Methodology

Every Diamond DFS projection is a transparent, weighted sum of public inputs. Nothing is a black box — you can tune every weight, see every input, and rebuild the projection from scratch.

Inputs

• Hitter rate stats — singles, doubles, triples, HR, BB, HBP, SB, R, and RBI per plate appearance. Season totals blended with last-14-day form (~35% weight on L14) and Empirical-Bayes shrunk toward league average for small samples. Source: MLB Stats API.
• Expected PA — lineup-slot table (slot 1 ≈ 4.6 PA, slot 9 ≈ 3.7 PA) scaled by the team's Vegas implied total. Pulled from official starting lineups on game day.
• Pitcher rate stats & role — K/IP, BB/IP, H/IP, ER/IP, HBP/IP from season totals. Recent gamelog usage classifies role (SP, Opener, Bulk, RP) and sets projected IP. Source: MLB Stats API.
• vs SP composite — pitcher dominance (K rate, last-30d WHIP, BAA, home/away, rest, quality starts) blended with opposing team vulnerability (team K%, on-base quality, BA L14, R/G L14, lineup strength). Hitter side adds platoon hand-OPS split and a K-rate mismatch adjustment.
• Park factor — multi-year runs, HR, and hits factors per venue. Coors boosts hitter ceilings; Petco suppresses them. See the full parks table.
• Weather — temperature, wind speed and direction relative to home plate, plus dome, turf, and day/night flags. Wind blowing out adds materially to HR expectation; domes are flat-flagged. Source: OpenWeather.
• Vegas implied team total — derived from moneyline and over/under, refreshed when lines move. Drives the team-total factor and feeds expected PA + pitcher win probability. Source: The Odds API.

Projection formula (simplified)

hitter_points =
    Σ over events {singles, doubles, triples, HR, BB, HBP, SB, R, RBI} of
      rate_per_PA(event)
    × expected_PA(lineup_slot, team_total)
    × event_multiplier(vsSP, recentTrend, parkFactor,
                       weather, teamTotal, environmentalSplits)
    × DK_points(event)

pitcher_points =
    Σ over events {K, ER, H, BB, IP} of
      rate_per_IP(event)
    × projected_IP(role: SP | Opener | Bulk | RP)
    × event_multiplier(vsSP, recentTrend, parkFactor,
                       weather, teamTotal)
    × DK_points(event)
  + win_probability(team_total, opp_total, isHome) × W_bonus

Weights

The six factors (vs SP, recent trend, park, weather, team total, environmental splits) are controlled by integer sliders that sum to a budget. Each factor's effective impact is its slider value divided by the total — moving one slider up automatically pulls impact away from the others. Defaults give every factor a roughly equal share; users can rebalance to lean harder on weather, downweight park, or change how aggressively the SP matchup fires.

Optimizer

Given projections, the optimizer solves the salary-cap problem with integer linear programming to maximize total projected points under DraftKings' roster constraints ($50,000 cap, 2 SP, 1 C, 1 1B, 1 2B, 1 3B, 1 SS, 3 OF). Locks, excludes, and stack rules are first-class constraints. A single solve runs in <100ms; multi-lineup portfolios run in 1–3s.

Data refresh cadence

• DraftKings player pool & salaries: every 15 minutes
• Probable pitchers & starting lineups: hourly until first pitch
• Weather: hourly, plus 90 minutes before each first pitch
• Vegas lines / implied totals: on line move (push from The Odds API)
• Season-to-date rate stats: nightly

Frequently asked