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WNBA · June 4, 2026
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Golden State Valkyries at Minnesota Lynx

GS 84 – MIN 87

June 4, 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 MIN

All 12 models’ predicted scores

GS — one dot per model MIN — one dot per model Actual: GS 84, MIN 87
Vince Chambers
GS 78.7–84.0 MIN 83.0–87.6 Overlap 0.95 pts Actual: GS 84, MIN 87

In 95 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.3) over Golden State Valkyries (avg. 81.3) — by 3.9 points. The ranges barely touch.

GS 79.1–83.6 MIN 83.4–87.2 Overlap 0.16 pts Actual: GS 84, MIN 87

In 90 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.3) over Golden State Valkyries (avg. 81.3) — by 3.9 points. The ranges barely touch.

GS 79.4–83.3 MIN 83.6–87.0 No overlap Actual: GS 84, MIN 87

In 85 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.3) over Golden State Valkyries (avg. 81.3) — by 3.9 points. The 85% ranges don't overlap at all — a confident model.

GS 79.6–83.1 MIN 83.8–86.8 No overlap Actual: GS 84, MIN 87

In 80 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.3) over Golden State Valkyries (avg. 81.3) — by 3.9 points. The 80% ranges don't overlap at all — a confident model.

GS 79.8–82.9 MIN 84.0–86.6 No overlap Actual: GS 84, MIN 87

In 75 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.3) over Golden State Valkyries (avg. 81.3) — by 3.9 points. The 75% ranges don't overlap at all — a confident model.

Show the math
Model
Vince Chambers (#1)
t-statistic
-23.93
p-value
4.60e-56

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 78.5–84.1 MIN 83.1–87.7 Overlap 0.93 pts Actual: GS 84, MIN 87

In 95 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.4) over Golden State Valkyries (avg. 81.3) — by 4.1 points. The ranges barely touch.

GS 78.9–83.6 MIN 83.5–87.3 Overlap 0.12 pts Actual: GS 84, MIN 87

In 90 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.4) over Golden State Valkyries (avg. 81.3) — by 4.1 points. The ranges barely touch.

GS 79.2–83.3 MIN 83.7–87.0 No overlap Actual: GS 84, MIN 87

In 85 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.4) over Golden State Valkyries (avg. 81.3) — by 4.1 points. The 85% ranges don't overlap at all — a confident model.

GS 79.4–83.1 MIN 83.9–86.9 No overlap Actual: GS 84, MIN 87

In 80 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.4) over Golden State Valkyries (avg. 81.3) — by 4.1 points. The 80% ranges don't overlap at all — a confident model.

GS 79.6–82.9 MIN 84.1–86.7 No overlap Actual: GS 84, MIN 87

In 75 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.4) over Golden State Valkyries (avg. 81.3) — by 4.1 points. The 75% ranges don't overlap at all — a confident model.

Show the math
Model
Reg (#2)
t-statistic
-24.17
p-value
4.43e-56

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 78.7–83.6 MIN 83.1–87.4 Overlap 0.5 pts Actual: GS 84, MIN 87

In 95 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.3) over Golden State Valkyries (avg. 81.2) — by 4.1 points. The ranges barely touch.

GS 79.1–83.2 MIN 83.5–87.1 No overlap Actual: GS 84, MIN 87

In 90 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.3) over Golden State Valkyries (avg. 81.2) — by 4.1 points. The 90% ranges don't overlap at all — a confident model.

GS 79.3–83.0 MIN 83.7–86.9 No overlap Actual: GS 84, MIN 87

In 85 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.3) over Golden State Valkyries (avg. 81.2) — by 4.1 points. The 85% ranges don't overlap at all — a confident model.

GS 79.5–82.8 MIN 83.9–86.7 No overlap Actual: GS 84, MIN 87

In 80 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.3) over Golden State Valkyries (avg. 81.2) — by 4.1 points. The 80% ranges don't overlap at all — a confident model.

GS 79.7–82.6 MIN 84.0–86.6 No overlap Actual: GS 84, MIN 87

In 75 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.3) over Golden State Valkyries (avg. 81.2) — by 4.1 points. The 75% ranges don't overlap at all — a confident model.

Show the math
Model
Dr. Wallace (#3)
t-statistic
-26.51
p-value
4.35e-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.

Kevin
GS 78.8–83.5 MIN 83.2–87.7 Overlap 0.31 pts Actual: GS 84, MIN 87

In 95 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.4) over Golden State Valkyries (avg. 81.1) — by 4.3 points. The ranges barely touch.

