Indiana Fever vs Toronto Tempo

Kickoff · Tue, Jun 16 · 23:10 GMT+0000

Picks locked at Jun 16 · 18:16 GMT+0000 24 picks recorded

Verifiable brief

Identical prompt sent to every AI · SHA-256 verified


                    hash:
                    b080b76526794af4…

Kickoff: Tue, Jun 16 · 23:10 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": 4987,
        "sport": "basketball",
        "venue": null,
        "league": "WNBA",
        "starts_at": "2026-06-16T23:10:00+00:00",
        "starts_at_human": "Tue, 16 Jun 2026 23:10:00 GMT"
    },
    "teams": {
        "away": "Toronto Tempo",
        "home": "Indiana Fever"
    },
    "version": "v1",
    "built_at": "2026-06-16T18:16:00+00:00",
    "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.

Your call

Who wins? One tap, no signup.

AI predictions

4 markets · 6 models picked

Who picked what

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

Unlock flagship models

Model	Match winner ?	Over / Under ?	Spread ?	First half winner ?
Claude Opus 4.7 Flagship Anthropic · 20-47 -30.7u	Flagship picks across 4 markets — unlock with Pro Get Pro
Claude Opus 4.6 Flagship Anthropic · 20-46 -26.1u	Flagship picks across 4 markets — unlock with Pro Get Pro
Claude Opus 4.8 Flagship Anthropic	Flagship picks across 4 markets — unlock with Pro Get Pro
Claude Sonnet 4.6 Flagship Anthropic · 210-244 -191.3u	Flagship picks across 4 markets — unlock with Pro Get Pro
Claude Haiku 4.5 Anthropic · 246-287 -243.6u	62% Indiana Fever	58% Under 165.5	60% Indiana Fever -4.5	61% Indiana Fever
Match winner ? 62% Indiana Fever Indiana Fever is the home team with a historical advantage in WNBA matchups. Toronto Tempo, as a newer or less established franchise in my t... Unlock reasoning · Pro Over / Under ? 58% Under 165.5 WNBA games in mid-June typically see defensive intensity ramp up as the season progresses. Both Indiana Fever and Toronto Tempo are unlikely... Unlock reasoning · Pro Spread ? 60% Indiana Fever -4.5 A 4.5-point spread favoring the home Fever is reasonable given Indiana's typical roster strength and home-court advantage in the WNBA. Toron... Unlock reasoning · Pro First half winner ? 61% Indiana Fever First-half winners often mirror the eventual game winner, especially when the home team has roster superiority. Indiana Fever should establi... Unlock reasoning · Pro
GPT-5 Flagship Openai	Flagship picks across 4 markets — unlock with Pro Get Pro
GPT-5 Mini Openai · 57-57 -1.5u	—	—	—	—
Match winner ? No pick on this market. Over / Under ? No pick on this market. Spread ? No pick on this market. First half winner ? No pick on this market.
GPT-4o Mini Openai · 89-117 -81.0u	68% Indiana Fever	56% under	54% Indiana Fever	60% Indiana Fever
Match winner ? 68% Indiana Fever Indiana Fever have won four of their last five games and have a strong home record of 5-2. Toronto Tempo have lost two straight games and ar... Unlock reasoning · Pro 🔍 researched 4 sources cited Over / Under ? 56% under Both teams have shown variability in scoring, and with Toronto's potential fatigue and Indiana's home-court advantage, the game is expected... Unlock reasoning · Pro 🔍 researched 4 sources cited Spread ? 54% Indiana Fever Indiana's home advantage and recent form make them likely to cover the spread. However, Toronto's ability to score from the perimeter and po... Unlock reasoning · Pro 🔍 researched 4 sources cited First half winner ? 60% Indiana Fever Indiana's strong home performance and recent momentum suggest they will lead at halftime. Toronto's potential fatigue and travel schedule ma... Unlock reasoning · Pro 🔍 researched 4 sources cited
o3 Flagship Openai	Flagship picks across 4 markets — unlock with Pro Get Pro
Grok 4.3 Flagship Xai	Flagship picks across 4 markets — unlock with Pro Get Pro
Grok 4 Fast Xai · 223-259 -172.2u	82% Indiana Fever	61% over	71% Indiana Fever -6.5	79% Indiana Fever
