Player Props · DraftKings Lines & Edges

May 4, 2026 MLB Hits Props

36 lines · Updated Jun 5, 12:58 AM ET

The Almanac's Take

The board is thin today — 36 lines are in, but the model returned zero top plays and carries no projections to work with. Worth flagging the methodology context anyway: the bias direction is high, meaning any overs that did surface would already be running hot out of the model, so the bar for trusting an edge here was elevated to begin with. With `top_plays` empty and `num_with_projection` at zero, there's nothing here worth forcing. Check back after lineups lock; projection coverage should fill in.

How we model these edges

Model method:: ml_monte_carlo
Approximation quality:: Reasonable
Bias direction:: Model edges on overs are biased high — real overs are slightly worse than reported.
Edge definition:: model_over_prob - no_vig(over_implied_prob)

·Per backtest, the model over-estimates hits overs at high predicted probs (sharpness issue); remediation is the projection engine (`src/projections/hitter.py`), not the probability layer.
·Probabilities come from a 1000-rep Monte Carlo on the per-PA outcome distribution. Lines outside the sim's threshold grid (very rare) fall back to the Poisson approximation — see the per-entry `model_over_prob_method` stamp.
·Integer lines (e.g. line=2.0) are treated as 'over wins on ≥2', which slightly overstates over_prob vs sportsbook push rules. Half-point lines (X.5) — the near-universal case for these markets — are unaffected.

Planned improvement: Backtest the Monte Carlo `prop_probs` against realized outcomes over a full 2024-2025 sample and recalibrate the per-PA distribution if the residuals exceed ±2 pp at any decile bucket. See `scripts/backtest_props.py`.

Hits Board

0 of 36 projected

Hits prop board sorted by signed model edge (over picks first).
#	Player	Line	Odds O/U	Proj	Model %	Market %	Edge	Pick	Conf
1	Andruw Monasterio	0.5	−133/ +100	—	—	53%	—	—	—
2	Bo Bichette	1.5	+111/ −147	—	—	44%	—	—	—
3	Brenton Doyle	1.5	+197/ −268	—	—	32%	—	—	—
4	Brett Baty	1.5	+175/ −237	—	—	34%	—	—	—
5	Caleb Durbin	0.5	−124/ −107	—	—	52%	—	—	—
6	Carson Benge	0.5	−257/ +190	—	—	68%	—	—	—
7	Ceddanne Rafaela	0.5	−164/ +123	—	—	58%	—	—	—
8	Connor Wong	0.5	+100/ −133	—	—	47%	—	—	—
9	Dillon Dingler	0.5	−220/ +164	—	—	64%	—	—	—
10	Ezequiel Tovar	0.5	−204/ +152	—	—	63%	—	—	—
11	Francisco Alvarez	1.5	+167/ −226	—	—	35%	—	—	—
12	Gleyber Torres	0.5	−176/ +132	—	—	60%	—	—	—
13	Hao-Yu Lee	0.5	−130/ −102	—	—	53%	—	—	—
14	Hunter Goodman	1.5	+179/ −243	—	—	34%	—	—	—
15	Isiah Kiner-Falefa	0.5	−149/ +112	—	—	56%	—	—	—
16	Jahmai Jones	0.5	−141/ +106	—	—	55%	—	—	—
17	Jordan Beck	0.5	−250/ +184	—	—	67%	—	—	—
18	Juan Soto	1.5	+148/ −198	—	—	38%	—	—	—
19	Kevin McGonigle	0.5	−212/ +157	—	—	64%	—	—	—
20	Kyle Karros	0.5	−208/ +155	—	—	63%	—	—	—
21	MJ Melendez	0.5	−215/ +160	—	—	64%	—	—	—
22	Marcus Semien	1.5	+175/ −237	—	—	34%	—	—	—
23	Mark Vientos	0.5	−264/ +194	—	—	68%	—	—	—
24	Matt Vierling	0.5	−146/ +110	—	—	55%	—	—	—
25	Riley Greene	0.5	−169/ +127	—	—	59%	—	—	—
26	Roman Anthony	0.5	−169/ +127	—	—	59%	—	—	—
27	Spencer Torkelson	0.5	−146/ +110	—	—	55%	—	—	—
28	TJ Rumfield	0.5	−231/ +171	—	—	65%	—	—	—
29	Trevor Story	0.5	−142/ +107	—	—	55%	—	—	—
30	Troy Johnston	0.5	−210/ +156	—	—	63%	—	—	—
31	Tyler Freeman	1.5	+172/ −233	—	—	34%	—	—	—
32	Tyrone Taylor	0.5	−233/ +173	—	—	66%	—	—	—
33	Wenceel Perez	0.5	−166/ +125	—	—	58%	—	—	—
34	Willi Castro	0.5	−265/ +195	—	—	68%	—	—	—
35	Willson Contreras	0.5	−161/ +121	—	—	58%	—	—	—
36	Wilyer Abreu	0.5	−166/ +124	—	—	58%	—	—	—

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