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Digvijay Pratap Singh vs Kazuki Nishiwaki

Tennis
2025-09-11 04:56
Start: 2025-09-11 04:47

Summary

Pick: home
EV: 0.0965

Current Odds

Home 2.7|Away 1.42
Best Odds

Match Info

Match key: Digvijay Pratap Singh_Kazuki Nishiwaki_2025-09-11

Analysis

Summary: We find modest positive value on the home player at 1.29 because the market underestimates the favorite versus an opponent with a 0-6 recorded start; expected ROI ~9.7%.

Highlights

  • Market implies 77.5% for home; we estimate 85%
  • Positive EV ≈ 0.097 (9.7% ROI) at current odds 1.29

Pros

  • + Opponent has no recorded wins in provided match history, indicating a clear form edge
  • + Current odds provide a measurable positive expected value using conservative probability estimate

Cons

  • - Small sample size and sparse information — opponent’s 0-6 could reflect limited play rather than clear long-term quality gap
  • - Unknown factors (injury, local conditions, actual head-to-head) not provided could invalidate the edge

Details

We compare market odds to our assessment based on the available form data. The market prices the home player (Digvijay Pratap Singh) at 1.29, which implies a win probability of ~77.5%. The only supplied research shows the opponent, Kazuki Nishiwaki, with a very poor early-career record (0-6) and no wins recorded on the provided hard-court results. Given Nishiwaki's lack of wins and limited experience, we assign a higher true win probability to the home player. Using our estimated true probability (85%), the bet at 1.29 yields positive expected value: EV = 0.85 * 1.29 - 1 ≈ 0.0965 (≈9.7% ROI). This is modest but positive value versus the market implied probability. We note the sample size for Nishiwaki is small and there are unknowns (injury status, match conditions), so while the market appears to underprice the home player, the margin is not huge.

Key factors

  • Opponent (Nishiwaki) has 0-6 career record in supplied data
  • Market-implied probability for home (1.29) is ~77.5% which is below our 85% estimate
  • Limited data/sample size on both players and potential unknowns (injury/conditions)