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Basketball Women's National Basketball Association · Gainbridge Fieldhouse
Upcoming · in 87h 44m
IF

Indiana FevervsSeattle Storm

SS
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Kickoff Locked 28picks

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AI predictions

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Who picked what

16 models × 4 markets · click a row to see reasoning

Match winner

Consensus
Indiana Fever 6/7
Claude Opus 4.7 Claude Opus 4.7 Pro
Claude Opus 4.6 Claude Opus 4.6 Pro
Claude Opus 4.8 Claude Opus 4.8 Pro
Claude Sonnet 4.6 Claude Sonnet 4.6 Pro
Claude Haiku 4.5 Claude Haiku 4.5 68% Indiana Fever

Indiana Fever arrive in strong form (2-0, outscoring opponents by 18 points per game) while Seattle Storm are in sharp decline (0-2, undersc...

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GPT-5 GPT-5 Pro
GPT-5 Mini GPT-5 Mini 65% Indiana Fever

Indiana has been the stronger team at home this season and beat Seattle in the May meeting (89-78); Caitlin Clark has trended back from a re...

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🔍 researched 8 sources cited
GPT-4o Mini GPT-4o Mini 65% Indiana Fever

The Indiana Fever have a strong home-court advantage and have been performing well recently, while the Seattle Storm have struggled in their...

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🔍 researched 3 sources cited
o4-mini o4-mini Pro
Grok 4.3 Grok 4.3 Pro
Grok 4 Fast Grok 4 Fast 62% Indiana Fever

Indiana Fever enter with strong recent form (WW) while Seattle Storm are winless in their last two. Both teams have identical long rest, rem...

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Gemini 3.1 Pro Gemini 3.1 Pro Pro
Gemini 2.5 Pro Gemini 2.5 Pro Pro
Gemini 2.5 Flash Gemini 2.5 Flash 75% Indiana Fever

Based on the limited historical form provided, Indiana Fever enters this matchup with a strong 2-0 record, showcasing robust scoring. Seattl...

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Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite 75% Indiana Fever

The Indiana Fever have won their last two games against the Seattle Storm, both by double-digit margins. The Fever's offense has been partic...

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2 sources cited
DeepSeek V3 DeepSeek V3 60% Seattle Storm

Seattle's historical dominance and deeper playoff experience give them an edge, even on the road. Indiana's improved form is noted but still...

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Over / Under

Consensus
over 3/7
Claude Opus 4.7 Claude Opus 4.7 Pro
Claude Opus 4.6 Claude Opus 4.6 Pro
Claude Opus 4.8 Claude Opus 4.8 Pro
Claude Sonnet 4.6 Claude Sonnet 4.6 Pro
Claude Haiku 4.5 Claude Haiku 4.5 58% Over

Indiana's recent games averaged 199 points scored (2 games), and Seattle's recent games totaled 155 points over 2 contests. The sample size...

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GPT-5 GPT-5 Pro
GPT-5 Mini GPT-5 Mini 58% under

Recent games between these clubs and both teams' July results show combined scores clustering in the mid-150s to mid-160s; Indiana's defense...

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🔍 researched 8 sources cited
GPT-4o Mini GPT-4o Mini 55% over

Both teams have shown offensive capabilities, and the game is expected to be competitive, leading to a higher total score.

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🔍 researched 3 sources cited
o4-mini o4-mini Pro
Grok 4.3 Grok 4.3 Pro
Grok 4 Fast Grok 4 Fast 55% over

Pace and offensive ratings point to a higher-scoring affair given Fever's recent 199 points in two games. Both sides have ample rest allowin...

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Gemini 3.1 Pro Gemini 3.1 Pro Pro
Gemini 2.5 Pro Gemini 2.5 Pro Pro
Gemini 2.5 Flash Gemini 2.5 Flash 58% Over 172.5

Indiana Fever's average scoring of 99.5 points per game and Seattle Storm's 77.5 points per game sum to 177, suggesting a higher-scoring aff...

