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Dallas Wings at Golden State Valkyries

DAL 80 – GS 104

September 27, 2026 · Final

Dr. Lila Shah

Best model for this game

Dr. Lila Shah
All models: 0-12
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 DAL at GS

All 12 models’ predicted scores

DAL — one dot per model GS — one dot per model Actual: DAL 80, GS 104
Vince Chambers
DAL 85.6–89.4 GS 78.5–83.7 No overlap Actual: DAL 80, GS 104

In 95 out of 100 simulated runs, leans Dallas Wings (avg. 87.5) over Golden State Valkyries (avg. 81.1) — by 6.3 points. The 95% ranges don't overlap at all — a confident model.

DAL 85.8–89.1 GS 78.9–83.3 No overlap Actual: DAL 80, GS 104

In 90 out of 100 simulated runs, leans Dallas Wings (avg. 87.5) over Golden State Valkyries (avg. 81.1) — by 6.3 points. The 90% ranges don't overlap at all — a confident model.

DAL 86.0–88.8 GS 79.2–83.0 No overlap Actual: DAL 80, GS 104

In 85 out of 100 simulated runs, leans Dallas Wings (avg. 87.5) over Golden State Valkyries (avg. 81.1) — by 6.3 points. The 85% ranges don't overlap at all — a confident model.

DAL 86.1–88.7 GS 79.4–82.8 No overlap Actual: DAL 80, GS 104

In 80 out of 100 simulated runs, leans Dallas Wings (avg. 87.5) over Golden State Valkyries (avg. 81.1) — by 6.3 points. The 80% ranges don't overlap at all — a confident model.

DAL 86.2–88.7 GS 79.6–82.7 No overlap Actual: DAL 80, GS 104

In 75 out of 100 simulated runs, leans Dallas Wings (avg. 87.5) over Golden State Valkyries (avg. 81.1) — by 6.3 points. The 75% ranges don't overlap at all — a confident model.

Show the math
Model
Vince Chambers (#1)
t-statistic
52.93
p-value
5.20e-181

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
DAL 84.8–89.6 GS 78.0–83.7 No overlap Actual: DAL 80, GS 104

In 95 out of 100 simulated runs, leans Dallas Wings (avg. 87.7) over Golden State Valkyries (avg. 81.2) — by 6.4 points. The 95% ranges don't overlap at all — a confident model.

DAL 85.3–89.5 GS 78.7–83.6 No overlap Actual: DAL 80, GS 104

In 90 out of 100 simulated runs, leans Dallas Wings (avg. 87.7) over Golden State Valkyries (avg. 81.2) — by 6.4 points. The 90% ranges don't overlap at all — a confident model.

DAL 86.0–89.4 GS 79.0–83.4 No overlap Actual: DAL 80, GS 104

In 85 out of 100 simulated runs, leans Dallas Wings (avg. 87.7) over Golden State Valkyries (avg. 81.2) — by 6.4 points. The 85% ranges don't overlap at all — a confident model.

DAL 86.2–89.3 GS 79.2–83.2 No overlap Actual: DAL 80, GS 104

In 80 out of 100 simulated runs, leans Dallas Wings (avg. 87.7) over Golden State Valkyries (avg. 81.2) — by 6.4 points. The 80% ranges don't overlap at all — a confident model.

DAL 86.4–89.0 GS 79.4–83.0 No overlap Actual: DAL 80, GS 104

In 75 out of 100 simulated runs, leans Dallas Wings (avg. 87.7) over Golden State Valkyries (avg. 81.2) — by 6.4 points. The 75% ranges don't overlap at all — a confident model.

Show the math
Model
Reg (#2)
t-statistic
47.06
p-value
9.52e-159

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
DAL 85.7–90.1 GS 78.4–83.8 No overlap Actual: DAL 80, GS 104

In 95 out of 100 simulated runs, leans Dallas Wings (avg. 87.5) over Golden State Valkyries (avg. 81.1) — by 6.4 points. The 95% ranges don't overlap at all — a confident model.

DAL 86.0–89.4 GS 78.8–83.3 No overlap Actual: DAL 80, GS 104

In 90 out of 100 simulated runs, leans Dallas Wings (avg. 87.5) over Golden State Valkyries (avg. 81.1) — by 6.4 points. The 90% ranges don't overlap at all — a confident model.

