Childhood Statistical Innocence: A Reminiscence

When I was about seven years old, I started ranking players based on statistics. I started with the Royals, because their totals were the easiest to come by in the newspaper. Later, I sorted players from other teams based on their back-of-the-baseball-card numbers. I organized the hierarchy by two measures: Batting average and ERA. A baseball board game, which assigned outcomes to dice-roll combinations, influenced this decision: Based on my experiences with this game, Wade Boggs was the best player in history, while Davey Concepcion was a complete disgrace to this ostensible All-Star roster. When I reached the 10-12 range, I started multiplying part-time statistics so that they reached the plate-appearances minimum for batting-title qualifiers, and firmly believed this is what those players could have accomplished had they gotten a chance/stayed healthy. I mourned the star-crossed Kal Daniels, who obviously would've gone 40/40 in 1987 if not for the wrathful fates.

I certainly appreciate the wealth of new statistical measures, and the Internet accessibility of figures both historical (the aforementioned Daniels career overview) and instantaneous (tonight's Game Day box score). However, I miss the silly thrill of believing in prospects who started strong, back before I knew anything about small sample sizes and OPS+. I'm not joining the chorus of baseball writers who advocate a return to the statistical dark ages, but the child in me has been wistful lately that I can't take any joy in Mitch Maier's .345 average. ("He's the best player on the team," that ignorant brat argues shrilly.) The real downside of this phenomenon -- at least during the 2008 season -- is that it has squashed optimism far more than it has encouraged unjustified pessimism (like the nagging kind I felt when David DeJesus was 1-23 going into June 2004). I couldn't take much joy in Bannister's budding stardom or Davies' first few starts, because daunting statistical evidence suggested their numbers were unsustainable. By contrast, this year's players who appear to be terrible in small sample sizes, such as Nomo and Jason Smith, are, in fact, terrible.

Sometimes I wish I had sound statistical basis to sustain my immature faith in illusions like Maier. Rationally, though, I'm just holding out hope that Butler, Gordon, Hosmer and Moustakas will be the kind of players whose actual, meaningful numbers impress without any need for willful deception or selective manipulation.