Han Shi vs Gabriella Da Silva Fick Head-to-Head Stats, Results & Performance Comparison

Insights into the upcoming tennis match:

Recent performance on second serves: Over the past six months, there has been a notable discrepancy in the second serve performance between Fick and Shi. Fick has successfully won 47.37% of points on their second serve, whereas Shi has achieved a slightly lower success rate of 43.26%. It is worth mentioning that there exists a strong correlation between this statistical measure and the accuracy of match predictions.

Analysis of return game statistics: Assessing the return game statistics for these players, Fick has demonstrated remarkable proficiency in recent matches. They have managed to win 55.21% of their opponent's second serve points. On the other hand, Shi has slightly outperformed Fick in this aspect, securing a win rate of 58.18%. In the domain of first serve returns, Fick's success rate stands at 34.72%, whereas Shi has shown a higher rate of 38.53%. This particular metric holds substantial significance when attempting to predict the favorite in their forthcoming head-to-head (H2H) encounter.

Analysis of performance under pressure: When it comes to saving breakpoints, Fick has effectively saved 48.51% of them in recent matches. In contrast, Shi has displayed an even more impressive ability to save breakpoints, successfully defending against 55.7% of them. Expectations regarding in-game betting predictions can be influenced by considering this useful statistic.

Overview of performance in the past year: Over the past year, Fick has achieved a noteworthy win rate of 56.67% in the matches played (with a win-loss ratio of 34/26), whereas Shi has enjoyed an even higher success rate of 62.82% (with a win-loss ratio of 49/29). These figures provide us with an overall view of their head-to-head prediction.

Examination of the best playing surface: Fick's most favorable playing surface throughout their career has been Grass, where they have won an impressive 73% of matches (W/L ratio of 11/4). Conversely, Hard courts seem to pose a challenge for Fick, resulting in a mere 50% win rate (W/L ratio of 103/101). On the other hand, Shi has experienced their greatest success on Clay courts, having achieved a remarkable win rate of 78% (W/L ratio of 7/2). However, similar to Fick, Shi also struggles on Hard courts, winning only 61% of matches (W/L ratio of 95/62).

Evaluation of player level: Over the past year, Fick has primarily participated in Challengers/ITF tournaments with prize money exceeding $10,000. In these tournaments, Fick has exhibited commendable performance, winning 57.63% of matches played (W/L ratio of 34/25). Conversely, Shi has predominantly competed in the same category of Challengers/ITF tournaments, winning 66.1% of matches (W/L ratio of 39/20). It should be noted that when comparing the statistics between these players to predict the favored player, their respective achievement levels in the events they have participated in come into play.

Assessment of opponent quality: In terms of the opponents faced over the past 12 months, Fick has encountered players with an average rank of 318.13, while Shi has contended against opponents with an average rank of 442.24. This information provides valuable context when evaluating their performances and predicting the outcome of their upcoming match.

Performance in deciding sets: For those interested in live predictions and betting, it should be noted that if this match advances to a deciding set, Fick has proven to be formidable in such situations, triumphing in 60% of deciding sets over the past 12 months. Conversely, Shi has a slightly lower success rate of 52% in all matches played on the tour. These figures become relevant during in-game decision-making and live betting scenarios.

Consideration of current event head-to-head statistics: Past player performance undoubtedly assists in predicting tennis matches, but once the event is underway, current event statistics provide crucial insights into the form of the players. For such indications, refer to the section below (if available) that details the players' performances in the ongoing event. Additionally, it is important to acknowledge that some players excel in particular events, which should be factored in when determining the likely outcome.

Breakpoint conversion analysis: Recent form highlights Fick's proficiency in converting breakpoint opportunities, succeeding in 45.52% of instances, while Shi has displayed a slightly lower conversion rate of 44.96% when attempting to break their opponents' serves. This statistic becomes invaluable for in-game live betting tips whenever either player finds themselves with a breakpoint opportunity.

If you are intrigued by models that predict tennis matches, you may find this article to be a commendable starting point. (Warning: Intended for statistics enthusiasts)