Forecasting the Winner of a Tennis Match

We propose a method to forecast the winner of a tennis match, not only at the beginning of the match, but also (and in particular) during the match.The method is based on a fast and exible computer program TENNISPROB, and on a statistical analysis of a large data set from Wimbledon, both at match and at point level.forecasting;panel data;sport

Forecasting the Winner of a Tennis Match

We propose a method to forecast the winner of a tennis match, not only at the beginning of the match, but also (and in particular) during the match.The method is based on a fast and exible computer program TENNISPROB, and on a statistical analysis of a large data set from Wimbledon, both at match and at point level.

"i have a feeling trump will win..................": Forecasting Winners and Losers from User Predictions on Twitter

Social media users often make explicit predictions about upcoming events. Such statements vary in the degree of certainty the author expresses toward the outcome:"Leonardo DiCaprio will win Best Actor" vs. "Leonardo DiCaprio may win" or "No way Leonardo wins!". Can popular beliefs on social media predict who will win? To answer this question, we build a corpus of tweets annotated for veridicality on which we train a log-linear classifier that detects positive veridicality with high precision. We then forecast uncertain outcomes using the wisdom of crowds, by aggregating users' explicit predictions. Our method for forecasting winners is fully automated, relying only on a set of contenders as input. It requires no training data of past outcomes and outperforms sentiment and tweet volume baselines on a broad range of contest prediction tasks. We further demonstrate how our approach can be used to measure the reliability of individual accounts' predictions and retrospectively identify surprise outcomes.Comment: Accepted at EMNLP 2017 (long paper

Selecting the Best? Spillover and Shadows in Elimination Tournaments

We consider how past, current, and future competition within an elimination tournament affect the probability that the stronger player wins. We present a two-stage model that yields the following main results: (1) a shadow effect—the stronger the expected future competitor, the lower the probability that the stronger player wins in the current stage and (2) an effort spillover effect—previous effort reduces the probability that the stronger player wins in the current stage. We test our theory predictions using data from high-stakes tournaments. Empirical results suggest that shadow and spillover effects influence match outcomes and have been already been priced into betting markets.

Towards Structured Analysis of Broadcast Badminton Videos

Sports video data is recorded for nearly every major tournament but remains archived and inaccessible to large scale data mining and analytics. It can only be viewed sequentially or manually tagged with higher-level labels which is time consuming and prone to errors. In this work, we propose an end-to-end framework for automatic attributes tagging and analysis of sport videos. We use commonly available broadcast videos of matches and, unlike previous approaches, does not rely on special camera setups or additional sensors. Our focus is on Badminton as the sport of interest. We propose a method to analyze a large corpus of badminton broadcast videos by segmenting the points played, tracking and recognizing the players in each point and annotating their respective badminton strokes. We evaluate the performance on 10 Olympic matches with 20 players and achieved 95.44% point segmentation accuracy, 97.38% player detection score ([email protected]), 97.98% player identification accuracy, and stroke segmentation edit scores of 80.48%. We further show that the automatically annotated videos alone could enable the gameplay analysis and inference by computing understandable metrics such as player's reaction time, speed, and footwork around the court, etc.Comment: 9 page

Issues in Sports Forecasting

A great amount of effort is spent in forecasting the outcome of sporting events, but few papers have focused exclusively on the characteristics of sports forecasts. Rather, many papers have been written about the efficiency of sports betting markets. As it turns out, it is possible to derive considerable information about the forecasts and the forecasting process from the studies that tested the markets for economic efficiency. Moreover, the huge number of observations provided by betting markets makes it possible to obtain robust tests of various forecasting hypotheses. This paper is concerned with a number of forecasting topics in horse racing and several team sports. The first topic involves the type of forecast that is made: picking a winner or predicting whether a particular team beats the point spread. Different evaluation procedures will be examined and alternative forecasting methods (models, experts, and the market) will be compared. The paper also examines the evidence about the existence of biases in the forecasts and concludes with the applicability of these results to forecasting in general.sports forecasting; betting markets; efficiency; bias; sports models