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Minnesota Lynx at New York Liberty

MIN 86 – NY 99

July 3, 2026 · Final

Jordan

Best model for this game

Jordan
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 MIN at NY

All 12 models’ predicted scores

MIN — one dot per model NY — one dot per model Actual: MIN 86, NY 99
Vince Chambers
MIN 85.8–90.9 NY 85.5–90.5 Overlap 4.65 pts Actual: MIN 86, NY 99

In 95 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.3) over New York Liberty (avg. 88.0) — by under a point. The ranges overlap almost entirely, so one game could go either way.

MIN 86.2–90.5 NY 85.9–90.1 Overlap 3.84 pts Actual: MIN 86, NY 99

In 90 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.3) over New York Liberty (avg. 88.0) — by under a point. The ranges overlap almost entirely, so one game could go either way.

MIN 86.5–90.2 NY 86.1–89.8 Overlap 3.31 pts Actual: MIN 86, NY 99

In 85 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.3) over New York Liberty (avg. 88.0) — by under a point. The ranges overlap almost entirely, so one game could go either way.

MIN 86.7–90.0 NY 86.3–89.6 Overlap 2.91 pts Actual: MIN 86, NY 99

In 80 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.3) over New York Liberty (avg. 88.0) — by under a point. The ranges overlap some — there's real uncertainty here.

MIN 86.9–89.8 NY 86.5–89.4 Overlap 2.57 pts Actual: MIN 86, NY 99

In 75 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.3) over New York Liberty (avg. 88.0) — by under a point. The ranges overlap some — there's real uncertainty here.

Show the math
Model
Vince Chambers (#1)
t-statistic
2.40
p-value
1.72e-02

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
MIN 85.6–91.8 NY 85.2–90.7 Overlap 5.15 pts Actual: MIN 86, NY 99

In 95 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.7) over New York Liberty (avg. 88.0) — by under a point. The ranges overlap almost entirely, so one game could go either way.

MIN 86.1–91.3 NY 85.6–90.3 Overlap 4.21 pts Actual: MIN 86, NY 99

In 90 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.7) over New York Liberty (avg. 88.0) — by under a point. The ranges overlap almost entirely, so one game could go either way.

MIN 86.4–90.9 NY 85.9–90.0 Overlap 3.59 pts Actual: MIN 86, NY 99

In 85 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.7) over New York Liberty (avg. 88.0) — by under a point. The ranges overlap almost entirely, so one game could go either way.

MIN 86.7–90.7 NY 86.1–89.8 Overlap 3.12 pts Actual: MIN 86, NY 99

In 80 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.7) over New York Liberty (avg. 88.0) — by under a point. The ranges overlap almost entirely, so one game could go either way.

MIN 86.9–90.5 NY 86.3–89.6 Overlap 2.72 pts Actual: MIN 86, NY 99

In 75 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.7) over New York Liberty (avg. 88.0) — by under a point. The ranges overlap some — there's real uncertainty here.

Show the math
Model
Reg (#2)
t-statistic
3.92
p-value
1.22e-04

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
MIN 86.4–90.2 NY 85.7–90.1 Overlap 3.65 pts Actual: MIN 86, NY 99

In 95 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.3) over New York Liberty (avg. 87.9) — by under a point. The ranges overlap almost entirely, so one game could go either way.

MIN 86.7–89.9 NY 86.1–89.7 Overlap 3.0 pts Actual: MIN 86, NY 99

In 90 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.3) over New York Liberty (avg. 87.9) — by under a point. The ranges overlap almost entirely, so one game could go either way.

MIN 86.9–89.7 NY 86.3–89.5 Overlap 2.57 pts Actual: MIN 86, NY 99

In 85 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.3) over New York Liberty (avg. 87.9) — by under a point. The ranges overlap some — there's real uncertainty here.

MIN 87.1–89.5 NY 86.5–89.3 Overlap 2.25 pts Actual: MIN 86, NY 99

In 80 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.3) over New York Liberty (avg. 87.9) — by under a point. The ranges overlap some — there's real uncertainty here.

MIN 87.2–89.4 NY 86.6–89.2 Overlap 1.97 pts Actual: MIN 86, NY 99

In 75 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.3) over New York Liberty (avg. 87.9) — by under a point. The ranges overlap some — there's real uncertainty here.

Show the math
Model
Dr. Wallace (#3)
t-statistic
3.22
p-value
1.49e-03

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
MIN 86.4–90.4 NY 85.9–90.5 Overlap 3.96 pts Actual: MIN 86, NY 99

In 95 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.5) over New York Liberty (avg. 88.0) — by under a point. The ranges overlap almost entirely, so one game could go either way.

