Eliakim Coulibaly vs Maik Steiner Head-to-Head Stats, Results & Performance Comparison

Predictions Related to Tennis Match Insights:

1. Second serve performance in recent times: In terms of recent form spanning over the past six months, the success rate for Coulibaly on their second serve stands at 45.61%, while Steiner has recorded a comparatively higher rate of 51.37%. It is imperative to note the strong correlation between this statistical measure and the accuracy of match predictions.
2. Recent return game statistics: Recent return game stats shed light on Coulibaly's performance in recent times. They have achieved a commendable success rate of 55% on their opponent's second serve points, whereas Steiner has secured 49.79% in the same category. In terms of first serve returns, Coulibaly possesses a rate of 35.7%, while Steiner has achieved 32.21%. Remarkably, these figures have a significant correlation when it comes to determining the favored player in this particular head-to-head (H2H) matchup.
3. Analysis under pressure: Analyzing the response of players under pressure scenarios, Coulibaly stands out by saving 56.67% of breakpoints in recent form. On the other hand, Steiner has been able to save 58.33% of breakpoints, illustrating the utility of this statistic in predicting in-game betting outcomes.
4. Performance overview: Examining the performance overview of the past year, Coulibaly boasts an impressive win rate of 72.84% in matches played (W/L: 59/22). In comparison, Steiner has a win rate of 60% (W/L: 36/24), providing us with a comprehensive head-to-head prediction overview.
5. Surface expertise: Concerning surface expertise, Coulibaly has displayed their finest career win rate of 73% on Hard courts (W/L: 141/52), whereas they tend to struggle more on Clay courts with a win rate of 54% (W/L: 14/12). In contrast, Steiner excels on Clay courts, securing a career win rate of 61% (W/L: 109/69), while I.hard surfaces prove to be more challenging for them with a win rate of 40% (W/L: 2/3).
6. Player level analysis: Delving into the player levels observed in the past year, Coulibaly has predominantly participated in Futures/Satellites/ITF tournaments with a prize pool of $10K. Remarkably, they have emerged victorious in 77.97% of their matches (W/L: 46/13). On the other hand, Steiner has also primarily competed in tournaments of similar nature, winning 60% of their matches (W/L: 36/24). It is essential to contextualize the relative nature of the stats when comparing players to predict a favorite, factoring in the event level they have consistently played at.
7. Opponent quality analysis: Over the course of the past 12 months, Coulibaly has encountered opponents with an average rank of 428.3, whereas Steiner has faced players with an average rank of 327.87.
8. Deciding set performance across matches: For those interested in live predictions and betting, should this match reach a deciding set, Coulibaly has emerged victorious in 78% of deciding sets throughout the past 12 months. Conversely, Steiner has achieved a winning rate of 47% across all matches played on tour.
9. Current event head-to-head statistics: While historical player performance provides valuable insights for predicting tennis matches, it is equally crucial to consider the current event statistics to gauge the form of each player. For relevant information on this matter, please refer to the section below (if the players have participated in the current event). Additionally, certain players may exhibit improved performance patterns at specific events, and this factor should be taken into account when determining the match outcome.
10. Break point conversion efficiency: In terms of recent form, Coulibaly has impressively converted 46.4% of their breakpoint opportunities, whereas Steiner has achieved a conversion rate of 42.98% when seizing chances to break their opponents' serve. This particular statistic holds significant value for in-game live betting recommendations, particularly when one of the players finds themselves with a breakpoint opportunity.
11. If you are interested in models that proficiently predict tennis matches, this article serves as an excellent starting point (Warning: Reserved for stats enthusiasts).