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Tennis Winston-Salem Open
Final
MR

Mees RottgeringvsHugo Grenier

HG
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Kickoff Locked 30picks

AI predictions

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Who picked what

32 models × 2 markets · click a row to see reasoning

Match winner

Consensus
Hugo Grenier 6/10
Claude Opus 4.7 Claude Opus 4.7 Pro
Claude Opus 4.7 Claude Opus 4.7 Pro
Claude Opus 4.6 Claude Opus 4.6 Pro
Claude Opus 4.6 Claude Opus 4.6 Pro
Claude Opus 4.8 Claude Opus 4.8 Pro
Claude Opus 4.8 Claude Opus 4.8 Pro
Claude Sonnet 4.6 Claude Sonnet 4.6 Pro
Claude Sonnet 4.6 Claude Sonnet 4.6 Pro
Claude Haiku 4.5 Claude Haiku 4.5 62% Mees Rottgering

Mees Rottgering is a Dutch hard-court specialist with solid ATP Challenger results on North American hard courts; Hugo Grenier is a French p...

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Claude Haiku 4.5 Claude Haiku 4.5 62% Mees Rottgering

Mees Rottgering is a Dutch hard-court specialist with solid ATP Challenger results on North American hard courts; Hugo Grenier is a French p...

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GPT-5 GPT-5 Pro
GPT-5 GPT-5 Pro
GPT-5 Mini GPT-5 Mini —
GPT-5 Mini GPT-5 Mini —
GPT-4o Mini GPT-4o Mini —
GPT-4o Mini GPT-4o Mini —
o4-mini o4-mini Pro
o4-mini o4-mini Pro
Grok 4.3 Grok 4.3 Pro
Grok 4.3 Grok 4.3 Pro
Grok 4 Fast Grok 4 Fast 62% Hugo Grenier

Grenier holds a higher ranking and more professional experience than the young Rottgering. Hard-court results favor Grenier in recent season...

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Grok 4 Fast Grok 4 Fast 62% Hugo Grenier

Grenier holds a higher ranking and more professional experience than the young Rottgering. Hard-court results favor Grenier in recent season...

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Gemini 3.1 Pro Gemini 3.1 Pro Pro
Gemini 3.1 Pro Gemini 3.1 Pro Pro
Gemini 2.5 Pro Gemini 2.5 Pro Pro
Gemini 2.5 Pro Gemini 2.5 Pro Pro
Gemini 2.5 Flash Gemini 2.5 Flash 65% Hugo Grenier

Based on training data up to my last knowledge cut-off, Hugo Grenier is the more experienced and established player on the professional circ...

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Gemini 2.5 Flash Gemini 2.5 Flash 65% Hugo Grenier

Based on training data up to my last knowledge cut-off, Hugo Grenier is the more experienced and established player on the professional circ...

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Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite 68% Hugo Grenier

Hugo Grenier is a more established player with a higher career-high ranking and more experience on the ATP Challenger Tour. While Mees Rottg...

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2 sources cited
Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite 68% Hugo Grenier

Hugo Grenier is a more established player with a higher career-high ranking and more experience on the ATP Challenger Tour. While Mees Rottg...

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2 sources cited
DeepSeek V3 DeepSeek V3 65% Mees Rottgering

Training data through 2025-09. Rottgering is a promising young talent with strong recent form on hard courts, while Grenier, though experien...

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DeepSeek V3 DeepSeek V3 65% Mees Rottgering

Training data through 2025-09. Rottgering is a promising young talent with strong recent form on hard courts, while Grenier, though experien...

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Over / Under

Consensus
Over 2.5 6/20
Claude Opus 4.7 Claude Opus 4.7 Pro
Claude Opus 4.7 Claude Opus 4.7 Pro
Claude Opus 4.6 Claude Opus 4.6 Pro
Claude Opus 4.6 Claude Opus 4.6 Pro
Claude Opus 4.8 Claude Opus 4.8 Pro
Claude Opus 4.8 Claude Opus 4.8 Pro
Claude Sonnet 4.6 Claude Sonnet 4.6 Pro
Claude Sonnet 4.6 Claude Sonnet 4.6 Pro
Claude Haiku 4.5 Claude Haiku 4.5 58% Over 2.5

Both players are mid-level ATP competitors (Rottgering and Grenier) likely to contest at least three sets in a Winston-Salem Open first or s...

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Claude Haiku 4.5 Claude Haiku 4.5 58% Over 2.5

Both players are mid-level ATP competitors (Rottgering and Grenier) likely to contest at least three sets in a Winston-Salem Open first or s...

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GPT-5 GPT-5 Pro
GPT-5 GPT-5 Pro
GPT-5 Mini GPT-5 Mini —
GPT-5 Mini GPT-5 Mini —
GPT-4o Mini GPT-4o Mini —
GPT-4o Mini GPT-4o Mini —
o4-mini o4-mini Pro
o4-mini o4-mini Pro
Grok 4.3 Grok 4.3 Pro
Grok 4.3 Grok 4.3 Pro
Grok 4 Fast Grok 4 Fast 68% under_2.5

Best-of-three format on outdoor hard typically produces straight-set wins when experience gap is large. Grenier's serve should limit breaks...

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Grok 4 Fast Grok 4 Fast 68% under_2.5

Best-of-three format on outdoor hard typically produces straight-set wins when experience gap is large. Grenier's serve should limit breaks...

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Gemini 3.1 Pro Gemini 3.1 Pro Pro
Gemini 3.1 Pro Gemini 3.1 Pro Pro
Gemini 2.5 Pro Gemini 2.5 Pro Pro
Gemini 2.5 Pro Gemini 2.5 Pro Pro
Gemini 2.5 Flash Gemini 2.5 Flash 55% Over 2.5

Even though Hugo Grenier is favored, younger or less established players like Mees Rottgering can often challenge more experienced opponents...

