Worked example
How much does this lineup change matter?
A lineup decision has two versions: the projected team and the team after a change. This example compares them over the same 100 simulated games, then shows the inputs and limits behind the difference.
Archived example from Fri 23 Oct, 09:21 UTC. This game has been played or the example expired; there is no current football example right now. It is kept so the walk-through still makes sense; a new one appears here when the next one is made.
Carolina Panthers v Tampa Bay Buccaneers
Kickoff
Scenario engine (approximate): not calibrated on held-out matches
1The decision being tested
Out: Bryce Young (QB, 22.1 fantasy points per game) · In: Kenny Pickett (QB, 1.0 fantasy points per game)
Out: Chuba Hubbard (RB, 19.6 fantasy points per game) · In: AJ Dillon (RB, 1.7 fantasy points per game)
Out: Tetairoa McMillan (WR, 17.8 fantasy points per game) · In: John Metchie III (WR, 5.2 fantasy points per game)
For Carolina Panthers.
The rule: The home team's three top-scoring starters are taken out and the best available player for each position comes in. Everything else is unchanged. We fixed it before running anything and ran it for 11 upcoming games. This is the one where it moved the result most; in a typical game it moved 4 points.
2 · What the simulation showed
Taking out Bryce Young, Chuba Hubbard and Tetairoa McMillan (Kenny Pickett, AJ Dillon and John Metchie III in) changed the simulated result: Tampa Bay Buccaneers win went from 43% to 51%.
The 95% interval for that change runs from +2.1 to +13.8 points, so it does not include zero.
Approximate scenario result. The engine has not been calibrated on held-out matches. These percentages describe its runs, not verified match-outcome probabilities. The 95% intervals measure simulation sampling variability; they exclude model and player-input error.
| Outcome | Before | After | Change, 95% interval | Reading |
|---|---|---|---|---|
| Carolina Panthers win | 55% | 48% (−7) | −12.5 to −1.4 pts | clear change |
| Draw | 2% | 1% (−1) | −6.8 to +4.5 pts | not clear |
| Tampa Bay Buccaneers win | 43% | 51% (+8) | +2.1 to +13.8 pts | clear change |
3Inspect the paired runs
The opponent and random seeds are unchanged; only the listed lineup swaps differ.
Projected lineup · 100 games
- Carolina Panthers win 55
- Draw 2
- Tampa Bay Buccaneers win 43
Changed lineup · the same 100 games
- Carolina Panthers win 48
- Draw 1
- Tampa Bay Buccaneers win 51
Every game we ran
- New Orleans Saints v Pittsburgh Steelers: New Orleans Saints win 52% → 49% (−3)
- Atlanta Falcons v San Francisco 49ers: Atlanta Falcons win 49% → 43% (−6)
- Tennessee Titans v Cleveland Browns: Tennessee Titans win 49% → 46% (−3)
- Minnesota Vikings v Indianapolis Colts: Minnesota Vikings win 49% → 46% (−3)
- New York Jets v Miami Dolphins: New York Jets win 52% → 52% (±0)
- Carolina Panthers v Tampa Bay Buccaneers: Carolina Panthers win 55% → 48% (−7)
- Baltimore Ravens v Cincinnati Bengals: Baltimore Ravens win 50% → 46% (−4)
- Houston Texans v New York Giants: Houston Texans win 52% → 49% (−3)
- Arizona Cardinals v Denver Broncos: Arizona Cardinals win 54% → 50% (−4)
- Las Vegas Raiders v Los Angeles Rams: Las Vegas Raiders win 50% → 45% (−5)
- Seattle Seahawks v Kansas City Chiefs: Seattle Seahawks win 49% → 44% (−5)
What the comparison can tell you
Match forecast when this example was saved (Live logit model, Fri 23 Oct, 09:18 UTC): Carolina Panthers 61.8% · Tampa Bay Buccaneers 38.2%.
The match forecast is the kind of number the sport pages and the forecast record show: a statistical model of final scores, fitted and checked against games played before kickoff. The one above is a saved snapshot; the forecast on the sport page is the current one and moves with new results, so the two can differ. See the current forecast.
The scenario engine (approximate) above is a different model, built to compare one lineup against another. It plays the game out with the chosen players and has not been calibrated on held-out matches, so its percentages can sit far from the forecast (here Carolina Panthers 55% against 61.8%). Use it to explore how the model responds to the listed change. It does not establish the true effect of a player, a recruitment decision or the chance of a result.
How this example was made
- The game
- Carolina Panthers v Tampa Bay Buccaneers: of the 11 upcoming games from the published schedule that we ran, the one where taking the three players out moved the home team’s win chance most.
- Paired runs
- Game i in each lineup uses the same random seed. The 95% change intervals use Newcombe’s paired score interval with continuity correction. They describe sampling variability within this simulation.
- The model
- Saved-player profile score sampler (approximation). Approximate score sampling; NBA/NHL ties resolved.
- What it is not
- 100 fast approximate scenarios, not 100 full animated matches. Same saved players; no automatic substitutions. Sampling intervals do not include model error. Not bookmaker prices.
- Squad data
- Published roster / depth chart: projected, not confirmed starters. Fetched Fri 23 Oct, 03:21 UTC.
- Computed
- Fri 23 Oct, 09:21 UTC. Shown until Sun 25 Oct, 17:00 UTC, then hidden until a new one is built.
- Starting lineups
- Carolina Panthers
- Tetairoa McMillan
- Jalen Coker
- Luke Fortner
- Robert Hunt
- Damien Lewis
- Taylor Moton
- Rasheed Walker
- Tommy Tremble
- Brycen Tremayne
- Bryce Young
- Chuba Hubbard
Tampa Bay Buccaneers- Emeka Egbuka
- Chris Godwin Jr.
- Graham Barton
- Ben Bredeson
- Luke Goedeke
- Cody Mauch
- Tristan Wirfs
- Cade Otton
- Ted Hurst III
- Baker Mayfield
- Bucky Irving
Honest About Uncertainty
A lineup comparison is an estimate from a model, never a guarantee or betting advice. The scenario engine has not been calibrated on held-out matches, and its intervals cover sampling only.
- The same opponent and random seeds in both lineups
- The selection rule fixed before anything was run
- The match forecast is checked separately, on the forecast record
Try your own change
Editing and saving a lineup is free. Running it uses simulations from a plan, and what you get back reads like the example above.
Get a Second Opinion Before Game Time
Compare every sportsbook’s price, simulate matchups and check our forecast record. Start free and upgrade when you need more. Every number is a model estimate, never a guarantee or betting advice.