Seattle Storm at Minnesota Lynx
SEA 68 – MIN 88
June 6, 2026 · Final
Best model for this game
In 95 out of 100 simulated runs, each team scored inside its band below — this is about where the final score lands, not a claim about the average.
In 90 out of 100 simulated runs, each team scored inside its band below — this is about where the final score lands, not a claim about the average.
In 85 out of 100 simulated runs, each team scored inside its band below — this is about where the final score lands, not a claim about the average.
In 80 out of 100 simulated runs, each team scored inside its band below — this is about where the final score lands, not a claim about the average.
In 75 out of 100 simulated runs, each team scored inside its band below — this is about where the final score lands, not a claim about the average.
All 12 models’ predicted scores
In 95 out of 100 simulated runs, leans Minnesota Lynx (avg. 86.1) over Seattle Storm (avg. 81.5) — by 4.6 points. The ranges barely touch.
In 90 out of 100 simulated runs, leans Minnesota Lynx (avg. 86.1) over Seattle Storm (avg. 81.5) — by 4.6 points. The 90% ranges don't overlap at all — a confident model.
In 85 out of 100 simulated runs, leans Minnesota Lynx (avg. 86.1) over Seattle Storm (avg. 81.5) — by 4.6 points. The 85% ranges don't overlap at all — a confident model.
In 80 out of 100 simulated runs, leans Minnesota Lynx (avg. 86.1) over Seattle Storm (avg. 81.5) — by 4.6 points. The 80% ranges don't overlap at all — a confident model.
In 75 out of 100 simulated runs, leans Minnesota Lynx (avg. 86.1) over Seattle Storm (avg. 81.5) — by 4.6 points. The 75% ranges don't overlap at all — a confident model.
Show the math
- Model
- Vince Chambers (#1)
- t-statistic
- -26.58
- p-value
- 4.44e-62
A low p-value means the two teams' average scores are statistically distinguishable — even when their ranges above overlap. Overlap describes where the final score could land; the test above is about whether the two averages differ. They are different claims, and either can hold without the other.
In 95 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.9) over Seattle Storm (avg. 81.5) — by 4.4 points. The ranges barely touch.
In 90 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.9) over Seattle Storm (avg. 81.5) — by 4.4 points. The 90% ranges don't overlap at all — a confident model.
In 85 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.9) over Seattle Storm (avg. 81.5) — by 4.4 points. The 85% ranges don't overlap at all — a confident model.
In 80 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.9) over Seattle Storm (avg. 81.5) — by 4.4 points. The 80% ranges don't overlap at all — a confident model.
In 75 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.9) over Seattle Storm (avg. 81.5) — by 4.4 points. The 75% ranges don't overlap at all — a confident model.
Show the math
- Model
- Reg (#2)
- t-statistic
- -25.94
- p-value
- 1.64e-60
A low p-value means the two teams' average scores are statistically distinguishable — even when their ranges above overlap. Overlap describes where the final score could land; the test above is about whether the two averages differ. They are different claims, and either can hold without the other.
In 95 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.7) over Seattle Storm (avg. 81.6) — by 4.2 points. The ranges barely touch.
In 90 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.7) over Seattle Storm (avg. 81.6) — by 4.2 points. The 90% ranges don't overlap at all — a confident model.
In 85 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.7) over Seattle Storm (avg. 81.6) — by 4.2 points. The 85% ranges don't overlap at all — a confident model.
In 80 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.7) over Seattle Storm (avg. 81.6) — by 4.2 points. The 80% ranges don't overlap at all — a confident model.
In 75 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.7) over Seattle Storm (avg. 81.6) — by 4.2 points. The 75% ranges don't overlap at all — a confident model.
Show the math
- Model
- Dr. Wallace (#3)
- t-statistic
- -27.38
- p-value
- 4.80e-63
A low p-value means the two teams' average scores are statistically distinguishable — even when their ranges above overlap. Overlap describes where the final score could land; the test above is about whether the two averages differ. They are different claims, and either can hold without the other.
In 95 out of 100 simulated runs, leans Minnesota Lynx (avg. 86.0) over Seattle Storm (avg. 81.6) — by 4.4 points. The ranges barely touch.