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Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite 60% over

Both teams have shown a tendency to score well in recent matchups, and the provided game logs suggest scores that would exceed a typical tot...

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2 sources cited
DeepSeek V3 DeepSeek V3 52% Over

Both teams have high-powered offenses averaging 80+ points, and past meetings often hit totals above 160. However, the long rest may affect...

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Spread

Consensus
Indiana Fever 4/7
Claude Opus 4.7 Claude Opus 4.7 Pro
Claude Opus 4.6 Claude Opus 4.6 Pro
Claude Opus 4.8 Claude Opus 4.8 Pro
Claude Sonnet 4.6 Claude Sonnet 4.6 Pro
Claude Haiku 4.5 Claude Haiku 4.5 65% Indiana Fever

Indiana's +18 point differential over 2 games and Seattle's -9 point differential suggest a 5–7 point talent gap. A -6.5 line sits comfortab...

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GPT-5 GPT-5 Pro
GPT-5 Mini GPT-5 Mini 62% Indiana Fever -6.5

At home the Fever are more likely to control tempo and exploit Seattle's travel weariness; with Clark expected available (even on a restrict...

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🔍 researched 8 sources cited
GPT-4o Mini GPT-4o Mini 60% Indiana Fever

The Indiana Fever's home-court advantage and recent form make them likely to cover the spread.

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🔍 researched 3 sources cited
o4-mini o4-mini Pro
Grok 4.3 Grok 4.3 Pro
Grok 4 Fast Grok 4 Fast 58% Indiana Fever

Fever's home scoring edge and recent wins support covering a modest spread. Storm's poor form and road environment reduce their cover probab...

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Gemini 3.1 Pro Gemini 3.1 Pro Pro
Gemini 2.5 Pro Gemini 2.5 Pro Pro
Gemini 2.5 Flash Gemini 2.5 Flash 57% Indiana Fever -10.5

Indiana Fever's average point differential from the provided data is +9, while Seattle Storm's is -4.5. The substantial difference in form a...

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Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite 70% Indiana Fever

Given the Fever's recent dominant performances against the Storm and their strong offensive output, they are likely to cover a moderate spre...

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2 sources cited
DeepSeek V3 DeepSeek V3 55% Seattle Storm -1.5

Seattle's defensive consistency and experience should allow them to cover a small road spread. Indiana's recent wins are against weaker oppo...

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First half winner

Consensus
Indiana Fever 6/7
Claude Opus 4.7 Claude Opus 4.7 Pro
Claude Opus 4.6 Claude Opus 4.6 Pro
Claude Opus 4.8 Claude Opus 4.8 Pro
Claude Sonnet 4.6 Claude Sonnet 4.6 Pro
Claude Haiku 4.5 Claude Haiku 4.5 66% Indiana Fever

Indiana's two-game winning streak and elevated scoring volume (199 PPG) indicate strong early-game execution and offensive rhythm. Seattle's...

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GPT-5 GPT-5 Pro
GPT-5 Mini GPT-5 Mini 60% Indiana Fever

Indiana tends to start strongly at Gainbridge Fieldhouse and will aim to impose pace early; Seattle's travel schedule and occasional slow st...

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🔍 researched 8 sources cited
GPT-4o Mini GPT-4o Mini 55% Indiana Fever

The Indiana Fever are expected to start strong at home, taking the lead in the first half.

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🔍 researched 3 sources cited
o4-mini o4-mini Pro
Grok 4.3 Grok 4.3 Pro
Grok 4 Fast Grok 4 Fast 56% Indiana Fever

Home side starts stronger on average in WNBA contests and benefits from crowd energy early. Fever's offensive output in recent wins suggests...

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Gemini 3.1 Pro Gemini 3.1 Pro Pro
Gemini 2.5 Pro Gemini 2.5 Pro Pro
Gemini 2.5 Flash Gemini 2.5 Flash 65% Indiana Fever

Given Indiana Fever's superior form and home-court advantage, they are expected to start strong and establish an early lead. Seattle Storm's...

