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Toronto Tempo at Indiana Fever

TOR 91 – IND 113

June 16, 2026 · Final

Dr. Lila Shah

Best model for this game

Dr. Lila Shah
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 TOR at IND

All 12 models’ predicted scores

TOR — one dot per model IND — one dot per model Actual: TOR 91, IND 113
Vince Chambers
TOR 83.5–88.8 IND 84.3–88.7 Overlap 4.39 pts Actual: TOR 91, IND 113

In 95 out of 100 simulated runs, leans Indiana Fever (avg. 86.5) over Toronto Tempo (avg. 86.2) — by under a point. The ranges overlap almost entirely, so one game could go either way.

TOR 84.0–88.4 IND 84.7–88.4 Overlap 3.68 pts Actual: TOR 91, IND 113

In 90 out of 100 simulated runs, leans Indiana Fever (avg. 86.5) over Toronto Tempo (avg. 86.2) — by under a point. The ranges overlap almost entirely, so one game could go either way.

TOR 84.2–88.1 IND 84.9–88.1 Overlap 3.22 pts Actual: TOR 91, IND 113

In 85 out of 100 simulated runs, leans Indiana Fever (avg. 86.5) over Toronto Tempo (avg. 86.2) — by under a point. The ranges overlap almost entirely, so one game could go either way.

TOR 84.5–87.9 IND 85.1–88.0 Overlap 2.84 pts Actual: TOR 91, IND 113

In 80 out of 100 simulated runs, leans Indiana Fever (avg. 86.5) over Toronto Tempo (avg. 86.2) — by under a point. The ranges overlap some — there's real uncertainty here.

TOR 84.6–87.7 IND 85.2–87.8 Overlap 2.51 pts Actual: TOR 91, IND 113

In 75 out of 100 simulated runs, leans Indiana Fever (avg. 86.5) over Toronto Tempo (avg. 86.2) — by under a point. The ranges overlap some — there's real uncertainty here.

Show the math
Model
Vince Chambers (#1)
t-statistic
-1.98
p-value
4.94e-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 Tol Warning
TOR 83.2–88.2 IND 83.7–88.7 Overlap 4.58 pts Actual: TOR 91, IND 113

In 95 out of 100 simulated runs, leans Indiana Fever (avg. 86.2) over Toronto Tempo (avg. 86.0) — by under a point. The ranges overlap almost entirely, so one game could go either way.

TOR 83.3–87.9 IND 84.1–88.3 Overlap 3.84 pts Actual: TOR 91, IND 113

In 90 out of 100 simulated runs, leans Indiana Fever (avg. 86.2) over Toronto Tempo (avg. 86.0) — by under a point. The ranges overlap almost entirely, so one game could go either way.

TOR 83.9–87.7 IND 84.3–88.0 Overlap 3.4 pts Actual: TOR 91, IND 113

In 85 out of 100 simulated runs, leans Indiana Fever (avg. 86.2) over Toronto Tempo (avg. 86.0) — by under a point. The ranges overlap almost entirely, so one game could go either way.

TOR 84.1–87.6 IND 84.5–87.8 Overlap 3.04 pts Actual: TOR 91, IND 113

In 80 out of 100 simulated runs, leans Indiana Fever (avg. 86.2) over Toronto Tempo (avg. 86.0) — by under a point. The ranges overlap almost entirely, so one game could go either way.

TOR 84.4–87.2 IND 84.7–87.7 Overlap 2.55 pts Actual: TOR 91, IND 113

In 75 out of 100 simulated runs, leans Indiana Fever (avg. 86.2) over Toronto Tempo (avg. 86.0) — by under a point. The ranges overlap some — there's real uncertainty here.

Show the math
Model
Reg (#2)
t-statistic
-1.12
p-value
2.66e-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.

Dr. Wallace Tol Warning
TOR 83.7–88.5 IND 84.5–88.1 Overlap 3.54 pts Actual: TOR 91, IND 113

In 95 out of 100 simulated runs, leans Indiana Fever (avg. 86.4) over Toronto Tempo (avg. 86.1) — by under a point. The ranges overlap almost entirely, so one game could go either way.

TOR 84.1–88.1 IND 85.2–87.9 Overlap 2.72 pts Actual: TOR 91, IND 113

In 90 out of 100 simulated runs, leans Indiana Fever (avg. 86.4) over Toronto Tempo (avg. 86.1) — by under a point. The ranges overlap some — there's real uncertainty here.

