Darts Analysis

TL;DR

This paper evaluates five mathematical models for predicting amateur darts game outcomes, demonstrating that a score-dependent Massey model outperforms others and can be adapted to various competitive scenarios.

Contribution

The paper introduces and tests a score-dependent Massey model for darts, showing its superior predictive performance and adaptability to other competitive environments.

Findings

Score-dependent Massey model has the best predictive accuracy.

Models improve predictions as the game progresses.

Massey model outperforms null, logistic, and simulation models.

Abstract

In this paper we examine the effectiveness of five mathematical models used to predict the outcomes of amateur darts games. These models not only predict the outcomes at the start of the game, but also update their estimations as the game score changes. The models were trained and tested on a dataset consisting of games played by amateur players involving students, faculty, and staff at Roanoke College. The five models are: the null model, which is based only on the live scores, a logistic regression model, a basic simulation model, a time-adjusted simulation model, and a new variation of the Massey model which updates based on the current score. We evaluate these models using two approaches. First, we compare their Brier scores. Second, we conduct head-to-head comparisons in a betting game in which one model sets the betting odds while the other places bets. In both cases, model performance is assessed not only at the start of the game but also at the start of each round. Across both evaluation methods, the score-dependent Massey model performs the best. We conclude by illustrating how this score-dependent Massey model framework can be adapted to other competitive settings beyond darts.