Tennis Prediction Market Data
Tick-by-tick trades and L2 order-book depth for 39 Tennis series on Kalshi & OG.com & Polymarket & predict.fun — ATP and WTA match winners, set winners, game totals and spreads, ITF and Challenger matches, tournament winners. 5,633 series-days covering July 2025 – present; 41.1M trades · $2.4B turnover over the last 30 captured days. Every contract captured co-located with nanosecond venue and receive timestamps — sold as day bundles, €1 per day, bought as a guest with instant download.
Tennis on Kalshi & OG.com & Polymarket & predict.fun
ATP, WTA, ITF and Challenger match winners, set winners, game totals, spreads and tournament winners.
Every series below is archived at full feed resolution — each contract file carries every trade print (price, size, side) and Level-2 order-book updates in one normalized, zstd-compressed schema. A series-day bundle holds all contracts of the series for one UTC day.
Most traded Tennis series (last 30 days)
Ranked by USD turnover from our own daily statistics — the series a market maker or bot builder should start with.
| # | Series | Venue | Trades (30d) | Turnover (30d) | Days | |
|---|---|---|---|---|---|---|
| 1 | Challenger ATP | Kalshi | 8,521,825 | $521.1M | 30 | Buy days |
| 2 | ATP Tennis Match | Kalshi | 4,658,826 | $410.8M | 30 | Buy days |
| 3 | ITF Women's Match | Kalshi | 10,078,466 | $381.9M | 30 | Buy days |
| 4 | WTA Tennis Match | Kalshi | 3,622,715 | $341.8M | 30 | Buy days |
| 5 | ITF Men's Match | Kalshi | 7,007,393 | $254.2M | 30 | Buy days |
| 6 | Challenger WTA | Kalshi | 1,991,517 | $150.1M | 25 | Buy days |
| 7 | ATP Tennis (matches & props) | Polymarket | 766,094 | $137.4M | 30 | Buy days |
| 8 | WTA Tennis (matches & props) | Polymarket | 422,848 | $77.2M | 30 | Buy days |
| 9 | ATP Set Winner | Kalshi | 731,089 | $41.8M | 30 | Buy days |
| 10 | WTA Set Winner | Kalshi | 708,072 | $38.1M | 30 | Buy days |
Trades and USD turnover summed over every contract of the series, from the recorded tick data; 30 days to 2026-11-01. Series without statistics in the window are omitted.
Tennis activity per venue
| Venue | Series | With stats | Trades (30d) | Turnover (30d) |
|---|---|---|---|---|
| Kalshi | 33 | 25 | 39,538,775 | $2.2B |
| Polymarket | 3 | 3 | 1,377,394 | $225.3M |
| OG.com | 1 | 1 | 177,538 | $26.3M |
| predict.fun | 2 | 2 | 28,919 | $3.4M |
30 days to 2026-11-01, from our own daily statistics per contract.
Tennis — development since capture
140,329,696 trades and $10.2B of USD turnover across 36 series since 2026-07-08; busiest single series-day 2026-08-31 with $64.1M, biggest contract KXATPMATCH-26SEP03ZVEHAL-ZVE (2026-09-04, $11.6M).
| Month | Series active | Days | Trades | Turnover (USD) | Avg / day | Contracts traded | L2 updates | vs previous month |
|---|---|---|---|---|---|---|---|---|
| Nov 2026 (month to date) | 30 | 1 | 1,028,118 | $57.7M | $57.7M | 1,923 | 121.1M | — |
| Oct 2026 | 31 | 31 | 43,038,515 | $2.6B | $83.3M | 72,365 | 4.8B | −2.8 % |
| Sep 2026 | 33 | 30 | 35,533,232 | $2.7B | $88.5M | 65,748 | 4.6B | −6.2 % |
| Aug 2026 | 30 | 31 | 33,322,525 | $2.8B | $91.3M | 51,488 | 3.4B | +39.9 % |
| Jul 2026 | 16 | 24 | 27,407,306 | $2.0B | $84.3M | — | — | — |
Tennis series — trades and USD turnover summed per calendar month (UTC) over every contract, from our own daily statistics of the recorded tick data. Days = captured UTC days; a partial month shows no month-over-month change. Contracts traded and L2 (order-book) updates exist for days captured with the extended statistics.
