Abstract:
With the popularity of the T20 cricket format, the
game of cricket has dramatically changed compared to several
decades ago. Every year there are more than 100s matches
played, which results in thousands of data that can be used
by sports analysts in cricket. Several studies have attempted
various analyses of the game, such as predicting the likelihood
of a team’s victory, analyzing individual player performances
and forecasting scores. However, forecasting scores has not been
studied extensively and limited to specific teams, rather than a
generalized approach. Our paper presents a generalised novel
deep neural network-based method to predict the score of the
first innings in a T20 international cricket match. The model
utilizes various attributes in three categories namely a) current
status of the match b) performance of the current batsmen and
c) performance of the bowler and provides predictions for each
over. We have used recent 5 years T20 international matches
from 14 teams and tested our method in the 2022 ICC Men’s
T20 World Cup. We demonstrate our findings quantitatively and
qualitatively in this paper.
Citation:
D. Abeysuriya, S. Fernando and R. Navarathna, "Beyond the Run-rate: Forecasting Framework for First Innings Score in T20 Cricket," 2023 Moratuwa Engineering Research Conference (MERCon), Moratuwa, Sri Lanka, 2023, pp. 48-53, doi: 10.1109/MERCon60487.2023.10355397.