DAL 86.1–89.0 GS 79.1–83.1 No overlap Actual: DAL 80, GS 104

In 85 out of 100 simulated runs, leans Dallas Wings (avg. 87.5) over Golden State Valkyries (avg. 81.1) — by 6.4 points. The 85% ranges don't overlap at all — a confident model.

DAL 86.2–88.7 GS 79.3–82.8 No overlap Actual: DAL 80, GS 104

In 80 out of 100 simulated runs, leans Dallas Wings (avg. 87.5) over Golden State Valkyries (avg. 81.1) — by 6.4 points. The 80% ranges don't overlap at all — a confident model.

DAL 86.3–88.6 GS 79.5–82.7 No overlap Actual: DAL 80, GS 104

In 75 out of 100 simulated runs, leans Dallas Wings (avg. 87.5) over Golden State Valkyries (avg. 81.1) — by 6.4 points. The 75% ranges don't overlap at all — a confident model.

Show the math
Model
Dr. Wallace (#3)
t-statistic
54.08
p-value
2.57e-182

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
DAL 85.7–89.7 GS 78.1–83.5 No overlap Actual: DAL 80, GS 104

In 95 out of 100 simulated runs, leans Dallas Wings (avg. 87.5) over Golden State Valkyries (avg. 81.0) — by 6.5 points. The 95% ranges don't overlap at all — a confident model.

DAL 86.0–89.3 GS 78.4–82.9 No overlap Actual: DAL 80, GS 104

In 90 out of 100 simulated runs, leans Dallas Wings (avg. 87.5) over Golden State Valkyries (avg. 81.0) — by 6.5 points. The 90% ranges don't overlap at all — a confident model.

DAL 86.1–89.0 GS 78.9–82.6 No overlap Actual: DAL 80, GS 104

In 85 out of 100 simulated runs, leans Dallas Wings (avg. 87.5) over Golden State Valkyries (avg. 81.0) — by 6.5 points. The 85% ranges don't overlap at all — a confident model.

DAL 86.2–88.7 GS 79.3–82.5 No overlap Actual: DAL 80, GS 104

In 80 out of 100 simulated runs, leans Dallas Wings (avg. 87.5) over Golden State Valkyries (avg. 81.0) — by 6.5 points. The 80% ranges don't overlap at all — a confident model.

DAL 86.3–88.6 GS 79.6–82.4 No overlap Actual: DAL 80, GS 104

In 75 out of 100 simulated runs, leans Dallas Wings (avg. 87.5) over Golden State Valkyries (avg. 81.0) — by 6.5 points. The 75% ranges don't overlap at all — a confident model.

Show the math
Model
Kevin (#4)
t-statistic
53.73
p-value
6.97e-178

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
DAL 84.8–89.2 GS 79.1–84.0 No overlap Actual: DAL 80, GS 104

In 95 out of 100 simulated runs, leans Dallas Wings (avg. 86.7) over Golden State Valkyries (avg. 81.5) — by 5.2 points. The 95% ranges don't overlap at all — a confident model.

DAL 85.1–88.9 GS 79.5–83.6 No overlap Actual: DAL 80, GS 104

In 90 out of 100 simulated runs, leans Dallas Wings (avg. 86.7) over Golden State Valkyries (avg. 81.5) — by 5.2 points. The 90% ranges don't overlap at all — a confident model.

DAL 85.3–88.6 GS 79.7–83.3 No overlap Actual: DAL 80, GS 104

In 85 out of 100 simulated runs, leans Dallas Wings (avg. 86.7) over Golden State Valkyries (avg. 81.5) — by 5.2 points. The 85% ranges don't overlap at all — a confident model.

DAL 85.4–88.3 GS 79.9–83.1 No overlap Actual: DAL 80, GS 104

In 80 out of 100 simulated runs, leans Dallas Wings (avg. 86.7) over Golden State Valkyries (avg. 81.5) — by 5.2 points. The 80% ranges don't overlap at all — a confident model.

DAL 85.5–88.1 GS 80.1–83.0 No overlap Actual: DAL 80, GS 104

In 75 out of 100 simulated runs, leans Dallas Wings (avg. 86.7) over Golden State Valkyries (avg. 81.5) — by 5.2 points. The 75% ranges don't overlap at all — a confident model.