GS 79.1–83.1 MIN 83.6–87.3 No overlap Actual: GS 84, MIN 87

In 90 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.4) over Golden State Valkyries (avg. 81.1) — by 4.3 points. The 90% ranges don't overlap at all — a confident model.

GS 79.4–82.8 MIN 83.8–87.1 No overlap Actual: GS 84, MIN 87

In 85 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.4) over Golden State Valkyries (avg. 81.1) — by 4.3 points. The 85% ranges don't overlap at all — a confident model.

GS 79.6–82.7 MIN 84.0–86.9 No overlap Actual: GS 84, MIN 87

In 80 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.4) over Golden State Valkyries (avg. 81.1) — by 4.3 points. The 80% ranges don't overlap at all — a confident model.

GS 79.7–82.5 MIN 84.1–86.8 No overlap Actual: GS 84, MIN 87

In 75 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.4) over Golden State Valkyries (avg. 81.1) — by 4.3 points. The 75% ranges don't overlap at all — a confident model.

Show the math
Model
Kevin (#4)
t-statistic
-27.80
p-value
4.84e-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.

Dr. Lila Shah
GS 78.1–83.3 MIN 82.4–87.1 Overlap 0.89 pts Actual: GS 84, MIN 87

In 95 out of 100 simulated runs, leans Minnesota Lynx (avg. 84.8) over Golden State Valkyries (avg. 80.7) — by 4.1 points. The ranges barely touch.

GS 78.5–82.9 MIN 82.8–86.7 Overlap 0.09 pts Actual: GS 84, MIN 87

In 90 out of 100 simulated runs, leans Minnesota Lynx (avg. 84.8) over Golden State Valkyries (avg. 80.7) — by 4.1 points. The ranges barely touch.

GS 78.8–82.6 MIN 83.1–86.5 No overlap Actual: GS 84, MIN 87

In 85 out of 100 simulated runs, leans Minnesota Lynx (avg. 84.8) over Golden State Valkyries (avg. 80.7) — by 4.1 points. The 85% ranges don't overlap at all — a confident model.

GS 79.0–82.4 MIN 83.2–86.3 No overlap Actual: GS 84, MIN 87

In 80 out of 100 simulated runs, leans Minnesota Lynx (avg. 84.8) over Golden State Valkyries (avg. 80.7) — by 4.1 points. The 80% ranges don't overlap at all — a confident model.

GS 79.1–82.2 MIN 83.4–86.1 No overlap Actual: GS 84, MIN 87

In 75 out of 100 simulated runs, leans Minnesota Lynx (avg. 84.8) over Golden State Valkyries (avg. 80.7) — by 4.1 points. The 75% ranges don't overlap at all — a confident model.

Show the math
Model
Dr. Lila Shah (#5)
t-statistic
-24.28
p-value
1.13e-56

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 78.5–83.3 MIN 83.1–87.4 Overlap 0.2 pts Actual: GS 84, MIN 87

In 95 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.3) over Golden State Valkyries (avg. 80.9) — by 4.3 points. The ranges barely touch.

GS 78.9–82.9 MIN 83.5–87.1 No overlap Actual: GS 84, MIN 87

In 90 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.3) over Golden State Valkyries (avg. 80.9) — by 4.3 points. The 90% ranges don't overlap at all — a confident model.

GS 79.2–82.7 MIN 83.7–86.9 No overlap Actual: GS 84, MIN 87

In 85 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.3) over Golden State Valkyries (avg. 80.9) — by 4.3 points. The 85% ranges don't overlap at all — a confident model.

GS 79.4–82.5 MIN 83.9–86.7 No overlap Actual: GS 84, MIN 87

In 80 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.3) over Golden State Valkyries (avg. 80.9) — by 4.3 points. The 80% ranges don't overlap at all — a confident model.

GS 79.5–82.3 MIN 84.0–86.5 No overlap Actual: GS 84, MIN 87

In 75 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.3) over Golden State Valkyries (avg. 80.9) — by 4.3 points. The 75% ranges don't overlap at all — a confident model.

Show the math
Model
Ice (#6)
t-statistic
-28.27
p-value
8.42e-66

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
GS 78.6–83.9 MIN 83.3–87.7 Overlap 0.55 pts Actual: GS 84, MIN 87

In 95 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.5) over Golden State Valkyries (avg. 81.3) — by 4.3 points. The ranges barely touch.

GS 79.0–83.5 MIN 83.7–87.4 No overlap Actual: GS 84, MIN 87

In 90 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.5) over Golden State Valkyries (avg. 81.3) — by 4.3 points. The 90% ranges don't overlap at all — a confident model.