In 90 out of 100 simulated runs, leans Minnesota Lynx (avg. 86.0) over Seattle Storm (avg. 81.6) — by 4.4 points. The 90% ranges don't overlap at all — a confident model.
In 85 out of 100 simulated runs, leans Minnesota Lynx (avg. 86.0) over Seattle Storm (avg. 81.6) — by 4.4 points. The 85% ranges don't overlap at all — a confident model.
In 80 out of 100 simulated runs, leans Minnesota Lynx (avg. 86.0) over Seattle Storm (avg. 81.6) — by 4.4 points. The 80% ranges don't overlap at all — a confident model.
In 75 out of 100 simulated runs, leans Minnesota Lynx (avg. 86.0) over Seattle Storm (avg. 81.6) — by 4.4 points. The 75% ranges don't overlap at all — a confident model.
Show the math
- Model
- Kevin (#4)
- t-statistic
- -27.24
- p-value
- 1.80e-63
A low p-value means the two teams' average scores are statistically distinguishable — even when their ranges above overlap. Overlap describes where the final score could land; the test above is about whether the two averages differ. They are different claims, and either can hold without the other.
In 95 out of 100 simulated runs, leans Minnesota Lynx (avg. 84.9) over Seattle Storm (avg. 81.2) — by 3.7 points. The ranges barely touch.
In 90 out of 100 simulated runs, leans Minnesota Lynx (avg. 84.9) over Seattle Storm (avg. 81.2) — by 3.7 points. The ranges barely touch.
In 85 out of 100 simulated runs, leans Minnesota Lynx (avg. 84.9) over Seattle Storm (avg. 81.2) — by 3.7 points. The 85% ranges don't overlap at all — a confident model.
In 80 out of 100 simulated runs, leans Minnesota Lynx (avg. 84.9) over Seattle Storm (avg. 81.2) — by 3.7 points. The 80% ranges don't overlap at all — a confident model.
In 75 out of 100 simulated runs, leans Minnesota Lynx (avg. 84.9) over Seattle Storm (avg. 81.2) — by 3.7 points. The 75% ranges don't overlap at all — a confident model.
Show the math
- Model
- Dr. Lila Shah (#5)
- t-statistic
- -23.46
- p-value
- 8.09e-55
A low p-value means the two teams' average scores are statistically distinguishable — even when their ranges above overlap. Overlap describes where the final score could land; the test above is about whether the two averages differ. They are different claims, and either can hold without the other.
In 95 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.8) over Seattle Storm (avg. 81.6) — by 4.2 points. The ranges barely touch.
In 90 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.8) over Seattle Storm (avg. 81.6) — by 4.2 points. The 90% ranges don't overlap at all — a confident model.
In 85 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.8) over Seattle Storm (avg. 81.6) — by 4.2 points. The 85% ranges don't overlap at all — a confident model.
In 80 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.8) over Seattle Storm (avg. 81.6) — by 4.2 points. The 80% ranges don't overlap at all — a confident model.
In 75 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.8) over Seattle Storm (avg. 81.6) — by 4.2 points. The 75% ranges don't overlap at all — a confident model.
Show the math
- Model
- Ice (#6)
- t-statistic
- -28.10
- p-value
- 2.19e-65
A low p-value means the two teams' average scores are statistically distinguishable — even when their ranges above overlap. Overlap describes where the final score could land; the test above is about whether the two averages differ. They are different claims, and either can hold without the other.
In 95 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.8) over Seattle Storm (avg. 81.5) — by 4.3 points. The ranges barely touch.
In 90 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.8) over Seattle Storm (avg. 81.5) — by 4.3 points. The ranges barely touch.
In 85 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.8) over Seattle Storm (avg. 81.5) — by 4.3 points. The 85% ranges don't overlap at all — a confident model.
In 80 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.8) over Seattle Storm (avg. 81.5) — by 4.3 points. The 80% ranges don't overlap at all — a confident model.
In 75 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.8) over Seattle Storm (avg. 81.5) — by 4.3 points. The 75% ranges don't overlap at all — a confident model.
