Statistical Prediction of Future Sports Records Based on Record Values

Christina Empacher, Udo Kamps () and Grigoriy Volovskiy
Additional contact information
Christina Empacher: Institute of Statistics, RWTH Aachen University, D-52056 Aachen, Germany
Udo Kamps: Institute of Statistics, RWTH Aachen University, D-52056 Aachen, Germany
Grigoriy Volovskiy: Institute of Statistics, RWTH Aachen University, D-52056 Aachen, Germany

Stats, 2023, vol. 6, issue 1, 1-17

Abstract: Point prediction of future record values based on sequences of previous lower or upper records is considered by means of the method of maximum product of spacings, where the underlying distribution is assumed to be a power function distribution and a Pareto distribution, respectively. Moreover, exact and approximate prediction intervals are discussed and compared with regard to their expected lengths and their percentages of coverage. The focus is on deriving explicit expressions in the point and interval prediction procedures. Predictions and forecasts are of interest, e.g., in sports analytics, which is gaining more and more attention in several sports disciplines. Previous works on forecasting athletic records have mainly been based on extreme value theory. The presented statistical prediction methods are exemplarily applied to data from various disciplines of athletics as well as to data from American football based on fantasy football points according to the points per reception scoring scheme. The results are discussed along with basic assumptions and the choice of underlying distributions.

Keywords: point and interval prediction; power function distribution; Pareto distribution; athletics; American football (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2571-905X/6/1/8/pdf (application/pdf)
https://www.mdpi.com/2571-905X/6/1/8/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jstats:v:6:y:2023:i:1:p:8-147:d:1031871

Access Statistics for this article

Stats is currently edited by Mrs. Minnie Li

More articles in Stats from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().