GS 79.3–83.2 MIN 83.9–87.2 No overlap Actual: GS 84, MIN 87

In 85 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.5) over Golden State Valkyries (avg. 81.3) — by 4.3 points. The 85% ranges don't overlap at all — a confident model.

GS 79.5–83.0 MIN 84.1–87.0 No overlap Actual: GS 84, MIN 87

In 80 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.5) over Golden State Valkyries (avg. 81.3) — by 4.3 points. The 80% ranges don't overlap at all — a confident model.

GS 79.7–82.8 MIN 84.3–86.8 No overlap Actual: GS 84, MIN 87

In 75 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.5) over Golden State Valkyries (avg. 81.3) — by 4.3 points. The 75% ranges don't overlap at all — a confident model.

Show the math
Model
Jamal (#7)
t-statistic
-26.16
p-value
1.62e-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.

Jordan
GS 78.7–83.5 MIN 83.0–87.9 Overlap 0.52 pts Actual: GS 84, MIN 87

In 95 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.4) over Golden State Valkyries (avg. 81.1) — by 4.3 points. The ranges barely touch.

GS 79.1–83.1 MIN 83.4–87.5 No overlap Actual: GS 84, MIN 87

In 90 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.4) over Golden State Valkyries (avg. 81.1) — by 4.3 points. The 90% ranges don't overlap at all — a confident model.

GS 79.3–82.9 MIN 83.6–87.2 No overlap Actual: GS 84, MIN 87

In 85 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.4) over Golden State Valkyries (avg. 81.1) — by 4.3 points. The 85% ranges don't overlap at all — a confident model.

GS 79.5–82.7 MIN 83.8–87.0 No overlap Actual: GS 84, MIN 87

In 80 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.4) over Golden State Valkyries (avg. 81.1) — by 4.3 points. The 80% ranges don't overlap at all — a confident model.

GS 79.7–82.5 MIN 84.0–86.9 No overlap Actual: GS 84, MIN 87

In 75 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.4) over Golden State Valkyries (avg. 81.1) — by 4.3 points. The 75% ranges don't overlap at all — a confident model.

Show the math
Model
Jordan (#8)
t-statistic
-26.31
p-value
1.87e-61

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
GS 78.9–83.7 MIN 83.0–87.5 Overlap 0.73 pts Actual: GS 84, MIN 87

In 95 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.2) over Golden State Valkyries (avg. 81.3) — by 3.9 points. The ranges barely touch.

GS 79.3–83.3 MIN 83.3–87.1 No overlap Actual: GS 84, MIN 87

In 90 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.2) over Golden State Valkyries (avg. 81.3) — by 3.9 points. The 90% ranges don't overlap at all — a confident model.

GS 79.5–83.1 MIN 83.6–86.9 No overlap Actual: GS 84, MIN 87

In 85 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.2) over Golden State Valkyries (avg. 81.3) — by 3.9 points. The 85% ranges don't overlap at all — a confident model.

GS 79.7–82.9 MIN 83.8–86.7 No overlap Actual: GS 84, MIN 87

In 80 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.2) over Golden State Valkyries (avg. 81.3) — by 3.9 points. The 80% ranges don't overlap at all — a confident model.

GS 79.9–82.7 MIN 83.9–86.6 No overlap Actual: GS 84, MIN 87

In 75 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.2) over Golden State Valkyries (avg. 81.3) — by 3.9 points. The 75% ranges don't overlap at all — a confident model.

Show the math
Model
Darren "Dimes" Lin (#9)
t-statistic
-24.85
p-value
4.84e-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.

Maya Jefferson
GS 78.1–84.1 MIN 83.2–87.6 Overlap 0.85 pts Actual: GS 84, MIN 87

In 95 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.4) over Golden State Valkyries (avg. 81.1) — by 4.3 points. The ranges barely touch.

GS 78.6–83.6 MIN 83.6–87.3 Overlap 0.02 pts Actual: GS 84, MIN 87

In 90 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.4) over Golden State Valkyries (avg. 81.1) — by 4.3 points. The ranges barely touch.

GS 78.9–83.3 MIN 83.8–87.0 No overlap Actual: GS 84, MIN 87

In 85 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.4) over Golden State Valkyries (avg. 81.1) — by 4.3 points. The 85% ranges don't overlap at all — a confident model.

GS 79.2–83.1 MIN 84.0–86.9 No overlap Actual: GS 84, MIN 87

In 80 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.4) over Golden State Valkyries (avg. 81.1) — by 4.3 points. The 80% ranges don't overlap at all — a confident model.