Show the math
- Model
- Jamal (#7)
- t-statistic
- -24.54
- p-value
- 2.14e-57
A low p-value means the two teams' average scores are statistically distinguishable — even when their ranges above overlap. Overlap describes where the final score could land; the test above is about whether the two averages differ. They are different claims, and either can hold without the other.
In 95 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.6) over Seattle Storm (avg. 81.6) — by 4.0 points. The ranges barely touch.
In 90 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.6) over Seattle Storm (avg. 81.6) — by 4.0 points. The 90% ranges don't overlap at all — a confident model.
In 85 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.6) over Seattle Storm (avg. 81.6) — by 4.0 points. The 85% ranges don't overlap at all — a confident model.
In 80 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.6) over Seattle Storm (avg. 81.6) — by 4.0 points. The 80% ranges don't overlap at all — a confident model.
In 75 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.6) over Seattle Storm (avg. 81.6) — by 4.0 points. The 75% ranges don't overlap at all — a confident model.
Show the math
- Model
- Jordan (#8)
- t-statistic
- -25.45
- p-value
- 3.16e-59
A low p-value means the two teams' average scores are statistically distinguishable — even when their ranges above overlap. Overlap describes where the final score could land; the test above is about whether the two averages differ. They are different claims, and either can hold without the other.
In 95 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.9) over Seattle Storm (avg. 81.5) — by 4.4 points. The ranges barely touch.
In 90 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.9) over Seattle Storm (avg. 81.5) — by 4.4 points. The 90% ranges don't overlap at all — a confident model.
In 85 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.9) over Seattle Storm (avg. 81.5) — by 4.4 points. The 85% ranges don't overlap at all — a confident model.
In 80 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.9) over Seattle Storm (avg. 81.5) — by 4.4 points. The 80% ranges don't overlap at all — a confident model.
In 75 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.9) over Seattle Storm (avg. 81.5) — by 4.4 points. The 75% ranges don't overlap at all — a confident model.
Show the math
- Model
- Darren "Dimes" Lin (#9)
- t-statistic
- -24.47
- p-value
- 2.86e-57
A low p-value means the two teams' average scores are statistically distinguishable — even when their ranges above overlap. Overlap describes where the final score could land; the test above is about whether the two averages differ. They are different claims, and either can hold without the other.
In 95 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.8) over Seattle Storm (avg. 81.6) — by 4.2 points. The ranges barely touch.
In 90 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.8) over Seattle Storm (avg. 81.6) — by 4.2 points. The 90% ranges don't overlap at all — a confident model.
In 85 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.8) over Seattle Storm (avg. 81.6) — by 4.2 points. The 85% ranges don't overlap at all — a confident model.
In 80 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.8) over Seattle Storm (avg. 81.6) — by 4.2 points. The 80% ranges don't overlap at all — a confident model.
In 75 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.8) over Seattle Storm (avg. 81.6) — by 4.2 points. The 75% ranges don't overlap at all — a confident model.
Show the math
- Model
- Maya Jefferson (#10)
- t-statistic
- -25.01
- p-value
- 1.94e-58
A low p-value means the two teams' average scores are statistically distinguishable — even when their ranges above overlap. Overlap describes where the final score could land; the test above is about whether the two averages differ. They are different claims, and either can hold without the other.
In 95 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.6) over Seattle Storm (avg. 81.7) — by 3.9 points. The ranges overlap some — there's real uncertainty here.
In 90 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.6) over Seattle Storm (avg. 81.7) — by 3.9 points. The ranges barely touch.
In 85 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.6) over Seattle Storm (avg. 81.7) — by 3.9 points. The 85% ranges don't overlap at all — a confident model.
In 80 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.6) over Seattle Storm (avg. 81.7) — by 3.9 points. The 80% ranges don't overlap at all — a confident model.
In 75 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.6) over Seattle Storm (avg. 81.7) — by 3.9 points. The 75% ranges don't overlap at all — a confident model.
Show the math
- Model
- Lexi (#11)
- t-statistic
- -23.33
- p-value
- 1.74e-54
A low p-value means the two teams' average scores are statistically distinguishable — even when their ranges above overlap. Overlap describes where the final score could land; the test above is about whether the two averages differ. They are different claims, and either can hold without the other.
