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ITF · ELO ESTIMATE · 2026-07-09

M. Dellavedova vs T. Yamanaka — prediction

M15 Tokyo 4 (Japan)
✓ Correct
DELLAVEDOVAWIN PROBABILITYYAMANAKA
82%
Elo prob.
@1.10
odds · 91% impl.
📈Form 9/10 · 7✓
WHAT THE ESTIMATE IS BASED ON

Tour Elo: 1792 vs 1524 — favorite by rating

ITF tier · 408 matches in the favorite's track record

Elo estimate (not the ATP factor model): these are softer, less-analyzed markets

WATCH FOR

!Soft market: the value edge in Challenger/ITF is NOT proven live — treat it as an estimate, not an opportunity.

Tour Elo estimate (Challenger/ITF markets, not covered by the factor model). The value edge here is unproven live — it's a reference, not a recommendation. 18+ · gamble responsibly.
@1.21
fair odds
−9.4%
expected value
HOW EACH FACTOR MATTERS
Level (Elo/ranking)▸ Dellavedova●●●
Dellavedova's 1792 Elo sits 268 points above Yamanaka's 1524, translating into the model's 82% win probability.
Form▸ Dellavedova●●
Dellavedova rides a 7-match win streak (9-1 last 10) versus Yamanaka's inconsistent 6-4 stretch with three recent losses in four.
Rest / workload▸ Yamanaka●●
Both had 1 day off, but Dellavedova has logged 9 matches in 14 days versus Yamanaka's 2 — heavier accumulated workload could blunt his legs late.
Market value= Even●●●
Market prices Dellavedova at 91% implied, well above the model's 82%, producing a -9.4% EV — no edge here.
ELO GAP

The core signal in this match is the rating gap: Dellavedova's 1792 Elo versus Yamanaka's 1524 is a substantial 268-point difference, which is why the model assigns him an 82% win probability. In a soft ITF/Challenger Elo pool, that gap still reflects a real difference in consistent match-level performance, even without granular serve or surface data to confirm the mechanism.

This is a rating-driven favorite, not a stylistic mismatch we can fully explain — no serve, return, or surface splits were available to show *how* Dellavedova's edge manifests on court, only that the aggregate rating history supports it.

FORM AND MOMENTUM

Recent form reinforces the level gap. Dellavedova is on a 7-match winning streak, going 9-1 in his last 10 — a sign of sustained sharpness rather than a hot patch. Yamanaka's form is choppier: 6-4 in his last 10 with three losses in his last four before two recent wins, suggesting less rhythm and confidence heading in.

This momentum differential adds a secondary, non-Elo confirmation of the favorite's edge, though it does not by itself change the probability — form is directional support, not a separate multiplier.

WORKLOAD RISK

Both players had just one day of rest, so short-term freshness is even. But the broader workload picture is lopsided: Dellavedova has played 9 matches in the last 14 days compared to Yamanaka's 2. That kind of match volume can accumulate physical fatigue over a tournament, a factor that tends to matter more as a event progresses even if it rarely shows up in a single match's headline probability.

This is the one data point that pushes back against the favorite, though it's a soft signal — his win streak suggests he's handling the workload fine so far.

VALUE READ

The model favors Dellavedova at 82%, but the market is pricing him even shorter, at an implied 91% (odds of 1.10). That gap generates a -9.4% expected value on the favorite — the market is more confident in Dellavedova than the model is, not less, so there is no mispricing to exploit in his favor.

This is a clear case of being the favorite without having value: Dellavedova is very likely to win on paper, but the price already reflects that and then some. Treat this as a soft, low-liquidity ITF market where the model's edge is unproven — there is no actionable value on either side here.

Impact and analysis from real match data (Elo, form, head-to-head, rest, surface vs baseline, weather, altitude). Soft-market estimate: the value is unproven live. 18+ · gamble responsibly.

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