Tennis — turnover by weekday
Tuesdays are the busiest day at about $102.2M of USD turnover, averaged over the captured days of the last 30.
| Weekday (UTC) | Avg turnover / day | USD | Avg trades | Days |
|---|---|---|---|---|
| Monday | $80.9M | 1.4M | 4 | |
| Tuesday | $102.2M | 1.7M | 4 | |
| Wednesday | $93.5M | 1.6M | 4 | |
| Thursday | $90.5M | 1.5M | 4 | |
| Friday | $91.8M | 1.6M | 4 | |
| Saturday | $65.3M | 1.0M | 5 | |
| Sunday | $57.5M | 961.3K | 5 |
Tennis series — average USD turnover and trades per weekday over the captured days of the last 30, from our own daily statistics; weekdays without a captured day show —.
Rising Tennis series — 30 days vs the 30 before
| # | Series | Venue | Turnover (30d) | Prior 30d | Growth (avg / day) | Trades (30d) |
|---|---|---|---|---|---|---|
| 1 | Will Player Qualify for ATP Finals? | Kalshi | $32.4K | $4.4K | +636.5 % | 474 |
| 2 | ITF Women's Doubles Match | Kalshi | $10.2M | $5.9M | +67.6 % | 586,188 |
| 3 | ATP #1 Ranked Player | Kalshi | $26.8K | $18.1K | +47.9 % | 594 |
| 4 | ITF Doubles Match | Kalshi | $7.4M | $6.1M | +22.1 % | 467,880 |
| 5 | ITF Women's Match | Kalshi | $381.9M | $319.6M | +19.5 % | 10,078,466 |
Growth of average USD turnover per captured day, the 30 days to 2026-11-01 against the 30 days before; series need ≥ 10 captured days in both windows and ≥ $10K of turnover in the current one. From our own daily statistics.
Tennis prediction markets — trades and turnover per day
Every captured UTC day of the last 30, summed over the 39 Tennis series — how much these markets trade day by day, from our own tick capture.
| UTC day | Series with stats | Contracts traded | Trades | Turnover | Buy share | L2 updates |
|---|---|---|---|---|---|---|
| 2026-11-01 | 30 | 1,923 | 1,028,118 | $57.7M | 67 % | 121.1M |
| 2026-10-31 | 30 | 1,683 | 1,137,422 | $66.6M | 69 % | 103.8M |
| 2026-10-30 | 30 | 1,868 | 1,663,795 | $98.4M | 68 % | 120.1M |
| 2026-10-29 | 30 | 2,462 | 1,626,123 | $109.0M | 68 % | 148.1M |
| 2026-10-28 | 30 | 3,112 | 1,618,058 | $104.3M | 68 % | 189.7M |
| 2026-10-27 | 30 | 3,516 | 1,859,249 | $118.6M | 68 % | 239.5M |
| 2026-10-26 | 30 | 2,291 | 1,391,446 | $81.4M | 70 % | 172.1M |
| 2026-10-25 | 31 | 1,912 | 947,519 | $55.5M | 68 % | 131.9M |
| 2026-10-24 | 31 | 1,781 | 1,132,392 | $75.0M | 68 % | 117.4M |
| 2026-10-23 | 30 | 1,798 | 1,631,518 | $97.8M | 69 % | 145.5M |
| 2026-10-22 | 30 | 2,253 | 1,473,634 | $81.1M | 70 % | 144.5M |
| 2026-10-21 | 29 | 2,694 | 1,589,969 | $78.2M | 69 % | 185.9M |
| 2026-10-20 | 29 | 3,437 | 1,888,896 | $101.5M | 68 % | 239.0M |
| 2026-10-19 | 29 | 2,653 | 1,532,395 | $74.1M | 69 % | 163.4M |
| 2026-10-18 | 31 | 2,347 | 1,116,586 | $58.8M | 66 % | 187.4M |
| 2026-10-17 | 31 | 1,697 | 1,074,264 | $60.1M | 66 % | 123.1M |
| 2026-10-16 | 31 | 1,525 | 1,609,046 | $87.9M | 68 % | 126.2M |
| 2026-10-15 | 31 | 1,957 | 1,412,889 | $80.7M | 69 % | 122.2M |
