ATP · ELO ESTIMATE · 2026-07-18

G. Bueno vs F. Rochaprediction

✓ Correct
BUENOWIN PROBABILITYROCHA
77%
Elo prob.
@1.20
odds · 83% impl.
Rest 6d vs 1d🎾Serve 59%📈Form 2/10 · 4✗
WHAT THE ESTIMATE IS BASED ON

Tour Elo: 1744 vs 1535 — favorite by rating

ATP qualifying / early round · 282 matches in the favorite's track record

Elo estimate (not the ATP factor model): qualifying draws have no clean main-tour history

WATCH FOR

!Qualifying/soft context: Elo estimate only — read the round context (already-through, lucky loser, dead rubber) from the dossier; it is not a proven edge.

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.
@1.30
fair odds
−7.7%
expected value
HOW EACH FACTOR MATTERS
Level (Elo/ranking)▸ Bueno●●●
Elo gap of 209 points (1744 vs 1535) puts Bueno's model probability at 77% versus Rocha's 23%.
Rest▸ Bueno●●●
Rocha has only 1 day of rest after 3 matches in 7 days, including a deep quarterfinal run; Bueno rests 6 days.
Form▸ Rocha●●
Rocha's last10 (LWLWWLLWWL, -1 streak) is steadier than Bueno's (LLLLWWLLLL, -4 streak), suggesting sharper current form.
Serve/return▸ Bueno●●
Bueno wins 59% of serve points and 37% of return points, a strong all-court profile with no comparable data for Rocha.
Value/Market= Even
Market implies 83% for Bueno versus the model's 77%, producing a -7.7% expected value — no edge at these odds.
LEVEL GAP

The 209-point Elo gap (1744 vs 1535) is the single largest input here, translating into a 77% win probability for Bueno. This is a real ratings edge built on a larger track record (282 matches for the favorite), but the method itself is a soft Challenger/ITF Elo estimate rather than a full ATP factor model, so the edge should be treated as directional, not exact.

In practical terms, the level gap explains most of why Bueno is favored, but it does not by itself justify the odds on offer — that gap has to be checked against the market price separately.

FATIGUE AND REST

The clearest tactical factor in this match is physical: Rocha is playing on 1 day of rest after 3 matches in the last 7 days, including a quarterfinal run at M15 Castelo Branco just a day before. Bueno, by contrast, arrives with 6 days of rest and only 2 matches in the same window.

Congestion and short turnaround typically show up in legs and shot tolerance rather than raw shot-making, so this factor leans toward Bueno holding up better physically over the course of the match, independent of the Elo gap.

FORM DIVERGENCE

Recent form actually cuts the other way. Rocha's last 10 results (LWLWWLLWWL, a -1 streak) show more wins mixed in than Bueno's (LLLLWWLLLL, a -4 streak), which is built on a longer current losing run. Neither player has a listed quality win, so this is a read on rhythm and consistency, not on beating strong opposition.

This is a genuine counterweight to the Elo and rest advantages: Bueno's rating and freshness are better, but his week-to-week results have been worse lately, which tempers how much confidence to place in the model number alone.

SERVE STRENGTH

Bueno's own serve and return numbers — 59% of serve points and 37% of return points won — describe a player competitive on both sides of the ball, which fits with a 1744 Elo rating. No equivalent serve or return data exists for Rocha, so this factor can only be read as a positive marker for Bueno rather than a head-to-head comparison.

VALUE READ

The model's 77% for Bueno sits notably below the market's implied 83%, producing a negative expected value of -7.7% at these odds. That means the market is pricing Bueno even more heavily than the rating gap, rest advantage, and serve profile already justify.

Bueno is the more likely winner on the numbers available — better Elo, more rest, and a stronger serve/return profile — but favorite status is not the same as value. With a soft Elo method behind an ATP qualifying-level match and the price already ahead of the model, there is no backed edge here; treat this as a case where the model roughly agrees with the market rather than beats it.

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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