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Golden State Valkyries at Seattle Storm

GS 76 – SEA 72

June 12, 2026 · Final

Coach Sarah Watanabe

Best model for this game

Coach Sarah Watanabe
All models: 12-0
Prediction ranges

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.

Confidence level for GS at SEA

All 12 models’ predicted scores

GS — one dot per model SEA — one dot per model Actual: GS 76, SEA 72
Vince Chambers
GS 79.3–84.9 SEA 77.7–81.5 Overlap 2.26 pts Actual: GS 76, SEA 72

In 95 out of 100 simulated runs, leans Golden State Valkyries (avg. 82.1) over Seattle Storm (avg. 79.6) — by 2.5 points. The ranges overlap some — there's real uncertainty here.

GS 79.7–84.4 SEA 78.0–81.2 Overlap 1.51 pts Actual: GS 76, SEA 72

In 90 out of 100 simulated runs, leans Golden State Valkyries (avg. 82.1) over Seattle Storm (avg. 79.6) — by 2.5 points. The ranges overlap some — there's real uncertainty here.

GS 80.0–84.2 SEA 78.2–81.0 Overlap 1.01 pts Actual: GS 76, SEA 72

In 85 out of 100 simulated runs, leans Golden State Valkyries (avg. 82.1) over Seattle Storm (avg. 79.6) — by 2.5 points. The ranges overlap some — there's real uncertainty here.

GS 80.2–83.9 SEA 78.4–80.9 Overlap 0.63 pts Actual: GS 76, SEA 72

In 80 out of 100 simulated runs, leans Golden State Valkyries (avg. 82.1) over Seattle Storm (avg. 79.6) — by 2.5 points. The ranges barely touch.

GS 80.4–83.7 SEA 78.5–80.7 Overlap 0.31 pts Actual: GS 76, SEA 72

In 75 out of 100 simulated runs, leans Golden State Valkyries (avg. 82.1) over Seattle Storm (avg. 79.6) — by 2.5 points. The ranges barely touch.

Show the math
Model
Vince Chambers (#1)
t-statistic
14.94
p-value
4.09e-31

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.

Reg
GS 78.6–85.5 SEA 77.1–82.2 Overlap 3.69 pts Actual: GS 76, SEA 72

In 95 out of 100 simulated runs, leans Golden State Valkyries (avg. 82.0) over Seattle Storm (avg. 79.7) — by 2.3 points. The ranges overlap almost entirely, so one game could go either way.

GS 79.1–84.9 SEA 77.5–81.8 Overlap 2.72 pts Actual: GS 76, SEA 72

In 90 out of 100 simulated runs, leans Golden State Valkyries (avg. 82.0) over Seattle Storm (avg. 79.7) — by 2.3 points. The ranges overlap some — there's real uncertainty here.

GS 79.5–84.6 SEA 77.8–81.6 Overlap 2.09 pts Actual: GS 76, SEA 72

In 85 out of 100 simulated runs, leans Golden State Valkyries (avg. 82.0) over Seattle Storm (avg. 79.7) — by 2.3 points. The ranges overlap some — there's real uncertainty here.

GS 79.8–84.3 SEA 78.0–81.4 Overlap 1.61 pts Actual: GS 76, SEA 72

In 80 out of 100 simulated runs, leans Golden State Valkyries (avg. 82.0) over Seattle Storm (avg. 79.7) — by 2.3 points. The ranges overlap some — there's real uncertainty here.

GS 80.0–84.0 SEA 78.2–81.2 Overlap 1.2 pts Actual: GS 76, SEA 72

In 75 out of 100 simulated runs, leans Golden State Valkyries (avg. 82.0) over Seattle Storm (avg. 79.7) — by 2.3 points. The ranges overlap some — there's real uncertainty here.

Show the math
Model
Reg (#2)
t-statistic
11.17
p-value
1.65e-21

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.

Dr. Wallace
GS 78.4–84.6 SEA 77.3–81.9 Overlap 3.49 pts Actual: GS 76, SEA 72

In 95 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.5) over Seattle Storm (avg. 79.6) — by 1.9 points. The ranges overlap almost entirely, so one game could go either way.