Show the math
Model
Dr. Lila Shah (#5)
t-statistic
44.05
p-value
3.79e-154

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
DAL 85.0–89.7 GS 78.0–83.1 No overlap Actual: DAL 80, GS 104

In 95 out of 100 simulated runs, leans Dallas Wings (avg. 87.4) over Golden State Valkyries (avg. 81.0) — by 6.4 points. The 95% ranges don't overlap at all — a confident model.

DAL 85.4–89.3 GS 78.8–82.9 No overlap Actual: DAL 80, GS 104

In 90 out of 100 simulated runs, leans Dallas Wings (avg. 87.4) over Golden State Valkyries (avg. 81.0) — by 6.4 points. The 90% ranges don't overlap at all — a confident model.

DAL 85.7–89.1 GS 79.0–82.7 No overlap Actual: DAL 80, GS 104

In 85 out of 100 simulated runs, leans Dallas Wings (avg. 87.4) over Golden State Valkyries (avg. 81.0) — by 6.4 points. The 85% ranges don't overlap at all — a confident model.

DAL 85.8–88.9 GS 79.3–82.5 No overlap Actual: DAL 80, GS 104

In 80 out of 100 simulated runs, leans Dallas Wings (avg. 87.4) over Golden State Valkyries (avg. 81.0) — by 6.4 points. The 80% ranges don't overlap at all — a confident model.

DAL 86.0–88.7 GS 79.3–82.4 No overlap Actual: DAL 80, GS 104

In 75 out of 100 simulated runs, leans Dallas Wings (avg. 87.4) over Golden State Valkyries (avg. 81.0) — by 6.4 points. The 75% ranges don't overlap at all — a confident model.

Show the math
Model
Ice (#6)
t-statistic
51.37
p-value
3.13e-171

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
DAL 85.5–89.6 GS 78.6–83.1 No overlap Actual: DAL 80, GS 104

In 95 out of 100 simulated runs, leans Dallas Wings (avg. 87.2) over Golden State Valkyries (avg. 80.9) — by 6.3 points. The 95% ranges don't overlap at all — a confident model.

DAL 85.9–89.0 GS 78.9–82.6 No overlap Actual: DAL 80, GS 104

In 90 out of 100 simulated runs, leans Dallas Wings (avg. 87.2) over Golden State Valkyries (avg. 80.9) — by 6.3 points. The 90% ranges don't overlap at all — a confident model.

DAL 86.0–88.6 GS 79.2–82.4 No overlap Actual: DAL 80, GS 104

In 85 out of 100 simulated runs, leans Dallas Wings (avg. 87.2) over Golden State Valkyries (avg. 80.9) — by 6.3 points. The 85% ranges don't overlap at all — a confident model.

DAL 86.0–88.4 GS 79.5–82.3 No overlap Actual: DAL 80, GS 104

In 80 out of 100 simulated runs, leans Dallas Wings (avg. 87.2) over Golden State Valkyries (avg. 80.9) — by 6.3 points. The 80% ranges don't overlap at all — a confident model.

DAL 86.2–88.3 GS 79.7–82.1 No overlap Actual: DAL 80, GS 104

In 75 out of 100 simulated runs, leans Dallas Wings (avg. 87.2) over Golden State Valkyries (avg. 80.9) — by 6.3 points. The 75% ranges don't overlap at all — a confident model.

Show the math
Model
Jamal (#7)
t-statistic
57.48
p-value
2.19e-192

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
DAL 85.6–89.9 GS 78.1–83.9 No overlap Actual: DAL 80, GS 104

In 95 out of 100 simulated runs, leans Dallas Wings (avg. 87.5) over Golden State Valkyries (avg. 81.0) — by 6.5 points. The 95% ranges don't overlap at all — a confident model.

DAL 86.0–89.5 GS 78.5–83.4 No overlap Actual: DAL 80, GS 104

In 90 out of 100 simulated runs, leans Dallas Wings (avg. 87.5) over Golden State Valkyries (avg. 81.0) — by 6.5 points. The 90% ranges don't overlap at all — a confident model.

DAL 86.1–89.1 GS 78.8–83.1 No overlap Actual: DAL 80, GS 104

In 85 out of 100 simulated runs, leans Dallas Wings (avg. 87.5) over Golden State Valkyries (avg. 81.0) — by 6.5 points. The 85% ranges don't overlap at all — a confident model.

