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Indiana Fever at Toronto Tempo

IND 101 – TOR 95

August 18, 2026 · Final

Kevin

Best model for this game

Kevin
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 IND at TOR

All 12 models’ predicted scores

IND — one dot per model TOR — one dot per model Actual: IND 101, TOR 95
Vince Chambers
IND 88.5–94.4 TOR 79.6–85.4 No overlap Actual: IND 101, TOR 95

In 95 out of 100 simulated runs, leans Indiana Fever (avg. 91.2) over Toronto Tempo (avg. 82.5) — by 8.7 points. The 95% ranges don't overlap at all — a confident model.

IND 88.6–93.9 TOR 80.1–84.9 No overlap Actual: IND 101, TOR 95

In 90 out of 100 simulated runs, leans Indiana Fever (avg. 91.2) over Toronto Tempo (avg. 82.5) — by 8.7 points. The 90% ranges don't overlap at all — a confident model.

IND 89.3–93.5 TOR 80.4–84.6 No overlap Actual: IND 101, TOR 95

In 85 out of 100 simulated runs, leans Indiana Fever (avg. 91.2) over Toronto Tempo (avg. 82.5) — by 8.7 points. The 85% ranges don't overlap at all — a confident model.

IND 89.6–93.3 TOR 80.6–84.4 No overlap Actual: IND 101, TOR 95

In 80 out of 100 simulated runs, leans Indiana Fever (avg. 91.2) over Toronto Tempo (avg. 82.5) — by 8.7 points. The 80% ranges don't overlap at all — a confident model.

IND 89.7–92.9 TOR 80.8–84.2 No overlap Actual: IND 101, TOR 95

In 75 out of 100 simulated runs, leans Indiana Fever (avg. 91.2) over Toronto Tempo (avg. 82.5) — by 8.7 points. The 75% ranges don't overlap at all — a confident model.

Show the math
Model
Vince Chambers (#1)
t-statistic
51.67
p-value
1.55e-150

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
IND 84.9–95.3 TOR 79.3–87.3 Overlap 2.32 pts Actual: IND 101, TOR 95

In 95 out of 100 simulated runs, leans Indiana Fever (avg. 91.0) over Toronto Tempo (avg. 83.3) — by 7.7 points. The ranges overlap some — there's real uncertainty here.

IND 86.3–94.7 TOR 80.0–86.6 Overlap 0.3 pts Actual: IND 101, TOR 95

In 90 out of 100 simulated runs, leans Indiana Fever (avg. 91.0) over Toronto Tempo (avg. 83.3) — by 7.7 points. The ranges barely touch.

IND 87.0–94.3 TOR 80.4–86.2 No overlap Actual: IND 101, TOR 95

In 85 out of 100 simulated runs, leans Indiana Fever (avg. 91.0) over Toronto Tempo (avg. 83.3) — by 7.7 points. The 85% ranges don't overlap at all — a confident model.

IND 87.2–94.1 TOR 80.7–85.9 No overlap Actual: IND 101, TOR 95

In 80 out of 100 simulated runs, leans Indiana Fever (avg. 91.0) over Toronto Tempo (avg. 83.3) — by 7.7 points. The 80% ranges don't overlap at all — a confident model.

IND 87.6–93.7 TOR 81.0–85.6 No overlap Actual: IND 101, TOR 95

In 75 out of 100 simulated runs, leans Indiana Fever (avg. 91.0) over Toronto Tempo (avg. 83.3) — by 7.7 points. The 75% ranges don't overlap at all — a confident model.

Show the math
Model
Reg (#2)
t-statistic
30.36
p-value
2.64e-90

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
IND 85.3–93.9 TOR 80.1–87.7 Overlap 2.39 pts Actual: IND 101, TOR 95

In 95 out of 100 simulated runs, leans Indiana Fever (avg. 89.7) over Toronto Tempo (avg. 83.3) — by 6.4 points. The ranges overlap some — there's real uncertainty here.

IND 85.6–93.2 TOR 80.4–87.2 Overlap 1.61 pts Actual: IND 101, TOR 95

In 90 out of 100 simulated runs, leans Indiana Fever (avg. 89.7) over Toronto Tempo (avg. 83.3) — by 6.4 points. The ranges overlap some — there's real uncertainty here.

IND 86.0–92.8 TOR 80.8–86.8 Overlap 0.82 pts Actual: IND 101, TOR 95

In 85 out of 100 simulated runs, leans Indiana Fever (avg. 89.7) over Toronto Tempo (avg. 83.3) — by 6.4 points. The ranges barely touch.

