2,646 research outputs found
Application of Probabilistic Ranking Systems on Women’s Junior Division Beach Volleyball
Women’s beach volleyball is one of the fastest growing collegiate sports today. The increase in popularity has come with an increase in valuable scholarship opportunities across the country. With thousands of athletes to sort through, college scouts depend on websites that aggregate tournament results and rank players nationally. This project partnered with the company Volleyball Life, who is the current market leader in the ranking space of junior beach volleyball players. Utilizing the tournament information provided by Volleyball Life, this study explored replacements to the current ranking systems, which are designed to aggregate player points from recent tournament placements. Three probabilistic/modern ranking techniques were tested, specifically an Elo variant, TrueSkill, and a random walker graph network. This study found that Elo could predict match outcomes with a 13% higher accuracy than the preexisting systems and TrueSkill with an 11% higher accuracy
Actions Speak Louder Than Goals: Valuing Player Actions in Soccer
Assessing the impact of the individual actions performed by soccer players
during games is a crucial aspect of the player recruitment process.
Unfortunately, most traditional metrics fall short in addressing this task as
they either focus on rare actions like shots and goals alone or fail to account
for the context in which the actions occurred. This paper introduces (1) a new
language for describing individual player actions on the pitch and (2) a
framework for valuing any type of player action based on its impact on the game
outcome while accounting for the context in which the action happened. By
aggregating soccer players' action values, their total offensive and defensive
contributions to their team can be quantified. We show how our approach
considers relevant contextual information that traditional player evaluation
metrics ignore and present a number of use cases related to scouting and
playing style characterization in the 2016/2017 and 2017/2018 seasons in
Europe's top competitions.Comment: Significant update of the paper. The same core idea, but with a
clearer methodology, applied on a different data set, and more extensive
experiments. 9 pages + 2 pages appendix. To be published at SIGKDD 201
A Dimension Reduction Approach to Player Rankings in European Football
Player performance evaluation is a challenging problem with multiple dimensions. Football (soccer) is the largest sports industry in terms of monetary value and it is paramount that teams can assess the performance of players for both financial and operational reasons. However, this is a difficult task, not only because performance differs from position to position, but also it is based on competition, time played and team play-styles. Because of this, raw player statistics are not comparable across players and must be processed to facilitate a fair performance evaluation. Furthermore, teams may have different requirements and a generic player performance evaluation does not directly serve the particular expectations of different clubs. In this study, we provide a generic framework for estimating player performance and performing player-fit-to-criteria assessment, under different objectives, for left and right backs from competitions worldwide. The results show that the players who have ranked high have increased their transfer values and they have moved to suitable teams. Global nature of the proposed methodology expands the analyzed player pool, facilitating the search for outstanding players from all available competitions
Football analytics: a literature analysis from 2010 to 2020
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe overall goal for the current study is to present a literature review of analytics, precisely machine
learning (ML) reference authors in terms of methods and applicable scopes of study, in football
where is a field that historically there are empirical decisions and the usage of analytics has been
growing intensely. The research aims to list relevant academic contributions published between 2010
and 2020, performing a comparable picture per authors across the following subsets: player
individual technical skills and team performance. Furthermore, the approach will provide a summary
of studies for machine learning methods applied in football.
Such outcomes of this study would contribute to the discussion about football analytics. Regarding
that these summaries can drive researchers to have a deep dive into the fields of interest straight to
references preview studied in the thesis. Results indicate that football analytics has broadly vast
opportunities in terms of research, regarding machine learning methods and a high potential to have
a deep exploration of team and player perspective. This study can leverage and pavement new
further in-depth and targeted investigation toward football analytics
Offensive and defensive plus-minus player ratings for soccer
Rating systems play an important part in professional sports, for example, as a source of entertainment for fans, by influencing decisions regarding tournament seedings, by acting as qualification criteria, or as decision support for bookmakers and gamblers. Creating good ratings at a team level is challenging, but even more so is the task of creating ratings for individual players of a team. This paper considers a plus–minus rating for individual players in soccer, where a mathematical model is used to distribute credit for the performance of a team as a whole onto the individual players appearing for the team. The main aim of the work is to examine whether the individual ratings obtained can be split into offensive and defensive contributions, thereby addressing the lack of defensive metrics for soccer players. As a result, insights are gained into how elements such as the effect of player age, the effect of player dismissals, and the home field advantage can be broken down into offensive and defensive consequences. View Full-Text
Keywords: association football, linear regression, regularization, rankingpublishedVersio
A Collaborative Kalman Filter for Time-Evolving Dyadic Processes
We present the collaborative Kalman filter (CKF), a dynamic model for
collaborative filtering and related factorization models. Using the matrix
factorization approach to collaborative filtering, the CKF accounts for time
evolution by modeling each low-dimensional latent embedding as a
multidimensional Brownian motion. Each observation is a random variable whose
distribution is parameterized by the dot product of the relevant Brownian
motions at that moment in time. This is naturally interpreted as a Kalman
filter with multiple interacting state space vectors. We also present a method
for learning a dynamically evolving drift parameter for each location by
modeling it as a geometric Brownian motion. We handle posterior intractability
via a mean-field variational approximation, which also preserves tractability
for downstream calculations in a manner similar to the Kalman filter. We
evaluate the model on several large datasets, providing quantitative evaluation
on the 10 million Movielens and 100 million Netflix datasets and qualitative
evaluation on a set of 39 million stock returns divided across roughly 6,500
companies from the years 1962-2014.Comment: Appeared at 2014 IEEE International Conference on Data Mining (ICDM
On the importance of the probabilistic model in identifying the most decisive game in a tournament
Identifying the decisive matches in international football tournaments is of great
relevance for a variety of decision makers such as organizers, team coaches and/or
media managers. This paper addresses this issue by analyzing the role of the statistical
approach used to estimate the outcome of the game on the identification of decisive
matches on international tournaments for national football teams. We extend the
measure of decisiveness proposed by Geenens (2014) in order to allow to predict or
evaluate the decisive matches before, during and after a particular game on the
tournament. Using information from the 2014 FIFA World Cup, our results suggest that
Poisson and kernel regressions significantly outperform the forecasts of ordered probit
models. Moreover, we find that although the identification of the most decisive matches
is independent of the model considered, the identification of other key matches is model
dependent. We also apply this methodology to identify the favorite teams and to predict
the most decisive matches in 2015 Copa America before the start of the competition.
Furthermore, we compare our forecast approach with respect to the original measure
during the knockout stage
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