HOW EACH FACTOR MATTERS
Level (Elo/ranking)▸ Shapovalov●●●
Shapovalov ranks #41 vs #82 and the baseline model gives him 52% to Hijikata's 38%, a clear 14-point gap.
Serve/return▸ Hijikata●●
Hijikata's serve (66%) tops Shapovalov's (62%), while Shapovalov's return edge (37% vs 35%) is smaller — net advantage on this axis to Hijikata.
Form▸ Shapovalov●●
Hijikata is 3-7 in his last 10 with a 2-match losing streak, though he owns quality wins over Lehecka (Elo 2028) and Prizmic (1986).
Rest▸ Shapovalov●●
Shapovalov has 40 days off vs Hijikata's 27, giving him fresher legs, but the long layoff also carries rustiness risk noted in the data.
Head-to-head▸ Shapovalov●
Shapovalov leads their only prior meeting 1-0, a minor psychological edge with limited sample size.
RANKING AND MODEL GAP
Shapovalov's #41 ranking against Hijikata's #82, combined with the baseline model's 52%-to-38% split, points to a real quality gap on paper. That 14-point baseline margin is the single largest signal in this match and underpins the favorite tag.
Still, Hijikata's ranking trend (+16 spots) shows he's moving up, not down, which tempers how much weight the current gap alone should carry going forward.
SERVE VS RETURN BATTLE
On the numbers, Hijikata is actually the better server: 66% of service points won compared to Shapovalov's 62%. Shapovalov's return game (37%) is only marginally ahead of Hijikata's (35%), so the net edge in this specific exchange tilts slightly toward Hijikata, not the favorite.
This is a meaningful counterweight to the ranking gap — it suggests service holds should be routine for both, with Hijikata perhaps enjoying a small structural advantage in free points on serve.
FORM AND FRESHNESS
Hijikata's recent form is uneven: a 3-7 record over his last 10 with a two-match losing streak, but with notable scalps against Lehecka (Elo 2028) and Prizmic (Elo 1986) mixed in. That combination suggests inconsistency rather than a clear decline in quality.
Shapovalov arrives with more rest (40 days vs. 27), which typically helps sustain intensity across a match, but the length of that layoff is flagged as a rustiness risk rather than a pure positive.
VALUE READ
The model sets Shapovalov at 56% to win, below the market's implied 59% at odds of 1.69. That gap produces a -5.9% expected value, meaning the price is not favorable even though Shapovalov is the model's favorite.
Being favored and offering value are not the same thing here: the market is pricing Shapovalov slightly higher than the model justifies, so this is not a spot where the data suggests an edge for backing him at these odds.
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.