IND 86.3–92.6 TOR 81.0–86.7 Overlap 0.4 pts Actual: IND 101, TOR 95

In 80 out of 100 simulated runs, leans Indiana Fever (avg. 89.7) over Toronto Tempo (avg. 83.3) — by 6.4 points. The ranges barely touch.

IND 86.4–92.5 TOR 81.1–86.3 No overlap Actual: IND 101, TOR 95

In 75 out of 100 simulated runs, leans Indiana Fever (avg. 89.7) over Toronto Tempo (avg. 83.3) — by 6.4 points. The 75% ranges don't overlap at all — a confident model.

Show the math
Model
Dr. Wallace (#3)
t-statistic
24.44
p-value
1.07e-73

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
IND 85.3–93.4 TOR 80.1–87.7 Overlap 2.35 pts Actual: IND 101, TOR 95

In 95 out of 100 simulated runs, leans Indiana Fever (avg. 89.5) over Toronto Tempo (avg. 83.8) — by 5.7 points. The ranges overlap some — there's real uncertainty here.

IND 85.7–93.1 TOR 80.4–87.6 Overlap 1.92 pts Actual: IND 101, TOR 95

In 90 out of 100 simulated runs, leans Indiana Fever (avg. 89.5) over Toronto Tempo (avg. 83.8) — by 5.7 points. The ranges overlap some — there's real uncertainty here.

IND 85.8–92.7 TOR 80.9–87.2 Overlap 1.4 pts Actual: IND 101, TOR 95

In 85 out of 100 simulated runs, leans Indiana Fever (avg. 89.5) over Toronto Tempo (avg. 83.8) — by 5.7 points. The ranges overlap some — there's real uncertainty here.

IND 86.0–92.5 TOR 81.2–87.0 Overlap 1.0 pts Actual: IND 101, TOR 95

In 80 out of 100 simulated runs, leans Indiana Fever (avg. 89.5) over Toronto Tempo (avg. 83.8) — by 5.7 points. The ranges overlap some — there's real uncertainty here.

IND 86.2–92.2 TOR 81.4–86.6 Overlap 0.42 pts Actual: IND 101, TOR 95

In 75 out of 100 simulated runs, leans Indiana Fever (avg. 89.5) over Toronto Tempo (avg. 83.8) — by 5.7 points. The ranges barely touch.

Show the math
Model
Kevin (#4)
t-statistic
21.50
p-value
4.36e-63

A low p-value means the two teams' average scores are statistically distinguishable — even when their ranges above overlap. Overlap describes where the final score could land; the test above is about whether the two averages differ. They are different claims, and either can hold without the other.

Dr. Lila Shah
IND 84.6–92.3 TOR 80.3–88.3 Overlap 3.71 pts Actual: IND 101, TOR 95

In 95 out of 100 simulated runs, leans Indiana Fever (avg. 87.9) over Toronto Tempo (avg. 84.2) — by 3.8 points. The ranges overlap almost entirely, so one game could go either way.

IND 84.8–91.9 TOR 80.4–87.8 Overlap 2.95 pts Actual: IND 101, TOR 95

In 90 out of 100 simulated runs, leans Indiana Fever (avg. 87.9) over Toronto Tempo (avg. 84.2) — by 3.8 points. The ranges overlap some — there's real uncertainty here.

IND 85.0–91.3 TOR 80.9–87.3 Overlap 2.33 pts Actual: IND 101, TOR 95

In 85 out of 100 simulated runs, leans Indiana Fever (avg. 87.9) over Toronto Tempo (avg. 84.2) — by 3.8 points. The ranges overlap some — there's real uncertainty here.

IND 85.2–91.1 TOR 81.2–87.0 Overlap 1.8 pts Actual: IND 101, TOR 95

In 80 out of 100 simulated runs, leans Indiana Fever (avg. 87.9) over Toronto Tempo (avg. 84.2) — by 3.8 points. The ranges overlap some — there's real uncertainty here.

IND 85.3–90.9 TOR 81.4–86.8 Overlap 1.55 pts Actual: IND 101, TOR 95

In 75 out of 100 simulated runs, leans Indiana Fever (avg. 87.9) over Toronto Tempo (avg. 84.2) — by 3.8 points. The ranges overlap some — there's real uncertainty here.

