There are many facets to a successful tennis prediction model. A good understanding of a player’s game is key, including their strengths and weaknesses, especially as it pertains to particular match surface types and weather conditions. Moreover, it is important to evaluate a player’s consistency over time, as their recent performance can offer valuable insights into their current abilities. This can help you determine how they might perform in future matches against opponents of similar strength.
Using a variety of approaches, data, and calibration methods, several studies on tenis prediction have been carried out so far. While the results vary, most of them report a prediction accuracy around 70-75% mark (with claims reaching up to 99%). In addition, most of them find that betting strategies based on such models fail to beat the predictions implied by bookmaker odds.
The most common type of tenis prediction model is the paired comparison approach, whereby players’ performance in historical matches against each other are used to estimate their strength ranking. Kovalchik (2016) reports that this method outperforms logistic regression-based models, but it still fails to beat bookmaker-implied forecasts. Another popular approach is the use of B-scores, which incorporate time-varying rate/ability information to account for dynamic relationships between players.
In terms of predictive performance, the majority of models report a similar level of accuracy, with some even higher than that reported in the literature on other sports, such as baseball and basketball. For example, Lisi and Zanella (2017) report a prediction accuracy of about 85% for their model, which uses the players’ rankings, their home advantage factor, the tennis surface, and certain information derived from bookmaker odds.
Another common method of predicting tennis matches is to apply machine learning techniques, such as neural networks. This method is able to take into account complex, multidimensional relationships among variables and to learn from previous observations. This makes it ideal for predicting tennis matches. In fact, the authors of this article suggest that a neural network model incorporating the underlying dynamics of player capabilities can significantly outperform a traditional logistic regression-based model, and also achieve better accuracy than other tenis prediction models relying on ranking information.
For example, Gu and Saaty (2019) propose a neural network model that integrates data and expert judgments in order to predict the winner of a match. They show that their model outperforms logistic regression models and other tenis prediction models based on expert judgments alone, and they are able to correctly classify 85% of the matches from the ATP Challenger Tour.
However, the profitability of a tenis prediction strategy depends on a number of factors, such as the market inefficiencies and the risk-adjusted betting returns. The average market inefficiencies in tennis, according to OLBG, are about 6%. This leaves very little room for consistent positive bettor returns. Despite this, the most profitable tipsters specialise in specific match markets such as 1st Set Winner. This is because a strong start by a lower ranked player can pay dividends, even if they eventually lose the match. tenis prediction