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: #54 vs #153 (better ranked)
›Recent form: 8/10 in recent matches
›On a streak: 5 wins in a row
›More rested: 17d vs opponent's 13d
›Model 77% vs market 62% → the model sees it as MORE likely than the odds
The model makes Q. Halys the favorite with a 77% win probability, against S. Kwon's 23% — a conviction read: the model sees the match clearly leaning one way. Converted to odds, that probability is worth about @1.29; the offered odds are around @1.62 (a 62% implied), slightly above the market, so the model is a touch more optimistic.
Several factors explain the number: #54 vs #153 (better ranked); 8/10 in recent matches; 5 wins in a row; 17d vs opponent's 13d.
Read it with perspective. Our probability is calibrated — when the model says 77%, 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 23 out of every 100 times Kwon 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.