T. Legout vs K. Miyoshi — prediction

The clearest signal here is the rating gap: Legout's 1730 Elo versus Miyoshi's 1646 is an 84-point advantage, translating into the model's 62% win probability. In Challenger/ITF tennis, gaps of this size are common and denote a real but not overwhelming quality difference — enough to make Legout a legitimate favorite, not enough to call this a mismatch.

Without surface, altitude, or head-to-head data to adjust that number, the Elo gap remains the single most substantive piece of evidence in this match.

Both players had just one day of rest, so on paper recovery time is equal. But the volume behind that rest differs sharply: Legout has played 8 matches in the last 14 days, more than double Miyoshi's 3. That kind of workload can accumulate physically over a tournament, particularly if matches go the distance, and it's the one factor here that cuts against the favorite.

This doesn't reverse the Elo-based edge, but it's a real friction point worth flagging — density of matches, not just days off, matters for legs and focus late in a tournament.

Legout wins 68% of his service points, a strong number in ITF terms and his most valuable individual weapon in this data set. Paired with a 44% return rate, his profile leans toward a serve-driven game, the kind of style that thrives when reliable and pressures opponents into quick service holds.

We don't have a matching serve or return number for Miyoshi, so this can't be framed as a head-to-head statistical mismatch — only as confirmation that Legout has a concrete tool to lean on if the match tightens.

Recent form is essentially a wash: both players carry 7-3 records over their last 10 matches and are riding 3-match winning streaks. Neither has a documented quality win in this data, so there's no signal here that either player is peaking against strong resistance versus padding a record against weaker fields.

The model's 62% probability sits only 2 points above the market's implied 60%, producing a modest +4% expected value at 1.68 odds. That's a small gap, and it comes from a soft Elo-based method in an ITF market that is thinly analyzed and not proven to hold an edge live.

Being the favorite is not the same as being undervalued. Here the model is essentially tracking the market rather than diverging from it — treat the +4% EV as a rough estimate, not an exploitable opportunity, and size any interest accordingly.