GS 78.9–84.1 SEA 77.7–81.6 Overlap 2.62 pts Actual: GS 76, SEA 72

In 90 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.5) over Seattle Storm (avg. 79.6) — by 1.9 points. The ranges overlap some — there's real uncertainty here.

GS 79.3–83.8 SEA 77.9–81.3 Overlap 2.06 pts Actual: GS 76, SEA 72

In 85 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.5) over Seattle Storm (avg. 79.6) — by 1.9 points. The ranges overlap some — there's real uncertainty here.

GS 79.5–83.6 SEA 78.1–81.1 Overlap 1.62 pts Actual: GS 76, SEA 72

In 80 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.5) over Seattle Storm (avg. 79.6) — by 1.9 points. The ranges overlap some — there's real uncertainty here.

GS 79.7–83.4 SEA 78.3–81.0 Overlap 1.26 pts Actual: GS 76, SEA 72

In 75 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.5) over Seattle Storm (avg. 79.6) — by 1.9 points. The ranges overlap some — there's real uncertainty here.

Show the math
Model
Dr. Wallace (#3)
t-statistic
10.22
p-value
5.74e-19

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.

Kevin
GS 78.7–84.4 SEA 76.9–82.4 Overlap 3.66 pts Actual: GS 76, SEA 72

In 95 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.6) over Seattle Storm (avg. 79.6) — by 1.9 points. The ranges overlap almost entirely, so one game could go either way.

GS 79.2–83.9 SEA 77.3–82.0 Overlap 2.77 pts Actual: GS 76, SEA 72

In 90 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.6) over Seattle Storm (avg. 79.6) — by 1.9 points. The ranges overlap some — there's real uncertainty here.

GS 79.5–83.6 SEA 77.6–81.7 Overlap 2.18 pts Actual: GS 76, SEA 72

In 85 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.6) over Seattle Storm (avg. 79.6) — by 1.9 points. The ranges overlap some — there's real uncertainty here.

GS 79.7–83.4 SEA 77.8–81.5 Overlap 1.74 pts Actual: GS 76, SEA 72

In 80 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.6) over Seattle Storm (avg. 79.6) — by 1.9 points. The ranges overlap some — there's real uncertainty here.

GS 79.9–83.2 SEA 78.0–81.3 Overlap 1.36 pts Actual: GS 76, SEA 72

In 75 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.6) over Seattle Storm (avg. 79.6) — by 1.9 points. The ranges overlap some — there's real uncertainty here.

Show the math
Model
Kevin (#4)
t-statistic
9.99
p-value
1.20e-18

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.

Dr. Lila Shah
GS 78.7–83.9 SEA 78.2–82.7 Overlap 4.03 pts Actual: GS 76, SEA 72

In 95 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.3) over Seattle Storm (avg. 80.5) — by under a point. The ranges overlap almost entirely, so one game could go either way.

GS 79.1–83.5 SEA 78.6–82.4 Overlap 3.25 pts Actual: GS 76, SEA 72

In 90 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.3) over Seattle Storm (avg. 80.5) — by under a point. The ranges overlap almost entirely, so one game could go either way.

GS 79.4–83.2 SEA 78.8–82.1 Overlap 2.75 pts Actual: GS 76, SEA 72

In 85 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.3) over Seattle Storm (avg. 80.5) — by under a point. The ranges overlap some — there's real uncertainty here.

GS 79.6–83.0 SEA 79.0–82.0 Overlap 2.36 pts Actual: GS 76, SEA 72

In 80 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.3) over Seattle Storm (avg. 80.5) — by under a point. The ranges overlap some — there's real uncertainty here.

GS 79.8–82.8 SEA 79.2–81.8 Overlap 2.03 pts Actual: GS 76, SEA 72

In 75 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.3) over Seattle Storm (avg. 80.5) — by under a point. The ranges overlap some — there's real uncertainty here.

Show the math
Model
Dr. Lila Shah (#5)
t-statistic
4.87
p-value
2.62e-06

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.

