MLBTop 2
CLE 0@MIL 0
-4.2pphigh
MLBTop 4
TOR 2@BOS 0
-42.9pplow
LAST NIGHT · MLBK. Bradish (BAL): 7.2 IP · 12 K · 1 ER in a win over SEA — Standout Index 94
LAST NIGHT · MLBK. Stowers (MIA): 4 H · 2 HR · 5 RBI in a win over PHI — Standout Index 93
LAST NIGHT · MLBN. McLean (NYM): 7.0 IP · 9 K in a win over CIN — Standout Index 91
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Portland Fire at Seattle Storm

POR
POR

POR

8-9

PregameSat, 9:00 PM EDT
SEA
SEA

SEA

3-13

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.

WNBASat, Jul 47/4 - 9:00 PM EDTSeriesPOR leads series 1-0

Preview · WNBA

ortland Fire visit Seattle Storm Saturday at 7/4 - 9:00 PM EDT.

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

Updated 0s ago

Season series

POR leads series 1-0

Jun 18SEASEA89@PORPOR94
Jul 5PORPOR@SEASEAtoday
Aug 9SEASEA@PORPORupcoming
Aug 15PORPOR@SEASEAupcoming

Scouting report

POR @ SEA

7/4 - 9:00 PM EDT
StorylineSEA lost 9 in a row.

Tale of the tape

PORmetricSEA

8-9

Record

3-13

#5

Conf rank

#8

-5.9

Pt diff

-6.6

W1

Streak

L9

4-6

Last 10

1-9

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.

Team stats

POR

Away

Stat

SEA

Home

45

FG %

41

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.

43.8%

ensemble · POR favored

  • Elo Static

    fallback · inputs missing

    50.0%

    P(SEA win)

    33%

    weight

  • Elo Recent

    fallback · inputs missing

    50.0%

    P(SEA win)

    31%

    weight

  • Pace Efficiency

    fallback · inputs missing

    50.0%

    P(SEA 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

SEA vs POR.

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

  • Natisha HiedemanSEA
    15.7± 6.3medium
  • Dominique MalongaSEA
    14.6± 9.9low
  • Carla LeitePOR
    13.6± 5.7medium

Rebounds

  • Dominique MalongaSEA
    7.1± 3.7low
  • Emily EngstlerPOR
    5.2± 3.4medium
  • Flau'jae JohnsonSEA
    5.1± 4.0medium

Assists

  • Carla LeitePOR
    5.9± 3.6medium
  • Natisha HiedemanSEA
    4.4± 2.0medium
  • Jade MelbourneSEA
    3.5± 2.8medium

Blocks

  • Emily EngstlerPOR
    1.9± 0.9medium
  • Dominique MalongaSEA
    1.1± 1.6low
  • Jordan HorstonSEA
    0.8± 1.5medium

Steals

  • Bridget CarletonPOR
    1.4± 1.2medium
  • Emily EngstlerPOR
    1.3± 1.4medium
  • Jordan HorstonSEA
    1.3± 1.7medium

Projections recompute every 30 minutes · prop lines plug in once sportsbook ingest lands

Matchup · 2026

Team rate stats vs league

wehoop

POR

POR

league avg

SEA

SEA

44.6%

FG%

◀

44.5

41.1%

34.1%

3PT %

◀

33.3

33.4%

82.2

PPG

◀

85.5

77.2

19.8

Assists / G

◀

18.0

18.0

14.8

Turnovers / G

13.0

▶

14.1

Data via ESPN · wehoop