GS 79.4–82.9 MIN 84.1–86.7 No overlap Actual: GS 84, MIN 87

In 75 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.4) over Golden State Valkyries (avg. 81.1) — by 4.3 points. The 75% ranges don't overlap at all — a confident model.

Show the math
Model
Maya Jefferson (#10)
t-statistic
-24.38
p-value
1.03e-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.

Lexi
GS 78.5–84.1 MIN 83.2–87.5 Overlap 0.9 pts Actual: GS 84, MIN 87

In 95 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.3) over Golden State Valkyries (avg. 81.3) — by 4.1 points. The ranges barely touch.

GS 78.9–83.6 MIN 83.5–87.2 Overlap 0.1 pts Actual: GS 84, MIN 87

In 90 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.3) over Golden State Valkyries (avg. 81.3) — by 4.1 points. The ranges barely touch.

GS 79.2–83.3 MIN 83.7–87.0 No overlap Actual: GS 84, MIN 87

In 85 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.3) over Golden State Valkyries (avg. 81.3) — by 4.1 points. The 85% ranges don't overlap at all — a confident model.

GS 79.4–83.1 MIN 83.9–86.8 No overlap Actual: GS 84, MIN 87

In 80 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.3) over Golden State Valkyries (avg. 81.3) — by 4.1 points. The 80% ranges don't overlap at all — a confident model.

GS 79.6–82.9 MIN 84.1–86.6 No overlap Actual: GS 84, MIN 87

In 75 out of 100 simulated runs, leans Minnesota Lynx (avg. 85.3) over Golden State Valkyries (avg. 81.3) — by 4.1 points. The 75% ranges don't overlap at all — a confident model.

Show the math
Model
Lexi (#11)
t-statistic
-24.00
p-value
1.08e-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.

Coach Sarah Watanabe
GS 79.4–85.3 MIN 83.0–89.1 Overlap 2.29 pts Actual: GS 84, MIN 87

In 95 out of 100 simulated runs, leans Minnesota Lynx (avg. 86.1) over Golden State Valkyries (avg. 82.4) — by 3.7 points. The ranges overlap some — there's real uncertainty here.

GS 79.9–84.8 MIN 83.5–88.6 Overlap 1.33 pts Actual: GS 84, MIN 87

In 90 out of 100 simulated runs, leans Minnesota Lynx (avg. 86.1) over Golden State Valkyries (avg. 82.4) — by 3.7 points. The ranges overlap some — there's real uncertainty here.

GS 80.2–84.5 MIN 83.8–88.3 Overlap 0.7 pts Actual: GS 84, MIN 87

In 85 out of 100 simulated runs, leans Minnesota Lynx (avg. 86.1) over Golden State Valkyries (avg. 82.4) — by 3.7 points. The ranges barely touch.

GS 80.4–84.3 MIN 84.1–88.1 Overlap 0.22 pts Actual: GS 84, MIN 87

In 80 out of 100 simulated runs, leans Minnesota Lynx (avg. 86.1) over Golden State Valkyries (avg. 82.4) — by 3.7 points. The ranges barely touch.

GS 80.6–84.1 MIN 84.3–87.9 No overlap Actual: GS 84, MIN 87

In 75 out of 100 simulated runs, leans Minnesota Lynx (avg. 86.1) over Golden State Valkyries (avg. 82.4) — by 3.7 points. The 75% ranges don't overlap at all — a confident model.

Show the math
Model
Coach Sarah Watanabe (#12)
t-statistic
-18.21
p-value
1.32e-41