Ice
GS 78.7–84.7 SEA 77.8–82.0 Overlap 3.35 pts Actual: GS 76, SEA 72

In 95 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.7) over Seattle Storm (avg. 79.9) — by 1.8 points. The ranges overlap almost entirely, so one game could go either way.

GS 79.2–84.3 SEA 78.2–81.7 Overlap 2.53 pts Actual: GS 76, SEA 72

In 90 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.7) over Seattle Storm (avg. 79.9) — by 1.8 points. The ranges overlap some — there's real uncertainty here.

GS 79.5–83.9 SEA 78.4–81.5 Overlap 1.99 pts Actual: GS 76, SEA 72

In 85 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.7) over Seattle Storm (avg. 79.9) — by 1.8 points. The ranges overlap some — there's real uncertainty here.

GS 79.7–83.7 SEA 78.6–81.3 Overlap 1.58 pts Actual: GS 76, SEA 72

In 80 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.7) over Seattle Storm (avg. 79.9) — by 1.8 points. The ranges overlap some — there's real uncertainty here.

GS 79.9–83.5 SEA 78.7–81.2 Overlap 1.23 pts Actual: GS 76, SEA 72

In 75 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.7) over Seattle Storm (avg. 79.9) — by 1.8 points. The ranges overlap some — there's real uncertainty here.

Show the math
Model
Ice (#6)
t-statistic
9.97
p-value
3.60e-18

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.

Jamal Tol Warning
GS 78.4–85.1 SEA 78.4–82.3 Overlap 3.87 pts Actual: GS 76, SEA 72

In 95 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.8) over Seattle Storm (avg. 80.0) — by 1.8 points. The ranges overlap almost entirely, so one game could go either way.

GS 79.0–84.6 SEA 78.5–82.1 Overlap 3.12 pts Actual: GS 76, SEA 72

In 90 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.8) over Seattle Storm (avg. 80.0) — by 1.8 points. The ranges overlap almost entirely, so one game could go either way.

GS 79.3–84.2 SEA 78.7–81.8 Overlap 2.47 pts Actual: GS 76, SEA 72

In 85 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.8) over Seattle Storm (avg. 80.0) — by 1.8 points. The ranges overlap some — there's real uncertainty here.

GS 79.6–83.9 SEA 78.8–81.5 Overlap 1.92 pts Actual: GS 76, SEA 72

In 80 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.8) over Seattle Storm (avg. 80.0) — by 1.8 points. The ranges overlap some — there's real uncertainty here.

GS 79.8–83.7 SEA 78.9–81.4 Overlap 1.57 pts Actual: GS 76, SEA 72

In 75 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.8) over Seattle Storm (avg. 80.0) — by 1.8 points. The ranges overlap some — there's real uncertainty here.

Show the math
Model
Jamal (#7)
t-statistic
8.85
p-value
2.24e-15

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.

Jordan Tol Warning
GS 78.8–85.0 SEA 78.1–82.8 Overlap 3.96 pts Actual: GS 76, SEA 72

In 95 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.9) over Seattle Storm (avg. 80.0) — by 1.9 points. The ranges overlap almost entirely, so one game could go either way.

GS 79.3–84.5 SEA 78.3–82.0 Overlap 2.7 pts Actual: GS 76, SEA 72

In 90 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.9) over Seattle Storm (avg. 80.0) — by 1.9 points. The ranges overlap some — there's real uncertainty here.

GS 79.7–84.2 SEA 78.5–81.7 Overlap 2.07 pts Actual: GS 76, SEA 72

In 85 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.9) over Seattle Storm (avg. 80.0) — by 1.9 points. The ranges overlap some — there's real uncertainty here.

GS 79.9–83.9 SEA 78.6–81.5 Overlap 1.63 pts Actual: GS 76, SEA 72

In 80 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.9) over Seattle Storm (avg. 80.0) — by 1.9 points. The ranges overlap some — there's real uncertainty here.

GS 80.1–83.7 SEA 78.7–81.3 Overlap 1.21 pts Actual: GS 76, SEA 72

In 75 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.9) over Seattle Storm (avg. 80.0) — by 1.9 points. The ranges overlap some — there's real uncertainty here.