TOR 84.4–87.9 IND 85.2–87.8 Overlap 2.57 pts Actual: TOR 91, IND 113

In 85 out of 100 simulated runs, leans Indiana Fever (avg. 86.4) over Toronto Tempo (avg. 86.1) — by under a point. The ranges overlap some — there's real uncertainty here.

TOR 84.6–87.7 IND 85.3–87.4 Overlap 2.1 pts Actual: TOR 91, IND 113

In 80 out of 100 simulated runs, leans Indiana Fever (avg. 86.4) over Toronto Tempo (avg. 86.1) — by under a point. The ranges overlap some — there's real uncertainty here.

TOR 84.7–87.5 IND 85.4–87.4 Overlap 1.97 pts Actual: TOR 91, IND 113

In 75 out of 100 simulated runs, leans Indiana Fever (avg. 86.4) over Toronto Tempo (avg. 86.1) — by under a point. The ranges overlap some — there's real uncertainty here.

Show the math
Model
Dr. Wallace (#3)
t-statistic
-1.69
p-value
9.22e-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.

Kevin
TOR 83.6–88.6 IND 84.1–89.0 Overlap 4.53 pts Actual: TOR 91, IND 113

In 95 out of 100 simulated runs, leans Indiana Fever (avg. 86.6) over Toronto Tempo (avg. 86.1) — by under a point. The ranges overlap almost entirely, so one game could go either way.

TOR 84.0–88.2 IND 84.5–88.6 Overlap 3.73 pts Actual: TOR 91, IND 113

In 90 out of 100 simulated runs, leans Indiana Fever (avg. 86.6) over Toronto Tempo (avg. 86.1) — by under a point. The ranges overlap almost entirely, so one game could go either way.

TOR 84.2–88.0 IND 84.8–88.4 Overlap 3.2 pts Actual: TOR 91, IND 113

In 85 out of 100 simulated runs, leans Indiana Fever (avg. 86.6) over Toronto Tempo (avg. 86.1) — by under a point. The ranges overlap almost entirely, so one game could go either way.

TOR 84.5–87.8 IND 85.0–88.2 Overlap 2.8 pts Actual: TOR 91, IND 113

In 80 out of 100 simulated runs, leans Indiana Fever (avg. 86.6) over Toronto Tempo (avg. 86.1) — by under a point. The ranges overlap some — there's real uncertainty here.

TOR 84.6–87.6 IND 85.1–88.0 Overlap 2.47 pts Actual: TOR 91, IND 113

In 75 out of 100 simulated runs, leans Indiana Fever (avg. 86.6) over Toronto Tempo (avg. 86.1) — by under a point. The ranges overlap some — there's real uncertainty here.

Show the math
Model
Kevin (#4)
t-statistic
-2.72
p-value
7.30e-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
TOR 82.3–88.6 IND 84.2–88.6 Overlap 4.39 pts Actual: TOR 91, IND 113

In 95 out of 100 simulated runs, leans Indiana Fever (avg. 86.4) over Toronto Tempo (avg. 85.5) — by under a point. The ranges overlap almost entirely, so one game could go either way.

TOR 82.8–88.1 IND 84.6–88.3 Overlap 3.53 pts Actual: TOR 91, IND 113

In 90 out of 100 simulated runs, leans Indiana Fever (avg. 86.4) over Toronto Tempo (avg. 85.5) — by under a point. The ranges overlap almost entirely, so one game could go either way.

TOR 83.2–87.8 IND 84.8–88.0 Overlap 2.97 pts Actual: TOR 91, IND 113

In 85 out of 100 simulated runs, leans Indiana Fever (avg. 86.4) over Toronto Tempo (avg. 85.5) — by under a point. The ranges overlap some — there's real uncertainty here.

TOR 83.4–87.5 IND 85.0–87.9 Overlap 2.54 pts Actual: TOR 91, IND 113

In 80 out of 100 simulated runs, leans Indiana Fever (avg. 86.4) over Toronto Tempo (avg. 85.5) — by under a point. The ranges overlap some — there's real uncertainty here.

TOR 83.6–87.3 IND 85.1–87.7 Overlap 2.18 pts Actual: TOR 91, IND 113

In 75 out of 100 simulated runs, leans Indiana Fever (avg. 86.4) over Toronto Tempo (avg. 85.5) — by under a point. The ranges overlap some — there's real uncertainty here.

