Last year JC Bradbury at Sabernomics created a list of position players who are not in the baseball hall of fame, but should be (more recent update here). I was intrigued by the list, but felt that, fair or not, winning and postseason success likely factor into voters decisions. I also wondered whether traditional stats such as hits and home runs might not predict voter behavior better than linear weights (about which voters are likely poorly informed).
The project became a bit long for one post so I’ll do it in parts. The next post (part 2) will compare model specifications and provide lists of players who are not enshrined, but best match the players already in the hall. Next I will look at hall of famers who had the lowest probability of induction and current or recently retired players who have the best chance of induction. Finally I may look at how changes in productivity would have affected players’ hall of fame chances (e.g. what difference would an additional all-star caliber season have made for Dale Murphy). For the last section I’m happy to take suggestions.
In his second post he finds something very interesting.
The real difference between [Bradbury's basic model] and [a model that includes the average winning percentage of a players teams as well as a dummy variable indicating whether he won a world series] shows up when comparing players. [The latter model] was particularly harsh on Professor Bradbury’s favorite player, Dale Murphy. According to it, Murphy’s chances were hurt by his lack of a World Series ring and the .443 winning percentage of the teams he played on.
It’s a shame, but I’m not surprised about the impact of team quality. I’m looking forward to the next two parts. I like what he’s doing here.