Consistent bounce, medium-fast: neutral conditions, no style favored.
Warm: the ball flies a little more and fitness counts.
Humid air: the ball loses some speed.
Some wind: makes baseline control harder.
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: #94 vs #167 (better ranked)
›Recent form: 4/10 in recent matches
›Hard specialist: performs +6% above baseline (44% career on the surface)
›Model 63% vs market 70% → the model sees it as less likely than the odds
The model makes C. Wong the favorite with a 63% win probability, against D. Lajovic's 37% — a solid favorite, though Lajovic keeps real chances. Converted to odds, that probability is worth about @1.59; the offered odds are around @1.43 (a 70% implied), slightly below the market, so the model is a touch more cautious.
Several factors explain the number: #94 vs #167 (better ranked); 4/10 in recent matches; performs +6% above baseline (44% career on the surface); model 63% vs market 70% → the model sees it as less likely than the odds.
Read it with perspective. Our probability is calibrated — when the model says 63%, 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 37 out of every 100 times Lajovic 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.