Blog
Methodology, match analysis, and transparent performance reports, including the losses.
- 01tennis
WTA Elo Movers: Who Actually Rose and Fell Most Over 16 Days
Sorana Cirstea's WTA Elo rating climbed 100 points in 16 days on our own leaderboard, while Jessica Pegula's dropped 104 — every mover, with exact dates.
- 02methodology
Bankroll Management for Tennis Betting: Size for the Drawdown, Not the Average
Bankroll management is a variance decision, not a money decision. What our own 9.91-unit drawdown says about how big a single stake should be.
- 03tennis
Are Jannik Sinner and Elena Rybakina No. 1 on Every Surface? A Top-of-the-Table Elo Deep Dive
ATP No. 1 Jannik Sinner and WTA No. 1 Elena Rybakina both top the hard-court leaderboard too, but our Sept. 27 data shows grass tells a different story.
- 04methodology
Surface Elo vs ATP Ranking: Which One Predicts a Match Better?
The elo vs atp ranking question, answered by what each number is built from, plus the rank swings our own surface ratings produce and what they cost.
- 05tennis
Carlos Alcaraz and Aryna Sabalenka: A Surface Elo Deep Dive
Carlos Alcaraz leads ATP grass Elo at 1928, his own lowest surface rating, while Aryna Sabalenka's 2018 hard-court Elo tops her overall rating of 2015.
- 06tennis
Hard, Clay and Grass: Where Tennis Surface Betting Prices Miss Most
Our hard, clay and grass tennis surface betting segments across 221 settled picks: what the splits show, and why none of them clears significance yet.
- 07tennis
Novak Djokovic and Iga Swiatek: A Surface Elo Deep Dive
Novak Djokovic ranks No. 5 overall on our Elo model but misses the ATP clay top 20, while Iga Swiatek, No. 6 overall, is No. 1 on the WTA grass leaderboard.
- 08tennis
Indoor Hard Court Tennis Betting: Why the Fall Swing Needs Its Own Rating
From late October most of the tour plays indoors, but our ratings only split hard, clay and grass. Here is what that blend hides and how thin our data is.
- 09tennis
Arthur Fils and Cori Gauff: A Surface Elo Deep Dive
WTA No. 1 Cori Gauff sits outside the grass Elo top 20 despite a 2028 overall rating, according to our October 4, 2026 leaderboard of 34,170 matches run.
- 10tennis
Best of Five vs. Best of Three: Why Slam Favorites Win More
The same rating gap gives a higher win probability over five sets than over three. The math behind it, and what it means when you price a Grand Slam match.
- 11tennis
Frances Tiafoe and Jessica Pegula: A Surface Elo Deep Dive
Tiafoe is top 10 ATP overall but outside our clay Elo top 20, while Pegula ranks inside the WTA top 20 on all three surfaces, per October 11, 2026 data.
- 12methodology
Why a Parlay Compounds the Margin, Not Just the Odds
A parlay multiplies the bookmaker margin along with the odds, so each extra leg costs more without an edge. With an edge, it multiplies the variance too.
- 13tennis
How Bookmakers Set Tennis Odds, and What Moves Them
Tennis odds are a probability estimate plus a margin, moved by news and money. Where the first price comes from, why it changes and why books disagree.
- 14methodology
What the Bookmaker Margin Costs You on a Tennis Bet
At a 3.4% bookmaker margin, a bet with no edge loses about 3.3% of each unit staked. How to measure the margin and the hit rate to break even.
- 15methodology
What a Confidence Interval Means in a Betting Record
A positive average with a confidence interval that includes zero is not a contradiction. What the interval tells you, and how to read any betting track record.
- 16tennis
How Often Do Tennis Matches End in Straight Sets?
Real closing odds show favorites win in straight sets about 10 to 12 points more often than a simple model predicts. Measured on 23,898 best-of-three matches.
- 17methodology
Is Value Betting Actually Profitable? An Honest Answer
Value betting can be profitable, but bookmaker odds are efficient and beating them is genuinely hard. A realistic answer, backed by real tracked numbers.
- 18tennis
Tennis Betting Odds for Beginners: A Plain-Language Guide
A beginner's guide to tennis betting odds: decimal odds, favorites vs. underdogs, moneyline vs. totals vs. spreads, and the vocabulary you need.
- 19methodology
Why We Publish Every Losing Pick, Not Just the Wins
A feed of only wins is the most common pattern among fake tipster accounts. Why our track record shows losses too, and what to check on any results page.
- 20methodology
Flat Staking vs. Kelly Criterion: Which Actually Wins?
We tested flat staking, graduated stakes, and real quarter-Kelly sizing on our own tracked picks. What actually won, and why the 'optimal' method didn't.
- 21tennis
Does Head-to-Head Record Actually Matter in Tennis Betting?
Head-to-head record is one of the most overused stats in tennis. What it actually predicts, and why sample size makes most H2H claims unreliable.
- 22tennis
Why Tennis Ratings Need to Be Surface-Specific
A single overall rating hides how much a player's level swings between hard, clay, and grass. How surface-specific Elo works and why it matters.
- 23methodology
Closing Line Value: Why Match Results Don't Matter
Closing line value isolates your decision quality from luck. Learn how the metric that ignores outcomes reveals whether you beat the market.
- 24tennis
The Favorite-Longshot Bias in Tennis: What 16,177 Bets Show
We backtested 16,177 tennis bets from 2020-2025 by claimed edge. The bigger the claimed edge, the worse the realized ROI — monotonic across four bands.
- 25methodology
How to Calculate Implied Probability From Betting Odds
Implied probability is 1 divided by the decimal odds. The formula for every odds format, why the numbers exceed 100%, and how to remove the bookmaker's margin.
- 26tennis
Tracking 203 Tennis Picks: Performance Data and What It Means
203 settled tennis picks, +26.48 units as of September 6, 2026. What the raw numbers reveal about value betting and where confidence still falls short.
- 27tennis
What Is Value Betting in Tennis? A Data-Based Explanation
Value betting means finding prices where the market's implied probability is lower than the real one. How we calculate it for tennis, and where it goes wrong.