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PECOTA, an acronym for Player Empirical Comparison and Optimization Test Algorithm, [1] is a sabermetric system for forecasting Major League Baseball player performance. The word is a backronym based on the name of journeyman major league player Bill Pecota, who, with a lifetime batting average of .249, is perhaps representative of the typical PECOTA entry.
In 2022, Kyle Harrison led Minor League Baseball with 14.8 strikeouts per 9 innings, the highest rate for a pitcher in the minor leagues in a season (minimum of 100 innings pitched) dating back to 1960. [6] [7]
Nate Silver, a former writer and managing partner of Baseball Prospectus, invented PECOTA (Player Empirical Comparison and Optimization Test Algorithm [25]) in 2002–2003, introducing it to the public in the book Baseball Prospectus in 2003. [26] It assumes that the careers of similar players will follow a similar trajectory. [27]
Player restrictions [3]: 100 Triple-A: 28 players: no restrictions Double-A: 28 players: no restrictions High-A: 30 players: No more than 2 players and 1 player-coach with 6 or more years of minor-league experience Single-A: 30 players: No more than 2 players with 5 or more years of minor-league experience US-based Rookie: no limit: No more ...
Baseball statistics include a variety of metrics used to evaluate player and team performance in the sport of baseball. Because the flow of a baseball game has natural breaks to it, and player activity is characteristically distinguishable individually, the sport lends itself to easy record-keeping and compiling statistics .
Minor League Baseball (MiLB) is a professional baseball organization below Major League Baseball (MLB), constituted of teams affiliated with MLB clubs. It was founded on September 5th, 1901 in response to the growing dominance of the National League and American League as the National Association of Professional Baseball Leagues, shortened to the NAPBL or NA.
Game length in Major League Baseball (MLB) has increased over time, with the 1988 New York Yankees being the first team to average over three hours per game. [2] From 2004 through 2014, MLB games increased from an average of 2.85 hours to 3.13 hours. [3]
VORP's usefulness is in the fact that it measures the marginal utility of individual players. Other statistics compare individual players to the league average, which is good for cross-era analysis. For example, 90 runs created in 1915 are much better than 90 runs created in 2015, because runs were more scarce in 1915. However, league average ...