Forecasting the winner of a tennis match

Klaassen, Franc; Magnus, Jan R.

doi:10.1016/s0377-2217(02)00682-3

Cited by 95 publications

(63 citation statements)

References 6 publications

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“…A second model excluded the rank predictors, and a third model excluded player physical characteristics. In terms of a Brier score, the predictive performance of the full model was superior Klaassen and Magnus (2003) considered a logit model to predict match outcomes at the beginning of a match. Letting R i be the rank of the ith player, who is favored to win the match, and R j the rank of the jth opponent, the logistic model they propose is…”

Section: Regression-basedmentioning

confidence: 99%

Searching for the GOAT of tennis win prediction

Kovalchik

2016

Journal of Quantitative Analysis in Sports

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Sports forecasting models -beyond their interest to bettors -are important resources for sports analysts and coaches. Like the best athletes, the best forecasting models should be rigorously tested and judged by how well their performance holds up against top competitors. Although a number of models have been proposed for predicting match outcomes in professional tennis, their comparative performance is largely unknown. The present paper tests the predictive performance of 11 published forecasting models for predicting the outcomes of 2395 singles matches during the 2014 season of the Association of Tennis Professionals Tour. The evaluated models fall into three categories: regressionbased, point-based, and paired comparison models. Bookmaker predictions were used as a performance benchmark. Using only 1 year of prior performance data, regression models based on player ranking and an Elo approach developed by FiveThirtyEight were the most accurate approaches. The FiveThirtyEight model predictions had an accuracy of 75% for matches of the most highly-ranked players, which was competitive with the bookmakers. The inclusion of careerto-date improved the FiveThirtyEight model predictions for lower-ranked players (from 59% to 64%) but did not change the performance for higher-ranked players. All models were 10-20 percentage points less accurate at predicting match outcomes among lower-ranked players than matches with the top players in the sport. The gap in performance according to player ranking and the simplicity of the information used in Elo ratings highlight directions for further model development that could improve the practical utility and generalizability of forecasting in tennis.

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Section: Regression-basedmentioning

confidence: 99%

Searching for the GOAT of tennis win prediction

Kovalchik

2016

Journal of Quantitative Analysis in Sports

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“…If we assume that points are independent and identically distributed, then the only difference between levels of aggregation is the counting system. For example, we may find that (p, p * ) = (0.65, 0.66) at point level, which translates to game winning probabilities (g, g * ) = (0.83, 0.85) (see Klaassen and Magnus, 2003), so that the inefficiency measure increases from 1 − p/p * = 1.5% to 1 − g/g * = 1.9%. If a game is defined differently, then the inefficiency measure will change, even if players behave in the same way.…”

Section: Accepted M Manuscriptmentioning

confidence: 99%

“…But a player serves many points in a tennis match. Thus we study the impact at higher levels of aggregation, applying the software developed in Klaassen and Magnus (2003) to all p and p * . If we consider a game, then the impact of inefficiency increases, not because the players perform differently but because of the structure of the tennis scoring system: from 0.7%-points at point level to 1.1%-points at game level for the men, and from 1.2%-points at point level to 2.5%-points at game level for the women.…”

Section: Accepted M Manuscriptmentioning

confidence: 99%

The efficiency of top agents: An analysis through service strategy in tennis

Klaassen

Magnus

2009

Journal of Econometrics

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We consider the question whether top tennis players in a top tournament (Wimbledon) employ an optimal (efficient) service strategy. While we show that top players do not, in general, follow an optimal strategy, our principal result is that the estimated inefficiencies are not large: the inefficiency regarding winning a point on service is on average 1.1% for men and 2.0% for women, implying that–by adopting an efficient service strategy–players can (on average) increase the probability of winning a match by 2.4%-points for men and 3.2%-points for women. While the inefficiencies may seem small, the financial consequences for the efficient player at Wimbledon can be substantial: the expected paycheck could rise by 18.7% for men and even by 32.8% for women. We use these findings to shed some light on the question of whether economic agents are successful optimizers

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“…Hence we also calculate the inefficiency at game-, set-, and match-level, using the software developed in Klaassen and Magnus (2003). If we consider a game, the inefficiency increases, not because the players perform differently but because of the structure of the tennis scoring system: from 1.1% at point level to 1.4% at game level for the men, and from 2.0% at point level to 4.0% at game level for the women.…”

Section: Efficiency Resultsmentioning

confidence: 99%

Are Economic Agents Successful Optimizers? An Analysis through Service Strategy in Tennis

Klaassen

Magnus

2006

SSRN Journal

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Are economic agents successful optimizers? An analysis through service strategy in tennis Klaassen, F.J.G.M.; Magnus, J.R. Link to publication General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. Download date: 11 May 2018Are economic agents successful optimizers? An analysis through service strategy in tennis Abstract: We consider the question whether top tennis players in a top tournament (Wimbledon) employ an optimal (efficient) service strategy. We show that top players do not, in general, follow an optimal strategy. The estimated inefficiencies are not large: the inefficiency regarding winning a point on service is on average 1.1% for men and 2.0% for women. Thus, by adopting an efficient service strategy, players can (on average) increase the probability of winning a match by 2.4% for men and 3.2% for women. We use these findings to shed some light on the question whether economic agents are successful optimizers.

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Forecasting the winner of a tennis match

Cited by 95 publications

References 6 publications

Searching for the GOAT of tennis win prediction

Searching for the GOAT of tennis win prediction

The efficiency of top agents: An analysis through service strategy in tennis

Are Economic Agents Successful Optimizers? An Analysis through Service Strategy in Tennis

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