Show the math
Model
Dr. Lila Shah (#5)
t-statistic
14.25
p-value
9.76e-36

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
IND 84.9–92.0 TOR 80.9–88.0 Overlap 3.01 pts Actual: IND 101, TOR 95

In 95 out of 100 simulated runs, leans Indiana Fever (avg. 87.8) over Toronto Tempo (avg. 84.9) — by 2.9 points. The ranges overlap almost entirely, so one game could go either way.

IND 85.2–91.6 TOR 81.2–87.7 Overlap 2.56 pts Actual: IND 101, TOR 95

In 90 out of 100 simulated runs, leans Indiana Fever (avg. 87.8) over Toronto Tempo (avg. 84.9) — by 2.9 points. The ranges overlap some — there's real uncertainty here.

IND 85.3–91.5 TOR 81.6–87.4 Overlap 2.06 pts Actual: IND 101, TOR 95

In 85 out of 100 simulated runs, leans Indiana Fever (avg. 87.8) over Toronto Tempo (avg. 84.9) — by 2.9 points. The ranges overlap some — there's real uncertainty here.

IND 85.5–90.9 TOR 81.9–87.1 Overlap 1.61 pts Actual: IND 101, TOR 95

In 80 out of 100 simulated runs, leans Indiana Fever (avg. 87.8) over Toronto Tempo (avg. 84.9) — by 2.9 points. The ranges overlap some — there's real uncertainty here.

IND 85.8–90.6 TOR 82.1–87.0 Overlap 1.22 pts Actual: IND 101, TOR 95

In 75 out of 100 simulated runs, leans Indiana Fever (avg. 87.8) over Toronto Tempo (avg. 84.9) — by 2.9 points. The ranges overlap some — there's real uncertainty here.

Show the math
Model
Ice (#6)
t-statistic
12.27
p-value
1.99e-28

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
IND 84.4–91.7 TOR 81.2–88.3 Overlap 3.85 pts Actual: IND 101, TOR 95

In 95 out of 100 simulated runs, leans Indiana Fever (avg. 87.3) over Toronto Tempo (avg. 85.2) — by 2.1 points. The ranges overlap almost entirely, so one game could go either way.

IND 84.8–91.3 TOR 81.8–87.7 Overlap 2.95 pts Actual: IND 101, TOR 95

In 90 out of 100 simulated runs, leans Indiana Fever (avg. 87.3) over Toronto Tempo (avg. 85.2) — by 2.1 points. The ranges overlap some — there's real uncertainty here.

IND 85.3–90.9 TOR 82.3–87.4 Overlap 2.12 pts Actual: IND 101, TOR 95

In 85 out of 100 simulated runs, leans Indiana Fever (avg. 87.3) over Toronto Tempo (avg. 85.2) — by 2.1 points. The ranges overlap some — there's real uncertainty here.

IND 85.4–90.3 TOR 82.5–87.2 Overlap 1.84 pts Actual: IND 101, TOR 95

In 80 out of 100 simulated runs, leans Indiana Fever (avg. 87.3) over Toronto Tempo (avg. 85.2) — by 2.1 points. The ranges overlap some — there's real uncertainty here.

IND 85.6–89.4 TOR 82.8–87.1 Overlap 1.46 pts Actual: IND 101, TOR 95

In 75 out of 100 simulated runs, leans Indiana Fever (avg. 87.3) over Toronto Tempo (avg. 85.2) — by 2.1 points. The ranges overlap some — there's real uncertainty here.

Show the math
Model
Jamal (#7)
t-statistic
9.74
p-value
1.01e-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.

Jordan
IND 84.0–92.2 TOR 81.6–87.9 Overlap 3.93 pts Actual: IND 101, TOR 95

In 95 out of 100 simulated runs, leans Indiana Fever (avg. 87.2) over Toronto Tempo (avg. 85.1) — by 2.0 points. The ranges overlap almost entirely, so one game could go either way.

IND 84.7–91.3 TOR 82.0–87.6 Overlap 2.94 pts Actual: IND 101, TOR 95

In 90 out of 100 simulated runs, leans Indiana Fever (avg. 87.2) over Toronto Tempo (avg. 85.1) — by 2.0 points. The ranges overlap some — there's real uncertainty here.

IND 84.8–90.5 TOR 82.5–87.4 Overlap 2.62 pts Actual: IND 101, TOR 95

In 85 out of 100 simulated runs, leans Indiana Fever (avg. 87.2) over Toronto Tempo (avg. 85.1) — by 2.0 points. The ranges overlap some — there's real uncertainty here.

