Seattle Storm at Indiana Fever
SEA
3-13
IND
9-6
Verdict
Pass · no edge tonight.
The model doesn't see daylight against the posted line on this game. We don't surface negative-EV picks; check the drill-down for sub-model context.
Preview · WNBA
eattle Storm visit Indiana Fever Friday at 7/17 - 7:30 PM EDT. IND is 5-3 in their last 8.
The market hasn't shipped a line worth tagging key numbers on yet — check back closer to first pitch.
ByTheOnemodel/auto-generated · live odds + scouting data/refreshes with the page
Team stats
SEA
Away
Stat
IND
Home
41
FG %
45
Season series
IND leads series 1-0
SEA78@IND89SEA@INDtodayIND@SEAupcomingScouting report
SEA @ IND
Tale of the tape
3-13
Record
9-6
#8
Conf rank
#3
-6.6
Pt diff
+4.1
L9
Streak
L1
1-9
Last 10
6-4
Composite signals from ESPN standings + 21-day power rolling. NBA pace / ORtg / DRtg from ESPN core team-stats; NFL yards-per-game from nflverse aggregation. Park factors, weather, KenPom-class metrics still on the roadmap.
Drill down
Sub-model tables · ensemble breakdown · last meeting · book shop · player props
Drill down
Sub-model tables · ensemble breakdown · last meeting · book shop · player props
Model ensemble · how the prediction is built
3 sub-models, blended.
Each sub-model uses a different rating substrate. Bayesian model averaging weights them by rolling Brier score so the ensemble inherits each model's strengths. Disagreement flags games where the sub-models don't see eye-to-eye — lower confidence, wider band.
43.9%
ensemble · SEA favored
Elo Static
fallback · inputs missing
50.0%
P(IND win)
33%
weight
Elo Recent
fallback · inputs missing
50.0%
P(IND win)
31%
weight
Pace Efficiency
fallback · inputs missing
50.0%
P(IND win)
35%
weight
Disagreement
0.00 pp
weighted σ across sub-models
Confidence
100% · high
maps from disagreement
Substrate count
0 / 3 active
ones with full inputs tonight
Weights recalibrated nightly on a 90-day rolling window with strict point-in-time correctness — no model gets credit for a game it hasn't seen. Headline % is Platt-scaled per league; sub-model rows show raw BMA inputs.
Player projections
IND vs SEA.
Per-player stat projections built from a recency-weighted blend of the last ten games, season average, and matchup context. Confidence reflects sample size and stability — the top of each list is who to watch.
115
projections · 0 high confidence
Points
- Kelsey MitchellIND20.2± 5.2medium
- Caitlin ClarkIND19.4± 8.8medium
- Aliyah BostonIND17.7± 7.4medium
Rebounds
- Aliyah BostonIND9.0± 3.4medium
- Dominique MalongaSEA7.1± 3.7low
- Flau'jae JohnsonSEA5.1± 4.0medium
Assists
- Caitlin ClarkIND7.7± 3.2medium
- Natisha HiedemanSEA4.4± 2.0medium
- Jade MelbourneSEA3.5± 2.8medium
Blocks
- Aliyah BostonIND1.4± 1.6medium
- Dominique MalongaSEA1.1± 1.6low
- Makayla TimpsonIND0.8± 1.1medium
Steals
- Jordan HorstonSEA1.3± 1.7medium
- Natisha HiedemanSEA1.2± 1.3medium
- Aliyah BostonIND1.0± 0.9medium
Projections recompute every 30 minutes · prop lines plug in once sportsbook ingest lands
Matchup · 2026
Team rate stats vs league
wehoop
SEA
league avg
IND
41.1%
FG%
44.6
▶46.2%
33.4%
3PT %
33.4
▶34.3%
77.2
PPG
85.7
▶92.2
18.0
Assists / G
18.0
▶20.9
14.1
Turnovers / G
13.0
15.2