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The ability to predict what shot a batsman will attempt given the type ofball and match situation is both one of the most challenging and strategicallyimportant tasks in cricket.  The goal of the batsman is to score as many runs without being dismissed,whilst for bowlers their goal is to stem the flow of runs and ideally todismiss their opponent. Getting the best batsman vs bowler match-up is ofparamount importance. For example, for the fielding team, the choice of bowleragainst the opposition star batsman could be the key difference between winningor losing. Therefore, the ability to have a predefined playbook (as in the NFL)which would allow a team to predict how best to set their fielders given thecontext of the game, the batsman they are bowling to and bowlers at theirdisposal would give them a significant strategic advantage.  To this end, we present a personalized deep neural network approach which canpredict the probabilities of where a specific batsman will hit a specificbowler and bowl type, in a specific game-scenario. We demonstrate how ourpersonalized predictions provide vital information to inform thedecision-making of coaches and captains, both in terms of pre-match and in-gametactical choices, using the 2019 World Cup final between England and NewZealand as a case study example.

You Cannot Do That Ben Stokes: Dynamically Predicting Shot Type in Cricket Using a Personalized Deep Neural Network