by Dariel Cruz Rodriguez, Rielley McNeill, and Derek Schneider
This analysis has several important limitations to consider. First, the data represents market prices rather than objective probabilities, which may reflect trader biases or liquidity constraints. Second, the 5-minute sampling interval may miss short-term volatility spikes. Third, we cannot definitively determine causality—high volatility may indicate uncertainty or simply reflect thin trading volume. Fourth, the coefficient of variation assumes that odds movements are meaningful even when they occur in low-probability ranges. Finally, this is a single-season analysis and patterns may vary across different tournament years or market conditions.
This section provides context on existing research related to prediction markets, volatility analysis, and sports betting markets. Research has shown that prediction markets can serve as accurate forecasting mechanisms for various events, including sporting outcomes. Studies on market efficiency suggest that odds movements reflect new information being incorporated into prices. The coefficient of variation (CV) as a normalized measure of volatility allows for comparison across markets with different base probabilities. Previous work on March Madness has examined predictive accuracy but less attention has been paid to market volatility patterns across tournament rounds.
This analysis has several important limitations to consider. First, the data represents market prices rather than objective probabilities, which may reflect trader biases or liquidity constraints. Second, the 5-minute sampling interval may miss short-term volatility spikes. Third, we cannot definitively determine causality—high volatility may indicate uncertainty or simply reflect thin trading volume. Fourth, the coefficient of variation assumes that odds movements are meaningful even when they occur in low-probability ranges. Finally, this is a single-season analysis and patterns may vary across different tournament years or market conditions.
This analysis has several important limitations to consider. First, the data represents market prices rather than objective probabilities, which may reflect trader biases or liquidity constraints. Second, the 5-minute sampling interval may miss short-term volatility spikes. Third, we cannot definitively determine causality—high volatility may indicate uncertainty or simply reflect thin trading volume. Fourth, the coefficient of variation assumes that odds movements are meaningful even when they occur in low-probability ranges. Finally, this is a single-season analysis and patterns may vary across different tournament years or market conditions.
This analysis has several important limitations to consider. First, the data represents market prices rather than objective probabilities, which may reflect trader biases or liquidity constraints. Second, the 5-minute sampling interval may miss short-term volatility spikes. Third, we cannot definitively determine causality—high volatility may indicate uncertainty or simply reflect thin trading volume. Fourth, the coefficient of variation assumes that odds movements are meaningful even when they occur in low-probability ranges. Finally, this is a single-season analysis and patterns may vary across different tournament years or market conditions.