DAL 86.2–89.0 GS 79.1–82.9 No overlap Actual: DAL 80, GS 104

In 80 out of 100 simulated runs, leans Dallas Wings (avg. 87.5) over Golden State Valkyries (avg. 81.0) — by 6.5 points. The 80% ranges don't overlap at all — a confident model.

DAL 86.4–88.8 GS 79.3–82.7 No overlap Actual: DAL 80, GS 104

In 75 out of 100 simulated runs, leans Dallas Wings (avg. 87.5) over Golden State Valkyries (avg. 81.0) — by 6.5 points. The 75% ranges don't overlap at all — a confident model.

Show the math
Model
Jordan (#8)
t-statistic
52.77
p-value
4.03e-176

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
DAL 85.4–89.5 GS 77.9–82.8 No overlap Actual: DAL 80, GS 104

In 95 out of 100 simulated runs, leans Dallas Wings (avg. 87.4) over Golden State Valkyries (avg. 80.8) — by 6.5 points. The 95% ranges don't overlap at all — a confident model.

DAL 85.7–89.2 GS 78.5–82.5 No overlap Actual: DAL 80, GS 104

In 90 out of 100 simulated runs, leans Dallas Wings (avg. 87.4) over Golden State Valkyries (avg. 80.8) — by 6.5 points. The 90% ranges don't overlap at all — a confident model.

DAL 85.9–88.9 GS 78.9–82.4 No overlap Actual: DAL 80, GS 104

In 85 out of 100 simulated runs, leans Dallas Wings (avg. 87.4) over Golden State Valkyries (avg. 80.8) — by 6.5 points. The 85% ranges don't overlap at all — a confident model.

DAL 86.2–88.7 GS 78.9–82.3 No overlap Actual: DAL 80, GS 104

In 80 out of 100 simulated runs, leans Dallas Wings (avg. 87.4) over Golden State Valkyries (avg. 80.8) — by 6.5 points. The 80% ranges don't overlap at all — a confident model.

DAL 86.3–88.5 GS 79.4–82.3 No overlap Actual: DAL 80, GS 104

In 75 out of 100 simulated runs, leans Dallas Wings (avg. 87.4) over Golden State Valkyries (avg. 80.8) — by 6.5 points. The 75% ranges don't overlap at all — a confident model.

Show the math
Model
Darren "Dimes" Lin (#9)
t-statistic
55.34
p-value
1.10e-185

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
DAL 85.5–89.8 GS 78.5–83.1 No overlap Actual: DAL 80, GS 104

In 95 out of 100 simulated runs, leans Dallas Wings (avg. 87.4) over Golden State Valkyries (avg. 81.1) — by 6.3 points. The 95% ranges don't overlap at all — a confident model.

DAL 85.7–89.1 GS 79.0–82.8 No overlap Actual: DAL 80, GS 104

In 90 out of 100 simulated runs, leans Dallas Wings (avg. 87.4) over Golden State Valkyries (avg. 81.1) — by 6.3 points. The 90% ranges don't overlap at all — a confident model.

DAL 86.0–88.9 GS 79.4–82.5 No overlap Actual: DAL 80, GS 104

In 85 out of 100 simulated runs, leans Dallas Wings (avg. 87.4) over Golden State Valkyries (avg. 81.1) — by 6.3 points. The 85% ranges don't overlap at all — a confident model.

DAL 86.1–88.7 GS 79.7–82.4 No overlap Actual: DAL 80, GS 104

In 80 out of 100 simulated runs, leans Dallas Wings (avg. 87.4) over Golden State Valkyries (avg. 81.1) — by 6.3 points. The 80% ranges don't overlap at all — a confident model.

DAL 86.2–88.5 GS 79.7–82.3 No overlap Actual: DAL 80, GS 104

In 75 out of 100 simulated runs, leans Dallas Wings (avg. 87.4) over Golden State Valkyries (avg. 81.1) — by 6.3 points. The 75% ranges don't overlap at all — a confident model.

Show the math
Model
Maya Jefferson (#10)
t-statistic
55.77
p-value
4.06e-187

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
DAL 85.5–89.9 GS 78.1–84.2 No overlap Actual: DAL 80, GS 104

In 95 out of 100 simulated runs, leans Dallas Wings (avg. 87.4) over Golden State Valkyries (avg. 81.1) — by 6.3 points. The 95% ranges don't overlap at all — a confident model.