MIN 86.8–90.2 NY 86.1–89.7 Overlap 2.92 pts Actual: MIN 86, NY 99

In 90 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.5) over New York Liberty (avg. 88.0) — by under a point. The ranges overlap some — there's real uncertainty here.

MIN 87.2–89.9 NY 86.5–89.5 Overlap 2.26 pts Actual: MIN 86, NY 99

In 85 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.5) over New York Liberty (avg. 88.0) — by under a point. The ranges overlap some — there's real uncertainty here.

MIN 87.4–89.9 NY 86.7–89.3 Overlap 1.9 pts Actual: MIN 86, NY 99

In 80 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.5) over New York Liberty (avg. 88.0) — by under a point. The ranges overlap some — there's real uncertainty here.

MIN 87.4–89.7 NY 87.0–88.9 Overlap 1.5 pts Actual: MIN 86, NY 99

In 75 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.5) over New York Liberty (avg. 88.0) — by under a point. The ranges overlap some — there's real uncertainty here.

Show the math
Model
Kevin (#4)
t-statistic
3.02
p-value
2.88e-03

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
MIN 85.5–90.0 NY 85.0–89.4 Overlap 3.85 pts Actual: MIN 86, NY 99

In 95 out of 100 simulated runs, leans Minnesota Lynx (avg. 87.8) over New York Liberty (avg. 87.0) — by under a point. The ranges overlap almost entirely, so one game could go either way.

MIN 85.9–89.7 NY 85.2–88.9 Overlap 2.99 pts Actual: MIN 86, NY 99

In 90 out of 100 simulated runs, leans Minnesota Lynx (avg. 87.8) over New York Liberty (avg. 87.0) — by under a point. The ranges overlap some — there's real uncertainty here.

MIN 86.1–89.4 NY 85.4–88.6 Overlap 2.52 pts Actual: MIN 86, NY 99

In 85 out of 100 simulated runs, leans Minnesota Lynx (avg. 87.8) over New York Liberty (avg. 87.0) — by under a point. The ranges overlap some — there's real uncertainty here.

MIN 86.3–89.3 NY 85.7–88.4 Overlap 2.11 pts Actual: MIN 86, NY 99

In 80 out of 100 simulated runs, leans Minnesota Lynx (avg. 87.8) over New York Liberty (avg. 87.0) — by under a point. The ranges overlap some — there's real uncertainty here.

MIN 86.4–89.1 NY 85.9–88.3 Overlap 1.83 pts Actual: MIN 86, NY 99

In 75 out of 100 simulated runs, leans Minnesota Lynx (avg. 87.8) over New York Liberty (avg. 87.0) — by under a point. The ranges overlap some — there's real uncertainty here.

Show the math
Model
Dr. Lila Shah (#5)
t-statistic
5.01
p-value
1.19e-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
MIN 85.8–90.7 NY 85.7–90.3 Overlap 4.53 pts Actual: MIN 86, NY 99

In 95 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.2) over New York Liberty (avg. 88.0) — by under a point. The ranges overlap almost entirely, so one game could go either way.

MIN 86.2–90.3 NY 86.1–89.9 Overlap 3.76 pts Actual: MIN 86, NY 99

In 90 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.2) over New York Liberty (avg. 88.0) — by under a point. The ranges overlap almost entirely, so one game could go either way.

MIN 86.4–90.0 NY 86.3–89.7 Overlap 3.26 pts Actual: MIN 86, NY 99

In 85 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.2) over New York Liberty (avg. 88.0) — by under a point. The ranges overlap almost entirely, so one game could go either way.

MIN 86.6–89.8 NY 86.5–89.5 Overlap 2.88 pts Actual: MIN 86, NY 99

In 80 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.2) over New York Liberty (avg. 88.0) — by under a point. The ranges overlap some — there's real uncertainty here.

MIN 86.8–89.7 NY 86.7–89.3 Overlap 2.56 pts Actual: MIN 86, NY 99

In 75 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.2) over New York Liberty (avg. 88.0) — by under a point. The ranges overlap some — there's real uncertainty here.

Show the math
Model
Ice (#6)
t-statistic
1.54
p-value
1.26e-01

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
MIN 86.2–90.5 NY 85.2–90.5 Overlap 4.32 pts Actual: MIN 86, NY 99

In 95 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.3) over New York Liberty (avg. 87.8) — by under a point. The ranges overlap almost entirely, so one game could go either way.

