Consistent bounce, medium-fast: neutral conditions, no style favored.
Strong heat: warm air speeds the ball up and physical wear tells in long matches.
Very dry air: the ball travels faster.
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: #83 vs #645 (better ranked)
›Recent form: 3/10 in recent matches
!Coming off 3 losses in a row
The model makes M. Giron the favorite with a 75% win probability, against D. Suresh's 25% — a conviction read: the model sees the match clearly leaning one way. Converted to odds, that probability is worth about @1.33; the offered odds are around @1.36 (a 74% implied), virtually the same as what the market prices in.
Several factors explain the number: #83 vs #645 (better ranked); 3/10 in recent matches.
Read it with perspective. Our probability is calibrated — when the model says 75%, 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 25 out of every 100 times Suresh 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. Watch out for: Coming off 3 losses in a row. This is informational analysis, not a betting recommendation. 18+ · play responsibly.