IND 85.1–90.0 TOR 82.7–87.3 Overlap 2.21 pts Actual: IND 101, TOR 95

In 80 out of 100 simulated runs, leans Indiana Fever (avg. 87.2) over Toronto Tempo (avg. 85.1) — by 2.0 points. The ranges overlap some — there's real uncertainty here.

IND 85.3–89.3 TOR 83.2–87.0 Overlap 1.64 pts Actual: IND 101, TOR 95

In 75 out of 100 simulated runs, leans Indiana Fever (avg. 87.2) over Toronto Tempo (avg. 85.1) — by 2.0 points. The ranges overlap some — there's real uncertainty here.

Show the math
Model
Jordan (#8)
t-statistic
9.76
p-value
9.24e-20

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
IND 84.9–92.6 TOR 81.4–88.9 Overlap 3.95 pts Actual: IND 101, TOR 95

In 95 out of 100 simulated runs, leans Indiana Fever (avg. 87.3) over Toronto Tempo (avg. 85.1) — by 2.2 points. The ranges overlap almost entirely, so one game could go either way.

IND 85.0–91.4 TOR 82.0–88.3 Overlap 3.26 pts Actual: IND 101, TOR 95

In 90 out of 100 simulated runs, leans Indiana Fever (avg. 87.3) over Toronto Tempo (avg. 85.1) — by 2.2 points. The ranges overlap almost entirely, so one game could go either way.

IND 85.2–90.8 TOR 82.4–87.9 Overlap 2.72 pts Actual: IND 101, TOR 95

In 85 out of 100 simulated runs, leans Indiana Fever (avg. 87.3) over Toronto Tempo (avg. 85.1) — by 2.2 points. The ranges overlap some — there's real uncertainty here.

IND 85.3–90.4 TOR 82.7–87.6 Overlap 2.31 pts Actual: IND 101, TOR 95

In 80 out of 100 simulated runs, leans Indiana Fever (avg. 87.3) over Toronto Tempo (avg. 85.1) — by 2.2 points. The ranges overlap some — there's real uncertainty here.

IND 85.5–89.8 TOR 82.9–87.3 Overlap 1.85 pts Actual: IND 101, TOR 95

In 75 out of 100 simulated runs, leans Indiana Fever (avg. 87.3) over Toronto Tempo (avg. 85.1) — by 2.2 points. The ranges overlap some — there's real uncertainty here.

Show the math
Model
Darren "Dimes" Lin (#9)
t-statistic
10.37
p-value
9.48e-22

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
IND 85.0–92.5 TOR 79.7–88.2 Overlap 3.2 pts Actual: IND 101, TOR 95

In 95 out of 100 simulated runs, leans Indiana Fever (avg. 87.8) over Toronto Tempo (avg. 84.8) — by 2.9 points. The ranges overlap almost entirely, so one game could go either way.

IND 85.3–91.4 TOR 81.2–87.8 Overlap 2.56 pts Actual: IND 101, TOR 95

In 90 out of 100 simulated runs, leans Indiana Fever (avg. 87.8) over Toronto Tempo (avg. 84.8) — by 2.9 points. The ranges overlap some — there's real uncertainty here.

IND 85.4–90.9 TOR 81.8–87.5 Overlap 2.12 pts Actual: IND 101, TOR 95

In 85 out of 100 simulated runs, leans Indiana Fever (avg. 87.8) over Toronto Tempo (avg. 84.8) — by 2.9 points. The ranges overlap some — there's real uncertainty here.

IND 85.7–90.8 TOR 82.0–87.1 Overlap 1.39 pts Actual: IND 101, TOR 95

In 80 out of 100 simulated runs, leans Indiana Fever (avg. 87.8) over Toronto Tempo (avg. 84.8) — by 2.9 points. The ranges overlap some — there's real uncertainty here.

IND 85.9–90.5 TOR 82.3–87.0 Overlap 1.09 pts Actual: IND 101, TOR 95

In 75 out of 100 simulated runs, leans Indiana Fever (avg. 87.8) over Toronto Tempo (avg. 84.8) — by 2.9 points. The ranges overlap some — there's real uncertainty here.

Show the math
Model
Maya Jefferson (#10)
t-statistic
12.61
p-value
1.29e-29

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
IND 85.0–94.6 TOR 79.9–87.4 Overlap 2.4 pts Actual: IND 101, TOR 95

In 95 out of 100 simulated runs, leans Indiana Fever (avg. 88.5) over Toronto Tempo (avg. 83.9) — by 4.6 points. The ranges overlap some — there's real uncertainty here.

