⚾ Bergwicz Deli Projections
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New here? Quick guide
Rate model Today's projections based on stat rates and matchup factors. Tap a card to see the full breakdown. Tap the blue dot to add or remove a player from your lineup.
Monte Carlo Probability-based projections from 10,000 simulations. Shows the likelihood of different scoring outcomes for each player today.
Accuracy Tracks how both models are performing over time. See projected vs actual by player and compare the two models head to head.
Rate model
Monte Carlo
Accuracy tracker
Projects today's score using each player's stat rates adjusted for pitcher, park, and platoon matchup.
How this model works
  1. 1Blend season + career stats — weights toward current season as plate appearances accumulate.
  2. 2Apply platoon splits — uses each player's real vs-left / vs-right rates from 2022–present.
  3. 3Apply matchup factors — pitcher difficulty, park factor, and recent form shift the rates up or down.
  4. 4Project a stat line — adjusted rates × expected ABs per game = projected H, HR, BB, K, etc.
  5. 5Score it — each stat is multiplied by your league's exact point values and summed.
High confidence Medium Low
Loading projections…Fetching today's data
Simulates 10,000 possible outcomes based on each player's historical game-by-game scoring, adjusted for today's matchup.
How this model works
  1. 1Fetch historical game logs — pulls every game from 2024–present for each player and computes their actual fantasy score.
  2. 2Compute a matchup multiplier — combines today's pitcher difficulty, platoon split, and park factor into a single adjustment (e.g. 0.73× for a tough matchup).
  3. 3Shift the distribution — multiplies each historical score by the matchup multiplier, reflecting today's conditions.
  4. 4Run 10,000 simulations — randomly samples from the shifted historical scores 10,000 times to simulate today's possible outcomes.
  5. 5Compute probabilities — counts how often each threshold (3+, 6+, negative) was hit across all simulations.
Loading historical projections...
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Model comparison
Metric Rate model Monte Carlo
Avg error——
Within 2.5 pts——
Bias——
In range %——
Rate model metrics
Avg error
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rate model
Within 2.5 pts
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rate model
Bias
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Best call
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In range
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landed in proj range
Monte Carlo calibration
No calibration data yet — check back after more games.
Proj vs actual — lineup total
No data yet.
Projected range (50% CI) Projected midpoint Actual
By player — avg error
No accuracy data yet.
Check back after the first game day.
Player chart
Yesterday
No data for yesterday yet.