The Market for English Premier League (EPL) Odds

TL;DR

This paper introduces a dynamic model using a Skellam process to analyze real-time betting odds for EPL games, estimating implied volatility and updating expectations as game events unfold.

Contribution

It develops a novel real-time implied volatility measure for EPL matches based on a Skellam process, enhancing understanding of market expectations during live games.

Findings

Model calibrates well to actual game outcomes

Implied volatility evolves with game events

Method demonstrated on real-time odds data

Abstract

This paper employs a Skellam process to represent real-time betting odds for English Premier League (EPL) soccer games. Given a matrix of market odds on all possible score outcomes, we estimate the expected scoring rates for each team. The expected scoring rates then define the implied volatility of an EPL game. As events in the game evolve, we re-estimate the expected scoring rates and our implied volatility measure to provide a dynamic representation of the market's expectation of the game outcome. Using a dataset of 1520 EPL games from 2012-2016, we show how our model calibrates well to the game outcome. We illustrate our methodology on real-time market odds data for a game between Everton and West Ham in the 2015-2016 season. We show how the implied volatility for the outcome evolves as goals, red cards, and corner kicks occur. Finally, we conclude with directions for future research.