| 2026-10-14 | 30 | 2,725 | 1,657,331 | $95.5M | 68 % | 223.7M |
| 2026-10-13 | 30 | 3,332 | 1,646,725 | $93.8M | 67 % | 233.7M |
| 2026-10-12 | 30 | 2,307 | 1,307,783 | $75.4M | 67 % | 151.8M |
| 2026-10-11 | 29 | 1,896 | 847,235 | $49.5M | 68 % | 120.3M |
| 2026-10-10 | 29 | 1,716 | 807,633 | $53.2M | 68 % | 111.1M |
| 2026-10-09 | 29 | 2,017 | 1,309,617 | $83.3M | 69 % | 126.7M |
| 2026-10-08 | 28 | 2,542 | 1,545,032 | $91.3M | 70 % | 150.5M |
| 2026-10-07 | 27 | 2,983 | 1,517,513 | $95.8M | 69 % | 215.5M |
| 2026-10-06 | 28 | 3,414 | 1,574,751 | $95.0M | 67 % | 220.9M |
| 2026-10-05 | 27 | 2,843 | 1,372,012 | $92.9M | 68 % | 144.5M |
| 2026-10-04 | 25 | 1,767 | 867,070 | $65.8M | 68 % | 96.6M |
| 2026-10-03 | 27 | 1,444 | 936,605 | $71.5M | 67 % | 80.4M |
Per UTC day, summed over every Tennis series that has statistics for that day, from every trade print of every contract in our tick archive. Trades and USD turnover are exact; contracts traded, buy share and L2 (order-book) updates exist for days captured with the extended statistics — earlier days show —.
Most traded Tennis contracts on 2026-11-01
The single event contracts with the most USD turnover on the newest captured day — with open and close price and the outcome implied by the last print.
| Contract | Series | Venue | Open → Close | Implied outcome | Trades | Turnover | |
|---|---|---|---|---|---|---|---|
| KXWTAMATCH-26OCT31FERSHN-SHN | WTA Tennis Match | Kalshi | 0.610 → 0.990 | Yes | 12,034 | $2.9M | buy file → |
| KXWTACHALLENGERMATCH-26NOV01TUBGRA-GRA | Challenger WTA | Kalshi | 0.550 → 0.010 | No | 21,222 | $1.4M | buy file → |
| KXWTAMATCH-26OCT31FERSHN-FER | WTA Tennis Match | Kalshi | 0.400 → 0.010 | No | 15,620 | $1.3M | buy file → |
| KXATPMATCH-26NOV01CERKEC-CER | ATP Tennis Match | Kalshi | 0.560 → 0.990 | Yes | 12,442 | $1.2M | buy file → |
| KXATPMATCH-26NOV01LEHVAC-VAC | ATP Tennis Match | Kalshi | 0.360 → 0.990 | Yes | 12,035 | $1.2M | buy file → |
| KXATPMATCH-26NOV01DEALC-ALC | ATP Tennis Match | Kalshi | 0.820 → 0.990 | Yes | 2,986 | $1.2M | buy file → |
| KXATPMATCH-26NOV01LEHVAC-LEH | ATP Tennis Match | Kalshi | 0.650 → 0.010 | No | 10,240 | $1.2M | buy file → |
| KXATPMATCH-26NOV01VALSON-SON | ATP Tennis Match | Kalshi | 0.580 → 0.990 | Yes | 8,603 | $1.1M | buy file → |
| KXWTACHALLENGERMATCH-26NOV01TUBGRA-TUB | Challenger WTA | Kalshi | 0.470 → 0.990 | Yes | 18,909 | $1.0M | buy file → |
| KXWTACHALLENGERMATCH-26OCT31MARFER-MAR | Challenger WTA | Kalshi | 0.520 → 0.990 | Yes | 10,037 | $893.2K | buy file → |
Single contracts ranked by USD turnover on 2026-11-01 (UTC), from every trade print in our tick archive. Prices quote 0..1; the implied outcome reads the last print of the day (≥ 0.97 Yes/Up, ≤ 0.03 No/Down) — venues publish the official result. Each contract’s day file is sold individually, the whole series day as a bundle.
