Here’s how Bing predicts possible outcomes of leading global sporting events

Pliskova Kristyna CZE Do you want to read the rest of this article? Aggregate features like the number of games a team has played helps determine experience. This means that the odds, more or less, reflect the wisdom of the crowd , making it a very good predictor. Tenis Schedule

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With the recent buzz about Elo rankings in tennis, both at FiveThirtyEight and here at Tennis Abstract , comes the ability to forecast the results of tennis matches. For inferring forecasts from the ATP ranking, we use a specific formula 1 and for Pinnacle, which is one of the biggest tennis bookmakers, we calculate the implied probabilities based on the provided odds minus the overround 2.

What we see here is how many percent of the predictions were actually right. Interestingly, the Elo model of Riles is outperformed by the predictions inferred from the ATP ranking.

Since there are several parameters that can be used to tweak an Elo model, Riles may still have some room left for improvement. However, just looking at the percentage of correctly called matches does not tell the whole story.

In fact, there are more granular metrics to investigate the performance of a prediction model: Calibration , for instance, captures the ability of a model to provide forecast probabilities that are close to the true probabilities.

Resolution measures how much the forecasts differ from the overall average. In other words, the more extreme and still correct forecasts are, the better. As we can see, the predictions are not always perfectly in line with what the corresponding bin would suggest. However, there are still two interesting cases marked in bold where sample size is better and which raised my interest. For the Riles model, this would maybe be a starting point to tweak the model. The Brier score combines Calibration and Resolution and the uncertainty of the outcomes into a single score for measuring the accuracy of predictions.

The models of FiveThirtyEight and Pinnacle for the used subset of data essentially perform equally good. At the beginning a problem of prediction of tennis player successfulness is presented. Factors that affect the successfulness are presented briefly, while the factors that are used in the study are discussed in detail. The study focuses on the motor and morphological factors. The objectives of this survey, the machine learning theory used in the solution and working methodology used in the survey are presented next.

The results and the interpretation of prediction of successfulness of tennis players for current and future age periods are given at the end. For prediction, classification and regression methods of machine learning with two different approaches for optimal attribute subset selection were used.

This research doesn't cite any other publications. Discover more publications, questions and projects in Tennis. The aim of the study was to examine the predictability of the competitive performance of Slovene tennis players by using the most promising morphological measures and motor tests selected by automatic computer methods and by experienced tennis coaches by means of machine learning methods.

The analysis included altogether 1, male and female tennis players who had undergone regular testing by Prediction of the successfulness of tennis players with machine learning methods. Classification of top male tennis players. The main objective of this study was to define different quality groups of tennis players based on their position on the ATP ranking list.