IND 85.4–93.4 TOR 80.7–87.2 Overlap 1.86 pts Actual: IND 101, TOR 95

In 90 out of 100 simulated runs, leans Indiana Fever (avg. 88.5) over Toronto Tempo (avg. 83.9) — by 4.6 points. The ranges overlap some — there's real uncertainty here.

IND 85.5–92.2 TOR 81.0–87.0 Overlap 1.48 pts Actual: IND 101, TOR 95

In 85 out of 100 simulated runs, leans Indiana Fever (avg. 88.5) over Toronto Tempo (avg. 83.9) — by 4.6 points. The ranges overlap some — there's real uncertainty here.

IND 85.6–91.7 TOR 81.3–86.7 Overlap 1.11 pts Actual: IND 101, TOR 95

In 80 out of 100 simulated runs, leans Indiana Fever (avg. 88.5) over Toronto Tempo (avg. 83.9) — by 4.6 points. The ranges overlap some — there's real uncertainty here.

IND 85.7–91.3 TOR 81.4–86.4 Overlap 0.73 pts Actual: IND 101, TOR 95

In 75 out of 100 simulated runs, leans Indiana Fever (avg. 88.5) over Toronto Tempo (avg. 83.9) — by 4.6 points. The ranges barely touch.

Show the math
Model
Lexi (#11)
t-statistic
16.83
p-value
5.23e-45

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
IND 85.4–93.2 TOR 80.3–88.0 Overlap 2.66 pts Actual: IND 101, TOR 95

In 95 out of 100 simulated runs, leans Indiana Fever (avg. 89.0) over Toronto Tempo (avg. 83.9) — by 5.1 points. The ranges overlap some — there's real uncertainty here.

IND 85.5–92.8 TOR 81.0–87.4 Overlap 1.9 pts Actual: IND 101, TOR 95

In 90 out of 100 simulated runs, leans Indiana Fever (avg. 89.0) over Toronto Tempo (avg. 83.9) — by 5.1 points. The ranges overlap some — there's real uncertainty here.

IND 85.7–92.6 TOR 81.3–87.3 Overlap 1.56 pts Actual: IND 101, TOR 95

In 85 out of 100 simulated runs, leans Indiana Fever (avg. 89.0) over Toronto Tempo (avg. 83.9) — by 5.1 points. The ranges overlap some — there's real uncertainty here.

IND 85.8–92.1 TOR 81.5–86.9 Overlap 1.02 pts Actual: IND 101, TOR 95

In 80 out of 100 simulated runs, leans Indiana Fever (avg. 89.0) over Toronto Tempo (avg. 83.9) — by 5.1 points. The ranges overlap some — there's real uncertainty here.

IND 86.1–91.9 TOR 81.6–86.5 Overlap 0.43 pts Actual: IND 101, TOR 95

In 75 out of 100 simulated runs, leans Indiana Fever (avg. 89.0) over Toronto Tempo (avg. 83.9) — by 5.1 points. The ranges barely touch.

Show the math
Model
Coach Sarah Watanabe (#12)
t-statistic
19.53
p-value
1.28e-55

A low p-value means the two teams' average scores are statistically distinguishable — even when their ranges above overlap. Overlap describes where the final score could land; the test above is about whether the two averages differ. They are different claims, and either can hold without the other.

