Machine Learning/Artificial Intelligence for Cricket Test Match Team Selection

Cricket is a fun sport. Millions of people worldwide follow cricket, particularly in places where it is widely played. People are enthusiastic about their teams, and players have massive fan bases all over the world.

Given that a well followed cricket game has far more at risk than just the few days or hours during which it is played.

Fans routinely analyze cricket matches and potential teams before every play, whether it is a Test Match, a One Day International, or a Twenty20 match.

The IPL has opened opportunities and introduced many good players through their excellent performances. These players are unproven at the international or national levels, but they have the potential to make an impact on the world stage. The IPL has also introduced a lot of good selection difficulties, mainly a lot more players to choose from.

When a national team is announced, it includes only players who have established themselves at the international level and does not include players who have gained prominence through franchise cricket.

Also, when the team management announces the team for a match before the match begins, the selections can surprise spectators all over the world. Even though they are not players, fans understand why a specific player should have been included or excluded from the team.

Here is where we must rely on the scientific method to deliver the much-needed result: a selection policy free of human errors, bias, or carelessness.

Whereas a captain may be able to appraise a player based on a few selection criteria, neither the captain nor team management are aware of the many other aspects that can influence the outcome of a match in relation to the set of players that may be part of the final squad.

A good machine learning algorithm or an AI algorithm/model can use a large amount of data (Big data) and can be constructed using several measured or partially measured selection criteria. These may include a player’s specific performance against an opponent using historical data, quantitatively measured performance of a player in a venue or weight/fitness parameters of a player, dietary pattern followed by a player, performance during the second innings, first innings, performance during a time of day, many more technical parameters related to batting and bowling, and match performance parameters such as how many times a player has won a match. All of these criteria can be quantified, and a final score can be calculated.

When combined with the goal of maximizing the team’s chances against an opponent in a venue, this balanced score card technique eliminates the whimsy and uneven selection rules that are sometimes seen by fans. This will help to improve the credibility of international cricket, which may be harmed by partisan or incompetent managerial decision-making.