Consistent bounce, medium-fast: neutral conditions, no style favored.
Mild: neutral conditions.
Very humid air: the ball gets heavy and points stretch out.
Light wind: no noticeable effect.
Surface feeds the model (surface specialization is one of its factors). Weather and altitude are context we publish for you — they do NOT move the probability.
›Ranking: #12 vs #157 (better ranked)
›Recent form: 7/10 in recent matches
›Match-sharp: 5 matches in the last 2 weeks
›Model 88% vs market 78% → the model sees it as MORE likely than the odds
The model makes Frances Tiafoe the favorite with a 88% win probability, against Rei Sakamoto's 12% — a conviction read: the model sees the match clearly leaning one way. Converted to odds, that probability is worth about @1.14; the offered odds are around @1.29 (a 78% implied), slightly above the market, so the model is a touch more optimistic.
Several factors explain the number: #12 vs #157 (better ranked); 7/10 in recent matches; 5 matches in the last 2 weeks; model 88% vs market 78% → the model sees it as more likely than the odds.
Read it with perspective. Our probability is calibrated — when the model says 88%, that outcome happens roughly that percentage of the time, with ~65% out-of-sample accuracy — but being the favorite is not being the winner: roughly 12 out of every 100 times Sakamoto wins. The model also tends to agree with the market, so the odds already capture almost all the edge: don't take it as a sure value. This is informational analysis, not a betting recommendation. 18+ · play responsibly.