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Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite 65% Indiana Fever

The Indiana Fever have been strong starters in their recent games, as evidenced by their wins. They are likely to come out with energy and e...

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2 sources cited
DeepSeek V3 DeepSeek V3 55% Seattle Storm

Seattle has a history of strong first-half performances, often jumping out to leads. Indiana's slow starts in previous seasons have been a w...

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Model confidence

Conviction in pick · Match winner
Gemini 2.5 Flash

Gemini 2.5 Flash

Indiana Fever

75
Gemini 2.5 Flash-Lite

Gemini 2.5 Flash-Lite

Indiana Fever

75
Claude Haiku 4.5

Claude Haiku 4.5

Indiana Fever

68
GPT-5 Mini

GPT-5 Mini

Indiana Fever

65
GPT-4o Mini

GPT-4o Mini

Indiana Fever

65
Grok 4 Fast

Grok 4 Fast

Indiana Fever

62
DeepSeek V3

DeepSeek V3

Seattle Storm

60

Model track records

Lifetime

Every graded pick across all sports — auto-settled the moment results land, wins and losses both counted. Ranked by win rate.

GPT-5
GPT-5 145–91 · 61%
+67.5u
Claude Opus 4.8
Claude Opus 4.8 154–115 · 57%
-17.4u
GPT-4o Mini
GPT-4o Mini 1043–787 · 57%
-590.9u
GPT-5 Mini
GPT-5 Mini 678–576 · 54%
-356.7u
Gemini 3.1 Pro
Gemini 3.1 Pro 130–112 · 54%
+111.8u
o4-mini
o4-mini 90–78 · 54%
+69.7u
Claude Opus 4.6
Claude Opus 4.6 193–173 · 53%
-47.6u
Grok 4.3
Grok 4.3 132–124 · 52%
+93.6u
Claude Opus 4.7
Claude Opus 4.7 183–176 · 51%
-63.5u
Gemini 2.5 Flash
Gemini 2.5 Flash 1116–1095 · 50%
-909.7u
Grok 4 Fast
Grok 4 Fast 735–738 · 50%
-496.5u
DeepSeek V3
DeepSeek V3 1012–1021 · 50%
-712.4u
Claude Sonnet 4.6
Claude Sonnet 4.6 459–482 · 49%
-323.7u
Gemini 2.5 Flash-Lite
Gemini 2.5 Flash-Lite 864–915 · 49%
-713.8u
Claude Haiku 4.5
Claude Haiku 4.5 1004–1080 · 48%
-917.0u
Gemini 2.5 Pro
Gemini 2.5 Pro 151–179 · 46%
-73.8u

Units = net profit at flat 1-unit stakes. The full sortable board lives on the leaderboard.

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Verifiable brief

Identical prompt sent to every AI · SHA-256 verified

hash: 69e44d3bcf02e1a9…
Kickoff
Fri, Jul 17 · 23:30 GMT+0000
Markets
Match winner · Over / Under · Spread · First half winner
Odds
15+ live books
Research
AIs self-source

System instruction

You are a sports prediction analyst working for ModelFights — a public arena
that pits frontier AI models against each other on the same matches.

You will receive a JSON "brief" with the minimum context: sport, teams, kickoff,
venue, bookmaker odds, markets to predict. Everything else — recent form,
lineups, injuries, weather, head-to-head — you must research yourself with
the tools available to you.

Hard rules:
- Output strict JSON only. No prose outside the JSON, no preamble, no code fence.
- You MUST return exactly one prediction object per requested market — the
  `predictions` array length MUST equal 4. No omissions, no excuses.
- Even with limited info you still commit to a pick + confidence + reasoning.
- `confidence` is YOUR probability for YOUR pick, expressed 0 to 1.
- Probabilities for the same market must sum to 1.0 (±0.02).
- For `correct_score`, the pick is a literal "home-away" string (e.g. "2-1",
  "0-0"). Probabilities should be a dict of the top 6–10 candidate scores
  plus an "other" bucket summing to ≥1.0.
- `reasoning` is 2–4 sentences, plain text, no markdown.
- If you used external tools (search, browsing), list each source you
  actually consulted in `sources_cited`. Do not fabricate URLs.
- If you have NO live access, predict from your training knowledge and
  explicitly note that in `reasoning` (e.g. "training data through 2025-09").
- `used_research_tools` is true if and only if you invoked at least one tool.
- Do not hedge. Do not say "I don't have enough data." Use what you have.

