Washington Mystics at Toronto Tempo
WSH
6-7
TOR
7-8
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
ashington Mystics visit Toronto Tempo Tuesday at 7/14 - 7:00 PM EDT. WSH is 3-4 in their last 7. TOR has lost 3 straight (3-4 in their last 7).
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
WSH
Away
Stat
TOR
Home
44
FG %
44
Season series
WSH leads series 2-0
WSH68@TOR65TOR85@WSH86WSH@TORtodayTOR@WSHupcomingScouting report
WSH @ TOR
Model edge vs market
Lean onlyMarket
—
Model
TOR -2.7
Edge
—
Market
—
Model
On the roadmap
Edge
—
Market
—
Model
TOR
Edge
—
Model spread derived from 21-day power-rank delta · not a true point-spread model. Total + ML model wires roadmap. Bet responsibly · 21+
Tale of the tape
6-7
Record
7-8
#5
Conf rank
#4
-4.3
Pt diff
-2.5
W1
Streak
L3
4-6
Last 10
4-6
39.0
Power score
41.7
#10
Power rank
#9
93.2
Sched ahead
61.0
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 · WSH favored
Elo Static
fallback · inputs missing
50.0%
P(TOR win)
33%
weight
Elo Recent
fallback · inputs missing
50.0%
P(TOR win)
31%
weight
Pace Efficiency
fallback · inputs missing
50.0%
P(TOR 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
TOR vs WSH.
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.
120
projections · 0 high confidence
Points
- Brittney SykesTOR18.1± 10.6medium
- Marina MabreyTOR17.1± 8.5medium
- Sonia CitronWSH16.4± 10.2low
Rebounds
- Kiki IriafenWSH8.7± 6.2low
- Shakira AustinWSH8.1± 4.2low
- Isabelle HarrisonTOR5.2± 2.9low
Assists
- Marina MabreyTOR4.0± 3.0medium
- Sonia CitronWSH3.6± 2.8low
- Julie AllemandTOR3.4± 2.2low
Blocks
- Shakira AustinWSH1.4± 1.4low
- Nyara SaballyTOR1.1± 1.0low
- Teonni KeyTOR0.7± 0.9medium
Steals
- Isabelle HarrisonTOR1.8± 2.2low
- Julie AllemandTOR1.6± 1.3low
- Laura JuskaiteTOR1.4± 1.6medium
Projections recompute every 30 minutes · prop lines plug in once sportsbook ingest lands
Matchup · 2026
Team rate stats vs league
wehoop
WSH
league avg
TOR
44.7%
FG%
44.6
43.5%
30.0%
3PT %
33.4
▶33.6%
81.9
PPG
85.7
▶88.8
18.8
Assists / G
18.0
18.9
16.0
Turnovers / G
13.0
▶12.7