  Professional baseball players are increasingly guaranteed expensive long-term
contracts, with over 70 deals signed in excess of \$90 million, mostly in the
last decade. These are substantial sums compared to a typical franchise
valuation of \$1-2 billion. Hence, the players to whom a team chooses to give
such a contract can have an enormous impact on both competitiveness and profit.
Despite this, most published approaches examining career progression in
baseball are fairly simplistic. We applied four machine learning algorithms to
the problem and soundly improved upon existing approaches, particularly for
batting data.