DAL 85.8–89.6 GS 78.6–83.7 No overlap Actual: DAL 80, GS 104

In 90 out of 100 simulated runs, leans Dallas Wings (avg. 87.4) over Golden State Valkyries (avg. 81.1) — by 6.3 points. The 90% ranges don't overlap at all — a confident model.

DAL 86.0–89.3 GS 78.9–83.4 No overlap Actual: DAL 80, GS 104

In 85 out of 100 simulated runs, leans Dallas Wings (avg. 87.4) over Golden State Valkyries (avg. 81.1) — by 6.3 points. The 85% ranges don't overlap at all — a confident model.

DAL 86.2–89.0 GS 79.1–83.1 No overlap Actual: DAL 80, GS 104

In 80 out of 100 simulated runs, leans Dallas Wings (avg. 87.4) over Golden State Valkyries (avg. 81.1) — by 6.3 points. The 80% ranges don't overlap at all — a confident model.

DAL 86.3–88.7 GS 79.4–82.9 No overlap Actual: DAL 80, GS 104

In 75 out of 100 simulated runs, leans Dallas Wings (avg. 87.4) over Golden State Valkyries (avg. 81.1) — by 6.3 points. The 75% ranges don't overlap at all — a confident model.

Show the math
Model
Lexi (#11)
t-statistic
48.63
p-value
1.85e-164

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
DAL 85.5–90.7 GS 78.3–84.0 No overlap Actual: DAL 80, GS 104

In 95 out of 100 simulated runs, leans Dallas Wings (avg. 88.1) over Golden State Valkyries (avg. 81.1) — by 7.0 points. The 95% ranges don't overlap at all — a confident model.

DAL 86.0–90.2 GS 78.8–83.5 No overlap Actual: DAL 80, GS 104

In 90 out of 100 simulated runs, leans Dallas Wings (avg. 88.1) over Golden State Valkyries (avg. 81.1) — by 7.0 points. The 90% ranges don't overlap at all — a confident model.

DAL 86.2–90.0 GS 79.0–83.2 No overlap Actual: DAL 80, GS 104

In 85 out of 100 simulated runs, leans Dallas Wings (avg. 88.1) over Golden State Valkyries (avg. 81.1) — by 7.0 points. The 85% ranges don't overlap at all — a confident model.

DAL 86.4–89.8 GS 79.3–83.0 No overlap Actual: DAL 80, GS 104

In 80 out of 100 simulated runs, leans Dallas Wings (avg. 88.1) over Golden State Valkyries (avg. 81.1) — by 7.0 points. The 80% ranges don't overlap at all — a confident model.

DAL 86.6–89.6 GS 79.5–82.8 No overlap Actual: DAL 80, GS 104

In 75 out of 100 simulated runs, leans Dallas Wings (avg. 88.1) over Golden State Valkyries (avg. 81.1) — by 7.0 points. The 75% ranges don't overlap at all — a confident model.

Show the math
Model
Coach Sarah Watanabe (#12)
t-statistic
54.85
p-value
2.67e-185

