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HomeTennis Tips

Tennis Betting Tips

Daily ATP, Challenger and Grand Slam picks built around price rather than noise. The aim is not to pretend we can call every winner in isolation; it is to find numbers that are too big, handicaps that are a touch loose, and totals that have been shaped by generic assumptions instead of the actual match.

634
Total Bets
+5.7%
ROI
51.1%
Win Rate
2.10
Avg Odds
Profit curve

All Tennis profit curve

Dashed line is our archive record before public tracking; the solid line is the verified public ledger.

Public ledgerArchive record
Net profit
+33.62u
At £100 per unit: +£3,362
Peak
+48.14u
Max drawdown
-17.76u
Largest dip from a high
Last 10 picks
-1.55u
Public picks
181
Selected point
25 Jun+0.66u
Budkov Kjaer vs Virtanen
Total Games - Over 36.5
Stake 1.00uRunning +33.62u

Record cards use the full tracked tennis category record. The recent selections table below is only a browsing sample from the latest 50 settled tennis picks, then filtered by the category tab you choose. The P/L progression shows any pre-tracking baseline as a dashed aggregate summary, then uses settled public ledger rows for the selected tab.

ACTIVE SELECTIONS

Current Picks

Stake in units (1u = your standard stake). We typically recommend 0.5u-2u per pick.

ML (Moneyline): A straight win bet with no handicap attached.

Wimbledon
30 JunPENDING
De Minaur vs Burruchaga
Total Games | Over 29.5
Odds
1.85
Book
BetMGM
Stake
0.75u
Wimbledon
29 JunPENDING
De Jong vs Hijikata
De Jong | +4.5 Games
Odds
1.97
Book
Unibet
Stake
0.5u
Wimbledon
29 JunPENDING
Rinderknech vs Tarvet
Total Games | Over 39.5
Odds
1.75
Book
Ladbrokes
Stake
0.75u
Wimbledon
29 JunPENDING
Mannarino vs Droguet
Mannarino | -2.5 Games
Odds
1.95
Book
Unibet
Stake
1u
Wimbledon
29 JunPENDING
Virtanen vs Shelton
Total Games | Over 38.5
Odds
1.73
Book
Betway
Stake
1u
Our methodology

How the model builds the card

01

Every match starts as fair odds

We do not begin with a tip or a hunch. We begin by pricing the match. Surface-specific serve and return data are blended with Elo so the model captures both underlying level and actual conditions, then a point-by-point tennis engine turns that into fair moneyline, handicap and total prices.

02

Raw output gets calibrated hard

Raw probabilities are not enough, especially below the very top tier. We shrink thin samples, weight tournament class properly, and account for things like venue speed, recent workload, rust, form and matchup shape. The point is not to force fake monster edges; it is to stop the fair odds drifting away from tennis reality.

03

We only bet when the price is wrong

Once our fair odds are set, we compare them to the live market and only move when the gap is worth taking. Sometimes that means moneyline, sometimes games, sometimes totals, and very often it means passing. The proof is not a lucky day; it is whether the number was strong enough to beat the market by the close.

Want the caveats behind the current surface model? Read the ATP clay model note and the track-record guide before treating any short sample as proof.
RESULTS

Recent Selections

This is a recent-results window, not the source of truth for the ROI card above. Older bets still count in the category record even when they have rolled out of the latest-50 settled feed.

25 Jun
Budkov Kjaer vs Virtanen
Total Games | Over 36.5
WON
Odds
1.66
Book
BetMGM
Stake
1u
P/L
+0.66u
24 Jun
Mejia vs Heide
Mejia | ML
WON
Odds
2.63
Book
Bet365
Stake
0.5u
P/L
+0.82u
24 Jun
Coria vs Sakellaridis
Total Games | Over 19.5
LOST
Odds
1.80
Book
Bet365
Stake
0.75u
P/L
-0.75u
24 Jun
Hussey vs Halys
Hussey | ML
LOST
Odds
2.50
Book
BoyleSports
Stake
0.5u
P/L
-0.50u
24 Jun
Samuel vs Tirante
Tirante | ML
LOST
Odds
2.43
Book
BetMGM
Stake
0.75u
P/L
-0.75u