Show the math
Model
Dr. Lila Shah (#5)
t-statistic
-5.11
p-value
9.91e-07

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
TOR 83.3–88.6 IND 84.1–88.8 Overlap 4.57 pts Actual: TOR 91, IND 113

In 95 out of 100 simulated runs, leans Indiana Fever (avg. 86.4) over Toronto Tempo (avg. 86.0) — by under a point. The ranges overlap almost entirely, so one game could go either way.

TOR 83.7–88.2 IND 84.4–88.4 Overlap 3.75 pts Actual: TOR 91, IND 113

In 90 out of 100 simulated runs, leans Indiana Fever (avg. 86.4) over Toronto Tempo (avg. 86.0) — by under a point. The ranges overlap almost entirely, so one game could go either way.

TOR 84.0–87.9 IND 84.7–88.2 Overlap 3.23 pts Actual: TOR 91, IND 113

In 85 out of 100 simulated runs, leans Indiana Fever (avg. 86.4) over Toronto Tempo (avg. 86.0) — by under a point. The ranges overlap almost entirely, so one game could go either way.

TOR 84.2–87.7 IND 84.9–88.0 Overlap 2.82 pts Actual: TOR 91, IND 113

In 80 out of 100 simulated runs, leans Indiana Fever (avg. 86.4) over Toronto Tempo (avg. 86.0) — by under a point. The ranges overlap some — there's real uncertainty here.

TOR 84.4–87.5 IND 85.0–87.8 Overlap 2.48 pts Actual: TOR 91, IND 113

In 75 out of 100 simulated runs, leans Indiana Fever (avg. 86.4) over Toronto Tempo (avg. 86.0) — by under a point. The ranges overlap some — there's real uncertainty here.

Show the math
Model
Ice (#6)
t-statistic
-2.76
p-value
6.53e-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.

Jamal Tol Warning
TOR 82.9–89.0 IND 85.0–88.4 Overlap 3.42 pts Actual: TOR 91, IND 113

In 95 out of 100 simulated runs, leans Indiana Fever (avg. 86.6) over Toronto Tempo (avg. 85.9) — by under a point. The ranges overlap almost entirely, so one game could go either way.

TOR 83.4–88.5 IND 85.3–88.0 Overlap 2.67 pts Actual: TOR 91, IND 113

In 90 out of 100 simulated runs, leans Indiana Fever (avg. 86.6) over Toronto Tempo (avg. 85.9) — by under a point. The ranges overlap some — there's real uncertainty here.

TOR 83.7–88.2 IND 85.4–87.9 Overlap 2.51 pts Actual: TOR 91, IND 113

In 85 out of 100 simulated runs, leans Indiana Fever (avg. 86.6) over Toronto Tempo (avg. 85.9) — by under a point. The ranges overlap some — there's real uncertainty here.

TOR 83.9–87.9 IND 85.6–87.6 Overlap 2.04 pts Actual: TOR 91, IND 113

In 80 out of 100 simulated runs, leans Indiana Fever (avg. 86.6) over Toronto Tempo (avg. 85.9) — by under a point. The ranges overlap some — there's real uncertainty here.

TOR 84.1–87.7 IND 85.6–87.5 Overlap 1.88 pts Actual: TOR 91, IND 113

In 75 out of 100 simulated runs, leans Indiana Fever (avg. 86.6) over Toronto Tempo (avg. 85.9) — by under a point. The ranges overlap some — there's real uncertainty here.

Show the math
Model
Jamal (#7)
t-statistic
-3.60
p-value
4.40e-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.

Jordan
TOR 83.3–88.5 IND 84.5–88.6 Overlap 4.01 pts Actual: TOR 91, IND 113

In 95 out of 100 simulated runs, leans Indiana Fever (avg. 86.6) over Toronto Tempo (avg. 85.9) — by under a point. The ranges overlap almost entirely, so one game could go either way.

TOR 83.7–88.1 IND 84.8–88.3 Overlap 3.26 pts Actual: TOR 91, IND 113

In 90 out of 100 simulated runs, leans Indiana Fever (avg. 86.6) over Toronto Tempo (avg. 85.9) — by under a point. The ranges overlap almost entirely, so one game could go either way.

TOR 83.9–87.8 IND 85.0–88.1 Overlap 2.76 pts Actual: TOR 91, IND 113

In 85 out of 100 simulated runs, leans Indiana Fever (avg. 86.6) over Toronto Tempo (avg. 85.9) — by under a point. The ranges overlap some — there's real uncertainty here.