View as table
Vince Chambers — IND at TOR — Actual: IND 101, TOR 95
Level IND range TOR range Overlap Tol Warning Actual landed in range?
95% 88.5–94.4 79.6–85.4 — pts No
90% 88.6–93.9 80.1–84.9 — pts No
85% 89.3–93.5 80.4–84.6 — pts No
80% 89.6–93.3 80.6–84.4 — pts No
75% 89.7–92.9 80.8–84.2 — pts No
Reg — IND at TOR — Actual: IND 101, TOR 95
Level IND range TOR range Overlap Tol Warning Actual landed in range?
95% 84.9–95.3 79.3–87.3 2.32 pts No
90% 86.3–94.7 80.0–86.6 0.3 pts No
85% 87.0–94.3 80.4–86.2 — pts No
80% 87.2–94.1 80.7–85.9 — pts No
75% 87.6–93.7 81.0–85.6 — pts No
Dr. Wallace — IND at TOR — Actual: IND 101, TOR 95
Level IND range TOR range Overlap Tol Warning Actual landed in range?
95% 85.3–93.9 80.1–87.7 2.39 pts No
90% 85.6–93.2 80.4–87.2 1.61 pts No
85% 86.0–92.8 80.8–86.8 0.82 pts No
80% 86.3–92.6 81.0–86.7 0.4 pts No
75% 86.4–92.5 81.1–86.3 — pts No
Kevin — IND at TOR — Actual: IND 101, TOR 95
Level IND range TOR range Overlap Tol Warning Actual landed in range?
95% 85.3–93.4 80.1–87.7 2.35 pts No
90% 85.7–93.1 80.4–87.6 1.92 pts No
85% 85.8–92.7 80.9–87.2 1.4 pts No
80% 86.0–92.5 81.2–87.0 1.0 pts No
75% 86.2–92.2 81.4–86.6 0.42 pts No
Dr. Lila Shah — IND at TOR — Actual: IND 101, TOR 95
Level IND range TOR range Overlap Tol Warning Actual landed in range?
95% 84.6–92.3 80.3–88.3 3.71 pts No
90% 84.8–91.9 80.4–87.8 2.95 pts No
85% 85.0–91.3 80.9–87.3 2.33 pts No
80% 85.2–91.1 81.2–87.0 1.8 pts No
75% 85.3–90.9 81.4–86.8 1.55 pts No
Ice — IND at TOR — Actual: IND 101, TOR 95
Level IND range TOR range Overlap Tol Warning Actual landed in range?
95% 84.9–92.0 80.9–88.0 3.01 pts No
90% 85.2–91.6 81.2–87.7 2.56 pts No
85% 85.3–91.5 81.6–87.4 2.06 pts No
80% 85.5–90.9 81.9–87.1 1.61 pts No
75% 85.8–90.6 82.1–87.0 1.22 pts No
Jamal — IND at TOR — Actual: IND 101, TOR 95
Level IND range TOR range Overlap Tol Warning Actual landed in range?
95% 84.4–91.7 81.2–88.3 3.85 pts No
90% 84.8–91.3 81.8–87.7 2.95 pts No
85% 85.3–90.9 82.3–87.4 2.12 pts No
80% 85.4–90.3 82.5–87.2 1.84 pts No
75% 85.6–89.4 82.8–87.1 1.46 pts No
Jordan — IND at TOR — Actual: IND 101, TOR 95
Level IND range TOR range Overlap Tol Warning Actual landed in range?
95% 84.0–92.2 81.6–87.9 3.93 pts No
90% 84.7–91.3 82.0–87.6 2.94 pts No
85% 84.8–90.5 82.5–87.4 2.62 pts No
80% 85.1–90.0 82.7–87.3 2.21 pts No
75% 85.3–89.3 83.2–87.0 1.64 pts No
Darren "Dimes" Lin — IND at TOR — Actual: IND 101, TOR 95
Level IND range TOR range Overlap Tol Warning Actual landed in range?
95% 84.9–92.6 81.4–88.9 3.95 pts No
90% 85.0–91.4 82.0–88.3 3.26 pts No
85% 85.2–90.8 82.4–87.9 2.72 pts No
80% 85.3–90.4 82.7–87.6 2.31 pts No
75% 85.5–89.8 82.9–87.3 1.85 pts No
Maya Jefferson — IND at TOR — Actual: IND 101, TOR 95
Level IND range TOR range Overlap Tol Warning Actual landed in range?
95% 85.0–92.5 79.7–88.2 3.2 pts No
90% 85.3–91.4 81.2–87.8 2.56 pts No
85% 85.4–90.9 81.8–87.5 2.12 pts No
80% 85.7–90.8 82.0–87.1 1.39 pts No
75% 85.9–90.5 82.3–87.0 1.09 pts No
Lexi — IND at TOR — Actual: IND 101, TOR 95
Level IND range TOR range Overlap Tol Warning Actual landed in range?
95% 85.0–94.6 79.9–87.4 2.4 pts No
90% 85.4–93.4 80.7–87.2 1.86 pts No
85% 85.5–92.2 81.0–87.0 1.48 pts No
80% 85.6–91.7 81.3–86.7 1.11 pts No
75% 85.7–91.3 81.4–86.4 0.73 pts No
Coach Sarah Watanabe — IND at TOR — Actual: IND 101, TOR 95
Level IND range TOR range Overlap Tol Warning Actual landed in range?
95% 85.4–93.2 80.3–88.0 2.66 pts No
90% 85.5–92.8 81.0–87.4 1.9 pts No
85% 85.7–92.6 81.3–87.3 1.56 pts No
80% 85.8–92.1 81.5–86.9 1.02 pts No
75% 86.1–91.9 81.6–86.5 0.43 pts No