Weighted Elo rating for tennis match predictions

Abstract

Originally applied to tennis by the data journalists of FiveThirtyEight.com, the Elo rating method estimates the strength of each player based on her/his career as well as the outcome of the last match played. Together with the regression-based, point-based and paired-comparison approaches, the Elo rating is a popular method to predict the probability of winning tennis matches. Notwithstanding its widely recognized merits in terms of ease of reproducibility and good performance, the Elo method does not completely take into account the current form of each player and their recent performances. This paper proposes a new version of the Elo rating method, labelled Weighted Elo (WElo), where the standard Elo updating is additionally weighted according to the scoreline of the players’ last match. The proposed method considers not only if a player has won (lost) a match, but also how the victory (defeat) was achieved. In the empirical application, the forecasting performance of the WElo method is evaluated and compared against the most popular forecasting methods in tennis, using a sample of over 60,000 men’s and women’s professional matches. Overall, the WElo method outperforms all these competing methods. Moreover, it provides meaningfully profitable opportunities, according to a simple betting strategy.