TOR 84.2–87.6 IND 85.2–87.9 Overlap 2.39 pts Actual: TOR 91, IND 113

In 80 out of 100 simulated runs, leans Indiana Fever (avg. 86.6) over Toronto Tempo (avg. 85.9) — by under a point. The ranges overlap some — there's real uncertainty here.

TOR 84.3–87.4 IND 85.3–87.8 Overlap 2.07 pts Actual: TOR 91, IND 113

In 75 out of 100 simulated runs, leans Indiana Fever (avg. 86.6) over Toronto Tempo (avg. 85.9) — by under a point. The ranges overlap some — there's real uncertainty here.

Show the math
Model
Jordan (#8)
t-statistic
-4.23
p-value
3.97e-05

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

Darren "Dimes" Lin Tol Warning
TOR 83.7–88.6 IND 84.2–88.1 Overlap 3.88 pts Actual: TOR 91, IND 113

In 95 out of 100 simulated runs, leans Indiana Fever (avg. 86.5) over Toronto Tempo (avg. 86.2) — by under a point. The ranges overlap almost entirely, so one game could go either way.

TOR 84.1–88.2 IND 85.1–88.0 Overlap 2.83 pts Actual: TOR 91, IND 113

In 90 out of 100 simulated runs, leans Indiana Fever (avg. 86.5) over Toronto Tempo (avg. 86.2) — by under a point. The ranges overlap some — there's real uncertainty here.

TOR 84.4–88.0 IND 85.4–87.8 Overlap 2.4 pts Actual: TOR 91, IND 113

In 85 out of 100 simulated runs, leans Indiana Fever (avg. 86.5) over Toronto Tempo (avg. 86.2) — by under a point. The ranges overlap some — there's real uncertainty here.

TOR 84.6–87.8 IND 85.5–87.8 Overlap 2.3 pts Actual: TOR 91, IND 113

In 80 out of 100 simulated runs, leans Indiana Fever (avg. 86.5) over Toronto Tempo (avg. 86.2) — by under a point. The ranges overlap some — there's real uncertainty here.

TOR 84.7–87.6 IND 85.7–87.7 Overlap 1.94 pts Actual: TOR 91, IND 113

In 75 out of 100 simulated runs, leans Indiana Fever (avg. 86.5) over Toronto Tempo (avg. 86.2) — by under a point. The ranges overlap some — there's real uncertainty here.

Show the math
Model
Darren "Dimes" Lin (#9)
t-statistic
-2.18
p-value
3.08e-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.

Maya Jefferson
TOR 83.2–88.6 IND 84.2–88.7 Overlap 4.4 pts Actual: TOR 91, IND 113

In 95 out of 100 simulated runs, leans Indiana Fever (avg. 86.4) over Toronto Tempo (avg. 85.9) — by under a point. The ranges overlap almost entirely, so one game could go either way.

TOR 83.7–88.1 IND 84.5–88.3 Overlap 3.61 pts Actual: TOR 91, IND 113

In 90 out of 100 simulated runs, leans Indiana Fever (avg. 86.4) over Toronto Tempo (avg. 85.9) — by under a point. The ranges overlap almost entirely, so one game could go either way.

TOR 83.9–87.9 IND 84.8–88.1 Overlap 3.09 pts Actual: TOR 91, IND 113

In 85 out of 100 simulated runs, leans Indiana Fever (avg. 86.4) over Toronto Tempo (avg. 85.9) — by under a point. The ranges overlap almost entirely, so one game could go either way.

TOR 84.1–87.6 IND 85.0–87.9 Overlap 2.69 pts Actual: TOR 91, IND 113

In 80 out of 100 simulated runs, leans Indiana Fever (avg. 86.4) over Toronto Tempo (avg. 85.9) — by under a point. The ranges overlap some — there's real uncertainty here.

TOR 84.3–87.5 IND 85.1–87.7 Overlap 2.37 pts Actual: TOR 91, IND 113

In 75 out of 100 simulated runs, leans Indiana Fever (avg. 86.4) over Toronto Tempo (avg. 85.9) — by under a point. The ranges overlap some — there's real uncertainty here.

Show the math
Model
Maya Jefferson (#10)
t-statistic
-3.08
p-value
2.45e-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.

Lexi
TOR 83.5–88.2 IND 84.6–88.3 Overlap 3.64 pts Actual: TOR 91, IND 113

In 95 out of 100 simulated runs, leans Indiana Fever (avg. 86.4) over Toronto Tempo (avg. 85.9) — by under a point. The ranges overlap almost entirely, so one game could go either way.

