THE PROTRADE JOURNAL: An Earnings Projection Method for Hitters

And here’s Part 1 of Bosox Bob’s work...using Dan’s ZiPS projections.

In my first pass using this approach, I pro-rated the ZiPS earnings projections for the remaining May-September ABs. The result? The average error dropped to 23.2% - less than either data set’s projections taken separately. This approach is flawed however, as it would overweight the ZiPS projection and underweight any performance level changes as the season progressed.

To counter this, I computed a combined earnings rates from the April and ZiPS data, weighting by ABs to date and projected ABs respectively (aprox. ? April data to ? ZiPS data). This rate was used to project the remaining earnings for the year. With this method, the average error went down to 21.5%. As a check of this projection method, I repeated the process using stats through the end of May, which yielded an average error of 19.2% - more accurate than a month prior.

2008 Hitter Projections

Now that we’ve established a projection method, let’s see what players it currently identifies as good buys. Using 2008 ZiPS and player stats to date for the season, I generated projections for all hitters (note: expected ABs were based on player’s average ABs per team games played, minus games missed on DL). The following table contains all hitters whose pace and projection are at least 25% greater than their current price: