Home / Current Issue / Paper 1718842
Exploiting Wicket-Driven Mispricings in Cricket Prediction Markets
Subject area: Science,Engineering and Technology · Area of research: Prediction Market
DOI: https://doi.org/10.64388/IREV9I12-1718842
Abstract
Prediction markets are exchange-traded contracts that pay out based on the outcome of a future event, with prices reflecting the market’s consensus probability of that outcome. When new information arrives, prices should adjust instantaneously under the efficient market hypothesis. In practice, however, thin limit-order-book markets may reprice gradually, creating transient windows in which informed participants can trade at stale prices. We study this phenomenon in the context of live Twenty20 (T20) cricket, where a wicket—the dismissal of a batter—is an instantaneous, unambiguous information event that discontinuously revises match-win probabilities. We develop a multiplicative wicket-impact signal model, calibrated on 353 Indian Premier League (IPL) matches and over 4,300 wicket events, that estimates the magnitude of the expected price shift as a function of player quality, match phase, innings context, and recent form. The model is paired with a systematic trading strategy that enters positions on the fielding team’s contract immediately after high-impact dismissals, exploiting the delay between the information event and full market repricing. We evaluate the strategy through three progressively demanding tests: a historical backtest across 52 IPL 2025 markets, an out-of-sample validation on 17 ICC Men’s T20 World Cup 2026 markets, and a live deployment spanning the full 64-match IPL 2026 season. Across all three settings, the strategy produces statistically significant positive returns. An innings-level decomposition further reveals that the repricing inefficiency is concentrated in the second innings, where wickets have a more direct and calculable effect on the probability of reaching a known target. These findings provide evidence that prediction markets for live sporting events exhibit systematic, exploitable inefficiencies in the immediate aftermath of discrete information shocks.
How to cite this paper
@article{1718842,
author = {Aviral Sharma, Chandrima Sabharwal},
title = {Exploiting Wicket-Driven Mispricings in Cricket Prediction Markets},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {12},
pages = {2056-2066},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1718842.pdf},
abstract = {Prediction markets are exchange-traded contracts that pay out based on the outcome of a future event, with prices reflecting the market’s consensus probability of that outcome. When new information arrives, prices should adjust instantaneously under the efficient market hypothesis. In practice, however, thin limit-order-book markets may reprice gradually, creating transient windows in which informed participants can trade at stale prices. We study this phenomenon in the context of live Twenty20 (T20) cricket, where a wicket—the dismissal of a batter—is an instantaneous, unambiguous information event that discontinuously revises match-win probabilities. We develop a multiplicative wicket-impact signal model, calibrated on 353 Indian Premier League (IPL) matches and over 4,300 wicket events, that estimates the magnitude of the expected price shift as a function of player quality, match phase, innings context, and recent form. The model is paired with a systematic trading strategy that enters positions on the fielding team’s contract immediately after high-impact dismissals, exploiting the delay between the information event and full market repricing. We evaluate the strategy through three progressively demanding tests: a historical backtest across 52 IPL 2025 markets, an out-of-sample validation on 17 ICC Men’s T20 World Cup 2026 markets, and a live deployment spanning the full 64-match IPL 2026 season. Across all three settings, the strategy produces statistically significant positive returns. An innings-level decomposition further reveals that the repricing inefficiency is concentrated in the second innings, where wickets have a more direct and calculable effect on the probability of reaching a known target. These findings provide evidence that prediction markets for live sporting events exhibit systematic, exploitable inefficiencies in the immediate aftermath of discrete information shocks.},
month = {June},
doi = {https://doi.org/10.64388/IREV9I12-1718842}
}