Match winner ? 82% Indiana Fever Indiana Fever is an established WNBA franchise while Toronto Tempo appears to be a 2026 expansion side with unknown roster quality. Historic... Unlock reasoning · Pro Over / Under ? 61% over WNBA games in June trend toward higher possessions and scoring. New teams often play at a faster uncontrolled pace early in seasons leading... Unlock reasoning · Pro Spread ? 71% Indiana Fever -6.5 Talent gap between an existing roster and an expansion side typically exceeds 6-8 points in season openers. Fever home-court advantage furth... Unlock reasoning · Pro First half winner ? 79% Indiana Fever Early-game execution favors the more cohesive veteran group. Tempo will need time to gel on both ends of the floor. Fever should build a lea... Unlock reasoning · Pro
Gemini 2.5 Pro Flagship Google · 16-49 -33.1u	Flagship picks across 4 markets — unlock with Pro Get Pro
Gemini 2.5 Flash Google · 185-239 -215.7u	60% Indiana Fever	52% Under 165.5	55% Indiana Fever -6.5	58% Indiana Fever
Match winner ? 60% Indiana Fever Based on my training data, the Indiana Fever are an established WNBA franchise with known players and league experience. Toronto Tempo is as... Unlock reasoning · Pro Over / Under ? 52% Under 165.5 WNBA games generally feature a lower scoring pace compared to the NBA, and 165.5 points is a common line for competitive matches. Given the... Unlock reasoning · Pro Spread ? 55% Indiana Fever -6.5 Aligning with the head-to-head prediction, the Indiana Fever are expected to be the stronger team due to their established presence in the l... Unlock reasoning · Pro First half winner ? 58% Indiana Fever The Indiana Fever, playing at home and possessing more league experience, are expected to assert control early in the game. An unproven Toro... Unlock reasoning · Pro
Gemini 2.5 Flash-Lite Google · 171-251 -185.8u	65% Toronto Tempo	55% over	52% Toronto Tempo	53% over
Match winner ? 65% Toronto Tempo The Indiana Fever have a stronger historical record and are generally considered a more established franchise in the WNBA. However, the Toro... Unlock reasoning · Pro Over / Under ? 55% over Basketball games, especially in professional leagues like the WNBA, tend to have a high number of scoring opportunities. Without specific ov... Unlock reasoning · Pro Spread ? 52% Toronto Tempo Given the slight preference for the Toronto Tempo in the head-to-head market and the general assumption of a competitive game, the spread is... Unlock reasoning · Pro First half winner ? 53% over Similar to the total game score, the first half of a basketball game often sees significant scoring as teams establish their rhythm and offe... Unlock reasoning · Pro
DeepSeek V3 Deepseek · 221-281 -237.9u	55% Indiana Fever	60% Over	50% Indiana Fever -1	55% Indiana Fever
Match winner ? 55% Indiana Fever Indiana Fever have a stronger roster on paper with Aliyah Boston and Kelsey Mitchell, while Toronto Tempo are a first-year expansion team st... Unlock reasoning · Pro Over / Under ? 60% Over Both teams have been playing at a moderately high pace, averaging combined 160+ points per game. Indiana's offense is efficient, and Toronto... Unlock reasoning · Pro Spread ? 50% Indiana Fever -1 Indiana's slight talent advantage and home court make them narrow favorites. However, Toronto's grit keeps the margin close. A -1 line is es... Unlock reasoning · Pro First half winner ? 55% Indiana Fever Indiana tends to start games well, especially at home, with strong first-quarter performances. Toronto may take time to settle in early. The... Unlock reasoning · Pro
Consensus	Indiana Fever 5/6	Under 165.5 2/6	Indiana Fever -6.5 2/6	Indiana Fever 5/6

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Research trail

What each AI looked up before picking

Unlock sources · Pro

Claude Haiku 4.5
0 tool calls · 0 sources

No live web access — picked from training-data knowledge.
Grok 4 Fast
0 tool calls · 0 sources

No live web access — picked from training-data knowledge.
GPT-4o Mini
4 tool calls · 4 sources

4 citations captured — unlock with Pro
Gemini 2.5 Flash-Lite
0 tool calls · 0 sources

No live web access — picked from training-data knowledge.
DeepSeek V3
0 tool calls · 0 sources

No live web access — picked from training-data knowledge.
Gemini 2.5 Flash
0 tool calls · 0 sources

No live web access — picked from training-data knowledge.

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