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Patrick Zahraj vs Cannon Kingsley

Tennis
2025-09-12 03:51
Start: 2025-09-12 18:10

Summary

No pick
EV: -0.072

Current Odds

Home 1.82|Away 2.07
Best Odds

Match Info

Match key: Patrick Zahraj_Cannon Kingsley_2025-09-12

Analysis

Summary: No value at current prices: Zahraj looks like a narrow favorite but 1.60 implies a bigger edge than the evidence supports, so we avoid betting either side.

Highlights

  • Market-implied probability for Zahraj (62.5%) exceeds our estimate (58%).
  • Insufficient, inconsistent stats and mixed recent form prevent confidence in a Cannon Kingsley upset-price play.

Pros

  • + Zahraj has marginally better overall win rate and more match experience.
  • + Both players are accustomed to hard courts, so surface is not a major unknown.

Cons

  • - Current moneyline overprices the favorite relative to our probability estimate (no positive EV).
  • - Provided match statistics contain inconsistencies, reducing confidence in any large edge assessment.

Details

We estimate Patrick Zahraj to be the marginal favorite based on a slightly better overall record (43-36 vs 23-21) and both players having match experience on hard courts, but the current moneyline (1.60, implied 62.5%) overstates his edge versus our read of form and sample data. Recent results for both players are mixed and the supplied match-level stats appear inconsistent, which reduces confidence in a large edge for either side. Conservatively we place Zahraj's true win probability at ~58.0%, below the ~62.5% implied by 1.60, producing a negative expected value at the available price. Cannon Kingsley could be undervalued if we had stronger evidence of a form swing, but current data does not support raising his true probability above the 44.8% break-even threshold for his 2.23 price. Therefore we do not recommend a side at the quoted prices.

Key factors

  • Zahraj has a slightly better overall record and more match volume (43-36 vs 23-21)
  • Both players have recent hard-court matches; form is mixed for each with no clear momentum
  • Quoted statistics in the research appear inconsistent, lowering confidence in a large edge and increasing model uncertainty