Can 25 years of tennis history teach us to predict upcoming tennis matches results

Completed

ARTIFICIAL INTELLIGENCE

PREDICTIVE ANALYSIS

WEB DEVELOPMENT

SPORTS

About Project
Predicting the outcome of tennis matches has always been a challenging task, with so many variables and factors to consider. From players' fitness, speed, to their past performance and the court type, it's no wonder that even the most experienced analysts often get it wrong. Odlica created the state-of-the-art prediction model for tennis matches results. Our system uses all mentioned factors to rank players and predict their chances of winning with unmatched accuracy.
our role

  • artificial intelligence

  • predictive analysis

  • web development

client background
MachtStat is a sports statistics platform that aim to provide live stats for all major sports worldwide.
challenges
Modeling someone’s performance in a specific skill is always challenging as it depends on a lot of factors and the skill level varies over time.
Factors like injury penalty, and performance on different field types should be taken into consideration. Modeling the players status is not sufficient for accurate prediction, extensive feature engineering should also be performed in order to reach the best possible quality.
  1. Modeling a player’s skills in any sport is challenging and differs in time
  2. Players ranking algorithms are not sufficient to model things like recent “form”, load from
  3. Overall rankings doesn’t take into consideration differences between players in speed, performance across different fields, and the statistical relation between all available factors
solution
A dataset for the last 20 tennis matches was collected and cleaned to provide enough data for the model to be trained on. Keen preprocessing and feature engineering were also performed in order to provide as much useful data as possible.
  • Unique ranking algorithm was used other than ATP rankings to enhance results
  • Cross validation was used to ensure accurate results
  • Hyperparameter tuning was used to get the full potential of the model
outcome
We were able to achieve a state of the art accuracy of 72%
Let's See Our Client's Testimonials

I have been building sports SaaS products for a long time and I am delighted every time I work with professional team that focus on quality and getting things done and done in the right way. This was one of those times.

James Morris

MachtStat, COO