Flashscore Win Rate Tracker

⚽ What can FlashScore Win Rate Tracker do?

FlashScore Win Rate Tracker collects recent historical matches for a team or player from FlashScore via a downstream Apify Actor, then analyzes the raw sports JSON with Gemini and returns a structured report in one final output.

Just run the Actor, and you will immediately get:

• 🔎 Search for a team or player by name
• 🏆 Recent completed matches for the selected sport
• 📊 Trends and win / draw / loss percentages
• 🥅 Goals scored and conceded, goal difference, and averages
• 🏠 Home and away match breakdowns
• 🟨 Incident analysis (goals, cards, substitutions, penalties, and other sport-specific events when available)
• 🧠 AI-generated structured analysis in your chosen language
• 📦 Raw scraper data stored together with the final analytical result
• ⌚️ Scheduled runs, exports, and integrations through API endpoints and webhooks

Use this Actor to track form, compare recent results, monitor consistency, build dashboards, support internal scouting workflows, or create compact sports summaries based on FlashScore data.

📊 What FlashScore data can be analyzed?

Each run can include structured match data and analytics, including:

🏷️ Sport type	🔎 Team or player name
🆔 Match IDs	🏆 Tournament / league
📅 Match kickoff time	⚔️ Home and away team
🥅 Score	📈 Win / draw / loss percentages
⚽ Goals scored / conceded	🏠 Home and away breakdowns
📜 Event / incident history	🏅 Top players or category leaders
🧪 Validation checks	📝 Human-readable summaries

The final result includes a generatedAt timestamp, normalized input, a matches section with indexed match metadata, scraper metadata, structured analysis, and the full rawScraperOutput used to build the report.

How to use FlashScore Win Rate Tracker

1. Create a free Apify account.
2. Open your Actor in Apify Console.
3. Fill in the input fields:
   • sport
   • name
   • historicalMatches
   • outputLanguage
   • additionalPrompt
4. Click Start and wait for the run to finish.
5. Download the final result from Output, review the default Dataset, or use it via API.

🧠 How it works

1. The Actor validates and normalizes your input.
2. It calls the downstream Actor statanow/flashscore-scraper-team-statistic with the parameters sport, entity_name, and historical_matches.
3. The raw scraper result is stored in the default key-value store under the key SCRAPER_OUTPUT.
4. The scraper JSON is compacted and checked against an internal size limit.
5. Gemini analyzes the raw data and returns schema-constrained JSON containing both machine-friendly metrics and short human-readable summaries.
6. The Actor stores the final report under the key OUTPUT and also adds it to the default dataset.

🏅 Sports with tailored analysis

This Actor includes analysis instructions tailored to specific sports for many common FlashScore categories. If a selected sport does not have its own dedicated configuration, a universal structured analysis is used.

⚽ Football	🏀 Basketball	🎾 Tennis	🏒 Hockey	⚾ Baseball
🏐 Volleyball	🤾 Handball	🏓 Table tennis	🏉 Rugby union / rugby league	🏸 Badminton
🥅 Futsal	🏈 American football	🥊 Boxing / MMA	⛳ Golf	🌊 Water polo

⬇️ Input

FlashScore Win Rate Tracker works immediately after launch, but requires input that defines the sport and the team or player to analyze.

✅ Supported sport input values

The sport field uses the same predefined select list as the downstream FlashScore Team Statistic scraper. Supported values:

skiing, american_football, badminton, bandy, baseball, basketball, beach_soccer, beach_volleyball, biathlon, boxing, cross_country_skiing, cycling, esports, field_hockey, floorball, football, futsal, golf, handball, hockey, mma, motorsport, netball, rugby_league, rugby_union, ski_jumping, table_tennis, tennis, volleyball, water_polo

The Actor input can be:

{
  "sport": "football",
  "name": "Barcelona",
  "historicalMatches": 20,
  "outputLanguage": "en",
  "additionalPrompt": ""
}

Input fields

• sport — sport selected from the supported list in the Actor input. Default: football
• name — required team or player name, for example Barcelona
• historicalMatches — how many recent completed matches to collect. Default: 20
• outputLanguage — language code or language name for the final report. Default: en
• additionalPrompt — optional extra instruction for a more specific final response

⬆️ Output

After the run is complete, FlashScore Win Rate Tracker stores:

• OUTPUT in the default key-value store — the final structured analytical result
• SCRAPER_OUTPUT in the default key-value store — the raw payload from the downstream scraper
• 1 dataset item in the default dataset — the same final report for API / export scenarios

Example of the final output structure

{
  "status": "success",
  "generatedAt": "2026-03-27T18:02:00+00:00",
  "input": {
    "sport": "football",
    "name": "Barcelona",
    "historicalMatches": 20,
    "outputLanguage": "en",
    "additionalPrompt": "",
    "tokenBudgetPolicy": {
      "maxOutputTokens": 3400,
      "additionalPromptMultiplier": 1.0
    },
    "jsonBudgetPolicy": {
      "maxJsonChars": 396000,
      "calculatedInternally": true,
      "calculatedFromHistoricalMatches": true,
      "sportAware": true
    }
  },
  "matches": {
    "count": 20,
    "matchIds": ["123456", "123457"],
    "index": [
      {
        "match_id": "123456",
        "kickoff": "2026-03-20T20:00:00Z",
        "competition": "SPAIN: LaLiga",
        "home_team": "Barcelona",
        "away_team": "Valencia",
        "score": "3-1",
        "url": "https://www.flashscore.com/match/..."
      }
    ]
  },
  "scraper": {
    "actorId": "statanow/flashscore-scraper-team-statistic",
    "runId": "abc123",
    "outputSource": "key_value_store",
    "outputKey": "OUTPUT",
    "defaultDatasetId": "...",
    "defaultKeyValueStoreId": "...",
    "rawOutputStoredAs": "SCRAPER_OUTPUT"
  },
  "analysis": {
    "provider": "gemini",
    "status": "completed",
    "model": "gemini-2.5-flash",
    "inputJsonChars": 185420,
    "maxJsonChars": 396000,
    "result": {
      "language": "en",
      "data_json": {
        "match_ids": ["123456", "123457"],
        "overall_statistics": [],
        "percentages": [],
        "goals": [],
        "breakdowns": [],
        "home_away": [],
        "incidents": [],
        "tops": [],
        "validations": [],
        "final_summary": "Compact analytical summary"
      },
      "text_json": {
        "general_statistics": "...",
        "percentages": "...",
        "goals": "...",
        "breakdowns": "...",
        "home_away": "...",
        "incidents": "...",
        "tops": "...",
        "validation": "...",
        "final_summary": "..."
      },
      "missing_fields": [],
      "warnings": []
    }
  },
  "rawScraperOutput": {}
}

❓ FAQ

Does it collect live matches?

No. This Actor is built for historical match analysis. It requests the latest completed matches for the selected team or player through the downstream FlashScore team-statistics scraper, then analyzes that data.

Can the final analysis be customized?

Yes. Use outputLanguage to change the language of the response, and additionalPrompt to request a more specific summary, analysis angle, or derived insight.

Where is the raw scraper result stored?

The raw downstream payload is stored in the default key-value store under the key SCRAPER_OUTPUT and is also embedded in the final OUTPUT payload as rawScraperOutput.

Can it be used in automations or external code?

Yes. You can run it from Apify Console, call it through the API, schedule it, or connect it to webhooks and external workflows.

Does it work only for football?

No. Football is the default, but the Actor includes sport-aware analysis rules for several sports. If the selected sport does not have a dedicated configuration, it falls back to a universal structured analysis based on the scraper JSON.