Acing Predictions
Logistic Regression in the 2024 US Open Men's Tennis Championship
DOI:
https://doi.org/10.58445/rars.1646Keywords:
tennis, US Open, sports forecasting, logistic regressionAbstract
Predicting match outcomes in tennis poses a significant challenge due to the sport's unpredictable nature and the influence of numerous factors on player performance. This study seeks to forecast the top 10 ranked athletes and their respective winning probabilities to win the 2024 US Open Men's Championship using Logistic Regression. The research analyzes data from US Opens from 2016 to 2023. The primary variables selected for the analysis are the winner's rank and the opponent's rank, applied in a logistic regression model using an 80/20 train-test split. The test accuracy was 68%. The probability of winning the US Open was also calculated for the top 10 ranked players, finding that the No.1 ranked player’s probability of winning the US Open was 3.1%. As No.1 seeds have won 28.9% of the men’s US Open singles tournaments, this suggests that using the player’s and opponent’s rank is insufficient to determine the probability of an individual winning the US Open.
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