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A tennis player may serve better than their opponent, hit more winners, make fewer errors, convert more break points, and still lose. How can that be? It could simply be that the player didn’t step up where it mattered most.
“Every sport is about numbers. How many points did you score, how many wins… It’s like watching a blackjack game and counting cards. Once you’ve learned to count, would you ever go back to relying on intuition?”
This quote, from Billy Beane, the figure behind the “Moneyball” revolution in American baseball, perfectly captures the logic of sports analytics in a single sentence. Since the early 2000s, this approach has spread across sports worldwide. Science provides empirically proven tools to help decide games, select the right lineup, and adopt the most effective tactics, so why not use them?
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.
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)
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 match despite winning five more points. His conclusion was pointed: “Tennis is definitely not basketball.”
True, tennis isn’t basketball, but the general principle holds: winning more points than your opponent is still the single best predictor of match victory.
According to Inpredictable, only 4.5% of ATP matches are won by a player who scored fewer points. At the set level, that figure drops to 2.4%. Winning 51% of points gives a player roughly an 85% chance of winning a best-of-three match; winning 52% pushes the probability over 95%. 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 a 15% chance to win.
Category 2: DR (Dominance Ratio in Return Games)
This may not be the first category fans think of, but it’s intuitive: it measures the percentage of return points won against your opponent. It’s not about winning more total return points, but about winning a higher percentage. In simpler terms: consistently getting closer to breaking your opponent’s serve.
This stat’s significance is clear: only 7.2% of matches are won by a player who is performs worse in this category, and only 7.4% of sets. When combined with winning more total points, the chance of losing drops to just 4%.
When considering betting, odds, and improving your odds of victory, it’s important to note that bookmakers use similar data to set their odds and may offer promotions to attract bettors, but most users lose money in 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.
