The predictive power of ranking systems in association football

Lašek, Jan; Szlávik, Zoltán; Bhulai, Sandjai

doi:10.1504/ijapr.2013.052339

Cited by 105 publications

(104 citation statements)

References 15 publications

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“…These findings provide further rationale for the wide use of Elo-based predictions in the media and adds tennis to a growing list of sports for which Elo ratings have proven useful (Stefani 2011;Lasek, Szlávik, and Bhulai 2013). Still, the performance of the FiveThirtyEight might seem surprising given that the information it uses is fairly basic.…”

Section: Discussionmentioning

confidence: 87%

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: Discussionmentioning

confidence: 87%

Searching for the GOAT of tennis win prediction

Kovalchik

2016

Journal of Quantitative Analysis in Sports

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“…See, for instance, the recent work of Kovalchik (2016) for tennis, Hvattum and Arntzen (2010) for English league football and Lasek, Szlávik and Bhulai (2013) for international football. 8 The latter article shows different methods having outcome MSEs in the range 12%-15%, though because of the frequency of draws in football these numbers are not directly comparable to our win-or-lose setting.…”

Section: Do Simulations Relate To Real Data?mentioning

confidence: 99%

Elo Ratings and the Sports Model: A Neglected Topic in Applied Probability?

Aldous¹

2017

Statist. Sci.

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Abstract. In a simple model for sports, the probability A beats B is a specified function of their difference in strength. One might think this would be a staple topic in Applied Probability textbooks (like the Galton-Watson branching process model, for instance) but it is curiously absent. Our first purpose is to point out that the model suggests a wide range of questions, suitable for "undergraduate research" via simulation but also challenging as professional research. Our second, more specific, purpose concerns Elo-type rating algorithms for tracking changing strengths. There has been little foundational research on their accuracy, despite a much-copied "30 matches suffice" claim, which our simulation study casts doubt upon.

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“…The number of prediction errors for game outcomes is computed assuming that a draw can be regarded as half a win, half a loss [12]. We present the experimental results in Figure 7.…”

Section: Xbox Title Halo2mentioning

confidence: 99%

Hypernode Graphs for Spectral Learning on Binary Relations over Sets

Ricatte¹,

Gilleron

Tommasi

2014

Machine Learning and Knowledge Discovery in Databases

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National audienceWe introduce hypernode graphs as (weighted) binary relations between sets of nodes : a hypernode is a set of nodes, a hyperedge is a pair of hypernodes, and each node in a hypernode of a hyperedge is given a non ne-gative weight that represents the node contribution to the relation. Hypernode graphs model binary relations between sets of individuals while allowing to reason at the level of individuals. We present a spectral theory for hypernode graphs that allows us to introduce an unnormalized Laplacian and a smoothness semi-norm. In this framework, we are able to extend existing spec-tral graph learning algorithms to the case of hypernode graphs. We show that hypernode graphs are a proper extension of graphs from the expressive power point of view and from the spectral analysis point of view. Therefore hypernode graphs allow to model higher or-der relations while it has been shown in [1] that it is not the case for (classical) hypergraphs. In order to prove the capabilities of the model, we represent mul-tiple players games with hypernode graphs and intro-duce a novel method to infer skill ratings from the game outcomes. We show that spectral learning algorithms over hypernode graphs obtain competitive results with skill ratings specialized algorithms such as Elo duelling and TrueSkill

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The predictive power of ranking systems in association football

Cited by 105 publications

References 15 publications

Searching for the GOAT of tennis win prediction

Searching for the GOAT of tennis win prediction

Elo Ratings and the Sports Model: A Neglected Topic in Applied Probability?

Hypernode Graphs for Spectral Learning on Binary Relations over Sets

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