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Even in our modern era of technology and measurement, some sports have lagged behind in adopting analytics. Tennis is one of them. While top players (like Novak Djokovic and Roger Federer) have used statistical insights to some extent, widespread adoption is limited, especially when considering what data is publicly available to fans, media, and coaches. Die hard tennis fans often seek greater depth and analysis in tennis data than what is currently available.
Craig O’Shannessy, arguably the world’s best-known tennis analyst, said in an interview a few years ago that, since 2015, he has saved more than 3,000 screenshots of match statistics on his phone, because most major tournaments (except Wimbledon) don’t maintain accessible historical data.
“In the NBA, you can see a heat map from a 1962 game,” he noted. “But if you want the first-serve percentage at Roland Garros 2018, be prepared for a long, frustrating search. The data exists, but it isn’t accessible in any centralized database – for media, fans, or coaches.” This makes it difficult for fans to find in-depth analysis and comprehensive data resources. The lack of a centralized database limits the depth of analysis possible for both casual viewers and die hard tennis fans.
Building a Comprehensive Stats Database
Jeff Sackmann, founder of Tennis Abstract, faced the same challenge. He launched a project that has since documented more than 10,000 ATP and WTA matches, with fans volunteering to watch full-length past matches and log every point. The goal was to build a database that could generate statistical and research insights about tennis. Users can view a comprehensive list of matches, player profiles, and head-to-head pages to analyze performance and performance trends. The website is regularly updated with the latest results from ATP and WTA tours, including detailed stats for WTA players. One of the earliest-and most intriguing-studies asked: Which statistics are most critical for a player to outperform their opponent and win a match?
Two Key Statistical Categories Predict Winners
Now take a moment and try to answer this question purely based on intuition: which statistic would give a player the highest chance of winning? More total points? A better winners-to-unforced-errors ratio? Higher first-serve percentages or net points won? Perhaps more break-point opportunities?
It turns out there are two categories that strongly predict which player will win a match. Tennis fans should pay attention to these during matches, as they indicate the favorite’s chances of victory. These insights are valuable for both fans and those interested in tennis betting, as they inform betting strategies and help users make more informed bets.
Category 1: TPW (Total Points Won)
Total points won (TPW) is the raw share of all points in a match that a player takes, and simple as it is, it remains the single best predictor of who wins.
In 2015, Rajeev Ram faced two opponents at a Manchester Challenger. He won the first match despite losing six more points overall than his rival, and lost the second despite winning five more. His conclusion was pointed: “Tennis is definitely not basketball.”
True, tennis isn’t basketball, but the principle holds: winning more points than your opponent is still the strongest signal of who takes the match.
According to Inpredictable, only 4.5% of ATP matches are won by a player who scored fewer points, and at the set level that drops to 2.4%. Winning 51% of points gives a player roughly an 86.7% chance of winning a best-of-three match, and 52% pushes the probability over 95.8%. For example, in a 100-point match between Jannik Sinner and Carlos Alcaraz, if the Italian wins 51 points to the Spaniard’s 49, Alcaraz has just a 15% chance to win.
| Statistical Edge | What It Means for the Result |
|---|---|
| Statistical Edge | What It Means for the Result |
| Win 51% of total points | Roughly 86.7% chance to win a best-of-three match |
| Win 52% of total points | Over 95.8% chance to win |
| Scored fewer points overall | Wins only about 4.5% of ATP matches |
| Weaker in return-game dominance | Wins only about 7.2% of matches |
| Leads in both TPW and DR | Loses only about 4% of the time |
This may not be the first stat fans think of, but it’s the second key signal. Dominance ratio (DR) is the percentage of return points a player wins divided by the percentage of serve points they lose. A DR above 1.0 means a player is winning a bigger share of return points than their opponent, and below 1.0 means they are being outreturned. In plain terms, it captures whether a player is consistently threatening to break serve, not just piling up return points in games already lost.
