Advances in relevance-based prediction
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From politics and finance to sports, accurately predicting outcomes, or forecasting, is an important (but difficult) task that requires sifting through vast amounts of data, scrutinizing many variables and detecting patterns.
Now, a team of State Street experts is offering another tool to prognosticators: a prediction system that focuses on the relevance of prior outcomes. Relevance-based prediction (RBP), as the forecasting model developed by Megan Czasonis, Mark Kritzman and David Turkington is called, relies on a mathematical measure to account for unusualness.
Kritzman, founding academic partner of State Street Associates and faculty member at MIT Sloan School of Management, presented the team’s research during State Street LIVE: Research Retreat, illustrating the effectiveness of the predictive method by applying it to the National Basketball Association (NBA) draft.
“Relevance-based prediction predicts the quality of the predictions. Before you even form the prediction, it’s telling you if this is going to be a good prediction or a bad prediction,” he said. “This foreknowledge enables us to discard predictions that we know in advance aren’t that trustworthy.”
Elements of relevance-based prediction
Advantages of relevance-based prediction
Relevance-based prediction and basketball
RBP may serve professional basketball teams well to gain insights on which players to take a closer look at and which players to avoid, according to Kritzman. Using data from the 2018 draft, he illustrated how the method effectively predicts outcomes for NBA draft prospects.
RBP predicted a statistic called "box score plus-minus" or BPM, which considers a player’s points scored, rebounds, assists, etc. It specifically measures a player’s contribution to the team’s success during actual playing time.
More than NBA predictions
RBP is an extremely valuable tool for investors, too. This method, which is “adaptable and automatically adjusts to new circumstances,” can be similarly applied to forecasting return risks or correlations, according to Kritzman.
“It is a good way to scale bets, by knowing the quality of each bet you’re making ahead of time,” concluded Kritzman, adding that the method is more transparent and adaptive than model-based machine learning algorithms.