TennisEdge Lab

Original tennis betting research from our 300,000-match database. Every finding is backed by data — and we publish the caveats too. No hunches, no "20 years of gut feel."

2026-08-14

Home Advantage in Tennis Is Real. We Tested 52,521 Matches, Then Checked Whether the Market Missed It.

Home players beat their rating by +2.0pp (ATP) and +1.0pp (WTA) on 52,521 matches. The market already prices about half of it. No edge claimed.

2026-08-14

One in 28 Tennis Matches Ends in a Retirement. Our Own Grader Thought It Was One in 1,100.

One in 28 matches ends in retirement. Base rates, age and surface gradients, and a 2.24x prior-retirement hazard, measured after fixing our detector. Twice.

2026-08-12

The Post-Title Letdown Is a Myth. We Tested It on 64,669 Event Transitions.

Players really do underperform after winning a title, by 3.5pp on 64,669 transitions. The cause is ELO overshoot, not emotion: runners-up dip the same.

2026-08-12

How Much Rust Is a Layoff Worth? We Measured It on 238,716 Player-Matches

Layoff rust measured on 238,716 player-matches: returning players underperform ELO by 0.9pp after 8-14 days off, 9.8pp past a year, fading over 4-6 matches.

2026-08-12

There Is No Marathon Cliff. Tennis Fatigue Is a Smooth Gradient, and Our Own Rule Had It Wrong.

No marathon cliff: 218,890 player-matches show tennis fatigue is a smooth games gradient, an easy win is a +3.4pp bonus, and we correct our own -4.5pp rule.

2026-08-11

Surface-Specific ELO Sounds Smarter. We Tested It on 62,744 Matches.

Surface-split tennis ELO loses to the pooled rating on 62,744 matches, both tours. The best mix is 0.7 overall + 0.3 surface. Tables included.

2026-08-07

First-Serve Percentage Barely Predicts Anything. The Serve Stat That Does Never Makes the Broadcast.

Landing more first serves wins the match 55.9% of the time. Winning more first-serve points wins it 84.9%. 14,082 ATP and WTA matches.

2026-08-03

How Accurate Are Tennis Predictions? The Honest Answer From 22,000 Matches

The market's favorite wins 71.5% and its odds are near-perfectly calibrated. Our model doesn't beat the close. Why 80% accuracy claims prove nothing.

2026-07-30

We Devigged Our Own CLV and the Number Got Worse. Here It Is Anyway.

Our 'beat the close 72%' stat included the bookmaker vig. Against the vig-stripped exchange close our real CLV is negative. We publish it.

2026-07-30

We Went Hunting for Style Edges: Lefties, Slice Queens, Weak Backhands. The Market Got There First.

Lefties win 46.3% vs 46.8% implied. Across 802 players we found fewer mispriced outliers than chance predicts. Style is not a betting edge.

2026-07-28

Tennis Fatigue, Measured on 209,000 Matches: Rest Barely Matters. A Three-Set War Does.

We tested the tennis fatigue myth on 209,000 matches. Rest alone barely helps (52.9%); a 3-setter yesterday costs ~4.5 points.

2026-07-28

The Favorite-Longshot Bias Is Real: 22,000 Matches Show Why Backing Underdogs Bleeds Money

Across 21,917 matches, flat-backing heavy favourites lost 3.2% and longshots lost 34.8%. The bookmaker margin is loaded onto underdogs.

2026-07-28

Why Our Bot Passes Most Matches, and Why That's the Point

We graded every match our model declined. Flat-betting those 'passes' would have lost 13.6%. Passing is where the edge is, and CLV proves it.

2026-07-28

Do Head-to-Head Records Matter in Tennis? 50,000 Rematches Say the Market Prices Them First

The H2H leader wins 59% of rematches, but against the betting odds the angle adds nothing: 36.0% actual vs 36.7% implied.