Consistent bounce, medium-fast: neutral conditions, no style favored.
Strong heat: warm air speeds the ball up and physical wear tells in long matches.
Humid air: the ball loses some speed.
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: #122 vs #133 (better ranked)
›Recent form: 3/10 in recent matches
›Model 71% vs market 42% → the model sees it as MORE likely than the odds
!Returning from a long layoff (42d) — possible rustiness
The model makes D. Sweeny the favorite with a 71% win probability, against C. O'Connell's 29% — a solid favorite, though O'Connell keeps real chances. Converted to odds, that probability is worth about @1.40; the offered odds are around @2.40 (a 42% implied), slightly above the market, so the model is a touch more optimistic.
Several factors explain the number: #122 vs #133 (better ranked); 3/10 in recent matches; model 71% vs market 42% → the model sees it as more likely than the odds.
Read it with perspective. Our probability is calibrated — when the model says 71%, 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 29 out of every 100 times O'Connell 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: Returning from a long layoff (42d) — possible rustiness. This is informational analysis, not a betting recommendation. 18+ · play responsibly.