A new methodology for generating and combining statistical forecasting models to enhance competitive event prediction

Stefan Lessmann*, Ming Chien Sung, Johnnie E.V. Johnson, Tiejun Ma

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Forecasting methods are routinely employed to predict the outcome of competitive events (CEs) and to shed light on the factors that influence participants' winning prospects (e.g.; in sports events, political elections). Combining statistical models' forecasts, shown to be highly successful in other settings, has been neglected in CE prediction. Two particular difficulties arise when developing model-based composite forecasts of CE outcomes: the intensity of rivalry among contestants, and the strength/diversity trade-off among individual models. To overcome these challenges we propose a range of surrogate measures of event outcome to construct a heterogeneous set of base forecasts. To effectively extract the complementary information concealed within these predictions, we develop a novel pooling mechanism which accounts for competition among contestants: a stacking paradigm integrating conditional logit regression and log-likelihood-ratio-based forecast selection. Empirical results using data related to horseracing events demonstrate that: (i) base model strength and diversity are important when combining model-based predictions for CEs; (ii) average-based pooling, commonly employed elsewhere, may not be appropriate for CEs (because average-based pooling exclusively focuses on strength); and (iii) the proposed stacking ensemble provides statistically and economically accurate forecasts. These results have important implications for regulators of betting markets associated with CEs and in particular for the accurate assessment of market efficiency.

Original languageEnglish
Pages (from-to)163-174
Number of pages12
JournalEuropean Journal of Operational Research
Issue number1
Early online date6 Nov 2011
Publication statusPublished - 1 Apr 2012


  • Competitive event prediction
  • Forecast combination
  • Forecasting


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