ITF · ELO ESTIMATE · 2026-07-09

T. Legout vs D. Chanprediction

M25 Laval
✓ Correct
LEGOUTWIN PROBABILITYCHAN
69%
Elo prob.
@1.58
odds · 63% impl.
Rest 1d vs 21d🎾Serve 68%📈Form 7/10
WHAT THE ESTIMATE IS BASED ON

Tour Elo: 1718 vs 1580 — favorite by rating

ITF tier · 156 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.
@1.45
fair odds
+8.8%
expected value
HOW EACH FACTOR MATTERS
Level (Elo)▸ Legout●●●
138-point Elo gap (1718 vs 1580) makes Legout the clear favorite, translating to a 69% model win probability.
Rest▸ Chan●●
Legout played 6 matches in 14 days with only 1 day off; Chan is fully rested at 21 days, a fatigue risk for the favorite.
Form▸ Legout
Legout won his last match and takes 7 of 10; Chan sits on a 1-match losing streak despite an identical 7-3 record.
Serve/return▸ Legout●●
Legout's 68% service-points-won rate shows a dominant serve, giving him a reliable path to holds against Chan.
Level (Elo/Market)= Even●●
Model's 69% vs market's 64% implied gives an 8.1% EV, but ITF Elo markets are soft and this edge is unproven live.
ELO GAP

The 138-point Elo difference (1718 vs 1580) is the single largest input in this match, and it lines up with the model's 69% win probability for Legout. At the ITF level, a gap this size usually reflects a real difference in consistency and point-construction ability built over the 156-match sample behind the favorite's rating.

This isn't a marginal edge — it's the kind of spread that typically shows up as a two-set difference in quality rather than a coin-flip encounter, though soft-market Elo at this tier still carries more noise than tour-level ratings.

WORKLOAD VS RUST

Legout's schedule is a real red flag: 6 matches in the last 14 days and just 1 day since his last outing. That kind of workload compounds physically over a best-of-three ITF match, particularly if any set goes long — accumulated fatigue can blunt the very serve numbers that make him favored.

Chan's situation is the mirror image: 21 days off with zero matches in the same window. That could mean he's fresh and recovered, or it could mean he's short on match rhythm and timing. Both effects are plausible here, so this factor is genuinely two-sided rather than a clean advantage for either player.

SERVE-RETURN PROFILE

The data we have on Legout paints a complete offensive picture: 68% of service points won is a high number for this level, suggesting he holds comfortably and rarely faces break pressure. Combined with a 44% return-points-won rate, he's also generating pressure on Chan's service games — a double-threat profile.

No serve or return numbers exist for Chan, so a direct head-to-head comparison isn't possible. But Legout's own figures are strong enough on their own to suggest he controls whether points are played on his terms.

RECENT FORM

Both players show identical 7-3 records over their last 10 matches, but the trend lines diverge: Legout closes his sample with a win and a positive 1-match streak, while Chan is on a 1-match losing skid. Momentum at this margin is modest, but it slightly reinforces the favorite's case.

HONEST VALUE READ

The model's 69% against the market's 64% implied probability produces a theoretical 8.1% EV at odds of 1.57. That's a genuine gap on paper, but it comes from a soft Challenger/ITF Elo market — one with far less liquidity and scrutiny than ATP markets, so any pricing edge here is unproven in practice.

Being the favorite is not the same as being a value bet: Legout's superior rating and serving numbers make him the more likely winner, but his brutal recent workload (1 day of rest after 6 matches in 14 days) is a tangible risk the model may be underweighting. Treat the edge as a modest lean, not a confirmed opportunity.

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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