Show the math
Model
Jordan (#8)
t-statistic
9.78
p-value
4.90e-18

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.

Darren "Dimes" Lin Tol Warning
GS 80.1–84.6 SEA 78.4–82.0 Overlap 1.92 pts Actual: GS 76, SEA 72

In 95 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.9) over Seattle Storm (avg. 79.9) — by 2.0 points. The ranges overlap some — there's real uncertainty here.

GS 80.3–83.7 SEA 78.5–81.8 Overlap 1.51 pts Actual: GS 76, SEA 72

In 90 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.9) over Seattle Storm (avg. 79.9) — by 2.0 points. The ranges overlap some — there's real uncertainty here.

GS 80.4–83.5 SEA 78.6–81.6 Overlap 1.15 pts Actual: GS 76, SEA 72

In 85 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.9) over Seattle Storm (avg. 79.9) — by 2.0 points. The ranges overlap some — there's real uncertainty here.

GS 80.6–83.4 SEA 78.7–81.3 Overlap 0.7 pts Actual: GS 76, SEA 72

In 80 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.9) over Seattle Storm (avg. 79.9) — by 2.0 points. The ranges barely touch.

GS 80.7–83.2 SEA 78.8–81.2 Overlap 0.52 pts Actual: GS 76, SEA 72

In 75 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.9) over Seattle Storm (avg. 79.9) — by 2.0 points. The ranges barely touch.

Show the math
Model
Darren "Dimes" Lin (#9)
t-statistic
10.90
p-value
5.61e-21

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.

Maya Jefferson
GS 78.6–84.7 SEA 77.6–82.1 Overlap 3.51 pts Actual: GS 76, SEA 72

In 95 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.7) over Seattle Storm (avg. 79.8) — by 1.8 points. The ranges overlap almost entirely, so one game could go either way.

GS 79.1–84.2 SEA 77.9–81.8 Overlap 2.65 pts Actual: GS 76, SEA 72

In 90 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.7) over Seattle Storm (avg. 79.8) — by 1.8 points. The ranges overlap some — there's real uncertainty here.

GS 79.4–83.9 SEA 78.2–81.5 Overlap 2.09 pts Actual: GS 76, SEA 72

In 85 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.7) over Seattle Storm (avg. 79.8) — by 1.8 points. The ranges overlap some — there's real uncertainty here.

GS 79.7–83.7 SEA 78.4–81.3 Overlap 1.66 pts Actual: GS 76, SEA 72

In 80 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.7) over Seattle Storm (avg. 79.8) — by 1.8 points. The ranges overlap some — there's real uncertainty here.

GS 79.9–83.5 SEA 78.5–81.2 Overlap 1.3 pts Actual: GS 76, SEA 72

In 75 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.7) over Seattle Storm (avg. 79.8) — by 1.8 points. The ranges overlap some — there's real uncertainty here.

Show the math
Model
Maya Jefferson (#10)
t-statistic
9.90
p-value
3.84e-18

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.

Lexi
GS 78.9–84.9 SEA 77.8–81.8 Overlap 2.92 pts Actual: GS 76, SEA 72

In 95 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.9) over Seattle Storm (avg. 79.8) — by 2.1 points. The ranges overlap some — there's real uncertainty here.

GS 79.4–84.5 SEA 78.2–81.5 Overlap 2.11 pts Actual: GS 76, SEA 72

In 90 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.9) over Seattle Storm (avg. 79.8) — by 2.1 points. The ranges overlap some — there's real uncertainty here.

GS 79.7–84.1 SEA 78.4–81.3 Overlap 1.59 pts Actual: GS 76, SEA 72

In 85 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.9) over Seattle Storm (avg. 79.8) — by 2.1 points. The ranges overlap some — there's real uncertainty here.

GS 79.9–83.9 SEA 78.5–81.1 Overlap 1.18 pts Actual: GS 76, SEA 72

In 80 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.9) over Seattle Storm (avg. 79.8) — by 2.1 points. The ranges overlap some — there's real uncertainty here.

