This is because it is unlikely that that 40% number came from values of 38%, 39%, 43%, 44% etc, but rahter values like 20%, 30%, 40%, 50%, 60% etc. So even though 40 is the average it does not have a small spread of values. Just because a value has a higher uncertainty, that doesn't mean that the overall average still isn't useful in predicting future occurances. As in all statistics, the more samples that are taken, the better the data taken will be. Obviously for blocks of 10 at-bats (like you mentioned), the results aren't likely to match the predicted outcome. In the long run, however, basing decisions on the averages will come out to your advantage. It would be foolish to disregard a statistic because of a high standard deviation, as the mean itself still has value. What I'm trying to say is, these 3 year compilations of data are obviously useful. Take a look at the Gomez situation. The chances of scoring 1 run with a man on 2nd and none out is 34.8%, with a total run expectancy of 1.15. The chances of scoring 1 run with a man on 3rd and one out is 47.8%, with a total run expectancy of .967. This obviously doesn't look at the possibilities of a failed bunt attempt, or a bunt for a hit, but lets just *assume* those would roughly cancel out. This data is obviously useful. If you're playing for one run, bunt him over. If you're playing for the big inning, swing away. It's that simple. The manager should use that data, and make a decision based on those two options. You have to take into account, most importantly, the score, the inning, the 3 batters due, and the pitcher, in that order (in my opinion). A computer and a "gut" manager are the two extremes of using the data. A computer disregards the personel, and the manager pays no attention to the data. All I really want is someone who will look at both to make a decision, and not just bunt because Gomez is fast.