Can a fantasy football novice automate his way to championship glory?
| Slot | Player | Tm | Proj |
|---|---|---|---|
| QB | Jalen Hurts | PHI | 21.6 |
| RB1 | Saquon Barkley | PHI | 16.4 |
| RB2 | Derrick Henry | BAL | 15.7 |
| WR1 | DeVonta Smith | PHI | 12.1 |
| WR2 | Carnell TateQues | TEN | 9.8 |
| TE | Jake Ferguson | DAL | 7.6 |
| W/R/T | Jeremiyah LoveQues | ARI | 12.8 |
| K | Cairo Santos | CHI | stream |
| DEF | Chiefs | KC | stream |
| Team | Title odds | Title | Playoff | |
|---|---|---|---|---|
| 1 | Hail Mary Andrew | 20.9% | 67% | |
| 2 | Say Hello To My Kittle Friend | 17.6% | 62% | |
| 3 | Let’s Get Weird! | 13.7% | 50% | |
| 4 | Bench Warmers Anonymous | 13.0% | 48% | |
| 5 | Lone Male Juror | 9.3% | 39% | |
| 6 | When Life Gives U Makai Lemon | 7.5% | 32% | |
| 7 | Jay Money | 5.7% | 28% | |
| 8 | Real Rout Daniel | 3.7% | 20% | |
| 9 | Them Scousers | 2.7% | 14% | |
| 10 | Saquon Deez Nuts | 2.6% | 18% | |
| 11 | HAIL | 2.1% | 12% | |
| 12 | Achane of Pace | 1.3% | 9% |
xFP · WOPR · snap share → buy-low / sell-high.
League + stack correlation with real right-skew.
Blends sources by graded accuracy, weekly.
Scans every roster for fair, +ΔP swaps.
Every strategy below was researched and graded before it was coded. SUPPORTED = backed by data; JUDGMENT = directionally sound but effect size unproven, so it’s implemented cautiously. Click any card for how it works, why it matters, and the source.
How: a weighted ensemble (consensus + ESPN), re-scored to your exact rules and reweighted each week toward whichever source has been most accurate for THIS league.
Value: in a 12-season backtest, the consensus average beat individual sources in 69% of head-to-head comparisons; single sources are volatile year to year.
Source: Fantasy Football Analytics
How: target share correlates ~0.70 year-over-year and explains ~60–70% of WR per-game variance; it stabilizes after ~4 games, and snap share stabilizes fastest. Efficiency stats (yards-per-carry) have almost no predictive value.
Value: stops the coach chasing a one-week spike (mostly noise) and buys players whose ROLE, not their luck, is rising.
Source: SumerSports research
How: of top-10 defenses through Week 12 (2022–24), only 40% stayed top-10 over the final six weeks. Kickers are nearly flat, 20 of 28 bunched between 7.6–9.6 points per game.
Value: the bench spot a backup K/DEF would waste becomes a weekly matchup edge instead.
Source: ESPN streaming data (Cockcroft / Clay).
How: implied total = (game total ÷ 2) − (spread ÷ 2). Historically teams implied under 14 scored ~13.7; 24–27.75 scored ~25.9; 28+ scored ~29.7.
Value: targets defenses facing offenses implied ≤ ~18–19 (the scoring-cliff zone) and kickers on high-total offenses.
Source: The Odds API
4-pt passing TDs flatten QB value (a 6-pt format spreads QB1–QB9 ~36% vs ~29%), making streaming QB viable and rushing QBs more valuable.
Kicker distance tiers (40–49 = 4, 50+ = 5) reward leg strength, Brandon Aubrey has averaged 10.44 pts, +2.27/game (+27.8%) above average.
The DEF cliff (21–27 allowed = ZERO, 28+ = negative) makes matchup selection matter more than unit quality.
How: grounded in Skinner’s variance-optimization framework: maximize Z = (μ_you − μ_opp) / √(σ²_you + σ²_opp). The crossover is exactly 50% win probability when adding variance is free.
Value: a real but modest edge, so it’s implemented conservatively (never sacrificing >10–15% of projected points); the head-to-head effect size is unpublished.
Source: Skinner, “Scoring strategies for the underdog,” Journal of Quantitative Analysis in Sports (2011).
How: winning a claim drops you to last and never replenishes, so the coach only spends #1 above ~1% championship gain; below that it says wait and grab the player off free agency for zero cost.
Value: avoids the classic mistake of burning your best claim on a two-week injury fill-in.
Eight-plus live feeds are fused into one player model every week.
Cadence: read continuously
Source: via your authenticated session
Cadence: daily
Source: docs.sleeper.com
Cadence: after every game
Source: nflverse.nflverse.com
Cadence: weekly, in-season
Source: ffopportunity.ffverse.com
Cadence: Tue & Sat
Source: fantasyfootballanalytics.net
Cadence: Sunday AM
Source: the-odds-api.com
Cadence: polled Sunday (most time-critical)
Source: www.nfl.com/inactives
Cadence: on demand
Source: fantasycalc.com
The engine is deterministic statistics, not vibes. Here’s the machinery.
Each week every team draws a score; the real Yahoo schedule decides wins; the top 4 seed; Weeks 16–17 are a reseeded bracket where the higher seed wins ties. ≥10,000 seasons per decision for stable odds.
So scores are modeled as gamma, not normal. Per-position weekly coefficient of variation: QB 0.36 · RB 0.54 · WR 0.58 · TE 0.63. TEs and deep WRs are the most volatile; QBs the steadiest, which is exactly why streaming a QB is safer here than streaming a TE.
A shared latent weekly factor makes all teams co-move slightly (~8% league-wide), and same-NFL-team starters correlate more (+0.2 to +0.6), the “stack” ceiling that wins two-week playoff brackets.
A deep roster loses less when a starter sits, because the sim swaps in your best backup and prices in the uncertainty. That is what finally makes handcuffs and depth pay off.
Every projection is logged and later scored against the real result; the mean-absolute-error per source becomes its weight, so the blend keeps shifting toward whatever is most accurate for your scoring.
Combines target share and air-yards share into a single opportunity score. In one study it explained about 75% of receiving fantasy points (R² ≈ 0.75).
This targets title probability directly, so a move that helps you win the whole thing outranks one that just scores more points on paper. Computed with common random numbers so even tiny edges are measured precisely, then every waiver, start/sit, and trade is ranked by this single delta.