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Gemini 2.5 Flash Gemini 2.5 Flash 55% Over 2.5

Even though Hugo Grenier is favored, younger or less established players like Mees Rottgering can often challenge more experienced opponents...

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

Given that Grenier is favored, it's plausible he will win in straight sets. However, Rottgering is capable of taking a set, especially if he...

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2 sources cited
Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite 60% 2.5

Given that Grenier is favored, it's plausible he will win in straight sets. However, Rottgering is capable of taking a set, especially if he...

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2 sources cited
DeepSeek V3 DeepSeek V3 55% Over 2.5

Both players have similar service holds and return games, suggesting a close match that could go the distance. Rottgering's youth and Grenie...

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DeepSeek V3 DeepSeek V3 55% Over 2.5

Both players have similar service holds and return games, suggesting a close match that could go the distance. Rottgering's youth and Grenie...

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Model confidence

Conviction in pick · Match winner
Gemini 2.5 Flash-Lite

Gemini 2.5 Flash-Lite

Hugo Grenier

68
Gemini 2.5 Flash-Lite

Gemini 2.5 Flash-Lite

Hugo Grenier

68
Gemini 2.5 Flash

Gemini 2.5 Flash

Hugo Grenier

65
Gemini 2.5 Flash

Gemini 2.5 Flash

Hugo Grenier

65
DeepSeek V3

DeepSeek V3

Mees Rottgering

65
DeepSeek V3

DeepSeek V3

Mees Rottgering

65
Claude Haiku 4.5

Claude Haiku 4.5

Mees Rottgering

62
Claude Haiku 4.5

Claude Haiku 4.5

Mees Rottgering

62
Grok 4 Fast

Grok 4 Fast

Hugo Grenier

62
Grok 4 Fast

Grok 4 Fast

Hugo Grenier

62

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-5 290–182 · 61%
+135.0u
GPT-5
GPT-5 290–182 · 61%
+135.0u
Claude Opus 4.8
Claude Opus 4.8 308–230 · 57%
-34.8u
Claude Opus 4.8
Claude Opus 4.8 308–230 · 57%
-34.8u
GPT-4o Mini
GPT-4o Mini 2473–2043 · 55%
-1,650.7u
GPT-4o Mini
GPT-4o Mini 2473–2043 · 55%
-1,650.7u
Gemini 3.1 Pro
Gemini 3.1 Pro 260–224 · 54%
+223.5u
Gemini 3.1 Pro
Gemini 3.1 Pro 260–224 · 54%
+223.5u
o4-mini
o4-mini 180–156 · 54%
+139.4u
o4-mini
o4-mini 180–156 · 54%
+139.4u
GPT-5 Mini
GPT-5 Mini 1494–1296 · 54%
-854.2u
GPT-5 Mini
GPT-5 Mini 1494–1296 · 54%
-854.2u
Claude Opus 4.6
Claude Opus 4.6 386–346 · 53%
-95.2u
Claude Opus 4.6
Claude Opus 4.6 386–346 · 53%
-95.2u
Grok 4.3
Grok 4.3 264–248 · 52%
+187.2u
Grok 4.3
Grok 4.3 264–248 · 52%
+187.2u
Claude Opus 4.7
Claude Opus 4.7 366–352 · 51%
-127.1u
Claude Opus 4.7
Claude Opus 4.7 366–352 · 51%
-127.1u
Claude Sonnet 4.6
Claude Sonnet 4.6 918–964 · 49%
-647.5u
Claude Sonnet 4.6
Claude Sonnet 4.6 918–964 · 49%
-647.5u
Claude Haiku 4.5
Claude Haiku 4.5 2202–2352 · 48%
-2,026.1u
Claude Haiku 4.5
Claude Haiku 4.5 2202–2352 · 48%
-2,026.1u
Grok 4 Fast
Grok 4 Fast 3583–3914 · 48%
-3,430.9u
Grok 4 Fast
Grok 4 Fast 3583–3914 · 48%
-3,430.9u
Gemini 2.5 Flash
Gemini 2.5 Flash 4058–4472 · 48%
-4,101.3u
Gemini 2.5 Flash
Gemini 2.5 Flash 4058–4472 · 48%
-4,101.3u
DeepSeek V3
DeepSeek V3 4278–4926 · 46%
-4,308.9u
DeepSeek V3
DeepSeek V3 4278–4926 · 46%
-4,308.9u
Gemini 2.5 Pro
Gemini 2.5 Pro 302–358 · 46%
-147.7u
Gemini 2.5 Pro
Gemini 2.5 Pro 302–358 · 46%
-147.7u
Gemini 2.5 Flash-Lite
Gemini 2.5 Flash-Lite 3384–4028 · 46%
-3,625.6u
Gemini 2.5 Flash-Lite
Gemini 2.5 Flash-Lite 3384–4028 · 46%
-3,625.6u

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

Verifiable brief

Identical prompt sent to every AI · SHA-256 verified

hash: 618b5808d3474523…
Kickoff
Sun, Aug 23 · 19:00 GMT+0200
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": 30525,
        "sport": "tennis",
        "venue": null,
        "league": "Winston-Salem Open",
        "starts_at": "2026-08-23T17:00:00+00:00",
        "starts_at_human": "Sun, 23 Aug 2026 17:00:00 GMT"
    },
    "teams": {
        "away": "Hugo Grenier",
        "home": "Mees Rottgering"
    },
    "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.

Research trail

What each AI looked up before picking

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