Win probabilities for every mathematically live driver, recalculated after each race from stage points, race points, and what's left on the schedule. Not a sportsbook line — this is what the model says a fair bet would look like.
| # | Driver | Car | Points | N=36 | N=37 |
|---|---|---|---|---|---|
| 1 | Denny Hamlin | 11 | 2,100 | 36.38% | 35.89% |
| 2 | Ryan Blaney | 12 | 2,075 | 15.16% | 15.11% |
| 3 | Tyler Reddick | 45 | 2,065 | 10.23% | 10.24% |
| 4 | Ty Gibbs | 54 | 2,060 | 8.32% | 8.35% |
| 5 | Chase Briscoe | 19 | 2,055 | 6.72% | 6.76% |
| 6 | Christopher Bell | 20 | 2,050 | 5.39% | 5.44% |
| 7 | Kyle Larson | 5 | 2,045 | 4.29% | 4.34% |
| 8 | Chase Elliott | 9 | 2,040 | 3.39% | 3.44% |
| 9 | Joey Logano | 22 | 2,035 | 2.66% | 2.71% |
| 10 | Chris Buescher | 17 | 2,030 | 2.07% | 2.12% |
| 11 | Daniel Suarez | 7 | 2,025 | 1.60% | 1.64% |
| 12 | Carson Hocevar | 77 | 2,020 | 1.23% | 1.26% |
| 13 | William Byron | 24 | 2,015 | 0.93% | 0.96% |
| 14 | Bubba Wallace | 23 | 2,010 | 0.70% | 0.73% |
| 15 | Austin Cindric | 2 | 2,005 | 0.53% | 0.55% |
| 16 | Ryan Preece | 60 | 2,000 | 0.39% | 0.41% |
Each driver's score in a race is built from three pieces: Stage 1 points, Stage 2 points, and race finish points. The model treats each of those as a random draw across the field, then works out the full range of outcomes for the races still left on the schedule by combining that distribution with itself once per remaining race.
A driver's win probability is the chance their season-ending total ends up higher than everyone else's, given those simulated outcomes. Two field sizes are shown side by side — 36 cars and 37 — since that one assumption has enough sensitivity to matter.
The model treats every driver as equally capable and every remaining race as statistically identical. That's a simplification: it doesn't account for a driver's actual form, or for track-specific strengths and weaknesses like superspeedway variance. Only drivers still mathematically able to catch the leader are shown.