The world of tennis is a fascinating one, and predicting the outcome of matches can be an exciting pursuit for fans and bettors. However, the task of making accurate predictions requires a solid understanding of various factors that influence player performance. Using data analytics can help to enhance the quality of predictions and maximize returns for bettors. In this article, we will explore ten strategies for improving tenis prediction, from analyzing player statistics to considering external factors and leveraging advanced analytics.
Despite the many challenges associated with tenis prediction, the potential rewards can be considerable. In fact, betting models based on machine learning techniques are able to outperform bookmaker odds-implied forecasts in most cases. However, the results obtained from these models vary widely and are often volatile. The average annual return of a betting strategy is well below the rate of growth of the stock market, and a bettor must be prepared to invest over a long period of time in order to achieve consistent positive returns.
The best performing model ensembles are able to generate slightly above average return-on-investment (ROI) but still do not achieve significant excesses over the stock market. In addition, even these high-performing models do not consistently deliver profitable returns over the course of a full season. This is due to the incredibly volatile nature of tennis, where betting odds are subject to wide fluctuations and often have large spreads.
For example, a model that incorporates historical match and player data such as tournament series and round, age differences between players, and home advantage hardly improves on predictions made purely with the help of a simple tennis ranking. Similarly, the addition of additional parameters such as tournament history, surface type, and head-to-head record only marginally improves prediction accuracy.
In the case of boys, only upper body power significantly predicts tennis performance at U13, and even this is not robust enough to explain a substantial portion of the variation in performance between highly ranked players. In contrast, a model that takes into account the dynamic evolution of a player’s ability by incorporating a B-score, as described in Lisi and Zanella (2017), outperforms those that are calibrated on rankings alone.
The upcoming US Open will be the first opportunity for some of these newer models to demonstrate their mettle on two different surfaces. Ruud, who lost in the 2022 French Open final to Alcaraz but reached another Slam title match at the US Open the following year, is likely to be a strong contender for the men’s crown. Likewise, Kyrgios could be in the hunt if he can find the form that led him to his first grand slam final appearance in Miami last year.
On the women’s side, Swiatek is a favorite to win her fourth straight US Open title and secure a Calendar Slam, matching Margaret Court’s record for most career Grand Slam titles. However, the challengers for the women’s title include a crop of talented players including Sinner, who reached the Miami final and semifinals this year, and Frances Tiafoe, who won Indian Wells and is a regular semifinalist at Wimbledon. tenis prediction