MIN 86.5–90.2 NY 85.6–90.1 Overlap 3.54 pts Actual: MIN 86, NY 99

In 90 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.3) over New York Liberty (avg. 87.8) — by under a point. The ranges overlap almost entirely, so one game could go either way.

MIN 86.7–89.9 NY 85.9–89.8 Overlap 3.04 pts Actual: MIN 86, NY 99

In 85 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.3) over New York Liberty (avg. 87.8) — by under a point. The ranges overlap almost entirely, so one game could go either way.

MIN 86.9–89.8 NY 86.1–89.6 Overlap 2.65 pts Actual: MIN 86, NY 99

In 80 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.3) over New York Liberty (avg. 87.8) — by under a point. The ranges overlap some — there's real uncertainty here.

MIN 87.1–89.6 NY 86.3–89.4 Overlap 2.33 pts Actual: MIN 86, NY 99

In 75 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.3) over New York Liberty (avg. 87.8) — by under a point. The ranges overlap some — there's real uncertainty here.

Show the math
Model
Jamal (#7)
t-statistic
3.32
p-value
1.09e-03

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
MIN 86.3–90.7 NY 86.2–90.0 Overlap 3.71 pts Actual: MIN 86, NY 99

In 95 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.2) over New York Liberty (avg. 88.0) — by under a point. The ranges overlap almost entirely, so one game could go either way.

MIN 86.6–90.0 NY 86.6–89.8 Overlap 3.23 pts Actual: MIN 86, NY 99

In 90 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.2) over New York Liberty (avg. 88.0) — by under a point. The ranges overlap almost entirely, so one game could go either way.

MIN 86.8–89.8 NY 86.7–89.7 Overlap 2.89 pts Actual: MIN 86, NY 99

In 85 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.2) over New York Liberty (avg. 88.0) — by under a point. The ranges overlap some — there's real uncertainty here.

MIN 87.1–89.6 NY 86.8–89.5 Overlap 2.41 pts Actual: MIN 86, NY 99

In 80 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.2) over New York Liberty (avg. 88.0) — by under a point. The ranges overlap some — there's real uncertainty here.

MIN 87.1–89.5 NY 87.0–89.4 Overlap 2.28 pts Actual: MIN 86, NY 99

In 75 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.2) over New York Liberty (avg. 88.0) — by under a point. The ranges overlap some — there's real uncertainty here.

Show the math
Model
Jordan (#8)
t-statistic
1.51
p-value
1.34e-01

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
MIN 86.4–90.3 NY 85.7–90.1 Overlap 3.66 pts Actual: MIN 86, NY 99

In 95 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.3) over New York Liberty (avg. 87.9) — by under a point. The ranges overlap almost entirely, so one game could go either way.

MIN 86.7–90.0 NY 86.0–89.7 Overlap 2.99 pts Actual: MIN 86, NY 99

In 90 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.3) over New York Liberty (avg. 87.9) — by under a point. The ranges overlap some — there's real uncertainty here.

MIN 86.9–89.8 NY 86.2–89.5 Overlap 2.56 pts Actual: MIN 86, NY 99

In 85 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.3) over New York Liberty (avg. 87.9) — by under a point. The ranges overlap some — there's real uncertainty here.

MIN 87.1–89.6 NY 86.4–89.3 Overlap 2.23 pts Actual: MIN 86, NY 99

In 80 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.3) over New York Liberty (avg. 87.9) — by under a point. The ranges overlap some — there's real uncertainty here.

MIN 87.2–89.5 NY 86.6–89.2 Overlap 1.95 pts Actual: MIN 86, NY 99

In 75 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.3) over New York Liberty (avg. 87.9) — by under a point. The ranges overlap some — there's real uncertainty here.

Show the math
Model
Darren "Dimes" Lin (#9)
t-statistic
3.66
p-value
3.17e-04

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
MIN 85.8–90.5 NY 85.9–90.0 Overlap 4.1 pts Actual: MIN 86, NY 99

In 95 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.2) over New York Liberty (avg. 87.6) — by under a point. The ranges overlap almost entirely, so one game could go either way.

MIN 86.2–90.1 NY 86.2–89.3 Overlap 3.13 pts Actual: MIN 86, NY 99

In 90 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.2) over New York Liberty (avg. 87.6) — by under a point. The ranges overlap almost entirely, so one game could go either way.

MIN 86.5–89.9 NY 86.3–89.1 Overlap 2.64 pts Actual: MIN 86, NY 99

In 85 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.2) over New York Liberty (avg. 87.6) — by under a point. The ranges overlap some — there's real uncertainty here.

