Rank Position Forecasting in Car Racing

by   Bo Peng, et al.

Forecasting is challenging since uncertainty resulted from exogenous factors exists. This work investigates the rank position forecasting problem in car racing, which predicts the rank positions at the future laps for cars. Among the many factors that bring changes to the rank positions, pit stops are critical but irregular and rare. We found existing methods, including statistical models, machine learning regression models, and state-of-the-art deep forecasting model based on encoder-decoder architecture, all have limitations in the forecasting. By elaborative analysis of pit stops events, we propose a deep model, RankNet, with the cause effects decomposition that modeling the rank position sequence and pit stop events separately. It also incorporates probabilistic forecasting to model the uncertainty inside each sub-model. Through extensive experiments, RankNet demonstrates a strong performance improvement over the baselines, e.g., MAE improves more than 10 consistently, and is also more stable when adapting to unseen new data. Details of model optimization, performance profiling are presented. It is promising to provide useful forecasting tools for the car racing analysis and shine a light on solutions to similar challenging issues in general forecasting problems.


page 1

page 2

page 3

page 4


ShuttleNet: Position-aware Fusion of Rally Progress and Player Styles for Stroke Forecasting in Badminton

The increasing demand for analyzing the insights in sports has stimulate...

Inaccuracy Minimization by Partioning Fuzzy Data Sets - Validation of Analystical Methodology

In the last two decades, a number of methods have been proposed for fore...

Seasonal Encoder-Decoder Architecture for Forecasting

Deep learning (DL) in general and Recurrent neural networks (RNNs) in pa...

Dynamic Network Models for Forecasting

We have developed a probabilistic forecasting methodology through a synt...

A Comparative Study on Forecasting of Retail Sales

Predicting product sales of large retail companies is a challenging task...

How to Learn from Others: Transfer Machine Learning with Additive Regression Models to Improve Sales Forecasting

In a variety of business situations, the introduction or improvement of ...

Code Repositories


rank predictor for indycar

view repo

Please sign up or login with your details

Forgot password? Click here to reset