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 — DAL at GS — Actual: DAL 80, GS 104
Level DAL range GS range Overlap Tol Warning Actual landed in range?
95% 85.6–89.4 78.5–83.7 — pts — No
90% 85.8–89.1 78.9–83.3 — pts — No
85% 86.0–88.8 79.2–83.0 — pts — No
80% 86.1–88.7 79.4–82.8 — pts — No
75% 86.2–88.7 79.6–82.7 — pts — No
Reg — DAL at GS — Actual: DAL 80, GS 104
Level DAL range GS range Overlap Tol Warning Actual landed in range?
95% 84.8–89.6 78.0–83.7 — pts — No
90% 85.3–89.5 78.7–83.6 — pts — No
85% 86.0–89.4 79.0–83.4 — pts — No
80% 86.2–89.3 79.2–83.2 — pts — No
75% 86.4–89.0 79.4–83.0 — pts — No
Dr. Wallace — DAL at GS — Actual: DAL 80, GS 104
Level DAL range GS range Overlap Tol Warning Actual landed in range?
95% 85.7–90.1 78.4–83.8 — pts — No
90% 86.0–89.4 78.8–83.3 — pts — No
85% 86.1–89.0 79.1–83.1 — pts — No
80% 86.2–88.7 79.3–82.8 — pts — No
75% 86.3–88.6 79.5–82.7 — pts — No
Kevin — DAL at GS — Actual: DAL 80, GS 104
Level DAL range GS range Overlap Tol Warning Actual landed in range?
95% 85.7–89.7 78.1–83.5 — pts — No
90% 86.0–89.3 78.4–82.9 — pts — No
85% 86.1–89.0 78.9–82.6 — pts — No
80% 86.2–88.7 79.3–82.5 — pts — No
75% 86.3–88.6 79.6–82.4 — pts — No
Dr. Lila Shah — DAL at GS — Actual: DAL 80, GS 104
Level DAL range GS range Overlap Tol Warning Actual landed in range?
95% 84.8–89.2 79.1–84.0 — pts — No
90% 85.1–88.9 79.5–83.6 — pts — No
85% 85.3–88.6 79.7–83.3 — pts — No
80% 85.4–88.3 79.9–83.1 — pts — No
75% 85.5–88.1 80.1–83.0 — pts — No
Ice — DAL at GS — Actual: DAL 80, GS 104
Level DAL range GS range Overlap Tol Warning Actual landed in range?
95% 85.0–89.7 78.0–83.1 — pts — No
90% 85.4–89.3 78.8–82.9 — pts — No
85% 85.7–89.1 79.0–82.7 — pts — No
80% 85.8–88.9 79.3–82.5 — pts — No
75% 86.0–88.7 79.3–82.4 — pts — No
Jamal — DAL at GS — Actual: DAL 80, GS 104
Level DAL range GS range Overlap Tol Warning Actual landed in range?
95% 85.5–89.6 78.6–83.1 — pts — No
90% 85.9–89.0 78.9–82.6 — pts — No
85% 86.0–88.6 79.2–82.4 — pts — No
80% 86.0–88.4 79.5–82.3 — pts — No
75% 86.2–88.3 79.7–82.1 — pts — No
Jordan — DAL at GS — Actual: DAL 80, GS 104
Level DAL range GS range Overlap Tol Warning Actual landed in range?
95% 85.6–89.9 78.1–83.9 — pts — No
90% 86.0–89.5 78.5–83.4 — pts — No
85% 86.1–89.1 78.8–83.1 — pts — No
80% 86.2–89.0 79.1–82.9 — pts — No
75% 86.4–88.8 79.3–82.7 — pts — No
Darren "Dimes" Lin — DAL at GS — Actual: DAL 80, GS 104
Level DAL range GS range Overlap Tol Warning Actual landed in range?
95% 85.4–89.5 77.9–82.8 — pts — No
90% 85.7–89.2 78.5–82.5 — pts — No
85% 85.9–88.9 78.9–82.4 — pts — No
80% 86.2–88.7 78.9–82.3 — pts — No
75% 86.3–88.5 79.4–82.3 — pts — No
Maya Jefferson — DAL at GS — Actual: DAL 80, GS 104
Level DAL range GS range Overlap Tol Warning Actual landed in range?
95% 85.5–89.8 78.5–83.1 — pts — No
90% 85.7–89.1 79.0–82.8 — pts — No
85% 86.0–88.9 79.4–82.5 — pts — No
80% 86.1–88.7 79.7–82.4 — pts — No
75% 86.2–88.5 79.7–82.3 — pts — No
Lexi — DAL at GS — Actual: DAL 80, GS 104
Level DAL range GS range Overlap Tol Warning Actual landed in range?
95% 85.5–89.9 78.1–84.2 — pts — No
90% 85.8–89.6 78.6–83.7 — pts — No
85% 86.0–89.3 78.9–83.4 — pts — No
80% 86.2–89.0 79.1–83.1 — pts — No
75% 86.3–88.7 79.4–82.9 — pts — No
Coach Sarah Watanabe — DAL at GS — Actual: DAL 80, GS 104
Level DAL range GS range Overlap Tol Warning Actual landed in range?
95% 85.5–90.7 78.3–84.0 — pts — No
90% 86.0–90.2 78.8–83.5 — pts — No
85% 86.2–90.0 79.0–83.2 — pts — No
80% 86.4–89.8 79.3–83.0 — pts — No
75% 86.6–89.6 79.5–82.8 — pts — No