But we have 4 or 5 players who aren't hitting home runs right now. That certainly becomes more statistically significant. Only if we knew nothing else about the players on the team would it become more stastically significant. But in this case, the more players you include in your team sample, the more information you add of prior knowlege. You wouldn't have any expectations of these players if you didn't have prior information (scouting and past performance) telling you to. The more players you add to your team sample, the more thousands of AB from previous seasons you need to consider. Unless we have a reason to believe that the perfomance of teamates are somehow linked (beyond sharing a specific ballpark and having common opponents), a team sample tells us nothing more than simply looking at the samples individually. This is why my in-season series probabilities are currently pretty flawed, even if I do wait until about midseason to start them. I'm trying to estimate a player's true skill from maybe 300 AB, which has a high degree of uncertainty associated with it. I should be adding the in-season sample to my preseason projection as my first step. By the way, has anyone ever actually does extensive regression analysis on spring training stats? Surely there must be some correlation between spring training success and regular season success. That would make for a great article. I vaguely recall a study that looked at the most extreme performances over spring training and concluded that it predicted a small increase or decrease in expected performance for that upcoming season. Something like 40 points of OPS above or below expectations in spring training translated to 4 points in the regular season. It was small but it existed. Spring training starts aren't worthless, but small samples, lower average opponent skill and players not always putting winning first adds a lot of noise to the data.