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Tennis Ennoble Care Philly Open
Final
EV

Esha VelagavsEkaterina Ovcharenko

EO
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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
Esha Velaga 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 58% Esha Velaga

Esha Velaga is predicted to win based on training knowledge through early 2025, though limited public data is available for both players in...

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Claude Haiku 4.5 Claude Haiku 4.5 58% Esha Velaga

Esha Velaga is predicted to win based on training knowledge through early 2025, though limited public data is available for both players in...

Unlock reasoning · Pro
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 58% Esha Velaga

No prior match data exists between Esha Velaga and Ekaterina Ovcharenko. Both players appear to be low-ranked juniors with minimal professio...

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Grok 4 Fast Grok 4 Fast 58% Esha Velaga

No prior match data exists between Esha Velaga and Ekaterina Ovcharenko. Both players appear to be low-ranked juniors with minimal professio...

Unlock reasoning · Pro
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% Ekaterina Ovcharenko

Based on my training data up to its last update, Ekaterina Ovcharenko has historically shown a slightly more consistent record on hard court...

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Gemini 2.5 Flash Gemini 2.5 Flash 55% Ekaterina Ovcharenko

Based on my training data up to its last update, Ekaterina Ovcharenko has historically shown a slightly more consistent record on hard court...

Unlock reasoning · Pro
Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite 55% Esha Velaga

Based on general knowledge up to my last training data, Esha Velaga is a more established player with a stronger overall record, giving her...

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Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite 55% Esha Velaga

Based on general knowledge up to my last training data, Esha Velaga is a more established player with a stronger overall record, giving her...

Unlock reasoning · Pro
DeepSeek V3 DeepSeek V3 65% Ekaterina Ovcharenko

Based on training data up to early 2025, Ovcharenko has consistently higher ITF rankings and more experience on hard courts, which is the ty...

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DeepSeek V3 DeepSeek V3 65% Ekaterina Ovcharenko

Based on training data up to early 2025, Ovcharenko has consistently higher ITF rankings and more experience on hard courts, which is the ty...

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Over / Under

Consensus
Over 21.5 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 52% Over 2.5

Women's tennis matches at WTA 250 level (Philly Open) typically go to three sets when both players are relatively evenly matched. Given the...

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Claude Haiku 4.5 Claude Haiku 4.5 52% Over 2.5

Women's tennis matches at WTA 250 level (Philly Open) typically go to three sets when both players are relatively evenly matched. Given the...

Unlock reasoning · Pro
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% under_2.5

Best-of-3 format in early-round ITF-level events often ends in straight sets when one player holds a modest edge. Limited player profiles su...

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Grok 4 Fast Grok 4 Fast 62% under_2.5

Best-of-3 format in early-round ITF-level events often ends in straight sets when one player holds a modest edge. Limited player profiles su...

Unlock reasoning · Pro
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

As both Esha Velaga and Ekaterina Ovcharenko compete at similar levels on the ITF tour, their matches are often competitive and prone to goi...

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Gemini 2.5 Flash Gemini 2.5 Flash 55% Over 2.5

As both Esha Velaga and Ekaterina Ovcharenko compete at similar levels on the ITF tour, their matches are often competitive and prone to goi...

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Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite 52% over

The match is projected to be close, with a slight advantage to Esha Velaga. This suggests a contest that could go the distance, making an 'o...

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Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite 52% over

The match is projected to be close, with a slight advantage to Esha Velaga. This suggests a contest that could go the distance, making an 'o...

Unlock reasoning · Pro
DeepSeek V3 DeepSeek V3 70% Over 1.5

In women's ITF matches, best-of-three sets are standard, and a competitive match is likely given that both players are relatively unknown at...

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DeepSeek V3 DeepSeek V3 70% Over 1.5

In women's ITF matches, best-of-three sets are standard, and a competitive match is likely given that both players are relatively unknown at...

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Model confidence

Conviction in pick · Match winner
DeepSeek V3

DeepSeek V3

Ekaterina Ovcharenko

65
DeepSeek V3

DeepSeek V3

Ekaterina Ovcharenko

65
Claude Haiku 4.5

Claude Haiku 4.5

Esha Velaga

58
Claude Haiku 4.5

Claude Haiku 4.5

Esha Velaga

58
Grok 4 Fast

Grok 4 Fast

Esha Velaga

58
Grok 4 Fast

Grok 4 Fast

Esha Velaga

58
Gemini 2.5 Flash

Gemini 2.5 Flash

Ekaterina Ovcharenko

55
Gemini 2.5 Flash

Gemini 2.5 Flash

Ekaterina Ovcharenko

55
Gemini 2.5 Flash-Lite

Gemini 2.5 Flash-Lite

Esha Velaga

55
Gemini 2.5 Flash-Lite

Gemini 2.5 Flash-Lite

Esha Velaga

55

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: cff09964e38d214e…
Kickoff
Sun, Aug 23 · 16:20 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": 30545,
        "sport": "tennis",
        "venue": null,
        "league": "Ennoble Care Philly Open",
        "starts_at": "2026-08-23T14:00:00+00:00",
        "starts_at_human": "Sun, 23 Aug 2026 14:00:00 GMT"
    },
    "teams": {
        "away": "Ekaterina Ovcharenko",
        "home": "Esha Velaga"
    },
    "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.

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