Kalshi Tennis series
Largest archive first. Each series page lists its exact coverage, day count and recent days; day bundles are bought in the shop calendar.
| Series | Venue | Interval | Days | Coverage | Size | Price/day | |
|---|---|---|---|---|---|---|---|
| ITF Men's Match | Kalshi | event | 205 | April 2026 – present | 108 GB | €1 | Buy days |
| ITF Women's Match | Kalshi | event | 205 | April 2026 – present | 104 GB | €1 | Buy days |
| Challenger ATP | Kalshi | event | 249 | February 2026 – present | 80.7 GB | €1 | Buy days |
| ATP Tennis Match | Kalshi | event | 242 | February 2026 – present | 32.3 GB | €1 | Buy days |
| WTA Tennis Match | Kalshi | event | 249 | February 2026 – present | 30.6 GB | €1 | Buy days |
| ATP Set Winner | Kalshi | event | 236 | March 2026 – present | 20.1 GB | €1 | Buy days |
| Challenger WTA | Kalshi | event | 191 | February 2026 – present | 18.0 GB | €1 | Buy days |
| WTA Set Winner | Kalshi | event | 186 | April 2026 – present | 17.0 GB | €1 | Buy days |
| ITF Doubles Match | Kalshi | event | 113 | July 2026 – present | 11.6 GB | €1 | Buy days |
| ATP Total Games (KXATPGTOTAL) | Kalshi | event | 172 | May 2026 – present | 11.0 GB | €1 | Buy days |
| ATO Game Spread | Kalshi | event | 182 | March 2026 – present | 10.2 GB | €1 | Buy days |
| ITF Women's Doubles Match | Kalshi | event | 113 | July 2026 – present | 9.98 GB | €1 | Buy days |
| WTA Total Games | Kalshi | event | 83 | August 2026 – present | 9.02 GB | €1 | Buy days |
| ATP Exact Match Score | Kalshi | event | 182 | March 2026 – present | 6.77 GB | €1 | Buy days |
| WTA Doubles Tennis Match | Kalshi | event | 110 | May 2026 – present | 4.15 GB | €1 | Buy days |
| ATP Doubles Tennis Match | Kalshi | event | 110 | March 2026 – present | 3.94 GB | €1 | Buy days |
| ATP Challenger Doubles Match | Kalshi | event | 76 | August 2026 – present | 2.53 GB | €1 | Buy days |
| WTA Exact Match Score | Kalshi | event | 26 | June 2026 – September 2026 | 1.37 GB | €1 | Buy days |
| Men's Tournament Winner | Kalshi | event | 162 | May 2026 – present | 786 MB | €1 | Buy days |
| Women's Tournament Winner | Kalshi | event | 161 | May 2026 – present | 603 MB | €1 | Buy days |
| ATP Stage Qualifiers | Kalshi | event | 41 | May 2026 – September 2026 | 445 MB | €1 | Buy days |
| ATP Total Sets | Kalshi | event | 14 | August 2026 – September 2026 | 373 MB | €1 | Buy days |
| ATP Set Spread | Kalshi | event | 14 | August 2026 – September 2026 | 361 MB | €1 | Buy days |
| Will Player Qualify for ATP Finals? | Kalshi | event | 153 | June 2026 – present | 336 MB | €1 | Buy days |
| WTA Stage Qualifiers | Kalshi | event | 38 | May 2026 – September 2026 | 320 MB | €1 | Buy days |
| French Open Men's | Kalshi | annual | 96 | March 2026 – June 2026 | 107 MB | €1 | Buy days |
| Will Player Qualify for WTA Finals | Kalshi | event | 153 | June 2026 – present | 100 MB | €1 | Buy days |
| Men and Women Tournament Winners | Kalshi | event | 38 | August 2026 – present | 85.3 MB | €1 | Buy days |