MIN 86.6–89.7 NY 86.3–89.0 Overlap 2.4 pts Actual: MIN 86, NY 99

In 80 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.2) over New York Liberty (avg. 87.6) — by under a point. The ranges overlap some — there's real uncertainty here.

MIN 86.8–89.5 NY 86.4–89.0 Overlap 2.17 pts Actual: MIN 86, NY 99

In 75 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.2) over New York Liberty (avg. 87.6) — by under a point. The ranges overlap some — there's real uncertainty here.

Show the math
Model
Maya Jefferson (#10)
t-statistic
3.66
p-value
3.18e-04

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
MIN 86.4–90.5 NY 85.9–89.5 Overlap 3.11 pts Actual: MIN 86, NY 99

In 95 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.2) over New York Liberty (avg. 87.8) — by under a point. The ranges overlap almost entirely, so one game could go either way.

MIN 86.9–89.7 NY 86.0–89.3 Overlap 2.38 pts Actual: MIN 86, NY 99

In 90 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.2) over New York Liberty (avg. 87.8) — by under a point. The ranges overlap some — there's real uncertainty here.

MIN 87.1–89.6 NY 86.1–89.1 Overlap 2.02 pts Actual: MIN 86, NY 99

In 85 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.2) over New York Liberty (avg. 87.8) — by under a point. The ranges overlap some — there's real uncertainty here.

MIN 87.1–89.5 NY 86.3–88.9 Overlap 1.74 pts Actual: MIN 86, NY 99

In 80 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.2) over New York Liberty (avg. 87.8) — by under a point. The ranges overlap some — there's real uncertainty here.

MIN 87.2–89.5 NY 86.4–88.8 Overlap 1.59 pts Actual: MIN 86, NY 99

In 75 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.2) over New York Liberty (avg. 87.8) — by under a point. The ranges overlap some — there's real uncertainty here.

Show the math
Model
Lexi (#11)
t-statistic
2.76
p-value
6.37e-03

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
MIN 87.0–91.0 NY 85.8–90.8 Overlap 3.81 pts Actual: MIN 86, NY 99

In 95 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.7) over New York Liberty (avg. 88.3) — by under a point. The ranges overlap almost entirely, so one game could go either way.

MIN 87.1–90.1 NY 86.2–90.4 Overlap 3.0 pts Actual: MIN 86, NY 99

In 90 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.7) over New York Liberty (avg. 88.3) — by under a point. The ranges overlap almost entirely, so one game could go either way.

MIN 87.3–90.1 NY 86.5–90.2 Overlap 2.8 pts Actual: MIN 86, NY 99

In 85 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.7) over New York Liberty (avg. 88.3) — by under a point. The ranges overlap some — there's real uncertainty here.

MIN 87.5–90.0 NY 86.7–90.0 Overlap 2.46 pts Actual: MIN 86, NY 99

In 80 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.7) over New York Liberty (avg. 88.3) — by under a point. The ranges overlap some — there's real uncertainty here.

MIN 87.5–90.0 NY 86.9–89.8 Overlap 2.27 pts Actual: MIN 86, NY 99

In 75 out of 100 simulated runs, leans Minnesota Lynx (avg. 88.7) over New York Liberty (avg. 88.3) — by under a point. The ranges overlap some — there's real uncertainty here.