The significance is clear: only 7.2% of matches are won by the player with the weaker DR, and only 7.4% of sets. Combine a stronger DR with winning more total points, and the chance of losing drops to just 4%.
When it comes to betting, bookmakers use similar data to set their odds, but most bettors lose money over the long run, so it’s important to bet responsibly and only risk what you can afford to lose.
Even when a player leads in key statistics, unexpected outcomes can occur. In these so-called “lottery matches,” rare or exceptional performances in critical moments or statistical parameters-such as clutch break-point conversions, a string of opponent double faults, or extraordinary execution in “money moments”-can override statistical expectations.
A famous example, one that probably still hurts Federer fans, is the 2019 Wimbledon final against Djokovic. Federer led in nearly every category: more points won, better return stats, more break points, more winners, dominance at the net, and superior serve stats. Yet Djokovic won. The difference? Djokovic committed zero unforced errors in three tiebreaks, compared to Federer’s 11. That single statistic, executed at the right moments, outweighed Federer’s overall statistical superiority.
Tennis may seem unpredictable, and anomalies happen. But if you want a simple way to improve your odds of victory-whether as a fan watching or a player competing-start with the fundamentals: win more points than your opponent, and excel in return games. Everything else follows.
For sportsbooks, these insights matter just as much. Understanding which statistics truly drive outcomes enables sharper pricing, smarter risk management, and more engaging markets. In a sport where small percentages swing entire matches, having the right statistical lens can mean the difference between exposure and profit.
Two stats predict tennis winners far better than aces, winners, or first-serve percentage: total points won (TPW) and dominance ratio (DR) in return games. Winning just 52% of total points pushes a player's chance of taking a best-of-three match above 95.8%, and only about 4.5% of ATP matches go to the player who scored fewer points. Lead in both TPW and DR, and your chance of losing drops to roughly 4%. The flashy numbers make highlight reels; these two decide matches.
For fans and analysts, public projects like Jeff Sackmann's Tennis Abstract and its Match Charting Project are the deepest free resources, with detailed data on more than 10,000 ATP and WTA matches logged point by point. The catch is that historical tennis data has long been scattered, with most tournaments outside Wimbledon keeping little accessible archive, which is exactly why analysts like Craig O'Shannessy resorted to saving thousands of match screenshots by hand. For sportsbooks and media that need accurate, real-time tennis stats at scale rather than a manual archive, professional feeds like LSports' Sports API deliver that depth continuously, in a format built to power live products.
Dominance ratio measures how much control a player has in return games. It is calculated as the percentage of return points won divided by the percentage of serve points lost, so a DR above 1.0 means a player is winning a higher share of return points than their opponent, and below 1.0 means they are being outreturned. Popularized by tennis analyst Carl Bialik, DR is a sharper read on match control than raw return-point totals, because it captures whether a player is consistently threatening to break serve rather than just collecting points in games already decided. Only about 7.2% of matches are won by the player with the weaker DR.
Yes, but it is rare. Only about 4.5% of ATP matches are won by the player who scored fewer total points, and at the set level that drops to around 2.4%. Tennis scoring is segmented into points, games, and sets, so points won in a blowout game count no more than points won in a tight one, which lets a player pile up totals without converting them where it matters. Rajeev Ram once won a challenger match despite losing six more points than his opponent, then lost his next despite winning five more, which led to his verdict: tennis is definitely not basketball.
Because statistics set expectations, they don't guarantee outcomes. In what analysts call lottery matches, a player can trail in nearly every category and still win by executing in the handful of moments that decide sets, through clutch break-point conversions, a timely run of opponent double faults, or flawless tiebreak play. The clearest example is the 2019 Wimbledon final, where Roger Federer led Novak Djokovic in points won, return stats, break points, winners, and net play, and still lost. The difference was Djokovic committing zero unforced errors across three tiebreaks against Federer's eleven. Over a full season the stats win out, but any single match can turn on a few points.