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 MIN — Actual: GS 84, MIN 87
Level GS range MIN range Overlap Tol Warning Actual landed in range?
95% 78.7–84.0 83.0–87.6 0.95 pts — Yes
90% 79.1–83.6 83.4–87.2 0.16 pts — No
85% 79.4–83.3 83.6–87.0 — pts — No
80% 79.6–83.1 83.8–86.8 — pts — No
75% 79.8–82.9 84.0–86.6 — pts — No
Reg — GS at MIN — Actual: GS 84, MIN 87
Level GS range MIN range Overlap Tol Warning Actual landed in range?
95% 78.5–84.1 83.1–87.7 0.93 pts — Yes
90% 78.9–83.6 83.5–87.3 0.12 pts — No
85% 79.2–83.3 83.7–87.0 — pts — No
80% 79.4–83.1 83.9–86.9 — pts — No
75% 79.6–82.9 84.1–86.7 — pts — No
Dr. Wallace — GS at MIN — Actual: GS 84, MIN 87
Level GS range MIN range Overlap Tol Warning Actual landed in range?
95% 78.7–83.6 83.1–87.4 0.5 pts — No
90% 79.1–83.2 83.5–87.1 — pts — No
85% 79.3–83.0 83.7–86.9 — pts — No
80% 79.5–82.8 83.9–86.7 — pts — No
75% 79.7–82.6 84.0–86.6 — pts — No
Kevin — GS at MIN — Actual: GS 84, MIN 87
Level GS range MIN range Overlap Tol Warning Actual landed in range?
95% 78.8–83.5 83.2–87.7 0.31 pts — No
90% 79.1–83.1 83.6–87.3 — pts — No
85% 79.4–82.8 83.8–87.1 — pts — No
80% 79.6–82.7 84.0–86.9 — pts — No
75% 79.7–82.5 84.1–86.8 — pts — No
Dr. Lila Shah — GS at MIN — Actual: GS 84, MIN 87
Level GS range MIN range Overlap Tol Warning Actual landed in range?
95% 78.1–83.3 82.4–87.1 0.89 pts — No
90% 78.5–82.9 82.8–86.7 0.09 pts — No
85% 78.8–82.6 83.1–86.5 — pts — No
80% 79.0–82.4 83.2–86.3 — pts — No
75% 79.1–82.2 83.4–86.1 — pts — No
Ice — GS at MIN — Actual: GS 84, MIN 87
Level GS range MIN range Overlap Tol Warning Actual landed in range?
95% 78.5–83.3 83.1–87.4 0.2 pts — No
90% 78.9–82.9 83.5–87.1 — pts — No
85% 79.2–82.7 83.7–86.9 — pts — No
80% 79.4–82.5 83.9–86.7 — pts — No
75% 79.5–82.3 84.0–86.5 — pts — No
Jamal — GS at MIN — Actual: GS 84, MIN 87
Level GS range MIN range Overlap Tol Warning Actual landed in range?
95% 78.6–83.9 83.3–87.7 0.55 pts — No
90% 79.0–83.5 83.7–87.4 — pts — No
85% 79.3–83.2 83.9–87.2 — pts — No
80% 79.5–83.0 84.1–87.0 — pts — No
75% 79.7–82.8 84.3–86.8 — pts — No
Jordan — GS at MIN — Actual: GS 84, MIN 87
Level GS range MIN range Overlap Tol Warning Actual landed in range?
95% 78.7–83.5 83.0–87.9 0.52 pts — No
90% 79.1–83.1 83.4–87.5 — pts — No
85% 79.3–82.9 83.6–87.2 — pts — No
80% 79.5–82.7 83.8–87.0 — pts — No
75% 79.7–82.5 84.0–86.9 — pts — No
Darren "Dimes" Lin — GS at MIN — Actual: GS 84, MIN 87
Level GS range MIN range Overlap Tol Warning Actual landed in range?
95% 78.9–83.7 83.0–87.5 0.73 pts — No
90% 79.3–83.3 83.3–87.1 — pts — No
85% 79.5–83.1 83.6–86.9 — pts — No
80% 79.7–82.9 83.8–86.7 — pts — No
75% 79.9–82.7 83.9–86.6 — pts — No
Maya Jefferson — GS at MIN — Actual: GS 84, MIN 87
Level GS range MIN range Overlap Tol Warning Actual landed in range?
95% 78.1–84.1 83.2–87.6 0.85 pts — Yes
90% 78.6–83.6 83.6–87.3 0.02 pts — No
85% 78.9–83.3 83.8–87.0 — pts — No
80% 79.2–83.1 84.0–86.9 — pts — No
75% 79.4–82.9 84.1–86.7 — pts — No
Lexi — GS at MIN — Actual: GS 84, MIN 87
Level GS range MIN range Overlap Tol Warning Actual landed in range?
95% 78.5–84.1 83.2–87.5 0.9 pts — Yes
90% 78.9–83.6 83.5–87.2 0.1 pts — No
85% 79.2–83.3 83.7–87.0 — pts — No
80% 79.4–83.1 83.9–86.8 — pts — No
75% 79.6–82.9 84.1–86.6 — pts — No
Coach Sarah Watanabe — GS at MIN — Actual: GS 84, MIN 87
Level GS range MIN range Overlap Tol Warning Actual landed in range?
95% 79.4–85.3 83.0–89.1 2.29 pts — Yes
90% 79.9–84.8 83.5–88.6 1.33 pts — Yes
85% 80.2–84.5 83.8–88.3 0.7 pts — Yes
80% 80.4–84.3 84.1–88.1 0.22 pts — Yes
75% 80.6–84.1 84.3–87.9 — pts — Yes