Filip Misolic vs Daniel Rincon Head-to-Head Stats, Results & Performance Comparison

Predictions Insights for an Upcoming Tennis Match:

Recent Form Analysis: Analyzing the performance of Rincon and Misolic in their recent matches over the past six months, Rincon has displayed an impressive second serve win rate of 48.88%, slightly surpassing Misolic's rate of 48.36%. This statistic holds a significant correlation with match prediction accuracy.

Return Game Statistics: Delving into the return game stats of both players, Rincon has proven to be formidable in recent form, winning 52.6% of their opponent's second serve points. In contrast, Misolic has achieved a slightly lower success rate of 46.53%. The numbers for first serve returns stand at 35.85% for Rincon and 35.31% for Misolic, respectively. These figures carry great significance when determining the favored player in their head-to-head matchup.

Under Pressure Analysis: When facing pressure situations, Rincon has demonstrated the ability to save a commendable 56.61% of breakpoints in recent matches. Conversely, Misolic has exhibited an even higher capability, saving 61.11% of breakpoints. This valuable statistic proves useful for in-game betting predictions.

Performance Overview: Considering their performance over the past year, Rincon has emerged victorious in 55.74% of matches played, with a win/loss record of 34/27. On the other hand, Misolic has achieved a success rate of 51.11%, recording 23 wins and 22 losses. This comprehensive overview sheds light on their head-to-head prediction.

Surface Specialization: Examining the players' career win percentages on various surfaces reveals intriguing insights. Rincon boasts a remarkable win rate of 65% on I.hard, while struggling on Grass with a win rate of 0% (0 wins, 5 losses). Misolic, in turn, excels on Clay courts with an impressive win rate of 66% (134 wins, 69 losses), but shares the same difficulties on Grass as Rincon. This surface analysis contributes significantly to predicting their performances.

Player Level: Over the past year, Rincon has predominantly competed in Challengers/ITF tournaments with a prize pool exceeding $10K, managing to seize victory in 53.49% of matches (23 wins, 20 losses). Similarly, Misolic has mainly participated in Challengers/ITF tournaments with a prize pool exceeding $10K, securing triumph in 46.88% of matches (15 wins, 17 losses). Comparing the statistics between these players to predict the favorite is contingent on the level of events they have been playing at.

Opponent Quality Statistics: During the previous 12 months, Rincon has faced opponents with an average rank of 269.51, while Misolic has encountered players with an average rank of 275.84. This data provides valuable context and allows for a deeper understanding of their performances against opponents of varying skill levels.

Deciding Set Performance: For those interested in live predictions and betting, a crucial factor is how well players perform in deciding sets. Exploring the past 12 months, Rincon has emerged victorious in 52% of deciding sets, while Misolic has proven themselves in 60% of all matches played on the tour. These statistics offer key insights for in-game predictions.

Current Event H2H Statistics: While evaluating past player performances proves beneficial in predicting tennis matches, it is essential to also consider the current event statistics to assess the current form of players. To access the relevant statistics for in-form players in the ongoing event, please refer to the section below. Furthermore, some players exhibit superior performances in specific events, which must be taken into account when determining the match outcome.

Break Point Conversion: Recent form indicates that Rincon has successfully converted 41% of breakpoint opportunities, while Misolic holds a conversion rate of 40.19% when given the chance to break their opponents' serve. This statistic becomes highly significant for in-game live betting tips whenever either player is presented with a breakpoint opportunity.

If you are interested in utilizing models that accurately predict tennis matches, this article serves as an excellent starting point. (Warning: Reserved for enthusiastic statisticians)