GS 80.1–83.7 SEA 78.7–81.0 Overlap 0.85 pts Actual: GS 76, SEA 72

In 75 out of 100 simulated runs, leans Golden State Valkyries (avg. 81.9) over Seattle Storm (avg. 79.8) — by 2.1 points. The ranges barely touch.

Show the math
Model
Lexi (#11)
t-statistic
11.94
p-value
3.77e-23

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.

Coach Sarah Watanabe
GS 78.2–85.8 SEA 76.9–81.9 Overlap 3.65 pts Actual: GS 76, SEA 72

In 95 out of 100 simulated runs, leans Golden State Valkyries (avg. 82.0) over Seattle Storm (avg. 79.4) — by 2.6 points. The ranges overlap almost entirely, so one game could go either way.

GS 78.8–85.2 SEA 77.3–81.5 Overlap 2.64 pts Actual: GS 76, SEA 72

In 90 out of 100 simulated runs, leans Golden State Valkyries (avg. 82.0) over Seattle Storm (avg. 79.4) — by 2.6 points. The ranges overlap some — there's real uncertainty here.

GS 79.2–84.8 SEA 77.5–81.2 Overlap 1.98 pts Actual: GS 76, SEA 72

In 85 out of 100 simulated runs, leans Golden State Valkyries (avg. 82.0) over Seattle Storm (avg. 79.4) — by 2.6 points. The ranges overlap some — there's real uncertainty here.

GS 79.5–84.5 SEA 77.7–81.0 Overlap 1.47 pts Actual: GS 76, SEA 72

In 80 out of 100 simulated runs, leans Golden State Valkyries (avg. 82.0) over Seattle Storm (avg. 79.4) — by 2.6 points. The ranges overlap some — there's real uncertainty here.

GS 79.8–84.2 SEA 77.9–80.8 Overlap 1.05 pts Actual: GS 76, SEA 72

In 75 out of 100 simulated runs, leans Golden State Valkyries (avg. 82.0) over Seattle Storm (avg. 79.4) — by 2.6 points. The ranges overlap some — there's real uncertainty here.

Show the math
Model
Coach Sarah Watanabe (#12)
t-statistic
12.00
p-value
2.45e-23

