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 Sun 25 Oct, 09:21 UTC. This game has been played or the example expired; there is no current basketball example right now. It is kept so the walk-through still makes sense; a new one appears here when the next one is made.
Charlotte Hornets v Washington Wizards
Kickoff
Scenario engine (approximate): not calibrated on held-out matches
1The decision being tested
Out: Naz Reid (C, 33.0 fantasy points per game) · In: Royce O'Neale (F, 14.6 fantasy points per game)
Out: Grayson Allen (G, 21.2 fantasy points per game) · In: Sion James (G, 10.2 fantasy points per game)
Out: Brandon Miller (F, 19.2 fantasy points per game) · In: Hannes Steinbach (F, 12.0 fantasy points per game)
For Charlotte Hornets.
The rule: The home team's three top-scoring starters are taken out and the best available player at the same position comes in. Everything else is unchanged. We fixed it before running anything and ran it for 8 upcoming games. This is the next game on the schedule, shown whatever it moved; in a typical game it moved 3 points.
2 · What the simulation showed
Taking out Naz Reid, Grayson Allen and Brandon Miller (Royce O'Neale, Sion James and Hannes Steinbach in): these runs do not establish a clear change in the simulated result.
The biggest shift was Charlotte Hornets win, from 57% to 58%; the 95% interval for that change runs from −1.9 to +3.9 points, which includes zero. That is not proof of no effect, only that 100 runs cannot tell.
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 |
|---|---|---|---|---|
| Charlotte Hornets win | 57% | 58% (+1) | −1.9 to +3.9 pts | not clear |
| Washington Wizards win | 43% | 42% (−1) | −3.9 to +1.9 pts | not clear |
3Inspect the paired runs
The opponent and random seeds are unchanged; only the listed lineup swaps differ.
Projected lineup · 100 games
- Charlotte Hornets win 57
- Washington Wizards win 43
Changed lineup · the same 100 games
- Charlotte Hornets win 58
- Washington Wizards win 42
Every game we ran
- Charlotte Hornets v Washington Wizards: Charlotte Hornets win 57% → 58% (+1)
- Boston Celtics v Chicago Bulls: Boston Celtics win 55% → 59% (+4)
- Miami Heat v Dallas Mavericks: Miami Heat win 55% → 61% (+6)
- Milwaukee Bucks v Indiana Pacers: Milwaukee Bucks win 50% → 52% (+2)
- Oklahoma City Thunder v Phoenix Suns: Oklahoma City Thunder win 49% → 49% (±0)
- Utah Jazz v Memphis Grizzlies: Utah Jazz win 60% → 59% (−1)
- Boston Celtics v Brooklyn Nets: Boston Celtics win 55% → 59% (+4)
- San Antonio Spurs v Sacramento Kings: San Antonio Spurs win 56% → 43% (−13)
What the comparison can tell you
Match forecast when this example was saved (Live logit model, Sun 25 Oct, 09:13 UTC): Charlotte Hornets 80.1% · Washington Wizards 19.9%.
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 Charlotte Hornets 57% against 80.1%). 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
- Charlotte Hornets v Washington Wizards: of the 8 upcoming games from the published schedule that we ran, the first one on the schedule, kept whatever the change did to it.
- 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 Sun 25 Oct, 09:20 UTC.
- Computed
- Sun 25 Oct, 09:21 UTC. Shown until Mon 26 Oct, 23:00 UTC, then hidden until a new one is built.
- Starting lineups
- Charlotte Hornets
- Dennis Schroder
- Grayson Allen
- Brandon Miller
- Naz Reid
- Moussa Diabate
Washington Wizards- Trae Young
- Kyshawn George
- Anthony Davis
- Khris Middleton
- Alex Sarr
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.