L. Zaar vs E. Malygina — prediction
Slow court, high bounce: longer points, rewards whoever holds up from the baseline.
Mild: neutral conditions.
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: #335 vs #590 (better ranked)
›Model 67% vs market 76% → the model sees it as less likely than the odds
›Recent form: 2/10 in recent matches
›Match-sharp: 3 matches in the last 2 weeks
!Coming off 4 losses in a row
Zaar holds the better ranking (#335) compared to Malygina (#590), a gap that typically reflects deeper results at higher-tier events. However, Malygina's Elo rating (1473) exceeds Zaar's (1447), suggesting more consistent point-for-point performance recently, which tempers the ranking-based case for the favorite.
Zaar's last ten matches read LLLWWLLLL, a four-match losing streak that signals real struggles with confidence and execution. Malygina's WWLLWLWLWL, while inconsistent, includes five wins and only a one-match skid, pointing to slightly better recent form heading into this meeting.
Both players serve at an identical 50%, but Malygina's 49% return rate outstrips Zaar's 47%, a two-point edge that can matter in tight service games. The evening's humid, breezy weather (61% humidity, 16 km/h wind) may stretch rallies, though without surface or player-style data this effect cannot be pinned to either player specifically.
Malygina also arrives fresher, with two matches in the past 14 days compared to Zaar's three, a modest rest advantage heading into the match.
The model rates Zaar's chances at 67%, notably below the market's implied 76% at odds of 1.31 — a gap that produces a projected expected value of -12.5%. In practical terms, the market is pricing Zaar as safer than the model's underlying factors justify, so backing the favorite at this price does not represent value, even though Zaar remains the more probable winner on paper.
One additional context note: a listed risk of a long layoff (61 days) raises the possibility of rustiness, though the data does not specify which player or quantify its impact, so it should be weighed only as background information.
Impact and analysis from real match data (Elo, form, head-to-head, rest, surface vs baseline, weather, altitude). The model ≈ the market on average; the odds already capture almost all the edge. 18+ · gamble responsibly.