Several methods are available in literature for estimating the probability of winning in tennis, such as the regression-based, point-based and pairedcomparison approaches, for instance. Among these latter, the ELO rating method plays a prominent role. Originally applied to tennis by the data journalists of FiveThirtyEight.com, the ELO rating method estimates the strength of each player on the basis of the last match in order to predict the probability of winning for the upcoming match. Notwithstanding its widely recognized merits in terms of ease of reproducibility and good performances, the ELO rating system does not take into account the number of games won by each player in the last match(es). The aim is to investigate the profitability of a variant of the standard ELO rating method, where also the games of the last match(es) concur to define the rating of each player.

Weighted ELO rating predictions in tennis / Candila, Vincenzo; De Angelis, Luca; Angelini, Giovanni. - (2019). (Intervento presentato al convegno 13th International Conference on Computational and Financial Econometrics (CFE 2019) tenutosi a London, UK).

Weighted ELO rating predictions in tennis

Vincenzo Candila;
2019

Abstract

Several methods are available in literature for estimating the probability of winning in tennis, such as the regression-based, point-based and pairedcomparison approaches, for instance. Among these latter, the ELO rating method plays a prominent role. Originally applied to tennis by the data journalists of FiveThirtyEight.com, the ELO rating method estimates the strength of each player on the basis of the last match in order to predict the probability of winning for the upcoming match. Notwithstanding its widely recognized merits in terms of ease of reproducibility and good performances, the ELO rating system does not take into account the number of games won by each player in the last match(es). The aim is to investigate the profitability of a variant of the standard ELO rating method, where also the games of the last match(es) concur to define the rating of each player.
2019
13th International Conference on Computational and Financial Econometrics (CFE 2019)
04 Pubblicazione in atti di convegno::04d Abstract in atti di convegno
Weighted ELO rating predictions in tennis / Candila, Vincenzo; De Angelis, Luca; Angelini, Giovanni. - (2019). (Intervento presentato al convegno 13th International Conference on Computational and Financial Econometrics (CFE 2019) tenutosi a London, UK).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1452171
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