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Tipsport Odds & Markets Export (Apify Actor)

Export structured Tipsport.cz pre-match odds and betting markets as clean JSON.

This Actor is built for market monitoring and odds evolution tracking: you can run it repeatedly, store snapshots, and analyze how prices move across time, competitions, and market types.

What you get

• Two data modes
  • Match list snapshot (broad coverage; lightweight)
  • Extended match markets (deep markets per selected match)
• JSON you can use immediately
  • Consistent keys
  • Explicit timestamps / close times
  • One object per match for extended mode (records + betsData)
• Reliability controls
  • Configurable maxRetries
  • Random backoff between retries (3–20s)

What you can do with the data (examples)

• Odds evolution
  • Run every X minutes, append results to a dataset, and compute deltas (odd_t - odd_t-1).
• Market discovery
  • For a given match, enumerate all available markets and outcomes; detect when markets open/close.
• Alerting
  • Trigger alerts on price moves, unusually low/high odds, or when a market becomes inactive.
• Segmentation
  • Slice by sport, competition, event table, market box, outcome name, etc.
• Backtesting / modeling
  • Build features from odds time series per outcome and evaluate strategies.

Output formats

1) Match list snapshot (downloadExtendedBets: false)

You get a flat dataset with match rows (good for "what’s coming up soon" views and scheduling deeper crawls).

In Apify Console, the match rows are stored in the named dataset small-match-dataset.

The default dataset contains only a single summary item per run (last_run, numberOfRecords).

Example item:

{
"match_id":7867838,
"match_name":"Galatasaray - Liverpool (1.z)",
"sport":"Fotbal - muži",
"competition_short":"Liga mistrů",
"match_url":"kurzy/zapas/fotbal-galatasaray-liverpool-1z/7867838",
"downloaded_at":"2026-03-04T21:21:00.123456"
}

2) Extended match markets (downloadExtendedBets: true)

For each input match URL you get one JSON object with:

• records = number of extracted betting outcomes
• betsData = array of all outcomes with match + market metadata

Example item:

{
"records":3,
"betsData":[
{
"match_id":7867838,
"match_name_full":"Galatasaray - Liverpool (1.z)",
"sport":"Fotbal - muži",
"competition":"Liga mistrů, Fotbal - muži",
"event_table_id":123,
"event_table_name":"Zápas",
"box_id":456,
"name_visible":true,
"cell_id":2537082717,
"bet_name":"1",
"odd":3.86,
"active":true,
"winning":false,
"event_id":2204967031,
"opp_number":1,
"bet_date_closed":"2026-03-10T18:45:00.000000Z"
},
{
"match_id":7867838,
"event_table_name":"Zápas",
"bet_name":"0",
"odd":3.40,
"active":true
},
{
"match_id":7867838,
"event_table_name":"Zápas",
"bet_name":"2",
"odd":1.95,
"active":true
}
]
}

Configuration (Actor input)

All inputs are plain JSON.

Time filter presets

Use these in startUrl:

• form.period.till1h
• form.period.till3h
• form.period.till6h
• form.period.till12h

Example startUrl:

{
"startUrl":"kurzy?timeFilter=form.period.till3h&withoutLive=true",
"downloadExtendedBets":false
}

Practical workflows

A) Build an odds time series (recommended)

1. Run snapshot mode every N minutes.
2. Select top matches you care about (or all).
3. Run extended mode for those matches.
4. Store snapshots and compute changes over time.

Key idea: treat each run as a market snapshot. Over time you get a dataset that enables:

• per-outcome odds charts
• volatility metrics
• alerting on sudden moves

B) Watchlist of matches (extended-only)

Provide urlsList (up to 20) and set downloadExtendedBets to true.

Tip: You can build urlsList by taking match_url values from the snapshot output dataset (small-match-dataset).

{
"downloadExtendedBets":true,
"urlsList":[
"kurzy/zapas/fotbal-galatasaray-liverpool-1z/7867838",
"kurzy/zapas/fotbal-ferencvarosi-braga-1z/7867985"
],
"maxRetries":5
}

Notes

• This Actor is designed for pre-match markets and structured export. Odds change frequently; scheduled runs unlock the real value.
• Respect applicable terms, policies, and local regulations for your intended usage.