TOR 83.9–87.8 IND 84.9–88.0 Overlap 2.97 pts Actual: TOR 91, IND 113

In 90 out of 100 simulated runs, leans Indiana Fever (avg. 86.4) over Toronto Tempo (avg. 85.9) — by under a point. The ranges overlap some — there's real uncertainty here.

TOR 84.2–87.6 IND 85.0–87.8 Overlap 2.53 pts Actual: TOR 91, IND 113

In 85 out of 100 simulated runs, leans Indiana Fever (avg. 86.4) over Toronto Tempo (avg. 85.9) — by under a point. The ranges overlap some — there's real uncertainty here.

TOR 84.3–87.4 IND 85.2–87.6 Overlap 2.19 pts Actual: TOR 91, IND 113

In 80 out of 100 simulated runs, leans Indiana Fever (avg. 86.4) over Toronto Tempo (avg. 85.9) — by under a point. The ranges overlap some — there's real uncertainty here.

TOR 84.5–87.2 IND 85.3–87.5 Overlap 1.91 pts Actual: TOR 91, IND 113

In 75 out of 100 simulated runs, leans Indiana Fever (avg. 86.4) over Toronto Tempo (avg. 85.9) — by under a point. The ranges overlap some — there's real uncertainty here.

Show the math
Model
Lexi (#11)
t-statistic
-3.72
p-value
2.85e-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.

Coach Sarah Watanabe
TOR 83.1–87.7 IND 83.3–88.3 Overlap 4.38 pts Actual: TOR 91, IND 113

In 95 out of 100 simulated runs, leans Indiana Fever (avg. 85.8) over Toronto Tempo (avg. 85.4) — by under a point. The ranges overlap almost entirely, so one game could go either way.

TOR 83.5–87.3 IND 83.7–87.9 Overlap 3.61 pts Actual: TOR 91, IND 113

In 90 out of 100 simulated runs, leans Indiana Fever (avg. 85.8) over Toronto Tempo (avg. 85.4) — by under a point. The ranges overlap almost entirely, so one game could go either way.

TOR 83.7–87.1 IND 84.0–87.6 Overlap 3.11 pts Actual: TOR 91, IND 113

In 85 out of 100 simulated runs, leans Indiana Fever (avg. 85.8) over Toronto Tempo (avg. 85.4) — by under a point. The ranges overlap almost entirely, so one game could go either way.

TOR 83.9–86.9 IND 84.2–87.4 Overlap 2.73 pts Actual: TOR 91, IND 113

In 80 out of 100 simulated runs, leans Indiana Fever (avg. 85.8) over Toronto Tempo (avg. 85.4) — by under a point. The ranges overlap some — there's real uncertainty here.

TOR 84.0–86.7 IND 84.3–87.2 Overlap 2.41 pts Actual: TOR 91, IND 113

In 75 out of 100 simulated runs, leans Indiana Fever (avg. 85.8) over Toronto Tempo (avg. 85.4) — by under a point. The ranges overlap some — there's real uncertainty here.