| French Open Women's | Kalshi | annual | 95 | March 2026 – June 2026 | 65.9 MB | €1 | Buy days |
| ATP #1 Ranked Player | Kalshi | event | 243 | March 2026 – present | 42.9 MB | €1 | Buy days |
| ATP Grand Slam | Kalshi | event | 243 | March 2026 – present | 32.8 MB | €1 | Buy days |
| WTA Grand Slam | Kalshi | event | 242 | March 2026 – present | 26.9 MB | €1 | Buy days |
| ATP Total Games (KXATPGAMETOTAL) | Kalshi | event | 10 | March 2026 | 4.33 MB | €1 | Buy days |
Polymarket Tennis series
Largest archive first. Each series page lists its exact coverage, day count and recent days; day bundles are bought in the shop calendar.
| Series | Venue | Interval | Days | Coverage | Size | Price/day | |
|---|---|---|---|---|---|---|---|
| ATP Tennis (matches & props) | Polymarket | event | 405 | July 2025 – present | 133 GB | €1 | Buy days |
| WTA Tennis (matches & props) | Polymarket | event | 360 | August 2025 – present | 68.4 GB | €1 | Buy days |
| ITF Tennis (matches & props) | Polymarket | event | 172 | May 2026 – present | 67.2 GB | €1 | Buy days |
predict.fun Tennis series
Largest archive first. Each series page lists its exact coverage, day count and recent days; day bundles are bought in the shop calendar.
| Series | Venue | Interval | Days | Coverage | Size | Price/day | |
|---|---|---|---|---|---|---|---|
| ATP Tennis (matches & props) | predict.fun | event | 19 | October 2026 – present | 80.3 MB | €1 | Buy days |
| WTA Tennis (matches & props) | predict.fun | event | 17 | October 2026 – present | 52.9 MB | €1 | Buy days |
OG.com Tennis series
Largest archive first. Each series page lists its exact coverage, day count and recent days; day bundles are bought in the shop calendar.
| Series | Venue | Interval | Days | Coverage | Size | Price/day | |
|---|---|---|---|---|---|---|---|
| Tennis (matches & props) | OG.com | event | 27 | October 2026 – present | 806 MB | €1 | Buy days |
Tennis — market types across 39 series
791,623 contracts listed as of 2026-11-02, read from each contract's venue identifier — Kalshi lists each type as its own series, Polymarket, predict.fun and OG.com one family per league.
| Market type | Contracts listed (all time) | Listed last 30 days | Share |
|---|---|---|---|
| total / over-under | 259,237 | 32,967 | 33 % |
| moneyline / winner | 205,436 | 33,382 | 26 % |
| player & team props | 150,637 | 14,527 | 19 % |
| spread / handicap | 86,827 | 27,046 | 11 % |
| period markets | 30,753 | 3,629 | 4 % |
| other | 58,733 | 10,523 | 7 % |
| Total | 791,623 | 122,074 | 100 % |
Tennis series— market type read from each contract's venue identifier (ticker suffix on Kalshi, slug suffix on Polymarket, market description on predict.fun and OG.com), counted over every contract listed in the family as of 2026-11-02; “listed last 30 days” counts contracts whose venue settlement time falls in that window. Every one of them is in the day bundle of its day.
Why buy Tennis prediction-market data here
Four things every page on this site is built on — and the reason the numbers above exist at all.
