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Federico Iannaccone vs Giannicola Misasi

Tennis
2025-09-07 05:09
Start: 2025-09-07 09:30

Summary

No pick
EV: -0.153

Current Odds

Home 1.48|Away 77.7
Best Odds

Match Info

Match key: Federico Iannaccone_Giannicola Misasi_2025-09-07

Analysis

Summary: The market heavily favors Iannaccone but the price (1.177) is too short relative to our ~72% win probability, so we find no value and recommend no bet.

Highlights

  • Book implied prob 84.9% vs our 72% estimate
  • Minimum fair odds ~1.389; current 1.177 offers negative EV

Pros

  • + Iannaccone shows a substantially better career record and is a logical favorite
  • + Qualifying matches can favor the more experienced player in matchplay and nerves

Cons

  • - Both players lack grass-specific form; surface uncertainty increases downside risk
  • - Current market price is too short to offer any positive expected value

Details

We compare the bookmaker-implied probability (1/1.177 = 84.9%) to our estimate of the true win probability for Federico Iannaccone (72%). Iannaccone appears the stronger player on paper: more matches and a substantially better career win-loss record than Giannicola Misasi, so a favorite is justified. However, both players have most of their match history on clay and hard courts with little or no grass data; that uncertainty and recent mixed form reduce the confidence that Iannaccone is as close to an 85% favorite as the market implies. At our estimated 72% true win probability, the minimum fair decimal odds would be ~1.389; the current market price of 1.177 offers a negative expected value (EV = 0.72*1.177 - 1 = -0.153). Because the listed price is below the fair threshold, there is no value to backing the favorite at current odds and we therefore recommend no bet.

Key factors

  • Market implies 84.9% for home; our model estimates ~72% — market is overpricing the favorite
  • Iannaccone has a stronger overall record (29-27 vs 12-23) but most matches were on clay/hard, not grass
  • Both players have limited or no documented grass form, increasing uncertainty and variance