Show the math
Model
Coach Sarah Watanabe (#12)
t-statistic
-2.44
p-value
1.57e-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 — TOR at IND — Actual: TOR 91, IND 113
Level TOR range IND range Overlap Tol Warning Actual landed in range?
95% 83.5–88.8 84.3–88.7 4.39 pts No
90% 84.0–88.4 84.7–88.4 3.68 pts No
85% 84.2–88.1 84.9–88.1 3.22 pts No
80% 84.5–87.9 85.1–88.0 2.84 pts No
75% 84.6–87.7 85.2–87.8 2.51 pts No
Reg — TOR at IND — Actual: TOR 91, IND 113
Level TOR range IND range Overlap Tol Warning Actual landed in range?
95% 83.2–88.2 83.7–88.7 4.58 pts Yes No
90% 83.3–87.9 84.1–88.3 3.84 pts Yes No
85% 83.9–87.7 84.3–88.0 3.4 pts Yes No
80% 84.1–87.6 84.5–87.8 3.04 pts Yes No
75% 84.4–87.2 84.7–87.7 2.55 pts Yes No
Dr. Wallace — TOR at IND — Actual: TOR 91, IND 113
Level TOR range IND range Overlap Tol Warning Actual landed in range?
95% 83.7–88.5 84.5–88.1 3.54 pts Yes No
90% 84.1–88.1 85.2–87.9 2.72 pts Yes No
85% 84.4–87.9 85.2–87.8 2.57 pts Yes No
80% 84.6–87.7 85.3–87.4 2.1 pts Yes No
75% 84.7–87.5 85.4–87.4 1.97 pts Yes No
Kevin — TOR at IND — Actual: TOR 91, IND 113
Level TOR range IND range Overlap Tol Warning Actual landed in range?
95% 83.6–88.6 84.1–89.0 4.53 pts No
90% 84.0–88.2 84.5–88.6 3.73 pts No
85% 84.2–88.0 84.8–88.4 3.2 pts No
80% 84.5–87.8 85.0–88.2 2.8 pts No
75% 84.6–87.6 85.1–88.0 2.47 pts No
Dr. Lila Shah — TOR at IND — Actual: TOR 91, IND 113
Level TOR range IND range Overlap Tol Warning Actual landed in range?
95% 82.3–88.6 84.2–88.6 4.39 pts No
90% 82.8–88.1 84.6–88.3 3.53 pts No
85% 83.2–87.8 84.8–88.0 2.97 pts No
80% 83.4–87.5 85.0–87.9 2.54 pts No
75% 83.6–87.3 85.1–87.7 2.18 pts No
Ice — TOR at IND — Actual: TOR 91, IND 113
Level TOR range IND range Overlap Tol Warning Actual landed in range?
95% 83.3–88.6 84.1–88.8 4.57 pts No
90% 83.7–88.2 84.4–88.4 3.75 pts No
85% 84.0–87.9 84.7–88.2 3.23 pts No
80% 84.2–87.7 84.9–88.0 2.82 pts No
75% 84.4–87.5 85.0–87.8 2.48 pts No
Jamal — TOR at IND — Actual: TOR 91, IND 113
Level TOR range IND range Overlap Tol Warning Actual landed in range?
95% 82.9–89.0 85.0–88.4 3.42 pts Yes No
90% 83.4–88.5 85.3–88.0 2.67 pts Yes No
85% 83.7–88.2 85.4–87.9 2.51 pts Yes No
80% 83.9–87.9 85.6–87.6 2.04 pts Yes No
75% 84.1–87.7 85.6–87.5 1.88 pts Yes No
Jordan — TOR at IND — Actual: TOR 91, IND 113
Level TOR range IND range Overlap Tol Warning Actual landed in range?
95% 83.3–88.5 84.5–88.6 4.01 pts No
90% 83.7–88.1 84.8–88.3 3.26 pts No
85% 83.9–87.8 85.0–88.1 2.76 pts No
80% 84.2–87.6 85.2–87.9 2.39 pts No
75% 84.3–87.4 85.3–87.8 2.07 pts No
Darren "Dimes" Lin — TOR at IND — Actual: TOR 91, IND 113
Level TOR range IND range Overlap Tol Warning Actual landed in range?
95% 83.7–88.6 84.2–88.1 3.88 pts Yes No
90% 84.1–88.2 85.1–88.0 2.83 pts Yes No
85% 84.4–88.0 85.4–87.8 2.4 pts Yes No
80% 84.6–87.8 85.5–87.8 2.3 pts Yes No
75% 84.7–87.6 85.7–87.7 1.94 pts Yes No
Maya Jefferson — TOR at IND — Actual: TOR 91, IND 113
Level TOR range IND range Overlap Tol Warning Actual landed in range?
95% 83.2–88.6 84.2–88.7 4.4 pts No
90% 83.7–88.1 84.5–88.3 3.61 pts No
85% 83.9–87.9 84.8–88.1 3.09 pts No
80% 84.1–87.6 85.0–87.9 2.69 pts No
75% 84.3–87.5 85.1–87.7 2.37 pts No
Lexi — TOR at IND — Actual: TOR 91, IND 113
Level TOR range IND range Overlap Tol Warning Actual landed in range?
95% 83.5–88.2 84.6–88.3 3.64 pts No
90% 83.9–87.8 84.9–88.0 2.97 pts No
85% 84.2–87.6 85.0–87.8 2.53 pts No
80% 84.3–87.4 85.2–87.6 2.19 pts No
75% 84.5–87.2 85.3–87.5 1.91 pts No
Coach Sarah Watanabe — TOR at IND — Actual: TOR 91, IND 113
Level TOR range IND range Overlap Tol Warning Actual landed in range?
95% 83.1–87.7 83.3–88.3 4.38 pts No
90% 83.5–87.3 83.7–87.9 3.61 pts No
85% 83.7–87.1 84.0–87.6 3.11 pts No
80% 83.9–86.9 84.2–87.4 2.73 pts No
75% 84.0–86.7 84.3–87.2 2.41 pts No