Show the math
Model
Coach Sarah Watanabe (#12)
t-statistic
2.32
p-value
2.14e-02

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 — MIN at NY — Actual: MIN 86, NY 99
Level MIN range NY range Overlap Tol Warning Actual landed in range?
95% 85.8–90.9 85.5–90.5 4.65 pts No
90% 86.2–90.5 85.9–90.1 3.84 pts No
85% 86.5–90.2 86.1–89.8 3.31 pts No
80% 86.7–90.0 86.3–89.6 2.91 pts No
75% 86.9–89.8 86.5–89.4 2.57 pts No
Reg — MIN at NY — Actual: MIN 86, NY 99
Level MIN range NY range Overlap Tol Warning Actual landed in range?
95% 85.6–91.8 85.2–90.7 5.15 pts No
90% 86.1–91.3 85.6–90.3 4.21 pts No
85% 86.4–90.9 85.9–90.0 3.59 pts No
80% 86.7–90.7 86.1–89.8 3.12 pts No
75% 86.9–90.5 86.3–89.6 2.72 pts No
Dr. Wallace — MIN at NY — Actual: MIN 86, NY 99
Level MIN range NY range Overlap Tol Warning Actual landed in range?
95% 86.4–90.2 85.7–90.1 3.65 pts No
90% 86.7–89.9 86.1–89.7 3.0 pts No
85% 86.9–89.7 86.3–89.5 2.57 pts No
80% 87.1–89.5 86.5–89.3 2.25 pts No
75% 87.2–89.4 86.6–89.2 1.97 pts No
Kevin — MIN at NY — Actual: MIN 86, NY 99
Level MIN range NY range Overlap Tol Warning Actual landed in range?
95% 86.4–90.4 85.9–90.5 3.96 pts No
90% 86.8–90.2 86.1–89.7 2.92 pts No
85% 87.2–89.9 86.5–89.5 2.26 pts No
80% 87.4–89.9 86.7–89.3 1.9 pts No
75% 87.4–89.7 87.0–88.9 1.5 pts No
Dr. Lila Shah — MIN at NY — Actual: MIN 86, NY 99
Level MIN range NY range Overlap Tol Warning Actual landed in range?
95% 85.5–90.0 85.0–89.4 3.85 pts No
90% 85.9–89.7 85.2–88.9 2.99 pts No
85% 86.1–89.4 85.4–88.6 2.52 pts No
80% 86.3–89.3 85.7–88.4 2.11 pts No
75% 86.4–89.1 85.9–88.3 1.83 pts No
Ice — MIN at NY — Actual: MIN 86, NY 99
Level MIN range NY range Overlap Tol Warning Actual landed in range?
95% 85.8–90.7 85.7–90.3 4.53 pts No
90% 86.2–90.3 86.1–89.9 3.76 pts No
85% 86.4–90.0 86.3–89.7 3.26 pts No
80% 86.6–89.8 86.5–89.5 2.88 pts No
75% 86.8–89.7 86.7–89.3 2.56 pts No
Jamal — MIN at NY — Actual: MIN 86, NY 99
Level MIN range NY range Overlap Tol Warning Actual landed in range?
95% 86.2–90.5 85.2–90.5 4.32 pts No
90% 86.5–90.2 85.6–90.1 3.54 pts No
85% 86.7–89.9 85.9–89.8 3.04 pts No
80% 86.9–89.8 86.1–89.6 2.65 pts No
75% 87.1–89.6 86.3–89.4 2.33 pts No
Jordan — MIN at NY — Actual: MIN 86, NY 99
Level MIN range NY range Overlap Tol Warning Actual landed in range?
95% 86.3–90.7 86.2–90.0 3.71 pts No
90% 86.6–90.0 86.6–89.8 3.23 pts No
85% 86.8–89.8 86.7–89.7 2.89 pts No
80% 87.1–89.6 86.8–89.5 2.41 pts No
75% 87.1–89.5 87.0–89.4 2.28 pts No
Darren "Dimes" Lin — MIN at NY — Actual: MIN 86, NY 99
Level MIN range NY range Overlap Tol Warning Actual landed in range?
95% 86.4–90.3 85.7–90.1 3.66 pts No
90% 86.7–90.0 86.0–89.7 2.99 pts No
85% 86.9–89.8 86.2–89.5 2.56 pts No
80% 87.1–89.6 86.4–89.3 2.23 pts No
75% 87.2–89.5 86.6–89.2 1.95 pts No
Maya Jefferson — MIN at NY — Actual: MIN 86, NY 99
Level MIN range NY range Overlap Tol Warning Actual landed in range?
95% 85.8–90.5 85.9–90.0 4.1 pts No
90% 86.2–90.1 86.2–89.3 3.13 pts No
85% 86.5–89.9 86.3–89.1 2.64 pts No
80% 86.6–89.7 86.3–89.0 2.4 pts No
75% 86.8–89.5 86.4–89.0 2.17 pts No
Lexi — MIN at NY — Actual: MIN 86, NY 99
Level MIN range NY range Overlap Tol Warning Actual landed in range?
95% 86.4–90.5 85.9–89.5 3.11 pts No
90% 86.9–89.7 86.0–89.3 2.38 pts No
85% 87.1–89.6 86.1–89.1 2.02 pts No
80% 87.1–89.5 86.3–88.9 1.74 pts No
75% 87.2–89.5 86.4–88.8 1.59 pts No
Coach Sarah Watanabe — MIN at NY — Actual: MIN 86, NY 99
Level MIN range NY range Overlap Tol Warning Actual landed in range?
95% 87.0–91.0 85.8–90.8 3.81 pts No
90% 87.1–90.1 86.2–90.4 3.0 pts No
85% 87.3–90.1 86.5–90.2 2.8 pts No
80% 87.5–90.0 86.7–90.0 2.46 pts No
75% 87.5–90.0 86.9–89.8 2.27 pts No