Sports Forecasting

A great amount of effort is spent in forecasting the outcome of sporting events, but few papers have focused exclusively on the characteristics of sports forecasts. Rather, many papers have been written about the efficiency of sports betting markets. As it turns out, it is possible to derive considerable information about the forecasts and the forecasting process from the studies that tested the markets for economic efficiency. Moreover, the huge number of observations provided by betting markets makes it possible to obtain robust tests of various forecasting hypotheses. This paper is concerned with a number of forecasting topics in horse racing and several team sports. The first topic involves the type of forecast that is made: picking a winner or predicting whether a particular team beats the point spread. Different evaluation procedures will be examined and alternative forecasting methods (models, experts, and the market) will be compared. The paper also examines the evidence about the existence of biases in the forecasts and concludes with the applicability of these results to forecasting in general.Sports forecasting, gambling markets, prediction markets

Evaluating Consensus Forecasts

The Census Bureau makes periodic long-term forecasts of both the total US population and the population of each of the states. Previous evaluations of these forecasts were based on the magnitude of the discrepancies between the projected and actual population figures. However, it might be inappropriate to evaluate these long-term projections with the specific quantitative statistics that have been useful in judging short-term forecasts. One of the purposes of a long range projection of each state¡¯s population is to provide a picture of the distribution of the aggregate US population among the various states. Thus the evaluation should compare the projected distribution of the total US population by states to the actual distribution. This paper uses the dissimilarity index to evaluate the accuracy of the Census projected percentage distributions of population by states.

What Do We Know About G-7 Macro Forecasts?

Fildes and Stekler’s (2002) survey of the state of knowledge about the quality of economic forecasts focused primarily on US and UK data. This paper will draw on some of their findings but it will not examine any additional US forecasts. The purpose is to determine whether their results are robust by examining the predictions of other countries. The focus will be on (1) directional errors, (2) the magnitude of the errors made in estimating growth and inflation, (3) whether there were biases and systematic errors, (4) the sources of the errors and (5) whether there has been an improvement in forecasting ability.G7 forecasts, evaluation techniques

Has the Accuracy of German Macroeconomic Forecasts Improved?

This paper asks whether the accuracy of German macroeconomic forecasts has improved over time. We examine one-year-ahead forecasts of rates of real GDP growth and inflation for the years 1967 to 2010 by three major German forecasters and the OECD. We find that overall error levels are high but not much different from those for the U.S. and U.K. In the 1980s and 1990s accuracy improved somewhat, but has now returned to its 1970s level, indicating that it reflects the variance of growth and inflation. Benchmark comparisons of these predictions with ex post forecasts of a macroecono-metric model indicate that accuracy can be improved but it will be difficult to achieve.Forecast evaluations; macroeconomic forecasting; accuracy limits

Issues in Sports Forecasting

A great amount of effort is spent in forecasting the outcome of sporting events, but few papers have focused exclusively on the characteristics of sports forecasts. Rather, many papers have been written about the efficiency of sports betting markets. As it turns out, it is possible to derive considerable information about the forecasts and the forecasting process from the studies that tested the markets for economic efficiency. Moreover, the huge number of observations provided by betting markets makes it possible to obtain robust tests of various forecasting hypotheses. This paper is concerned with a number of forecasting topics in horse racing and several team sports. The first topic involves the type of forecast that is made: picking a winner or predicting whether a particular team beats the point spread. Different evaluation procedures will be examined and alternative forecasting methods (models, experts, and the market) will be compared. The paper also examines the evidence about the existence of biases in the forecasts and concludes with the applicability of these results to forecasting in general.sports forecasting; betting markets; efficiency; bias; sports models

Measuring Consensus in Binary Forecasts: NFL Game Predictions

Previous research on defining and measuring consensus (agreement) among forecasters has been concerned with evaluation of forecasts of continuous variables. This previous work is not relevant when the forecasts involve binary decisions: up-down or win-lose. In this paper we use Cohen¡¯s kappa coefficient, a measure of inter-rater agreement involving binary choices, to evaluate forecasts of National Football League games. This statistic is applied to the forecasts of 74 experts and 31 statistical systems that predicted the outcomes of games during two NFL seasons. We conclude that the forecasters, particularly the systems, displayed significant levels of agreement and that levels of agreement in picking game winners were higher than in picking against the betting line. There is greater agreement among statistical systems in picking game winners or picking winners against the line as the season progresses, but no change in levels of agreement among experts. High levels of consensus among forecasters are associated with greater accuracy in picking game winners, but not in picking against the line.binary forecasts, NFL, agreement, consensus, kappa coefficient

Can the Fed Predict the State of the Economy?

Recent research has documented that the Federal Reserve produces systematic errors in forecasting inflation, real GDP growth, and the unemployment rate, even though these forecasts are unbiased. We show that these systematic errors reveal that the Fed is “surprised” by real and inflationary cycles. Using a modified Mincer-Zarnowitz regression, we show that the Fed knows the state of the economy for the current quarter, but cannot predict it one quarter ahead.Forecast Evaluation; Federal Reserve; Systematic Errors; Recessions

Are 'unbiased' forecasts really unbiased? Another look at the Fed forecasts

This paper reconciles contradictory findings obtained from forecast evaluations: the existence of systematic errors and the failure to reject rationality in the presence of such errors. Systematic errors in one economic state may offset the opposite types of errors in the other state such that the null of rationality is not rejected. A modified test applied to the Fed forecasts shows that the forecasts were ex post biased.Greenbook Forecasts, forecast evaluation, systematic errors

Monthly Output Index for the U.S. Transportation Sector

We develop a monthly output index of the U.S. Transportation sector over 1980:1-2002:4 covering air, rail, water, truck, transit and pipeline activities. Separate indexes for freight and passenger are also constructed. Our total transportation output index matches very well with the annual transportation output figures produced by the Bureau of Labor Statistics (BLS) and the Bureau of Economic Analysis (BEA). The strong cyclical movements in the transportation output appear to be more synchronized with the growth slowdowns rather than full-fledged recessions of the U.S. economy. The index has led the turning points of the six NBER-defined growth cycles over the period with an average lead-time of 6 months at peaks and 5 months at troughs.