Dalila SpiterivsJoelle Lilly Sophie Steur
JLYour call
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AI predictions
2 markets · 4 models
Ask the AIsWho picked what
16 models × 2 markets · click a row to see reasoning
| Model |
Over / Under
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Over / Under
Over / Under total points. Will the combined score be above or below the line.
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Match winner
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Match winner
Match winner. Pick the team that wins in regular time (or who advances in cup formats).
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| Consensus |
under 2/8 models |
Dalila Spiteri 4/4 models |
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Claude Opus 4.7 FlagshipAnthropic |
Flagship picks across 2 markets — unlock with Pro
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Claude Opus 4.6 FlagshipAnthropic |
Flagship picks across 2 markets — unlock with Pro
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Claude Opus 4.8 FlagshipAnthropic |
Flagship picks across 2 markets — unlock with Pro
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Claude Sonnet 4.6 FlagshipAnthropic |
Flagship picks across 2 markets — unlock with Pro
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Claude Haiku 4.5 Anthropic |
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Over / Under
?
Over / Under
Over / Under total points. Will the combined score be above or below the line.
No pick on this market.
Match winner
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Match winner
Match winner. Pick the team that wins in regular time (or who advances in cup formats).
No pick on this market. |
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GPT-5 FlagshipOpenai |
Flagship picks across 2 markets — unlock with Pro
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GPT-5 Mini Openai |
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Over / Under
?
Over / Under
Over / Under total points. Will the combined score be above or below the line.
No pick on this market.
Match winner
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Match winner
Match winner. Pick the team that wins in regular time (or who advances in cup formats).
No pick on this market. |
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GPT-4o Mini Openai |
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Over / Under
?
Over / Under
Over / Under total points. Will the combined score be above or below the line.
No pick on this market.
Match winner
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Match winner
Match winner. Pick the team that wins in regular time (or who advances in cup formats).
No pick on this market. |
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o4-mini FlagshipOpenai |
Flagship picks across 2 markets — unlock with Pro
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Grok 4.3 FlagshipXai |
Flagship picks across 2 markets — unlock with Pro
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Grok 4 Fast Xai |
58%
under |
62%
Dalila Spiteri |
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Over / Under
?
Over / Under
Over / Under total points. Will the combined score be above or below the line.
58%
under Surface and experience gap favor straight-sets outcome for Spiteri. Clay matches at this level frequently end 2-0 when one player holds clea...
Match winner
?
Match winner
Match winner. Pick the team that wins in regular time (or who advances in cup formats).
62%
Dalila Spiteri Dalila Spiteri is the more established player with greater experience on European clay events; training data through 2023 shows her holding... |
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Gemini 3.1 Pro Flagship |
Flagship picks across 2 markets — unlock with Pro
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Gemini 2.5 Pro Flagship |
Flagship picks across 2 markets — unlock with Pro
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Gemini 2.5 Flash |
55%
Over 2.5 Sets |
53%
Dalila Spiteri |
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Over / Under
?
Over / Under
Over / Under total points. Will the combined score be above or below the line.
55%
Over 2.5 Sets Matches between players of similar ranking on the ITF/Challenger circuit often tend to be competitive and go the distance, especially on cla...
Match winner
?
Match winner
Match winner. Pick the team that wins in regular time (or who advances in cup formats).
53%
Dalila Spiteri Based on training data up to my last update, Dalila Spiteri generally performs better on clay courts, which is the likely surface for the BB... |
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Gemini 2.5 Flash-Lite |
60%
Joelle Lilly Sophie Steur |
65%
Dalila Spiteri |
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Over / Under
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Over / Under
Over / Under total points. Will the combined score be above or below the line.
60%
Joelle Lilly Sophie Steur Given that Dalila Spiteri is favored, it's likely the match will be decided in two sets. However, Joelle Lilly Sophie Steur has shown the ab...
Match winner
?
Match winner
Match winner. Pick the team that wins in regular time (or who advances in cup formats).
65%
Dalila Spiteri Based on my training data, Dalila Spiteri is generally a more established player with a higher probability of winning matches. Joelle Lilly... |
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DeepSeek V3 Deepseek |
54%
Over 2.5 |
62%
Dalila Spiteri |
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Over / Under
?
Over / Under
Over / Under total points. Will the combined score be above or below the line.
54%
Over 2.5 Two lower-profile clay-court qualifiers-level players tend to produce tight, break-heavy sets, and neither has a dominant serve to blow the...
Match winner
?
Match winner
Match winner. Pick the team that wins in regular time (or who advances in cup formats).
62%
Dalila Spiteri It is 2026, well beyond my reliable training data (through 2025-09), so this is a knowledge-based projection rather than a live-scouted call... |
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Over / Under
Consensusunder 2/8
Surface and experience gap favor straight-sets outcome for Spiteri. Clay matches at this level frequently end 2-0 when one player holds clea...
Matches between players of similar ranking on the ITF/Challenger circuit often tend to be competitive and go the distance, especially on cla...
Given that Dalila Spiteri is favored, it's likely the match will be decided in two sets. However, Joelle Lilly Sophie Steur has shown the ab...
Two lower-profile clay-court qualifiers-level players tend to produce tight, break-heavy sets, and neither has a dominant serve to blow the...
Match winner
ConsensusDalila Spiteri 4/4
Dalila Spiteri is the more established player with greater experience on European clay events; training data through 2023 shows her holding...
Based on training data up to my last update, Dalila Spiteri generally performs better on clay courts, which is the likely surface for the BB...
Based on my training data, Dalila Spiteri is generally a more established player with a higher probability of winning matches. Joelle Lilly...
It is 2026, well beyond my reliable training data (through 2025-09), so this is a knowledge-based projection rather than a live-scouted call...
Model confidence
Conviction in pick · Match winnerGemini 2.5 Flash-Lite
Dalila Spiteri
Grok 4 Fast
Dalila Spiteri
DeepSeek V3
Dalila Spiteri
Gemini 2.5 Flash
Dalila Spiteri
Model track records
LifetimeEvery graded pick across all sports — auto-settled the moment results land, wins and losses both counted. Ranked by win rate.
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Verifiable brief
Identical prompt sent to every AI · SHA-256 verified
hash:
3f2a53eede3705cd…
- Kickoff
- Mon, Sep 14 · 10:30 GMT+0000
- Markets
- Match winner · Total sets · Total games
- 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 3. 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 3, in this order): h2h | totals_sets | totals_games
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": 43452,
"sport": "tennis",
"venue": null,
"league": "BBVA Open Internacional De Valencia",
"starts_at": "2026-09-14T10:30:00+00:00",
"starts_at_human": "Mon, 14 Sep 2026 10:30:00 GMT"
},
"teams": {
"away": "Joelle Lilly Sophie Steur",
"home": "Dalila Spiteri"
},
"version": "v2",
"sport_focus": [
"Surface is paramount — weigh each player's record and movement on THIS surface (hard/clay/grass), not their overall ranking.",
"Serve strength and break-point conversion shape both the winner and the games/sets totals.",
"Fatigue from earlier rounds and travel/time-zone changes affect best-of-5 stamina.",
"Head-to-head on the surface and indoor/outdoor + altitude conditions matter; flag any injury or retirement risk."
],
"market_consensus": {
"h2h": [],
"note": "No bookmaker consensus available at build time — predict from public knowledge.",
"extra_markets": []
},
"markets_requested": [
"h2h",
"totals_sets",
"totals_games"
],
"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.
Results settle automatically once the final score lands. Picks are permanent — no hindsight edits.
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