MODEL LIVE // APR 17, 2026 SCANNING 18+ BOOKS
ATP · WTA · GRAND SLAMS 18 MATCHES TODAY 62 SIGNALS ACTIVE
TENNIS · NEURAL EDGE ENGINE

The model sees
every serve,
every break point.

2.4M points modeled. Every ATP and WTA match, every set, every prop — scored against model probability in real time. Not rankings. Not vibes. Pure expected value.

APPLY FOR ACCESS → SEE TODAY'S MATCHES
// SERVE MODELLIVE
+19.8u7D PNL
WIN RATE60.8%
TOP EDGE+148.6%
// PROPS SCANNER380
CONFIDENCE92.4%
TENNIS WIN RATE · YTD
60.8%
348W · 224L
TENNIS P&L · YTD
+143.7u
+$14,370 @ $100/u
AVG EV EDGE
+35.6%
Over implied book probability
PROPS TRACKED
380
ACES · DF · SETS · GAMES · BREAKS · TB
// LIVE SIGNALS →
// picks.today()

Today's tennis board,
ranked by edge.

MODEL LIVE LAST RUN · 00:07 AGO 18 MATCHES
edge-terminal — tennis/picks.live $ streaming_
ALL18
MATCH ML18
GAME SPREAD18
TOTAL GAMES18
SETS18
PROPS380
LIVE3
# MATCHUP START MARKET ODDS PICK EV EDGE PROB EDGE %
// props.top()

Top player prop edges.

L15 HIT RATE SHOWN · 380 tracked
// model.architecture

Built for the baseline.
Not retrofit from a team-sport model.

Tennis is a one-on-one serve game. Surface swings probability by 8%. Best-of-three vs. best-of-five changes variance entirely. Travel, altitude, heat, tournament fatigue, head-to-head history on surface — every tennis-specific variable is encoded. The result: calibrated probabilities within 0.4% deviation across 8 seasons of ATP and WTA outcomes.

[ 01 ] SERVE & RETURN ENGINE
Point-by-point calibration
The model is trained on 2.4M individual points over 8 seasons. Every serve, every return, every break point feeds the probability layer. Per-point priors beat per-match regressions every time.
[ 02 ] SURFACE ADJUSTMENT
Hard, clay, grass — priced separately
Rankings lie on surface. The model re-prices every match against surface-specific Elo and serve/return splits — factoring ball speed, bounce height, and handedness matchups.
[ 03 ] FATIGUE & SCHEDULE
Tournament-aware totals
Most books use recent form. We use cumulative points played, recovery window, and previous-round length. On deep-run players coming off a 3-set battle, totals books lag by 1.2 games on average.
[ 04 ] PROP REGRESSION
380 props, re-priced in real-time
ACES · DF · 1ST SERVE % · BREAKS · TIEBREAKS · TOTAL GAMES — every prop line is compared against our surface-weighted serve projection and opponent return-game splits.
[ 05 ] LIVE ODDS SIGNALS
Cross-book movement alerts
When smart money hits a book, we see it first. 62 signals active right now. Average time-to-alert: 1.4 seconds across 18 books.
[ 06 ] IMMUTABLE AUDIT
Every pick, timestamped.
No retroactive edits. No deleted losses. Every tennis pick is logged before first serve with full line, odds, and probability. Zero cappers can match this transparency.
tennis_pnl.log — bash
// last 15 tennis picks — @ $100/unit $ edge-terminal --sport=tennis --last=15 [W] Sinner v Medvedev · ML -145 · +$68.97 [W] Alcaraz v Zverev · SETS -1.5 +110 · +$110.00 [W] ALCARAZ ACES · OVER 7.5 · +$95.24 [L] Djokovic v Rune · O/U 22.5 · -$100.00 [W] Swiatek v Gauff · ML -165 · +$60.61 [W] Sabalenka v Rybakina · OVER 20.5 · +$87.72 [W] FRITZ ACES · OVER 9.5 · +$104.00 [L] Tsitsipas v Ruud · GAME SP -2.5 · -$100.00 [W] Sinner v Rublev · TB NO · +$82.64 [W] SABALENKA DF · UNDER 3.5 · +$90.91 [W] Alcaraz v Dimitrov · SETS 2-0 · +$140.00 [L] GAUFF GAMES · OVER 10.5 · -$100.00 [W] Djokovic v Hurkacz · ML -220 · +$45.45 [W] Pegula v Jabeur · OVER 21.5 · +$95.24 [W] MEDVEDEV ACES · OVER 5.5 · +$86.96 ───────────────────────────────────────── RECORD: 12W - 3L WIN%: 80.0% NET P&L: +$867.78 UNITS: +8.68u ROI: +57.9% CLV: +2.5% ─────────────────────────────────────────
// coverage.tennis()

Every tour.
Every surface.
Every prop.

Full ATP and WTA coverage — all four Grand Slams, every Masters 1000, 500, and 250 series event. Challenger and WTA 125 for deep markets. Every major line priced against our model in real time, every 60 seconds.

MARKETS COVERED
MATCH ML SET SPREAD GAME SPREAD TOTAL GAMES TOTAL SETS CORRECT SETS 1ST SET WINNER TIEBREAK Y/N LIVE IN-MATCH CROSS-BOOK
PLAYER PROPS
ACES DOUBLE FAULTS 1ST SERVE % BREAK POINTS WON PLAYER GAMES TIEBREAKS PLAYED SETS WON LONGEST GAME MATCH LENGTH RETIREMENT Y/N
// compare.execute()

Edge Terminal vs.
your favorite tennis tipster.

Tennis is a serve-game. A single break flips the match. Lines move fast on weather, withdrawals, and surface changes. If you're not faster than the book, you're paying the vig. Most tennis tipsters lose money long-term. We don't.

EDGE TERMINAL / TENNIS
Model trained on 2.4M points · 8 ATP and WTA seasons
Re-prices every line in 1.4 seconds across 18 books
Verified 60.8% tennis win rate · every pick timestamped
Flat 1.0u sizing — no "LOCK OF THE WEEK" nonsense
Withdrawal news triggers automatic re-pricing
380 player props scanned every refresh
Positive CLV: +2.5% · beats the closing line consistently
Pure expected value. Emotionless. Published losses.
VS
TENNIS "TIPSTER"
"Trust me, Djokovic can't lose this"
Posts the line 30 min after it moves
"74% all-time" — but Discord reset last month
"10-UNIT LOCK" three times a tournament
Hasn't checked the surface switch mid-season
Covers match ML only, game spreads are "too volatile"
Almost always taking the stale number
Emotional. Biased. Long-term losing.
// access.apply()

Stop guessing.
Start computing.

Tennis access is invite-only. We cap membership to protect line value for existing users. Apply now — if you qualify, we'll send you this week's tournament board inside 24 hours.

APPLY FOR ACCESS →
SEE TODAY'S MATCHES
60.8%
TENNIS WIN RATE · YTD
+143.7u
YTD PROFIT @ $100/u
TOP 0.05%
OF ALL TENNIS BETTORS