Platform comparison
| Platform | YES odds | NO odds | Fee | KYC | Settlement | |
|---|---|---|---|---|---|---|
Polymarket (via March Madness Predictions) Pick polygram.ink (preferred broker) |
27% | 73% | 0% (USDC on-chain) | No-KYC up to $1,500 | USDC, auto via UMA oracle | See live odds → |
Polymarket (direct) polymarket.com |
27% | 73% | 0% | Geo-blocked in US/UK/EU | USDC, on-chain | See live odds → |
Kalshi kalshi.com |
— | — | Up to 7% per trade | US-only, KYC required | USD | See live odds → |
Betfair Exchange betfair.com |
— | — | 2-5% commission | Full KYC from first trade | GBP / EUR | See live odds → |
Manifold Markets manifold.markets |
— | — | Play-money (mana) | None — play-money | Mana (no cash-out) | See live odds → |
Outcome probabilities
Current market-implied probability for each outcome, from the live order book.
| Outcome | Probability |
|---|---|
| James Wood | 27% |
| Josh Jung | 26% |
| Kevin McGonigle | 19% |
| Willy Adames | 10% |
| Matt Olson | 10% |
| Bobby Witt Jr. | 8% |
| Freddie Freeman | 7% |
| Ernie Clement | 5% |
| Ezequiel Tovar | 4% |
| Nico Hoerner | 3% |
| Mauricio Dubón | 3% |
| Taylor Ward | 2% |
| Bo Bichette | 2% |
| Bryan Reynolds | 2% |
| Francisco Lindor | 2% |
| Vladimir Guerrero Jr. | 2% |
| Gavin Sheets | 1% |
| Casey Schmitt | 1% |
| Pete Alonso | 1% |
| Jarren Duran | 1% |
| Maikel Garcia | 1% |
| Pete Crow-Armstrong | 1% |
| Salvador Perez | 1% |
| Bryce Harper | 1% |
| Ian Happ | 1% |
| Juan Soto | 1% |
| George Springer | 1% |
| Corbin Carroll | 1% |
| Riley Greene | 1% |
| Colt Keith | 0% |
| Christian Walker | 0% |
| Adley Rutschman | 0% |
| Rhys Hoskins | 0% |
| Gabriel Moreno | 0% |
| Brent Rooker | 0% |
| CJ Abrams | 0% |
| Shohei Ohtani | 0% |
| Aaron Judge | 0% |
| Andy Pages | 0% |
| Nick Kurtz | 0% |
| Yordan Alvarez | 0% |
| Player B | 0% |
| Player C | 0% |
| Player D | 0% |
| Player E | 0% |
| Player F | 0% |
| Player G | 0% |
| Player H | 0% |
| Player I | 0% |
| Player J | 0% |
| Player K | 0% |
| Player L | 0% |
| Player M | 0% |
| Player N | 0% |
| Player O | 0% |
| Player P | 0% |
| Player Q | 0% |
| Player R | 0% |
| Player S | 0% |
| Player T | 0% |
| Player U | 0% |
| Player V | 0% |
| Player W | 0% |
| Player X | 0% |
| Player Y | 0% |
| Player Z | 0% |
| Player AA | 0% |
| Player AB | 0% |
| Player AC | 0% |
| Player AD | 0% |
| Player AE | 0% |
| Player AF | 0% |
| Player AG | 0% |
| Player AH | 0% |
| Player AI | 0% |
| Player AJ | 0% |
| Player AK | 0% |
| Player AL | 0% |
| Player AM | 0% |
| Player AN | 0% |
| Player AO | 0% |
| Player AP | 0% |
| Player AQ | 0% |
| Player AR | 0% |
| Player AS | 0% |
| Player AT | 0% |
| Player AU | 0% |
| Player AV | 0% |
| Player AW | 0% |
| Player AX | 0% |
| Other | 0% |
Market context
The 2026 MLB regular season will determine which player accumulates the most doubles across all 162 games, with the crown typically going to a high-contact hitter who sees consistent playing time. Historical leaders in this category have ranged from 40 to 52 doubles per season, with the honour usually claimed by established veterans or emerging stars in their prime years rather than surprise candidates. The 2% implied probability reflects the extreme difficulty of predicting a single statistical leader across an entire league nearly two years in advance, when roster composition, injuries, and performance trajectories remain largely unknowable.
Recent seasons show doubles leaders emerging from teams with sustained offensive production rather than flash performers. Players like Kyle Schwarber and Mookie Betts have led or contended for the title in recent years, both benefiting from full seasons at the plate and lineups generating consistent scoring opportunities. The metric favours players who hit for contact and average rather than power, meaning high strikeout rates or injury-prone profiles face structural disadvantages.
Traders should monitor spring training performance in March 2026 and early-season batting averages, as the doubles leader typically establishes themselves within the first two months. Trade deadline moves in late July will also prove significant—a player acquired by a contender might see elevated plate appearances down the stretch. Coaching staff changes affecting batting approaches and ballpark dimensions for any relocated franchises could shift expected doubles totals materially.
Methodology
Sports-specific comparison page for MLB: Doubles Leader. Polymarket's live quote (Polygon order book) plus platform attributes for the three reference venues. Sports markets reward liquidity — Polymarket and Betfair are materially deeper than Kalshi or Manifold.
Resolution & payout
Sports markets typically settle on official final-whistle plus league confirmation. Polymarket uses UMA Optimistic Oracle with a source URL per contract — usually official league data feeds or ESPN/Soccerway. Two-hour dispute window, then smart-contract payout in USDC.
FAQ
- Are prediction markets better than sports betting?
- Prediction markets tend toward tighter odds than bookmakers because they use peer-to-peer exchange rather than bookmaker margin. On major matches, Polymarket quotes typically sit 2-5% closer to the true probability model than bet365 or DraftKings.
- Can I bet on individual matches?
- Yes, Polymarket lists every major Premier League / Champions League / World Cup match as its own market. Liquidity varies — top matches like El Clásico or a semi-final often have six-figure pools, lower-league games closer to three-figure.
- Which sports markets are available?
- Football (soccer) dominates — Champions League, World Cup, Premier League, La Liga, Bundesliga — followed by NFL, NBA, tennis Grand Slams, Formula 1, boxing/MMA. Resolution via official league source confirmation.
- What's the difference between match odds and outright odds?
- Match odds cover a single game ("Bayern beats BVB"). Outright odds are long-term aggregates ("Bayern wins the league"). Outright markets have deeper liquidity; match markets have faster resolution.
- Can I import form data and lineups?
- This page shows the live Polymarket quote as an implied probability. Form data and lineups must be researched separately via sports data providers (e.g. Soccerway, ESPN, FBref) — we surface the market probability, not the sport analysis.
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