Get election results early with CSIR’s prediction model
The CSIR model relies on the analysis of voter behaviour patterns and the sequence in which voting results are announced on election day
A general view at the IEC results centre. Picture: Jacques Nelles
With the country on the cusp of the most hotly contested elections since the dawn of democracy, South Africans will get a glimpse into which political party is leading the race and early results, thanks to the Council for Scientific and Industrial Research (CSIR) prediction model.
The CSIR will once again be part of the South African elections, utilising its election night prediction model for the 2024 national and provincial elections.
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View MapThis model was first introduced by the CSIR during the 1999 general elections.
Prediction
The CSIR said its election prediction model relies on two core principles.
“The analysis of voter behaviour patterns and the sequence in which voting results are announced on election day. When combined, these two principles enable the team to group voters or voting districts based on their past voting behaviour, utilising a statistical clustering method.
“When applied in previous elections, the model typically achieved a high degree of accuracy at a national level once approximately 5% of the results had been tallied. Due to the way the model works, the predictions become more stable and accurate as more voting districts are counted, ultimately converging to the final results once all voting districts have been declared,” the CSIR said.
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Not polling system
CSIR Chief Executive Officer, Dr Thulani Dlamini said emphasised that the CSIR’s election prediction model is not a polling system, but a model that uses statistical and mathematical analysis to predict election outcomes.
“It showcases how statistical clustering and some mathematical algorithms can achieve good predictions from a small sample of results. The election prediction model operates on the basis of reducing the bias resulting from the ‘non-randomness’ of the incoming results that arise from the order in which the results are received.”
Dlamini said the CSIR’s predictive modelling capabilities allow for this to be tailor-made for other predictive analysis work.
“As one of its kind in the continent, CSIR has the capability of assisting other countries that may require such a tool to support election transparency and engagement,” Dlamini said.
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