3. Politics 16.1% 4. Other 5.3% 5. Culture 1.7% 6. Business 1.6% 7. Economy 1.1% 8. Weather 0.8% 9. Tech 0.3%

@ordinalpunk72 dm us

@PredictParity invite code plz

Prediction Market Accuracy By Category 181,333 resolved markets across Kalshi & Polymarket. We compared the differences in accuracy over time. Findings: -Weather markets on Kalshi are 81.7% accurate 1 day out, but in the final 4 hours it jumps to 96.8% accuracy right before resolution. -Entertainment is the most consistent category across both platforms -Politics has the biggest cross platform gap

@MickBransfield x.com/MovieTimeDev/s…

@PredictParity How are you scoring this?

@Kalshi @Polymarket @cryptocom @opinionlabsxyz @trylimitless @IBKR Note* Polymarkets revenue displayed here is from the month of Feb. Recent revenue is up and currently sits around ~$7.5m.

Prediction markets revenue* (last 30) 1. @Kalshi $110m 2. @Polymarket $4.2m 3. @cryptocom $4.1m 4. @opinionlabsxyz $1.3m 5. @trylimitless $1.1m 6. @IBKR $597k

Great question! The "expected" isn't from the data, it's the benchmark for perfect calibration. We bucket markets by their last traded price (e.g., all markets priced 20-25%), then check what % actually resolved YES. If the market is well calibrated, ~22.5% of markets in that bucket should resolve YES. The midpoint is what the prices claim should happen. The "actual" is what did happen. The gap is the calibration error.

@PredictParity Why is the "expected" value exactly the midpoint of every category? Doesn't seem plausible that the data would perfectly work out that way. How are you performing the calculations?

Prediction Market Accuracy 1d before resolution Out of 146,960 resolved markets. Odds at 50% are the most mispriced. Interestingly both platforms have a negative bias meaning these markets consistently overprice yes as a outcome.

@0GAntD Would you believe me if i told you most of their volume is from Robinhood

kalshi running the binance aster playbook nice wash trading/money laundering cuz aint no way

@stacy_muur @Polymarket thats us, you can find traders here predictparity.com/traders

14/20 most profitable traders on @Polymarket are bots. The team that builds a proper agentic infrastructure layer for prediction markets will easily be a billion-dollar project.

Overview of the prediction market badge economy

92.7% of market making on Polymarket are profitable. Out of 711 maker makers who earned $100+ in rewards last month, 659 were net profitable. This is looking at the net pnl from trading and rewards activities

Note: Kalshi user data is based on publicly available opt-in data. Actual new user numbers are likely higher.

Prediction markets peaked at 24,058 new users per day during the week of Feb 2nd-9th Current growth is 10k new users / day

*Kalshi new user data is based on users that have their data public. *Retention Data is calculated from polymarket data only.

Prediction markets are onboarding *273,908+ new users every month The retention is what's interesting 50.2% stay more than 1 month 30% stay more than 3 months

@XenyPM chad

@PredictParity I’m only top 8000? wtf 😔

Out of 2,244,926 Polymarket traders, 730,158 are profitable roughly 32.52%