Emily Appleton vs Matilde Paoletti Head-to-Head Stats, Results & Performance Comparison

Prediction Insights for the Upcoming Tennis Match:

The performance of tennis players in recent form provides valuable insights into their chances of winning. Examining specific statistics can help make accurate match predictions.

1. Second Serve Performance: When analyzing their second serve performance in the last six months, it becomes evident that Appleton has a slight advantage, winning 46.96% of points, compared to Paoletti's 44.69%. This statistic has a strong correlation with match prediction accuracy.
2. Return Game Statistics: Looking at return stats, Appleton has shown remarkable ability in recent form, winning 51.91% of their opponent's second serve points. On the other hand, Paoletti has performed even better, securing 57.67%. When considering first serve returns, Appleton's success rate is 34.04%, whereas Paoletti's is 42.89%. This information proves crucial in determining who is favored in their head-to-head matchup.
3. Under Pressure Analysis: When a player faces breakpoints, their ability to save them becomes significant. Appleton has demonstrated resilience, saving 67.14% of breakpoints in recent matches. In comparison, Paoletti has managed to save 51.75%. This statistic is particularly useful for in-game betting predictions.
4. Performance Overview: Over the past year, Appleton has won 54.55% of matches played (W/L 30/25), while Paoletti has an impressive record of 65.71% (W/L 23/12). Considering their head-to-head history, this provides an overall prediction overview.
5. Surface Preferences: Each player has specific surface preferences based on their historical win percentages. Appleton excels on I.hard courts with a 56% win rate (W/L 53/42), while Grass has proven to be their least successful surface, with a 41% win rate (W/L 13/19). Conversely, Paoletti performs best on I.hard courts, winning 67% (W/L 8/4), and struggles on Grass, winning 0% (W/L 0/1).
6. Player Level: Comparing the players' stats requires considering the tournament level at which they have been competing. In the past year, Appleton has played the majority of their matches in Challengers/ITF tournaments > $10K, winning 59.57% (W/L 28/19). Meanwhile, Paoletti's focus has also been on Challengers/ITF tournaments > $10K, achieving a 65.71% success rate (W/L 23/12). When predicting the favorite, it is essential to consider the level of competition they have faced.
7. Opponent Quality Statistics: Examining the average rank of their opponents in the last 12 months provides further insights. Appleton has faced players with an average rank of 327.98, whereas Paoletti has encountered opponents with an average rank of 367.46.
8. Deciding Set Performance: If the match reaches a deciding set, a player's track record in such situations becomes crucial. Over the last 12 months, Appleton has won 50% of deciding sets, while Paoletti boasts an impressive 68% victory rate in all matches played on the tour.
9. Current Event H2H Statistics: Past performance is valuable, but current event stats are equally important for predicting in-form players. Evaluating the section below (if they have played a match in the current event) provides valuable insights. Additionally, the influence of certain events on players' performance should not be overlooked in determining the likely outcome.
10. Break Point Conversion: Another significant factor in live betting is break point conversion. Appleton has successfully converted 40.63% of breakpoint opportunities, whereas Paoletti has a higher conversion rate of 48.14% when seizing the chance to break their opponents' serves. This statistic can provide useful tips for in-game betting when either player has a breakpoint opportunity.
11. If you are interested in models that predict tennis matches, this article offers a comprehensive starting point. (Warning: Only for discerning statisticians)