Issues in sports forecasting

Stekler, H. O.; Sendor, David; Verlander, Richard

doi:10.1016/j.ijforecast.2010.01.003

Cited by 90 publications

(48 citation statements)

References 90 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Sports result prediction is nowadays very popular among fans around the world, mostly due to the expansion of sports betting (Stekler, Sendor, & Verlander, 2010). Major League Baseball (MLB) is a multi-billion dollar statistically filled business, and many people are strongly 92 interested in developing systems with the aim of providing the best prediction of the winner in many specific baseball games (Baumer & Zimbalist, 2014).…”

Section: Introductionmentioning

confidence: 99%

Predicting Win-Loss outcomes in MLB regular season games – A comparative study using data mining methods

Soto-Valero

2016

International Journal of Computer Science in Sport

View full text Add to dashboard Cite

Baseball is a statistically filled sport, and predicting the winner of a particular Major League Baseball (MLB) game is an interesting and challenging task. Up to now, there is no definitive formula for determining what factors will conduct a team to victory, but through the analysis of many years of historical records many trends could emerge. Recent studies concentrated on using and generating new statistics called sabermetrics in order to rank teams and players according to their perceived strengths and consequently applying these rankings to forecast specific games. In this paper, we employ sabermetrics statistics with the purpose of assessing the predictive capabilities of four data mining methods (classification and regression based) for predicting outcomes (win or loss) in MLB regular season games. Our model approach uses only past data when making a prediction, corresponding to ten years of publicly available data. We create a dataset with accumulative sabermetrics statistics for each MLB team during this period for which data contamination is not possible. The inherent difficulties of attempting this specific sports prediction are confirmed using two geometry or topology based measures of data complexity. Results reveal that the classification predictive scheme forecasts game outcomes better than regression scheme, and of the four data mining methods used, SVMs produce the best predictive results with a mean of nearly 60% prediction accuracy for each team. The evaluation of our model is performed using stratified 10-fold cross-validation.

show abstract

Section: Introductionmentioning

confidence: 99%

Predicting Win-Loss outcomes in MLB regular season games – A comparative study using data mining methods

Soto-Valero

2016

International Journal of Computer Science in Sport

View full text Add to dashboard Cite

show abstract

“…To pierwszy etap weryfikacji. Tego typu testy zostały wprowadzone po raz pierwszy przez Preston i Baratta (1948), a następnie były wykorzystywane przez yaariego (1965), Rosetta (1971) oraz Grifitha (1949, który wykonywał je w warunkach rzeczywistych dla amerykańskich wyścigów konnych, − test związków między kursami bukmacherskimi a prawdopodobieństwami wygranych -w tym przypadku wykrycie negatywnych relacji podważa założenia słabej formy efektywności rynku, − test na istotność parametrów modelu prognostycznego dla wyników meczów oraz dla prawdopodobieństw -w tym wypadku uzasadnia się możliwość wykorzystania modeli ilościowych we wspieraniu procesu inwestycyjnego [Goddard, 2013;Goddard, Asimakopoulos, 2004;Stekler, Sendor, Verlander, 2010], − test na normalność rozkładu stóp zwrotu oraz rozkładu niepewności gry -brak normalności rozkładu oznacza, że bukmacherzy intuicyjnie zniekształcają szanse wygranej faworyta, powodując zmniejszanie wygranej [levitt, 2004]. Półsilna forma efektywności rynku zakładów bukmacherskich oznacza, że oczekiwane stopy zwrotu na dowolny zakład lub typ zakładu, dotyczący dowolnego zdarzenia z identycznym prawdopodobieństwem wygranej, są identyczne [Williams, 1999].…”

Section: Efektywność Rynkuunclassified

The Research on the Efficiency of Online Bookmakers’ Market in Poland

Majewski

2017

View full text Add to dashboard Cite

WstępWeryfikacja hipotez efektywności rynku jest jednym z najpopularniejszych tematów prac naukowych dotyczących rynku kapitałowego na całym świecie. Wprawdzie w głównej mierze dotyczą one rynku giełdowego, jednak można również odnaleźć prace odnoszące się do innych rynków. Jednym z nich jest rynek bukmacherski. Jest on bardzo specyficzny ze względu na organizację. To rynek nastawiony na spekulację, przez co konieczne jest wprowadzanie wielu specyficznych założeń.Inwestowanie na rynku zakładów sportowych nie wymaga znajomości ekonomii i finansów (analizy technicznej, fundamentalnej czy portfelowej) ani zaangażowania dużego kapitału czy obserwacji sytuacji makroekonomicznej. "Obstawianie wyników zdarzeń w zakładach sportowych" jest hazardem w czystej postaci, gdzie niezbędna jest "fachowa" wiedza o dyscyplinie sportu, której dotyczy zdarzenie oraz o wszystkich czynnikach mogących mieć wpływ na wynik końcowy. Można zatem stwierdzić, że jest to sposób inwestowania dla ludzi pasjonujących się sportem i lubiących ryzyko.Pobrane z czasopisma Annales H -Oeconomia http://oeconomia.annales.umcs.pl

show abstract

“…Finally, in terms of forecasting in sports leagues, many different types of models have been constructed to predict the outcomes of sporting events, but, unfortunately, many of these models have never been used in forecasting beyond the period of fit [Stekler et al 2010]. We believe that the main reason is the difficulty of competing with one of the simplest but most powerful models, the Yesterday model.…”

Section: Related Workmentioning

confidence: 99%

Forecasting in the NBA and other team sports

Melo

Almeida

Loureiro

et al. 2012

ACM Trans. Knowl. Discov. Data

View full text Add to dashboard Cite

The multi-million sports-betting market is based on the fact that the task of predicting the outcome of a sports event is very hard. Even with the aid of an uncountable number of descriptive statistics and background information, only a few can correctly guess the outcome of a game or a league. In this work, our approach is to move away from the traditional way of predicting sports events, and instead to model sports leagues as networks of players and teams where the only information available is the work relationships among them. We propose two network-based models to predict the behavior of teams in sports leagues. These models are parameter-free, that is, they do not have a single parameter, and moreover are sport-agnostic: they can be applied directly to any team sports league. First, we view a sports league as a network in evolution, and we infer the implicit feedback behind network changes and properties over the years. Then, we use this knowledge to construct the network-based prediction models, which can, with a significantly high probability, indicate how well a team will perform over a season. We compare our proposed models with other prediction models in two of the most popular sports leagues: the National Basketball Association (NBA) and the Major League Baseball (MLB). Our model shows consistently good results in comparison with the other models and, relying upon the network properties of the teams, we achieved a ≈ 14% rank prediction accuracy improvement over our best competitor.

show abstract

Issues in sports forecasting

Cited by 90 publications

References 90 publications

Predicting Win-Loss outcomes in MLB regular season games – A comparative study using data mining methods

Predicting Win-Loss outcomes in MLB regular season games – A comparative study using data mining methods

The Research on the Efficiency of Online Bookmakers’ Market in Poland

Forecasting in the NBA and other team sports

Contact Info

Product

Resources

About