Club Friendlies historical data
Club Friendlies is a Polymarket sports series with markets recurring daily. Marketlens has captured 649 markets since August 1, 2026, of which 633 resolved, with $2.1M in traded volume. Order books are stored at full depth, with every individual price change captured at millisecond resolution on the most actively traded markets, alongside trades, candles, and the final resolution outcome.
Data as of 2026-09-04 · series slug clf-games
Markets by bet type
Market counts by bet type across the series. Pass any of these as subtype= to backtest that type on its own.
Markets in this series
The most recently resolved of 649 markets.
Backtest this series
from marketlens import MarketLens
client = MarketLens()
result = client.backtest(
MyStrategy(), "clf-games", subtype="moneyline",
after="2026-08-09", before="2026-08-17", initial_cash=1_000,
)
result.show() # inspect the run in the dashboardAvailable datasets
Common questions
How many Club Friendlies markets are in the archive?
649 markets from August 1, 2026 through August 16, 2026, of which 633 have resolved and 16 are active.
What data exists for each Club Friendlies market?
Full depth L2 order book snapshots at regular intervals, individual trades, OHLC candles, and the resolution outcome, with the full price change stream at millisecond resolution on the most actively traded markets in the series. All of it is queryable by the series slug "clf-games" through the API and Python SDK, or downloadable as Parquet.
How much does a typical Club Friendlies market trade?
Average traded volume is $4.0K per market, $2.1M across the series.
Can I backtest strategies on Club Friendlies?
Yes. Pass the slug "clf-games" to client.backtest() in the Python SDK. Execution mode replays order books against real historical depth; Alpha mode, one bar per market, covers every market in the series and suits its longer horizons. This series mixes several bet types, so pass subtype= (e.g. "moneyline") to backtest one type at a time.
Related series: FIFA World Cup, MLB, ATP, WTA, UFC, WNBA, or all sports series.
Try it
Pull Club Friendlies books in one call
The free tier includes 25M rows per day with full API and full archive access, no card required.
$ pip install marketlens