In 95 out of 100 simulated runs, leans Minnesota Lynx (avg. 86.0) over Seattle Storm (avg. 80.6) — by 5.4 points. The 95% ranges don't overlap at all — a confident model.
In 90 out of 100 simulated runs, leans Minnesota Lynx (avg. 86.0) over Seattle Storm (avg. 80.6) — by 5.4 points. The 90% ranges don't overlap at all — a confident model.
In 85 out of 100 simulated runs, leans Minnesota Lynx (avg. 86.0) over Seattle Storm (avg. 80.6) — by 5.4 points. The 85% ranges don't overlap at all — a confident model.
In 80 out of 100 simulated runs, leans Minnesota Lynx (avg. 86.0) over Seattle Storm (avg. 80.6) — by 5.4 points. The 80% ranges don't overlap at all — a confident model.
In 75 out of 100 simulated runs, leans Minnesota Lynx (avg. 86.0) over Seattle Storm (avg. 80.6) — by 5.4 points. The 75% ranges don't overlap at all — a confident model.
Show the math
- Model
- Coach Sarah Watanabe (#12)
- t-statistic
- -30.99
- p-value
- 1.42e-70
A low p-value means the two teams' average scores are statistically distinguishable — even when their ranges above overlap. Overlap describes where the final score could land; the test above is about whether the two averages differ. They are different claims, and either can hold without the other.
View as table
| Level | SEA range | MIN range | Overlap | Tol Warning | Actual landed in range? |
|---|---|---|---|---|---|
| 95% | 78.9–84.0 | 83.7–88.1 | 0.3 pts | Yes | No |
| 90% | 79.3–83.6 | 83.9–87.7 | — pts | Yes | No |
| 85% | 79.6–83.4 | 84.0–87.4 | — pts | Yes | No |
| 80% | 79.8–83.2 | 84.4–87.3 | — pts | Yes | No |
| 75% | 80.0–83.0 | 84.7–87.2 | — pts | Yes | No |
| Level | SEA range | MIN range | Overlap | Tol Warning | Actual landed in range? |
|---|---|---|---|---|---|
| 95% | 79.1–83.9 | 83.3–88.5 | 0.6 pts | — | No |
| 90% | 79.5–83.5 | 83.8–88.1 | — pts | — | No |
| 85% | 79.7–83.3 | 84.0–87.8 | — pts | — | No |
| 80% | 79.9–83.1 | 84.2–87.6 | — pts | — | No |
| 75% | 80.1–82.9 | 84.4–87.4 | — pts | — | No |
| Level | SEA range | MIN range | Overlap | Tol Warning | Actual landed in range? |
|---|---|---|---|---|---|
| 95% | 79.5–83.6 | 83.3–88.1 | 0.31 pts | — | No |
| 90% | 79.9–83.3 | 83.7–87.8 | — pts | — | No |
| 85% | 80.1–83.1 | 84.0–87.5 | — pts | — | No |
| 80% | 80.2–82.9 | 84.2–87.3 | — pts | — | No |
| 75% | 80.4–82.8 | 84.3–87.2 | — pts | — | No |
| Level | SEA range | MIN range | Overlap | Tol Warning | Actual landed in range? |
|---|---|---|---|---|---|
| 95% | 79.3–83.9 | 83.6–88.4 | 0.36 pts | — | No |
| 90% | 79.7–83.6 | 84.0–88.0 | — pts | — | No |
| 85% | 79.9–83.3 | 84.2–87.8 | — pts | — | No |
| 80% | 80.1–83.1 | 84.4–87.6 | — pts | — | No |
| 75% | 80.3–83.0 | 84.6–87.4 | — pts | — | No |
| Level | SEA range | MIN range | Overlap | Tol Warning | Actual landed in range? |
