Player Props · DraftKings Lines & Edges

May 4, 2026 MLB Hits Props

36 lines · Updated Jun 4, 11:25 PM ET

The Almanac's Take

The board is thin today — 36 lines are in the system but the model returned zero top plays, and with `num_with_projection` sitting at 0, there are no underlying hit projections to generate edges against. That alone makes any strong lean unreliable. Worth noting that the methodology flags a high bias direction, meaning model overs already run hot on this market, so any borderline edge that did surface would carry an extra skepticism tax. Check back as projections populate; right now there's nothing here worth forcing.

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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