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
Vince Chambers — GS at SEA — Actual: GS 76, SEA 72
Level GS range SEA range Overlap Tol Warning Actual landed in range?
95% 79.3–84.9 77.7–81.5 2.26 pts — No
90% 79.7–84.4 78.0–81.2 1.51 pts — No
85% 80.0–84.2 78.2–81.0 1.01 pts — No
80% 80.2–83.9 78.4–80.9 0.63 pts — No
75% 80.4–83.7 78.5–80.7 0.31 pts — No
Reg — GS at SEA — Actual: GS 76, SEA 72
Level GS range SEA range Overlap Tol Warning Actual landed in range?
95% 78.6–85.5 77.1–82.2 3.69 pts — No
90% 79.1–84.9 77.5–81.8 2.72 pts — No
85% 79.5–84.6 77.8–81.6 2.09 pts — No
80% 79.8–84.3 78.0–81.4 1.61 pts — No
75% 80.0–84.0 78.2–81.2 1.2 pts — No
Dr. Wallace — GS at SEA — Actual: GS 76, SEA 72
Level GS range SEA range Overlap Tol Warning Actual landed in range?
95% 78.4–84.6 77.3–81.9 3.49 pts — No
90% 78.9–84.1 77.7–81.6 2.62 pts — No
85% 79.3–83.8 77.9–81.3 2.06 pts — No
80% 79.5–83.6 78.1–81.1 1.62 pts — No
75% 79.7–83.4 78.3–81.0 1.26 pts — No
Kevin — GS at SEA — Actual: GS 76, SEA 72
Level GS range SEA range Overlap Tol Warning Actual landed in range?
95% 78.7–84.4 76.9–82.4 3.66 pts — No
90% 79.2–83.9 77.3–82.0 2.77 pts — No
85% 79.5–83.6 77.6–81.7 2.18 pts — No
80% 79.7–83.4 77.8–81.5 1.74 pts — No
75% 79.9–83.2 78.0–81.3 1.36 pts — No
Dr. Lila Shah — GS at SEA — Actual: GS 76, SEA 72
Level GS range SEA range Overlap Tol Warning Actual landed in range?
95% 78.7–83.9 78.2–82.7 4.03 pts — No
90% 79.1–83.5 78.6–82.4 3.25 pts — No
85% 79.4–83.2 78.8–82.1 2.75 pts — No
80% 79.6–83.0 79.0–82.0 2.36 pts — No
75% 79.8–82.8 79.2–81.8 2.03 pts — No
Ice — GS at SEA — Actual: GS 76, SEA 72
Level GS range SEA range Overlap Tol Warning Actual landed in range?
95% 78.7–84.7 77.8–82.0 3.35 pts — No
90% 79.2–84.3 78.2–81.7 2.53 pts — No
85% 79.5–83.9 78.4–81.5 1.99 pts — No
80% 79.7–83.7 78.6–81.3 1.58 pts — No
75% 79.9–83.5 78.7–81.2 1.23 pts — No
Jamal — GS at SEA — Actual: GS 76, SEA 72
Level GS range SEA range Overlap Tol Warning Actual landed in range?
95% 78.4–85.1 78.4–82.3 3.87 pts Yes No
90% 79.0–84.6 78.5–82.1 3.12 pts Yes No
85% 79.3–84.2 78.7–81.8 2.47 pts Yes No
80% 79.6–83.9 78.8–81.5 1.92 pts Yes No
75% 79.8–83.7 78.9–81.4 1.57 pts Yes No
Jordan — GS at SEA — Actual: GS 76, SEA 72
Level GS range SEA range Overlap Tol Warning Actual landed in range?
95% 78.8–85.0 78.1–82.8 3.96 pts Yes No
90% 79.3–84.5 78.3–82.0 2.7 pts Yes No
85% 79.7–84.2 78.5–81.7 2.07 pts Yes No
80% 79.9–83.9 78.6–81.5 1.63 pts Yes No
75% 80.1–83.7 78.7–81.3 1.21 pts Yes No
Darren "Dimes" Lin — GS at SEA — Actual: GS 76, SEA 72
Level GS range SEA range Overlap Tol Warning Actual landed in range?
95% 80.1–84.6 78.4–82.0 1.92 pts Yes No
90% 80.3–83.7 78.5–81.8 1.51 pts Yes No
85% 80.4–83.5 78.6–81.6 1.15 pts Yes No
80% 80.6–83.4 78.7–81.3 0.7 pts Yes No
75% 80.7–83.2 78.8–81.2 0.52 pts Yes No
Maya Jefferson — GS at SEA — Actual: GS 76, SEA 72
Level GS range SEA range Overlap Tol Warning Actual landed in range?
95% 78.6–84.7 77.6–82.1 3.51 pts — No
90% 79.1–84.2 77.9–81.8 2.65 pts — No
85% 79.4–83.9 78.2–81.5 2.09 pts — No
80% 79.7–83.7 78.4–81.3 1.66 pts — No
75% 79.9–83.5 78.5–81.2 1.3 pts — No
Lexi — GS at SEA — Actual: GS 76, SEA 72
Level GS range SEA range Overlap Tol Warning Actual landed in range?
95% 78.9–84.9 77.8–81.8 2.92 pts — No
90% 79.4–84.5 78.2–81.5 2.11 pts — No
85% 79.7–84.1 78.4–81.3 1.59 pts — No
80% 79.9–83.9 78.5–81.1 1.18 pts — No
75% 80.1–83.7 78.7–81.0 0.85 pts — No
Coach Sarah Watanabe — GS at SEA — Actual: GS 76, SEA 72
Level GS range SEA range Overlap Tol Warning Actual landed in range?
95% 78.2–85.8 76.9–81.9 3.65 pts — No
90% 78.8–85.2 77.3–81.5 2.64 pts — No
85% 79.2–84.8 77.5–81.2 1.98 pts — No
80% 79.5–84.5 77.7–81.0 1.47 pts — No
75% 79.8–84.2 77.9–80.8 1.05 pts — No