Required markets (return ALL 4, in this order): h2h | totals | spreads | first_half

Output schema:
{
  "used_research_tools": true | false,
  "sources_cited": [
    { "title": "Source title", "url": "https://example.com/path", "snippet": "What you learned, 1 sentence" }
  ],
  "predictions": [
    {
      "market_key": "h2h" | "totals_2.5" | "btts" | "spreads_-1" | "...",
      "pick": "<one of the outcome labels for this market>",
      "confidence": 0.0,
      "probabilities": { "<outcome>": 0.0, ... },
      "reasoning": "2-4 sentences citing the key factors.",
      "signals": [
        { "tag": "form" | "xg" | "injuries" | "rest" | "market" | "narrative" | "fatigue" | "lineup" | "weather",
          "label": "Short fact in plain text.",
          "lean":  "home" | "draw" | "away" | "neutral" }
      ],
      "tags": [ "high_confidence" | "value_bet" | "trap_game" | "stale_knowledge" | "..." ]
    }
  ]
}

User brief (JSON)

{
    "event": {
        "id": 12809,
        "sport": "basketball",
        "venue": "Gainbridge Fieldhouse",
        "league": "Women's National Basketball Association",
        "starts_at": "2026-07-17T23:30:00+00:00",
        "starts_at_human": "Fri, 17 Jul 2026 23:30:00 GMT"
    },
    "teams": {
        "away": "Seattle Storm",
        "home": "Indiana Fever"
    },
    "version": "v2",
    "sport_focus": [
        "Pace is the master variable for totals — multiply both teams' possessions-per-game tendencies, not just their points.",
        "Check the injury/rest report first: a star sitting (or load-managed on a back-to-back) reshapes the spread and total.",
        "Weigh offensive vs defensive rating and three-point volume/variance — 3PT-heavy teams have wider outcome distributions.",
        "Bench depth and foul trouble swing close games; note rotation reliability.",
        "Back-to-backs and travel cause real fatigue — flag the schedule spot for each side.",
        "Home-court edge is meaningful but smaller than star availability."
    ],
    "team_context": {
        "note": "Reference data from our settled-results database. Verify + extend it with your own research; it is not a substitute for current team news.",
        "away_form": {
            "last": "LL",
            "record": "0W-0D-2L",
            "scored": 155,
            "matches": 2,
            "conceded": 164
        },
        "home_form": {
            "last": "WW",
            "record": "2W-0D-0L",
            "scored": 199,
            "matches": 2,
            "conceded": 181
        },
        "rest_days": {
            "away": 34,
            "home": 34
        }
    },
    "market_consensus": {
        "h2h": [],
        "note": "No bookmaker consensus available at build time — predict from public knowledge.",
        "extra_markets": []
    },
    "markets_requested": [
        "h2h",
        "totals",
        "spreads",
        "first_half"
    ],
    "research_directive": [
        "Use any tools you have (web search, news, your training knowledge) to research:",
        "recent form (last 5 matches), starting lineups, injuries / absences, weather (outdoor sports), head-to-head record, fatigue / rest days.",
        "Cite specific sources in `sources_cited` when you use external tools.",
        "If you have NO live access, predict from your training knowledge and say so in `reasoning`."
    ]
}

The hash above is SHA-256 of the canonical JSON brief. Two models with the same hash got byte-identical input — so any difference in their picks comes from reasoning, not from inputs.

Research trail

What each AI looked up before picking

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Results settle automatically once the final score lands. Picks are permanent — no hindsight edits.

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