Can Statistical Models Out-predict Human Judgment?: Comparing Statistical Models to the NCAA Selection Committee
نویسنده
چکیده
The NCAA selects teams for their championship tournaments using a selection committee. For the Men’s Division I NCAA Basketball Tournament the committee must factor in the results of over 5,000 regular season games, as well as other qualitative factors. One of the key summary statistics used by the committee during the process is the Rating Percentage Index (RPI). This paper evaluates the committee’s selection process, as well as the RPI and winning percentage statistics, from the 2002-03 through the 2010-11 seasons. In the process of evaluating the committee, RPI, and winning percentage, a series of Bradley-Terry Models are developed to use as comparison models. Results suggest that the RPI statistic and the committee predictions are significantly correlated. Additionally, the all Bradley-Terry Models predict more games correctly than the committee, suggesting that a statistical model might be better suited in seeding teams for the NCAA tournament.
منابع مشابه
A New Model Selection Test with Application to the Censored Data of Carbon Nanotubes Coating
Model selection of nano and micro droplet spreading can be widely used to predict and optimize of different coating processes such as ink jet printing, spray painting and plasma spraying. The idea of model selection is beginning with a set of data and rival models to choice the best one. The decision making on this set is an important question in statistical inference. Some tests and criteria a...
متن کاملModel Selection Based on Tracking Interval Under Unified Hybrid Censored Samples
The aim of statistical modeling is to identify the model that most closely approximates the underlying process. Akaike information criterion (AIC) is commonly used for model selection but the precise value of AIC has no direct interpretation. In this paper we use a normalization of a difference of Akaike criteria in comparing between the two rival models under unified hybrid cens...
متن کاملApplying Combined Approach of Sequential Floating Forward Selection and Support Vector Machine to Predict Financial Distress of Listed Companies in Tehran Stock Exchange Market
Objective: Nowadays, financial distress prediction is one of the most important research issues in the field of risk management that has always been interesting to banks, companies, corporations, managers and investors. The main objective of this study is to develop a high performance predictive model and to compare the results with other commonly used models in financial distress prediction M...
متن کاملComparing Prediction Power of Artificial Neural Networks Compound Models in Predicting Credit Default Swap Prices through Black–Scholes–Merton Model
Default risk is one of the most important types of risks, and credit default swap (CDS) is one of the most effective financial instruments to cover such risks. The lack of these instruments may reduce investment attraction, particularly for international investors, and impose potential losses on the economy of the countries lacking such financial instruments, among them, Iran. After the 2007 fi...
متن کاملAn Overview of the New Feature Selection Methods in Finite Mixture of Regression Models
Variable (feature) selection has attracted much attention in contemporary statistical learning and recent scientific research. This is mainly due to the rapid advancement in modern technology that allows scientists to collect data of unprecedented size and complexity. One type of statistical problem in such applications is concerned with modeling an output variable as a function of a sma...
متن کامل