The "mid-game" phase lasts from the beginning of the game up until there are nine or fewer tiles left in the bag. The program uses a rapid algorithm to find all possible plays from the given rack, and then part of the program called the "kibitzer" uses simple heuristics to sort them into rough order of quality. The most promising moves are then evaluated by "simming", in which the program simulates the random drawing of tiles, plays forward a set number of plays, and compares the points spread of the moves' outcomes. By simulating thousands of random drawings, the program can give a very accurate quantitative evaluation of the different plays. (While a Monte Carlo search, Maven does not use Monte Carlo tree search because it evaluates game trees only 2-ply deep, rather than playing out to the end of the game, and does not reallocate rollouts to more promising branches for deeper exploration; in reinforcement learning terminology, the Maven search strategy might be considered "truncated Monte Carlo simulation". A true MCTS strategy is unnecessary because the endgame can be solved. The shallow search is because the Maven author argues that, due to the fast turnover of letters in one's bag, it is typically not useful to look more than 2-ply deep, because if one instead looked deeper, e.g. 4-ply, the variance of rewards will be larger and the simulations will take several times longer, while only helping in a few exotic situations: "We maintain that if it requires an extreme situation like CACIQUE to see the value of a four-ply simulation then they are not worth doing." As the board value can be evaluated with very high accuracy in Scrabble, unlike games such as Go, deeper simulations are unlikely to change the initial evaluation.)