Prediction for Marcelo Tomas Barrios Vera - Oliver Bonding 15 June 2024
Head-to-Head Encounters: Marcelo Tomas Barrios Vera - Oliver Bonding
Marcelo Tomas Barrios Vera and Oliver Bonding don't know what to expect from each other, as they will be crossing racquets for the first time. The history of face-to-face encounters is undoubtedly important for making predictions in tennis matches, so the absence of head-to-head stats is a challenge but not critical. Our choice, based on statistics and other indicators, has already been made. To find out more, read below.Statistical Preview of Marcelo Tomas Barrios Vera and Oliver Bonding
Marcelo Tomas Barrios Vera
Let's examine the results of the last ten matches in the current season for Marcelo Tomas Barrios Vera. Here are the key indicators:
- Win/Loss: 5 / 5
- Win/Loss in the first set: 6 / 4
- Average total games: 21.8
Results of victories:
- 4 with a score of 2-0 (80%);
- 1 resulted in a victory with a score of 2-1 (20%).
Results of defeats:
- 3 with a score of 0-2 (60%);
- 2 was lost with a score of 1-2 (40%).
Oliver Bonding
Let's talk about how the current season is going for Oliver Bonding. Data for the last ten games:
- Win/Loss: 0 / 7
- Win/Loss in the first set: 2 / 5
- Average total games: 21.1
Victory results:
- 0 with a score of 2-0 (0%);
- 0 with a result of 2-1 (0%).
Defeat results:
- 5 with a score of 0-2 (71.4%);
- 2 was lost 1-2 (28.6%).
Prediction for the match between Marcelo Tomas Barrios Vera and Oliver Bonding
We suggest identifying the likely winner of the upcoming confrontation based on their average winning game performance per match. To do this, let's look at the data for both sides over the past year. Marcelo Tomas Barrios Vera averages 23.3 games per match, winning 11.5 of them, and 4.9 games in the first set. Oliver Bonding has the following results: the average total games over the past year – 21.1, with 8.1 victories, and an average of 4.1 games in the starting set. Considering this statistics, we choose the bet Total under (21.5).
This forecast was generated using AI Scores24. The product is in beta testing and may contain minor errors.