We record everything
The complete public feed of each venue as it was published: every Level-2 snapshot and update at the venue's full book depth, every trade with its aggressor side, every quote, funding, mark-price and liquidation event — for every instrument the venue lists, every UTC day since we added the venue. Nothing sampled, no top-N cut, no on-demand capture.
Institutional grade
Captured co-located at the venue with the exchange timestamp and our receive timestamp in integer nanoseconds, an event-id chain that makes any gap visible, and one normalized schema across 35+ venues — the same capture our own high-frequency trading engine and enterprise feeds run on.
€1 per instrument-day
Any instrument-day is €1, series-day bundles start at €1 — no subscription, no minimum order, no tiers to unlock. Credit packs lower the effective price and never expire, and every venue has free full-day samples to test against first.
Self-service for everyone
Pick the days in the Data Shop, pay by card as a guest and download immediately — no sales call, no enterprise contract, no KYC. Coding agents buy the same files through the MCP server, and the free Agent Skill teaches them the format.
Agents: /llms.txt · MCP server /mcp · every page as markdown via Accept: text/markdown
Tennis prediction-market data — FAQ
Does Kalshi have tennis markets?
Yes — ATP, WTA, ITF (men’s and women’s, singles and doubles) and ATP Challenger match winners, set winners, exact match scores, game totals, game spreads, stage qualifiers and tournament winners. Polymarket ATP, WTA and ITF matches and props are each a family.
How much tennis prediction-market data is there?
Tennis is among the busiest sports on both venues — hundreds of matches per day in season; the ATP, ITF and WTA match series are consistently among the most-traded Kalshi sports series.
How far back does the Tennis data go?
Coverage runs July 2025 – present — 5,633 series-days across 39 series on Kalshi & OG.com & Polymarket & predict.fun. Each series page lists its exact span.
Can I get Tennis prediction-market data as CSV or Parquet?
Yes — trades export as gzipped CSV or Parquet at no extra cost, with integer microsecond UTC timestamps that load straight into pandas, polars or DuckDB. Kalshi, OG.com, Polymarket and predict.fun record full order-book depth without a separate BBO stream, so book data ships in the native tick file — the free reader script converts it locally. See the formats overview.
How much does Tennis prediction-market data cost?
€1 per series-day bundle — every contract of the series for that UTC day — no subscription, no minimum order. Pick days in the shop calendar, pay as a guest, download immediately.
Do I need an account or a sales call?
No. Pick the Tennis series-days in the Data Shop, pay by card as a guest and download immediately — no subscription, no minimum order, no KYC, no enterprise contract, no sales call. Coding agents buy the same bundles through the MCP server.
How is the Tennis data captured?
Co-located at the venue, message by message: every Level-2 update and every trade print carries the exchange timestamp and our receive timestamp in integer nanoseconds, an event-id chain makes any gap visible, and one normalized schema across 35+ venues means a parser written for Binance reads these files unchanged. It is the same capture our own high-frequency trading engine runs on — nothing sampled, no top-N cut.
Which market types are in the Tennis data?
As of 2026-11-02 the 39 Tennis series with a market-type snapshot had listed 791,623 contracts: total / over-under 259,237 · moneyline / winner 205,436 · player & team props 150,637 · spread / handicap 86,827 · period markets 30,753 · other 58,733 — counted from every contract file of each family across Kalshi, OG.com, Polymarket, predict.fun. Kalshi splits these into separate series; Polymarket, predict.fun and OG.com list one family per league. Each series page shows its own mix.
What was the busiest Tennis trading day?
2026-08-31 — $64.1M of USD turnover over 458,870 trades in a single series-day. Since our statistics began on 2026-07-08, 36 Tennis series recorded 140,329,696 trades and $10.2B in total; the biggest single contract was KXATPMATCH-26SEP03ZVEHAL-ZVE on 2026-09-04 at $11.6M. Tuesdays are the busiest weekday at about $102.2M per day.