|---|---|---|---|---|---|
| 95% | 78.9–83.5 | 82.5–87.3 | 0.96 pts | — | No |
| 90% | 79.3–83.1 | 82.9–87.0 | 0.2 pts | — | No |
| 85% | 79.5–82.9 | 83.2–86.7 | — pts | — | No |
| 80% | 79.7–82.7 | 83.4–86.5 | — pts | — | No |
| 75% | 79.9–82.5 | 83.5–86.3 | — pts | — | No |
| Level | SEA range | MIN range | Overlap | Tol Warning | Actual landed in range? |
|---|---|---|---|---|---|
| 95% | 79.5–83.8 | 83.6–88.0 | 0.21 pts | — | No |
| 90% | 79.8–83.5 | 84.0–87.7 | — pts | — | No |
| 85% | 80.0–83.3 | 84.2–87.4 | — pts | — | No |
| 80% | 80.2–83.1 | 84.4–87.3 | — pts | — | No |
| 75% | 80.4–82.9 | 84.5–87.1 | — pts | — | No |
| Level | SEA range | MIN range | Overlap | Tol Warning | Actual landed in range? |
|---|---|---|---|---|---|
| 95% | 78.8–84.1 | 83.2–88.4 | 0.88 pts | — | No |
| 90% | 79.2–83.7 | 83.7–88.0 | 0.04 pts | — | No |
| 85% | 79.5–83.4 | 83.9–87.7 | — pts | — | No |
| 80% | 79.7–83.2 | 84.1–87.5 | — pts | — | No |
| 75% | 79.9–83.0 | 84.3–87.3 | — pts | — | No |
| Level | SEA range | MIN range | Overlap | Tol Warning | Actual landed in range? |
|---|---|---|---|---|---|
| 95% | 79.4–83.8 | 83.2–88.1 | 0.63 pts | — | No |
| 90% | 79.7–83.4 | 83.6–87.7 | — pts | — | No |
| 85% | 80.0–83.2 | 83.8–87.4 | — pts | — | No |
| 80% | 80.1–83.0 | 84.0–87.2 | — pts | — | No |
| 75% | 80.3–82.9 | 84.2–87.1 | — pts | — | No |
| Level | SEA range | MIN range | Overlap | Tol Warning | Actual landed in range? |
|---|---|---|---|---|---|
| 95% | 79.2–83.7 | 83.3–88.5 | 0.45 pts | Yes | No |
| 90% | 79.4–83.3 | 83.7–88.1 | — pts | Yes | No |
| 85% | 80.0–83.1 | 84.0–87.8 | — pts | Yes | No |
| 80% | 80.1–82.7 | 84.2–87.6 | — pts | Yes | No |
| 75% | 80.2–82.6 | 84.4–87.4 | — pts | Yes | No |
| Level | SEA range | MIN range | Overlap | Tol Warning | Actual landed in range? |
|---|---|---|---|---|---|
| 95% | 79.1–84.1 | 83.4–88.2 | 0.75 pts | — | No |
| 90% | 79.5–83.7 | 83.8–87.8 | — pts | — | No |
| 85% | 79.7–83.5 | 84.0–87.5 | — pts | — | No |
| 80% | 79.9–83.2 | 84.2–87.3 | — pts | — | No |
| 75% | 80.1–83.1 | 84.4–87.2 | — pts | — | No |
| Level | SEA range | MIN range | Overlap | Tol Warning | Actual landed in range? |
|---|---|---|---|---|---|
| 95% | 79.3–84.1 | 83.1–88.2 | 1.03 pts | — | No |
| 90% | 79.7–83.7 | 83.5–87.8 | 0.24 pts | — | No |
| 85% | 79.9–83.5 | 83.8–87.5 | — pts | — | No |
| 80% | 80.1–83.3 | 84.0–87.3 | — pts | — | No |
| 75% | 80.3–83.1 | 84.1–87.1 | — pts | — | No |
| Level | SEA range | MIN range | Overlap | Tol Warning | Actual landed in range? |
|---|---|---|---|---|---|
| 95% | 78.2–82.9 | 83.2–88.8 | — pts | — | No |
| 90% | 78.5–82.6 | 83.7–88.3 | — pts | — | No |
| 85% | 78.8–82.3 | 84.0–88.0 | — pts | — | No |
| 80% | 79.0–82.1 | 84.2–87.8 | — pts | — | No |
| 75% | 79.1–82.0 | 84.4–87.6 | — pts | — | No |